empirical research in the literature

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Introduction to Empirical Research

Databases for finding empirical research, guided search, google scholar, examples of empirical research, sources and further reading.

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Introductory Video This video covers what empirical research is, what kinds of questions and methods empirical researchers use, and some tips for finding empirical research articles in your discipline.

Guided Search: Finding Empirical Research Articles This is a hands-on tutorial that will allow you to use your own search terms to find resources.

Study on radiation transfer in human skin for cosmetics
Long-Term Mobile Phone Use and the Risk of Vestibular Schwannoma: A Danish Nationwide Cohort Study
Emissions Impacts and Benefits of Plug-In Hybrid Electric Vehicles and Vehicle-to-Grid Services
Review of design considerations and technological challenges for successful development and deployment of plug-in hybrid electric vehicles
Endocrine disrupters and human health: could oestrogenic chemicals in body care cosmetics adversely affect breast cancer incidence in women?

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Last Updated: Apr 5, 2024 9:55 AM
URL: https://guides.lib.purdue.edu/research_approaches

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Module 2 Chapter 3: What is Empirical Literature & Where can it be Found?

In Module 1, you read about the problem of pseudoscience. Here, we revisit the issue in addressing how to locate and assess scientific or empirical literature . In this chapter you will read about:

distinguishing between what IS and IS NOT empirical literature
how and where to locate empirical literature for understanding diverse populations, social work problems, and social phenomena.

Probably the most important take-home lesson from this chapter is that one source is not sufficient to being well-informed on a topic. It is important to locate multiple sources of information and to critically appraise the points of convergence and divergence in the information acquired from different sources. This is especially true in emerging and poorly understood topics, as well as in answering complex questions.

What Is Empirical Literature

Social workers often need to locate valid, reliable information concerning the dimensions of a population group or subgroup, a social work problem, or social phenomenon. They might also seek information about the way specific problems or resources are distributed among the populations encountered in professional practice. Or, social workers might be interested in finding out about the way that certain people experience an event or phenomenon. Empirical literature resources may provide answers to many of these types of social work questions. In addition, resources containing data regarding social indicators may also prove helpful. Social indicators are the “facts and figures” statistics that describe the social, economic, and psychological factors that have an impact on the well-being of a community or other population group.The United Nations (UN) and the World Health Organization (WHO) are examples of organizations that monitor social indicators at a global level: dimensions of population trends (size, composition, growth/loss), health status (physical, mental, behavioral, life expectancy, maternal and infant mortality, fertility/child-bearing, and diseases like HIV/AIDS), housing and quality of sanitation (water supply, waste disposal), education and literacy, and work/income/unemployment/economics, for example.

Three characteristics stand out in empirical literature compared to other types of information available on a topic of interest: systematic observation and methodology, objectivity, and transparency/replicability/reproducibility. Let’s look a little more closely at these three features.

Systematic Observation and Methodology. The hallmark of empiricism is “repeated or reinforced observation of the facts or phenomena” (Holosko, 2006, p. 6). In empirical literature, established research methodologies and procedures are systematically applied to answer the questions of interest.

Objectivity. Gathering “facts,” whatever they may be, drives the search for empirical evidence (Holosko, 2006). Authors of empirical literature are expected to report the facts as observed, whether or not these facts support the investigators’ original hypotheses. Research integrity demands that the information be provided in an objective manner, reducing sources of investigator bias to the greatest possible extent.

Transparency and Replicability/Reproducibility. Empirical literature is reported in such a manner that other investigators understand precisely what was done and what was found in a particular research study—to the extent that they could replicate the study to determine whether the findings are reproduced when repeated. The outcomes of an original and replication study may differ, but a reader could easily interpret the methods and procedures leading to each study’s findings.

What is NOT Empirical Literature

By now, it is probably obvious to you that literature based on “evidence” that is not developed in a systematic, objective, transparent manner is not empirical literature. On one hand, non-empirical types of professional literature may have great significance to social workers. For example, social work scholars may produce articles that are clearly identified as describing a new intervention or program without evaluative evidence, critiquing a policy or practice, or offering a tentative, untested theory about a phenomenon. These resources are useful in educating ourselves about possible issues or concerns. But, even if they are informed by evidence, they are not empirical literature. Here is a list of several sources of information that do not meet the standard of being called empirical literature:

your course instructor’s lectures
political statements
advertisements
newspapers & magazines (journalism)
television news reports & analyses (journalism)
many websites, Facebook postings, Twitter tweets, and blog postings
the introductory literature review in an empirical article

You may be surprised to see the last two included in this list. Like the other sources of information listed, these sources also might lead you to look for evidence. But, they are not themselves sources of evidence. They may summarize existing evidence, but in the process of summarizing (like your instructor’s lectures), information is transformed, modified, reduced, condensed, and otherwise manipulated in such a manner that you may not see the entire, objective story. These are called secondary sources, as opposed to the original, primary source of evidence. In relying solely on secondary sources, you sacrifice your own critical appraisal and thinking about the original work—you are “buying” someone else’s interpretation and opinion about the original work, rather than developing your own interpretation and opinion. What if they got it wrong? How would you know if you did not examine the primary source for yourself? Consider the following as an example of “getting it wrong” being perpetuated.

Example: Bullying and School Shootings . One result of the heavily publicized April 1999 school shooting incident at Columbine High School (Colorado), was a heavy emphasis placed on bullying as a causal factor in these incidents (Mears, Moon, & Thielo, 2017), “creating a powerful master narrative about school shootings” (Raitanen, Sandberg, & Oksanen, 2017, p. 3). Naturally, with an identified cause, a great deal of effort was devoted to anti-bullying campaigns and interventions for enhancing resilience among youth who experience bullying. However important these strategies might be for promoting positive mental health, preventing poor mental health, and possibly preventing suicide among school-aged children and youth, it is a mistaken belief that this can prevent school shootings (Mears, Moon, & Thielo, 2017). Many times the accounts of the perpetrators having been bullied come from potentially inaccurate third-party accounts, rather than the perpetrators themselves; bullying was not involved in all instances of school shooting; a perpetrator’s perception of being bullied/persecuted are not necessarily accurate; many who experience severe bullying do not perpetrate these incidents; bullies are the least targeted shooting victims; perpetrators of the shooting incidents were often bullying others; and, bullying is only one of many important factors associated with perpetrating such an incident (Ioannou, Hammond, & Simpson, 2015; Mears, Moon, & Thielo, 2017; Newman &Fox, 2009; Raitanen, Sandberg, & Oksanen, 2017). While mass media reports deliver bullying as a means of explaining the inexplicable, the reality is not so simple: “The connection between bullying and school shootings is elusive” (Langman, 2014), and “the relationship between bullying and school shooting is, at best, tenuous” (Mears, Moon, & Thielo, 2017, p. 940). The point is, when a narrative becomes this publicly accepted, it is difficult to sort out truth and reality without going back to original sources of information and evidence.

What May or May Not Be Empirical Literature: Literature Reviews

Investigators typically engage in a review of existing literature as they develop their own research studies. The review informs them about where knowledge gaps exist, methods previously employed by other scholars, limitations of prior work, and previous scholars’ recommendations for directing future research. These reviews may appear as a published article, without new study data being reported (see Fields, Anderson, & Dabelko-Schoeny, 2014 for example). Or, the literature review may appear in the introduction to their own empirical study report. These literature reviews are not considered to be empirical evidence sources themselves, although they may be based on empirical evidence sources. One reason is that the authors of a literature review may or may not have engaged in a systematic search process, identifying a full, rich, multi-sided pool of evidence reports.

There is, however, a type of review that applies systematic methods and is, therefore, considered to be more strongly rooted in evidence: the systematic review .

Systematic review of literature. A systematic reviewis a type of literature report where established methods have been systematically applied, objectively, in locating and synthesizing a body of literature. The systematic review report is characterized by a great deal of transparency about the methods used and the decisions made in the review process, and are replicable. Thus, it meets the criteria for empirical literature: systematic observation and methodology, objectivity, and transparency/reproducibility. We will work a great deal more with systematic reviews in the second course, SWK 3402, since they are important tools for understanding interventions. They are somewhat less common, but not unheard of, in helping us understand diverse populations, social work problems, and social phenomena.

Locating Empirical Evidence

Social workers have available a wide array of tools and resources for locating empirical evidence in the literature. These can be organized into four general categories.

Journal Articles. A number of professional journals publish articles where investigators report on the results of their empirical studies. However, it is important to know how to distinguish between empirical and non-empirical manuscripts in these journals. A key indicator, though not the only one, involves a peer review process . Many professional journals require that manuscripts undergo a process of peer review before they are accepted for publication. This means that the authors’ work is shared with scholars who provide feedback to the journal editor as to the quality of the submitted manuscript. The editor then makes a decision based on the reviewers’ feedback:

Accept as is
Accept with minor revisions
Request that a revision be resubmitted (no assurance of acceptance)

When a “revise and resubmit” decision is made, the piece will go back through the review process to determine if it is now acceptable for publication and that all of the reviewers’ concerns have been adequately addressed. Editors may also reject a manuscript because it is a poor fit for the journal, based on its mission and audience, rather than sending it for review consideration.

Word cloud of social work related publications

Indicators of journal relevance. Various journals are not equally relevant to every type of question being asked of the literature. Journals may overlap to a great extent in terms of the topics they might cover; in other words, a topic might appear in multiple different journals, depending on how the topic was being addressed. For example, articles that might help answer a question about the relationship between community poverty and violence exposure might appear in several different journals, some with a focus on poverty, others with a focus on violence, and still others on community development or public health. Journal titles are sometimes a good starting point but may not give a broad enough picture of what they cover in their contents.

In focusing a literature search, it also helps to review a journal’s mission and target audience. For example, at least four different journals focus specifically on poverty:

Journal of Children & Poverty
Journal of Poverty
Journal of Poverty and Social Justice
Poverty & Public Policy

Let’s look at an example using the Journal of Poverty and Social Justice . Information about this journal is located on the journal’s webpage: http://policy.bristoluniversitypress.co.uk/journals/journal-of-poverty-and-social-justice . In the section headed “About the Journal” you can see that it is an internationally focused research journal, and that it addresses social justice issues in addition to poverty alone. The research articles are peer-reviewed (there appear to be non-empirical discussions published, as well). These descriptions about a journal are almost always available, sometimes listed as “scope” or “mission.” These descriptions also indicate the sponsorship of the journal—sponsorship may be institutional (a particular university or agency, such as Smith College Studies in Social Work ), a professional organization, such as the Council on Social Work Education (CSWE) or the National Association of Social Work (NASW), or a publishing company (e.g., Taylor & Frances, Wiley, or Sage).

Indicators of journal caliber. Despite engaging in a peer review process, not all journals are equally rigorous. Some journals have very high rejection rates, meaning that many submitted manuscripts are rejected; others have fairly high acceptance rates, meaning that relatively few manuscripts are rejected. This is not necessarily the best indicator of quality, however, since newer journals may not be sufficiently familiar to authors with high quality manuscripts and some journals are very specific in terms of what they publish. Another index that is sometimes used is the journal’s impact factor . Impact factor is a quantitative number indicative of how often articles published in the journal are cited in the reference list of other journal articles—the statistic is calculated as the number of times on average each article published in a particular year were cited divided by the number of articles published (the number that could be cited). For example, the impact factor for the Journal of Poverty and Social Justice in our list above was 0.70 in 2017, and for the Journal of Poverty was 0.30. These are relatively low figures compared to a journal like the New England Journal of Medicine with an impact factor of 59.56! This means that articles published in that journal were, on average, cited more than 59 times in the next year or two.

Impact factors are not necessarily the best indicator of caliber, however, since many strong journals are geared toward practitioners rather than scholars, so they are less likely to be cited by other scholars but may have a large impact on a large readership. This may be the case for a journal like the one titled Social Work, the official journal of the National Association of Social Workers. It is distributed free to all members: over 120,000 practitioners, educators, and students of social work world-wide. The journal has a recent impact factor of.790. The journals with social work relevant content have impact factors in the range of 1.0 to 3.0 according to Scimago Journal & Country Rank (SJR), particularly when they are interdisciplinary journals (for example, Child Development , Journal of Marriage and Family , Child Abuse and Neglect , Child Maltreatmen t, Social Service Review , and British Journal of Social Work ). Once upon a time, a reader could locate different indexes comparing the “quality” of social work-related journals. However, the concept of “quality” is difficult to systematically define. These indexes have mostly been replaced by impact ratings, which are not necessarily the best, most robust indicators on which to rely in assessing journal quality. For example, new journals addressing cutting edge topics have not been around long enough to have been evaluated using this particular tool, and it takes a few years for articles to begin to be cited in other, later publications.

Beware of pseudo-, illegitimate, misleading, deceptive, and suspicious journals . Another side effect of living in the Age of Information is that almost anyone can circulate almost anything and call it whatever they wish. This goes for “journal” publications, as well. With the advent of open-access publishing in recent years (electronic resources available without subscription), we have seen an explosion of what are called predatory or junk journals . These are publications calling themselves journals, often with titles very similar to legitimate publications and often with fake editorial boards. These “publications” lack the integrity of legitimate journals. This caution is reminiscent of the discussions earlier in the course about pseudoscience and “snake oil” sales. The predatory nature of many apparent information dissemination outlets has to do with how scientists and scholars may be fooled into submitting their work, often paying to have their work peer-reviewed and published. There exists a “thriving black-market economy of publishing scams,” and at least two “journal blacklists” exist to help identify and avoid these scam journals (Anderson, 2017).

This issue is important to information consumers, because it creates a challenge in terms of identifying legitimate sources and publications. The challenge is particularly important to address when information from on-line, open-access journals is being considered. Open-access is not necessarily a poor choice—legitimate scientists may pay sizeable fees to legitimate publishers to make their work freely available and accessible as open-access resources. On-line access is also not necessarily a poor choice—legitimate publishers often make articles available on-line to provide timely access to the content, especially when publishing the article in hard copy will be delayed by months or even a year or more. On the other hand, stating that a journal engages in a peer-review process is no guarantee of quality—this claim may or may not be truthful. Pseudo- and junk journals may engage in some quality control practices, but may lack attention to important quality control processes, such as managing conflict of interest, reviewing content for objectivity or quality of the research conducted, or otherwise failing to adhere to industry standards (Laine & Winker, 2017).

One resource designed to assist with the process of deciphering legitimacy is the Directory of Open Access Journals (DOAJ). The DOAJ is not a comprehensive listing of all possible legitimate open-access journals, and does not guarantee quality, but it does help identify legitimate sources of information that are openly accessible and meet basic legitimacy criteria. It also is about open-access journals, not the many journals published in hard copy.

An additional caution: Search for article corrections. Despite all of the careful manuscript review and editing, sometimes an error appears in a published article. Most journals have a practice of publishing corrections in future issues. When you locate an article, it is helpful to also search for updates. Here is an example where data presented in an article’s original tables were erroneous, and a correction appeared in a later issue.

Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2017). A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 12(8): e0181722. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558917/
Marchant, A., Hawton, K., Stewart A., Montgomery, P., Singaravelu, V., Lloyd, K., Purdy, N., Daine, K., & John, A. (2018).Correction—A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: The good, the bad and the unknown. PLoS One, 13(3): e0193937. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193937

Search Tools. In this age of information, it is all too easy to find items—the problem lies in sifting, sorting, and managing the vast numbers of items that can be found. For example, a simple Google® search for the topic “community poverty and violence” resulted in about 15,600,000 results! As a means of simplifying the process of searching for journal articles on a specific topic, a variety of helpful tools have emerged. One type of search tool has previously applied a filtering process for you: abstracting and indexing databases . These resources provide the user with the results of a search to which records have already passed through one or more filters. For example, PsycINFO is managed by the American Psychological Association and is devoted to peer-reviewed literature in behavioral science. It contains almost 4.5 million records and is growing every month. However, it may not be available to users who are not affiliated with a university library. Conducting a basic search for our topic of “community poverty and violence” in PsychINFO returned 1,119 articles. Still a large number, but far more manageable. Additional filters can be applied, such as limiting the range in publication dates, selecting only peer reviewed items, limiting the language of the published piece (English only, for example), and specified types of documents (either chapters, dissertations, or journal articles only, for example). Adding the filters for English, peer-reviewed journal articles published between 2010 and 2017 resulted in 346 documents being identified.

Just as was the case with journals, not all abstracting and indexing databases are equivalent. There may be overlap between them, but none is guaranteed to identify all relevant pieces of literature. Here are some examples to consider, depending on the nature of the questions asked of the literature:

Academic Search Complete—multidisciplinary index of 9,300 peer-reviewed journals
AgeLine—multidisciplinary index of aging-related content for over 600 journals
Campbell Collaboration—systematic reviews in education, crime and justice, social welfare, international development
Google Scholar—broad search tool for scholarly literature across many disciplines
MEDLINE/ PubMed—National Library of medicine, access to over 15 million citations
Oxford Bibliographies—annotated bibliographies, each is discipline specific (e.g., psychology, childhood studies, criminology, social work, sociology)
PsycINFO/PsycLIT—international literature on material relevant to psychology and related disciplines
SocINDEX—publications in sociology
Social Sciences Abstracts—multiple disciplines
Social Work Abstracts—many areas of social work are covered
Web of Science—a “meta” search tool that searches other search tools, multiple disciplines

Placing our search for information about “community violence and poverty” into the Social Work Abstracts tool with no additional filters resulted in a manageable 54-item list. Finally, abstracting and indexing databases are another way to determine journal legitimacy: if a journal is indexed in a one of these systems, it is likely a legitimate journal. However, the converse is not necessarily true: if a journal is not indexed does not mean it is an illegitimate or pseudo-journal.

Government Sources. A great deal of information is gathered, analyzed, and disseminated by various governmental branches at the international, national, state, regional, county, and city level. Searching websites that end in.gov is one way to identify this type of information, often presented in articles, news briefs, and statistical reports. These government sources gather information in two ways: they fund external investigations through grants and contracts and they conduct research internally, through their own investigators. Here are some examples to consider, depending on the nature of the topic for which information is sought:

Agency for Healthcare Research and Quality (AHRQ) at https://www.ahrq.gov/
Bureau of Justice Statistics (BJS) at https://www.bjs.gov/
Census Bureau at https://www.census.gov
Morbidity and Mortality Weekly Report of the CDC (MMWR-CDC) at https://www.cdc.gov/mmwr/index.html
Child Welfare Information Gateway at https://www.childwelfare.gov
Children’s Bureau/Administration for Children & Families at https://www.acf.hhs.gov
Forum on Child and Family Statistics at https://www.childstats.gov
National Institutes of Health (NIH) at https://www.nih.gov , including (not limited to):
National Institute on Aging (NIA at https://www.nia.nih.gov
National Institute on Alcohol Abuse and Alcoholism (NIAAA) at https://www.niaaa.nih.gov
National Institute of Child Health and Human Development (NICHD) at https://www.nichd.nih.gov
National Institute on Drug Abuse (NIDA) at https://www.nida.nih.gov
National Institute of Environmental Health Sciences at https://www.niehs.nih.gov
National Institute of Mental Health (NIMH) at https://www.nimh.nih.gov
National Institute on Minority Health and Health Disparities at https://www.nimhd.nih.gov
National Institute of Justice (NIJ) at https://www.nij.gov
Substance Abuse and Mental Health Services Administration (SAMHSA) at https://www.samhsa.gov/
United States Agency for International Development at https://usaid.gov

Each state and many counties or cities have similar data sources and analysis reports available, such as Ohio Department of Health at https://www.odh.ohio.gov/healthstats/dataandstats.aspx and Franklin County at https://statisticalatlas.com/county/Ohio/Franklin-County/Overview . Data are available from international/global resources (e.g., United Nations and World Health Organization), as well.

Other Sources. The Health and Medicine Division (HMD) of the National Academies—previously the Institute of Medicine (IOM)—is a nonprofit institution that aims to provide government and private sector policy and other decision makers with objective analysis and advice for making informed health decisions. For example, in 2018 they produced reports on topics in substance use and mental health concerning the intersection of opioid use disorder and infectious disease, the legal implications of emerging neurotechnologies, and a global agenda concerning the identification and prevention of violence (see http://www.nationalacademies.org/hmd/Global/Topics/Substance-Abuse-Mental-Health.aspx ). The exciting aspect of this resource is that it addresses many topics that are current concerns because they are hoping to help inform emerging policy. The caution to consider with this resource is the evidence is often still emerging, as well.

Numerous “think tank” organizations exist, each with a specific mission. For example, the Rand Corporation is a nonprofit organization offering research and analysis to address global issues since 1948. The institution’s mission is to help improve policy and decision making “to help individuals, families, and communities throughout the world be safer and more secure, healthier and more prosperous,” addressing issues of energy, education, health care, justice, the environment, international affairs, and national security (https://www.rand.org/about/history.html). And, for example, the Robert Woods Johnson Foundation is a philanthropic organization supporting research and research dissemination concerning health issues facing the United States. The foundation works to build a culture of health across systems of care (not only medical care) and communities (https://www.rwjf.org).

While many of these have a great deal of helpful evidence to share, they also may have a strong political bias. Objectivity is often lacking in what information these organizations provide: they provide evidence to support certain points of view. That is their purpose—to provide ideas on specific problems, many of which have a political component. Think tanks “are constantly researching solutions to a variety of the world’s problems, and arguing, advocating, and lobbying for policy changes at local, state, and federal levels” (quoted from https://thebestschools.org/features/most-influential-think-tanks/ ). Helpful information about what this one source identified as the 50 most influential U.S. think tanks includes identifying each think tank’s political orientation. For example, The Heritage Foundation is identified as conservative, whereas Human Rights Watch is identified as liberal.

While not the same as think tanks, many mission-driven organizations also sponsor or report on research, as well. For example, the National Association for Children of Alcoholics (NACOA) in the United States is a registered nonprofit organization. Its mission, along with other partnering organizations, private-sector groups, and federal agencies, is to promote policy and program development in research, prevention and treatment to provide information to, for, and about children of alcoholics (of all ages). Based on this mission, the organization supports knowledge development and information gathering on the topic and disseminates information that serves the needs of this population. While this is a worthwhile mission, there is no guarantee that the information meets the criteria for evidence with which we have been working. Evidence reported by think tank and mission-driven sources must be utilized with a great deal of caution and critical analysis!

In many instances an empirical report has not appeared in the published literature, but in the form of a technical or final report to the agency or program providing the funding for the research that was conducted. One such example is presented by a team of investigators funded by the National Institute of Justice to evaluate a program for training professionals to collect strong forensic evidence in instances of sexual assault (Patterson, Resko, Pierce-Weeks, & Campbell, 2014): https://www.ncjrs.gov/pdffiles1/nij/grants/247081.pdf . Investigators may serve in the capacity of consultant to agencies, programs, or institutions, and provide empirical evidence to inform activities and planning. One such example is presented by Maguire-Jack (2014) as a report to a state’s child maltreatment prevention board: https://preventionboard.wi.gov/Documents/InvestmentInPreventionPrograming_Final.pdf .

When Direct Answers to Questions Cannot Be Found. Sometimes social workers are interested in finding answers to complex questions or questions related to an emerging, not-yet-understood topic. This does not mean giving up on empirical literature. Instead, it requires a bit of creativity in approaching the literature. A Venn diagram might help explain this process. Consider a scenario where a social worker wishes to locate literature to answer a question concerning issues of intersectionality. Intersectionality is a social justice term applied to situations where multiple categorizations or classifications come together to create overlapping, interconnected, or multiplied disadvantage. For example, women with a substance use disorder and who have been incarcerated face a triple threat in terms of successful treatment for a substance use disorder: intersectionality exists between being a woman, having a substance use disorder, and having been in jail or prison. After searching the literature, little or no empirical evidence might have been located on this specific triple-threat topic. Instead, the social worker will need to seek literature on each of the threats individually, and possibly will find literature on pairs of topics (see Figure 3-1). There exists some literature about women’s outcomes for treatment of a substance use disorder (a), some literature about women during and following incarceration (b), and some literature about substance use disorders and incarceration (c). Despite not having a direct line on the center of the intersecting spheres of literature (d), the social worker can develop at least a partial picture based on the overlapping literatures.

Figure 3-1. Venn diagram of intersecting literature sets.

Take a moment to complete the following activity. For each statement about empirical literature, decide if it is true or false.

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Empirical Research: Definition, Methods, Types and Examples

Content Index

Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and qualitative market research methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

LEARN ABOUT: Behavioral Research

You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

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For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

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Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

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Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

Observational method: Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

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Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

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There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

It is used to authenticate traditional research through various experiments and observations.
This research methodology makes the research being conducted more competent and authentic.
It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
The level of control in such a research is high so the researcher can control multiple variables.
It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 35min read

Ever wondered how we gather the facts, unveil hidden truths, and make informed decisions in a world filled with questions? Empirical research holds the key.

In this guide, we'll delve deep into the art and science of empirical research, unraveling its methods, mysteries, and manifold applications. From defining the core principles to mastering data analysis and reporting findings, we're here to equip you with the knowledge and tools to navigate the empirical landscape.

What is Empirical Research?

Empirical research is the cornerstone of scientific inquiry, providing a systematic and structured approach to investigating the world around us. It is the process of gathering and analyzing empirical or observable data to test hypotheses, answer research questions, or gain insights into various phenomena. This form of research relies on evidence derived from direct observation or experimentation, allowing researchers to draw conclusions based on real-world data rather than purely theoretical or speculative reasoning.

Characteristics of Empirical Research

Empirical research is characterized by several key features:

Observation and Measurement : It involves the systematic observation or measurement of variables, events, or behaviors.
Data Collection : Researchers collect data through various methods, such as surveys, experiments, observations, or interviews.
Testable Hypotheses : Empirical research often starts with testable hypotheses that are evaluated using collected data.
Quantitative or Qualitative Data : Data can be quantitative (numerical) or qualitative (non-numerical), depending on the research design.
Statistical Analysis : Quantitative data often undergo statistical analysis to determine patterns , relationships, or significance.
Objectivity and Replicability : Empirical research strives for objectivity, minimizing researcher bias . It should be replicable, allowing other researchers to conduct the same study to verify results.
Conclusions and Generalizations : Empirical research generates findings based on data and aims to make generalizations about larger populations or phenomena.

Importance of Empirical Research

Empirical research plays a pivotal role in advancing knowledge across various disciplines. Its importance extends to academia, industry, and society as a whole. Here are several reasons why empirical research is essential:

Evidence-Based Knowledge : Empirical research provides a solid foundation of evidence-based knowledge. It enables us to test hypotheses, confirm or refute theories, and build a robust understanding of the world.
Scientific Progress : In the scientific community, empirical research fuels progress by expanding the boundaries of existing knowledge. It contributes to the development of theories and the formulation of new research questions.
Problem Solving : Empirical research is instrumental in addressing real-world problems and challenges. It offers insights and data-driven solutions to complex issues in fields like healthcare, economics, and environmental science.
Informed Decision-Making : In policymaking, business, and healthcare, empirical research informs decision-makers by providing data-driven insights. It guides strategies, investments, and policies for optimal outcomes.
Quality Assurance : Empirical research is essential for quality assurance and validation in various industries, including pharmaceuticals, manufacturing, and technology. It ensures that products and processes meet established standards.
Continuous Improvement : Businesses and organizations use empirical research to evaluate performance, customer satisfaction, and product effectiveness. This data-driven approach fosters continuous improvement and innovation.
Human Advancement : Empirical research in fields like medicine and psychology contributes to the betterment of human health and well-being. It leads to medical breakthroughs, improved therapies, and enhanced psychological interventions.
Critical Thinking and Problem Solving : Engaging in empirical research fosters critical thinking skills, problem-solving abilities, and a deep appreciation for evidence-based decision-making.

Empirical research empowers us to explore, understand, and improve the world around us. It forms the bedrock of scientific inquiry and drives progress in countless domains, shaping our understanding of both the natural and social sciences.

How to Conduct Empirical Research?

So, you've decided to dive into the world of empirical research. Let's begin by exploring the crucial steps involved in getting started with your research project.

1. Select a Research Topic

Selecting the right research topic is the cornerstone of a successful empirical study. It's essential to choose a topic that not only piques your interest but also aligns with your research goals and objectives. Here's how to go about it:

Identify Your Interests : Start by reflecting on your passions and interests. What topics fascinate you the most? Your enthusiasm will be your driving force throughout the research process.
Brainstorm Ideas : Engage in brainstorming sessions to generate potential research topics. Consider the questions you've always wanted to answer or the issues that intrigue you.
Relevance and Significance : Assess the relevance and significance of your chosen topic. Does it contribute to existing knowledge? Is it a pressing issue in your field of study or the broader community?
Feasibility : Evaluate the feasibility of your research topic. Do you have access to the necessary resources, data, and participants (if applicable)?

2. Formulate Research Questions

Once you've narrowed down your research topic, the next step is to formulate clear and precise research questions . These questions will guide your entire research process and shape your study's direction. To create effective research questions:

Specificity : Ensure that your research questions are specific and focused. Vague or overly broad questions can lead to inconclusive results.
Relevance : Your research questions should directly relate to your chosen topic. They should address gaps in knowledge or contribute to solving a particular problem.
Testability : Ensure that your questions are testable through empirical methods. You should be able to gather data and analyze it to answer these questions.
Avoid Bias : Craft your questions in a way that avoids leading or biased language. Maintain neutrality to uphold the integrity of your research.

3. Review Existing Literature

Before you embark on your empirical research journey, it's essential to immerse yourself in the existing body of literature related to your chosen topic. This step, often referred to as a literature review, serves several purposes:

Contextualization : Understand the historical context and current state of research in your field. What have previous studies found, and what questions remain unanswered?
Identifying Gaps : Identify gaps or areas where existing research falls short. These gaps will help you formulate meaningful research questions and hypotheses.
Theory Development : If your study is theoretical, consider how existing theories apply to your topic. If it's empirical, understand how previous studies have approached data collection and analysis.
Methodological Insights : Learn from the methodologies employed in previous research. What methods were successful, and what challenges did researchers face?

4. Define Variables

Variables are fundamental components of empirical research. They are the factors or characteristics that can change or be manipulated during your study. Properly defining and categorizing variables is crucial for the clarity and validity of your research. Here's what you need to know:

Independent Variables : These are the variables that you, as the researcher, manipulate or control. They are the "cause" in cause-and-effect relationships.
Dependent Variables : Dependent variables are the outcomes or responses that you measure or observe. They are the "effect" influenced by changes in independent variables.
Operational Definitions : To ensure consistency and clarity, provide operational definitions for your variables. Specify how you will measure or manipulate each variable.
Control Variables : In some studies, controlling for other variables that may influence your dependent variable is essential. These are known as control variables.

Understanding these foundational aspects of empirical research will set a solid foundation for the rest of your journey. Now that you've grasped the essentials of getting started, let's delve deeper into the intricacies of research design.

Empirical Research Design

Now that you've selected your research topic, formulated research questions, and defined your variables, it's time to delve into the heart of your empirical research journey – research design . This pivotal step determines how you will collect data and what methods you'll employ to answer your research questions. Let's explore the various facets of research design in detail.

Types of Empirical Research

Empirical research can take on several forms, each with its own unique approach and methodologies. Understanding the different types of empirical research will help you choose the most suitable design for your study. Here are some common types:

Experimental Research : In this type, researchers manipulate one or more independent variables to observe their impact on dependent variables. It's highly controlled and often conducted in a laboratory setting.
Observational Research : Observational research involves the systematic observation of subjects or phenomena without intervention. Researchers are passive observers, documenting behaviors, events, or patterns.
Survey Research : Surveys are used to collect data through structured questionnaires or interviews. This method is efficient for gathering information from a large number of participants.
Case Study Research : Case studies focus on in-depth exploration of one or a few cases. Researchers gather detailed information through various sources such as interviews, documents, and observations.
Qualitative Research : Qualitative research aims to understand behaviors, experiences, and opinions in depth. It often involves open-ended questions, interviews, and thematic analysis.
Quantitative Research : Quantitative research collects numerical data and relies on statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys.

Your choice of research type should align with your research questions and objectives. Experimental research, for example, is ideal for testing cause-and-effect relationships, while qualitative research is more suitable for exploring complex phenomena.

Experimental Design

Experimental research is a systematic approach to studying causal relationships. It's characterized by the manipulation of one or more independent variables while controlling for other factors. Here are some key aspects of experimental design:

Control and Experimental Groups : Participants are randomly assigned to either a control group or an experimental group. The independent variable is manipulated for the experimental group but not for the control group.
Randomization : Randomization is crucial to eliminate bias in group assignment. It ensures that each participant has an equal chance of being in either group.
Hypothesis Testing : Experimental research often involves hypothesis testing. Researchers formulate hypotheses about the expected effects of the independent variable and use statistical analysis to test these hypotheses.

Observational Design

Observational research entails careful and systematic observation of subjects or phenomena. It's advantageous when you want to understand natural behaviors or events. Key aspects of observational design include:

Participant Observation : Researchers immerse themselves in the environment they are studying. They become part of the group being observed, allowing for a deep understanding of behaviors.
Non-Participant Observation : In non-participant observation, researchers remain separate from the subjects. They observe and document behaviors without direct involvement.
Data Collection Methods : Observational research can involve various data collection methods, such as field notes, video recordings, photographs, or coding of observed behaviors.

Survey Design

Surveys are a popular choice for collecting data from a large number of participants. Effective survey design is essential to ensure the validity and reliability of your data. Consider the following:

Questionnaire Design : Create clear and concise questions that are easy for participants to understand. Avoid leading or biased questions.
Sampling Methods : Decide on the appropriate sampling method for your study, whether it's random, stratified, or convenience sampling.
Data Collection Tools : Choose the right tools for data collection, whether it's paper surveys, online questionnaires, or face-to-face interviews.

Case Study Design

Case studies are an in-depth exploration of one or a few cases to gain a deep understanding of a particular phenomenon. Key aspects of case study design include:

Single Case vs. Multiple Case Studies : Decide whether you'll focus on a single case or multiple cases. Single case studies are intensive and allow for detailed examination, while multiple case studies provide comparative insights.
Data Collection Methods : Gather data through interviews, observations, document analysis, or a combination of these methods.

Qualitative vs. Quantitative Research

In empirical research, you'll often encounter the distinction between qualitative and quantitative research . Here's a closer look at these two approaches:

Qualitative Research : Qualitative research seeks an in-depth understanding of human behavior, experiences, and perspectives. It involves open-ended questions, interviews, and the analysis of textual or narrative data. Qualitative research is exploratory and often used when the research question is complex and requires a nuanced understanding.
Quantitative Research : Quantitative research collects numerical data and employs statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys. Quantitative research is ideal for testing hypotheses and establishing cause-and-effect relationships.

Understanding the various research design options is crucial in determining the most appropriate approach for your study. Your choice should align with your research questions, objectives, and the nature of the phenomenon you're investigating.

Data Collection for Empirical Research

Now that you've established your research design, it's time to roll up your sleeves and collect the data that will fuel your empirical research. Effective data collection is essential for obtaining accurate and reliable results.

Sampling Methods

Sampling methods are critical in empirical research, as they determine the subset of individuals or elements from your target population that you will study. Here are some standard sampling methods:

Random Sampling : Random sampling ensures that every member of the population has an equal chance of being selected. It minimizes bias and is often used in quantitative research.
Stratified Sampling : Stratified sampling involves dividing the population into subgroups or strata based on specific characteristics (e.g., age, gender, location). Samples are then randomly selected from each stratum, ensuring representation of all subgroups.
Convenience Sampling : Convenience sampling involves selecting participants who are readily available or easily accessible. While it's convenient, it may introduce bias and limit the generalizability of results.
Snowball Sampling : Snowball sampling is instrumental when studying hard-to-reach or hidden populations. One participant leads you to another, creating a "snowball" effect. This method is common in qualitative research.
Purposive Sampling : In purposive sampling, researchers deliberately select participants who meet specific criteria relevant to their research questions. It's often used in qualitative studies to gather in-depth information.

The choice of sampling method depends on the nature of your research, available resources, and the degree of precision required. It's crucial to carefully consider your sampling strategy to ensure that your sample accurately represents your target population.

Data Collection Instruments

Data collection instruments are the tools you use to gather information from your participants or sources. These instruments should be designed to capture the data you need accurately. Here are some popular data collection instruments:

Questionnaires : Questionnaires consist of structured questions with predefined response options. When designing questionnaires, consider the clarity of questions, the order of questions, and the response format (e.g., Likert scale , multiple-choice).
Interviews : Interviews involve direct communication between the researcher and participants. They can be structured (with predetermined questions) or unstructured (open-ended). Effective interviews require active listening and probing for deeper insights.
Observations : Observations entail systematically and objectively recording behaviors, events, or phenomena. Researchers must establish clear criteria for what to observe, how to record observations, and when to observe.
Surveys : Surveys are a common data collection instrument for quantitative research. They can be administered through various means, including online surveys, paper surveys, and telephone surveys.
Documents and Archives : In some cases, data may be collected from existing documents, records, or archives. Ensure that the sources are reliable, relevant, and properly documented.

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Data Collection Procedures

Data collection procedures outline the step-by-step process for gathering data. These procedures should be meticulously planned and executed to maintain the integrity of your research.

Training : If you have a research team, ensure that they are trained in data collection methods and protocols. Consistency in data collection is crucial.
Pilot Testing : Before launching your data collection, conduct a pilot test with a small group to identify any potential problems with your instruments or procedures. Make necessary adjustments based on feedback.
Data Recording : Establish a systematic method for recording data. This may include timestamps, codes, or identifiers for each data point.
Data Security : Safeguard the confidentiality and security of collected data. Ensure that only authorized individuals have access to the data.
Data Storage : Properly organize and store your data in a secure location, whether in physical or digital form. Back up data to prevent loss.

Ethical Considerations

Ethical considerations are paramount in empirical research, as they ensure the well-being and rights of participants are protected.

Informed Consent : Obtain informed consent from participants, providing clear information about the research purpose, procedures, risks, and their right to withdraw at any time.
Privacy and Confidentiality : Protect the privacy and confidentiality of participants. Ensure that data is anonymized and sensitive information is kept confidential.
Beneficence : Ensure that your research benefits participants and society while minimizing harm. Consider the potential risks and benefits of your study.
Honesty and Integrity : Conduct research with honesty and integrity. Report findings accurately and transparently, even if they are not what you expected.
Respect for Participants : Treat participants with respect, dignity, and sensitivity to cultural differences. Avoid any form of coercion or manipulation.
Institutional Review Board (IRB) : If required, seek approval from an IRB or ethics committee before conducting your research, particularly when working with human participants.

Adhering to ethical guidelines is not only essential for the ethical conduct of research but also crucial for the credibility and validity of your study. Ethical research practices build trust between researchers and participants and contribute to the advancement of knowledge with integrity.

With a solid understanding of data collection, including sampling methods, instruments, procedures, and ethical considerations, you are now well-equipped to gather the data needed to answer your research questions.

Empirical Research Data Analysis

Now comes the exciting phase of data analysis, where the raw data you've diligently collected starts to yield insights and answers to your research questions. We will explore the various aspects of data analysis, from preparing your data to drawing meaningful conclusions through statistics and visualization.

Data Preparation

Data preparation is the crucial first step in data analysis. It involves cleaning, organizing, and transforming your raw data into a format that is ready for analysis. Effective data preparation ensures the accuracy and reliability of your results.

Data Cleaning : Identify and rectify errors, missing values, and inconsistencies in your dataset. This may involve correcting typos, removing outliers, and imputing missing data.
Data Coding : Assign numerical values or codes to categorical variables to make them suitable for statistical analysis. For example, converting "Yes" and "No" to 1 and 0.
Data Transformation : Transform variables as needed to meet the assumptions of the statistical tests you plan to use. Common transformations include logarithmic or square root transformations.
Data Integration : If your data comes from multiple sources, integrate it into a unified dataset, ensuring that variables match and align.
Data Documentation : Maintain clear documentation of all data preparation steps, as well as the rationale behind each decision. This transparency is essential for replicability.

Effective data preparation lays the foundation for accurate and meaningful analysis. It allows you to trust the results that will follow in the subsequent stages.

Descriptive Statistics

Descriptive statistics help you summarize and make sense of your data by providing a clear overview of its key characteristics. These statistics are essential for understanding the central tendencies, variability, and distribution of your variables. Descriptive statistics include:

Measures of Central Tendency : These include the mean (average), median (middle value), and mode (most frequent value). They help you understand the typical or central value of your data.
Measures of Dispersion : Measures like the range, variance, and standard deviation provide insights into the spread or variability of your data points.
Frequency Distributions : Creating frequency distributions or histograms allows you to visualize the distribution of your data across different values or categories.

Descriptive statistics provide the initial insights needed to understand your data's basic characteristics, which can inform further analysis.

Inferential Statistics

Inferential statistics take your analysis to the next level by allowing you to make inferences or predictions about a larger population based on your sample data. These methods help you test hypotheses and draw meaningful conclusions. Key concepts in inferential statistics include:

Hypothesis Testing : Hypothesis tests (e.g., t-tests, chi-squared tests) help you determine whether observed differences or associations in your data are statistically significant or occurred by chance.
Confidence Intervals : Confidence intervals provide a range within which population parameters (e.g., population mean) are likely to fall based on your sample data.
Regression Analysis : Regression models (linear, logistic, etc.) help you explore relationships between variables and make predictions.
Analysis of Variance (ANOVA) : ANOVA tests are used to compare means between multiple groups, allowing you to assess whether differences are statistically significant.

Inferential statistics are powerful tools for drawing conclusions from your data and assessing the generalizability of your findings to the broader population.

Qualitative Data Analysis

Qualitative data analysis is employed when working with non-numerical data, such as text, interviews, or open-ended survey responses. It focuses on understanding the underlying themes, patterns, and meanings within qualitative data. Qualitative analysis techniques include:

Thematic Analysis : Identifying and analyzing recurring themes or patterns within textual data.
Content Analysis : Categorizing and coding qualitative data to extract meaningful insights.
Grounded Theory : Developing theories or frameworks based on emergent themes from the data.
Narrative Analysis : Examining the structure and content of narratives to uncover meaning.

Qualitative data analysis provides a rich and nuanced understanding of complex phenomena and human experiences.

Data Visualization

Data visualization is the art of representing data graphically to make complex information more understandable and accessible. Effective data visualization can reveal patterns, trends, and outliers in your data. Common types of data visualization include:

Bar Charts and Histograms : Used to display the distribution of categorical or discrete data.
Line Charts : Ideal for showing trends and changes in data over time.
Scatter Plots : Visualize relationships and correlations between two variables.
Pie Charts : Display the composition of a whole in terms of its parts.
Heatmaps : Depict patterns and relationships in multidimensional data through color-coding.
Box Plots : Provide a summary of the data distribution, including outliers.
Interactive Dashboards : Create dynamic visualizations that allow users to explore data interactively.

Data visualization not only enhances your understanding of the data but also serves as a powerful communication tool to convey your findings to others.

As you embark on the data analysis phase of your empirical research, remember that the specific methods and techniques you choose will depend on your research questions, data type, and objectives. Effective data analysis transforms raw data into valuable insights, bringing you closer to the answers you seek.

How to Report Empirical Research Results?

At this stage, you get to share your empirical research findings with the world. Effective reporting and presentation of your results are crucial for communicating your research's impact and insights.

1. Write the Research Paper

Writing a research paper is the culmination of your empirical research journey. It's where you synthesize your findings, provide context, and contribute to the body of knowledge in your field.

Title and Abstract : Craft a clear and concise title that reflects your research's essence. The abstract should provide a brief summary of your research objectives, methods, findings, and implications.
Introduction : In the introduction, introduce your research topic, state your research questions or hypotheses, and explain the significance of your study. Provide context by discussing relevant literature.
Methods : Describe your research design, data collection methods, and sampling procedures. Be precise and transparent, allowing readers to understand how you conducted your study.
Results : Present your findings in a clear and organized manner. Use tables, graphs, and statistical analyses to support your results. Avoid interpreting your findings in this section; focus on the presentation of raw data.
Discussion : Interpret your findings and discuss their implications. Relate your results to your research questions and the existing literature. Address any limitations of your study and suggest avenues for future research.
Conclusion : Summarize the key points of your research and its significance. Restate your main findings and their implications.
References : Cite all sources used in your research following a specific citation style (e.g., APA, MLA, Chicago). Ensure accuracy and consistency in your citations.
Appendices : Include any supplementary material, such as questionnaires, data coding sheets, or additional analyses, in the appendices.

Writing a research paper is a skill that improves with practice. Ensure clarity, coherence, and conciseness in your writing to make your research accessible to a broader audience.

2. Create Visuals and Tables

Visuals and tables are powerful tools for presenting complex data in an accessible and understandable manner.

Clarity : Ensure that your visuals and tables are clear and easy to interpret. Use descriptive titles and labels.
Consistency : Maintain consistency in formatting, such as font size and style, across all visuals and tables.
Appropriateness : Choose the most suitable visual representation for your data. Bar charts, line graphs, and scatter plots work well for different types of data.
Simplicity : Avoid clutter and unnecessary details. Focus on conveying the main points.
Accessibility : Make sure your visuals and tables are accessible to a broad audience, including those with visual impairments.
Captions : Include informative captions that explain the significance of each visual or table.

Compelling visuals and tables enhance the reader's understanding of your research and can be the key to conveying complex information efficiently.

3. Interpret Findings

Interpreting your findings is where you bridge the gap between data and meaning. It's your opportunity to provide context, discuss implications, and offer insights. When interpreting your findings:

Relate to Research Questions : Discuss how your findings directly address your research questions or hypotheses.
Compare with Literature : Analyze how your results align with or deviate from previous research in your field. What insights can you draw from these comparisons?
Discuss Limitations : Be transparent about the limitations of your study. Address any constraints, biases, or potential sources of error.
Practical Implications : Explore the real-world implications of your findings. How can they be applied or inform decision-making?
Future Research Directions : Suggest areas for future research based on the gaps or unanswered questions that emerged from your study.

Interpreting findings goes beyond simply presenting data; it's about weaving a narrative that helps readers grasp the significance of your research in the broader context.

With your research paper written, structured, and enriched with visuals, and your findings expertly interpreted, you are now prepared to communicate your research effectively. Sharing your insights and contributing to the body of knowledge in your field is a significant accomplishment in empirical research.

Examples of Empirical Research

To solidify your understanding of empirical research, let's delve into some real-world examples across different fields. These examples will illustrate how empirical research is applied to gather data, analyze findings, and draw conclusions.

Social Sciences

In the realm of social sciences, consider a sociological study exploring the impact of socioeconomic status on educational attainment. Researchers gather data from a diverse group of individuals, including their family backgrounds, income levels, and academic achievements.

Through statistical analysis, they can identify correlations and trends, revealing whether individuals from lower socioeconomic backgrounds are less likely to attain higher levels of education. This empirical research helps shed light on societal inequalities and informs policymakers on potential interventions to address disparities in educational access.

Environmental Science

Environmental scientists often employ empirical research to assess the effects of environmental changes. For instance, researchers studying the impact of climate change on wildlife might collect data on animal populations, weather patterns, and habitat conditions over an extended period.

By analyzing this empirical data, they can identify correlations between climate fluctuations and changes in wildlife behavior, migration patterns, or population sizes. This empirical research is crucial for understanding the ecological consequences of climate change and informing conservation efforts.

Business and Economics

In the business world, empirical research is essential for making data-driven decisions. Consider a market research study conducted by a business seeking to launch a new product. They collect data through surveys, focus groups, and consumer behavior analysis.

By examining this empirical data, the company can gauge consumer preferences, demand, and potential market size. Empirical research in business helps guide product development, pricing strategies, and marketing campaigns, increasing the likelihood of a successful product launch.

Psychological studies frequently rely on empirical research to understand human behavior and cognition. For instance, a psychologist interested in examining the impact of stress on memory might design an experiment. Participants are exposed to stress-inducing situations, and their memory performance is assessed through various tasks.

By analyzing the data collected, the psychologist can determine whether stress has a significant effect on memory recall. This empirical research contributes to our understanding of the complex interplay between psychological factors and cognitive processes.

These examples highlight the versatility and applicability of empirical research across diverse fields. Whether in medicine, social sciences, environmental science, business, or psychology, empirical research serves as a fundamental tool for gaining insights, testing hypotheses, and driving advancements in knowledge and practice.

Conclusion for Empirical Research

Empirical research is a powerful tool for gaining insights, testing hypotheses, and making informed decisions. By following the steps outlined in this guide, you've learned how to select research topics, collect data, analyze findings, and effectively communicate your research to the world. Remember, empirical research is a journey of discovery, and each step you take brings you closer to a deeper understanding of the world around you. Whether you're a scientist, a student, or someone curious about the process, the principles of empirical research empower you to explore, learn, and contribute to the ever-expanding realm of knowledge.

How to Collect Data for Empirical Research?

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Empirical research in the social sciences and education.

What is Empirical Research and How to Read It
Finding Empirical Research in Library Databases
Designing Empirical Research
Ethics, Cultural Responsiveness, and Anti-Racism in Research
Citing, Writing, and Presenting Your Work

Contact the Librarian at your campus for more help!

Introduction: What is Empirical Research?

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief.

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology." Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

Specific research questions to be answered
Definition of the population, behavior, or phenomena being studied
Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools used in the present study
Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Reading and Evaluating Scholarly Materials

Reading research can be a challenge. However, the tutorials and videos below can help. They explain what scholarly articles look like, how to read them, and how to evaluate them:

CRAAP Checklist A frequently-used checklist that helps you examine the currency, relevance, authority, accuracy, and purpose of an information source.
IF I APPLY A newer model of evaluating sources which encourages you to think about your own biases as a reader, as well as concerns about the item you are reading.
Credo Video: How to Read Scholarly Materials (4 min.)
Credo Tutorial: How to Read Scholarly Materials
Credo Tutorial: Evaluating Information
Credo Video: Evaluating Statistics (4 min.)
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Empirical Research: A Comprehensive Guide for Academics

Empirical research relies on gathering and studying real, observable data. The term ’empirical’ comes from the Greek word ’empeirikos,’ meaning ‘experienced’ or ‘based on experience.’ So, what is empirical research? Instead of using theories or opinions, empirical research depends on real data obtained through direct observation or experimentation.

Why Empirical Research?

Empirical research plays a key role in checking or improving current theories, providing a systematic way to grow knowledge across different areas. By focusing on objectivity, it makes research findings more trustworthy, which is critical in research fields like medicine, psychology, economics, and public policy. In the end, the strengths of empirical research lie in deepening our awareness of the world and improving our capacity to tackle problems wisely. 1,2

Qualitative and Quantitative Methods

There are two main types of empirical research methods – qualitative and quantitative. 3,4 Qualitative research delves into intricate phenomena using non-numerical data, such as interviews or observations, to offer in-depth insights into human experiences. In contrast, quantitative research analyzes numerical data to spot patterns and relationships, aiming for objectivity and the ability to apply findings to a wider context.

Steps for Conducting Empirical Research

When it comes to conducting research, there are some simple steps that researchers can follow. 5,6

Create Research Hypothesis: Clearly state the specific question you want to answer or the hypothesis you want to explore in your study.
Examine Existing Research: Read and study existing research on your topic. Understand what’s already known, identify existing gaps in knowledge, and create a framework for your own study based on what you learn.
Plan Your Study: Decide how you’ll conduct your research—whether through qualitative methods, quantitative methods, or a mix of both. Choose suitable techniques like surveys, experiments, interviews, or observations based on your research question.
Develop Research Instruments: Create reliable research collection tools, such as surveys or questionnaires, to help you collate data. Ensure these tools are well-designed and effective.
Collect Data: Systematically gather the information you need for your research according to your study design and protocols using the chosen research methods.
Data Analysis: Analyze the collected data using suitable statistical or qualitative methods that align with your research question and objectives.
Interpret Results: Understand and explain the significance of your analysis results in the context of your research question or hypothesis.
Draw Conclusions: Summarize your findings and draw conclusions based on the evidence. Acknowledge any study limitations and propose areas for future research.

Advantages of Empirical Research

Empirical research is valuable because it stays objective by relying on observable data, lessening the impact of personal biases. This objectivity boosts the trustworthiness of research findings. Also, using precise quantitative methods helps in accurate measurement and statistical analysis. This precision ensures researchers can draw reliable conclusions from numerical data, strengthening our understanding of the studied phenomena. 4

Disadvantages of Empirical Research

While empirical research has notable strengths, researchers must also be aware of its limitations when deciding on the right research method for their study.4 One significant drawback of empirical research is the risk of oversimplifying complex phenomena, especially when relying solely on quantitative methods. These methods may struggle to capture the richness and nuances present in certain social, cultural, or psychological contexts. Another challenge is the potential for confounding variables or biases during data collection, impacting result accuracy.

Tips for Empirical Writing

In empirical research, the writing is usually done in research papers, articles, or reports. The empirical writing follows a set structure, and each section has a specific role. Here are some tips for your empirical writing. 7

Define Your Objectives: When you write about your research, start by making your goals clear. Explain what you want to find out or prove in a simple and direct way. This helps guide your research and lets others know what you have set out to achieve.
Be Specific in Your Literature Review: In the part where you talk about what others have studied before you, focus on research that directly relates to your research question. Keep it short and pick studies that help explain why your research is important. This part sets the stage for your work.
Explain Your Methods Clearly : When you talk about how you did your research (Methods), explain it in detail. Be clear about your research plan, who took part, and what you did; this helps others understand and trust your study. Also, be honest about any rules you follow to make sure your study is ethical and reproducible.
Share Your Results Clearly : After doing your empirical research, share what you found in a simple way. Use tables or graphs to make it easier for your audience to understand your research. Also, talk about any numbers you found and clearly state if they are important or not. Ensure that others can see why your research findings matter.
Talk About What Your Findings Mean: In the part where you discuss your research results, explain what they mean. Discuss why your findings are important and if they connect to what others have found before. Be honest about any problems with your study and suggest ideas for more research in the future.
Wrap It Up Clearly: Finally, end your empirical research paper by summarizing what you found and why it’s important. Remind everyone why your study matters. Keep your writing clear and fix any mistakes before you share it. Ask someone you trust to read it and give you feedback before you finish.

References:

Empirical Research in the Social Sciences and Education, Penn State University Libraries. Available online at https://guides.libraries.psu.edu/emp
How to conduct empirical research, Emerald Publishing. Available online at https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research
Empirical Research: Quantitative & Qualitative, Arrendale Library, Piedmont University. Available online at https://library.piedmont.edu/empirical-research
Bouchrika, I. What Is Empirical Research? Definition, Types & Samples in 2024. Research.com, January 2024. Available online at https://research.com/research/what-is-empirical-research
Quantitative and Empirical Research vs. Other Types of Research. California State University, April 2023. Available online at https://libguides.csusb.edu/quantitative
Empirical Research, Definitions, Methods, Types and Examples, Studocu.com website. Available online at https://www.studocu.com/row/document/uganda-christian-university/it-research-methods/emperical-research-definitions-methods-types-and-examples/55333816
Writing an Empirical Paper in APA Style. Psychology Writing Center, University of Washington. Available online at https://psych.uw.edu/storage/writing_center/APApaper.pdf

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Ethics in Science: Importance, Principles & Guidelines

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Encyclopedia of Psychology and Religion pp 782–783 Cite as

Empirical Research

Emeka Thaddues Njoku 3
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The term “empirical” entails gathered data based on experience, observations, or experimentation. In empirical research, knowledge is developed from factual experience as opposed to theoretical assumption and usually involved the use of data sources like datasets or fieldwork, but can also be based on observations within a laboratory setting. Testing hypothesis or answering definite questions is a primary feature of empirical research. Empirical research, in other words, involves the process of employing working hypothesis that are tested through experimentation or observation. Hence, empirical research is a method of uncovering empirical evidence.

Through the process of gathering valid empirical data, scientists from a variety of fields, ranging from the social to the natural sciences, have to carefully design their methods. This helps to ensure quality and accuracy of data collection and treatment. However, any error in empirical data collection process could inevitably render such...

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Bibliography

Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. Textbooks Collection . Book 3.

Google Scholar

Comte, A., & Bridges, J. H. (Tr.) (1865). A general view of positivism . Trubner and Co. (reissued by Cambridge University Press, 2009).

Dilworth, C. B. (1982). Empirical research in the literature class. English Journal, 71 (3), 95–97.

Article Google Scholar

Heisenberg, W. (1971). Positivism, metaphysics and religion. In R. N. Nanshen (Ed.), Werner Heisenberg – Physics and beyond – Encounters and conversations , World Perspectives. 42. Translator: Arnold J. Pomerans. New York: Harper and Row.

Hossain, F. M. A. (2014). A critical analysis of empiricism. Open Journal of Philosophy, 2014 (4), 225–230.

Kant, I. (1783). Prolegomena to any future metaphysic (trans: Bennett, J.). Early Modern Texts. www.earlymoderntexts.com

Koch, S. (1992). Psychology’s Bridgman vs. Bridgman’s Bridgman: An essay in reconstruction. Theory and Psychology, 2 (3), 261–290.

Matin, A. (1968). An outline of philosophy . Dhaka: Mullick Brothers.

Mcleod, S. (2008). Psychology as science. http://www.simplypsychology.org/science-psychology.html

Popper, K. (1963). Conjectures and refutations: The growth of scientific knowledge . London: Routledge.

Simmel, G. (1908). The problem areas of sociology in Kurt H. Wolf: The sociology of Georg Simmel . London: The Free Press.

Weber, M. (1991). The nature of social action. In W. G. Runciman (Ed.), Weber: Selections in translation . Cambridge: Cambridge University Press.

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Njoku, E.T. (2020). Empirical Research. In: Leeming, D.A. (eds) Encyclopedia of Psychology and Religion. Springer, Cham. https://doi.org/10.1007/978-3-030-24348-7_200051

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Empirical Research in the Social Sciences and Education

What is empirical research.

Finding Empirical Research
Designing Empirical Research
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Citing, Writing, and Presenting Your Work

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An empirical research article is a primary source where the authors reported on experiments or observations that they conducted. Their research includes their observed and measured data that they derived from an actual experiment rather than theory or belief.

How do you know if you are reading an empirical article? Ask yourself: "What did the authors actually do?" or "How could this study be re-created?"

Key characteristics to look for:

Specific research questions to be answered
Definition of the population, behavior, or phenomena being studied
Description of the process or methodology used to study this population or phenomena, including selection criteria, controls, and testing instruments (example: surveys, questionnaires, etc)
You can readily describe what the authors actually did

Layout of Empirical Articles

Scholarly journals sometimes use a specific layout for empirical articles, called the "IMRaD" format, to communicate empirical research findings. There are four main components:

Introduction : aka "literature review". This section summarizes what is known about the topic at the time of the article's publication. It brings the reader up-to-speed on the research and usually includes a theoretical framework
Methodology : aka "research design". This section describes exactly how the study was done. It describes the population, research process, and analytical tools
Results : aka "findings". This section describes what was learned in the study. It usually contains statistical data or substantial quotes from research participants
Discussion : aka "conclusion" or "implications". This section explains why the study is important, and also describes the limitations of the study. While research results can influence professional practices and future studies, it's important for the researchers to clarify if specific aspects of the study should limit its use. For example, a study using undergraduate students at a small, western, private college can not be extrapolated to include all undergraduates.
Next: Finding Empirical Research >>
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Empirical Research: Quantitative & Qualitative

Empirical Research

Introduction: What is Empirical Research?

Quantitative methods, qualitative methods.

Quantitative vs. Qualitative
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Empirical research is based on phenomena that can be observed and measured. Empirical research derives knowledge from actual experience rather than from theory or belief.

Key characteristics of empirical research include:

Specific research questions to be answered;
Definitions of the population, behavior, or phenomena being studied;
Description of the methodology or research design used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys);
Two basic research processes or methods in empirical research: quantitative methods and qualitative methods (see the rest of the guide for more about these methods).

(based on the original from the Connelly LIbrary of LaSalle University)

Empirical Research: Qualitative vs. Quantitative

Learn about common types of journal articles that use APA Style, including empirical studies; meta-analyses; literature reviews; and replication, theoretical, and methodological articles.

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Quantitative Research

A quantitative research project is characterized by having a population about which the researcher wants to draw conclusions, but it is not possible to collect data on the entire population.

For an observational study, it is necessary to select a proper, statistical random sample and to use methods of statistical inference to draw conclusions about the population.
For an experimental study, it is necessary to have a random assignment of subjects to experimental and control groups in order to use methods of statistical inference.

Statistical methods are used in all three stages of a quantitative research project.

For observational studies, the data are collected using statistical sampling theory. Then, the sample data are analyzed using descriptive statistical analysis. Finally, generalizations are made from the sample data to the entire population using statistical inference.

For experimental studies, the subjects are allocated to experimental and control group using randomizing methods. Then, the experimental data are analyzed using descriptive statistical analysis. Finally, just as for observational data, generalizations are made to a larger population.

Iversen, G. (2004). Quantitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.), Encyclopedia of social science research methods . (pp. 897-898). Thousand Oaks, CA: SAGE Publications, Inc.

Qualitative Research

What makes a work deserving of the label qualitative research is the demonstrable effort to produce richly and relevantly detailed descriptions and particularized interpretations of people and the social, linguistic, material, and other practices and events that shape and are shaped by them.

Qualitative research typically includes, but is not limited to, discerning the perspectives of these people, or what is often referred to as the actor’s point of view. Although both philosophically and methodologically a highly diverse entity, qualitative research is marked by certain defining imperatives that include its case (as opposed to its variable) orientation, sensitivity to cultural and historical context, and reflexivity.

In its many guises, qualitative research is a form of empirical inquiry that typically entails some form of purposive sampling for information-rich cases; in-depth interviews and open-ended interviews, lengthy participant/field observations, and/or document or artifact study; and techniques for analysis and interpretation of data that move beyond the data generated and their surface appearances.

Sandelowski, M. (2004). Qualitative research . In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.), Encyclopedia of social science research methods . (pp. 893-894). Thousand Oaks, CA: SAGE Publications, Inc.

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Identifying Empirical Research Articles

Identifying empirical articles.

Searching for Empirical Research Articles

What is Empirical Research?

An empirical research article reports the results of a study that uses data derived from actual observation or experimentation. Empirical research articles are examples of primary research. To learn more about the differences between primary and secondary research, see our related guide:

Primary and Secondary Sources

By the end of this guide, you will be able to:

Identify common elements of an empirical article
Use a variety of search strategies to search for empirical articles within the library collection

Look for the IMRaD layout in the article to help identify empirical research. Sometimes the sections will be labeled differently, but the content will be similar.

I ntroduction: why the article was written, research question or questions, hypothesis, literature review
M ethods: the overall research design and implementation, description of sample, instruments used, how the authors measured their experiment
R esults: output of the author's measurements, usually includes statistics of the author's findings
D iscussion: the author's interpretation and conclusions about the results, limitations of study, suggestions for further research

Parts of an Empirical Research Article

Parts of an empirical article.

The screenshots below identify the basic IMRaD structure of an empirical research article.

Introduction

The introduction contains a literature review and the study's research hypothesis.

The method section outlines the research design, participants, and measures used.

Results

The results section contains statistical data (charts, graphs, tables, etc.) and research participant quotes.

The discussion section includes impacts, limitations, future considerations, and research.

Learn the IMRaD Layout: How to Identify an Empirical Article

This short video overviews the IMRaD method for identifying empirical research.

Next: Searching for Empirical Research Articles >>
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How to Write a Literature Review | Guide, Examples, & Templates

How to Write a Literature Review | Guide, Examples, & Templates

Published on January 2, 2023 by Shona McCombes . Revised on September 11, 2023.

What is a literature review? A literature review is a survey of scholarly sources on a specific topic. It provides an overview of current knowledge, allowing you to identify relevant theories, methods, and gaps in the existing research that you can later apply to your paper, thesis, or dissertation topic .

There are five key steps to writing a literature review:

Search for relevant literature
Evaluate sources
Identify themes, debates, and gaps
Outline the structure
Write your literature review

A good literature review doesn’t just summarize sources—it analyzes, synthesizes , and critically evaluates to give a clear picture of the state of knowledge on the subject.

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What is the purpose of a literature review, examples of literature reviews, step 1 – search for relevant literature, step 2 – evaluate and select sources, step 3 – identify themes, debates, and gaps, step 4 – outline your literature review’s structure, step 5 – write your literature review, free lecture slides, other interesting articles, frequently asked questions, introduction.

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Step 1 & 2

When you write a thesis , dissertation , or research paper , you will likely have to conduct a literature review to situate your research within existing knowledge. The literature review gives you a chance to:

Demonstrate your familiarity with the topic and its scholarly context
Develop a theoretical framework and methodology for your research
Position your work in relation to other researchers and theorists
Show how your research addresses a gap or contributes to a debate
Evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

Writing literature reviews is a particularly important skill if you want to apply for graduate school or pursue a career in research. We’ve written a step-by-step guide that you can follow below.

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Writing literature reviews can be quite challenging! A good starting point could be to look at some examples, depending on what kind of literature review you’d like to write.

Example literature review #1: “Why Do People Migrate? A Review of the Theoretical Literature” ( Theoretical literature review about the development of economic migration theory from the 1950s to today.)
Example literature review #2: “Literature review as a research methodology: An overview and guidelines” ( Methodological literature review about interdisciplinary knowledge acquisition and production.)
Example literature review #3: “The Use of Technology in English Language Learning: A Literature Review” ( Thematic literature review about the effects of technology on language acquisition.)
Example literature review #4: “Learners’ Listening Comprehension Difficulties in English Language Learning: A Literature Review” ( Chronological literature review about how the concept of listening skills has changed over time.)

You can also check out our templates with literature review examples and sample outlines at the links below.

Download Word doc Download Google doc

Before you begin searching for literature, you need a clearly defined topic .

If you are writing the literature review section of a dissertation or research paper, you will search for literature related to your research problem and questions .

Make a list of keywords

Start by creating a list of keywords related to your research question. Include each of the key concepts or variables you’re interested in, and list any synonyms and related terms. You can add to this list as you discover new keywords in the process of your literature search.

Social media, Facebook, Instagram, Twitter, Snapchat, TikTok
Body image, self-perception, self-esteem, mental health
Generation Z, teenagers, adolescents, youth

Search for relevant sources

Use your keywords to begin searching for sources. Some useful databases to search for journals and articles include:

Your university’s library catalogue
Google Scholar
Project Muse (humanities and social sciences)
Medline (life sciences and biomedicine)
EconLit (economics)
Inspec (physics, engineering and computer science)

You can also use boolean operators to help narrow down your search.

Make sure to read the abstract to find out whether an article is relevant to your question. When you find a useful book or article, you can check the bibliography to find other relevant sources.

You likely won’t be able to read absolutely everything that has been written on your topic, so it will be necessary to evaluate which sources are most relevant to your research question.

For each publication, ask yourself:

What question or problem is the author addressing?
What are the key concepts and how are they defined?
What are the key theories, models, and methods?
Does the research use established frameworks or take an innovative approach?
What are the results and conclusions of the study?
How does the publication relate to other literature in the field? Does it confirm, add to, or challenge established knowledge?
What are the strengths and weaknesses of the research?

Make sure the sources you use are credible , and make sure you read any landmark studies and major theories in your field of research.

You can use our template to summarize and evaluate sources you’re thinking about using. Click on either button below to download.

Take notes and cite your sources

As you read, you should also begin the writing process. Take notes that you can later incorporate into the text of your literature review.

It is important to keep track of your sources with citations to avoid plagiarism . It can be helpful to make an annotated bibliography , where you compile full citation information and write a paragraph of summary and analysis for each source. This helps you remember what you read and saves time later in the process.

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To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
Themes: what questions or concepts recur across the literature?
Debates, conflicts and contradictions: where do sources disagree?
Pivotal publications: are there any influential theories or studies that changed the direction of the field?
Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?

This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

Most research has focused on young women.
There is an increasing interest in the visual aspects of social media.
But there is still a lack of robust research on highly visual platforms like Instagram and Snapchat—this is a gap that you could address in your own research.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological

The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological

If you draw your sources from different disciplines or fields that use a variety of research methods , you might want to compare the results and conclusions that emerge from different approaches. For example:

Look at what results have emerged in qualitative versus quantitative research
Discuss how the topic has been approached by empirical versus theoretical scholarship
Divide the literature into sociological, historical, and cultural sources

Theoretical

A literature review is often the foundation for a theoretical framework . You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.

Like any other academic text , your literature review should have an introduction , a main body, and a conclusion . What you include in each depends on the objective of your literature review.

The introduction should clearly establish the focus and purpose of the literature review.

Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
Analyze and interpret: don’t just paraphrase other researchers — add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
Critically evaluate: mention the strengths and weaknesses of your sources
Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts

In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

When you’ve finished writing and revising your literature review, don’t forget to proofread thoroughly before submitting. Not a language expert? Check out Scribbr’s professional proofreading services !

This article has been adapted into lecture slides that you can use to teach your students about writing a literature review.

Scribbr slides are free to use, customize, and distribute for educational purposes.

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If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Sampling methods
Simple random sampling
Stratified sampling
Cluster sampling
Likert scales
Reproducibility

Statistics

Null hypothesis
Statistical power
Probability distribution
Effect size
Poisson distribution

Research bias

Optimism bias
Cognitive bias
Implicit bias
Hawthorne effect
Anchoring bias
Explicit bias

A literature review is a survey of scholarly sources (such as books, journal articles, and theses) related to a specific topic or research question .

It is often written as part of a thesis, dissertation , or research paper , in order to situate your work in relation to existing knowledge.

There are several reasons to conduct a literature review at the beginning of a research project:

To familiarize yourself with the current state of knowledge on your topic
To ensure that you’re not just repeating what others have already done
To identify gaps in knowledge and unresolved problems that your research can address
To develop your theoretical framework and methodology
To provide an overview of the key findings and debates on the topic

Writing the literature review shows your reader how your work relates to existing research and what new insights it will contribute.

The literature review usually comes near the beginning of your thesis or dissertation . After the introduction , it grounds your research in a scholarly field and leads directly to your theoretical framework or methodology .

A literature review is a survey of credible sources on a topic, often used in dissertations , theses, and research papers . Literature reviews give an overview of knowledge on a subject, helping you identify relevant theories and methods, as well as gaps in existing research. Literature reviews are set up similarly to other academic texts , with an introduction , a main body, and a conclusion .

An annotated bibliography is a list of source references that has a short description (called an annotation ) for each of the sources. It is often assigned as part of the research process for a paper .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, September 11). How to Write a Literature Review | Guide, Examples, & Templates. Scribbr. Retrieved April 15, 2024, from https://www.scribbr.com/dissertation/literature-review/

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2.3 Reviewing the Research Literature

Learning objectives.

Define the research literature in psychology and give examples of sources that are part of the research literature and sources that are not.
Describe and use several methods for finding previous research on a particular research idea or question.

Reviewing the research literature means finding, reading, and summarizing the published research relevant to your question. An empirical research report written in American Psychological Association (APA) style always includes a written literature review, but it is important to review the literature early in the research process for several reasons.

It can help you turn a research idea into an interesting research question.
It can tell you if a research question has already been answered.
It can help you evaluate the interestingness of a research question.
It can give you ideas for how to conduct your own study.
It can tell you how your study fits into the research literature.

What Is the Research Literature?

The research literature in any field is all the published research in that field. The research literature in psychology is enormous—including millions of scholarly articles and books dating to the beginning of the field—and it continues to grow. Although its boundaries are somewhat fuzzy, the research literature definitely does not include self-help and other pop psychology books, dictionary and encyclopedia entries, websites, and similar sources that are intended mainly for the general public. These are considered unreliable because they are not reviewed by other researchers and are often based on little more than common sense or personal experience. Wikipedia contains much valuable information, but the fact that its authors are anonymous and its content continually changes makes it unsuitable as a basis of sound scientific research. For our purposes, it helps to define the research literature as consisting almost entirely of two types of sources: articles in professional journals, and scholarly books in psychology and related fields.

Professional Journals

Professional journals are periodicals that publish original research articles. There are thousands of professional journals that publish research in psychology and related fields. They are usually published monthly or quarterly in individual issues, each of which contains several articles. The issues are organized into volumes, which usually consist of all the issues for a calendar year. Some journals are published in hard copy only, others in both hard copy and electronic form, and still others in electronic form only.

Most articles in professional journals are one of two basic types: empirical research reports and review articles. Empirical research reports describe one or more new empirical studies conducted by the authors. They introduce a research question, explain why it is interesting, review previous research, describe their method and results, and draw their conclusions. Review articles summarize previously published research on a topic and usually present new ways to organize or explain the results. When a review article is devoted primarily to presenting a new theory, it is often referred to as a theoretical article .

Figure 2.6 Small Sample of the Thousands of Professional Journals That Publish Research in Psychology and Related Fields

A Small sample of the thousands of professional journals that publish research in psychology and related fields

Most professional journals in psychology undergo a process of peer review . Researchers who want to publish their work in the journal submit a manuscript to the editor—who is generally an established researcher too—who in turn sends it to two or three experts on the topic. Each reviewer reads the manuscript, writes a critical review, and sends the review back to the editor along with his or her recommendations. The editor then decides whether to accept the article for publication, ask the authors to make changes and resubmit it for further consideration, or reject it outright. In any case, the editor forwards the reviewers’ written comments to the researchers so that they can revise their manuscript accordingly. Peer review is important because it ensures that the work meets basic standards of the field before it can enter the research literature.

Scholarly Books

Scholarly books are books written by researchers and practitioners mainly for use by other researchers and practitioners. A monograph is written by a single author or a small group of authors and usually gives a coherent presentation of a topic much like an extended review article. Edited volumes have an editor or a small group of editors who recruit many authors to write separate chapters on different aspects of the same topic. Although edited volumes can also give a coherent presentation of the topic, it is not unusual for each chapter to take a different perspective or even for the authors of different chapters to openly disagree with each other. In general, scholarly books undergo a peer review process similar to that used by professional journals.

Literature Search Strategies

Using psycinfo and other databases.

The primary method used to search the research literature involves using one or more electronic databases. These include Academic Search Premier, JSTOR, and ProQuest for all academic disciplines, ERIC for education, and PubMed for medicine and related fields. The most important for our purposes, however, is PsycINFO , which is produced by the APA. PsycINFO is so comprehensive—covering thousands of professional journals and scholarly books going back more than 100 years—that for most purposes its content is synonymous with the research literature in psychology. Like most such databases, PsycINFO is usually available through your college or university library.

PsycINFO consists of individual records for each article, book chapter, or book in the database. Each record includes basic publication information, an abstract or summary of the work, and a list of other works cited by that work. A computer interface allows entering one or more search terms and returns any records that contain those search terms. (These interfaces are provided by different vendors and therefore can look somewhat different depending on the library you use.) Each record also contains lists of keywords that describe the content of the work and also a list of index terms. The index terms are especially helpful because they are standardized. Research on differences between women and men, for example, is always indexed under “Human Sex Differences.” Research on touching is always indexed under the term “Physical Contact.” If you do not know the appropriate index terms, PsycINFO includes a thesaurus that can help you find them.

Given that there are nearly three million records in PsycINFO, you may have to try a variety of search terms in different combinations and at different levels of specificity before you find what you are looking for. Imagine, for example, that you are interested in the question of whether women and men differ in terms of their ability to recall experiences from when they were very young. If you were to enter “memory for early experiences” as your search term, PsycINFO would return only six records, most of which are not particularly relevant to your question. However, if you were to enter the search term “memory,” it would return 149,777 records—far too many to look through individually. This is where the thesaurus helps. Entering “memory” into the thesaurus provides several more specific index terms—one of which is “early memories.” While searching for “early memories” among the index terms returns 1,446 records—still too many too look through individually—combining it with “human sex differences” as a second search term returns 37 articles, many of which are highly relevant to the topic.

Depending on the vendor that provides the interface to PsycINFO, you may be able to save, print, or e-mail the relevant PsycINFO records. The records might even contain links to full-text copies of the works themselves. (PsycARTICLES is a database that provides full-text access to articles in all journals published by the APA.) If not, and you want a copy of the work, you will have to find out if your library carries the journal or has the book and the hard copy on the library shelves. Be sure to ask a librarian if you need help.

Using Other Search Techniques

In addition to entering search terms into PsycINFO and other databases, there are several other techniques you can use to search the research literature. First, if you have one good article or book chapter on your topic—a recent review article is best—you can look through the reference list of that article for other relevant articles, books, and book chapters. In fact, you should do this with any relevant article or book chapter you find. You can also start with a classic article or book chapter on your topic, find its record in PsycINFO (by entering the author’s name or article’s title as a search term), and link from there to a list of other works in PsycINFO that cite that classic article. This works because other researchers working on your topic are likely to be aware of the classic article and cite it in their own work. You can also do a general Internet search using search terms related to your topic or the name of a researcher who conducts research on your topic. This might lead you directly to works that are part of the research literature (e.g., articles in open-access journals or posted on researchers’ own websites). The search engine Google Scholar is especially useful for this purpose. A general Internet search might also lead you to websites that are not part of the research literature but might provide references to works that are. Finally, you can talk to people (e.g., your instructor or other faculty members in psychology) who know something about your topic and can suggest relevant articles and book chapters.

What to Search For

When you do a literature review, you need to be selective. Not every article, book chapter, and book that relates to your research idea or question will be worth obtaining, reading, and integrating into your review. Instead, you want to focus on sources that help you do four basic things: (a) refine your research question, (b) identify appropriate research methods, (c) place your research in the context of previous research, and (d) write an effective research report. Several basic principles can help you find the most useful sources.

First, it is best to focus on recent research, keeping in mind that what counts as recent depends on the topic. For newer topics that are actively being studied, “recent” might mean published in the past year or two. For older topics that are receiving less attention right now, “recent” might mean within the past 10 years. You will get a feel for what counts as recent for your topic when you start your literature search. A good general rule, however, is to start with sources published in the past five years. The main exception to this rule would be classic articles that turn up in the reference list of nearly every other source. If other researchers think that this work is important, even though it is old, then by all means you should include it in your review.

Second, you should look for review articles on your topic because they will provide a useful overview of it—often discussing important definitions, results, theories, trends, and controversies—giving you a good sense of where your own research fits into the literature. You should also look for empirical research reports addressing your question or similar questions, which can give you ideas about how to operationally define your variables and collect your data. As a general rule, it is good to use methods that others have already used successfully unless you have good reasons not to. Finally, you should look for sources that provide information that can help you argue for the interestingness of your research question. For a study on the effects of cell phone use on driving ability, for example, you might look for information about how widespread cell phone use is, how frequent and costly motor vehicle crashes are, and so on.

How many sources are enough for your literature review? This is a difficult question because it depends on how extensively your topic has been studied and also on your own goals. One study found that across a variety of professional journals in psychology, the average number of sources cited per article was about 50 (Adair & Vohra, 2003). This gives a rough idea of what professional researchers consider to be adequate. As a student, you might be assigned a much lower minimum number of references to use, but the principles for selecting the most useful ones remain the same.

Key Takeaways

The research literature in psychology is all the published research in psychology, consisting primarily of articles in professional journals and scholarly books.
Early in the research process, it is important to conduct a review of the research literature on your topic to refine your research question, identify appropriate research methods, place your question in the context of other research, and prepare to write an effective research report.
There are several strategies for finding previous research on your topic. Among the best is using PsycINFO, a computer database that catalogs millions of articles, books, and book chapters in psychology and related fields.
Practice: Use the techniques discussed in this section to find 10 journal articles and book chapters on one of the following research ideas: memory for smells, aggressive driving, the causes of narcissistic personality disorder, the functions of the intraparietal sulcus, or prejudice against the physically handicapped.

Adair, J. G., & Vohra, N. (2003). The explosion of knowledge, references, and citations: Psychology’s unique response to a crisis. American Psychologist, 58 , 15–23.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Empirical Research

Introduction, what is empirical research, attribution.

Finding Empirical Research in Library Databases
Designing Empirical Research
Case Sudies

Empirical research is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief.

Key characteristics to look for:

Specific research questions to be answered
Definition of the population, behavior, or phenomena being studied
Description of the process used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
Methodology: sometimes called "research design" -- how to recreate the study -- usually describes the population, research process, and analytical tools
Results : sometimes called "findings" -- what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies

Portions of this guide were built using suggestions from other libraries, including Penn State and Utah State University libraries.

Next: Finding Empirical Research in Library Databases >>
Last Updated: Jan 10, 2023 8:31 AM
URL: https://enmu.libguides.com/EmpiricalResearch

University of Memphis Libraries
Research Guides

Empirical Research: Defining, Identifying, & Finding

Searching for empirical research.

Defining Empirical Research
Introduction

Where Do I Find Empirical Research?

How do i find more empirical research in my search.

Database Tools
Search Terms
Image Descriptions

Because empirical research refers to the method of investigation rather than a method of publication, it can be published in a number of places. In many disciplines empirical research is most commonly published in scholarly, peer-reviewed journals . Putting empirical research through the peer review process helps ensure that the research is high quality.

Finding Peer-Reviewed Articles

You can find peer-reviewed articles in a general web search along with a lot of other types of sources. However, these specialized tools are more likely to find peer-reviewed articles:

Library databases
Academic search engines such as Google Scholar

Common Types of Articles That Are Not Empirical

However, just finding an article in a peer-reviewed journal is not enough to say it is empirical, since not all the articles in a peer-reviewed journal will be empirical research or even peer reviewed. Knowing how to quickly identify some types non-empirical research articles in peer-reviewed journals can help speed up your search.

Peer-reviewed articles that systematically discuss and propose abstract concepts and methods for a field without primary data collection.
Example: Grosser, K. & Moon, J. (2019). CSR and feminist organization studies: Towards an integrated theorization for the analysis of gender issues .
Peer-reviewed articles that systematically describe, summarize, and often categorize and evaluate previous research on a topic without collecting new data.
Example: Heuer, S. & Willer, R. (2020). How is quality of life assessed in people with dementia? A systematic literature review and a primer for speech-language pathologists .
Note: empirical research articles will have a literature review section as part of the Introduction , but in an empirical research article the literature review exists to give context to the empirical research, which is the primary focus of the article. In a literature review article, the literature review is the focus.
While these articles are not empirical, they are often a great source of information on previous empirical research on a topic with citations to find that research.
Non-peer-reviewed articles where the authors discuss their thoughts on a particular topic without data collection and a systematic method. There are a few differences between these types of articles.
Written by the editors or guest editors of the journal.
Example: Naples, N. A., Mauldin, L., & Dillaway, H. (2018). From the guest editors: Gender, disability, and intersectionality .
Written by guest authors. The journal may have a non-peer-reviewed process for authors to submit these articles, and the editors of the journal may invite authors to write opinion articles.
Example: García, J. J.-L., & Sharif, M. Z. (2015). Black lives matter: A commentary on racism and public health .
Written by the readers of a journal, often in response to an article previously-published in the journal.
Example: Nathan, M. (2013). Letters: Perceived discrimination and racial/ethnic disparities in youth problem behaviors .
Non-peer-reviewed articles that describe and evaluate books, products, services, and other things the audience of the journal would be interested in.
Example: Robinson, R. & Green, J. M. (2020). Book review: Microaggressions and traumatic stress: Theory, research, and clinical treatment .

Even once you know how to recognize empirical research and where it is published, it would be nice to improve your search results so that more empirical research shows up for your topic.

There are two major ways to find the empirical research in a database search:

Use built-in database tools to limit results to empirical research.
Include search terms that help identify empirical research.
<< Previous: Discussion
Next: Database Tools >>
Last Updated: Apr 2, 2024 11:25 AM
URL: https://libguides.memphis.edu/empirical-research

Meriam Library

SWRK 330 - Social Work Research Methods

Literature Reviews and Empirical Research
Databases and Search Tips
Article Citations
Scholarly Journal Evaulation
Statistical Sources
Books and eBooks

What is a Literature Review?

Empirical research.

Annotated Bibliographies

A literature review summarizes and discusses previous publications on a topic.

It should also:

explore past research and its strengths and weaknesses.

be used to validate the target and methods you have chosen for your proposed research.

consist of books and scholarly journals that provide research examples of populations or settings similar to your own, as well as community resources to document the need for your proposed research.

The literature review does not present new primary scholarship.

be completed in the correct citation format requested by your professor (see the C itations Tab)

Access Purdue OWL's Social Work Literature Review Guidelines here .

Empirical Research is research that is based on experimentation or observation, i.e. Evidence. Such research is often conducted to answer a specific question or to test a hypothesis (educated guess).

These are some key features to look for when identifying empirical research.

NOTE: Not all of these features will be in every empirical research article, some may be excluded, use this only as a guide.

Statement of methodology
Research questions are clear and measurable
Individuals, group, subjects which are being studied are identified/defined
Data is presented regarding the findings
Controls or instruments such as surveys or tests were conducted
There is a literature review
There is discussion of the results included
Citations/references are included

Literature Reviews, Theoretical Frameworks, and Conceptual Frameworks: An Introduction for New Biology Education Researchers

Julie a. luft.

† Department of Mathematics, Social Studies, and Science Education, Mary Frances Early College of Education, University of Georgia, Athens, GA 30602-7124

Sophia Jeong

‡ Department of Teaching & Learning, College of Education & Human Ecology, Ohio State University, Columbus, OH 43210

Robert Idsardi

§ Department of Biology, Eastern Washington University, Cheney, WA 99004

Grant Gardner

∥ Department of Biology, Middle Tennessee State University, Murfreesboro, TN 37132

Associated Data

To frame their work, biology education researchers need to consider the role of literature reviews, theoretical frameworks, and conceptual frameworks as critical elements of the research and writing process. However, these elements can be confusing for scholars new to education research. This Research Methods article is designed to provide an overview of each of these elements and delineate the purpose of each in the educational research process. We describe what biology education researchers should consider as they conduct literature reviews, identify theoretical frameworks, and construct conceptual frameworks. Clarifying these different components of educational research studies can be helpful to new biology education researchers and the biology education research community at large in situating their work in the broader scholarly literature.

INTRODUCTION

Discipline-based education research (DBER) involves the purposeful and situated study of teaching and learning in specific disciplinary areas ( Singer et al. , 2012 ). Studies in DBER are guided by research questions that reflect disciplines’ priorities and worldviews. Researchers can use quantitative data, qualitative data, or both to answer these research questions through a variety of methodological traditions. Across all methodologies, there are different methods associated with planning and conducting educational research studies that include the use of surveys, interviews, observations, artifacts, or instruments. Ensuring the coherence of these elements to the discipline’s perspective also involves situating the work in the broader scholarly literature. The tools for doing this include literature reviews, theoretical frameworks, and conceptual frameworks. However, the purpose and function of each of these elements is often confusing to new education researchers. The goal of this article is to introduce new biology education researchers to these three important elements important in DBER scholarship and the broader educational literature.

The first element we discuss is a review of research (literature reviews), which highlights the need for a specific research question, study problem, or topic of investigation. Literature reviews situate the relevance of the study within a topic and a field. The process may seem familiar to science researchers entering DBER fields, but new researchers may still struggle in conducting the review. Booth et al. (2016b) highlight some of the challenges novice education researchers face when conducting a review of literature. They point out that novice researchers struggle in deciding how to focus the review, determining the scope of articles needed in the review, and knowing how to be critical of the articles in the review. Overcoming these challenges (and others) can help novice researchers construct a sound literature review that can inform the design of the study and help ensure the work makes a contribution to the field.

The second and third highlighted elements are theoretical and conceptual frameworks. These guide biology education research (BER) studies, and may be less familiar to science researchers. These elements are important in shaping the construction of new knowledge. Theoretical frameworks offer a way to explain and interpret the studied phenomenon, while conceptual frameworks clarify assumptions about the studied phenomenon. Despite the importance of these constructs in educational research, biology educational researchers have noted the limited use of theoretical or conceptual frameworks in published work ( DeHaan, 2011 ; Dirks, 2011 ; Lo et al. , 2019 ). In reviewing articles published in CBE—Life Sciences Education ( LSE ) between 2015 and 2019, we found that fewer than 25% of the research articles had a theoretical or conceptual framework (see the Supplemental Information), and at times there was an inconsistent use of theoretical and conceptual frameworks. Clearly, these frameworks are challenging for published biology education researchers, which suggests the importance of providing some initial guidance to new biology education researchers.

Fortunately, educational researchers have increased their explicit use of these frameworks over time, and this is influencing educational research in science, technology, engineering, and mathematics (STEM) fields. For instance, a quick search for theoretical or conceptual frameworks in the abstracts of articles in Educational Research Complete (a common database for educational research) in STEM fields demonstrates a dramatic change over the last 20 years: from only 778 articles published between 2000 and 2010 to 5703 articles published between 2010 and 2020, a more than sevenfold increase. Greater recognition of the importance of these frameworks is contributing to DBER authors being more explicit about such frameworks in their studies.

Collectively, literature reviews, theoretical frameworks, and conceptual frameworks work to guide methodological decisions and the elucidation of important findings. Each offers a different perspective on the problem of study and is an essential element in all forms of educational research. As new researchers seek to learn about these elements, they will find different resources, a variety of perspectives, and many suggestions about the construction and use of these elements. The wide range of available information can overwhelm the new researcher who just wants to learn the distinction between these elements or how to craft them adequately.

Our goal in writing this paper is not to offer specific advice about how to write these sections in scholarly work. Instead, we wanted to introduce these elements to those who are new to BER and who are interested in better distinguishing one from the other. In this paper, we share the purpose of each element in BER scholarship, along with important points on its construction. We also provide references for additional resources that may be beneficial to better understanding each element. Table 1 summarizes the key distinctions among these elements.

Comparison of literature reviews, theoretical frameworks, and conceptual reviews

This article is written for the new biology education researcher who is just learning about these different elements or for scientists looking to become more involved in BER. It is a result of our own work as science education and biology education researchers, whether as graduate students and postdoctoral scholars or newly hired and established faculty members. This is the article we wish had been available as we started to learn about these elements or discussed them with new educational researchers in biology.

LITERATURE REVIEWS

Purpose of a literature review.

A literature review is foundational to any research study in education or science. In education, a well-conceptualized and well-executed review provides a summary of the research that has already been done on a specific topic and identifies questions that remain to be answered, thus illustrating the current research project’s potential contribution to the field and the reasoning behind the methodological approach selected for the study ( Maxwell, 2012 ). BER is an evolving disciplinary area that is redefining areas of conceptual emphasis as well as orientations toward teaching and learning (e.g., Labov et al. , 2010 ; American Association for the Advancement of Science, 2011 ; Nehm, 2019 ). As a result, building comprehensive, critical, purposeful, and concise literature reviews can be a challenge for new biology education researchers.

Building Literature Reviews

There are different ways to approach and construct a literature review. Booth et al. (2016a) provide an overview that includes, for example, scoping reviews, which are focused only on notable studies and use a basic method of analysis, and integrative reviews, which are the result of exhaustive literature searches across different genres. Underlying each of these different review processes are attention to the s earch process, a ppraisa l of articles, s ynthesis of the literature, and a nalysis: SALSA ( Booth et al. , 2016a ). This useful acronym can help the researcher focus on the process while building a specific type of review.

However, new educational researchers often have questions about literature reviews that are foundational to SALSA or other approaches. Common questions concern determining which literature pertains to the topic of study or the role of the literature review in the design of the study. This section addresses such questions broadly while providing general guidance for writing a narrative literature review that evaluates the most pertinent studies.

The literature review process should begin before the research is conducted. As Boote and Beile (2005 , p. 3) suggested, researchers should be “scholars before researchers.” They point out that having a good working knowledge of the proposed topic helps illuminate avenues of study. Some subject areas have a deep body of work to read and reflect upon, providing a strong foundation for developing the research question(s). For instance, the teaching and learning of evolution is an area of long-standing interest in the BER community, generating many studies (e.g., Perry et al. , 2008 ; Barnes and Brownell, 2016 ) and reviews of research (e.g., Sickel and Friedrichsen, 2013 ; Ziadie and Andrews, 2018 ). Emerging areas of BER include the affective domain, issues of transfer, and metacognition ( Singer et al. , 2012 ). Many studies in these areas are transdisciplinary and not always specific to biology education (e.g., Rodrigo-Peiris et al. , 2018 ; Kolpikova et al. , 2019 ). These newer areas may require reading outside BER; fortunately, summaries of some of these topics can be found in the Current Insights section of the LSE website.

In focusing on a specific problem within a broader research strand, a new researcher will likely need to examine research outside BER. Depending upon the area of study, the expanded reading list might involve a mix of BER, DBER, and educational research studies. Determining the scope of the reading is not always straightforward. A simple way to focus one’s reading is to create a “summary phrase” or “research nugget,” which is a very brief descriptive statement about the study. It should focus on the essence of the study, for example, “first-year nonmajor students’ understanding of evolution,” “metacognitive prompts to enhance learning during biochemistry,” or “instructors’ inquiry-based instructional practices after professional development programming.” This type of phrase should help a new researcher identify two or more areas to review that pertain to the study. Focusing on recent research in the last 5 years is a good first step. Additional studies can be identified by reading relevant works referenced in those articles. It is also important to read seminal studies that are more than 5 years old. Reading a range of studies should give the researcher the necessary command of the subject in order to suggest a research question.

Given that the research question(s) arise from the literature review, the review should also substantiate the selected methodological approach. The review and research question(s) guide the researcher in determining how to collect and analyze data. Often the methodological approach used in a study is selected to contribute knowledge that expands upon what has been published previously about the topic (see Institute of Education Sciences and National Science Foundation, 2013 ). An emerging topic of study may need an exploratory approach that allows for a description of the phenomenon and development of a potential theory. This could, but not necessarily, require a methodological approach that uses interviews, observations, surveys, or other instruments. An extensively studied topic may call for the additional understanding of specific factors or variables; this type of study would be well suited to a verification or a causal research design. These could entail a methodological approach that uses valid and reliable instruments, observations, or interviews to determine an effect in the studied event. In either of these examples, the researcher(s) may use a qualitative, quantitative, or mixed methods methodological approach.

Even with a good research question, there is still more reading to be done. The complexity and focus of the research question dictates the depth and breadth of the literature to be examined. Questions that connect multiple topics can require broad literature reviews. For instance, a study that explores the impact of a biology faculty learning community on the inquiry instruction of faculty could have the following review areas: learning communities among biology faculty, inquiry instruction among biology faculty, and inquiry instruction among biology faculty as a result of professional learning. Biology education researchers need to consider whether their literature review requires studies from different disciplines within or outside DBER. For the example given, it would be fruitful to look at research focused on learning communities with faculty in STEM fields or in general education fields that result in instructional change. It is important not to be too narrow or too broad when reading. When the conclusions of articles start to sound similar or no new insights are gained, the researcher likely has a good foundation for a literature review. This level of reading should allow the researcher to demonstrate a mastery in understanding the researched topic, explain the suitability of the proposed research approach, and point to the need for the refined research question(s).

The literature review should include the researcher’s evaluation and critique of the selected studies. A researcher may have a large collection of studies, but not all of the studies will follow standards important in the reporting of empirical work in the social sciences. The American Educational Research Association ( Duran et al. , 2006 ), for example, offers a general discussion about standards for such work: an adequate review of research informing the study, the existence of sound and appropriate data collection and analysis methods, and appropriate conclusions that do not overstep or underexplore the analyzed data. The Institute of Education Sciences and National Science Foundation (2013) also offer Common Guidelines for Education Research and Development that can be used to evaluate collected studies.

Because not all journals adhere to such standards, it is important that a researcher review each study to determine the quality of published research, per the guidelines suggested earlier. In some instances, the research may be fatally flawed. Examples of such flaws include data that do not pertain to the question, a lack of discussion about the data collection, poorly constructed instruments, or an inadequate analysis. These types of errors result in studies that are incomplete, error-laden, or inaccurate and should be excluded from the review. Most studies have limitations, and the author(s) often make them explicit. For instance, there may be an instructor effect, recognized bias in the analysis, or issues with the sample population. Limitations are usually addressed by the research team in some way to ensure a sound and acceptable research process. Occasionally, the limitations associated with the study can be significant and not addressed adequately, which leaves a consequential decision in the hands of the researcher. Providing critiques of studies in the literature review process gives the reader confidence that the researcher has carefully examined relevant work in preparation for the study and, ultimately, the manuscript.

A solid literature review clearly anchors the proposed study in the field and connects the research question(s), the methodological approach, and the discussion. Reviewing extant research leads to research questions that will contribute to what is known in the field. By summarizing what is known, the literature review points to what needs to be known, which in turn guides decisions about methodology. Finally, notable findings of the new study are discussed in reference to those described in the literature review.

Within published BER studies, literature reviews can be placed in different locations in an article. When included in the introductory section of the study, the first few paragraphs of the manuscript set the stage, with the literature review following the opening paragraphs. Cooper et al. (2019) illustrate this approach in their study of course-based undergraduate research experiences (CUREs). An introduction discussing the potential of CURES is followed by an analysis of the existing literature relevant to the design of CUREs that allows for novel student discoveries. Within this review, the authors point out contradictory findings among research on novel student discoveries. This clarifies the need for their study, which is described and highlighted through specific research aims.

A literature reviews can also make up a separate section in a paper. For example, the introduction to Todd et al. (2019) illustrates the need for their research topic by highlighting the potential of learning progressions (LPs) and suggesting that LPs may help mitigate learning loss in genetics. At the end of the introduction, the authors state their specific research questions. The review of literature following this opening section comprises two subsections. One focuses on learning loss in general and examines a variety of studies and meta-analyses from the disciplines of medical education, mathematics, and reading. The second section focuses specifically on LPs in genetics and highlights student learning in the midst of LPs. These separate reviews provide insights into the stated research question.

Suggestions and Advice

A well-conceptualized, comprehensive, and critical literature review reveals the understanding of the topic that the researcher brings to the study. Literature reviews should not be so big that there is no clear area of focus; nor should they be so narrow that no real research question arises. The task for a researcher is to craft an efficient literature review that offers a critical analysis of published work, articulates the need for the study, guides the methodological approach to the topic of study, and provides an adequate foundation for the discussion of the findings.

In our own writing of literature reviews, there are often many drafts. An early draft may seem well suited to the study because the need for and approach to the study are well described. However, as the results of the study are analyzed and findings begin to emerge, the existing literature review may be inadequate and need revision. The need for an expanded discussion about the research area can result in the inclusion of new studies that support the explanation of a potential finding. The literature review may also prove to be too broad. Refocusing on a specific area allows for more contemplation of a finding.

It should be noted that there are different types of literature reviews, and many books and articles have been written about the different ways to embark on these types of reviews. Among these different resources, the following may be helpful in considering how to refine the review process for scholarly journals:

Booth, A., Sutton, A., & Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. This book addresses different types of literature reviews and offers important suggestions pertaining to defining the scope of the literature review and assessing extant studies.
Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago: University of Chicago Press. This book can help the novice consider how to make the case for an area of study. While this book is not specifically about literature reviews, it offers suggestions about making the case for your study.
Galvan, J. L., & Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). Routledge. This book offers guidance on writing different types of literature reviews. For the novice researcher, there are useful suggestions for creating coherent literature reviews.

THEORETICAL FRAMEWORKS

Purpose of theoretical frameworks.

As new education researchers may be less familiar with theoretical frameworks than with literature reviews, this discussion begins with an analogy. Envision a biologist, chemist, and physicist examining together the dramatic effect of a fog tsunami over the ocean. A biologist gazing at this phenomenon may be concerned with the effect of fog on various species. A chemist may be interested in the chemical composition of the fog as water vapor condenses around bits of salt. A physicist may be focused on the refraction of light to make fog appear to be “sitting” above the ocean. While observing the same “objective event,” the scientists are operating under different theoretical frameworks that provide a particular perspective or “lens” for the interpretation of the phenomenon. Each of these scientists brings specialized knowledge, experiences, and values to this phenomenon, and these influence the interpretation of the phenomenon. The scientists’ theoretical frameworks influence how they design and carry out their studies and interpret their data.

Within an educational study, a theoretical framework helps to explain a phenomenon through a particular lens and challenges and extends existing knowledge within the limitations of that lens. Theoretical frameworks are explicitly stated by an educational researcher in the paper’s framework, theory, or relevant literature section. The framework shapes the types of questions asked, guides the method by which data are collected and analyzed, and informs the discussion of the results of the study. It also reveals the researcher’s subjectivities, for example, values, social experience, and viewpoint ( Allen, 2017 ). It is essential that a novice researcher learn to explicitly state a theoretical framework, because all research questions are being asked from the researcher’s implicit or explicit assumptions of a phenomenon of interest ( Schwandt, 2000 ).

Selecting Theoretical Frameworks

Theoretical frameworks are one of the most contemplated elements in our work in educational research. In this section, we share three important considerations for new scholars selecting a theoretical framework.

The first step in identifying a theoretical framework involves reflecting on the phenomenon within the study and the assumptions aligned with the phenomenon. The phenomenon involves the studied event. There are many possibilities, for example, student learning, instructional approach, or group organization. A researcher holds assumptions about how the phenomenon will be effected, influenced, changed, or portrayed. It is ultimately the researcher’s assumption(s) about the phenomenon that aligns with a theoretical framework. An example can help illustrate how a researcher’s reflection on the phenomenon and acknowledgment of assumptions can result in the identification of a theoretical framework.

In our example, a biology education researcher may be interested in exploring how students’ learning of difficult biological concepts can be supported by the interactions of group members. The phenomenon of interest is the interactions among the peers, and the researcher assumes that more knowledgeable students are important in supporting the learning of the group. As a result, the researcher may draw on Vygotsky’s (1978) sociocultural theory of learning and development that is focused on the phenomenon of student learning in a social setting. This theory posits the critical nature of interactions among students and between students and teachers in the process of building knowledge. A researcher drawing upon this framework holds the assumption that learning is a dynamic social process involving questions and explanations among students in the classroom and that more knowledgeable peers play an important part in the process of building conceptual knowledge.

It is important to state at this point that there are many different theoretical frameworks. Some frameworks focus on learning and knowing, while other theoretical frameworks focus on equity, empowerment, or discourse. Some frameworks are well articulated, and others are still being refined. For a new researcher, it can be challenging to find a theoretical framework. Two of the best ways to look for theoretical frameworks is through published works that highlight different frameworks.

When a theoretical framework is selected, it should clearly connect to all parts of the study. The framework should augment the study by adding a perspective that provides greater insights into the phenomenon. It should clearly align with the studies described in the literature review. For instance, a framework focused on learning would correspond to research that reported different learning outcomes for similar studies. The methods for data collection and analysis should also correspond to the framework. For instance, a study about instructional interventions could use a theoretical framework concerned with learning and could collect data about the effect of the intervention on what is learned. When the data are analyzed, the theoretical framework should provide added meaning to the findings, and the findings should align with the theoretical framework.

A study by Jensen and Lawson (2011) provides an example of how a theoretical framework connects different parts of the study. They compared undergraduate biology students in heterogeneous and homogeneous groups over the course of a semester. Jensen and Lawson (2011) assumed that learning involved collaboration and more knowledgeable peers, which made Vygotsky’s (1978) theory a good fit for their study. They predicted that students in heterogeneous groups would experience greater improvement in their reasoning abilities and science achievements with much of the learning guided by the more knowledgeable peers.

In the enactment of the study, they collected data about the instruction in traditional and inquiry-oriented classes, while the students worked in homogeneous or heterogeneous groups. To determine the effect of working in groups, the authors also measured students’ reasoning abilities and achievement. Each data-collection and analysis decision connected to understanding the influence of collaborative work.

Their findings highlighted aspects of Vygotsky’s (1978) theory of learning. One finding, for instance, posited that inquiry instruction, as a whole, resulted in reasoning and achievement gains. This links to Vygotsky (1978) , because inquiry instruction involves interactions among group members. A more nuanced finding was that group composition had a conditional effect. Heterogeneous groups performed better with more traditional and didactic instruction, regardless of the reasoning ability of the group members. Homogeneous groups worked better during interaction-rich activities for students with low reasoning ability. The authors attributed the variation to the different types of helping behaviors of students. High-performing students provided the answers, while students with low reasoning ability had to work collectively through the material. In terms of Vygotsky (1978) , this finding provided new insights into the learning context in which productive interactions can occur for students.

Another consideration in the selection and use of a theoretical framework pertains to its orientation to the study. This can result in the theoretical framework prioritizing individuals, institutions, and/or policies ( Anfara and Mertz, 2014 ). Frameworks that connect to individuals, for instance, could contribute to understanding their actions, learning, or knowledge. Institutional frameworks, on the other hand, offer insights into how institutions, organizations, or groups can influence individuals or materials. Policy theories provide ways to understand how national or local policies can dictate an emphasis on outcomes or instructional design. These different types of frameworks highlight different aspects in an educational setting, which influences the design of the study and the collection of data. In addition, these different frameworks offer a way to make sense of the data. Aligning the data collection and analysis with the framework ensures that a study is coherent and can contribute to the field.

New understandings emerge when different theoretical frameworks are used. For instance, Ebert-May et al. (2015) prioritized the individual level within conceptual change theory (see Posner et al. , 1982 ). In this theory, an individual’s knowledge changes when it no longer fits the phenomenon. Ebert-May et al. (2015) designed a professional development program challenging biology postdoctoral scholars’ existing conceptions of teaching. The authors reported that the biology postdoctoral scholars’ teaching practices became more student-centered as they were challenged to explain their instructional decision making. According to the theory, the biology postdoctoral scholars’ dissatisfaction in their descriptions of teaching and learning initiated change in their knowledge and instruction. These results reveal how conceptual change theory can explain the learning of participants and guide the design of professional development programming.

The communities of practice (CoP) theoretical framework ( Lave, 1988 ; Wenger, 1998 ) prioritizes the institutional level , suggesting that learning occurs when individuals learn from and contribute to the communities in which they reside. Grounded in the assumption of community learning, the literature on CoP suggests that, as individuals interact regularly with the other members of their group, they learn about the rules, roles, and goals of the community ( Allee, 2000 ). A study conducted by Gehrke and Kezar (2017) used the CoP framework to understand organizational change by examining the involvement of individual faculty engaged in a cross-institutional CoP focused on changing the instructional practice of faculty at each institution. In the CoP, faculty members were involved in enhancing instructional materials within their department, which aligned with an overarching goal of instituting instruction that embraced active learning. Not surprisingly, Gehrke and Kezar (2017) revealed that faculty who perceived the community culture as important in their work cultivated institutional change. Furthermore, they found that institutional change was sustained when key leaders served as mentors and provided support for faculty, and as faculty themselves developed into leaders. This study reveals the complexity of individual roles in a COP in order to support institutional instructional change.

It is important to explicitly state the theoretical framework used in a study, but elucidating a theoretical framework can be challenging for a new educational researcher. The literature review can help to identify an applicable theoretical framework. Focal areas of the review or central terms often connect to assumptions and assertions associated with the framework that pertain to the phenomenon of interest. Another way to identify a theoretical framework is self-reflection by the researcher on personal beliefs and understandings about the nature of knowledge the researcher brings to the study ( Lysaght, 2011 ). In stating one’s beliefs and understandings related to the study (e.g., students construct their knowledge, instructional materials support learning), an orientation becomes evident that will suggest a particular theoretical framework. Theoretical frameworks are not arbitrary , but purposefully selected.

With experience, a researcher may find expanded roles for theoretical frameworks. Researchers may revise an existing framework that has limited explanatory power, or they may decide there is a need to develop a new theoretical framework. These frameworks can emerge from a current study or the need to explain a phenomenon in a new way. Researchers may also find that multiple theoretical frameworks are necessary to frame and explore a problem, as different frameworks can provide different insights into a problem.

Finally, it is important to recognize that choosing “x” theoretical framework does not necessarily mean a researcher chooses “y” methodology and so on, nor is there a clear-cut, linear process in selecting a theoretical framework for one’s study. In part, the nonlinear process of identifying a theoretical framework is what makes understanding and using theoretical frameworks challenging. For the novice scholar, contemplating and understanding theoretical frameworks is essential. Fortunately, there are articles and books that can help:

Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. This book provides an overview of theoretical frameworks in general educational research.
Ding, L. (2019). Theoretical perspectives of quantitative physics education research. Physical Review Physics Education Research , 15 (2), 020101-1–020101-13. This paper illustrates how a DBER field can use theoretical frameworks.
Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems. Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 . This paper articulates the need for studies in BER to explicitly state theoretical frameworks and provides examples of potential studies.
Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Sage. This book also provides an overview of theoretical frameworks, but for both research and evaluation.

CONCEPTUAL FRAMEWORKS

Purpose of a conceptual framework.

A conceptual framework is a description of the way a researcher understands the factors and/or variables that are involved in the study and their relationships to one another. The purpose of a conceptual framework is to articulate the concepts under study using relevant literature ( Rocco and Plakhotnik, 2009 ) and to clarify the presumed relationships among those concepts ( Rocco and Plakhotnik, 2009 ; Anfara and Mertz, 2014 ). Conceptual frameworks are different from theoretical frameworks in both their breadth and grounding in established findings. Whereas a theoretical framework articulates the lens through which a researcher views the work, the conceptual framework is often more mechanistic and malleable.

Conceptual frameworks are broader, encompassing both established theories (i.e., theoretical frameworks) and the researchers’ own emergent ideas. Emergent ideas, for example, may be rooted in informal and/or unpublished observations from experience. These emergent ideas would not be considered a “theory” if they are not yet tested, supported by systematically collected evidence, and peer reviewed. However, they do still play an important role in the way researchers approach their studies. The conceptual framework allows authors to clearly describe their emergent ideas so that connections among ideas in the study and the significance of the study are apparent to readers.

Constructing Conceptual Frameworks

Including a conceptual framework in a research study is important, but researchers often opt to include either a conceptual or a theoretical framework. Either may be adequate, but both provide greater insight into the research approach. For instance, a research team plans to test a novel component of an existing theory. In their study, they describe the existing theoretical framework that informs their work and then present their own conceptual framework. Within this conceptual framework, specific topics portray emergent ideas that are related to the theory. Describing both frameworks allows readers to better understand the researchers’ assumptions, orientations, and understanding of concepts being investigated. For example, Connolly et al. (2018) included a conceptual framework that described how they applied a theoretical framework of social cognitive career theory (SCCT) to their study on teaching programs for doctoral students. In their conceptual framework, the authors described SCCT, explained how it applied to the investigation, and drew upon results from previous studies to justify the proposed connections between the theory and their emergent ideas.

In some cases, authors may be able to sufficiently describe their conceptualization of the phenomenon under study in an introduction alone, without a separate conceptual framework section. However, incomplete descriptions of how the researchers conceptualize the components of the study may limit the significance of the study by making the research less intelligible to readers. This is especially problematic when studying topics in which researchers use the same terms for different constructs or different terms for similar and overlapping constructs (e.g., inquiry, teacher beliefs, pedagogical content knowledge, or active learning). Authors must describe their conceptualization of a construct if the research is to be understandable and useful.

There are some key areas to consider regarding the inclusion of a conceptual framework in a study. To begin with, it is important to recognize that conceptual frameworks are constructed by the researchers conducting the study ( Rocco and Plakhotnik, 2009 ; Maxwell, 2012 ). This is different from theoretical frameworks that are often taken from established literature. Researchers should bring together ideas from the literature, but they may be influenced by their own experiences as a student and/or instructor, the shared experiences of others, or thought experiments as they construct a description, model, or representation of their understanding of the phenomenon under study. This is an exercise in intellectual organization and clarity that often considers what is learned, known, and experienced. The conceptual framework makes these constructs explicitly visible to readers, who may have different understandings of the phenomenon based on their prior knowledge and experience. There is no single method to go about this intellectual work.

Reeves et al. (2016) is an example of an article that proposed a conceptual framework about graduate teaching assistant professional development evaluation and research. The authors used existing literature to create a novel framework that filled a gap in current research and practice related to the training of graduate teaching assistants. This conceptual framework can guide the systematic collection of data by other researchers because the framework describes the relationships among various factors that influence teaching and learning. The Reeves et al. (2016) conceptual framework may be modified as additional data are collected and analyzed by other researchers. This is not uncommon, as conceptual frameworks can serve as catalysts for concerted research efforts that systematically explore a phenomenon (e.g., Reynolds et al. , 2012 ; Brownell and Kloser, 2015 ).

Sabel et al. (2017) used a conceptual framework in their exploration of how scaffolds, an external factor, interact with internal factors to support student learning. Their conceptual framework integrated principles from two theoretical frameworks, self-regulated learning and metacognition, to illustrate how the research team conceptualized students’ use of scaffolds in their learning ( Figure 1 ). Sabel et al. (2017) created this model using their interpretations of these two frameworks in the context of their teaching.

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Conceptual framework from Sabel et al. (2017) .

A conceptual framework should describe the relationship among components of the investigation ( Anfara and Mertz, 2014 ). These relationships should guide the researcher’s methods of approaching the study ( Miles et al. , 2014 ) and inform both the data to be collected and how those data should be analyzed. Explicitly describing the connections among the ideas allows the researcher to justify the importance of the study and the rigor of the research design. Just as importantly, these frameworks help readers understand why certain components of a system were not explored in the study. This is a challenge in education research, which is rooted in complex environments with many variables that are difficult to control.

For example, Sabel et al. (2017) stated: “Scaffolds, such as enhanced answer keys and reflection questions, can help students and instructors bridge the external and internal factors and support learning” (p. 3). They connected the scaffolds in the study to the three dimensions of metacognition and the eventual transformation of existing ideas into new or revised ideas. Their framework provides a rationale for focusing on how students use two different scaffolds, and not on other factors that may influence a student’s success (self-efficacy, use of active learning, exam format, etc.).

In constructing conceptual frameworks, researchers should address needed areas of study and/or contradictions discovered in literature reviews. By attending to these areas, researchers can strengthen their arguments for the importance of a study. For instance, conceptual frameworks can address how the current study will fill gaps in the research, resolve contradictions in existing literature, or suggest a new area of study. While a literature review describes what is known and not known about the phenomenon, the conceptual framework leverages these gaps in describing the current study ( Maxwell, 2012 ). In the example of Sabel et al. (2017) , the authors indicated there was a gap in the literature regarding how scaffolds engage students in metacognition to promote learning in large classes. Their study helps fill that gap by describing how scaffolds can support students in the three dimensions of metacognition: intelligibility, plausibility, and wide applicability. In another example, Lane (2016) integrated research from science identity, the ethic of care, the sense of belonging, and an expertise model of student success to form a conceptual framework that addressed the critiques of other frameworks. In a more recent example, Sbeglia et al. (2021) illustrated how a conceptual framework influences the methodological choices and inferences in studies by educational researchers.

Sometimes researchers draw upon the conceptual frameworks of other researchers. When a researcher’s conceptual framework closely aligns with an existing framework, the discussion may be brief. For example, Ghee et al. (2016) referred to portions of SCCT as their conceptual framework to explain the significance of their work on students’ self-efficacy and career interests. Because the authors’ conceptualization of this phenomenon aligned with a previously described framework, they briefly mentioned the conceptual framework and provided additional citations that provided more detail for the readers.

Within both the BER and the broader DBER communities, conceptual frameworks have been used to describe different constructs. For example, some researchers have used the term “conceptual framework” to describe students’ conceptual understandings of a biological phenomenon. This is distinct from a researcher’s conceptual framework of the educational phenomenon under investigation, which may also need to be explicitly described in the article. Other studies have presented a research logic model or flowchart of the research design as a conceptual framework. These constructions can be quite valuable in helping readers understand the data-collection and analysis process. However, a model depicting the study design does not serve the same role as a conceptual framework. Researchers need to avoid conflating these constructs by differentiating the researchers’ conceptual framework that guides the study from the research design, when applicable.

Explicitly describing conceptual frameworks is essential in depicting the focus of the study. We have found that being explicit in a conceptual framework means using accepted terminology, referencing prior work, and clearly noting connections between terms. This description can also highlight gaps in the literature or suggest potential contributions to the field of study. A well-elucidated conceptual framework can suggest additional studies that may be warranted. This can also spur other researchers to consider how they would approach the examination of a phenomenon and could result in a revised conceptual framework.

It can be challenging to create conceptual frameworks, but they are important. Below are two resources that could be helpful in constructing and presenting conceptual frameworks in educational research:

Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. Chapter 3 in this book describes how to construct conceptual frameworks.
Ravitch, S. M., & Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. This book explains how conceptual frameworks guide the research questions, data collection, data analyses, and interpretation of results.

CONCLUDING THOUGHTS

Literature reviews, theoretical frameworks, and conceptual frameworks are all important in DBER and BER. Robust literature reviews reinforce the importance of a study. Theoretical frameworks connect the study to the base of knowledge in educational theory and specify the researcher’s assumptions. Conceptual frameworks allow researchers to explicitly describe their conceptualization of the relationships among the components of the phenomenon under study. Table 1 provides a general overview of these components in order to assist biology education researchers in thinking about these elements.

It is important to emphasize that these different elements are intertwined. When these elements are aligned and complement one another, the study is coherent, and the study findings contribute to knowledge in the field. When literature reviews, theoretical frameworks, and conceptual frameworks are disconnected from one another, the study suffers. The point of the study is lost, suggested findings are unsupported, or important conclusions are invisible to the researcher. In addition, this misalignment may be costly in terms of time and money.

Conducting a literature review, selecting a theoretical framework, and building a conceptual framework are some of the most difficult elements of a research study. It takes time to understand the relevant research, identify a theoretical framework that provides important insights into the study, and formulate a conceptual framework that organizes the finding. In the research process, there is often a constant back and forth among these elements as the study evolves. With an ongoing refinement of the review of literature, clarification of the theoretical framework, and articulation of a conceptual framework, a sound study can emerge that makes a contribution to the field. This is the goal of BER and education research.

Supplementary Material

Allee, V. (2000). Knowledge networks and communities of learning . OD Practitioner , 32 ( 4 ), 4–13. [ Google Scholar ]
Allen, M. (2017). The Sage encyclopedia of communication research methods (Vols. 1–4 ). Los Angeles, CA: Sage. 10.4135/9781483381411 [ CrossRef ] [ Google Scholar ]
American Association for the Advancement of Science. (2011). Vision and change in undergraduate biology education: A call to action . Washington, DC. [ Google Scholar ]
Anfara, V. A., Mertz, N. T. (2014). Setting the stage . In Anfara, V. A., Mertz, N. T. (eds.), Theoretical frameworks in qualitative research (pp. 1–22). Sage. [ Google Scholar ]
Barnes, M. E., Brownell, S. E. (2016). Practices and perspectives of college instructors on addressing religious beliefs when teaching evolution . CBE—Life Sciences Education , 15 ( 2 ), ar18. https://doi.org/10.1187/cbe.15-11-0243 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Boote, D. N., Beile, P. (2005). Scholars before researchers: On the centrality of the dissertation literature review in research preparation . Educational Researcher , 34 ( 6 ), 3–15. 10.3102/0013189x034006003 [ CrossRef ] [ Google Scholar ]
Booth, A., Sutton, A., Papaioannou, D. (2016a). Systemic approaches to a successful literature review (2nd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., Fitzgerald, W. T. (2016b). The craft of research (4th ed.). Chicago, IL: University of Chicago Press. [ Google Scholar ]
Brownell, S. E., Kloser, M. J. (2015). Toward a conceptual framework for measuring the effectiveness of course-based undergraduate research experiences in undergraduate biology . Studies in Higher Education , 40 ( 3 ), 525–544. https://doi.org/10.1080/03075079.2015.1004234 [ Google Scholar ]
Connolly, M. R., Lee, Y. G., Savoy, J. N. (2018). The effects of doctoral teaching development on early-career STEM scholars’ college teaching self-efficacy . CBE—Life Sciences Education , 17 ( 1 ), ar14. https://doi.org/10.1187/cbe.17-02-0039 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Cooper, K. M., Blattman, J. N., Hendrix, T., Brownell, S. E. (2019). The impact of broadly relevant novel discoveries on student project ownership in a traditional lab course turned CURE . CBE—Life Sciences Education , 18 ( 4 ), ar57. https://doi.org/10.1187/cbe.19-06-0113 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Creswell, J. W. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Los Angeles, CA: Sage. [ Google Scholar ]
DeHaan, R. L. (2011). Education research in the biological sciences: A nine decade review (Paper commissioned by the NAS/NRC Committee on the Status, Contributions, and Future Directions of Discipline Based Education Research) . Washington, DC: National Academies Press. Retrieved May 20, 2022, from www7.nationalacademies.org/bose/DBER_Mee ting2_commissioned_papers_page.html [ Google Scholar ]
Ding, L. (2019). Theoretical perspectives of quantitative physics education research . Physical Review Physics Education Research , 15 ( 2 ), 020101. [ Google Scholar ]
Dirks, C. (2011). The current status and future direction of biology education research . Paper presented at: Second Committee Meeting on the Status, Contributions, and Future Directions of Discipline-Based Education Research, 18–19 October (Washington, DC). Retrieved May 20, 2022, from http://sites.nationalacademies.org/DBASSE/BOSE/DBASSE_071087 [ Google Scholar ]
Duran, R. P., Eisenhart, M. A., Erickson, F. D., Grant, C. A., Green, J. L., Hedges, L. V., Schneider, B. L. (2006). Standards for reporting on empirical social science research in AERA publications: American Educational Research Association . Educational Researcher , 35 ( 6 ), 33–40. [ Google Scholar ]
Ebert-May, D., Derting, T. L., Henkel, T. P., Middlemis Maher, J., Momsen, J. L., Arnold, B., Passmore, H. A. (2015). Breaking the cycle: Future faculty begin teaching with learner-centered strategies after professional development . CBE—Life Sciences Education , 14 ( 2 ), ar22. https://doi.org/10.1187/cbe.14-12-0222 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Galvan, J. L., Galvan, M. C. (2017). Writing literature reviews: A guide for students of the social and behavioral sciences (7th ed.). New York, NY: Routledge. https://doi.org/10.4324/9781315229386 [ Google Scholar ]
Gehrke, S., Kezar, A. (2017). The roles of STEM faculty communities of practice in institutional and departmental reform in higher education . American Educational Research Journal , 54 ( 5 ), 803–833. https://doi.org/10.3102/0002831217706736 [ Google Scholar ]
Ghee, M., Keels, M., Collins, D., Neal-Spence, C., Baker, E. (2016). Fine-tuning summer research programs to promote underrepresented students’ persistence in the STEM pathway . CBE—Life Sciences Education , 15 ( 3 ), ar28. https://doi.org/10.1187/cbe.16-01-0046 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Institute of Education Sciences & National Science Foundation. (2013). Common guidelines for education research and development . Retrieved May 20, 2022, from www.nsf.gov/pubs/2013/nsf13126/nsf13126.pdf
Jensen, J. L., Lawson, A. (2011). Effects of collaborative group composition and inquiry instruction on reasoning gains and achievement in undergraduate biology . CBE—Life Sciences Education , 10 ( 1 ), 64–73. https://doi.org/10.1187/cbe.19-05-0098 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Kolpikova, E. P., Chen, D. C., Doherty, J. H. (2019). Does the format of preclass reading quizzes matter? An evaluation of traditional and gamified, adaptive preclass reading quizzes . CBE—Life Sciences Education , 18 ( 4 ), ar52. https://doi.org/10.1187/cbe.19-05-0098 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Labov, J. B., Reid, A. H., Yamamoto, K. R. (2010). Integrated biology and undergraduate science education: A new biology education for the twenty-first century? CBE—Life Sciences Education , 9 ( 1 ), 10–16. https://doi.org/10.1187/cbe.09-12-0092 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lane, T. B. (2016). Beyond academic and social integration: Understanding the impact of a STEM enrichment program on the retention and degree attainment of underrepresented students . CBE—Life Sciences Education , 15 ( 3 ), ar39. https://doi.org/10.1187/cbe.16-01-0070 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life . New York, NY: Cambridge University Press. [ Google Scholar ]
Lo, S. M., Gardner, G. E., Reid, J., Napoleon-Fanis, V., Carroll, P., Smith, E., Sato, B. K. (2019). Prevailing questions and methodologies in biology education research: A longitudinal analysis of research in CBE — Life Sciences Education and at the Society for the Advancement of Biology Education Research . CBE—Life Sciences Education , 18 ( 1 ), ar9. https://doi.org/10.1187/cbe.18-08-0164 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Lysaght, Z. (2011). Epistemological and paradigmatic ecumenism in “Pasteur’s quadrant:” Tales from doctoral research . In Official Conference Proceedings of the Third Asian Conference on Education in Osaka, Japan . Retrieved May 20, 2022, from http://iafor.org/ace2011_offprint/ACE2011_offprint_0254.pdf
Maxwell, J. A. (2012). Qualitative research design: An interactive approach (3rd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
Miles, M. B., Huberman, A. M., Saldaña, J. (2014). Qualitative data analysis (3rd ed.). Los Angeles, CA: Sage. [ Google Scholar ]
Nehm, R. (2019). Biology education research: Building integrative frameworks for teaching and learning about living systems . Disciplinary and Interdisciplinary Science Education Research , 1 , ar15. https://doi.org/10.1186/s43031-019-0017-6 [ Google Scholar ]
Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice . Los Angeles, CA: Sage. [ Google Scholar ]
Perry, J., Meir, E., Herron, J. C., Maruca, S., Stal, D. (2008). Evaluating two approaches to helping college students understand evolutionary trees through diagramming tasks . CBE—Life Sciences Education , 7 ( 2 ), 193–201. https://doi.org/10.1187/cbe.07-01-0007 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Posner, G. J., Strike, K. A., Hewson, P. W., Gertzog, W. A. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change . Science Education , 66 ( 2 ), 211–227. [ Google Scholar ]
Ravitch, S. M., Riggan, M. (2016). Reason & rigor: How conceptual frameworks guide research . Los Angeles, CA: Sage. [ Google Scholar ]
Reeves, T. D., Marbach-Ad, G., Miller, K. R., Ridgway, J., Gardner, G. E., Schussler, E. E., Wischusen, E. W. (2016). A conceptual framework for graduate teaching assistant professional development evaluation and research . CBE—Life Sciences Education , 15 ( 2 ), es2. https://doi.org/10.1187/cbe.15-10-0225 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Reynolds, J. A., Thaiss, C., Katkin, W., Thompson, R. J. Jr. (2012). Writing-to-learn in undergraduate science education: A community-based, conceptually driven approach . CBE—Life Sciences Education , 11 ( 1 ), 17–25. https://doi.org/10.1187/cbe.11-08-0064 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Rocco, T. S., Plakhotnik, M. S. (2009). Literature reviews, conceptual frameworks, and theoretical frameworks: Terms, functions, and distinctions . Human Resource Development Review , 8 ( 1 ), 120–130. https://doi.org/10.1177/1534484309332617 [ Google Scholar ]
Rodrigo-Peiris, T., Xiang, L., Cassone, V. M. (2018). A low-intensity, hybrid design between a “traditional” and a “course-based” research experience yields positive outcomes for science undergraduate freshmen and shows potential for large-scale application . CBE—Life Sciences Education , 17 ( 4 ), ar53. https://doi.org/10.1187/cbe.17-11-0248 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Sabel, J. L., Dauer, J. T., Forbes, C. T. (2017). Introductory biology students’ use of enhanced answer keys and reflection questions to engage in metacognition and enhance understanding . CBE—Life Sciences Education , 16 ( 3 ), ar40. https://doi.org/10.1187/cbe.16-10-0298 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Sbeglia, G. C., Goodridge, J. A., Gordon, L. H., Nehm, R. H. (2021). Are faculty changing? How reform frameworks, sampling intensities, and instrument measures impact inferences about student-centered teaching practices . CBE—Life Sciences Education , 20 ( 3 ), ar39. https://doi.org/10.1187/cbe.20-11-0259 [ PMC free article ] [ PubMed ] [ Google Scholar ]
Schwandt, T. A. (2000). Three epistemological stances for qualitative inquiry: Interpretivism, hermeneutics, and social constructionism . In Denzin, N. K., Lincoln, Y. S. (Eds.), Handbook of qualitative research (2nd ed., pp. 189–213). Los Angeles, CA: Sage. [ Google Scholar ]
Sickel, A. J., Friedrichsen, P. (2013). Examining the evolution education literature with a focus on teachers: Major findings, goals for teacher preparation, and directions for future research . Evolution: Education and Outreach , 6 ( 1 ), 23. https://doi.org/10.1186/1936-6434-6-23 [ Google Scholar ]
Singer, S. R., Nielsen, N. R., Schweingruber, H. A. (2012). Discipline-based education research: Understanding and improving learning in undergraduate science and engineering . Washington, DC: National Academies Press. [ Google Scholar ]
Todd, A., Romine, W. L., Correa-Menendez, J. (2019). Modeling the transition from a phenotypic to genotypic conceptualization of genetics in a university-level introductory biology context . Research in Science Education , 49 ( 2 ), 569–589. https://doi.org/10.1007/s11165-017-9626-2 [ Google Scholar ]
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes . Cambridge, MA: Harvard University Press. [ Google Scholar ]
Wenger, E. (1998). Communities of practice: Learning as a social system . Systems Thinker , 9 ( 5 ), 2–3. [ Google Scholar ]
Ziadie, M. A., Andrews, T. C. (2018). Moving evolution education forward: A systematic analysis of literature to identify gaps in collective knowledge for teaching . CBE—Life Sciences Education , 17 ( 1 ), ar11. https://doi.org/10.1187/cbe.17-08-0190 [ PMC free article ] [ PubMed ] [ Google Scholar ]

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Original research article, impact of industrial policy on urban green innovation: empirical evidence of china’s national high-tech zones based on double machine learning.

College of Economics and Management, Taiyuan University of Technology, Taiyuan, China

Effective industrial policies need to be implemented, particularly aligning with environmental protection goals to drive the high-quality growth of China’s economy in the new era. Setting up national high-tech zones falls under the purview of both regional and industrial policies. Using panel data from 163 prefecture-level cities in China from 2007 to 2019, this paper empirically analyzes the impact of national high-tech zones on the level of urban green innovation and its underlying mechanisms. It utilizes the national high-tech zones as a quasi-natural experiment and employs a double machine learning model. The study findings reveal that the policy for national high-tech zones greatly enhances urban green innovation. This conclusion remains consistent even after adjusting the measurement method, empirical samples, and controlling for other policy interferences. The findings from the heterogeneity analysis reveal that the impact of the national high-tech zone policy on green innovation exhibits significant regional heterogeneity, with a particularly significant effect in the central and western regions. Among cities, there is a notable push for green innovation levels in second-tier, third-tier, and fourth-tier cities. The moderating effect results indicate that, at the current stage of development, transportation infrastructure primarily exerts a negative moderating effect on how the national high-tech zone policy impacts the level of urban green innovation. This research provides robust empirical evidence for informing the optimization of the industrial policy of China and the establishment of a future ecological civilization system.

1 Introduction

The Chinese economy currently focuses on high-quality development rather than quick growth. The traditional demographic and resource advantages gradually diminish, making the earlier crude development model reliant on excessive resource input and consumption unsustainable. Simultaneously, resource impoverishment, environmental pollution, and carbon emissions are growing more severe ( Wang F. et al., 2022 ). Consequently, pursuing a mutually beneficial equilibrium between the economy and the environment has emerged as a critical concern in China’s economic growth. Green innovation, the integration of innovation with sustainability development ideas, is progressively gaining significance within the framework of reshaping China’s economic development strategy and addressing the challenges associated with resource and environmental limitations. In light of the present circumstances, and with the objectives outlined in the “3060 Plan” for carbon peak and carbon neutral, the pursuit of a green and innovative development trajectory, emphasizing heightened innovation alongside environ-mental preservation, has emerged as a pivotal concern within the context of China’s contemporary economic progress.

Industrial policy is pivotal in government intervention within market-driven resource allocation and correcting structural disparities. The government orchestrates this initiative to bolster industrial expansion and operational effectiveness. In contrast to Western industrial policies, those in China are predominantly crafted within the administrative framework and promulgated through administrative regulations. Over an extended period, numerous industrial policies have been devised in response to regional disparities in industrial development. These policies aim to identify new growth opportunities in diverse regions, focusing on optimizing and upgrading industrial structures. These strategies have been implemented at various administrative levels, from the central government to local authorities ( Sun and Sun, 2015 ). As a distinctive regional economic policy in China, the national high-tech zone represents one of the foremost supportive measures a city can acquire at the national level. Its crucial role involves facilitating the dissemination and advancement of regional economic growth. Over more than three decades, it has evolved into the primary platform through which China executes its strategy of concentrating on high-tech industries and fostering development driven by innovation. Concurrently, the national high-tech zone, operating as a geographically focused policy customized for a specific region ( Cao, 2019 ), enhances the precision of policy support for the industries under its purview, covering a more limited range of municipalities, counties, and regions. Contrasting with conventional regional industrial policies, the industry-focused policy within national high-tech zones prioritizes comprehensive resource allocation advice and economic foundations to maximize synergy and promote the long-term sustainable growth of the regional economy, and this represents a significant paradigm shift in location-based policies within the framework of carrying out the new development idea. Its inception embodies a combination of central authorization, high-level strategic planning, local grassroots decision-making, and innovative system development. In recent years, driven by the objective of dual carbon, national high-tech have proactively promoted environmentally friendly innovation. Nevertheless, given the proliferation of new industrial policies and the escalating complexity of the policy framework, has the setting up of national high-tech zones genuinely elevated the level of urban green innovation in contrast to conventional regional industrial policies? What are the underlying mechanisms? Simultaneously, concerning the variations among different cities, have the industrial policy tools within the national high-tech zones been employed judiciously and adaptable? What are the concrete practical outcomes? Investigating these matters has emerged as a significant subject requiring resolution by government, industry and academia.

2 Literature review and research hypothesis

2.1 literature review.

When considering industrial policy, the setting up national high-tech zones embodies the intersection of regional and industrial policies. Domestic and international academic research concerning setting up national high-tech zones primarily centers on economic activities and innovation. Notably, the economic impact of national high-tech zones encompasses a wide range of factors, including their influence on total factor productivity ( Tan and Zhang, 2018 ; Wang and Liu, 2023 ), foreign trade ( Alder et al., 2016 ), industrial structure upgrades ( Yuan and Zhu, 2018 ), and economic growth ( Liu and Zhao, 2015 ; Huang and Fernández-Maldonado, 2016 ; Wang Z. et al., 2022 ). Regarding innovation, numerous researchers have confirmed the positive effects of national high-tech zones on company innovation ( Vásquez-Urriago et al., 2014 ; Díez-Vial and Fernández-Olmos, 2017 ; Wang and Xu, 2020 ); Nevertheless, a few scholars have disagreed on this matter ( Hong et al., 2016 ; Sosnovskikh, 2017 ). In general, the consensus among scholars is that setting up high-tech national zones fosters regional innovation significantly. This consensus is supported by various aspects of innovation, including innovation efficiency ( Park and Lee, 2004 ; Chandrashekar and Bala Subrahmanya, 2017 ), agglomeration effect ( De Beule and Van Beveren, 2012 ), innovation capability ( Yang and Guo, 2020 ), among other relevant dimensions. The existing literature predominantly delves into the correlation between the setting up of national high-tech zones, innovation, and economic significance. However, the rise of digital economic developments, notably industrial digitization, has accentuated the limitations of the traditional innovation paradigm. These shortcomings, such as the inadequate exploration of the social importance and sustainability of innovation, have become apparent in recent years. As the primary driver of sustainable development, green innovation represents a potent avenue for achieving economic benefits and environmental value ( Weber et al., 2014 ). Its distinctiveness from other innovation forms lies in its potential to facilitate the transformation of development modes, reshape economic structures, and address pollution prevention and control challenges. However, in the context of green innovation, based on the double-difference approach, Wang et al. (2020) has pointed out that national high-tech zones enhance the effectiveness of urban green innovation, but this is only significant in the eastern region.

Furthermore, scholars have also explored the mechanisms underlying the innovation effects of national high-tech. For example, Cattapan et al. (2012) focused on science parks in Italy. They found that green innovation represents a potent avenue for achieving economic benefits as the primary driver of sustainable development, and environmental value technology transfer services positively influence product innovation. Albahari et al. (2017) confirmed that higher education institutions’ involvement in advancing corporate innovation within technology and science parks has a beneficial moderating effect. Using the moderating effect of spatial agglomeration as a basis, Li WH. et al. (2022) found that industrial agglomeration has a significantly unfavorable moderating influence on the effectiveness of performance transformation in national high-tech zones. Multiple studies have examined the national high-tech zone industrial policy’s regulatory framework and urban innovation. However, in the age of rapidly expanding new infrastructure, infrastructure construction is concentrated on information technologies like blockchain, big data, cloud computing, artificial intelligence, and the Internet; Further research is needed to explore whether traditional infrastructure, particularly transportation infrastructure, can promote urban green innovation. Transportation infrastructure has consistently been vital in fostering economic expansion, integrating regional resources, and facilitating coordinated development ( Behrens et al., 2007 ; Zhang et al., 2018 ; Pokharel et al., 2021 ). Therefore, it is necessary to investigate whether transportation infrastructure can continue encouraging innovative urban green practices in the digital economy.

In summary, the existing literature has extensively examined the influence of national high-tech zones on economic growth and innovation from various levels and perspectives, establishing a solid foundation and offering valuable research insights for this study. Nonetheless, previous studies frequently overlooked the impact of national high-tech zones on urban green innovation levels, and a subsequent series of work in this paper aims to address this issue. Further exploration and expansion are needed to understand the industrial policy framework’s strategy for relating national high-tech zones to urban green innovation. Furthermore, there is a need for further improvement and refinement of the research model and methodology. Based on these, this paper aims to discuss the industrial policy effects of national high-tech zones from the perspective of urban green innovation to enrich and expand the existing research.

In contrast to earlier research, the marginal contribution of this paper is organized into three dimensions: 1) Most scholars have primarily focused on the effects of national high-tech zones on economic activity and innovation, with less emphasis on green innovation and rare studies according to the level of green innovation perspective. The study on national high-tech zones as an industrial policy that has already been done is enhanced by this work. 2) Regarding the research methodology, the Double Machine Learning (DML) approach is used to evaluate the policy effects of national high-tech zones, leveraging the advantages of machine learning algorithms for high-dimensional and non-parametric prediction. This approach circumvents the problems of model setting bias and the “curse of dimensionality” encountered in traditional econometric models ( Chernozhukov et al., 2018 ), enhancing the credibility of the research findings. 3) By introducing transportation infrastructure as a moderator variable, this study investigates the underlying mechanism of national high-tech zones on urban green innovation, offering suggestions for maximizing the influence of these zones on policy.

2.2 Theoretical analysis and hypotheses

2.2.1 national high-tech zones’ industrial policies and urban green innovation.

As one of the ways to land industrial policies at the national level, national high-tech zones serve as effective driving forces for enhancing China’s ability to innovate regionally and its contribution to economic growth ( Xu et al., 2022 ). Green innovation is a novel form of innovation activity that harmoniously balances the competing goals of environmental preservation and technological advancement, facilitating the superior expansion of the economy by alleviating the strain on resources and the environment ( Li, 2015 ). National high-tech zones mainly impact urban green innovation through three main aspects. Firstly, based on innovation compensation effects, national high-tech zones, established based on the government’s strategic planning, receive special treatment in areas such as land, taxation, financing, credit, and more, serving as pioneering special zones and experimental fields established by the government to promote high-quality regional development. When the government offers R&D subsidies to enterprises engaged in green innovation activities within the zones, enterprises are inclined to respond positively to the government’s policy support and enhance their level of green innovation as a means of seeking external legitimacy ( Fang et al., 2021 ), thereby contributing to the advancement of urban green innovation. Secondly, based on the industrial restructuring effect, strict regulation of businesses with high emissions, high energy consumption, and high pollution levels is another aspect of implementing the national high-tech zone program. Consequently, businesses with significant emissions and energy consumption are required to optimize their industrial structure to access various benefits within the park, resulting in the gradual transformation and upgrading of high-energy-consumption industries towards green practices, thereby further contributing to regional green innovation. Based on Porter’s hypothesis, the green and low-carbon requirements of the park policy increase the production costs for polluting industries, prompting polluting enterprises to upgrade their existing technology and adopt green innovation practices. Lastly, based on the theory of industrial agglomeration, the national high-tech zones’ industrial policy facilitates the concentration of innovative talents to a certain extent, resulting in intensified competition in the green innovation market. Increased competition fosters the sharing of knowledge, technology, and talent, stimulating a market environment where the survival of the fittest prevails ( Melitz and Ottaviano, 2008 ). These increase the effectiveness of urban green innovation, helping to propel urban green innovation forward. Furthermore, the infrastructure development within the national high-tech zones establishes a favorable physical environment for enterprises to engage in creative endeavors. Also, it enables the influx of high-quality innovation capital from foreign sources, complementing the inherent characteristics of national high-tech zones that attract such capital and concentrate green innovation resources, ultimately resulting in both environmental and economic benefits. Based on the above analysis, Hypothesis 1 is proposed:

Hypothesis 1. Implementing industrial policies in national high-tech zones enhances levels of urban green innovation.

2.2.2 Heterogeneity analysis

Given the variations in economic foundations, industrial statuses, and population distributions across different regions, development strategies in different regions are also influenced by these variations ( Chen and Zheng, 2008 ). Theoretically, when using administrative boundaries or geographic locations as benchmarks, the impact of national high-tech zone industrial policy on urban green innovation should be achieved through strategies like aligning with the region’s existing industrial structure. Compared to the western and central regions, the eastern region exhibits more incredible innovation and dynamism due to advantages such as a developed economy, good infrastructure, advanced management concepts, and technologies, combined with a relatively high initial level of green innovation factor endowment. Considering the diminishing marginal effect principle of green innovation, the industrial policy implementation in national high-tech zones favors an “icing on the cake” approach in the eastern region, contrasting with a “send carbon in the snow” approach in the central and western regions. In other words, the economic benefits of national high-tech zones for promoting urban green innovation may need to be more robust than their impact on the central and western regions. Literature confirms that establishing national high-tech zones yields a more beneficial technology agglomeration effect in the less developed central and western regions ( Liu and Zhao, 2015 ), leading to a more substantial impact on enhancing the level of urban green innovation.

Moreover, local governments consider economic development, industrial structure, and infrastructure levels when establishing national high-tech zones. These factors serve as the foundation for regional classification to address variations in regional quality and to compensate for gaps in theoretical research on the link between national high-tech zone industrial policy implementation and urban green innovation. Consequently, the execution of industrial policies in national high-tech zones relies on other vital factors influencing urban green innovation. Significant variations exist in economic development and infrastructure levels among cities of different grades ( Luo and Wang, 2023 ). Generally, cities with higher rankings exhibit strong economic growth and infrastructure, contrasting those with lower rankings. Consequently, the effect of establishing a national high-tech zone on green innovation may vary across different city grades. Thus, considering the disparities across city rankings, we delve deeper into identifying the underlying reasons for regional diversity in the green innovation outcomes of industrial policies implemented in national high-tech zones based on city grades. Based on the above analysis, Hypothesis 2 is proposed:

Hypothesis 2. There is regional heterogeneity and city-level heterogeneity in the impact of national high-tech zone policies on the level of urban green innovation.

2.2.3 The moderating effect of transportation infrastructure

Implementing industrial policies and facilitating the flow of innovation factors are closely intertwined with the role of transport infrastructure as carriers and linkages. Generally, enhanced transportation infrastructure facilitates the absorption of local factors and improves resource allocation efficiency, thereby influencing the spatial redistribution of production factors like labor, resources, and technology across cities. Enhanced transportation infrastructure fosters the development of more robust and advanced innovation networks ( Fritsch and Slavtchev, 2011 ). Banister and Berechman (2001) highlighted that transportation infrastructure exhibits network properties that are fundamental to its agglomeration or diffusion effects. From this perspective, robust infrastructure impacts various economic activities, including interregional labor mobility, factor agglomeration, and knowledge exchange among firms, thereby expediting the spillover effects of green technological innovations ( Yu et al., 2013 ). In turn, this could positively moderate the influence of national hi-tech zone policies on green innovation. On the other hand, while transportation infrastructure facilitates the growth of national high-tech zone policies, it also brings negative impacts, including high pollution, emissions, and ecological landscape fragmentation. Improving transportation infrastructure can also lead to the “relative congestion effect” in national high-tech zones. This phenomenon, observed in specific regions, refers to the excessive concentration of similar enterprises across different links of the same industrial chain, which exacerbates the competition for innovation resources among enterprises, making it challenging for enterprises in the region to allocate their limited innovation resources to technological research and development activities ( Li et al., 2015 ). As a result, there needs to be a higher green innovation level. Therefore, the impact of transportation infrastructure in the current stage of development will be more complex. When the level of transport infrastructure is moderate, adequate transport infrastructure supports the promotion of urban green innovation through national high-tech zone policies. However, the impact of transport infrastructure regulation may be harmful. Based on the above analysis, Hypothesis 3 is proposed:

Hypothesis 3. Transportation infrastructure moderates the relationship between national high-tech zones and levels of urban green invention.

3 Research design

3.1 model setting.

This research explores the impact of industrial policies of national high-tech zones on the level of urban green innovation. Many related studies utilize traditional causal inference models to assess the impact of these policies. However, these models have several limitations in their application. For instance, the commonly used double-difference model in the parallel trend test has stringent requirements for the sample data. Although the synthetic control approach can create a virtual control group that meets parallel trends’ needs, it is limited to addressing the ‘one-to-many’ problem and requires excluding groups with extreme values. The selection of matching variables in propensity score matching is subjective, among other limitations ( Zhang and Li, 2023 ). To address the limitations of conventional causal inference models, scholars have started to explore applying machine learning to infer causality ( Chernozhukov et al., 2018 ; Knittel and Stolper, 2021 ). Machine learning algorithms excel at an impartial assessment of the effect on the intended target variable for making accurate predictions.

In contrast to traditional machine learning algorithms, the formal proposal of DML was made in 2018 ( Chernozhukov et al., 2018 ). This approach offers a more robust approach to causal inference by mitigating bias through the incorporation of residual modeling. Currently, some scholars utilize DML to assess causality in economic phenomena. For instance, Hull and Grodecka-Messi (2022) examined the effects of local taxation, crime, education, and public services on migration using DML in the context of Swedish cities between 2010 and 2016. These existing research findings serve as valuable references for this study. Compared to traditional causal inference models, DML offers distinct advantages in variable selection and model estimation ( Zhang and Li, 2023 ). However, in promoting urban green innovation in China, there is a high probability of non-linear relationships between variables, and the traditional linear regression model may lead to bias and errors. Moreover, the double machine learning model can effectively avoid problems such as setting bias. Based on this, the present study employs a DML model to evaluate the policy implications of establishing a national high-tech zone.

3.1.1 Double machine learning framework

Prior to applying the DML algorithm, this paper refers to the practice of Chernozhukov et al. (2018) to construct a partially linear DML model, as depicted in Eq. 1 below:

where i represents the city, t represents the year, and l n G I i t represents the explained variable, which in this paper is the green innovation level of the city. Z o n e i t represents the disposition variable, which in this case is a national high-tech zone’s policy variable. It takes a value of 1 after the implementation of the pilot and 0 otherwise. θ 0 is the disposal factor that is the focus of this paper. X i t represents the set of high-dimensional control variables. Machine learning algorithms are utilized to estimate the specific form of g ^ X i t , whereas U i t , which has a conditional mean of 0, stands for the error term. n represents the sample size. Direct estimation of Eq. 1 provides an estimate for the coefficient of dispositions.

We can further explore the estimation bias by combining Eqs 1 , 2 as depicted in Eq. ( 3 ) below:

where a = 1 n ∑ i ∈ I , t ∈ T Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T Z o n e i t U i t , by a normal distribution having 0 as the mean, b = 1 n ∑ i ∈ I , t ∈ T Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T Z o n e i t g X i t − g ^ X i t . It is important to note that DML utilizes machine learning and a regularization algorithm to estimate a specific functional form g ^ X i t . The introduction of “canonical bias” is inevitable as it prevents the estimates from having excessive variance while maintaining their unbiasedness. Specifically, the convergence of g ^ X i t to g X i t , n −φg > n −1/2 , as n tends to infinity, b also tends to infinity, θ ^ 0 is difficult to converge to θ 0 . To expedite convergence and ensure unbiasedness of the disposal coefficient estimates with small samples, an auxiliary regression is constructed as follows:

where m X i t represents the disposition variable’s regression function on the high-dimensional control variable, this function also requires estimation using a machine learning algorithm in the specific form of m ^ X i t . Additionally, V i t represents the error term with a 0 conditional mean.

3.1.2 The test of the mediating effect within the DML framework

This study investigates how the national high-tech zone industrial policy influences the urban green innovation. It incorporates moderating variables within the DML framework, drawing on the testing procedure outlined by Jiang (2022) , and integrates it with the practice of He et al. (2022) , as outlined below:

Equation 5 is based on Eq. 1 with the addition of variables l n t r a i t and Z o n e i t * l n t r a i t .Where l n t r a i t represents the moderating variable, which in this paper is the transportation infrastructure. Z o n e i t * l n t r a i t represents the interaction term of the moderating variable and the disposition variable. The variables l n t r a i t and Z o n e i t are added to the high-dimensional control variables X i t , and the rest of the variables in Eq. 5 are identical to Eq. 1 . θ 1 represents the disposal factor to focus on.

3.2 Variable selection

3.2.1 dependent variable: level of urban green innovation (lngi).

Nowadays, many academics use indicators like the number of applications for patents or authorizations to assess the degree of urban innovation. To be more precise, the quantity of patent applications is a measure of technological innovation effort, while the number of patents authorized undergoes strict auditing and can provide a more direct reflection of the achievements and capacity of scientific and technological innovation. Thus, this paper refers to the studies of Zhou and Shen (2020) and Li X. et al. (2022) to utilize the count of authorized green invention patents in each prefecture-level city to indicate the level of green innovation. For the empirical study, the count of authorized green patents plus 1 is transformed using logarithm.

3.2.2 Disposal variable: dummy variables for national high-tech zones (Zone)

The national high-tech zone dummy variable’s value correlates with the city in which it is located and the list of national high-tech zones released by China’s Ministry of Science and Technology. If a national high-tech zone was established in the city by 2017, the value is set to 1 for the year the high-tech zone is established and subsequent years. Otherwise, it is set to 0.

3.2.3 Moderating variable: transportation infrastructure (lntra)

Previous studies have shown that China’s highway freight transport comprises 75% of the total freight transport ( Li and Tang, 2015 ). Highway transportation infrastructure has a significant influence on the evolution of the Chinese economy. The development and improvement of highway infrastructure are crucial for modern transportation. This paper uses the research methods of Wu (2019) and uses the roadway mileage (measured in kilometers) to population as a measure of the quality of the transportation system.

3.2.4 Control variables

(1) Foreign direct investment (lnfdi): There is general agreement among academics that foreign direct investment (FDI) significantly influences urban green innovation, as FDI provides expertise in management, human resources, and cutting-edge industrial technology ( Luo et al., 2021 ). Thus, it is necessary to consider and control the level of FDI. This paper uses the ratio of foreign investment to the local GDP in a million yuan.

(2) Financial development level (lnfd): Innovation in science and technology is greatly aided by finance. For the green innovation-driven strategy to advance, it is imperative that funding for science and technology innovation be strengthened. The amount of capital raised for innovation is strongly impacted by the state of urban financial development ( Zhou and Du, 2021 ). Thus, this paper uses the loan balance to GDP ratio as an indicator.

(3) Human capital (lnhum): Highly skilled human capital is essential for cities to drive green innovation. Generally, highly qualified human capital significantly boosts green innovation ( Ansaris et al., 2016 ). Therefore, a measure was employed: the proportion of people in the city who had completed their bachelor’s degree or above.

(4) Industrial structure (lnind): Generally, the secondary industry in China is the primary source of pollution, and there is a significant impact of industrial structure on green innovation ( Qiu et al., 2023 ). The metric used in this paper is the secondary industry-to-GDP ratio for the area.

(5) Regional economic development level (lnagdp): A region’s level of economic growth is indicative of the material foundation for urban green innovation and in-fluences the growth of green innovation in the region ( Bo et al., 2020 ). This research uses the annual gross domestic product per capita as a measurement.

3.3 Data source

By 2017, China had developed 157 national high-tech zones in total. In conjunction with the study’s objectives, this study performs sample adjustments and a screening process. The study’s sample period spans from 2007 to 2019. 57 national high-tech zones that were created prior to 2000 are omitted to lessen the impact on the test results of towns having high-tech zones founded before 2007. Due to the limitations of high-tech areas in cities at the county level in promoting urban green innovation, 8 high-tech zones located in county-level cities are excluded. And 4 high-tech zones with missing severe data are excluded. Among the list of established national high-tech zones, 88 high-tech zones are distributed across 83 prefecture-level cities due to multiple districts within a single city. As a result, 83 cities are selected as the experimental group for this study. Additionally, a control group of 80 cities was selected from among those that did not have high-tech zones by the end of 2019, resulting in a final sample size of 163 cities. This paper collects green patent data for each city from the China Green Patent Statistical Report published by the State Intellectual Property Office. The author compiled the list of national high-tech zones and the starting year of their establishment on the official government website. In addition, the remaining data in this paper primarily originated from the China Urban Statistical Yearbook (2007–2019), the EPS database, and the official websites of the respective city’s Bureau of Statistics. Missing values were addressed through linear interpolation. To address heteroskedasticity in the model, the study logarithmically transforms the variables, excluding the disposal variable. Table 1 shows the descriptive analysis of the variables.

Table 1 . Descriptive analysis.

4 Empirical analysis

4.1 national high-tech zones’ policy effects on urban green innovation.

This study utilizes the DML model to estimate the impact of industrial policies implemented in national high-tech zones at the level of urban green innovation. Following the approach of Zhang and Li (2023) , the sample is split in a ratio of 1:4, and the random forest algorithm is used to perform predictions and combine Eq. ( 1 ) with Eq. ( 4 ) for the regression. Table 2 presents the results with and without controlling for time and city effects. The results indicate that the treatment effect sizes for these four columns are 0.376, 0.293, 0.396, and 0.268, correspondingly, each of which was significant at a 1% level. Thus, Hypothesis 1 is supported.

Table 2 . Benchmark regression results.

4.2 Robustness tests

4.2.1 eliminate the influence of extreme values.

To reduce the impact of extreme values on the estimation outcomes, all variables on the benchmark regression, excluding the disposal variable, undergo a shrinkage process based on the upper and lower 1% and 5% quantiles. Values lower than the lowest and higher than the highest quantile are replaced accordingly. Regression analyses are conducted. Table 3 demonstrates that removing outliers did not substantially alter the findings of this study.

Table 3 . Extreme values removal results.

4.2.2 Considering province-time interaction fixed effects

Since provinces are critical administrative units in the governance system of the Chinese government, cities within the same province often share similarities in policy environment and location characteristics. Therefore, to account for the influence of temporal changes across different provinces, this study incorporates province-time interaction fixed effects based on the benchmark regression. Table 4 presents the individual regression results. Based on the regression results, after accounting for the correlation between different city characteristics within the same province, national high-tech zone policies continue to significantly influence urban green innovation, even at the 1% level.

Table 4 . The addition of province and time fixed effects interaction terms.

4.2.3 Excluding other policy disturbances

When analyzing how national high-tech zones affect strategy for urban green innovation, it is susceptible to the influence of concurrent policies. This study accounts for other comparable policies during the same period to ensure an accurate estimation of the policy effect. Since 2007, national high-tech zone policies have been successively implemented, including the development of “smart cities.” Therefore, this study incorporates a policy dummy variable for “smart cities” in the benchmark regression. The specific regression findings are shown in Table 5 . After controlling for the impact of concurrent policies, the importance of national high-tech zones’ policy impact remains consistent.

Table 5 . Results of removing the impact of parallel policies.

4.2.4 Resetting the DML model

To mitigate the potential bias introduced by the settings in the DML model on the conclusions, the purpose of this study is to assess the conclusions’ robustness using the following methods. First, the sample split ratio of the DML model is adjusted from 1:4 to 1:2 to examine the potential impact of the sample split ratio on the conclusions of this study. Second, the machine learning algorithm is substituted, replacing the random forest algorithm, which has been utilized as a prediction algorithm, with lasso regression, gradient boosting, and neural networks to investigate the potential influence of prediction algorithms on the conclusions of this study. Third, regarding benchmark regression, additional linear models were constructed and analyzed using DML, which involves subjective decisions regarding model form selection. Therefore, DML was employed to construct more comprehensive interactive models, aiming to assess the influence of model settings on the conclusions of this study. The main and auxiliary regressions utilized for the analysis were modified as follows:

Combining Eqs ( 7 ), ( 8 ) for the regression, the interactive model yielded estimated coefficients for the disposition effect:

The results of Eq. ( 9 ) are shown in column (5) of Table 6 . And all the regression results obtained from the modified DML model are presented in Table 6 .

Table 6 . Results of resetting the DML model.

The findings indicate that the sample split ratio in the DML model, the prediction algorithm used, or the model estimation approach does not impact the conclusion that the national high-tech zone policy raises urban areas’ level of green innovation. These factors only modify the magnitude of the policy effect to some degree.

4.3 Heterogeneity analysis

4.3.1 regional heterogeneity.

The sample cities were further divided into the east, central, and west regions based on the three major economic subregions to examine regional variations in national high-tech zone policies ' effects on urban green innovation, with the results presented in Table 7 . National high-tech zone policies do not statistically significantly affect urban green innovation in the eastern region. However, they have a considerable beneficial influence in the central and western areas. The lack of statistical significance may be explained by the possibility that the setting up of national high-tech zones in the eastern region will provide obstacles to the growth of urban green innovation, such as resource strain and environmental pollution. Given the central and western regions’ relatively underdeveloped economic status and industrial structure, coupled with the preceding theoretical analysis, establishing national high-tech zones is a crucial catalyst, significantly boosting urban green innovation levels. Furthermore, the central government emphasizes that setting high-tech national zones should consider regional resource endowments and local conditions, implementing tailored policies. The central and western regions possess unique geographic locations and natural conditions that make them well-suited for developing solar energy, wind energy, and other forms of green energy. Compared to the central region, the national high-tech zone initiative has a more pronounced impact on promoting urban green innovation in the western region. While further optimization is needed for the western region’s urban innovation environment, the policy on national high-tech zones has a more substantial incentive effect in this region due to its more significant development potential, positive transformation of industrial structure, and increased policy support from the state, including the development strategy for the western region.

Table 7 . Heterogeneity test results for different regions.

4.3.2 Urban hierarchical heterogeneity

The New Tier 1 Cities Institute’s ‘2020 City Business Charm Ranking’ is the basis for this study, with the sample cities categorized into Tier 1 (New Tier 1), Tier 2, Tier 3, Tier 4, and Tier 5. Table 8 presents the regression findings for each of the groups.

Table 8 . Heterogeneity test results for different classes of cities.

The results in Table 8 reveal significant heterogeneity at the city level regarding national high-tech zones’ effects on urban green innovation, confirming Hypothesis 2 . In particular, the coefficients for the first-tier cities are not statistically significant due to the small sample size, and the same applies to the fifth-tier cities. This could be attributed to the relatively weak economy and infrastructure development issues in the fifth-tier cities. Additionally, due to their limited level of development, the fifth-tier cities may have a relatively homogeneous industrial structure, with a dominance of traditional industries or agriculture and a need for a more diversified industrial layout. National high-tech zones have not greatly aided the development of green innovation in these cities. In contrast, national high-tech zone policies in second-tier, third-tier, and fourth-tier cities have a noteworthy favorable impact on green innovation, indicating their favorable influence on enhancing green innovation in these cities. Despite the lower level of economic development in fourth-tier cities compared to second-tier and third-tier cities, the fourth-tier cities’ national high-tech zones have the most pronounced impact on promoting green innovation. This could be attributed to the ongoing transformation of industries in fourth-tier cities, which are still in the technology diffusion and imitation stage, allowing these cities’ national high-tech zones to maintain a high marginal effect. Thus, Hypothesis 2 is supported.

5 Further analysis

According to the empirical findings, setting high-tech national zones significantly raises the bar for urban green innovation. Therefore, it is essential to understand the underlying factors and mechanisms that contribute to the positive correlation. This paper constructs a moderating effect test model using Eqs 5 , 6 and provides a detailed discussion by introducing transportation infrastructure as a moderating variable.

The empirical finding of the moderating impact of transportation infrastructure is shown in Table 9 . The dichotomous interaction term Zone*lntra is significantly negative at the 5% level, suggesting that the impact of national high-tech zone policies on the level of urban green innovation is negatively moderated by transportation infrastructure. This result deviates from the general expectation, but it aligns with the complexity of the role played by transportation infrastructure in the context of modern economic development, as discussed in the previous theoretical analysis. This could be attributed to the insufficient green innovation benefits generated by the policy on national high-tech zones at the current stage, which fails to compensate for the adverse effects of excessive resource consumption and environmental pollution caused by the construction of the zone. Furthermore, transportation infrastructure can lead to an excessive concentration of similar enterprises in the high-tech zones. This excessive concentration creates a relative crowding effect, intensifying competition among enterprises. It diminishes their inclination to engage in green innovation collaboration and investment and hinders their effective implementation of technological research and development activities. Moreover, the excessive clustering of similar enterprises implies a need for more diversity in green innovation activities among businesses located in national high-tech zones. This results in duplicated green innovation outputs and hinders the advancement of green innovation. Thus, Hypothesis 3 is supported.

Table 9 . Empirical results of moderating effects.

6 Conclusion and policy recommendations

6.1 conclusion.

Based on panel data from 163 prefecture-level cities in China from 2007 to 2019, the net effect of setting national high-tech zones on urban green innovation was analyzed using the double machine learning model. The results found that: firstly, the national high-tech zone policy significantly raises the degree of local green innovation, and these results remain robust even after accounting for various factors that could affect the estimation results. Secondly, in the central and western regions, the level of urban green innovation is positively impacted by the national high-tech zone policy; However, this impact is less significant in the eastern region. In the western region compared to the central region, the national high-tech zone initiative has a stronger impact on increasing the level of urban green innovation. Across different city levels, compared to second-tier and third-tier cities, the high-tech zone policy has a more substantial impact on increasing the level of green innovation in fourth-tier cities. Thirdly, based on the moderating effect mechanism test, the construction of transportation infrastructure weakens the promotional effect of national high-tech zones on urban green innovation.

6.2 Policy recommendations

In order that national high-tech zones can better promote China’s high-quality development, this paper proposes the following policy recommendations:

(1) Urban green innovation in China depends on accelerating the setting up of national high-tech zones and creating an atmosphere that supports innovation. Establishing national high-tech zones as testbeds for high-quality development and green innovation has significantly elevated urban green innovation. Thus, cities can efficiently foster urban green innovation by supporting the development of national high-tech zones. Cities that have already established national high-tech zones should further encourage enterprises within these zones to increase their investment in research and development. They should also proceed to foster the leadership of national high-tech zones for urban green innovation, assuming the role of pilot cities as models and leaders. Additionally, it is essential to establish mechanisms for cooperation and synergy between the pilot cities and their neighboring cities to promote collective green development in the region.

(2) Expanding the pilot program and implementing tailored policies based on local conditions are essential. Industrial policies about national high-tech zones have differing effects on urban green innovation. Regions should leverage their comparative advantages, consider urban development’s commonalities and unique aspects, and foster a stable and sustainable green innovation ecosystem. The western and central regions should prioritize constructing and enhancing new infrastructure and bolster support for the high-tech green industry. The western region should seize the opportunity presented by national policies that prioritize support, quicken the rate of environmental innovation, and progressively bridge the gap with the eastern and central regions in various aspects. Furthermore, second-tier, third-tier, and fourth-tier cities should enhance the advantages of national high-tech zone policies, further maintaining the high standard of green innovation and keeping green innovation at an elevated level. Regions facing challenges in green innovation, particularly fifth-tier cities, should learn from the development experiences of advanced regions with national high-tech zones to compensate for their deficiencies in green innovation.

(3) Highlighting the importance of transportation regulation and enhancing collaboration in green innovation is crucial. Firstly, transportation infrastructure should be maximized to strengthen coordination and cooperation among regions, facilitate the smooth movement of innovative talents across regions, and facilitate the rational sharing of innovative resources, collectively enhancing green innovation. Additionally, attention ought to be given to the industrial clustering effect of parks to prevent the wastage of resources and inefficiencies resulting from the excessive clustering of similar industries. Efforts should be focused on effectively harnessing the latent potential of crucial transportation infrastructure areas as long-term drivers of development, promptly mitigating the negative impact of transportation infrastructure construction, and gradually achieving the synergistic promotion of the setting up of national high-tech zones and the raising of urban levels of green innovation, among other overarching objectives.

6.3 Limitations and future research

Our study has some limitations because the research in this paper is conducted in the institutional context of China. For example, not all countries are suitable for implementing similar industrial policies to develop the economy while focusing on environmental protection. However, we recognize that this study is interesting and relevant, and it encourages us to focus more intensely on environmental protection from an industrial policy perspective. Moreover, this paper exhibits certain limitations in the research process. Firstly, the urban green innovation measurement index was developed using the quantity of green patent authorizations. Future studies could focus on green innovation processes, such as the quality of green patents granted. Secondly, the paper employs machine learning techniques for causal inference. Subsequent investigations could delve further into the potential applications of machine learning algorithms in environmental sciences to maximize the benefits of innovative research methodologies.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

WC: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing–review and editing. YJ: Conceptualization, Data curation, Formal Analysis, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. BT: Investigation, Project administration, Writing–review and editing.

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Youth Fund for Humanities and Social Science research of Ministry of Education (20YJC790004).

Acknowledgments

The authors are grateful to the editors and the reviewers for their insightful comments.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Albahari, A., Pérez-Canto, S., Barge-Gil, A., and Modrego, A. (2017). Technology parks versus science parks: does the university make the difference? Technol. Forecast. Soc. Change 116, 13–28. doi:10.1016/j.techfore.2016.11.012

CrossRef Full Text | Google Scholar

Alder, S., Shao, L., and Zilibotti, F. (2016). Economic reforms and industrial policy in a panel of Chinese cities. J. Econ. Growth 21, 305–349. doi:10.1007/s10887-016-9131-x

Ansaris, M., Ashrafi, S., and Jebellie, H. (2016). The impact of human capital on green innovation. Industrial Manag. J. 8 (2), 141–162. doi:10.22059/imj.2016.60653

Banister, D., and Berechman, Y. (2001). Transport investment and the promotion of economic growth. J. Transp. Geogr. 9 (3), 209–218. doi:10.1016/s0966-6923(01)00013-8

Behrens, K., Lamorgese, A. R., Ottaviano, G. I., and Tabuchi, T. (2007). Changes in transport and non-transport costs: local vs global impacts in a spatial network. Regional Sci. Urban Econ. 37 (6), 625–648. doi:10.1016/j.regsciurbeco.2007.08.003

Bo, W., Yongzhong, Z., Lingshan, C., and Xing, Y. (2020). Urban green innovation level and decomposition of its determinants in China. Sci. Res. Manag. 41 (8), 123. doi:10.19571/j.cnki.1000-2995.2020.08.013

Cao, Q. F. (2019). The latest researches on place based policy and its implications for the construction of xiong’an national new district. Sci. Technol. Prog. Policy 36 (2), 36–43. (in Chinese).

Google Scholar

Cattapan, P., Passarelli, M., and Petrone, M. (2012). Brokerage and SME innovation: an analysis of the technology transfer service at area science park, Italy. Industry High. Educ. 26 (5), 381–391. doi:10.5367/ihe.2012.0119

Chandrashekar, D., and Bala Subrahmanya, M. H. (2017). Absorptive capacity as a determinant of innovation in SMEs: a study of Bengaluru high-tech manufacturing cluster. Small Enterp. Res. 24 (3), 290–315. doi:10.1080/13215906.2017.1396491

Chen, M., and Zheng, Y. (2008). China's regional disparity and its policy responses. China & World Econ. 16 (4), 16–32. doi:10.1111/j.1749-124x.2008.00119.x

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., et al. (2018). Double/debiased machine learning for treatment and structural parameters. Econ. J. 21 (1), C1–C68. doi:10.1111/ectj.12097

De Beule, F., and Van Beveren, I. (2012). Does firm agglomeration drive product innovation and renewal? An application for Belgium. Tijdschr. Econ. Soc. Geogr. 103 (4), 457–472. doi:10.1111/j.1467-9663.2012.00715.x

Díez-Vial, I., and Fernández-Olmos, M. (2017). The effect of science and technology parks on firms’ performance: how can firms benefit most under economic downturns? Technol. Analysis Strategic Manag. 29 (10), 1153–1166. doi:10.1080/09537325.2016.1274390

Fang, Z., Kong, X., Sensoy, A., Cui, X., and Cheng, F. (2021). Government’s awareness of environmental protection and corporate green innovation: a natural experiment from the new environmental protection law in China. Econ. Analysis Policy 70, 294–312. doi:10.1016/j.eap.2021.03.003

Fritsch, M., and Slavtchev, V. (2011). Determinants of the efficiency of regional innovation systems. Reg. Stud. 45 (7), 905–918. doi:10.1080/00343400802251494

He, J. A., Peng, F. P., and Xie, X. Y. (2022). Mixed-ownership reform, political connection and enterprise innovation: based on the double/unbiased machine learning method. Sci. Technol. Manag. Res. 42 (11), 116–126. (in Chinese).

Hong, J., Feng, B., Wu, Y., and Wang, L. (2016). Do government grants promote innovation efficiency in China's high-tech industries? Technovation 57, 4–13. doi:10.1016/j.technovation.2016.06.001

Huang, W. J., and Fernández-Maldonado, A. M. (2016). High-tech development and spatial planning: comparing The Netherlands and Taiwan from an institutional perspective. Eur. Plan. Stud. 24 (9), 1662–1683. doi:10.1080/09654313.2016.1187717

Hull, I., and Grodecka-Messi, A. (2022). Measuring the impact of taxes and public services on property values: a double machine learning approach . arXiv preprint arXiv:2203.14751.

Jiang, T. (2022). Mediating effects and moderating effects in causal inference. China Ind. Econ. 5, 100–120. doi:10.19581/j.cnki.ciejournal.2022.05.005

Knittel, C. R., and Stolper, S. (2021). Machine learning about treatment effect heterogeneity: the case of household energy use. Nashv. TN 37203, 440–444. doi:10.1257/pandp.20211090

Li, H., and Tang, L. (2015). Transportation infrastructure investment, spatial spillover effect and enterprise inventory. Manag. World 4, 126–136. doi:10.19744/j.cnki.11-1235/f.2015.04.012

Li, W. H., Liu, F., and Liu, T. S. (2022a). Can national high-tech zones improve the urban innovation efficiency? an empirical test based on the effect of spatial agglomeration regulation. Manag. Rev. 34 (5), 93. doi:10.14120/j.cnki.cn11-5057/f.2022.05.007

Li, X. (2015). Analysis and outlook of the related researches on green innovation. R&D Manag. 27 (2), 1–11. doi:10.13581/j.cnki.rdm.2015.02.001

Li, X., Shao, X., Chang, T., and Albu, L. L. (2022b). Does digital finance promote the green innovation of China's listed companies? Energy Econ. 114, 106254. doi:10.1016/j.eneco.2022.106254

Li, X. P., Li, P., Lu, D. G., and Jiang, F. T. (2015). Economic agglomeration, selection effects and firm productivity. J. Manag. World 4, 25–37+51. (in Chinese). doi:10.19744/j.cnki.11-1235/f.2015.04.004

Liu, R. M., and Zhao, R. J. (2015). Does the national high-tech zone promote regional economic development? A verification based on differences-in-differences method. J. Manag. World 8, 30–38. doi:10.19744/j.cnki.11-1235/f.2015.08.005

Luo, R., and Wang, Q. M. (2023). Does the construction of national demonstration logistics park produce economic growth effect? Econ. Surv. 40 (1), 47–56. doi:10.15931/j.cnki.1006-1096.2023.01.015

Luo, Y., Salman, M., and Lu, Z. (2021). Heterogeneous impacts of environmental regulations and foreign direct investment on green innovation across different regions in China. Sci. total Environ. 759, 143744. doi:10.1016/j.scitotenv.2020.143744

PubMed Abstract | CrossRef Full Text | Google Scholar

Melitz, M. J., and Ottaviano, G. I. (2008). Market size, trade, and productivity. Rev. Econ. Stud. 75 (1), 295–316. doi:10.1111/j.1467-937x.2007.00463.x

Park, S. C., and Lee, S. K. (2004). The regional innovation system in Sweden: a study of regional clusters for the development of high technology. Ai Soc. 18 (3), 276–292. doi:10.1007/s00146-003-0277-7

Pokharel, R., Bertolini, L., Te Brömmelstroet, M., and Acharya, S. R. (2021). Spatio-temporal evolution of cities and regional economic development in Nepal: does transport infrastructure matter? J. Transp. Geogr. 90, 102904. doi:10.1016/j.jtrangeo.2020.102904

Qiu, Y., Wang, H., and Wu, J. (2023). Impact of industrial structure upgrading on green innovation: evidence from Chinese cities. Environ. Sci. Pollut. Res. 30 (2), 3887–3900. doi:10.1007/s11356-022-22162-1

Sosnovskikh, S. (2017). Industrial clusters in Russia: the development of special economic zones and industrial parks. Russ. J. Econ. 3 (2), 174–199. doi:10.1016/j.ruje.2017.06.004

Sun, Z., and Sun, J. C. (2015). The effect of Chinese industrial policy: industrial upgrading or short-term economic growth. China Ind. Econ. 7, 52–67. (in Chinese). doi:10.19581/j.cnki.ciejournal.2015.07.004

Tan, J., and Zhang, J. (2018). Does national high-tech development zones promote the growth of urban total factor productivity? —based on" quasi-natural experiments" of 277 cities. Res. Econ. Manag. 39 (9), 75–90. doi:10.13502/j.cnki.issn1000-7636.2018.09.007

Vásquez-Urriago, Á. R., Barge-Gil, A., Rico, A. M., and Paraskevopoulou, E. (2014). The impact of science and technology parks on firms’ product innovation: empirical evidence from Spain. J. Evol. Econ. 24, 835–873. doi:10.1007/s00191-013-0337-1

Wang, F., Dong, M., Ren, J., Luo, S., Zhao, H., and Liu, J. (2022a). The impact of urban spatial structure on air pollution: empirical evidence from China. Environ. Dev. Sustain. 24, 5531–5550. doi:10.1007/s10668-021-01670-z

Wang, M., and Liu, X. (2023). The impact of the establishment of national high-tech zones on total factor productivity of Chinese enterprises. China Econ. 18 (3), 68–93. doi:10.19602/j.chinaeconomist.2023.05.04

Wang, Q., She, S., and Zeng, J. (2020). The mechanism and effect identification of the impact of National High-tech Zones on urban green innovation: based on a DID test. China Popul. Resour. Environ. 30 (02), 129–137.

Wang, W. S., and Xu, T. S. (2020). A research on the impact of national high-teach zone establishment on enterprise innovation performance. Econ. Surv. 37 (6), 76–87. doi:10.15931/j.cnki.1006-1096.20201010.001

Wang, Z., Yang, Y., and Wei, Y. (2022b). Has the construction of national high-tech zones promoted regional economic growth? empirical research from prefecture-level cities in China. Sustainability 14 (10), 6349. doi:10.3390/su14106349

Weber, M., Driessen, P. P., and Runhaar, H. A. (2014). Evaluating environmental policy instruments mixes; a methodology illustrated by noise policy in The Netherlands. J. Environ. Plan. Manag. 57 (9), 1381–1397. doi:10.1080/09640568.2013.808609

Wu, Y. B. (2019). Does fiscal decentralization promote technological innovation. Mod. Econ. Sci. 41, 13–25.

Xu, S. D., Jiang, J., and Zheng, J. (2022). Has the establishment of national high-tech zones promoted industrial Co-Agglomeration? an empirical test based on difference in difference method. Inq. into Econ. Issues 11, 113–127. (in Chinese).

Yang, F., and Guo, G. (2020). Fuzzy comprehensive evaluation of innovation capability of Chinese national high-tech zone based on entropy weight—taking the northern coastal comprehensive economic zone as an example. J. Intelligent Fuzzy Syst. 38 (6), 7857–7864. doi:10.3233/jifs-179855

Yu, N., De Jong, M., Storm, S., and Mi, J. (2013). Spatial spillover effects of transport infrastructure: evidence from Chinese regions. J. Transp. Geogr. 28, 56–66. doi:10.1016/j.jtrangeo.2012.10.009

Yuan, H., and Zhu, C. L. (2018). Do national high-tech zones promote the transformation and upgrading of China’s industrial structure. China Ind. Econ. 8, 60–77. doi:10.19581/j.cnki.ciejournal.2018.08.004

Zhang, T., Chen, L., and Dong, Z. (2018). Highway construction, firm dynamics and regional economic efficiency. China Ind. Econ. 1, 79–99. doi:10.19581/j.cnki.ciejournal.20180115.003

Zhang, T., and Li, J. C. (2023). Network infrastructure, inclusive green growth, and regional inequality: from causal inference based on double machine learning. J. Quantitative Technol. Econ. 40 (4), 113–135. doi:10.13653/j.cnki.jqte.20230310.005

Zhou, L., and Shen, K. (2020). National city group construction and green innovation. China Popul. Resour. Environ. 30 (8), 92–99.

Zhou, X., and Du, J. (2021). Does environmental regulation induce improved financial development for green technological innovation in China? J. Environ. Manag. 300, 113685. doi:10.1016/j.jenvman.2021.113685

Keywords: national high-tech zone, industrial policy, green innovation, heterogeneity analysis, moderating effect, double machine learning

Citation: Cao W, Jia Y and Tan B (2024) Impact of industrial policy on urban green innovation: empirical evidence of China’s national high-tech zones based on double machine learning. Front. Environ. Sci. 12:1369433. doi: 10.3389/fenvs.2024.1369433

Received: 12 January 2024; Accepted: 15 March 2024; Published: 04 April 2024.

Reviewed by:

Copyright © 2024 Cao, Jia and Tan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yu Jia, [email protected]

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Published: 16 April 2024

Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study

Luning Shao 1 ,
Yuxin Miao 2 ,
Shengce Ren 3 ,
Sanfa Cai 4 &
Fei Fan ORCID: orcid.org/0000-0001-8756-5140 5 , 6

Humanities and Social Sciences Communications volume 11 , Article number: 519 ( 2024 ) Cite this article

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Business and management

Entrepreneurship education (EE) has rapidly evolved within higher education and has emerged as a pivotal mechanism for cultivating innovative and entrepreneurial talent. In China, while EE has made positive strides, it still faces a series of practical challenges. These issues cannot be effectively addressed solely through the efforts of universities. Based on the triple helix (TH) theory, this study delves into the unified objectives and practical content of EE in Chinese higher education. Through a comprehensive literature review on EE, coupled with educational objectives, planned behavior, and entrepreneurship process theories, this study introduces the 4H objective model of EE. 4H stands for Head (mindset), Hand (skill), Heart (attitude), and Help (support). Additionally, the research extends to a corresponding content model that encompasses entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate as tools for achieving the objectives. Based on a single-case approach, this study empirically explores the application of the content model at T-University. Furthermore, this paper elucidates how the university plays a role through the comprehensive development of entrepreneurial learning, practices, services, and climate in nurturing numerous entrepreneurs and facilitating the flourishing of the regional entrepreneurial ecosystem. This paper provides important contributions in its application of TH theory to develop EE within the Chinese context, and it provides clear guidance by elucidating the core objectives and practical content of EE. The proposed conceptual framework serves not only as a guiding tool but also as a crucial conduit for fostering the collaborative development of the EE ecosystem. To enhance the robustness of the framework, this study advocates strengthening empirical research on TH theory through multiple and comparative case studies.

Extra-curricular support for entrepreneurship among engineering students: development of entrepreneurial self-efficacy and intentions

Deepa Subhadrammal, Martin Bliemel, … Helene de Burgh-Woodman

The mediating effects of entrepreneurial self-efficacy in the relationship between entrepreneurship education and start-up readiness

Adeshina Olushola Adeniyi

Individual entrepreneurial orientation for entrepreneurial readiness

Adeshina Olushola Adeniyi, Vangeli Gamede & Evelyn Derera

Introduction

In the era of the knowledge economy, entrepreneurship has emerged as a fundamental driver of social and economic development. As early as 1911, Schumpeter proposed the well-known theory of economic development, wherein he first introduced the concepts of entrepreneurship and creative destruction as driving forces behind socioeconomic development. Numerous endogenous growth theories, such as the entrepreneurial ecosystem mechanism of Acs et al. ( 2018 ), which also underscores the pivotal role of entrepreneurship in economic development, are rooted in Schumpeter’s model. Recognized as a key means of cultivating entrepreneurs and enhancing their capabilities (Jin et al., 2023 ), entrepreneurship education (EE) has received widespread attention over the past few decades, especially in the context of higher education (Wong & Chan, 2022 ).

Driven by international trends and economic demands, China places significant emphasis on nurturing innovative talent and incorporating EE into the essential components of its national education system. The State Council’s “Implementation Opinions on Deepening the Reform of Innovation and Entrepreneurship Education in Higher Education” (hereafter referred to as the report) underscores the urgent necessity for advancing reforms in innovation and EE in higher education institutions. This initiative aligns with the national strategy of promoting innovation-driven development and enhancing economic quality and efficiency. Furthermore, institutions at various levels are actively and eagerly engaging in EE.

Despite the positive strides made in EE in China, its development still faces a series of formidable practical challenges. As elucidated in the report, higher education institutions face challenges such as a delay in the conceptualization of EE, inadequate integration with specialized education, and a disconnect from practical applications. Furthermore, educators exhibit a deficiency in awareness and capabilities, which manifests in a singular and less effective teaching methodology. The shortage of practical platforms, guidance, and support emphasizes the pressing need for comprehensive innovation and EE systems. These issues necessitate collaborative efforts from universities, industry, and policymakers.

Internationally established solutions for the current challenges have substantially matured, providing invaluable insights and guidance for the development of EE in the Chinese context. In the late 20th century, the concept of the entrepreneurial university gained prominence (Etzkowitz et al., 2000 ). Then, entrepreneurial universities expanded their role from traditional research and teaching to embrace a “third mission” centered on economic development. This transformation entailed fostering student engagement in entrepreneurial initiatives by offering resources and guidance to facilitate the transition of ideas into viable entrepreneurial ventures. Additionally, these entrepreneurial universities played a pivotal role in advancing the triple helix (TH) model (Henry, 2009 ). The TH model establishes innovation systems that facilitate knowledge conversion into economic endeavors by coordinating the functions of universities, government entities, and industry. The robustness of this perspective has been substantiated through comprehensive theoretical and empirical investigations (Mandrup & Jensen, 2017 ).

Therefore, this study aims to explore how EE in Chinese universities can adapt to new societal trends and demands through the guidance of TH theory. This research involves two major themes: educational objectives and content. Educational objectives play a pivotal role in regulating the entire process of educational activities, ensuring alignment with the principles and norms of education (Whitehead, 1967 ), while content provides a practical pathway to achieving these objectives. Specifically, the study has three pivotal research questions:

RQ1: What is the present landscape of EE research?

RQ2: What unified macroscopic goals should be formulated to guide EE in Chinese higher education?

RQ3: What specific EE system should be implemented to realize the identified goals in Chinese higher education?

The structure of this paper is as follows: First, we conduct a comprehensive literature review on EE to answer RQ1 , thereby establishing a robust theoretical foundation. Second, we outline our research methodology, encompassing both framework construction and case studies and providing a clear and explicit approach to our research process. Third, we derive the objectives and content model of EE guided by educational objectives, entrepreneurial motivations, and entrepreneurial process theories. Fourth, focusing on a typical university in China as our research subject, we conduct a case study to demonstrate the practical application of our research framework. Finally, we end the paper with the findings for RQ2 and RQ3 , discussions on the framework, and conclusions.

Literature review

The notion of TH first appeared in the early 1980s, coinciding with the global transition from an industrial to a knowledge-based economy (Cai & Etzkowitz, 2020 ). At that time, the dramatic increase in productivity led to overproduction, and knowledge became a valuable mechanism for driving innovation and economic growth (Mandrup & Jensen, 2017 ). Recognizing the potential of incorporating cutting-edge university technologies into industry and facilitating technology transfer and innovation, the US government took proactive steps to enhance the international competitiveness of American industries. This initiative culminated in the enactment of relevant legislation in 1980, which triggered a surge in technology transfer, patent licensing, and the establishment of new enterprises within the United States. Subsequently, European and Asian nations adopted similar measures, promoting the transformation of universities’ identity (Grimaldi et al., 2011 ). Universities assumed a central role in technology transfer, the formation of businesses, and regional revitalization within the knowledge society rather than occupying a secondary position within the industrial community. The conventional one-to-one relationships between universities, companies, and the government evolved into a dynamic TH model (Cai & Etzkowitz, 2020 ). Beyond their traditional roles in knowledge creation, wealth production, and policy coordination, these sectors began to engage in multifaceted interactions, effectively “playing the role of others” (Ranga & Etzkowitz, 2013 ).

The TH model encompasses three fundamental elements: 1) In a knowledge-based society, universities assume a more prominent role in innovation than in industry; 2) The three entities engage in collaborative relationships, with innovation policies emerging as a result of their mutual interactions rather than being solely dictated by the government; and 3) Each entity, while fulfilling its traditional functions, also takes on the roles of the other two parties (Henry, 2009 ). This model is closely aligned with EE.

On the one hand, EE can enhance the effectiveness of TH theory by strengthening the links between universities, industry, and government. The TH concept was developed based on entrepreneurial universities. The emerging entrepreneurial university model integrates economic development as an additional function. Etzkowitz’s research on the entrepreneurial university identified a TH model of academia-industry-government relations implemented by universities in an increasingly knowledge-based society (Galvao et al., 2019 ). Alexander and Evgeniy ( 2012 ) articulated that entrepreneurial universities are crucial to the implementation of triple-helix arrangements and that by integrating EE into their curricula, universities have the potential to strengthen triple-helix partnerships and boost the effectiveness of the triple-helix model.

On the other hand, TH theory also drives EE to achieve high-quality development. Previously, universities were primarily seen as sources of knowledge and human resources. However, they are now also regarded as reservoirs of technology. Within EE and incubation programs, universities are expanding their educational capabilities beyond individual education to shaping organizations (Henry, 2009 ). Surpassing their role as sources of new ideas for existing companies, universities blend their research and teaching processes in a novel way, emerging as pivotal sources for the formation of new companies, particularly in high-tech domains. Furthermore, innovation within one field of the TH influences others (Piqué et al., 2020 ). An empirical study by Alexander and Evgeniy ( 2012 ) outlined how the government introduced a series of initiatives to develop entrepreneurial universities, construct innovation infrastructure, and foster EE growth.

Overview of EE

EE occupies a crucial position in driving economic advancement, and this domain has been the focal point of extensive research. Fellnhofer ( 2019 ) examined 1773 publications from 1975 to 2014, introducing a more closely aligned taxonomy of EE research. This taxonomy encompasses eight major clusters: social and policy-driven EE, human capital studies related to self-employment, organizational EE and TH, (Re)design and evaluation of EE initiatives, entrepreneurial learning, EE impact studies, and the EE opportunity-related environment at the organizational level. Furthermore, Mohamed and Sheikh Ali ( 2021 ) conducted a systematic literature review of 90 EE articles published from 2009 to 2019. The majority of these studies focused on the development of EE (32%), followed by its benefits (18%) and contributions (12%). The selected research also addressed themes such as the relationship between EE and entrepreneurial intent, the effectiveness of EE, and its assessment (each comprising 9% of the sample).

Spanning from 1975 to 2019, these two reviews offer a comprehensive landscape of EE research. The perspective on EE has evolved, extending into multiple dimensions (Zaring et al., 2021 ). However, EE does not always achieve the expected outcomes, as challenges such as limited student interest and engagement as well as persistent negative attitudes are often faced (Mohamed & Sheikh Ali, 2021 ). In fact, the challenges faced by EE in most countries may be similar. However, the solutions may vary due to contextual differences (Fred Awaah et al., 2023 ). Furthermore, due to this evolution, there is a need for a more comprehensive grasp of pedagogical concepts and the foundational elements of modern EE (Hägg & Gabrielsson, 2020 ). Based on the objectives of this study, four specific themes were chosen for an in-depth literature review: the objectives, contents and methods, outcomes, and experiences of EE.

Objectives of EE

The objectives of EE may provide significant guidance for its implementation and the assessment of its effectiveness, and EE has evolved to form a diversified spectrum. Mwasalwiba ( 2010 ) presented a multifaceted phenomenon in which EE objectives are closely linked to entrepreneurial outcomes. These goals encompass nurturing entrepreneurial attitudes (34%), promoting new ventures (27%), contributing to local community development (24%), and imparting entrepreneurial skills (15%). Some current studies still emphasize particular dimensions of these goals, such as fostering new ventures or value creation (Jones et al., 2018 ; Ratten & Usmanij, 2021 ). These authors further stress the significance of incorporating practical considerations related to the business environment, which prompts learners to contemplate issues such as funding and resource procurement. This goal inherently underscores the importance of entrepreneurial thinking and encourages learners to transition from merely being students to developing entrepreneurial mindsets.

Additionally, Kuratko and Morris ( 2018 ) posit that the goal of EE should not be to produce entrepreneurs but to cultivate entrepreneurial mindsets in students, equipping them with methods for thinking and acting entrepreneurially and enabling them to perceive opportunities rapidly in uncertain conditions and harness resources as entrepreneurs would. While the objectives of EE may vary based on the context of the teaching institution, the fundamental goal is increasingly focused on conveying and nurturing an entrepreneurial mindset among diverse stakeholders. Hao’s ( 2017 ) research contends that EE forms a comprehensive system in which multidimensional educational objectives are established. These objectives primarily encompass cultivating students’ foundational qualities and innovative entrepreneurial personalities, equipping them with essential awareness of entrepreneurship, psychological qualities conducive to entrepreneurship, and a knowledge structure for entrepreneurship. Such a framework guides students towards independent entrepreneurship based on real entrepreneurial scenarios.

Various studies and practices also contain many statements about entrepreneurial goals. The Entrepreneurship Competence Framework, which was issued by the EU in 2016, delineates three competency domains: ideas and opportunities, resources and action. Additionally, the framework outlines 15 specific entrepreneurship competencies (Jun, 2017 ). Similarly, the National Content Standards for EE published by the US Consortium encompass three overarching strategies for articulating desired competencies for aspiring entrepreneurs: entrepreneurial skills, ready skills, and business functions (Canziani & Welsh, 2021 ). First, entrepreneurial skills are unique characteristics, behaviors, and experiences that distinguish entrepreneurs from ordinary employees or managers. Second, ready skills, which include business and entrepreneurial knowledge and skills, are prerequisites and auxiliary conditions for EE. Third, business functions help entrepreneurs create and operate business processes in business activities. These standards explain in the broadest terms what students need to be self-employed or to develop and grow a new venture. Although entrepreneurial skills may be addressed in particular courses offered by entrepreneurship faculties, it is evident that business readiness and functional skills significantly contribute to entrepreneurial success (Canziani & Welsh, 2021 ).

Contents and methods of EE

The content and methods employed in EE are pivotal factors for ensuring the delivery of high-quality entrepreneurial instruction, and they have significant practical implications for achieving educational objectives. The conventional model of EE, which is rooted in the classroom setting, typically features an instructor at the front of the room delivering concepts and theories through lectures and readings (Mwasalwiba, 2010 ). However, due to limited opportunities for student engagement in the learning process, lecture-based teaching methods prove less effective at capturing students’ attention and conveying new concepts (Rahman, 2020 ). In response, Okebukola ( 2020 ) introduced the Culturo-Techno-Contextual Approach (CTCA), which offers a hybrid teaching and learning method that integrates cultural, technological, and geographical contexts. Through a controlled experiment involving 400 entrepreneurship development students from Ghana, CTCA has been demonstrated to be a model for enhancing students’ comprehension of complex concepts (Awaah, 2023 ). Furthermore, learners heavily draw upon their cultural influences to shape their understanding of EE, emphasizing the need for educators to approach the curriculum from a cultural perspective to guide students in comprehending entrepreneurship effectively.

In addition to traditional classroom approaches, research has highlighted innovative methods for instilling entrepreneurial spirit among students. For instance, students may learn from specific university experiences or even engage in creating and running a company (Kolb & Kolb, 2011 ). Some scholars have developed an educational portfolio that encompasses various activities, such as simulations, games, and real company creation, to foster reflective practice (Neck & Greene, 2011 ). However, some studies have indicated that EE, when excessively focused on applied and practical content, yields less favorable outcomes for students aspiring to engage in successful entrepreneurship (Martin et al., 2013 ). In contrast, students involved in more academically oriented courses tend to demonstrate improved intellectual skills and often achieve greater success as entrepreneurs (Zaring et al., 2021 ). As previously discussed, due to the lack of a coherent theoretical framework in EE, there is a lack of uniformity and consistency in course content and methods (Ribeiro et al., 2018 ).

Outcomes of EE

Research on the outcomes of EE is a broad and continually evolving field, with most related research focusing on immediate or short-term impact factors. For example, Anosike ( 2019 ) demonstrated the positive effect of EE on human capital, and Chen et al. ( 2022 ) proposed that EE significantly moderates the impact of self-efficacy on entrepreneurial competencies in higher education students through an innovative learning environment. In particular, in the comprehensive review by Kim et al. ( 2020 ), six key EE outcomes were identified: entrepreneurial creation, entrepreneurial intent, opportunity recognition, entrepreneurial self-efficacy and orientation, need for achievement and locus of control, and other entrepreneurial knowledge. One of the more popular directions is the examination of the impact of EE on entrepreneurial intentions. Bae et al. ( 2014 ) conducted a meta-analysis of 73 studies to examine the relationship between EE and entrepreneurial intention and revealed little correlation. However, a meta-analysis of 389 studies from 2010 to 2020 by Zhang et al. ( 2022 ) revealed a positive association between the two variables.

Nabi et al. ( 2017 ) conducted a systematic review to determine the impact of EE in higher education. Their findings highlight that studies exploring the outcomes of EE have primarily concentrated on short-term and subjective assessments, with insufficient consideration of longer-term effects spanning five or even ten years. These longer-term impacts encompass factors such as the nature and quantity of startups, startup survival rates, and contributions to society and the economy. As noted in the Eurydice report, a significant impediment to advancing EE is the lack of comprehensive delineation concerning education outcomes (Bourgeois et al., 2016 ).

Experiences in the EE system

With the deepening exploration of EE, researchers have turned to studying university-centered entrepreneurship ecosystems (Allahar and Sookram, 2019 ). Such ecosystems are adopted to fill gaps in “educational and economic development resources”, such as entrepreneurship curricula. A growing number of universities have evolved an increasingly complex innovation system that extends from technology transfer offices, incubators, and technology parks to translational research and the promotion of EE across campuses (Cai & Etzkowitz, 2020 ). In the university context, the entrepreneurial ecosystem aligns with TH theory, in which academia, government, and industry create a trilateral network and hybrid organization (Ranga & Etzkowitz, 2013 ).

The EE system is also a popular topic in China. Several researchers have summarized the Chinese experience in EE, including case studies and overall experience, such as the summary of the progress and system development of EE in Chinese universities over the last decade by Weiming et al. ( 2013 ) and the summary of the Chinese experience in innovation and EE by Maoxin ( 2017 ). Other researchers take an in-depth look at the international knowledge of EE, such as discussions on the EE system of Denmark by Yuanyuan ( 2015 ), analyzes of the ecological system of EE at the Technical University of Munich by Yubing and Ziyan ( 2015 ), and comparisons of international innovation and EE by Ke ( 2017 ).

In general, although there has been considerable discussion on EE, the existing body of work has not properly addressed the practical challenges faced by EE in China. On the one hand, the literature is fragmented and has not yet formed a unified and mature theoretical framework. Regarding what should be taught and how it can be taught and assessed, the answers in related research are ambiguous (Hoppe, 2016 ; Wong & Chan, 2022 ). On the other hand, current research lacks empirical evidence in the context of China, and guidance on how to put the concept of EE into practice is relatively limited. These dual deficiencies impede the effective and in-depth development of EE in China. Consequently, it is imperative to comprehensively redefine the objectives and contents of EE to provide clear developmental guidance for Chinese higher education institutions.

Research methodology

To answer the research questions, this study employed a comprehensive approach by integrating both literature-based and empirical research methods. The initial phase focused on systematically reviewing the literature related to entrepreneurial education, aiming to construct a clear set of frameworks for the objectives and content of EE in higher education institutions. The second phase involved conducting a case study at T-University, in which the theoretical frameworks were applied to a real-world context. This case not only contributed to validating the theoretical constructs established through the literature review but also provided valuable insights into the practical operational dynamics of entrepreneurial education within the specific university setting.

Conceptual framework stage

This paper aims to conceptualize the objective and content frameworks for EE. The methodology sequence is as follows: First, we examine the relevant EE literature to gain insights into existing research themes. Subsequently, we identify specific research articles based on these themes, such as “entrepreneurial intention”, “entrepreneurial self-efficacy”, and “entrepreneurial approach”, among others. Third, we synthesize the shared objectives of EE across diverse research perspectives through an analysis of the selected literature. Fourth, we construct an objective model for EE within higher education by integrating Bloom’s educational objectives ( 1956 ) and Gagne’s five learning outcomes ( 1984 ), complemented by entrepreneurship motivation and process considerations. Finally, we discuss the corresponding content framework.

Case study stage

To further elucidate the conceptual framework, this paper delves into the methods for the optimization of EE in China through a case analysis. Specifically, this paper employs a single-case approach. While a single case study may have limited external validity (Onjewu et al., 2021 ), if a case study informs current theory and conceptualizes the explored issues, it can still provide valuable insights from its internal findings (Buchanan, 1999 ).

T-University, which is a comprehensive university in China, is chosen as the subject of the case study for the following reasons. First, T-University is located in Shanghai, which is a Chinese international technological innovation center approved by the State Council. Shanghai’s “14th Five-Year Plan” proposes the establishment of a multichannel international innovation collaboration platform and a global innovation cooperation network. Second, T-University has initiated curriculum reforms and established a regional knowledge economy ecosystem by utilizing EE as a guiding principle, which aligns with the characteristics of its geographical location, history, culture, and disciplinary settings. This case study will showcase T-University’s experiences in entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate, elucidating the positive outcomes of this triangular interaction and offering practical insights for EE in other contexts.

The data collection process of this study was divided into two main stages: field research and archival research. The obtained data included interview transcripts, field notes, photos, internal documents, websites, reports, promotional materials, and published articles. In the initial stage, we conducted a 7-day field trip, including visits to the Innovation and Entrepreneurship Institute, the Career Development Centre, the Academic Affairs Office, and the Graduate School. Moreover, we conducted semistructured interviews with several faculty members and students involved in entrepreneurship education at the university to understand the overall state of implementation of entrepreneurship education at the university. In the second stage, we contacted the Academic Affairs Office and the Student Affairs Office at the university and obtained internal materials related to entrepreneurship education. Additionally, we conducted a comprehensive collection and created a summary of publicly available documents, official school websites, public accounts, and other electronic files. To verify the validity of the multisource data, we conducted triangulation and ultimately used consistent information as the basis for the data analysis.

For the purpose of our study, thematic analysis was employed to delve deeply into the TH factors, the objective and content frameworks, and their interrelationships. Thematic analysis is a method for identifying, analyzing, and reporting patterns within data. This approach emphasizes a comprehensive interpretation of the data, as it extracts information from multiple perspectives and derives valuable conclusions through summary and induction (Onjewu et al., 2021 ). Therefore, thematic analysis likely serves as the foundation for most other qualitative data analysis methods (Willig, 2013 ). In this study, three researchers individually conducted rigorous analyses and comprehensive reviews to ensure the accuracy and reliability of the data. Subsequently, they engaged in collaborative discussions to explore their differences and ultimately reach a consensus.

Framework construction

Theoretical basis of ee in universities.

The study is grounded in the theories of educational objectives, planned behavior, and the entrepreneurial process. Planned behavior theory can serve to elucidate the emergence of entrepreneurial activity, while entrepreneurial process theory can be used to delineate the essential elements of successful entrepreneurship.

Theory of educational objectives. The primary goal of education is to assist students in shaping their future. Furthermore, education should directly influence students and facilitate their future development. Education can significantly enhance students’ prospects by imparting specific skills and fundamental principles and cultivating the correct attitudes and mindsets (Bruner, 2009 ). According to “The Aims of Education” by Whitehead, the objective of education is to stimulate creativity and vitality. Gagne identifies five learning outcomes that enable teachers to design optimal learning conditions based on the presentation of these outcomes, encompassing “attitude,” “motor skills,” “verbal information,” “intellectual skills,” and “cognitive strategies”. Bloom et al. ( 1956 ) argue that education has three aims, which concern the cognitive, affective, and psychomotor domains. Gedeon ( 2017 ) posits that EE involves critical input and output elements. The key objectives encompass mindset (Head), skill (hand), attitude (heart), and support (help). The input objectives include EE teachers, resources, facilities, courses, and teaching methods. The output objectives encompass the impacts of the input factors, such as the number of students, the number of awards, and the establishment of new companies. The primary aims of Gedeon ( 2017 ) correspond to those of Bloom et al. ( 1956 ).

Theory of planned behavior. The theory of planned behavior argues that human behavior is the outcome of well-thought-out planning (Ajzen, 1991 ). Human behavior depends on behavioral intentions, which are affected by three main factors. The first is derived from the individual’s “attitude” towards taking a particular action; the second is derived from the influence of “subjective norms” from society; and the third is derived from “perceived behavioral control” (Ajzen, 1991 ). Researchers have adopted this theory to study entrepreneurial behavior and EE.

Theory of the entrepreneurship process. Researchers have proposed several entrepreneurial models, most of which are processes (Baoshan & Baobao, 2008 ). The theory of the entrepreneurship process focuses on the critical determinants of entrepreneurial success. The essential variables of the entrepreneurial process model significantly impact entrepreneurial performance. Timmons et al. ( 2004 ) argue that successful entrepreneurial activities require an appropriate match among opportunities, entrepreneurial teams, resources, and a dynamic balance as the business develops. Their model emphasizes flexibility and equilibrium, and it is believed that entrepreneurial activities change with time and space. As a result, opportunities, teams, and resources will be unbalanced and need timely adjustment.

4H objective model of EE

Guided by TH theory, the objectives of EE should consider universities’ transformational identity in the knowledge era and promote collaboration among students, faculty, researchers, and external players (Mandrup & Jensen, 2017 ). Furthermore, through a comprehensive analysis of the literature and pertinent theoretical underpinnings, the article introduces the 4H model for the EE objectives, as depicted in Fig. 1 .

The 4H objective model of entrepreneurship education.

The model comprises two levels. The first level pertains to outcomes at the entrepreneurial behavior level, encompassing entrepreneurial intention and entrepreneurial performance. These two factors support universities’ endeavors to nurture individuals with an entrepreneurial mindset and potential and contribute to the region’s growth of innovation and entrepreneurship. The second level pertains to fundamentals, which form the foundation of the first level. The article defines these as the 4H model, representing mindset (Head), skill (Hand), attitude (Heart), and support (Help). This model integrates key theories, including educational objectives, the entrepreneurship process, and planned behavior.

First, according to the theory of educational objectives, the cognitive, emotional, and skill objectives proposed by Bloom et al. ( 1956 ) correspond to the key goals of education offered by Gedeon ( 2017 ), namely, Head, Hand, and Heart; thus, going forward, in this study, these three objectives are adopted. Second, according to the theory of planned behavior, for the promotion of entrepreneurial intention, reflection on the control of beliefs, social norms, and perceptual behaviors must be included. EE’s impact on the Head, Hand, and Heart will promote the power of entrepreneurs’ thoughts and perceptual actions. Therefore, this approach is beneficial for enhancing entrepreneurial intentions. Third, according to entrepreneurship process theory, entrepreneurial performance is affected by various factors, including entrepreneurial opportunities, teams, and resources. Consideration of the concepts of Head, Hand, and Heart can enhance entrepreneurial opportunity recognition and entrepreneurial team capabilities. However, as the primary means of obtaining external resources, social networks play an essential role in improving the performance of innovation and entrepreneurship companies (Gao et al., 2023 ). Therefore, an effective EE program should tell students how to take action, connect them with those who can help them succeed (Ronstadt, 1985 ), and help them access the necessary resources. If EE institutions can provide relevant help, they will consolidate entrepreneurial intentions and improve entrepreneurial performance, enabling the EE’s objective to better support the Head, Hand, and Heart.

Content model of EE

EE necessitates establishing a systematic implementation framework to achieve the 4H objectives. Current research on EE predominantly focuses on two facets: one focuses on EE methods to improve students’ skills, and the other focuses on EE outcome measurements, which consider the impact of EE on different stakeholders. Based on this, to foster innovation in EE approaches and enable long-term sustainable EE outcomes, the 4H Model of EE objectives mandates that pertinent institutions provide entrepreneurial learning, entrepreneurial practice, startup services, and a suitable entrepreneurial climate. These components constitute the four integral facets of the content model for EE, as depicted in Fig. 2 .

The content model of entrepreneurship education.

Entrepreneurial learning

Entrepreneurial learning mainly refers to the learning of innovative entrepreneurial knowledge and theory. This factor represents the core of EE and can contribute significantly to the Head component. It can also improve the entrepreneurial thinking ability of academic subjects through classroom teaching, lectures, information reading and analysis, discussion, debates, etc. Additionally, it can positively affect the Hand and Heart elements of EE.

Entrepreneurial practice

Entrepreneurial practice mainly refers to academic subjects comprehensively enhancing their cognition and ability by participating in entrepreneurial activities. This element is also a key component of EE and plays a significant role in the cultivation of the Hand element. Entrepreneurial practice is characterized by participation in planning and implementing entrepreneurial programs, competitions, and simulation activities. Furthermore, it positively impacts EE’s Head, Heart, and Help factors.

Startup services

Startup services mainly refer to entrepreneurial-related support services provided by EE institutions, which include investment and financing, project declaration, financial and legal support, human resources, marketing, and intermediary services. These services can improve the success of entrepreneurship projects. Therefore, they can reinforce the expectations of entrepreneurs’ success and positively impact the Heart, Hand, and Head objectives of EE.

Entrepreneurial climate

The entrepreneurial climate refers to the entrepreneurial environment created by EE institutions and their community and is embodied mainly in the educational institutions’ external and internal entrepreneurial culture and ecology. The environment can impact the entrepreneurial attitude of educated individuals and the Heart objective of EE. Additionally, it is beneficial for realizing EE’s Head, Hand, and Help goals.

Case study: EE practice of T-University

Overview of ee at t-university.

T-University is one of the first in China to promote innovation and EE. Since the 1990s, a series of policies have been introduced, and different platforms have been set up. After more than 20 years of teaching, research, and practice, an innovation and entrepreneurship education system with unique characteristics has gradually evolved. The overall goal of this system is to ensure that 100% of students receive such education, with 10% of students completing the program and 1% achieving entrepreneurship with a high-quality standard. The overall employment rate of 2020 graduates reached 97.49%. In recent years, the proportion of those pursuing entrepreneurship has been more than 1% almost every year. The T-Rim Knowledge-Based Economic Circle, an industrial cluster formed around knowledge spillover from T-University’s dominant disciplines, employs more than 400 T-University graduates annually.

In 2016, T-University established the School of Innovation & Entrepreneurship, with the president serving as its dean. This school focuses on talent development and is pivotal in advancing innovation-driven development strategies. It coordinates efforts across various departments and colleges to ensure comprehensive coverage of innovation and EE, the integration of diverse academic disciplines, and the transformation of interdisciplinary scientific and technological advancements (see Fig. 3 ).

T-University innovation and entrepreneurship education map.

T-University is dedicated to integrating innovation and EE into every stage of talent development. As the guiding framework for EE, the university has established the Innovation and EE sequence featuring “three-dimensional, linked, and cross-university cooperation” with seven educational elements. These elements include the core curriculum system of innovation and entrepreneurship, the “one top-notch and three excellences” and experimental zones of innovation and entrepreneurship talent cultivation model, the four-level “China-Shanghai-University-School” training programs for innovation and entrepreneurship, four-level “International-National-Municipal-University” science and technology competitions, four-level “National-Municipal-University-School” innovation and entrepreneurship practice bases, three-level “Venture Valley-Entrepreneurship Fund-Industry Incubation” startup services and a high-level teaching team with both full-time and part-time personnel.

T-University has implemented several initiatives. First, the university has implemented 100% student innovation and EE through reforming the credit setting and curriculum system. Through the Venture Valley class, mobile class, and “joint summer school”, more than 10% of the students completed the Innovation and EE program. Moreover, through the professional reform pilot and eight professional incubation platforms in the National Science and Technology Park of T-University and other measures, 1% of the students established high-quality entrepreneurial enterprises. Second, the university is committed to promoting the integration of innovation and entrepreneurship and training programs, exploring and practising a variety of innovative talent cultivation models, and adding undergraduate innovation ability development as a mandatory component of the training program. In addition, pilot reforms have been conducted in engineering, medicine, and law majors, focusing on integrating research and education.

T-University has constructed a high-level integrated innovation and entrepreneurship practice platform by combining internal and external resources. This platform serves as the central component in Fig. 3 , forming a sequence of innovation and entrepreneurship practice opportunities, including 1) the On-and-off Campus Basic Practice Platform, 2) the Entrepreneurship Practice Platform with the Integration of Production, Learning, and Research, 3) the Transformation Platform of Major Scientific Research Facilities and Achievements, and 4) the Strategic Platform of the T-Rim Knowledge-Based Economic Circle. All these platforms are accessible to students based on their specific tasks and objectives.

Moreover, the university has reinforced its support for entrepreneurship and collaborated with local governments in Sichuan, Dalian, and Shenzhen to establish off-campus bases jointly. In 2016, in partnership with other top universities in China, the university launched the Innovation and Entrepreneurship Alliance of Universities in the Yangtze River Delta. This alliance effectively brings together government bodies, businesses, social communities, universities, and funding resources in the Yangtze River Delta, harnessing the synergistic advantages of these institutions. In 2018, the university assumed the director role for the Ministry of Education’s Steering Committee for Innovation and Entrepreneurship. Through collaborations with relevant government agencies and enterprises, T-University has continued its efforts to reform and advance innovation and EE, establishing multiple joint laboratories to put theory into practice.

Startup service

In terms of entrepreneurial services, T-University has focused on the employment guidance center and the science and technology Park, working closely with the local industrial and commercial bureaus in the campus area to provide centralized entrepreneurial services. Through entities such as the Shanghai Municipal College Entrepreneurship Guidance Station, entrepreneurship seedling gardens, the science and technology park, and off-campus bases such as the entrepreneurship valley, the university has established a full-cycle service system that is tailored to students’ innovative and entrepreneurial activities, providing continuous professional guidance and support from the early startup stage to maturity.

Notably, the T-University Science and Technology Park has set up nine professional incubation service platforms that cover investment and financing, human resources, entrepreneurship training, project declaration, financial services, professional intermediaries, market promotion, advanced assessment, and the labor union. Moreover, the Technology Park has established a corporate service mechanism for liaison officers, counselors, and entrepreneurship mentors to ensure that enterprises receive comprehensive support and guidance. Through these services, T-University has successfully cultivated numerous high-tech backbone enterprises, such as New Vision Healthcare, Zhong Hui Ecology, Tongjie Technology, Tonglei Civil Engineering, and Tongchen Environmental Protection, which indicates the positive effect of these entrepreneurial services.

T-University places significant emphasis on fostering the entrepreneurial climate, which is effectively nurtured through the T-Rim Knowledge-Based Economic Circle and on-campus entrepreneurship activities. Moreover, T-University is dedicated to establishing and cultivating a dynamic T-Rim Knowledge-Based Economic Circle in strategic alignment with the district government and key agencies. This innovative ecosystem strategically centers around three prominent industrial clusters: the creative and design industry, the international engineering consulting services industry, and the new energy/materials and environmental technology industry. These industrial clusters provide fertile ground for graduates’ employment and entrepreneurial pursuits and have yielded remarkable economic outputs. In 2020, the combined value of these clusters surged to a staggering RMB 50 billion, with 80% of entrepreneurs being teachers, students, or alumni from T-University.

This commitment has led to the establishment of an intricate design industry chain featuring architectural design and urban planning design; it also supports services in automobile design, landscape design, software design, environmental engineering design, art media design, and associated services such as graphic production, architectural modeling, and engineering consulting.

The EE system at T-University

T-University has undertaken a comprehensive series of initiatives to promote EE, focusing on four key aspects: entrepreneurial learning, entrepreneurial practice, startup service, and the entrepreneurial climate. As of the end of 2021, the National Technology Park at T-University has cumulatively supported more than 3000 enterprises. Notably, the park has played a pivotal role in assisting more than 300 enterprises established by college students.

In its commitment to EE, the university maintains an open approach to engaging with society. Simultaneously, it integrates innovative elements such as technology, information, and talent to facilitate students’ entrepreneurial endeavors. Through the synergy between the university, government entities, and the market, EE cultivates a cadre of entrepreneurial talent. The convergence of these talents culminates in the formation of an innovative and creative industry cluster within the region, representing the tangible outcome of the university’s “disciplinary chain—technology chain—industry chain” approach to EE. This approach has gradually evolved into the innovative ecosystem of the T-Rim Knowledge-Based Economic Circle.

Findings and discussion

Unified macroscopic objectives of ee.

To date, a widespread consensus on defining EE in practical terms has yet to be achieved (Mwasalwiba, 2010 ; Nabi et al., 2017 ). Entrepreneurial education should strive towards a common direction, which is reflected in the agreement on educational objectives and recommended teaching methods(Aparicio et al., 2019 ). Mason and Arshed ( 2013 ) criticized that entrepreneurial education should teach about entrepreneurship rather than for entrepreneurship. Therefore, EE should not only focus on singular outcome-oriented aspects but also emphasize the cultivation of fundamental aspects such as cognition, abilities, attitudes, and skills.

This study embarks on a synthesis of the EE-related literature, integrating educational objective theory, planned behavior theory, and entrepreneurial process theory. The 4H model of EE objectives, which consists of basic and outcome levels, is proposed. This model aims to comprehensively capture the core elements of EE, addressing both students’ performance in entrepreneurial outcomes and their development of various aspects of foundational cognitive attributes and skills.

The basic level of the EE objective model includes the 4Hs, namely Head (mindset), Hand (skill), Heart (attitude), and Help (support). First, Head has stood out as a prominent learning outcome within EE over the past decade (Fretschner & Lampe, 2019 ). Attention given to the “Head” aspect not only highlights the development of individuals recognized as “entrepreneurs” (Mitra, 2017 ) but also underscores its role in complementing the acquisition of skills and practical knowledge necessary for initiating new ventures and leading more productive lives (Neck & Corbett, 2018 ).

Second, the Hand aspect also constitutes a significant developmental goal and learning outcome of EE. The trajectory of EE is evolving towards a focus on entrepreneurial aspects, and the learning outcomes equip students with skills relevant to entrepreneurship (Wong & Chan, 2022 ). Higher education institutions should go beyond fundamental principles associated with knowledge and actively cultivate students’ entrepreneurial skills and spirit.

Third, Heart represents EE objectives that are related to students’ psychological aspects, as students’ emotions, attitudes, and other affective factors impact their perception of entrepreneurship (Cao, 2021 ). Moreover, the ultimate goal of EE is to instill an entrepreneurial attitude and pave the way for future success as entrepreneurs in establishing new businesses and fostering job creation (Kusumojanto et al., 2021 ). Thus, the cultivation of this mindset is not only linked to the understanding of entrepreneurship but also intricately tied to the aspiration for personal fulfillment (Yang, 2013 ).

Fourth, entrepreneurship support (Help) embodies the goal of providing essential resource support to students to establish a robust foundation for their entrepreneurial endeavors. The establishment of a comprehensive support system is paramount for EE in universities. This establishment encompasses the meticulous design of the curriculum, the development of training bases, and the cultivation of teacher resources (Xu, 2017 ). A well-structured support system is crucial for equipping students with the necessary knowledge and skills to successfully navigate the complexities of entrepreneurship (Greene & Saridakis, 2008 ).

The outcome level of the EE objective model encompasses entrepreneurial intention and entrepreneurial performance, topics that have been extensively discussed in the previous literature. Entrepreneurial intention refers to individuals’ subjective willingness and plans for entrepreneurial behavior (Wong & Chan, 2022 ) and represents the starting point of the entrepreneurial process. Entrepreneurial performance refers to individuals’ actual behaviors and achievements in entrepreneurial activities (Wang et al., 2021 ) and represents the ultimate manifestation of entrepreneurial goals. In summary, the proposed 4H model of the EE objectives covers fundamental attitudes, cognition, skills, support, and ultimate outcomes, thus answering the question of what EE should teach.

Specific implementable system of EE

To facilitate the realization of EE goals, this study developed a corresponding content model as an implementable system and conducted empirical research through a case university. Guided by the 4H objectives, the content model also encompasses four dimensions: entrepreneurial learning, entrepreneurial practice, startup service, and entrepreneurial climate. Through a detailed exposition of the practical methods at T-university, this study provides support for addressing the question of how to teach EE.

In the traditional EE paradigm, there is often an overreliance on the transmission of theoretical knowledge, which leads to a deficiency in students’ practical experience and capabilities (Kremel and Wetter-Edman, 2019 ). Moreover, due to the rapidly changing and dynamic nature of the environment, traditional educational methods frequently become disconnected from real-world demands. In response to these issues, the approach of “learning by doing” has emerged as a complementary and improved alternative to traditional methods (Colombelli et al., 2022 ).

The proposed content model applies the “learning by doing” approach to the construction of the EE system. For entrepreneurial learning, the university has constructed a comprehensive innovation and EE chain that encompasses courses, experimental areas, projects, competitions, practice bases, and teaching teams. For entrepreneurial practice, the university has built a high-level, integrated innovation and entrepreneurship practice platform that provides students with the opportunity to turn their ideas into actual projects. For startup services, the university has established close collaborative relationships with local governments and enterprises and has set up nine professional incubation service platforms. For the entrepreneurial climate, the university cultivated a symbiotic innovation and EE ecosystem by promoting the construction of the T-Rim Knowledge-Based Economic Circle. Through the joint efforts of multiple parties, the entrepreneurial activities of teachers, students, and alumni have become vibrant and have formed a complete design industry chain and an enterprise ecosystem that coexists with numerous SMEs.

Development of a framework based on the TH theory

Through the exploration of the interactive relationships among universities, governments, and industries, TH theory points out a development direction for solving the dilemma of EE. Through the lens of TH theory, this study developed a comprehensive framework delineating the macroscopic objectives and practical methods of EE, as depicted in Fig. 4 . In this context, EE has become a common undertaking for multiple participants. Therefore, universities can effectively leverage the featured external and internal resources, facilitating the organic integration of entrepreneurial learning, practice, services, and climate. This, in turn, will lead to better achievement of the unified goals of EE.

Practical contents and objectives based on the triple helix theory.

Numerous scholars have explored the correlation between EE and the TH theory. Zhou and Peng ( 2008 ) articulated the concept of an entrepreneurial university as “the university that strongly influences the regional development of industries as well as economic growth through high-tech entrepreneurship based on strong research, technology transfer, and entrepreneurship capability.” Moreover, Tianhao et al. ( 2020 ) emphasized the significance of fostering collaboration among industry, academia, and research as the optimal approach to enhancing the efficacy of EE. Additionally, Ribeiro et al. ( 2018 ) underscored the pivotal role of MIT’s entrepreneurial ecosystem in facilitating startup launches. They called upon educators, university administrators, and policymakers to allocate increased attention to how university ecosystems can cultivate students’ knowledge, skills, and entrepreneurial mindsets. Rather than viewing EE within the confines of universities in isolation, we advocate for establishing an integrated system that encompasses universities, government bodies, and businesses. Such a system would streamline their respective roles and ultimately bolster regional innovation and entrepreneurship efforts.

Jones et al. ( 2021 ) reported that with the widespread embrace of EE by numerous countries, the boundaries between universities and external ecosystems are becoming increasingly blurred. This convergence not only fosters a stronger entrepreneurial culture within universities but also encourages students to actively establish startups. However, these startups often face challenges related to limited value and long-term sustainability. From the perspective of TH theory, each university can cultivate an ecosystem conducive to specialized entrepreneurial activities based on its unique resources and advantages. To do so, universities should actively collaborate with local governments and industries, leveraging shared resources and support to create a more open, inclusive, and innovation-supporting ecosystem that promotes lasting reform and sustainability.

There are two main ways in which this paper contributes to the literature. First, this study applies TH theory to both theoretical and empirical research on EE in China, presenting a novel framework for the operation of EE. Previous research has applied TH theory in contexts such as India, Finland, and Russia, showcasing the unique contributions of TH in driving social innovation. This paper introduces the TH model to the Chinese context, illustrating collaborative efforts and support for EE from universities, industries, and governments through the construction of EE objectives and content models. Therefore, this paper not only extends the applicability of the TH theory globally but also provides valuable insights for EE in the Chinese context.

Second, the proposed conceptual framework clarifies the core goals and practical content of EE. By emphasizing the comprehensive cultivation of knowledge, skills, attitudes, and resources, this framework provides a concrete reference for designing EE courses, activities, and support services. Moreover, the framework underscores the importance of collaborative efforts among stakeholders, facilitating resource integration to enhance the quality and impact of EE. Overall, the conceptual framework presented in this paper serves not only as a guiding tool but also as a crucial bridge for fostering the collaborative development of the EE ecosystem.

While EE has widespread global recognition, many regions still face similar developmental challenges, such as a lack of organized objectives and content delivery methods. This article, grounded in the context of EE in Chinese higher education institutions, seeks to address the current challenges guided by TH theory. By aligning EE with socioeconomic demands and leveraging TH theory, this study offers insights into the overall goals and practical content of EE.

This study presents a 4H objective model of EE comprising two levels. The first level focuses on outcomes related to entrepreneurial behavior, including entrepreneurial intentions and performance, which highlight the practical effects of EE. The second level is built as the foundation of the outcomes and encompasses the four elements of mindset, skill, attitude, and support. This multilayered structure provides a more systematic and multidimensional consideration for the cultivation of entrepreneurial talent. The framework offers robust support for practical instructional design and goal setting. Additionally, the research extends to the corresponding content model, incorporating four elements: entrepreneurial learning, entrepreneurial practice, startup services, and the entrepreneurial climate. This content model serves as a practical instructional means to achieve EE goals, enhancing the feasibility of implementing these objectives in practice.

Moreover, this study focused on a representative Chinese university, T-University, to showcase the successful implementation of the 4H and content models. Through this case, we may observe how the university, through comprehensive development in entrepreneurial learning, practice, services, and climate, nurtured many entrepreneurs and facilitated the formation of the innovation and entrepreneurship industry cluster. This approach not only contributes to the university’s reputation and regional economic growth but also offers valuable insights for other regions seeking to advance EE.

This study has several limitations that need to be acknowledged. First, the framework proposed is still preliminary. While its application has been validated through a case study, further exploration is required to determine the detailed classification and elaboration of its constituent elements to deepen the understanding of the EE system. Second, the context of this study is specific to China, and the findings may not be directly generalizable to other regions. Future research should investigate the adaptability of the framework in various cultural and educational contexts from a broader international perspective. Finally, the use of a single-case approach limits the generalizability of the research conclusions. Subsequent studies can enhance comprehensiveness by employing a comparative or multiple-case approach to assess the framework’s reliability and robustness.

In conclusion, this study emphasizes the need to strengthen the application of TH theory in EE and advocates for the enhancement of framework robustness through multiple and comparative case studies. An increase in the quantity of evidence will not only generate greater public interest but also deepen the dynamic interactions among universities, industries, and the nation. This, in turn, may expedite the development of EE in China and foster the optimization of the national economy and the overall employment environment.

Data availability

The datasets generated during and/or analyzed during the current study are not publicly available. Making the full data set publicly available could potentially breach the privacy that was promised to participants when they agreed to take part, in particular for the individual informants who come from a small, specific population, and may breach the ethics approval for the study. The data are available from the corresponding author on reasonable request.

Acs ZJ, Estrin S, Mickiewicz T et al. (2018) Entrepreneurship, institutional economics, and economic growth: an ecosystem perspective. Small Bus Econ 51(2):501–514. https://doi.org/10.1007/s11187-018-0013-9

Article Google Scholar

Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211

Alexander U, Evgeniy P (2012) The entrepreneurial university in Russia: from idea to reality. Paper presented at the 10TH triple helix conference 2012

Allahar H, Sookram R (2019) Emergence of university-centred entrepreneurial ecosystems in the Caribbean. Ind Higher Educ 33(4):246–259. https://doi.org/10.1177/0950422219838220

Anosike P (2019) Entrepreneurship education as human capital: Implications for youth self-employment and conflict mitigation in Sub-Saharan Africa. Ind Higher Educ 33(1):42–54. https://doi.org/10.1177/0950422218812631

Aparicio G, Iturralde T, Maseda A (2019) Conceptual structure and perspectives on entrepreneurship education research: A bibliometric review. Eur res on manage and bus econ 25(3):105–113

Awaah F, Okebukola P, Shabani J et al. (2023) Students’ career interests and entrepreneurship education in a developing country. High Educ Skills Work-Based Learn 13(1):148–160

Awaah F (2023) In the classroom I enhance students understanding of entrepreneurship development—the culturo–techno-contextual approach. J Res Innov Teach Learn https://doi.org/10.1108/JRIT-08-2022-0047

Bae TJ, Qian S, Miao C et al. (2014) The relationship between entrepreneurship education and entrepreneurial intentions: a meta–analytic review. Entrep Theory Pract 38(2):217–254

Baoshan G, Baobao D (2008) 创业模型比较研究 [A comparative retrospective study of classic entrepreneurial models]. Foreign Econ Manag. 3:19–28

Google Scholar

Bloom BS, Engelhart MD, Furst EJ, Hill WH, Krathwohl DR (1956) Taxonomy of educational objectives: the classification of educational goals. Handbook 1: Cognitive domain. David McKay, New York

Bourgeois A, Balcon M-P & Riiheläinen JM (2016) Entrepreneurship education at school in Europe. Eurydice Report. Education, Audiovisual and Culture Executive Agency, European Commission

Bruner JS (2009) The process of education. Harvard University Press

Buchanan DA (1999) The Logic of Political Action: an experiment with the epistemology of the particular. Br J Manag 10(s1):73–88. https://doi.org/10.1111/1467-8551.10.s1.7

Cai Y, Etzkowitz H (2020) Theorizing the triple helix model: past, present, and future. Triple Helix J 1–38. https://doi.org/10.1163/21971927-bja10003

Canziani BF, Welsh DHB (2021) How entrepreneurship influences other disciplines: an examination of learning goals. Int J Manag Educ 19(1). https://doi.org/10.1016/j.ijme.2019.01.003

Cao Q (2021) Entrepreneurial psychological quality and quality cultivation of college students in the higher education and moral education perspectives. Front Psychol 12:700334

Article ADS PubMed PubMed Central Google Scholar

Chen H, Tang Y, Han J (2022) Building students’ entrepreneurial competencies in Chinese universities: diverse learning environment, knowledge transfer, and entrepreneurship education. Sustainability 14(15). https://doi.org/10.3390/su14159105

Colombelli A, Panelli A, Serraino F (2022) A learning-by-doing approach to entrepreneurship education: evidence from a short intensive online international program. Admin Sci 12(1). https://doi.org/10.3390/admsci12010016

Etzkowitz H, Webster A, Gebhardt C et al. (2000) The future of the university and the university of the future: evolution of ivory tower to entrepreneurial paradigm. Res Policy 29(2):313–330. https://doi.org/10.1016/S0048-7333(99)00069-4

Fellnhofer K (2019) Toward a taxonomy of entrepreneurship education research literature: a bibliometric mapping and visualization. Educ Res Rev 27:28–55

Fretschner M, Lampe HW (2019) Detecting hidden sorting and alignment effects of entrepreneurship education. J Small Bus Manage 57(4):1712–1737

Gagne RM (1984) Learning outcomes and their effects: useful categories of human performance. Am Psychol 39(4):377

Galvao A, Mascarenhas C, Marques C et al. (2019) Triple helix and its evolution: a systematic literature review. J Sci Technol Policy Manag 10(3):812–833. https://doi.org/10.1108/JSTPM-10-2018-0103

Gao J, Cheng Y, He H et al. (2023) The mechanism of entrepreneurs’ Social Networks on Innovative Startups’ innovation performance considering the moderating effect of the entrepreneurial competence and motivation. Entrep Res J 13(1):31–69. https://doi.org/10.1515/erj-2020-0541

Gedeon SA (2017) Measuring student transformation in entrepreneurship education programs. Educ Res Int 2017:8475460. https://doi.org/10.1155/2017/8475460

Greene FJ, Saridakis G (2008) The role of higher education skills and support in graduate self-employment. Stud High Educ 33(6):653–672. https://doi.org/10.1080/03075070802457082

Grimaldi R, Kenney M, Siegel DS et al. (2011) 30 years after Bayh–Dole: reassessing academic entrepreneurship. Res Policy 40(8):1045–1057

Hägg G, Gabrielsson J (2020) A systematic literature review of the evolution of pedagogy in EE research. Int J Entrep Behav Res 26(5):829–861

Hao Y (2017) Research on building curriculum system of entrepreneurship education for college students in China. In: Paper presented at the Proceedings of the 7th international conference on education, management, computer and medicine (EMCM 2016)

Henry E (2009) The entrepreneurial university and the triple helix model of innovation. Stud Sci Sci 27(4):481–488

Hoppe M (2016) Policy and entrepreneurship education. Small Bus Econ. 46(1):13–29

Jin D, Liu X, Zhang F et al. (2023) Entrepreneurial role models and college students’ entrepreneurial calling: a moderated mediation model. Front Psychol 14:1129495

Article PubMed PubMed Central Google Scholar

Jones C, Penaluna K, Penaluna A et al. (2018) The changing nature of enterprise: addressing the challenge of Vesper and Gartner. Ind High Educ 32(6):430–437

Jones P, Maas G, Kraus S et al. (2021) An exploration of the role and contribution of entrepreneurship centres in UK higher education institutions. J Small Bus Enterp Dev 28(2):205–228. https://doi.org/10.1108/JSBED-08-2018-0244

Jun C (2017) 欧盟创业能力框架: 创业教育行动新指南 [EU’s entrepreneurship competence framework: a new guide to entrepreneurship education]. Int Comp Educ 1:45–51

MathSciNet Google Scholar

Ke L (2017) 创新创业教育的国际比较与借鉴 [International comparison and reference of innovation and entrepreneurship education]. Stud Dialect Nat 9:73–78

Kim G, Kim D, Lee WJ, et al (2020) The effect of youth entrepreneurship education programs: two large-scale experimental studies. Sage Open 10(3). https://doi.org/10.1177/2158244020956976

Kolb A, Kolb D (2011) Experiential learning theory: a dynamic, holistic approach to management learning, education and development. In Armstrong SJ, Fukami C (Eds) Handbook of management learning, education and development. https://doi.org/10.4135/9780857021038.n3

Kremel A, Wetter-Edman K (2019) Implementing design thinking as didactic method in entrepreneurship education, the importance of through. Des J 22:163–175. https://doi.org/10.1080/14606925.2019.1595855

Kuratko DF, Morris MH (2018) Examining the future trajectory of entrepreneurship. J Small Bus Manag 56(1):11–23. https://doi.org/10.1111/jsbm.12364

Kusumojanto DD, Wibowo A, Kustiandi J, et al (2021) Do entrepreneurship education and environment promote students’ entrepreneurial intention? The role of entrepreneurial attitude. Cogent Educ 8(1). https://doi.org/10.1080/2331186X.2021.1948660

Mandrup M, Jensen TL (2017) Educational Action Research and Triple Helix principles in entrepreneurship education: introducing the EARTH design to explore individuals in Triple Helix collaboration. Triple Helix 4(1). https://doi.org/10.1186/s40604-017-0048-y

Maoxin Y (2017) 创业教育的中国经验 [“China’s experiences” of entrepreneurship education]. Educ Res 38(9):70–75

Martin BC, McNally JJ, Kay MJ (2013) Examining the formation of human capital in entrepreneurship: a meta-analysis of entrepreneurship education outcomes. J Bus Ventur 28(2):211–224

Mason C, Arshed N (2013) Teaching entrepreneurship to university students through experiential Learning: a case study. Ind High Educ 27(6):449–463

Mitra J (2017) Holistic experimentation for emergence: a creative approach to postgraduate entrepreneurship education and training. Ind High Educ 31(1):34–50

Mohamed NA, Sheikh Ali AY (2021) Entrepreneurship education: systematic literature review and future research directions. World J Entrep Manag Sustain Dev 17(4):644–661

Mwasalwiba ES (2010) Entrepreneurship education: a review of its objectives, teaching methods, and impact indicators. Educ+ Train 52(1):20–47

Nabi G, Liñán F, Fayolle A et al. (2017) The impact of entrepreneurship education in higher education: a systematic review and research agenda. Acad Manag Learn Educ 16(2):277–299. https://doi.org/10.5465/amle.2015.0026

Neck HM, Greene PG (2011) Entrepreneurship education: known worlds and new frontiers. J Small Bus Manag 49(1):55–70. https://doi.org/10.1111/j.1540-627X.2010.00314.x

Neck HM, Corbett AC (2018) The scholarship of teaching and learning entrepreneurship. Entrep Educ Pedagog 1(1):8–41

Okebukola P (2020) Breaking barriers to learning: the culture techno-contextual approach (CTCA). Sterling Publishers, Slough

Onjewu AKE, Sukumar A, Prakash KVD et al (2021) The triple helix: a case study of Centurion University of Technology and Management. In Jones P, Apostolopoulos N, Kakouris A, Moon C, Ratten V & Walmsley A (Eds), Universities and entrepreneurship: meeting the educational and social challenges (Vol 11, pp 199–218)

Piqué JM, Berbegal-Mirabent J, Etzkowitz H (2020) The role of universities in shaping the evolution of Silicon Valley’s ecosystem of innovation. Triple Helix J 1–45. https://doi.org/10.1163/21971927-bja10009

Rahman S (2020) Improving the power of lecture method in higher education. In Teaching learning and new technologies in higher education (pp 135–147)

Ranga M, Etzkowitz H (2013) Triple helix systems: an analytical framework for innovation policy and practice in the knowledge society. Ind. High Educ. 27(4):237–262. https://doi.org/10.5367/ihe.2013.0165

Ratten V, Usmanij P (2021) Entrepreneurship education: time for a change in research direction? Int J Manag Educ 19(1). https://doi.org/10.1016/j.ijme.2020.100367

Ribeiro ATVB, Uechi JN, Plonski GA (2018) Building builders: entrepreneurship education from an ecosystem perspective at MIT. Triple Helix 5(1). https://doi.org/10.1186/s40604-018-0051-y

Ronstadt R (1985) The educated entrepreneurs: a new era of EE is beginning. Am J Small Bus 10(1):7–23. https://doi.org/10.1177/104225878501000102

Tianhao L, Beiwei L, Yang L (2020) 国外创新创业教育发展述评与启示 [The development of innovation and entrepreneurship education in foreign countries: review and enlightenment]. Manag Innov Entrep 1:23–36

Timmons JA, Spinelli S, Tan Y (2004) New venture creation: entrepreneurship for the 21st century (Vol 6). McGraw-Hill/Irwin, New York

Wang SY, Wu XL, Xu M et al. (2021) The evaluation of synergy between university entrepreneurship education ecosystem and university students’ entrepreneurship performance. Math Probl Eng, https://doi.org/10.1155/2021/3878378

Weiming L, Chunyan L, Xiaohua D (2013) 我国高校创业教育十年: 演进, 问题与体系建设 [Research on ten-year entrepreneurship education in Chinese universities: evolution, problems and system construction]. Educ Res 6:42–51

Whitehead AN (1967) Aims of education: Simon and Schuster

Willig C (2013) EBOOK: introducing qualitative research in psychology. McGraw-Hill Education, UK

Wong HY, Chan CK (2022) A systematic review on the learning outcomes in entrepreneurship education within higher education settings. Assess Eval High Educ 47(8):1213–1230

Xu Y (2017) Research on the Construction of Support System of University Students’ Entrepreneurship Education under the Background of the New Normal [Proceedings Paper]. 2017 INTERNATIONAL CONFERENCE ON FRONTIERS IN EDUCATIONAL TECHNOLOGIES AND MANAGEMENT SCIENCES (FETMS 2017)

Yang J (2013) The theory of planned behavior and prediction of entrepreneurial intention among Chinese undergraduates. Soc Behav Personal Int J 41(3):367–376

Yuanyuan C (2015) 从 ABC 到 PhD: 丹麦创业教育体系的框架设计与特点 [From ABC to PhD: the guiding ideas and development framework of Danish EE (Entrepreneurship Education) system]. Int Comp Educ 8:7–13

Yubing H, Ziyan G (2015) 慕尼黑工业大学创业教育生态系统建设及启示 [The EE ecosystem of TUM and some recommendations to China]. Sci Sci Manag 10:41–49

Zaring O, Gifford E, McKelvey M (2021) Strategic choices in the design of entrepreneurship education: an explorative study of Swedish higher education institutions. Stud High Educ 46(2):343–358

Zhang W, Li Y, Zeng Q, et al. (2022) Relationship between entrepreneurship education and entrepreneurial intention among college students: a meta-analysis. Int J Environ Res Public Health, 19(19) https://doi.org/10.3390/ijerph191912158

Zhou C, Peng X-M (2008) The entrepreneurial university in China: nonlinear paths. Sci Public Policy 35(9):637–646

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Acknowledgements

We express our sincere gratitude to all individuals who contributed to the data collection process. Furthermore, we extend our appreciation to Linlin Yang and Jinxiao Chen from Tongji University for their invaluable suggestions on the initial draft. Special thanks are also due to Prof. Yuzhuo Cai from Tampere University for his insightful contributions to this paper. Funding for this study was provided by the Chinese National Social Science Funds [BIA190205] and the Shanghai Educational Science Research General Project [C2023033].

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All the authors contributed to the study’s conception and design. Material preparation, data collection, and analysis were performed by Luning Shao, Yuxin Miao, Sanfa Cai and Fei Fan. The first Chinese outline and draft were written by Luning Shao, Yuxin Miao, and Shengce Ren. The English draft of the manuscript was prepared by Fei Fan. All the authors commented on previous versions of the manuscript. All the authors read and approved the final manuscript.

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Correspondence to Fei Fan .

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This research was approved by the Tongji University Ethics Committee for Human Research (No. tjdxsr079). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

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Shao, L., Miao, Y., Ren, S. et al. Designing a framework for entrepreneurship education in Chinese higher education: a theoretical exploration and empirical case study. Humanit Soc Sci Commun 11 , 519 (2024). https://doi.org/10.1057/s41599-024-03024-2

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Received : 22 May 2023

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Published : 16 April 2024

DOI : https://doi.org/10.1057/s41599-024-03024-2

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What is Empirical Research? Definition, Methods, Examples

What is Empirical Research?

Characteristics of Empirical Research

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