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Research Results Section – Writing Guide and Examples

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Research Results

Research Results

Research results refer to the findings and conclusions derived from a systematic investigation or study conducted to answer a specific question or hypothesis. These results are typically presented in a written report or paper and can include various forms of data such as numerical data, qualitative data, statistics, charts, graphs, and visual aids.

Results Section in Research

The results section of the research paper presents the findings of the study. It is the part of the paper where the researcher reports the data collected during the study and analyzes it to draw conclusions.

In the results section, the researcher should describe the data that was collected, the statistical analysis performed, and the findings of the study. It is important to be objective and not interpret the data in this section. Instead, the researcher should report the data as accurately and objectively as possible.

Structure of Research Results Section

The structure of the research results section can vary depending on the type of research conducted, but in general, it should contain the following components:

  • Introduction: The introduction should provide an overview of the study, its aims, and its research questions. It should also briefly explain the methodology used to conduct the study.
  • Data presentation : This section presents the data collected during the study. It may include tables, graphs, or other visual aids to help readers better understand the data. The data presented should be organized in a logical and coherent way, with headings and subheadings used to help guide the reader.
  • Data analysis: In this section, the data presented in the previous section are analyzed and interpreted. The statistical tests used to analyze the data should be clearly explained, and the results of the tests should be presented in a way that is easy to understand.
  • Discussion of results : This section should provide an interpretation of the results of the study, including a discussion of any unexpected findings. The discussion should also address the study’s research questions and explain how the results contribute to the field of study.
  • Limitations: This section should acknowledge any limitations of the study, such as sample size, data collection methods, or other factors that may have influenced the results.
  • Conclusions: The conclusions should summarize the main findings of the study and provide a final interpretation of the results. The conclusions should also address the study’s research questions and explain how the results contribute to the field of study.
  • Recommendations : This section may provide recommendations for future research based on the study’s findings. It may also suggest practical applications for the study’s results in real-world settings.

Outline of Research Results Section

The following is an outline of the key components typically included in the Results section:

I. Introduction

  • A brief overview of the research objectives and hypotheses
  • A statement of the research question

II. Descriptive statistics

  • Summary statistics (e.g., mean, standard deviation) for each variable analyzed
  • Frequencies and percentages for categorical variables

III. Inferential statistics

  • Results of statistical analyses, including tests of hypotheses
  • Tables or figures to display statistical results

IV. Effect sizes and confidence intervals

  • Effect sizes (e.g., Cohen’s d, odds ratio) to quantify the strength of the relationship between variables
  • Confidence intervals to estimate the range of plausible values for the effect size

V. Subgroup analyses

  • Results of analyses that examined differences between subgroups (e.g., by gender, age, treatment group)

VI. Limitations and assumptions

  • Discussion of any limitations of the study and potential sources of bias
  • Assumptions made in the statistical analyses

VII. Conclusions

  • A summary of the key findings and their implications
  • A statement of whether the hypotheses were supported or not
  • Suggestions for future research

Example of Research Results Section

An Example of a Research Results Section could be:

  • This study sought to examine the relationship between sleep quality and academic performance in college students.
  • Hypothesis : College students who report better sleep quality will have higher GPAs than those who report poor sleep quality.
  • Methodology : Participants completed a survey about their sleep habits and academic performance.

II. Participants

  • Participants were college students (N=200) from a mid-sized public university in the United States.
  • The sample was evenly split by gender (50% female, 50% male) and predominantly white (85%).
  • Participants were recruited through flyers and online advertisements.

III. Results

  • Participants who reported better sleep quality had significantly higher GPAs (M=3.5, SD=0.5) than those who reported poor sleep quality (M=2.9, SD=0.6).
  • See Table 1 for a summary of the results.
  • Participants who reported consistent sleep schedules had higher GPAs than those with irregular sleep schedules.

IV. Discussion

  • The results support the hypothesis that better sleep quality is associated with higher academic performance in college students.
  • These findings have implications for college students, as prioritizing sleep could lead to better academic outcomes.
  • Limitations of the study include self-reported data and the lack of control for other variables that could impact academic performance.

V. Conclusion

  • College students who prioritize sleep may see a positive impact on their academic performance.
  • These findings highlight the importance of sleep in academic success.
  • Future research could explore interventions to improve sleep quality in college students.

Example of Research Results in Research Paper :

Our study aimed to compare the performance of three different machine learning algorithms (Random Forest, Support Vector Machine, and Neural Network) in predicting customer churn in a telecommunications company. We collected a dataset of 10,000 customer records, with 20 predictor variables and a binary churn outcome variable.

Our analysis revealed that all three algorithms performed well in predicting customer churn, with an overall accuracy of 85%. However, the Random Forest algorithm showed the highest accuracy (88%), followed by the Support Vector Machine (86%) and the Neural Network (84%).

Furthermore, we found that the most important predictor variables for customer churn were monthly charges, contract type, and tenure. Random Forest identified monthly charges as the most important variable, while Support Vector Machine and Neural Network identified contract type as the most important.

Overall, our results suggest that machine learning algorithms can be effective in predicting customer churn in a telecommunications company, and that Random Forest is the most accurate algorithm for this task.

Example 3 :

Title : The Impact of Social Media on Body Image and Self-Esteem

Abstract : This study aimed to investigate the relationship between social media use, body image, and self-esteem among young adults. A total of 200 participants were recruited from a university and completed self-report measures of social media use, body image satisfaction, and self-esteem.

Results: The results showed that social media use was significantly associated with body image dissatisfaction and lower self-esteem. Specifically, participants who reported spending more time on social media platforms had lower levels of body image satisfaction and self-esteem compared to those who reported less social media use. Moreover, the study found that comparing oneself to others on social media was a significant predictor of body image dissatisfaction and lower self-esteem.

Conclusion : These results suggest that social media use can have negative effects on body image satisfaction and self-esteem among young adults. It is important for individuals to be mindful of their social media use and to recognize the potential negative impact it can have on their mental health. Furthermore, interventions aimed at promoting positive body image and self-esteem should take into account the role of social media in shaping these attitudes and behaviors.

Importance of Research Results

Research results are important for several reasons, including:

  • Advancing knowledge: Research results can contribute to the advancement of knowledge in a particular field, whether it be in science, technology, medicine, social sciences, or humanities.
  • Developing theories: Research results can help to develop or modify existing theories and create new ones.
  • Improving practices: Research results can inform and improve practices in various fields, such as education, healthcare, business, and public policy.
  • Identifying problems and solutions: Research results can identify problems and provide solutions to complex issues in society, including issues related to health, environment, social justice, and economics.
  • Validating claims : Research results can validate or refute claims made by individuals or groups in society, such as politicians, corporations, or activists.
  • Providing evidence: Research results can provide evidence to support decision-making, policy-making, and resource allocation in various fields.

How to Write Results in A Research Paper

Here are some general guidelines on how to write results in a research paper:

  • Organize the results section: Start by organizing the results section in a logical and coherent manner. Divide the section into subsections if necessary, based on the research questions or hypotheses.
  • Present the findings: Present the findings in a clear and concise manner. Use tables, graphs, and figures to illustrate the data and make the presentation more engaging.
  • Describe the data: Describe the data in detail, including the sample size, response rate, and any missing data. Provide relevant descriptive statistics such as means, standard deviations, and ranges.
  • Interpret the findings: Interpret the findings in light of the research questions or hypotheses. Discuss the implications of the findings and the extent to which they support or contradict existing theories or previous research.
  • Discuss the limitations : Discuss the limitations of the study, including any potential sources of bias or confounding factors that may have affected the results.
  • Compare the results : Compare the results with those of previous studies or theoretical predictions. Discuss any similarities, differences, or inconsistencies.
  • Avoid redundancy: Avoid repeating information that has already been presented in the introduction or methods sections. Instead, focus on presenting new and relevant information.
  • Be objective: Be objective in presenting the results, avoiding any personal biases or interpretations.

When to Write Research Results

Here are situations When to Write Research Results”

  • After conducting research on the chosen topic and obtaining relevant data, organize the findings in a structured format that accurately represents the information gathered.
  • Once the data has been analyzed and interpreted, and conclusions have been drawn, begin the writing process.
  • Before starting to write, ensure that the research results adhere to the guidelines and requirements of the intended audience, such as a scientific journal or academic conference.
  • Begin by writing an abstract that briefly summarizes the research question, methodology, findings, and conclusions.
  • Follow the abstract with an introduction that provides context for the research, explains its significance, and outlines the research question and objectives.
  • The next section should be a literature review that provides an overview of existing research on the topic and highlights the gaps in knowledge that the current research seeks to address.
  • The methodology section should provide a detailed explanation of the research design, including the sample size, data collection methods, and analytical techniques used.
  • Present the research results in a clear and concise manner, using graphs, tables, and figures to illustrate the findings.
  • Discuss the implications of the research results, including how they contribute to the existing body of knowledge on the topic and what further research is needed.
  • Conclude the paper by summarizing the main findings, reiterating the significance of the research, and offering suggestions for future research.

Purpose of Research Results

The purposes of Research Results are as follows:

  • Informing policy and practice: Research results can provide evidence-based information to inform policy decisions, such as in the fields of healthcare, education, and environmental regulation. They can also inform best practices in fields such as business, engineering, and social work.
  • Addressing societal problems : Research results can be used to help address societal problems, such as reducing poverty, improving public health, and promoting social justice.
  • Generating economic benefits : Research results can lead to the development of new products, services, and technologies that can create economic value and improve quality of life.
  • Supporting academic and professional development : Research results can be used to support academic and professional development by providing opportunities for students, researchers, and practitioners to learn about new findings and methodologies in their field.
  • Enhancing public understanding: Research results can help to educate the public about important issues and promote scientific literacy, leading to more informed decision-making and better public policy.
  • Evaluating interventions: Research results can be used to evaluate the effectiveness of interventions, such as treatments, educational programs, and social policies. This can help to identify areas where improvements are needed and guide future interventions.
  • Contributing to scientific progress: Research results can contribute to the advancement of science by providing new insights and discoveries that can lead to new theories, methods, and techniques.
  • Informing decision-making : Research results can provide decision-makers with the information they need to make informed decisions. This can include decision-making at the individual, organizational, or governmental levels.
  • Fostering collaboration : Research results can facilitate collaboration between researchers and practitioners, leading to new partnerships, interdisciplinary approaches, and innovative solutions to complex problems.

Advantages of Research Results

Some Advantages of Research Results are as follows:

  • Improved decision-making: Research results can help inform decision-making in various fields, including medicine, business, and government. For example, research on the effectiveness of different treatments for a particular disease can help doctors make informed decisions about the best course of treatment for their patients.
  • Innovation : Research results can lead to the development of new technologies, products, and services. For example, research on renewable energy sources can lead to the development of new and more efficient ways to harness renewable energy.
  • Economic benefits: Research results can stimulate economic growth by providing new opportunities for businesses and entrepreneurs. For example, research on new materials or manufacturing techniques can lead to the development of new products and processes that can create new jobs and boost economic activity.
  • Improved quality of life: Research results can contribute to improving the quality of life for individuals and society as a whole. For example, research on the causes of a particular disease can lead to the development of new treatments and cures, improving the health and well-being of millions of people.

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Reporting Research Results in APA Style | Tips & Examples

Published on December 21, 2020 by Pritha Bhandari . Revised on January 17, 2024.

The results section of a quantitative research paper is where you summarize your data and report the findings of any relevant statistical analyses.

The APA manual provides rigorous guidelines for what to report in quantitative research papers in the fields of psychology, education, and other social sciences.

Use these standards to answer your research questions and report your data analyses in a complete and transparent way.

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Table of contents

What goes in your results section, introduce your data, summarize your data, report statistical results, presenting numbers effectively, what doesn’t belong in your results section, frequently asked questions about results in apa.

In APA style, the results section includes preliminary information about the participants and data, descriptive and inferential statistics, and the results of any exploratory analyses.

Include these in your results section:

  • Participant flow and recruitment period. Report the number of participants at every stage of the study, as well as the dates when recruitment took place.
  • Missing data . Identify the proportion of data that wasn’t included in your final analysis and state the reasons.
  • Any adverse events. Make sure to report any unexpected events or side effects (for clinical studies).
  • Descriptive statistics . Summarize the primary and secondary outcomes of the study.
  • Inferential statistics , including confidence intervals and effect sizes. Address the primary and secondary research questions by reporting the detailed results of your main analyses.
  • Results of subgroup or exploratory analyses, if applicable. Place detailed results in supplementary materials.

Write up the results in the past tense because you’re describing the outcomes of a completed research study.

Prevent plagiarism. Run a free check.

Before diving into your research findings, first describe the flow of participants at every stage of your study and whether any data were excluded from the final analysis.

Participant flow and recruitment period

It’s necessary to report any attrition, which is the decline in participants at every sequential stage of a study. That’s because an uneven number of participants across groups sometimes threatens internal validity and makes it difficult to compare groups. Be sure to also state all reasons for attrition.

If your study has multiple stages (e.g., pre-test, intervention, and post-test) and groups (e.g., experimental and control groups), a flow chart is the best way to report the number of participants in each group per stage and reasons for attrition.

Also report the dates for when you recruited participants or performed follow-up sessions.

Missing data

Another key issue is the completeness of your dataset. It’s necessary to report both the amount and reasons for data that was missing or excluded.

Data can become unusable due to equipment malfunctions, improper storage, unexpected events, participant ineligibility, and so on. For each case, state the reason why the data were unusable.

Some data points may be removed from the final analysis because they are outliers—but you must be able to justify how you decided what to exclude.

If you applied any techniques for overcoming or compensating for lost data, report those as well.

Adverse events

For clinical studies, report all events with serious consequences or any side effects that occured.

Descriptive statistics summarize your data for the reader. Present descriptive statistics for each primary, secondary, and subgroup analysis.

Don’t provide formulas or citations for commonly used statistics (e.g., standard deviation) – but do provide them for new or rare equations.

Descriptive statistics

The exact descriptive statistics that you report depends on the types of data in your study. Categorical variables can be reported using proportions, while quantitative data can be reported using means and standard deviations . For a large set of numbers, a table is the most effective presentation format.

Include sample sizes (overall and for each group) as well as appropriate measures of central tendency and variability for the outcomes in your results section. For every point estimate , add a clearly labelled measure of variability as well.

Be sure to note how you combined data to come up with variables of interest. For every variable of interest, explain how you operationalized it.

According to APA journal standards, it’s necessary to report all relevant hypothesis tests performed, estimates of effect sizes, and confidence intervals.

When reporting statistical results, you should first address primary research questions before moving onto secondary research questions and any exploratory or subgroup analyses.

Present the results of tests in the order that you performed them—report the outcomes of main tests before post-hoc tests, for example. Don’t leave out any relevant results, even if they don’t support your hypothesis.

Inferential statistics

For each statistical test performed, first restate the hypothesis , then state whether your hypothesis was supported and provide the outcomes that led you to that conclusion.

Report the following for each hypothesis test:

  • the test statistic value,
  • the degrees of freedom ,
  • the exact p- value (unless it is less than 0.001),
  • the magnitude and direction of the effect.

When reporting complex data analyses, such as factor analysis or multivariate analysis, present the models estimated in detail, and state the statistical software used. Make sure to report any violations of statistical assumptions or problems with estimation.

Effect sizes and confidence intervals

For each hypothesis test performed, you should present confidence intervals and estimates of effect sizes .

Confidence intervals are useful for showing the variability around point estimates. They should be included whenever you report population parameter estimates.

Effect sizes indicate how impactful the outcomes of a study are. But since they are estimates, it’s recommended that you also provide confidence intervals of effect sizes.

Subgroup or exploratory analyses

Briefly report the results of any other planned or exploratory analyses you performed. These may include subgroup analyses as well.

Subgroup analyses come with a high chance of false positive results, because performing a large number of comparison or correlation tests increases the chances of finding significant results.

If you find significant results in these analyses, make sure to appropriately report them as exploratory (rather than confirmatory) results to avoid overstating their importance.

While these analyses can be reported in less detail in the main text, you can provide the full analyses in supplementary materials.

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result in research paper sample

To effectively present numbers, use a mix of text, tables , and figures where appropriate:

  • To present three or fewer numbers, try a sentence ,
  • To present between 4 and 20 numbers, try a table ,
  • To present more than 20 numbers, try a figure .

Since these are general guidelines, use your own judgment and feedback from others for effective presentation of numbers.

Tables and figures should be numbered and have titles, along with relevant notes. Make sure to present data only once throughout the paper and refer to any tables and figures in the text.

Formatting statistics and numbers

It’s important to follow capitalization , italicization, and abbreviation rules when referring to statistics in your paper. There are specific format guidelines for reporting statistics in APA , as well as general rules about writing numbers .

If you are unsure of how to present specific symbols, look up the detailed APA guidelines or other papers in your field.

It’s important to provide a complete picture of your data analyses and outcomes in a concise way. For that reason, raw data and any interpretations of your results are not included in the results section.

It’s rarely appropriate to include raw data in your results section. Instead, you should always save the raw data securely and make them available and accessible to any other researchers who request them.

Making scientific research available to others is a key part of academic integrity and open science.

Interpretation or discussion of results

This belongs in your discussion section. Your results section is where you objectively report all relevant findings and leave them open for interpretation by readers.

While you should state whether the findings of statistical tests lend support to your hypotheses, refrain from forming conclusions to your research questions in the results section.

Explanation of how statistics tests work

For the sake of concise writing, you can safely assume that readers of your paper have professional knowledge of how statistical inferences work.

In an APA results section , you should generally report the following:

  • Participant flow and recruitment period.
  • Missing data and any adverse events.
  • Descriptive statistics about your samples.
  • Inferential statistics , including confidence intervals and effect sizes.
  • Results of any subgroup or exploratory analyses, if applicable.

According to the APA guidelines, you should report enough detail on inferential statistics so that your readers understand your analyses.

  • the test statistic value
  • the degrees of freedom
  • the exact p value (unless it is less than 0.001)
  • the magnitude and direction of the effect

You should also present confidence intervals and estimates of effect sizes where relevant.

In APA style, statistics can be presented in the main text or as tables or figures . To decide how to present numbers, you can follow APA guidelines:

  • To present three or fewer numbers, try a sentence,
  • To present between 4 and 20 numbers, try a table,
  • To present more than 20 numbers, try a figure.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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How to Write the Results/Findings Section in Research

result in research paper sample

What is the research paper Results section and what does it do?

The Results section of a scientific research paper represents the core findings of a study derived from the methods applied to gather and analyze information. It presents these findings in a logical sequence without bias or interpretation from the author, setting up the reader for later interpretation and evaluation in the Discussion section. A major purpose of the Results section is to break down the data into sentences that show its significance to the research question(s).

The Results section appears third in the section sequence in most scientific papers. It follows the presentation of the Methods and Materials and is presented before the Discussion section —although the Results and Discussion are presented together in many journals. This section answers the basic question “What did you find in your research?”

What is included in the Results section?

The Results section should include the findings of your study and ONLY the findings of your study. The findings include:

  • Data presented in tables, charts, graphs, and other figures (may be placed into the text or on separate pages at the end of the manuscript)
  • A contextual analysis of this data explaining its meaning in sentence form
  • All data that corresponds to the central research question(s)
  • All secondary findings (secondary outcomes, subgroup analyses, etc.)

If the scope of the study is broad, or if you studied a variety of variables, or if the methodology used yields a wide range of different results, the author should present only those results that are most relevant to the research question stated in the Introduction section .

As a general rule, any information that does not present the direct findings or outcome of the study should be left out of this section. Unless the journal requests that authors combine the Results and Discussion sections, explanations and interpretations should be omitted from the Results.

How are the results organized?

The best way to organize your Results section is “logically.” One logical and clear method of organizing research results is to provide them alongside the research questions—within each research question, present the type of data that addresses that research question.

Let’s look at an example. Your research question is based on a survey among patients who were treated at a hospital and received postoperative care. Let’s say your first research question is:

results section of a research paper, figures

“What do hospital patients over age 55 think about postoperative care?”

This can actually be represented as a heading within your Results section, though it might be presented as a statement rather than a question:

Attitudes towards postoperative care in patients over the age of 55

Now present the results that address this specific research question first. In this case, perhaps a table illustrating data from a survey. Likert items can be included in this example. Tables can also present standard deviations, probabilities, correlation matrices, etc.

Following this, present a content analysis, in words, of one end of the spectrum of the survey or data table. In our example case, start with the POSITIVE survey responses regarding postoperative care, using descriptive phrases. For example:

“Sixty-five percent of patients over 55 responded positively to the question “ Are you satisfied with your hospital’s postoperative care ?” (Fig. 2)

Include other results such as subcategory analyses. The amount of textual description used will depend on how much interpretation of tables and figures is necessary and how many examples the reader needs in order to understand the significance of your research findings.

Next, present a content analysis of another part of the spectrum of the same research question, perhaps the NEGATIVE or NEUTRAL responses to the survey. For instance:

  “As Figure 1 shows, 15 out of 60 patients in Group A responded negatively to Question 2.”

After you have assessed the data in one figure and explained it sufficiently, move on to your next research question. For example:

  “How does patient satisfaction correspond to in-hospital improvements made to postoperative care?”

results section of a research paper, figures

This kind of data may be presented through a figure or set of figures (for instance, a paired T-test table).

Explain the data you present, here in a table, with a concise content analysis:

“The p-value for the comparison between the before and after groups of patients was .03% (Fig. 2), indicating that the greater the dissatisfaction among patients, the more frequent the improvements that were made to postoperative care.”

Let’s examine another example of a Results section from a study on plant tolerance to heavy metal stress . In the Introduction section, the aims of the study are presented as “determining the physiological and morphological responses of Allium cepa L. towards increased cadmium toxicity” and “evaluating its potential to accumulate the metal and its associated environmental consequences.” The Results section presents data showing how these aims are achieved in tables alongside a content analysis, beginning with an overview of the findings:

“Cadmium caused inhibition of root and leave elongation, with increasing effects at higher exposure doses (Fig. 1a-c).”

The figure containing this data is cited in parentheses. Note that this author has combined three graphs into one single figure. Separating the data into separate graphs focusing on specific aspects makes it easier for the reader to assess the findings, and consolidating this information into one figure saves space and makes it easy to locate the most relevant results.

results section of a research paper, figures

Following this overall summary, the relevant data in the tables is broken down into greater detail in text form in the Results section.

  • “Results on the bio-accumulation of cadmium were found to be the highest (17.5 mg kgG1) in the bulb, when the concentration of cadmium in the solution was 1×10G2 M and lowest (0.11 mg kgG1) in the leaves when the concentration was 1×10G3 M.”

Captioning and Referencing Tables and Figures

Tables and figures are central components of your Results section and you need to carefully think about the most effective way to use graphs and tables to present your findings . Therefore, it is crucial to know how to write strong figure captions and to refer to them within the text of the Results section.

The most important advice one can give here as well as throughout the paper is to check the requirements and standards of the journal to which you are submitting your work. Every journal has its own design and layout standards, which you can find in the author instructions on the target journal’s website. Perusing a journal’s published articles will also give you an idea of the proper number, size, and complexity of your figures.

Regardless of which format you use, the figures should be placed in the order they are referenced in the Results section and be as clear and easy to understand as possible. If there are multiple variables being considered (within one or more research questions), it can be a good idea to split these up into separate figures. Subsequently, these can be referenced and analyzed under separate headings and paragraphs in the text.

To create a caption, consider the research question being asked and change it into a phrase. For instance, if one question is “Which color did participants choose?”, the caption might be “Color choice by participant group.” Or in our last research paper example, where the question was “What is the concentration of cadmium in different parts of the onion after 14 days?” the caption reads:

 “Fig. 1(a-c): Mean concentration of Cd determined in (a) bulbs, (b) leaves, and (c) roots of onions after a 14-day period.”

Steps for Composing the Results Section

Because each study is unique, there is no one-size-fits-all approach when it comes to designing a strategy for structuring and writing the section of a research paper where findings are presented. The content and layout of this section will be determined by the specific area of research, the design of the study and its particular methodologies, and the guidelines of the target journal and its editors. However, the following steps can be used to compose the results of most scientific research studies and are essential for researchers who are new to preparing a manuscript for publication or who need a reminder of how to construct the Results section.

Step 1 : Consult the guidelines or instructions that the target journal or publisher provides authors and read research papers it has published, especially those with similar topics, methods, or results to your study.

  • The guidelines will generally outline specific requirements for the results or findings section, and the published articles will provide sound examples of successful approaches.
  • Note length limitations on restrictions on content. For instance, while many journals require the Results and Discussion sections to be separate, others do not—qualitative research papers often include results and interpretations in the same section (“Results and Discussion”).
  • Reading the aims and scope in the journal’s “ guide for authors ” section and understanding the interests of its readers will be invaluable in preparing to write the Results section.

Step 2 : Consider your research results in relation to the journal’s requirements and catalogue your results.

  • Focus on experimental results and other findings that are especially relevant to your research questions and objectives and include them even if they are unexpected or do not support your ideas and hypotheses.
  • Catalogue your findings—use subheadings to streamline and clarify your report. This will help you avoid excessive and peripheral details as you write and also help your reader understand and remember your findings. Create appendices that might interest specialists but prove too long or distracting for other readers.
  • Decide how you will structure of your results. You might match the order of the research questions and hypotheses to your results, or you could arrange them according to the order presented in the Methods section. A chronological order or even a hierarchy of importance or meaningful grouping of main themes or categories might prove effective. Consider your audience, evidence, and most importantly, the objectives of your research when choosing a structure for presenting your findings.

Step 3 : Design figures and tables to present and illustrate your data.

  • Tables and figures should be numbered according to the order in which they are mentioned in the main text of the paper.
  • Information in figures should be relatively self-explanatory (with the aid of captions), and their design should include all definitions and other information necessary for readers to understand the findings without reading all of the text.
  • Use tables and figures as a focal point to tell a clear and informative story about your research and avoid repeating information. But remember that while figures clarify and enhance the text, they cannot replace it.

Step 4 : Draft your Results section using the findings and figures you have organized.

  • The goal is to communicate this complex information as clearly and precisely as possible; precise and compact phrases and sentences are most effective.
  • In the opening paragraph of this section, restate your research questions or aims to focus the reader’s attention to what the results are trying to show. It is also a good idea to summarize key findings at the end of this section to create a logical transition to the interpretation and discussion that follows.
  • Try to write in the past tense and the active voice to relay the findings since the research has already been done and the agent is usually clear. This will ensure that your explanations are also clear and logical.
  • Make sure that any specialized terminology or abbreviation you have used here has been defined and clarified in the  Introduction section .

Step 5 : Review your draft; edit and revise until it reports results exactly as you would like to have them reported to your readers.

  • Double-check the accuracy and consistency of all the data, as well as all of the visual elements included.
  • Read your draft aloud to catch language errors (grammar, spelling, and mechanics), awkward phrases, and missing transitions.
  • Ensure that your results are presented in the best order to focus on objectives and prepare readers for interpretations, valuations, and recommendations in the Discussion section . Look back over the paper’s Introduction and background while anticipating the Discussion and Conclusion sections to ensure that the presentation of your results is consistent and effective.
  • Consider seeking additional guidance on your paper. Find additional readers to look over your Results section and see if it can be improved in any way. Peers, professors, or qualified experts can provide valuable insights.

One excellent option is to use a professional English proofreading and editing service  such as Wordvice, including our paper editing service . With hundreds of qualified editors from dozens of scientific fields, Wordvice has helped thousands of authors revise their manuscripts and get accepted into their target journals. Read more about the  proofreading and editing process  before proceeding with getting academic editing services and manuscript editing services for your manuscript.

As the representation of your study’s data output, the Results section presents the core information in your research paper. By writing with clarity and conciseness and by highlighting and explaining the crucial findings of their study, authors increase the impact and effectiveness of their research manuscripts.

For more articles and videos on writing your research manuscript, visit Wordvice’s Resources page.

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How to Write a Results Section | Tips & Examples

Published on 27 October 2016 by Bas Swaen . Revised on 25 October 2022 by Tegan George.

A results section is where you report the main findings of the data collection and analysis you conducted for your thesis or dissertation . You should report all relevant results concisely and objectively, in a logical order. Don’t include subjective interpretations of why you found these results or what they mean – any evaluation should be saved for the discussion section .

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Table of contents

How to write a results section, reporting quantitative research results, reporting qualitative research results, results vs discussion vs conclusion, checklist: research results, frequently asked questions about results sections.

When conducting research, it’s important to report the results of your study prior to discussing your interpretations of it. This gives your reader a clear idea of exactly what you found and keeps the data itself separate from your subjective analysis.

Here are a few best practices:

  • Your results should always be written in the past tense.
  • While the length of this section depends on how much data you collected and analysed, it should be written as concisely as possible.
  • Only include results that are directly relevant to answering your research questions . Avoid speculative or interpretative words like ‘appears’ or ‘implies’.
  • If you have other results you’d like to include, consider adding them to an appendix or footnotes.
  • Always start out with your broadest results first, and then flow into your more granular (but still relevant) ones. Think of it like a shoe shop: first discuss the shoes as a whole, then the trainers, boots, sandals, etc.

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If you conducted quantitative research , you’ll likely be working with the results of some sort of statistical analysis .

Your results section should report the results of any statistical tests you used to compare groups or assess relationships between variables . It should also state whether or not each hypothesis was supported.

The most logical way to structure quantitative results is to frame them around your research questions or hypotheses. For each question or hypothesis, share:

  • A reminder of the type of analysis you used (e.g., a two-sample t test or simple linear regression ). A more detailed description of your analysis should go in your methodology section.
  • A concise summary of each relevant result, both positive and negative. This can include any relevant descriptive statistics (e.g., means and standard deviations ) as well as inferential statistics (e.g., t scores, degrees of freedom , and p values ). Remember, these numbers are often placed in parentheses.
  • A brief statement of how each result relates to the question, or whether the hypothesis was supported. You can briefly mention any results that didn’t fit with your expectations and assumptions, but save any speculation on their meaning or consequences for your discussion  and conclusion.

A note on tables and figures

In quantitative research, it’s often helpful to include visual elements such as graphs, charts, and tables , but only if they are directly relevant to your results. Give these elements clear, descriptive titles and labels so that your reader can easily understand what is being shown. If you want to include any other visual elements that are more tangential in nature, consider adding a figure and table list .

As a rule of thumb:

  • Tables are used to communicate exact values, giving a concise overview of various results
  • Graphs and charts are used to visualise trends and relationships, giving an at-a-glance illustration of key findings

Don’t forget to also mention any tables and figures you used within the text of your results section. Summarise or elaborate on specific aspects you think your reader should know about rather than merely restating the same numbers already shown.

Example of using figures in the results section

Figure 1: Intention to donate to environmental organisations based on social distance from impact of environmental damage.

In qualitative research , your results might not all be directly related to specific hypotheses. In this case, you can structure your results section around key themes or topics that emerged from your analysis of the data.

For each theme, start with general observations about what the data showed. You can mention:

  • Recurring points of agreement or disagreement
  • Patterns and trends
  • Particularly significant snippets from individual responses

Next, clarify and support these points with direct quotations. Be sure to report any relevant demographic information about participants. Further information (such as full transcripts , if appropriate) can be included in an appendix .

‘I think that in role-playing games, there’s more attention to character design, to world design, because the whole story is important and more attention is paid to certain game elements […] so that perhaps you do need bigger teams of creative experts than in an average shooter or something.’

Responses suggest that video game consumers consider some types of games to have more artistic potential than others.

Your results section should objectively report your findings, presenting only brief observations in relation to each question, hypothesis, or theme.

It should not  speculate about the meaning of the results or attempt to answer your main research question . Detailed interpretation of your results is more suitable for your discussion section , while synthesis of your results into an overall answer to your main research question is best left for your conclusion .

I have completed my data collection and analyzed the results.

I have included all results that are relevant to my research questions.

I have concisely and objectively reported each result, including relevant descriptive statistics and inferential statistics .

I have stated whether each hypothesis was supported or refuted.

I have used tables and figures to illustrate my results where appropriate.

All tables and figures are correctly labelled and referred to in the text.

There is no subjective interpretation or speculation on the meaning of the results.

You've finished writing up your results! Use the other checklists to further improve your thesis.

The results chapter of a thesis or dissertation presents your research results concisely and objectively.

In quantitative research , for each question or hypothesis , state:

  • The type of analysis used
  • Relevant results in the form of descriptive and inferential statistics
  • Whether or not the alternative hypothesis was supported

In qualitative research , for each question or theme, describe:

  • Recurring patterns
  • Significant or representative individual responses
  • Relevant quotations from the data

Don’t interpret or speculate in the results chapter.

Results are usually written in the past tense , because they are describing the outcome of completed actions.

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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The results section is where you report the findings of your study based upon the methodology [or methodologies] you applied to gather information. The results section should state the findings of the research arranged in a logical sequence without bias or interpretation. A section describing results should be particularly detailed if your paper includes data generated from your own research.

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070.

Importance of a Good Results Section

When formulating the results section, it's important to remember that the results of a study do not prove anything . Findings can only confirm or reject the hypothesis underpinning your study. However, the act of articulating the results helps you to understand the problem from within, to break it into pieces, and to view the research problem from various perspectives.

The page length of this section is set by the amount and types of data to be reported . Be concise. Use non-textual elements appropriately, such as figures and tables, to present findings more effectively. In deciding what data to describe in your results section, you must clearly distinguish information that would normally be included in a research paper from any raw data or other content that could be included as an appendix. In general, raw data that has not been summarized should not be included in the main text of your paper unless requested to do so by your professor.

Avoid providing data that is not critical to answering the research question . The background information you described in the introduction section should provide the reader with any additional context or explanation needed to understand the results. A good strategy is to always re-read the background section of your paper after you have written up your results to ensure that the reader has enough context to understand the results [and, later, how you interpreted the results in the discussion section of your paper that follows].

Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Brett, Paul. "A Genre Analysis of the Results Section of Sociology Articles." English for Specific Speakers 13 (1994): 47-59; Go to English for Specific Purposes on ScienceDirect;Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008; Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit; "Reporting Findings." In Making Sense of Social Research Malcolm Williams, editor. (London;: SAGE Publications, 2003) pp. 188-207.

Structure and Writing Style

I.  Organization and Approach

For most research papers in the social and behavioral sciences, there are two possible ways of organizing the results . Both approaches are appropriate in how you report your findings, but use only one approach.

  • Present a synopsis of the results followed by an explanation of key findings . This approach can be used to highlight important findings. For example, you may have noticed an unusual correlation between two variables during the analysis of your findings. It is appropriate to highlight this finding in the results section. However, speculating as to why this correlation exists and offering a hypothesis about what may be happening belongs in the discussion section of your paper.
  • Present a result and then explain it, before presenting the next result then explaining it, and so on, then end with an overall synopsis . This is the preferred approach if you have multiple results of equal significance. It is more common in longer papers because it helps the reader to better understand each finding. In this model, it is helpful to provide a brief conclusion that ties each of the findings together and provides a narrative bridge to the discussion section of the your paper.

NOTE :   Just as the literature review should be arranged under conceptual categories rather than systematically describing each source, you should also organize your findings under key themes related to addressing the research problem. This can be done under either format noted above [i.e., a thorough explanation of the key results or a sequential, thematic description and explanation of each finding].

II.  Content

In general, the content of your results section should include the following:

  • Introductory context for understanding the results by restating the research problem underpinning your study . This is useful in re-orientating the reader's focus back to the research problem after having read a review of the literature and your explanation of the methods used for gathering and analyzing information.
  • Inclusion of non-textual elements, such as, figures, charts, photos, maps, tables, etc. to further illustrate key findings, if appropriate . Rather than relying entirely on descriptive text, consider how your findings can be presented visually. This is a helpful way of condensing a lot of data into one place that can then be referred to in the text. Consider referring to appendices if there is a lot of non-textual elements.
  • A systematic description of your results, highlighting for the reader observations that are most relevant to the topic under investigation . Not all results that emerge from the methodology used to gather information may be related to answering the " So What? " question. Do not confuse observations with interpretations; observations in this context refers to highlighting important findings you discovered through a process of reviewing prior literature and gathering data.
  • The page length of your results section is guided by the amount and types of data to be reported . However, focus on findings that are important and related to addressing the research problem. It is not uncommon to have unanticipated results that are not relevant to answering the research question. This is not to say that you don't acknowledge tangential findings and, in fact, can be referred to as areas for further research in the conclusion of your paper. However, spending time in the results section describing tangential findings clutters your overall results section and distracts the reader.
  • A short paragraph that concludes the results section by synthesizing the key findings of the study . Highlight the most important findings you want readers to remember as they transition into the discussion section. This is particularly important if, for example, there are many results to report, the findings are complicated or unanticipated, or they are impactful or actionable in some way [i.e., able to be pursued in a feasible way applied to practice].

NOTE:   Always use the past tense when referring to your study's findings. Reference to findings should always be described as having already happened because the method used to gather the information has been completed.

III.  Problems to Avoid

When writing the results section, avoid doing the following :

  • Discussing or interpreting your results . Save this for the discussion section of your paper, although where appropriate, you should compare or contrast specific results to those found in other studies [e.g., "Similar to the work of Smith [1990], one of the findings of this study is the strong correlation between motivation and academic achievement...."].
  • Reporting background information or attempting to explain your findings. This should have been done in your introduction section, but don't panic! Often the results of a study point to the need for additional background information or to explain the topic further, so don't think you did something wrong. Writing up research is rarely a linear process. Always revise your introduction as needed.
  • Ignoring negative results . A negative result generally refers to a finding that does not support the underlying assumptions of your study. Do not ignore them. Document these findings and then state in your discussion section why you believe a negative result emerged from your study. Note that negative results, and how you handle them, can give you an opportunity to write a more engaging discussion section, therefore, don't be hesitant to highlight them.
  • Including raw data or intermediate calculations . Ask your professor if you need to include any raw data generated by your study, such as transcripts from interviews or data files. If raw data is to be included, place it in an appendix or set of appendices that are referred to in the text.
  • Be as factual and concise as possible in reporting your findings . Do not use phrases that are vague or non-specific, such as, "appeared to be greater than other variables..." or "demonstrates promising trends that...." Subjective modifiers should be explained in the discussion section of the paper [i.e., why did one variable appear greater? Or, how does the finding demonstrate a promising trend?].
  • Presenting the same data or repeating the same information more than once . If you want to highlight a particular finding, it is appropriate to do so in the results section. However, you should emphasize its significance in relation to addressing the research problem in the discussion section. Do not repeat it in your results section because you can do that in the conclusion of your paper.
  • Confusing figures with tables . Be sure to properly label any non-textual elements in your paper. Don't call a chart an illustration or a figure a table. If you are not sure, go here .

Annesley, Thomas M. "Show Your Cards: The Results Section and the Poker Game." Clinical Chemistry 56 (July 2010): 1066-1070; Bavdekar, Sandeep B. and Sneha Chandak. "Results: Unraveling the Findings." Journal of the Association of Physicians of India 63 (September 2015): 44-46; Burton, Neil et al. Doing Your Education Research Project . Los Angeles, CA: SAGE, 2008;  Caprette, David R. Writing Research Papers. Experimental Biosciences Resources. Rice University; Hancock, Dawson R. and Bob Algozzine. Doing Case Study Research: A Practical Guide for Beginning Researchers . 2nd ed. New York: Teachers College Press, 2011; Introduction to Nursing Research: Reporting Research Findings. Nursing Research: Open Access Nursing Research and Review Articles. (January 4, 2012); Kretchmer, Paul. Twelve Steps to Writing an Effective Results Section. San Francisco Edit ; Ng, K. H. and W. C. Peh. "Writing the Results." Singapore Medical Journal 49 (2008): 967-968; Reporting Research Findings. Wilder Research, in partnership with the Minnesota Department of Human Services. (February 2009); Results. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Schafer, Mickey S. Writing the Results. Thesis Writing in the Sciences. Course Syllabus. University of Florida.

Writing Tip

Why Don't I Just Combine the Results Section with the Discussion Section?

It's not unusual to find articles in scholarly social science journals where the author(s) have combined a description of the findings with a discussion about their significance and implications. You could do this. However, if you are inexperienced writing research papers, consider creating two distinct sections for each section in your paper as a way to better organize your thoughts and, by extension, your paper. Think of the results section as the place where you report what your study found; think of the discussion section as the place where you interpret the information and answer the "So What?" question. As you become more skilled writing research papers, you can consider melding the results of your study with a discussion of its implications.

Driscoll, Dana Lynn and Aleksandra Kasztalska. Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University.

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The Principles of Biomedical Scientific Writing: Results

Zahra bahadoran.

1 Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Parvin Mirmiran

2 Department of Clinical Nutrition and Diet Therapy, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Azita Zadeh-Vakili

3 Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Farhad Hosseinpanah

4 Obesity Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Asghar Ghasemi

5 Endocrine Physiology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran

The “results section” of a scientific paper provides the results related to all measurements and outcomes that have been posted earlier in the materials and methods section. This section consists of text, figures, and tables presenting detailed data and facts without interpretation and discussion. Results may be presented in chronological order, general to specific order, most to least important order, or may be organized according to the topic/study groups or experiment/measured parameters. The primary content of this section includes the most relevant results that correspond to the central question stated in the introduction section, whether they support the hypothesis or not. Findings related to secondary outcomes and subgroup analyses may be reported in this section. All results should be presented in a clear, concise, and sensible manner. In this review, we discuss the function, content, and organization of the “results section,” as well as the principles and the most common tips for the writing of this section.

The “results section” is the heart of the paper, around which the other sections are organized ( 1 ). Research is about results and the reader comes to the paper to discover the results ( 2 ). In this section, authors contribute to the development of scientific literature by providing novel, hitherto unknown knowledge ( 3 ). In addition to the results, this section contains data and statistical information for supporting or refuting the hypothesis proposed in the introduction ( 4 ).

“Results section” should provide an objective description of the main findings, clearly and concisely, without interpretation ( 5 , 6 ). The authors need to use an interesting combination of text, tables, and figures to answer the study questions and to tell the story without diversions ( 7 ). The systemic assessment of published articles highlights the fact that the literature frequently suffers from selective reporting of results only for certain assessed outcomes, selective reporting of statistical analyses, and confused, ambiguous, incomplete, or misleading presentation of data ( 8 , 9 ).

In this section of our series on the principles of biomedical scientific writing ( 10 , 11 ), we describe the function, content, and organization of the “results section” in a scientific paper (mostly for hypothesis-testing papers) and provide common recommendations that can help authors to write this section more effectively.

2. The Function of the Results Section

The function of the “results section” is to present the main results of experiments described in the materials and methods section ( 12 , 13 ) and to present the supporting data in the form of text, tables, and figures ( 13 ). This section should answer the basic question: “What did the authors find in research?” By providing the results, authors try to elucidate the research data, making it to the point and meaningful ( 13 ).

3. Content of the Results Section

The “results section” includes both results and data that are presented in text, tables, and figures. Results are presented in the text; data (the most important) are presented in figures and tables, with a limited amount presented in the text ( 13 ). Statistically relevant parameters including sample size, P values, and the type of statistics used are also presented in this section ( 13 ).

3.1. Difference Between Data and Results

Data and results are not the same ( 14 ); providing results but no data vs. data but no results should be avoided ( 14 , 15 ). Results are general statements in the main text that summarize or explain what the data (facts and numbers) show ( 13 , 14 ); in other words, results are text descriptions of what is important about data ( 16 ) and give meaning to the data ( 15 ). When reporting data or results, make sure that they are logical ( 2 ). See Box 1 for more differences between results and data.

a The text presented in square brackets is data and the remainder is a result.

3.2. The Appropriate Format for Presenting Data/Results

Depending on how the data best support the findings of the study, the “results section” is structured as text, tables, and figures ( 12 ) and should consist of a dynamic interplay between text and figures/tables; the most important data are usually presented in both formats ( 17 ). The reader should select the mode of presentation in a way that optimizes comprehension of the data; however, as a general rule, if you want to present three or fewer numbers, you should use a sentence; otherwise, you consider a table or a graph ( 18 ).

Selecting the best format for presenting results/data depends on the level of details (exact values or patterns) to present ( 19 ). Tables are useful to present specific information or exact values ( 19 ), and function as reference tools for readers ( 20 ) whereas figures are useful to show comparisons and patterns ( 19 ), functioning as analytic tools ( 20 ).

Tables are meant to summarize large amounts of data, to organize and display data more clearly than words, to compare groups of data, to simplify found information, and to facilitate calculations ( 19 ). A table typically has three or more interrelated columns and three or more interrelated rows; otherwise, presenting the information in the text may be more appropriate ( 19 ).

The functions of figures include: (1) showing the underlying patterns of data that are not presentable in text or tables, (2) displaying data more clearly than they can be done in text or tables, (3) more summarizing a large amount of data than they can be done in text or tables, and (4) improving the understanding and locating the specific information easily and rapidly ( 21 ).

3.3. Results

The primary content of this section includes the most relevant (but not all) results corresponding to the central question posed in the introduction section, whether they support the hypothesis or not ( 12 , 13 ). The secondary findings, e.g., results related to secondary outcomes and subgroup analyses, may also be reported in this section ( 22 ). Results must be presented for both experimental and control groups ( 13 ). Results of each item mentioned in the materials and methods should be given in the results section ( 12 , 15 ).

The text of the “results section” should state and summarize the main results and explain the data presented within tables and/or figures ( 23 ); reiteration of all numbers presented in tables and figures is not recommended ( 22 ); however, readers must be given the main messages derived from a table or figure without having to interpret the data themselves ( 7 ). It means that if there is a large amount of data in a table or figure, restating a key piece of data in the text is acceptable and helps the reader zero in on important data ( 14 ).

3.3.1. Reporting Negative Findings

Authors are highly recommended excluding irrelevant results but not ignoring valid anomalous results that contradict the research hypothesis or do not support the current scientific literature ( 22 ). The Feynman, says “if you are doing an experiment, you should report everything that you think might make it invalid-not only what you think is right about it” ( 24 ). Although reporting null or negative findings is not as straightforward as positive findings, it may lead to reexamining current scientific thinking, and guide scientists towards unabridged science ( 25 ). Reporting negative findings can also prevent the replication of the study and prevent the waste of time and resources ( 25 ). The ignorance of null or negative findings also leads to an overestimation of an effect size or treatment effect in available data ( 9 ).

3.3.2. Referring to Unpublished Results

Referring to unpublished results is not recommend unless there is a strong argument supporting their inclusion ( 14 ); therefore, authors are advised to avoid using the term “data not shown” ( 4 ).

3.3.3. Methods or Interpretation in the Results Section

Generally, the “results section” is not the place for presenting methods and experimental details or interpreting data ( 14 ). When experiments are described in this section, if a result leads to additional experiments, it is better to report the new experimental details in the “results section” ( 14 ). Sometimes authors want to refer to a specific experiment or method in results; in these cases, they should not repeat experimental details, but preferably use a transition phrase to link methods with results ( 14 ). To justify the rationale behind the experiment, using topic sentences/phrases (e.g. in order to determine whether…) provides an overview before giving details ( 12 ); however, in this case, the method statement should not be used as a topic sentence and the main verbs should describe results, not methods (e.g., “ when propranolol was administered during normal ventilation, phospholipids decreased ”; here “ method ” is subordinated in a transition clause and result is the main clause) ( 13 ). Two patterns of sentence structure are recommended for including methods in a result statement: making the method the subject of the sentence or stating the method using a transition phrase or clause and the result in the main clause ( 13 ).

The traditional view of writing the “results section” is just to report data and results without any interpretation; accordingly, the result is not expected to contain statements that need to be referenced (comparisons of findings) ( 13 , 26 ). In another view, some interpretation or brief comparisons that do not fit into the discussion may be included ( 13 , 27 ).

Data are facts and numbers, mostly presented as non-textual elements (usually in tables and figures) where they are easy to read ( 13 , 14 , 28 ). A limited amount of data may also be presented in the text, following a result statement ( 13 ) although too much data in the text make it too long ( Box 1 ) ( 28 ). Data may be in the form of raw data, summarized data, or transformed data ( 13 ); however, it is suggested that raw data (i.e. patients’ records, individual observations) not be presented in results ( 12 ). Note that numerical data are absolute while some data, e.g. microscopic data, are subjective ( 2 ).

3.4.1. Non-Textual Elements

Providing study findings visually, rather than entire textualizing, enables authors to summarize a great deal of data compactly within the text with an appropriate reference; some images convey more than words ( 29 ). The primary purpose of non-textual elements, i.e. tables, graphs, figures, and maps, is to present data such that they can be easily and quickly grasped ( 23 ) while being more informative than when appearing in the text ( 6 ). Tables and figures should be complete/comprehensible, being able to stand alone without the text ( 5 , 12 ).

Non-textual elements should be referred to in the text at the appropriate point ( 5 , 6 , 12 ). Location statements, i.e. statements referring to non-textual elements, may be presented in different patterns (e.g., A. X is shown in table/figure; B. table/figure shows; C. see table/figure; D. as shown in table/figure); pattern B is more and pattern C is less common ( 27 ).

An external file that holds a picture, illustration, etc.
Object name is ijem-17-02-92113-i001.jpg

Some general tips about using non-textual elements in the “results section” are reviewed in Box 2 . The most common rules in organizing tables and figures are given in the following. For more information about designing different types of tables/figures/graphs, please refer to additional references ( 7 , 19 , 20 , 30 , 31 ).

3.4.1.1. Tables

The use of tables is an effective way to summarize demographic information and descriptive statistics ( 23 ). Note that tables must have a purpose and be integrated into the text ( 21 ). Tables are most useful to present counts, proportions, and percentages ( 8 ), and are appropriate also for presenting details especially when exact values matter ( 32 ), being are more informative than graphs ( 29 ). However, limited information should be presented in tables; otherwise, most readers find them difficult to read and thus, may ignore them ( 5 , 23 ). Data in tables can be arranged horizontally or vertically; whenever possible, primary comparisons are preferably presented horizontally from left to right ( 19 ).

3.4.1.1.1. Basic Elements of Tables

Tables usually have at least six elements: (1) table number, (2) table title, (3) row headings (stubs), and (4) column headings (boxes), identifying information in rows and columns, (5) data in data field, and (6) horizontal lines (rules). Most also have footnotes, row subheadings, spanner headings (identifying subgroups in column headings), and expanded forms of abbreviations in the table ( 19 , 21 , 31 , 33 ).

The table title should clearly state what appears in it and provide sufficient information on the study, i.e. provide a context helping readers interpret the table information ( 19 ). Some specific details may also be provided including the type and number of subjects or the period of study ( 30 ). For developing the title of a table, one can describe the main cell entries, followed by qualification or more description ( 32 ). The table’s title is presented as a phrase not a full sentence ( 19 ). Authors need to refer to the journal’s style for rules on which words in titles are capitalized.

As a rule, comparing two (or even three) numbers should be side-by-side rather than above and below ( 30 ). Column and row headings help readers find information and they should be included group sizes and measurement units ( 19 ). Tables should be in borderless grids of rows and columns ( 5 , 32 ) with no vertical rule and limited horizontal rules ( 32 ). The first column of a table includes usually a list of variables that are presented in the table; although the first column usually does not need a header, sometimes a simple description of what appears in each row may be provided as the heading of the first column. Units for variables may be placed in parentheses immediately below the row descriptions ( 30 ).

Headings for other columns should also be informative without vague labels, e.g. group A, group B, group C, etc.; instead, a brief description summarizing group characteristics is used ( 30 ). The last column may show P values for comparison between study groups ( 34 ), except for randomized clinical trials, where P values are not needed to compare baseline characteristics of participants ( 7 ). The first letters of lines and column headings in tables should be capitalized.

The fields of tables are points at which columns and rows intersect ( 19 ). Cells of a table are the data field of the table, other than those containing row and column headings ( 21 ). Cells contain information as numerals, text, or symbols ( 19 ). Every cell must contain information; if no information is available, one can use NA in the cell and define it in the footnote as not available or not applicable; alternatively, a dash mark may be inserted ( 19 ). The content of columns need to be aligned ( 19 ); words are usually left aligned, numerals are aligned at decimals, parenthesis, and factors of 10 ( 19 , 21 ).

Table footnotes should be brief, and define abbreviations, provide statistical results, and explain discrepancies in data, e.g., “percentages do not total 100 because of rounding” ( 19 , 30 ). In addition to asterisks usually used to show statistical significance ( 33 ), the following symbols are used, in sequence, for further notes: †, ‡, §, ¶, #, ††, ‡‡ ( 30 ).

3.4.1.1.2. Different Types of Tables

Table of lists, table of baseline or clinical characteristics of subjects, table of comparisons, and table of multivariable results are various types of tables that may be used ( 30 ). The table’s format should be selected according to the purpose of the table ( 30 ). A table of lists just presents a list of items including diagnostic criteria or causes of a disease; it is critical to arrange such tables based on their contents by order (e.g., alphabetical order) or their importance (most to least) ( 30 ). Tables of study participants’ characteristics usually provide a general overview of the essential characteristics of subjects, such as age, sex, race, disease stage, and selected risk factors ( 30 ). The table of comparisons (≥ two groups) provides details for each group and differences between the groups. Tables of multivariable results elaborate results of statistical analyses assessing relationships between predictor (independent) and outcome (dependent) variables, and usually include regression coefficients, standard errors, slopes, partial correlation coefficients, and P values or odds ratio, hazard ratios, and 95% confidence intervals for regression models ( 30 ).

3.4.1.2. Figures

Graphical elements convey the important messages of research ( 20 ). A figure is “any graphical display to present information or data” ( 20 ), and it effectively presents complicated patterns ( 32 ), best used for presenting an important point at a glance or indicating trends or relationships ( 20 ). Like tables, figures should have a purpose and be integrated with the rest of the text ( 21 ).

3.4.1.2.1. Basic Elements of Figures

Most figures that present quantitative information (charts and graphs) have at least seven elements, including figure number, figure caption/legend, data field, vertical scale, horizontal scale, labels, and data (plotting symbols, lines, and so on) ( 21 ). Some figures also have reference lines in the data field to help orient readers and keys that identify data ( 21 ).

Figure caption/legend, usually given below the figure, describes the figure and must reflect the figure entirely, independent of the main text ( 21 , 31 ). For the figure to stand alone, a figure legend needs to be included four parts (a brief title, experimental or statistical information/details, definitions of symbols, line, or bar patterns, and abbreviations) ( 31 ).

Data field is a space in the figure in which data are presented; it is usually bordered on the left by the X-axis (abscissa) and on the bottom by the Y-axis (ordinate) ( 20 , 21 ). Labels identify the variables graphed and the units of measurement ( 21 ). Figure lines should be broad and the labeling text should be large enough to be legible after reduction to a single- or two-column size ( 32 ). Appropriate font size should be used to maintain legibility after fitting figures to publication size ( 31 ).

Scales on each axis should match the data range and be slightly above the highest value ( 20 ). Symbols should be uniform across the figures ( 20 ). The data point symbols should be easily distinguishable; using black and white circles (● - ∘) is the easiest way when two are needed ( 31 ); if more are needed, using up-pointing triangles (▲ - Δ) and squares (■ - □) is suggested ( 31 ). Using symbols, line types, and colors is also effective in differentiating important strata in figures ( 8 ).

3.4.1.2.2. Emphasizing Important Data on Figures

To make figures visually efficient, the subordination of all non-data elements vs. data elements is advised (gridlines should be used as thin as possible and very faint). Directly labeling objects, instead of legends, may keep readers’ attention on the most important parts of the figure ( 8 ). Using different line weights may also be helpful to emphasize the important information/data in figures ( 31 ). The use of color, shading, or 3D perspectives is not suggested unless they serve a specific explanatory function in figure ( 8 ).

3.4.1.2.3. Different Types of Figures

Two major categories of figures are statistical figures (graphs) and non-statistical figures (clinical images, photographs, diagrams, illustrations, and textual figures) ( 20 ). Graphs are suitable for presenting relationships whereas non-statistical figures are used to confirm findings or provide explanatory information ( 20 ).

In statistical figures, selecting a graphical format (bar graph, line graph, dot plot, and scatterplot) is done according to the type of relationship that authors wish to communicate ( 20 ); for example, line graphs are appropriate for showing trends and bar graphs for magnitudes ( 20 ). Using a graphing format that is easy to interpret is preferred ( 20 ); pie graphs are sparingly used because comparing different angles is complicated with them ( 20 ). Graphs should accurately represent findings; when possible, scales should start at zero, and figure axes should not be altered in order to make data more meaningful ( 20 ).

Non-statistical figures are those that visually present information that does not contain data ( 20 ). Clinical images and photographs [ultrasonograms, computed tomographic scans (CT scans), magnetic resonance images (MRI), images of patients, tissue samples, microscopic findings, and so on] provide absolute proof of findings ( 20 ). Illustrations are used for explaining structures (parts of a cell), mechanisms, and relationships ( 20 ). Diagrams (flowcharts, algorithms, pedigrees, and maps) are useful for displaying complex relations ( 20 ). Textual figures, containing only text, are mostly used for describing steps of a procedure or summarizing guidelines ( 20 ). For photographs, patient information or identifiers should be removed ( 20 ).

3.5. Statistics in the Results Section

Statistics in the “results section” must report data in a way that enables readers to assess the degree of experimental variation and to estimate the variability or precision of the findings ( 22 ). For more details, one can see SAMPL (Statistical Analysis and methods in the Published Literature) guidelines ( 35 ). To report normally distributed data, the mean and estimated variation from mean should be stated ( 13 ). Variability should be reported using standard deviation (SD), which is a descriptive statistic ( 36 ) and reflects the dispersion of individual sample observation of the sample mean ( 37 ). The standard error (SE), an inferential statistic ( 36 ) reflecting the theoretical dispersion of sample means about some population means, characterizes uncertainty about true values of population means ( 37 ). It is useful for assessing the precision of an estimator ( 36 ) and is not an appropriate estimate of the variability in observations ( 37 ). Using “mean (SD or SE)” is preferred to “mean ± SD or SE” because the “±” sign can cause confusion ( 22 ). Increasing sample size decreases SE but not SD ( 36 ). To report data with a skewed distribution, the median and the interquartile range (between 25th and 75th percentiles) should be provided ( 22 ).

To report risk, rates, and ratios, one should use a type of rate (incidence rate, survival rate), ratio (odds ratio, hazards ratio), or risk (absolute risk, relative risk, relative risk reduction) ( 35 ). The measure of precision (95% CI) for estimated risks, rates, and ratios should also be provided ( 35 ). For correlation analysis, the exact values of the correlation coefficient and 95% CI should be reported. Describing correlation using qualitative words (low, moderate, high) without providing a clear definition is not acceptable ( 35 ). Results of regression analysis should include regression coefficients (β) of each explanatory variable, corresponding 95% CI and/or P value and a measure of the “goodness-of-fit” of the model ( 35 ).

3.5.1. Significance Levels

A P value is the probability of consistency between data and the hypothesis being tested ( 38 ). Reporting the exact P values ( P = 0.34 or P = 0.02) rather than the conventional P ( P < 0.05) is recommended for all primary analyses ( 12 , 37 ) as it conveys more information ( 37 ). The use of the term “partially significant” or “marginally significant”, where the P value is almost significant (e.g. P = 0.057) is not acceptable if the significance level is defined as P = 0.05 ( 39 ). Some, however, argue that it is not always necessary to stick to P = 0.05 for the interpretation of results and it is better to report the exact P value and confidence interval for the estimator ( 40 ).

The use of the 95% confidence interval (95% CI) can provide further information compared to P values per se, and prefigures the direction of the effect size (negative or positive), its magnitude, and the degree of precision ( 17 ). A confidence interval characterizes uncertainty about the true value of population parameters ( 37 ). It is essential to provide the sample size (n) and probability values for tests of statistical significance ( 13 ).

Statements about significance must be qualified numerically ( 41 ). In the text, it is suggested that P values be reported as equalities rather than as inequalities in relation to the alpha criterion ( 41 ). In tables and figures, inequalities may be useful for groups of data ( 41 ) where asterisks *, **, and *** are usually used to show statistical significance at 0.05, 0.01, and 0.001 probability levels, respectively ( 33 ).

Although not consistent, P values < 0.001 are reported as P < 0.001; for 0.001 ≤ P values < 0.01, a three-significant digit is recommended, e.g. P = 0.003; for 0.01 ≤ P values < 0.1, a two-significant digit is sufficient (e.g. P = 0.05); for 0.1 ≤ P values ≤ 0.9, a one-significant digit is sufficient (e.g. P = 0.4); and P values > 0.9 are reported as P > 0.9 ( 42 ). For genome-wide association studies, the power of 10 is used for reporting P values, e.g. 6 × 10 -9 ( 42 ). It is generally suggested that zero be used before a decimal point when the value is below one, e.g. 0.37 ( 43 ). According to the American Psychological Association, zero before a decimal point is used for numbers that are below one, but it can also be used for values that may exceed one (e.g. 0.23 cm). Therefore, when statistics cannot be greater than one (e.g. correlations, proportions, and P values), do not use a zero before decimal fraction, e.g. P = .028 not P = 0.028 ( 18 ); this recommendation, however, is not always adopted by everyone. The international standard is P (large italic) although both ‘p’ and ‘P’ are allowed ( 40 ).

4. Organization of the Results Section

There are different ways for organizing the “results section” including ( 1 , 12 , 14 , 22 , 44 ): (1) chronological order, (2) general to specific, (3) most to least important, and (4) grouping results by topic/study groups or experiment/measured parameters. Authors decide which format is more appropriate for the presentation of their data ( 12 ); anyway, results should be presented in a logical manner ( 4 ).

4.1. Different Ways of Organizing the Results Section

4.1.1. chronological order.

The best order for organizing “results section” may be the chronological order ( 22 ). It is considered as the most straightforward approach using subheadings that parallel methods ( 14 ). This order facilitates referring to a method associated with a given result ( 14 ) such that results are presented in the same order as methods ( 15 ).

4.1.2. General to Specific

This format is mostly used in clinical studies involving multiple groups of individuals receiving different treatments ( 14 ). The “results section” usually proceeds from general to more specific findings ( 1 ). Characteristics of the overall study population (sex and age distribution and dropouts) are first given ( 14 ), followed by data and results for each group starting with the control group or the group receiving the standard treatment ( 14 ); finally, the disease group or group receiving the experimental treatment are addressed ( 14 ). As a general rule, secondary results should be given after presenting more important (primary) results, followed by any supporting information ( 22 ). A common order is stating recruitment/response, characteristics of the sample/study participants, findings from the primary analyses, findings from secondary analyses, and any additional or unexpected findings ( 17 ). In other words, the “results section” should be initiated by univariate statistics, followed by bivariate analyses to describe associations between explanatory and outcome variables; finally, it gets through by any multivariate analyses ( 7 ).

4.1.3. Most to Least Important

This format is used in case that the order of presenting results is not critical to their being comprehendible and allows the author to immediately highlight important findings ( 14 ). Results that answer the main question are presented at the beginning of the “results section,” followed by other results in next paragraphs ( 13 ).

4.1.4. Grouping by Topic or Experiment

Comparison of the diagnostic and analytical performance of a number of assays for analytes is an example of using this format ( 14 ).

4.2. Paragraphing of the Results Section

The “results section” may be initiated by two approaches: (1) by giving a general (not detailed) overview of the experiment and (2) by going directly to the results by referring to tables or figures ( 44 ). The first paragraph of this section, along with table 1, describes the characteristics of the study population (number, sex, age, and symptoms) ( 23 ). These data show the comparability of the study groups at baseline and the distribution of potential confounders between groups, as a source of bias that can affect the study findings ( 7 ). It allows the reader to decide whether or not the case and control groups are similar and represent the patient population in their private practice ( 23 ).

For clinical trials, the number of patients completing the protocol in each treatment/study group, the number of patients lost to follow-up, and the number and reasons for excluded/withdrawn subjects should be given. Commenting on whether baseline characteristics of study groups are statistically similar or different is also important ( 1 ). For further information, authors can consult reporting guidelines for the main study types available at http://www.equator-network.org.

The number of the middle paragraphs depends on the number of research questions/hypotheses and the types of statistical analyses; each hypothesis or specific analysis typically devotes at least a paragraph to itself ( 1 ). Figure legends, description of the methods and results for control groups should not be given at the beginning of paragraphs, as they do not narrate the story ( 28 ). However, sometimes, it is needed that results of the control group are presented first (e.g. for establishing the stability of baseline) ( 13 ).

5. Emphasizing Important Results

Since not all results are equally important, the reader must be able to distinguish important results and authors have to emphasize important information and de-emphasize less important information ( 13 ). There are various techniques for emphasizing important information, including condensing or omitting less important information, subordinating less important information, placing important results at the power position, and labeling, stating, and repeating important information ( 13 ).

For condensing or omitting less important information, you should be careful not to duplicate/repeat data in tables and figures or repeat them in the text ( 4 , 6 , 12 ); one or two values from tables/figures can be repeated in the text for emphasis ( 13 ).

For subordinating less important information, one should not use table titles, figure legends or methods statement as a topic sentence in the text ( 13 , 22 ). Instead, after stating the first result relevant to the table/figure, you can cite it in parenthesis ( 13 ). Since a result states a message and creates an expectation, it is a more powerful topic sentence than a figure legend or table title ( 13 ). Sometimes, control results can be subordinated by incorporating them into experimental results ( 13 ).

To highlight more important results (those that help answer questions), authors can put these results at the beginning of paragraphs, the strongest power position ( 12 , 22 , 28 ), followed by supporting details and control results ( 28 ).

Moreover, key findings may receive more attention by using a signal (e.g. we found or we observed) at the beginning of the sentence ( 13 ).

6. Other Considerations

6.1. length and paragraphing.

To see the forest for the tree, the “results section” should be as brief and uncluttered as possible ( 13 ), which can be accomplished by having a well-organized “materials and methods” section ( 3 ) and avoiding unnecessary repetition ( 13 ); for example, similar results for several variables can be reported together. The “results section” of an original manuscript usually includes 2 - 3 pages (~1000 words) with a 1.5 line spacing, font size 11 (including tables and figures) ( 45 ), and 4 - 9 paragraphs (each 130 words) on average ( 45 ); a paragraph should be devoted to one or more closely related figures ( 4 ).

Presenting additional results/data as supplementary materials is a suggestion for keeping the “results section” brief ( 17 ). In addition to save the text space, supplementary materials improve the presentation and facilitate communications among scientists ( 46 , 47 ). According to Springer, supplementary materials can be used for presenting data that are not needed to support the major conclusions but are still interesting. However, keep in mind that the unregulated use of supplementary materials is harmful to science ( 47 ). Supplementary materials should be referred to at the appropriate points in the main text.

For referring to results obtained in hypothesis testing studies, using past tenses is recommended ( 4 , 12 - 14 ); non-textual elements should be referred using present tenses, e.g. “as seen in table 1 …” or “table 1 shows …” in descriptive studies, results are reported in the present tense ( 13 ).

6.3. Word Choice

Although adverbs/adjectives are commonly used to highlight the importance of results, it is recommended altogether avoiding the use of such qualitative/emotive words in the “results section” ( 7 , 13 ). Some believe that qualitative words should not be used because they may imply an interpretation of findings ( 17 ). In biomedical publications, the terms ‘significant, significance, and significantly’ (followed by P values) are used to show statistical relationships and should not be used for other purposes for which, other terms such as substantial, considerable, or noteworthy can be used ( 14 ). See Box 3 for appropriate word choice for the “results section.”

In the “results section,” to make a comparison between the results, i.e. stating the similarity/equivalence or difference/non-equivalence, using appropriate signals is recommended ( 27 ). To show a similarity, a signal to the reader may be used such as “like”, “alike”, “similar to”, and “the same as”; to show differences, the following signals can be used: “but”, “while”, “however”, “in contrast”, “more likely than”, and “less likely than” ( 27 ).

6.4. Reporting Numbers

Numbers play an important role in scientific communication and there are some golden rules for reporting numbers in a scientific paper ( 43 , 48 ). Significant figures (significant digits) should reflect the degree of precision of the original measurement ( 12 ). The number of digits reported for a quantity should be consistent with scientific relevance ( 37 ); for example, a resolution to 0.001 units is necessary for pH but a resolution of < 1 mm Hg is unimportant for blood pressure ( 37 ). Avoid using “about” or “approximately” to qualify a measurement or calculation ( 12 ). The use of percentage for sample sizes of < 20 and decimal for sample sizes of < 100 is not recommended ( 43 ).

The numbers should be spelled out at the beginning of a sentence or when they are less than 10, e.g., twelve students improved… ( 43 ). In a sentence, the authors should be consistent where they use numbers as numerals or spelled-out ( 43 ). Before a unit of a measure, time, dates, and points, numbers should be used as numerals, e.g. 12 cm; 1 h 34 min; at 12:30 A.M., and on a 7-point scale ( 18 ).

A space between the numeral and the unit should be considered, except in the case of %. Because the terms “billion,” “trillion,” and “quadrillion” imply different numbers in Europe and the USA, they should not be used ( 48 ). To express ranges in text, the terms “to” or “through” are preferred to dashes; in tables, the use of dashes or hyphens is recommended ( 48 ).

7. Conclusions

The “results section” of a biomedical manuscript should clearly present findings of the study using an effective combination of results and data. Some dos and don’ts of writing the “results section” are provided in Box 4 . Authors should try to find the best format using a dynamic interplay between text and figures/tables. Results can be organized in different ways including chronological order or most to least important; however, results should be presented in a manner that makes sense.

Acknowledgments

The authors wish to acknowledge Ms. Niloofar Shiva for critical editing of English grammar and syntax of the manuscript.

Conflict of Interests: It is not declared by the authors.

Funding/Support: Research Institute for Endocrine Sciences supported the study.

How to Write an Effective Results Section

Affiliation.

  • 1 Rothman Orthopaedics Institute, Philadelphia, PA.
  • PMID: 31145152
  • DOI: 10.1097/BSD.0000000000000845

Developing a well-written research paper is an important step in completing a scientific study. This paper is where the principle investigator and co-authors report the purpose, methods, findings, and conclusions of the study. A key element of writing a research paper is to clearly and objectively report the study's findings in the Results section. The Results section is where the authors inform the readers about the findings from the statistical analysis of the data collected to operationalize the study hypothesis, optimally adding novel information to the collective knowledge on the subject matter. By utilizing clear, concise, and well-organized writing techniques and visual aids in the reporting of the data, the author is able to construct a case for the research question at hand even without interpreting the data.

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Writing a scientific paper.

  • Writing a lab report
  • INTRODUCTION

Writing a "good" results section

Figures and Captions in Lab Reports

"Results Checklist" from: How to Write a Good Scientific Paper. Chris A. Mack. SPIE. 2018.

Additional tips for results sections.

  • LITERATURE CITED
  • Bibliography of guides to scientific writing and presenting
  • Peer Review
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  • Lab Report Writing Guides on the Web

This is the core of the paper. Don't start the results sections with methods you left out of the Materials and Methods section. You need to give an overall description of the experiments and present the data you found.

  • Factual statements supported by evidence. Short and sweet without excess words
  • Present representative data rather than endlessly repetitive data
  • Discuss variables only if they had an effect (positive or negative)
  • Use meaningful statistics
  • Avoid redundancy. If it is in the tables or captions you may not need to repeat it

A short article by Dr. Brett Couch and Dr. Deena Wassenberg, Biology Program, University of Minnesota

  • Present the results of the paper, in logical order, using tables and graphs as necessary.
  • Explain the results and show how they help to answer the research questions posed in the Introduction. Evidence does not explain itself; the results must be presented and then explained. 
  • Avoid: presenting results that are never discussed;  presenting results in chronological order rather than logical order; ignoring results that do not support the conclusions; 
  • Number tables and figures separately beginning with 1 (i.e. Table 1, Table 2, Figure 1, etc.).
  • Do not attempt to evaluate the results in this section. Report only what you found; hold all discussion of the significance of the results for the Discussion section.
  • It is not necessary to describe every step of your statistical analyses. Scientists understand all about null hypotheses, rejection rules, and so forth and do not need to be reminded of them. Just say something like, "Honeybees did not use the flowers in proportion to their availability (X2 = 7.9, p<0.05, d.f.= 4, chi-square test)." Likewise, cite tables and figures without describing in detail how the data were manipulated. Explanations of this sort should appear in a legend or caption written on the same page as the figure or table.
  • You must refer in the text to each figure or table you include in your paper.
  • Tables generally should report summary-level data, such as means ± standard deviations, rather than all your raw data.  A long list of all your individual observations will mean much less than a few concise, easy-to-read tables or figures that bring out the main findings of your study.  
  • Only use a figure (graph) when the data lend themselves to a good visual representation.  Avoid using figures that show too many variables or trends at once, because they can be hard to understand.

From:  https://writingcenter.gmu.edu/guides/imrad-results-discussion

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How to Write an APA Results Section

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

result in research paper sample

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

result in research paper sample

Verywell / Nusha Ashjaee 

What to Include in an APA Results Section

  • Justify Claims
  • Summarize Results

Report All Relevant Results

  • Report Statistical Findings

Include Tables and Figures

What not to include in an apa results section.

Psychology papers generally follow a specific structure. One important section of a paper is known as the results section. An APA results section of a psychology paper summarizes the data that was collected and the statistical analyses that were performed. The goal of this section is to report the results of your study or experiment without any type of subjective interpretation.

At a Glance

The results section is a vital part of an APA paper that summarizes a study's findings and statistical analysis. This section often includes descriptive text, tables, and figures to help summarize the findings. The focus is purely on summarizing and presenting the findings and should not include any interpretation, since you'll cover that in the subsequent discussion section.

This article covers how to write an APA results section, including what to include and what to avoid.

The results section is the third section of a psychology paper. It will appear after the introduction and methods sections and before the discussion section.

The results section should include:

  • A summary of the research findings.
  • Information about participant flow, recruitment, retention, and attrition. If some participants started the study and later left or failed to complete the study, then this should be described. 
  • Information about any reasons why some data might have been excluded from the study. 
  • Statistical information including samples sizes and statistical tests that were used. It should report standard deviations, p-values, and other measures of interest.

Results Should Justify Your Claims

Report data in order to sufficiently justify your conclusions. Since you'll be talking about your own interpretation of the results in the discussion section, you need to be sure that the information reported in the results section justifies your claims.

When you start writing your discussion section, you can then look back on your results to ensure that all the data you need are there to fully support your conclusions. Be sure not to make claims in your discussion section that are not supported by the findings described in your results section.

Summarize Your Results

Remember, you are summarizing the results of your psychological study, not reporting them in full detail. The results section should be a relatively brief overview of your findings, not a complete presentation of every single number and calculation.

If you choose, you can create a supplemental online archive where other researchers can access the raw data if they choose.

How long should a results section be?

The length of your results section will vary depending on the nature of your paper and the complexity of your research. In most cases, this will be the shortest section of your paper.

Just as the results section of your psychology paper should sufficiently justify your claims, it should also provide an accurate look at what you found in your study. Be sure to mention all relevant information.

Don't omit findings simply because they failed to support your predictions.

Your hypothesis may have expected more statistically significant results or your study didn't support your hypothesis , but that doesn't mean that the conclusions you reach are not useful. Provide data about what you found in your results section, then save your interpretation for what the results might mean in the discussion section.

While your study might not have supported your original predictions, your finding can provide important inspiration for future explorations into a topic.

How is the results section different from the discussion section?

The results section provides the results of your study or experiment. The goal of the section is to report what happened and the statistical analyses you performed. The discussion section is where you will examine what these results mean and whether they support or fail to support your hypothesis.

Report Your Statistical Findings

Always assume that your readers have a solid understanding of statistical concepts. There's no need to explain what a t-test is or how a one-way ANOVA works. Your responsibility is to report the results of your study, not to teach your readers how to analyze or interpret statistics.

Include Effect Sizes

The Publication Manual of the American Psychological Association recommends including effect sizes in your results section so that readers can appreciate the importance of your study's findings.

Your results section should include both text and illustrations. Presenting data in this way makes it easier for readers to quickly look at your results.

Structure your results section around tables or figures that summarize the results of your statistical analysis. In many cases, the easiest way to accomplish this is to first create your tables and figures and then organize them in a logical way. Next, write the summary text to support your illustrative materials.

Only include tables and figures if you are going to talk about them in the body text of your results section.

In addition to knowing what you should include in the results section of your psychology paper, it's also important to be aware of things that you should avoid putting in this section:

Cause-and-Effect Conclusions

Don't draw cause-effect conclusions. Avoid making any claims suggesting that your result "proves" that something is true. 

Interpretations

Present the data without editorializing it. Save your comments and interpretations for the discussion section of your paper. 

Statistics Without Context

Don't include statistics without narration. The results section should not be a numbers dump. Instead, you should sequentially narrate what these numbers mean.

Don't include the raw data in the results section. The results section should be a concise presentation of the results. If there is raw data that would be useful, include it in the appendix .

Don't only rely on descriptive text. Use tables and figures to present these findings when appropriate. This makes the results section easier to read and can convey a great deal of information quickly.

Repeated Data

Don't present the same data twice in your illustrative materials. If you have already presented some data in a table, don't present it again in a figure. If you have presented data in a figure, don't present it again in a table.

All of Your Findings

Don't feel like you have to include everything. If data is irrelevant to the research question, don't include it in the results section.

But Don't Skip Relevant Data

Don't leave out results because they don't support your claims. Even if your data does not support your hypothesis, including it in your findings is essential if it's relevant.

More Tips for Writing a Results Section

If you are struggling, there are a few things to remember that might help:

  • Use the past tense . The results section should be written in the past tense.
  • Be concise and objective . You will have the opportunity to give your own interpretations of the results in the discussion section.
  • Use APA format . As you are writing your results section, keep a style guide on hand. The Publication Manual of the American Psychological Association is the official source for APA style.
  • Visit your library . Read some journal articles that are on your topic. Pay attention to how the authors present the results of their research.
  • Get a second opinion . If possible, take your paper to your school's writing lab for additional assistance.

What This Means For You

Remember, the results section of your paper is all about providing the data from your study. This section is often the shortest part of your paper, and in most cases, the most clinical.

Be sure not to include any subjective interpretation of the results. Simply relay the data in the most objective and straightforward way possible. You can then provide your own analysis of what these results mean in the discussion section of your paper.

Bavdekar SB, Chandak S. Results: Unraveling the findings . J Assoc Physicians India . 2015 Sep;63(9):44-6. PMID:27608866.

Snyder N, Foltz C, Lendner M, Vaccaro AR. How to write an effective results section .  Clin Spine Surg . 2019;32(7):295-296. doi:10.1097/BSD.0000000000000845

American Psychological Association.  Publication Manual of the American Psychological Association  (7th ed.). Washington DC: The American Psychological Association; 2019.

Purdue Online Writing Lab. APA sample paper: Experimental psychology .

Berkeley University. Reviewing test results .

Tuncel A, Atan A. How to clearly articulate results and construct tables and figures in a scientific paper ? Turk J Urol . 2013;39(Suppl 1):16-19. doi:10.5152/tud.2013.048

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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APA Results Section – Explanation & Examples

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APA-Results-Section-Definition

The APA results section summarizes data and includes reporting statistics in a quantitative research study. The APA results section is an essential part of your research paper and typically begins with a brief overview of the data followed by a systematic and detailed reporting of each hypothesis tested. The interpreted results will then be presented in the discussion sections. Ensure you adhere to APA style guidelines consistently throughout the paper.

Inhaltsverzeichnis

  • 1 APA Results Section – In a Nutshell
  • 2 Definition: APA results section
  • 3 What’s included in the APA results section?
  • 4 APA results section: Introducing the data
  • 5 APA results section: Summarizing the data
  • 6 APA results section: Reporting the results
  • 7 APA results section: Formatting numbers
  • 8 APA results section: Don’t include these

APA Results Section – In a Nutshell

  • The APA results section of empirical manuscripts reports the quantitative results of a study conducted on a data set.
  • The APA results section provides concrete evidence to disprove or confirm the hypothesis.

Definition: APA results section

The American Psychological Association recommends the APA style guide for presenting results in a manuscript. A research manuscript’s APA results section describes the researcher’s findings following a thorough data analysis and interpretation of the results. It uses obtained data to test or refute the theory of a research study.

What’s included in the APA results section?

The APA results section includes preliminary details on the data, participants, statistics , and the results of the explanatory analysis , as discussed below.

  • Participants – The number of participants is reported at every study stage
  • Missing data – Identifying the amount of data excluded from the final analysis.
  • Adverse effects – Report any unforeseen events for clinical studies
  • Descriptive statistics – Summarize the secondary and primary outcomes of a study
  • Inferential statistics – Helps researchers draw conclusions and make predictions from the data.
  • Confidence interval and effect size  – Confidence intervals are a range of possible values for the data set mean.
  • Results of explanatory analysis – An exploratory research investigates data to test a hypothesis, check assumptions, and find anomalies.

APA results section: Introducing the data

Before you discuss your research findings, start by clearly describing the participants at each study stage. If any data was excluded from the eventual analysis, indicate that too.

Participants

Recruitment, participant flow, and attrition should be reported. Attrition bias affects external and internal validity and produces erroneous results.

A flow chart is often the best way to report the number of participants per group per stage and their reasons for attrition. Below is an example of how to report participant flow.

  • 25% of the 400 participants who signed up and completed the first survey were eliminated for not fitting the research criteria.
  • 15% didn’t use fiber optics internet exclusively.
  • 10% did not have internet access.
  • 300 participants progressed to the final survey round for a gift bag.
  • 52 people didn’t complete the survey.

This resulted in 248 research participants.

Missing data & adverse effects

In any study, missing data must be reported. Unexpected events, poor storage, and equipment failures can cause missing data. In any instance, clearly explain why you couldn’t use the data.

Data outliers can be excluded from the final study, but you must explain why. Include how you handled missing data. Standard procedures include mean-value imputation, interpolation, extrapolation, and substitution.

  • Results of 33 participants were excluded from the study as they did not meet the research criteria.
  • The data for another 4 participants were lost due to human error.

APA results section: Summarizing the data

It is important to note that you should provide a summary of your study’s results. However, you can create a supplemental archive for other researchers to access raw data. 2

Descriptive Statistics

Descriptive statistics are concise coefficients that summarize a specific data collection , such as a population sample or APA results section. APA results section can include descriptive statistics such as:

  • Central tendency measures describe a data set by identifying the center of the data set. ( mode , median, mean )
  • Measures of variability describe the score dispersion within a data set. ( standard deviation , range, variance , and interquartile range )
  • Sample sizes
  • Variables of interest, which are measured, changing quantities in experimental studies. Be sure to explain how you operationalized any variable of interest you use.
  • 20 athletes in five trials were given 400 mg of a performance-enhancing substance to measure their speed (m/s ) and reaction time(s).
  • After averaging each athlete’s speed and response time, the group’s averages were calculated.

The group that used the performance-enhancing drug had a higher speed (m/s) than the group that did not use the drug ( M = 4, SD=1.25 )

APA results section: Reporting the results

APA journal standards require all the appropriate hypothesis tests, confidence intervals, and effect size estimates to be reported in the APA results section.

Inferential statistics

Inferential statistics help researchers draw conclusions and make predictions based on the data.

When you are reporting the inferential statistics in the APA results section, use the following:

  • Degrees of freedom
  • Test statistic (includes the z-score, t-value, and f-ratio )
  • Error term (if needed, though it is not included in correlations and non-parametric tests.)
  • The exact p-value (unless . 001)

In keeping with the hypotheses, athletes who take performance-enhancing drugs have increased reaction times, and speeds, t (20) = 1s , p .001

Confidence intervals & effect sizes

A confidence interval can be described as a range of possible values for the mean derived from the sample data. It helps show the variability that is around point estimates. You should include confidence intervals any time you report estimates for population parameters.

Night guards consume an average of 600 mg of caffeine weekly, 93% CI [90, 200}

Effect size measures an experiment’s magnitude. It explains the research’s significance. Since effect size is an estimate, confidence intervals should be included.

Moderate amounts of performance-enhancing drugs increase speed significantly, Cohen’s d =1.4, 93% CI [0.92, 1.57]

Subgroup & exploratory analyses

Exploratory analysis tests a hypothesis, checks assumptions , and finds patterns and anomalies in data . If you find notable results, report them as exploratory, not confirming, to avoid overstating their value.

APA results section: Formatting numbers

Use figures, text, and tables to show numbers in APA results sections properly.

✓ For three or fewer numbers, use a sentence, a table for 4 and 20 numbers, and a figure for more than 20 .

✓ Number and title the APA tables and figures , as well as relevant notes. If you have already presented the data in a table, do not repeat it in a figure and vice versa.

✓ Statistics in your APA results section must be abbreviated, capitalized, and italicized.

✓ Use APA norms for reporting statistics and writing numbers.

✓ Look up these guidelines if you are unsure how to present certain symbols.

Ireland

APA results section: Don’t include these

Besides knowing what to include in an APA results section, it is just as important to know what not to have. Below is an outline of what you should exclude from an APA results section.

What should be included in the APA results section?

The APA results section should include details on the participants, descriptive statistics and inferential statistics , missing data , and the results of any exploratory analysis.

What tense should I use to write my results?

Write the APA results section in the past tense.

When should I include tables and figures?

Include tables and figures if you will discuss them in the body text of the APA results section.

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Results Section Of A Research Paper: How To Write It Properly

results section of a research paper

The results section of a research paper refers to the part that represents the study’s core findings from the methods that the researcher used to collect and analyze data. This section presents the results logically without interpretation or bias from the author.

Thus, this part of a research paper sets up the read for evaluation and analysis of the findings in the discussion section. Essentially, this section breaks down the information into several sentences, showing its importance to the research question. Writing results section in a research paper entails summarizing the gathered data and the performed statistical analysis. That way, the author presents or reports the results without subjective interpretation.

What Is The Results Section Of A Research Paper?

In its simplest definition, a research paper results section is where the researcher reports the findings of a study based on the applied methodology for gathering information. It’s the part where the author states the research findings in a logical sequence without interpreting them. If the research paper has data from actual research, this section should feature a detailed description of the results.

When writing a dissertation, a thesis, or any other academic paper, the result section should come third in sections’ sequence. It should follow the Methods and Materials presentation and the Discussion section comes after it. But most scientific papers present the Results and Discussion sections together. However, the results section answers the question, “What did your research uncover?”

Ideally, this section allows you to report findings in research paper, creating the basis for sufficiently justified conclusions. After writing the study findings in the results section, you interpret them in the subsequent discussion part. Therefore, your results section should report information that will justify your claims. That way, you can look back on the results section when writing the discussion part to ensure that your report supports your conclusions.

What Goes in the Results Section of a Research Paper?

This section should present results in research paper. The findings part of a research paper can differ in structure depending on the study, discipline, and journal. Nevertheless, the results section presents a description of the experiment while presenting the research results. When writing this part of your research paper, you can use graphs and tables if necessary.

However, state the findings without interpreting them. For instance, you can find a correlation between variables when analyzing data. In that case, your results section can explain this correlation without speculating about the causes of this correlation.

Here’s what to include in the results section of research paper:

A brief introductory of the context, repeating the research questions to help the readers understand the results A report about information collection, participants, and recruitment: for instance, you can include a demographic summary with the participants’ characteristics A systematic findings’ description, with a logical presentation highlighting relevant and crucial results A contextual data analysis explaining the meaning in sentences Information corresponding to the primary research questions Secondary findings like subgroup analysis and secondary outcomes Visual elements like charts, figures, tables, and maps, illustrating and summarizing the findings

Ensure that your results section cites and numbers visual elements in an orderly manner. Every table or figure should stand alone without text. That means visual elements should have adequate non-textual content to enable the audiences to understand their meanings.

If your study has a broad scope, several variables, or used methodologies that yielded different results, state the most relevant results only based on the research question you presented in your Introduction section.

The general rule is to leave out any data that doesn’t present your study’s direct outcome or findings. Unless the professor, advisor, university faulty, or your target journal requests you to combine the Results and Discussion sections, omit the interpretations and explanations of the results in this section.

How Long Should A Results Section Be?

The findings section of a research paper ranges between two and three pages, with tables, text, and figures. In most cases, universities and journals insist that this section shouldn’t exceed 1,000 words over four to nine paragraphs, usually with no references.

But a good findings section occupies 5% of the entire paper. For instance, this section should have 500 words if a dissertation has 10,000 words. If the educator didn’t specify the number of words to include in this chapter, use the data you collect to determine its length. Nevertheless, be as concise as possible by featuring only relevant results that answer your research question.

How To Write Results Section Of Research Paper

Perhaps, you have completed researching and writing the preceding sections, and you’re now wondering how to write results. By the time you’re composing this section, you already have findings or answers to your research questions. However, you don’t even know how to start a results section. And your search for guidelines landed you on this page.

Well, every research project is different and unique. That’s why researchers use different strategies when writing this section of their research papers. The scientific or academic discipline, specialization field, target journal, and the author are factors influencing how you write this section. Nevertheless, there’s a general way of writing this section, although it might differ slightly between disciplines. Here’s how to write results section in a research paper.

Check the instructions or guidelines. Check their instructions or guidelines first, whether you’re writing the research paper as part of your coursework or for an academic journal. These guidelines outline the requirements for presenting results in research papers. Also, check the published articles to know how to approach this section. When reviewing the procedures, check content restrictions and length. Essentially, learn everything you can about this section from the instructions or guidelines before you start writing. Reflect on your research findings. With instructions and guidelines in mind, reflect on your research findings to determine how to present them in your research paper. Decide on the best way to show the results so that they can answer the research question. Also, strive to clarify and streamline your report, especially with a complex and lengthy results section. You can use subheadings to avoid peripheral and excessive details. Additionally, consider breaking down the content to make it easy for the readers to understand or remember. Your hypothesis, research question, or methodologies might influence the structure of the findings sections. Nevertheless, a hierarchy of importance, chronological order, or meaningful grouping of categories or themes can be an effective way of presenting your findings. Design your visual presentations. Visual presentations improve the textual report of the research findings. Therefore, decide on the figures and styles to use in your tables, graphs, photos, and maps. However, check the instructions and guidelines of your faculty or journal to determine the visual aids you can use. Also, check what the guidelines say about their formats and design elements. Ideally, number the figures and tables according to their mention in the text. Additionally, your figures and tables should be self-explanatory. Write your findings section. Writing the results section of a research paper entails communicating the information you gathered from your study. Ideally, be as objective and factual as possible. If you gathered complex information, try to simplify and present it accurately, precisely, and clearly. Therefore, use well-structured sentences instead of complex expressions and phrases. Also, use an active voice and past tense since you’ve already done the research. Additionally, use correct spelling, grammar, and punctuation. Take your time to present the findings in the best way possible to focus your readers on your study objectives while preparing them for the coming speculations, interpretations, and recommendations. Edit Your Findings Section. Once you’ve written the results part of your paper, please go through it to ensure that you’ve presented your study findings in the best way possible. Make sure that the content of this section is factual, accurate, and without errors. You’ve taken a considerable amount of time to compose the results scientific paper audiences will find interesting to read. Therefore, take a moment to go through the draft and eliminate all errors.

Practical Tips on How to Write a Results Section of a Research Paper

The results part of a research paper aims to present the key findings objectively in a logical and orderly sequence using text and illustrative materials. A common mistake that many authors make is confusing the information in the discussion and the results sections. To avoid this, focus on presenting your research findings without interpreting them or speculating about them.

The following tips on how to write a results section should make this task easier for you:

Summarize your study results: Instead of reporting the findings in full detail, summarize them. That way, you can develop an overview of the results. Present relevant findings only: Don’t report everything you found during your research. Instead, present pertinent information only. That means taking time to analyze your results to know what your audiences want to know. Report statistical findings: When writing this section, assume that the audiences understand statistical concepts. Therefore, don’t try to explain the nitty-gritty in this section. Remember that your work is to report your study’s findings in this section. Be objective and concise: You can interpret the findings in the discussion sections. Therefore, focus on presenting the results objectively and concisely in this section. Use the suitable format: Use the correct style to present the findings depending on your study field.

Get Professional Help with the Research Section

Maybe you’re pursuing your graduate or undergraduate studies but cannot write the results part of your paper. Perhaps, you’re done researching and analyzing information, but this section proves too tricky for you to write. Well, you’re not alone because many students across the world struggle to present their research findings.

Luckily, our highly educated, talented, and experienced writers are always ready to assist such learners. If you are stuck with the results part of your paper, our professionals can help you . We offer high-quality, custom writing help online. We’re a reliable team of experts with a sterling reputation for providing comprehensive assistance to college, high school, and university learners. We deliver highly informative academic papers after conducting extensive and in-depth research. Contact us saying something like, “please do my thesis” to get quality help with your paper!

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APA Results Section

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The APA results section is a part of a research paper where the findings and statistical analyses are presented. You should briefly summarize the research outcomes stivking to specific APA style guidelines.

By now you probably have conducted your research and all that’s left is to share your findings in APA Results section. American Psychological Association has established multiple rules for designing your research outcomes. Chances are that you have numerous questions regarding this part of a paper, but only a limited time to find any sound answers. That’s why we have prepared this quick guide. Keep reading and find out what goes in the Result section and how to properly format it in APA writing style . 

APA Results Section: Basics

APA Results section is a part of a research paper where scientists share their findings. After all, it is impossible to tell otherwise about the work’s significance. There is no need to elaborate on your research topic. Rather it just focuses on statistics and numerical data.  APA Results section should provide data that answers your research question. Here’s what you should include in this part of a paper:

  • Number of participants
  • Descriptive statistical data
  • Inferential statistical data
  • Missing details
  • Side effects
  • Written reports.

You should maintain a consistent structure and offer an easy-to-follow flow of ideas. It’s usually written using the past tense. You must present the outcomes of a study that has already been finished.

How to Write Results Section: APA

When designing an APA format Results section, you should work out each block step by step. Let us walk you through each stage of the writing process:

  • Preliminary discussion
  • Analysis of obtained data
  • Presenting your research findings.

Note that these details should only be summarized. Keep interpretations for your Discussion section.

Preliminary Discussion in Your APA Results Section

APA Results section of a research paper should start with a brief reminder. Briefly restate your main goal and hypotheses that you wanted to test. (We have the whole blog on how to write a hypothesis .) Then, you should mention a number of participants, excluded data (if there is any) and adverse effects.  Report how many people participated in your research. A number of participants may vary depending on each stage of your study. This being said, you should explain the reasons for attrition to ensure internal validity.  Your research depends much on how complete your data is. But sometimes, you might lack some necessary equipment or have things going the way you don’t expect. That’s why you should inform your readers about any missing data and reasons behind this. If it’s a clinical research, you should also report any  side effects that have happened.  Pay extra attention to reporting style, as you must convince readers that your research was conducted according to set conditions. Without this, it won’t be possible to achieve a desired result. Wonder how to cite a report APA ? We have a special blog that contains all rules with every detail.

APA Results Section: Summarize Your Data Analysis

Writing the APA results section relies on preparing an explanation of your outcomes. Dry statistics isn’t your best option. Instead, you should make a descriptive analysis of data that you have collected. Introduce descriptive statistics for each type of analysis – preliminary, secondary and subgroup one.  Make sure you properly report descriptive statistics in your APA Results section.  The means of reporting may vary depending on the nature of your data and conditions.

                                                                                                               Means of reporting data

Besides, you should also include such elements:

  • Sample sizes
  • Measures of central tendency
  • Measures of variability (for point estimate).

Provide verified information from trusted sources. Losing your readers’ trust is easy. APA recommends using citations in cases when rare statistics is integrated. However, you shouldn’t bother citing common knowledge. 

Presenting Outcomes in Your APA Results Section

To introduce outcomes in your APA results section, report hypothesis tests. Then, mention if it was confirmed by presenting numbers. Make sure you specify such information:

  • Test statistic
  • Degree of freedom
  • Your p -value
  • Magnitude and direction.

Readers don’t have to guess what details you have omitted and should be able to draw conclusions based on real data. Besides, you should estimate effect sizes and provide information on confidence intervals.  There is one good way to organize your statistical results – moving from the most important to the least important. First, you should focus on the primary questions and then address secondary research questions until you cover subgroups. Follow this structure and provide information in stages. Your work formatting is one of the most important steps to success. So, follow American Psychological statistics to cope with numbers.

APA Results Section: How to Format

After having decided on the format of an APA results section, you should consider the  general requirements. The manual contains information about such details:

  • Font: Times New Roman.
  • Size: 12 pt. font size.
  • Spacing: Double-spaced.
  • Margins: 1 inch on all sides.

You might also want to integrate visual elements to enhance your research. For example, you can use figures, graphs, charts or tables to present numerical data. According to APA 7th edition, you should create an appendix and make respective references. Number figures and graphs in the order they appear in your APA results section.

APA Results Section: Writing Tips

Before writing the APA results section, make sure that the data is meaningful and can potentially contribute to further research. Academic writing is peculiar as the presentation of information should be carried out according to all rules and requirements. However, this is not the end. A few tips will help to write a worthwhile Results section. Consider the following:

  • Tense All outcomes of a study must be described in the past tense, because the objective is to describe the obtained results.
  • Brevity Any deviation from a topic is unacceptable, nor the provision of useless information is. Staying on point and being concise is the right decision.
  • Objectivity Present an unbiased synopsis of outcomes, as this will allow you to present information in a convenient and useful format. Readers will be grateful.

Preparing a paper takes a lot of effort and this is a good reason to take advantage of the advice from academic professionals.

Example of APA Results Section

Sometimes, all you need to get started is an APA results section example. A decent sample is easy to find here. Pay attention to the key points and keep them in mind as you write. Moreover, you can use this template to format this paper’s part  with APA requirements in mind.

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APA Results Section: Final Thoughts

The APA results section requires a special attention from students. Hypothesis and presentation of evidence are the basis for project development. Reporting your main findings in this section will help you prove your hypothesis and enhance your stuy.

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Delegate this tedious task to StudyCrumb and get skilled writers to write paper . Our experts have got a solid track record in delivering high-quality research papers in a timely manner and will be eager to help you, too.

Frequently Asked Questions About APA Results Section

1. how many words should an apa results section contain.

An APA results section is presented in a concise style, so the number of words is limited. It shouldn’t exceed 1000 words, which is 2-3 pages of double spaced text. Be specific and don’t deviate from your main point.

2. What’s the difference between APA Results section and APA Discussion section?

APA results section presents the outcomes of research. Here, you should focus on the results, statistical and other data as proof of your hypothesis. A Discussion section, in turn, involves an analysis of findings. In this part of your study, you should evaluate hypotheses and interpret your results.

3. When should I use tables or figures to present numbers in my APA results section?

APA results section includes not only textual information about your research outcomes, but also other ways of presenting information. Create tables, figures and archives to present your findings. Here are several rules you should keep in mind before using visual elements:

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  • Published: 14 March 2024

Study on the impact of digital transformation on the innovation potential based on evidence from Chinese listed companies

  • Xu Zhao 1 , 2 ,
  • Qi-an Chen 1 ,
  • Xiaoshu Yuan 2 ,
  • Yannan Yu 2 &
  • Haitao Zhang 3  

Scientific Reports volume  14 , Article number:  6183 ( 2024 ) Cite this article

Metrics details

  • Information technology
  • Socioeconomic scenarios
  • Sustainability

Digital transformation has emerged as a powerful force in reshaping the business landscape and enabling organizations to enhance their capabilities. One critical aspect of this change is how it impacts an enterprise’s innovation ability. To explore this question, we select data regarding China’s A-share listed enterprises from 2007 to 2021 as the research sample. We employ crawler technology to gather keywords related to “digital transformation” from annual reports, portraying detailed journeys of enterprises’ digital transformation. Through descriptive statistics and multiple covariance tests, a linear relationship is established between digital transformation and innovation ability. Benchmark regression is conducted and a robustness test is utilized to determine the robustness of the benchmark regression. The mechanism, heterogeneity, and moderating effects of this study are also tested. The results reveal that digital transformation makes a significant positive contribution to the innovation capability of enterprises. Meanwhile, among different types of enterprises, the impact of digital transformation on enterprise innovation capability shows heterogeneity. In terms of the impact mechanism, digital transformation can enhance the innovation output of enterprises by reducing the agency cost and improving the risk-taking level of enterprises, so as to further improve the innovation capability of enterprises. The research results of this paper provide essential theoretical support for the digital transformation of enterprises and the government’s formulation of enterprises’ digitalization strategies. More profoundly, it provides significant reference for how to further promote the digital transformation of Chinese enterprises.

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Introduction

With the integration of digital technology and the real economy, the digital economy is gradually replacing the traditional economy as a new driving force for global economic development. According to Xu and Zhang 1 , the explosive development and application of artificial intelligence, blockchain, cloud computing, and big data in recent years have enabled data to gradually become a new engine driving economic development in addition to land, labor, and capital factors. The report of the 19th National Congress of the Communist Party of China points out the need to “promote the deep integration of the internet, big data, artificial intelligence, and the real economy” and build a “digital China”. The report of the 20th National Congress of the Communist Party of China further clarifies the need to “accelerate the development of the digital economy and promote the deep integration of the digital economy and the real economy”. Since the outbreak of the bilateral trade conflict between China and the US in 2018, downward pressure on China’s economy has increased, coupled with a sharp rise in external input risks. Some export-oriented enterprises have also been greatly influenced by this economic and trade friction, and the impacts of this war are still lingering. In addition, the COVID-19 pandemic that broke out in 2020 has had a significant impact on the global economy that cannot be ignored. During this period, the application of the digital economy has enabled enterprises to gradually improve epidemic prevention and control, realize the resumption of production, and inject new impetus into the revival of China and even the global economy. Therefore, driven by the dual factors of existing policies and the above backdrop, enterprises actively try to turn crises into opportunities. Yang and Liu 2 point that, taking digital transformation as a form of breakthrough, enterprises seek opportunities to complete innovation and remodeling in their harsh living environments, and vigorously promote the epoch-making process of the digital economy and digital transformation’s reform and innovation.

According to the report “White Paper on the Development of China’s Digital Economy” 3 , the scale of China’s digital economy reached 45.5 trillion yuan in 2021, with a year-on-year nominal growth of 16.2%. The structure of the digital economy has also been further optimized. The human economy and society itself have officially entered a new era, with “data” as its core 4 . The development environment of enterprises has been significantly subverted. Moreover, the process and results of their digital transformation have become a hot issue in political studies. In essence, the digital economy can be divided into digital industrialization and industrial digitalization. One of the core components of industrial digitalization is the digital transformation of enterprises. Digital transformation reflects the transformation process involved in an enterprise’s movement from a traditional to a digital management mode. Therefore, the introduction of new digital technology has led to the macro and systematic evolution of the enterprise at the micro level of competition, business model, operation process, and even business ecology 5 , driving the enterprise’s resource allocation in the direction of intelligence, precision, and efficiency, while simultaneously empowering the innovation performance and value of enterprises 6 .

Meanwhile, as the integration of the real economy and digital technology deepen, the digital economy is gradually replacing the traditional economy as the new engine of global economic development. The integration of enterprises and digital technology reflects the process by which enterprises move from tradition to digitization—the digital transformation. Innovation has always been a key element throughout this transformation process. The concept of digital transformation was proposed by Negroponte 7 , who viewed enterprises’ digital transformation as the digital penetration of production factors, the digital restructuring of production relationships, and the digital innovation of business activities. Some scholars have suggested that enterprises’ digital transformation is a process of transforming production processes and organizational structures through digital technology 8 . Based on this, Frynas, Mol and Mellahi 5 provided an explanation of digital transformation, which states that the introduction of digital technology leads to a systematic evolution across the concepts of competition, business models, operational processes, and even business ecosystems, driving resource allocation in enterprises toward intelligent, precise, and efficient directions. Therefore, Wei, Gong and Liu 9 summarized enterprise digital transformation as the advanced transformation involved in utilizing a new generation of information technology to change and upgrade existing technological and production systems, in order to optimize production methods and improve management levels.

Scholars have mainly focused on three elements when studying the impact of digital transformation on enterprise innovation capabilities: technological innovation, institutional innovation, and management innovation. First, in terms of technological innovation, enterprises’ technological innovation is an explicit ability, and digitization is a process—not a single link—that can enhance innovation capabilities through new product development, process improvement, and technological application innovations 10 , 11 . Second, institutional innovation involves updating organizational structures, practices, and cultures, and formulating employee stock ownership plans to maximize innovation capabilities 12 , 13 , 14 . Finally, in terms of management innovation, personalized services are provided by big data analysis to increase customer viscosity, a flat organizational structure is constructed to strengthen inter-departmental collaboration, and enterprises’ digital division of labor promotes platform ecosystem integration 15 , 16 . Based on the literature above, it can be seen that research related to enterprise digital transformation and innovation capabilities mainly focuses on the manifestation of innovation capabilities and their positive effects on enterprises, but further exploration of their specific impact mechanisms is still needed. Therefore, this chapter identifies the impact mechanism of digital transformation on enterprise innovation capabilities as an important component of this field of research.

The main innovations of this research are as follows: First, previous studies on innovation capabilities have mostly used the number of patent applications as a proxy variable, which limits the innovative behavior of enterprises to a single strategic behavior 17 , 18 , 19 . This approach also limits the guidance and practical significance of related literature in regard to enhancing enterprises’ own innovation capabilities 20 . Therefore, this chapter applies an innovation variable group, taking cumulative acquired R&D patents as the core dependent variable and further dividing it into invention creation patents, utility model patents, and design patents. This balances the measurement of innovation capabilities while refining enterprise R&D results, providing verifiable evidence in regard to utilizing digital transformation to enhance enterprises’ own innovation capabilities. Second, to address the problem of reverse causal interference and time lag in previous research on the relationship between digital transformation and innovation capabilities, this chapter borrows from Fishman and Svensson 21 and handles the lag of the independent variable by using the industry average value of digital transformation as an instrumental variable for 2SLS estimation, effectively weakening the endogeneity problem. Third, based on the verification of the impact of digital transformation on enterprise innovation capability, this chapter discusses its impact mechanism from the perspectives of agency cost and risk-bearing level. Finally, in-depth research on the impact of digital transformation on enterprise innovation capabilities has practical guidance and inspirational significance for enterprise operators and policy makers. On the one hand, it helps enterprise operators better understand and promote their own digital transformation, especially by selecting appropriate risk-bearing levels according to their own development needs, thereby effectively enhancing their innovation capabilities. On the other hand, to maximize the enhancement of innovation capabilities through digital transformation, policymakers should not only focus on the enterprise’s own technological strength but also fully leverage the role of the capital market to guide and encourage enterprises to improve their innovation capabilities.

Theoretical analysis and hypothesis formulation

The impact of digital transformation on enterprises’ innovation capability.

The advent of the digital economy has led to the emergence of numerous new enterprises in the market. Digital transformation has become a necessary path for existing enterprises to seek breakthroughs. In order to achieve sustainable development, enterprises must constantly evolve and transform. Digital transformation can help optimize operational processes, enrich business models, and reshape organizational structures, thereby enabling enterprises to achieve self-innovation in multiple respects 22 . Therefore, an increasing number of enterprises are aiming to become “digital enterprises” in order to accelerate their transformation and unleash their innovative potential. Among them, the introduction of advanced digital technologies is one of the main features of digital transformation. An 22 believes that the essence of digital transformation lies in the utilization of digital technologies to solve complex and uncertain problems, thereby enhancing innovation capability and operational efficiency. Vial 10 point out that the application of digital technologies can help enterprises achieve improvements and optimization in various aspects. Based on the above research, this study defines digital transformation as the change and upgrade of corporate governance at the enterprise level due to the integration of digital technologies, with the goal of achieving the rational utilization of resources in daily operations.

Enterprise innovation is a continuous economic process. Schumpeter 23 first mentioned in his book, The Theory of Economic Development , that innovation needs to be improved in various respects, such as procurement models, production methods, organizational structures, and research and development. Therefore, the forms innovation can take are diverse, with the most significant being innovation output. Research and development activities are the foundation and core of enterprise innovation output, and digital transformation provides new methods and ideas for enterprise research and development activities. By analyzing and processing resources and data information through digital technology, enterprises can optimize the research and development process and match various target tasks with the best resources and talents 24 , thereby achieving optimal outputs at each stage of the research and development activities and improving their innovation capability. Most existing studies regard the number of patent applications or research and development investments as indicators of enterprise innovation capability 20 , 25 , 26 . However, different types of patents actually reflect different levels of enterprise innovation capability, and there are also significant risks in transforming research and development investment into innovation capability. This study enriches the measurement of innovation capability by classifying the output of innovation into three categories according to the types of patent applications in China.

According to resource-based theory, the integration and allocation of resources are important factors for the success of enterprise innovation, including internal and external factors. From an internal perspective, Loebbecke and Picot 27 believe that, with the deepening of digital transformation, the operational efficiency of enterprises will be greatly improved. Digital technology enables enterprises to improve resource utilization at lower costs, thereby enabling enterprises to achieve higher outputs under existing innovation resource conditions. Li, Dang and Zhao 28 point out that digital technology can more effectively utilize innovation resources and reduce repeated investments and resource waste in traditional enterprise operating structures. From an external perspective, enterprises can use digital technology to quickly capture target resources and other associated resources, assisting in establishing “weak ties” outside the organization 29 . Autio, Nambisan, Thomas and Wright 30 propose that, when enterprises interact with external stakeholders, they can use digital technology to build better management systems and better platforms for efficient communication. Enterprises can acquire more advanced external knowledge through digital technology to improve product innovation speed 31 .

It is worth mentioning that the innovation capability of enterprises is also influenced by many other factors. At the macro level, industrial policies 32 , fiscal and technological investment 33 , institutional environment 34 , and capital market reforms 35 can all bring about changes in innovation capability. At the micro level, innovation capability is often affected by factors such as the leverage levels of enterprises 36 , the characteristics of top management 37 , corporate culture 38 , and forms of corporate ownership 39 . Meanwhile, enterprise innovation capability is also influenced by many negative factors, such as weak internal control processes and high uncertainty risks 40 , 41 . According to the research results of Li and Wang 42 , as environmental uncertainty factors increase, the role of digital transformation in promoting innovation by enterprises will become greater. Based on the above analysis, this study posits that, the stronger the digital transformation is in enterprises, the stronger their innovation capability will be.

This paper thus proposes hypothesis H1: Digital transformation can enhance enterprise innovation capability.

The impact of digital transformation on enterprise innovation capability through agency costs

Traced back to its origins, the internal costs caused by the conflicts of interests between shareholders (principals) and management (agents), as well as the costs incurred in handling these differences and contradictions, are called agency costs. Foundational studies in this field suggest that there is often information asymmetry between shareholders and management teams 43 , and the consideration of moral hazards in management teams cannot be ignored 44 . The distribution of interests between shareholders and management teams is also part of corporate governance, and mishandling it can result in agency costs, including contract costs, management costs, and regulatory costs. Therefore, shareholders need to take certain measures to minimize agency costs and to supervise and control management teams to the maximum extent possible.

According to Jensen and Meckling 45 , the agency problem that exists between principal and agent is referred to as the first type of agency problem. In the process of corporate governance, it is not ideal for the principal to have complete supervision over the agent. Under conditions where the principal is at an informational disadvantage, the agent tends to act in their own interests, seeking a higher fixed wage income rather than taking on the income risk associated with innovation, uncertainty, and long-term considerations 46 . Furthermore, the second type of agency problem between large and small shareholders essentially involves the oppression of small shareholders by large shareholders, leading to high coordination costs among shareholders 47 . Given the significant risks involved in the innovation process and the need for long-term considerations, the high transaction costs of reaching a consensus between the two types of shareholders in the enterprise’s innovation decision-making process can lead to a tendency for the actual controllers of the enterprise to engage in asset stripping, thereby exacerbating the enterprise’s financing constraints 48 .

To effectively address and mitigate such agency problems, various measures have been introduced both domestically and internationally, such as the establishment of an external director system and the formation of specialized state-owned asset management institutions, but their effectiveness remains limited. Regarding the external director system, many scholars have raised doubts about whether independent directors can truly exercise regulatory functions 49 . However, with the increasing number of companies joining the wave of digital transformation, digital technology has effectively addressed issues such as information asymmetry, information transaction costs, and agency costs 50 . Principals have achieved convenient, fast, and low-cost information acquisition through digital networks. This indicates that digital technology plays a significant role in addressing information asymmetry issues. Digital technology has become a major factor in improving corporate management, leading to a downward trend in the agency costs of principals in this context. Furthermore, digital transformation is being fully integrated into the business ecosystem of enterprises, indicating that the data resources within enterprises are gradually being managed in a more refined and scientific manner 51 . Consequently, agents find it difficult to determine the direction of innovation based on personal desires, and digital technology will also promote further transparency in the enterprise management process. This approach can reduce the monitoring costs of principals in terms of management behavior and the agency costs between managers and general employees. Overall, the organizational transformation brought about by the digital transformation of enterprises has led to the effective and comprehensive penetration of digital technology in both business and functional management processes 52 .

At the same time, research findings related to agency costs and enterprises’ innovation capabilities suggest there are severe agency problems arising from conflicting interests within the enterprise, which affect the decision-making and efficiency of the enterprise’s technological innovation 53 . Peng and Luo 54 believe that, by reducing agency costs, shareholders can obtain more information, correct the information disadvantage in competition with senior managers, and effectively suppress the pressure of the management in regard to innovative activities, thus enhancing the enterprise’s innovation performance from a long-term perspective. Tang and Zuo 55 argue that the high agency costs in enterprises lead to enterprises’ reluctance to engage in long-term, high-risk research and development activities. Therefore, to achieve sustainable development, it is necessary to reduce the agency costs of enterprises through digital transformation, thereby enhancing enterprises’ innovation capabilities.

This paper thus proposes hypothesis H2a: The digital transformation of enterprises can reduce agency costs and thereby enhance the innovation capabilities of enterprises.

The impact of digital transformation on enterprise innovation capability through risk-bearing level

Risk-bearing level refers to the voluntary assumption of risks by enterprises, whether rational or irrational, which manifest in the tendency of enterprises to bear daily operational costs in exchange for substantial profits 56 . The specific level of risk-bearing mainly reflects the degree of operational and financial risk undertaken by enterprises. In the daily operations and investment activities of enterprise digital transformation, risk is a key factor determining whether internal economic activities can proceed smoothly. In terms of the innovation capabilities of an enterprise, the uncertainty of risk-bearing level can hinder the smooth conduct of innovation activities, thus imposing higher demands on an enterprise’s risk-bearing level for its own innovation activities.

According to resource-based theory, first, new technologies such as artificial intelligence, big data, and cloud computing are effectively utilized in the new era of digital transformation, enabling enterprises to break traditional constraints, flexibly integrate market dynamic information with their own operating conditions, continuously analyze and identify personalized consumer needs, and effectively enhance their sensitive responses to market fluctuations. New technologies also improve the efficiency of resource allocation in daily operations 57 , significantly reducing operational risks and increasing the risk-bearing level of enterprises. Second, data circulating within the enterprise are maximally developed through the application of digital technology 58 , further maximizing the effective and reliable financial data available for grasping the current development of enterprise operations, achieving the maximization of resource allocation, and enhancing the risk-bearing level of enterprises. Finally, because digital transformation is a procedural behavior, resource-rich enterprises can tolerate partial or temporary stagnation resulting from failure 59 , alleviating the impact of digital innovation uncertainty on enterprises 60 . In addition, digital transformation can mitigate the principal–agent problem and promote the learning of relevant knowledge by management. By improving the information transparency of enterprises, digital transformation enables stakeholders to better supervise managers and restrain their risk-avoidance behavior driven by personal interests. It mitigates the issues of adverse selection and moral hazards, thereby enhancing the risk-bearing level of enterprises 14 , 61 . Meanwhile, in the optimization and restructuring of internal and external environments, digital transformation requires managers to learn advanced technology theories and management skills 62 . Based on the theory of higher-order gradients, the cognitive level of managers influences their decision-making, and their management abilities continuously improve with the accumulation of theoretical knowledge. This enables managers to adapt to rapidly changing environments and dare to choose high-risk investment projects 63 , ultimately enhancing the risk-bearing level of enterprises.

The level of risk-bearing by enterprises affects the innovation capabilities of enterprises 64 . On the one hand, a high risk-bearing level helps enterprises to raise funds to a greater extent, providing financial support for innovative activities, enhancing the motivation and preventive mechanisms of cash holdings 65 , 66 , and making it easier for enterprises to fully realize the value of resources and invest them in long-term innovative projects such as research and development 67 . Second, the enhanced confidence of the management resulting from a high risk-bearing level will expand the range of choices available for the daily operations and project investments of enterprises, further driving the expansion of innovative activities within the enterprises 68 , 69 . On the other hand, the complexity of digital technology and the uncertainty of innovation increase the risk of transformation failure 50 . Enterprises with low risk-bearing levels urgently need stable investment and expansion, making it difficult to invest limited resources in complex innovation activities 59 , 60 . Based on the above studies, this paper suggests that digital transformation can enhance the innovation capabilities of enterprises by improving their risk-bearing levels.

This paper thus proposes hypothesis H2b: The digital transformation of enterprises can enhance their risk-bearing level and thereby improve the innovation capabilities of enterprises.

The moderating effects of corporate ownership nature, percentage of institutional investors, percentage of overseas business income, and industry concentration on the relationship between digital transformation and enterprise innovation capability

Research has found that the level of innovation capability resulting from enterprises’ digital transformation is influenced by the nature of corporate ownership 70 . However, most studies have focused on the comparison between state-owned enterprises and private enterprises, with little research on the differences between state-owned and non-state-owned enterprises. First, compared to non-state-owned enterprises, state-owned enterprises are more likely to obtain government resources and information support, breaking through funding constraints and technological barriers in the innovation and research process 49 . Second, state-owned enterprises have accumulated scientific research, talent, and technology, which can generate economies of scale in collaborative innovation with upstream and downstream companies in the industrial chain 71 , facilitating the evolution of the enterprise’s innovation ecology and the enhancement of its innovation capability. Finally, state-owned enterprises often exhibit a stronger sense of social responsibility; to better fulfill their responsibilities in ensuring employment and maintaining social stability, they are more likely to demonstrate a strong motivation for policy implementation and innovation breakthroughs 72 .

Based on the above analysis, hypothesis H3a is proposed: The effect of digital transformation on the innovation capability of state-owned enterprises is stronger than that on non-state-owned enterprises.

From the perspective of institutional investors, digital transformation is currently a popular topic in the investment field. Companies that choose digital transformation and disclose relevant information externally signal their active participation in the capital market, arousing investor interest and obtaining more business investments, thereby attracting more attention in the capital market. Jensen and Meckling 45 argue that the long-term ownership and high exit costs associated with institutional investors make them more concerned about innovative activities that can generate returns. Most institutional investors actively participate in the supervision, management, and governance of companies, minimizing the possibility of independent decision-making errors by the corporate management and motivating them to innovate 73 . Therefore, there is a clear positive correlation between the shareholding proportion of institutional investors and an enterprise’s innovation capability 74 . However, there is currently limited research on the moderating effect of institutional investors’ shareholding on the relationship between digital transformation and enterprise innovation capability.

This paper thus proposes hypothesis H3b: Institutional investors’ shareholding proportion has a positive moderating effect on the relationship between digital transformation and enterprises’ innovation capability.

According to dynamic capability theory, the transformation and upgrading of enterprise development strategies will bring about fundamental changes in various aspects of operation and management, directly affecting the formulation and implementation of international business strategies 75 . With the expansion of overseas business and the accompanying increased level of openness, the implementation of the new strategy of digital transformation will further strengthen the dynamic capabilities of enterprises, enhancing their adaptability and innovation capabilities. In order to better enter international markets, enterprises must fully leverage the competitive ability and improved innovation capabilities brought about by digital transformation. First, to expand to overseas markets, enterprises improve factor allocation through digital transformation, enhance production efficiency 76 , and continuously increase the spillover of innovative technology. Second, to further expand their level of openness, enterprises pay more attention to digital transformation and the application of digital technology, effectively reducing the costs and risks of overseas business operations 77 , with multidimensional forms of innovation across technology, operations, and management playing a critical role.

Based on the above analysis, hypothesis H3c is proposed: An enterprise’s level of openness positively moderates the relationship between an enterprise’s digital transformation and innovation capability.

As China’s high-tech enterprises continue to grow, digital transformation is actively leading companies toward further innovation. However, enterprise innovation significantly increases its operational risks. Enterprises do not naturally prefer innovation; innovation is determined by the level of industry competition. When industry concentration is low, enterprises with lower market shares attempt to seize a greater market share through innovation 78 , 79 , 80 , 81 . Conversely, when industry concentration is too high, market monopolization leads to decreased competition, making it difficult for enterprises to focus on their innovation capabilities 82 , 83 . In the long run, larger enterprises with higher industry concentration will dominate the market. In order to obtain higher profits, these enterprises will often manipulate product prices to gain profits, overlooking research and development, resulting in a decrease in the intensity of the enterprise’s research and development activities 84 and thereby inhibiting enterprise innovation.

Therefore, this paper proposes hypothesis H3d: Industry concentration negatively moderates the effect of digital transformation on the innovation capability of enterprises.

Research design

Data source.

This paper selects A-share listed enterprises from 2007 to 2021 as the initial sample. It thus covers both enterprise samples before digital transformation and samples that have not yet undergone digital transformation at the sample observation time point, so as to avoid the endogenous interference caused by selective errors as far as possible and ensure the reliability of the regression results. The financial data regarding the enterprise comes from the Wind database. To eliminate the interference of certain special observation samples on the empirical results, this article processed the data as follows: (1) Exclude financial industry samples to avoid interference, such as differences in accounting standards; (2) exclude companies with an abnormal listing status, such as ST and PT, and avoid interfering with the overall regression results due to any abnormal business operations of the companies themselves; (3) eliminate observation samples with large quantities of data missing; (4) the continuous variables in the data shall be shrunk at the level of 1% from the beginning to the end to avoid interference due to extreme outliers. Based on the cumulative total number of R&D patents obtained ( \({{\text{ln}}\_patentcul}_{it}\) ), the cumulative number of invention and creation patents obtained ( \({{\text{ln}}\_inventioncul}_{it}\) ), the cumulative number of utility model patents obtained ( \({{\text{ln}}\_utilitymodelcul}_{it}\) ), and the cumulative number of design patents obtained ( \({{\text{ln}}\_designcul}_{it}\) ), the final observations are 4292, 3987, 2821, and 1752, respectively. The software used in this study is Stata 17.0.

Variable settings

Dependent variables.

Innovation variable group. Previous studies have mostly used the number of enterprise patent applications or R&D investment as proxy variables for enterprise innovation capability. However, in reality, different types of patents portray varying degrees of an enterprise’s innovation capability, and there is a significant risk that R&D investment will not effectively transform into innovation capability. Accordingly, this article takes the cumulative total number of R&D patents obtained by the enterprise ( \({{\text{ln}}\_patentcul}_{it}\) ) as the proxy variable for the innovation ability of the enterprise, and classifies patents according to different types. The first is the cumulative number of invention and creation patents obtained ( \({{\text{ln}}\_inventioncul}_{it}\) ); the second is the cumulative number of utility model patents obtained ( \({{\text{ln}}\_utilitymodelcul}_{it}\) ); and the third is the cumulative number of design patents obtained ( \({{\text{ln}}\_designcul}_{it}\) ). This approach can both eliminate the interference brought about by design patents with low technology and preliminarily test the robustness level of the results.

Independent variables

The independent variable in this paper is the enterprises’ digital transformation. Both the business community and the academic community have discussed how to measure the strength of enterprises’ digital transformation. Qi and Xiao 15 believe that enterprises’ digital transformation takes “ABCD” (artificial intelligence, blockchain, cloud computing, big data) technology as the core infrastructure, and this level of digital transformation focuses on embedding digital technology into the daily operations of enterprises. Furthermore, in this way, enterprises aim to empower production technology, management, and sales through the application of digital technology. From a technical standpoint in regard to variable design, this article utilizes the Python web scraping functionality to compile annual reports of all A-share listed companies. The Java PDFbox library was employed to extract the content of these reports. Drawing on established research, the study measured the digital transformation in sampled enterprises by calculating the total frequency of five key terms: “artificial intelligence technology”, “blockchain technology”, “cloud computing technology”, “big data technology”, and “digital technology application” 4 , 85 . To mitigate issues such as heteroscedasticity, the study added one to the count of each term’s occurrence per year and then applied natural logarithm transformation.

Mediated transmission variables

The agency cost (ATO). This paper draws on the practices of Singh, Davidson and Suchard 86 , Xiao and Chen 87 , and Xu and Zhang 1 , and selects the total asset turnover (ATO) as the proxy variable to measure the enterprises agency cost. The higher the total asset turnover of ATO, the lower the enterprise’s agency cost.

Risk-bearing (Risk). Using the approach adopted by Yu, Li and Pan 88 and Song, Wen, Wang and Shen 63 , the volatility of corporate profits (Risk) is used to measure the risk-bearing level of enterprises. The higher the value of Risk, the higher the enterprise’s earnings volatility.

Control variables

To overcome the impact of missing variables as much as possible, this article refers to previous literature and includes multiple variables at the micro level of the enterprise. This includes five control variables: enterprise size (Size), asset structure (Lev), profitability (ROA), proportion of independent directors (Indep), equity concentration ratio (Top10), and cash flow situation (Cashflow) 89 .

Considering the way in which enterprises’ digital transformation may correspond with changes in internal and external factors, based on the literature review, this paper utilizes enterprise attributes (SOE), overseas business income proportion (Overinc), institutional investor shareholding ratio (INST), and industry concentration ratio (HHI) as moderating variables.

The specific variable definitions are detailed in Table 1 .

Model setting and empirical testing

Referring to existing research methods, this article establishes the following model:

In regression Eqs. ( 1 ), ( 2 ), ( 3 ), and ( 4 ), the dependent variables are the cumulative total number of R&D patents obtained by the enterprise, invention and creation patents obtained, utility model patents obtained, and design patents obtained. The core independent variable is the enterprise’s digital transformation ( \({a}_{1}{{\text{ln}}\_digi2}_{i,t-1}\) ); the control variable consists of relevant financial indicators and operational indicators of enterprise i in year t; and \({\in }_{it}\) is a random error term. This paper carries out the following processing: First, considering that the impact of digital transformation on enterprise innovation capability may have a time lag and in order to avoid endogenous interference caused by potential reverse causality, this paper lags the core dependent variable for a period of time. Second, to absorb related fixed effects and avoid the interference of omitted variables, this article adopts both individual and time fixed effects, while considering the possibility of certain industry characteristics changing with different industries. This study also controls industry fixed effects for testing.

Empirical results and analysis

Descriptive statistical analysis.

Table 2 presents the descriptive statistics for the main variables in this paper. The cumulative total number of R&D patents acquired by firms ( \({{\text{ln}}\_patentcul}_{it}\) ) is the main independent variable in this paper, reflecting the innovation capabilities of enterprises. To further compare the impact of digital transformation on the different types of patents in this study, the cumulative total number of R&D patents acquired is also broken down into the cumulative total number of invention patents acquired ( \({{\text{ln}}\_inventioncul}_{it}\) ), utility model patents ( \({{\text{ln}}\_utilitymodelcul}_{it}\) ), and the cumulative number of designs acquired ( \({{\text{ln}}\_designcul}_{it}\) ). The sample mean and standard deviation of the cumulative total number of R&D patents acquired ( \({{\text{ln}}\_patentcul}_{it}\) ) have a sample mean and standard deviation of 4.8559 and 1.3222, and minimum and maximum values of 0.6931 and 10.6085, respectively, indicating that the firms in the study sample of this article generally have a certain number of patents, but the patent data have a large dispersion (i.e., reflecting the differences in R&D performance among firms. The companies have patents for inventions ( \({{\text{ln}}\_inventioncul}_{it}\) ) and patents for utility models ( \({{\text{ln}}\_utilitymodelcul}_{it}\) ). The utility models ( \({{\text{ln}}\_designcul}_{it}\) ) have averages of 3.2211, 3.9675, and 2.5861, respectively, indicating that patents acquired by A-share listed companies consist mainly of patents for inventions and utility models, while patents for industrial designs and with low technology are relatively rare.

Multicollinearity test

To prevent multicollinearity between the main independent variables and the control variables from affecting the accuracy of the regression results, this paper excludes multicollinearity using a variance inflation factor (VIF) test.

Table 3 shows the VIF values between the main independent variables, as well as the control variables. All values are below the threshold of five that detects the presence of multicollinearity; this test thus rules out relevant interference.

Results of the benchmark regression

Columns (1) to (4) of Table 4 show the impact of enterprises’ digital transformation on the output of the above four types of patents, where the independent variables are the total number of R&D patents acquired, the total number of patents for inventions acquired, the total number of patents for utility models acquired, and the total number of patents for industrial designs acquired, respectively. Table 4 shows the results of the regression of Models (1), (2), (3), and (4) in regard to the effect of digital transformation on innovation capabilities. Columns (1) to (4) show that the estimated coefficients of the main independent variable of digital transformation are significantly positive (all at the 1% significance level) after controlling for other variables, indicating that enterprises’ digital transformation is positively correlated with the total number of patents acquired in each category (i.e., the higher an enterprise’s digital transformation, the higher its innovation potential). This result confirms hypothesis H1.

Second, the estimated coefficients of the independent variables represent the influence elasticity, reflecting in turn the degree of improvement in the cumulative total number of patents acquired by firms when digital transformation increases by 1%, since the main independent variables and the dependent variables in this study are treated with natural logarithms. Taking the cumulative total number of R&D patents acquired in column (1) as an example, the estimated coefficient of the main independent variable is 0.1161%, indicating that the cumulative total number of R&D patents acquired increases by 0.1161% for every 1% increase in digital transformation. This degree of influence is relatively significant considering that the number of R&D patents in this paper is a cumulative variable. Comparing the impact of the different types of patents, the cumulative number of design patents obtained has the largest impact, followed by the cumulative total number of R&D patents obtained, the cumulative number of invention patents obtained, and the cumulative number of utility model patents obtained. Invention patents tend to be less complex to develop and are therefore the easiest to modernize. R&D patents and utility model patents are more technically complex and often require a long technical accumulation process and repeated testing and review, so their impact is relatively slow. Nevertheless, the results of this paper show that the impact of digital transformation on the improvement of business capabilities is still highly significant. Additionally, at the R&D level, the economic impact of digital transformation on business innovation is present.

There are many factors that affect enterprise innovation capability; among the control variables considered in this paper, the effect of firm size on innovativeness is most evident, as shown in Table 4 . The larger a firm is, the more R&D resource reserves and far-sighted strategic vision it has, and it can thus identify the direction of potential technology development in the industry as early as possible, organize researchers, and invest R&D funds in research and development. Small enterprises, on the other hand, have relatively weak human and material resources and strategic planning capacity, and are therefore, all other things being equal, unable to accumulate R&D results. In addition, sufficient cash flow and low debt levels can help to increase the innovation capability of enterprises.

Robustness tests

In this paper, several methods are used to test the robustness of the baseline model. First, controlling for fixed effects; second, substituting independent variables; third, substituting the main independent variables; and fourth, using the instrumental variables approach to address possible endogeneity problems and revalidate the model in conjunction with the calibration of the main independent variables.

Controlling fixed effects

Individual differences between different companies are more obvious than other types of differences; for example, some companies have a strong innovation atmosphere and a good R&D environment, while others tend to acquire R&D patents through mergers and acquisitions and other external channels. These factors not only affect the amount of R&D patents held by companies, but are also difficult to fully quantify. At the same time, we control for time fixed effects, as different years can be affected by factors such as policies that change over time and are difficult to observe. Similarly, we also control for industry fixed effects, to account for differences in the degree of competition for R&D across industries, industry incentives, and other factors that are independent of individuals but are industry-specific. Moreover, as described in the model building section, the main independent variables in this paper are lagged by one period because of the lag in patent acquisition. The results in Table 4 show that the previous results of the study remain robust even after accounting for changes in these factors.

Replacing the window period

Considering the 2008 financial crisis and the 2018 US–China trade war, the three years before and after these two external environments appeared (i.e., 2007–2009 and 2017–2019) are excluded from the full sample and the benchmark regression is re-run. As shown in Tables 5 and 6 , the regression results of each model after excluding some years are basically consistent with the benchmark regression; thus, the benchmark model can be considered robust.

Changing the measurement method of dependent variables

Since patent applications take a long time and have a certain time lag, this paper, following Chen, He and Zhang 90 , does not use the number of patents obtained in the current period as the independent variable, but rather the cumulative number of patents obtained in the base part of the regression, which allows us to better represent the impact of digital transformation on innovativeness over time. At the same time, by treating enterprises’ digital transformation with a lag of one period, we can to some extent eliminate the endogenous disturbances caused by possible reverse causality and reflect the time lag. However, the aggregate of acquired patents is not necessarily the result of an enterprise’s own R&D. There may be a small portion of patents acquired through mergers and acquisitions of other companies, and this portion of innovation capability may be independent of the topic of the digital transformation studied in this paper. To avoid this possible source of confounding, this paper, referring to Hall and Harhoff 18 , replaces the original dependent variables with the cumulative number of patents filed by firms and reruns the test. The results presented in Table 7 show that the estimated coefficients of the main independent variables in columns (1) to (4) are significantly positive (in descending order, at the 1%, 1%, 1% and 5% significance levels), indicating a significant upward effect of digital transformation on the cumulative number of R&D patents filed (i.e., the greater the enterprises’ digital transformation, the greater) their innovation potential. These findings confirm the relative robustness of the control results.

Changing the measurement method of the core independent variables

In this paper, following Xiao, Sun, Yuan and Sun 91 , Huang, Xie, Meng and Zhang 14 , and Wu, Chang and Ren 4 , different terms are used to measure the enterprises’ digital transformation, excluding the term “application of digital technology” at the application level and keeping only “artificial intelligence technology” at the basic digital technology level. “Blockchain technology”, “cloud computing technology,” and “big data technology” are retained at the basic digital technology level, and the natural logarithm of their commonality is used as a surrogate variable to test robustness. According to the regression results presented in Table 8 , the estimated coefficients of the main independent variables in each column of the results remain significantly positive (all at the 1% significance level) when different measures of digital transformation are used. Furthermore, comparing the elasticity coefficients of the impact of digital transformation on the total volume of patents acquired, design patents continue to have the largest impact, followed by aggregate R&D patents, invention patents and utility model patents. These findings are consistent with the benchmark regression results.

Endogeneity test

Given the possible endogeneity of the relationship between digital transformation and enterprises’ innovation capability, the digital transformation variable in the base section of the regression is shifted by one period to avoid a violation of endogeneity due to possible reverse causality. At the same time, this paper continues to search for instrumental variables, building on a related study by Fishman and Svensson 21 in which the industry average of digital transformation is used as an instrumental variable for the least squares estimation (2SLS), in order to observe the net effect of digital transformation on innovation capability.

As shown in Table 9 , the estimated coefficients of the main independent variables for digital transformation in the results in columns (1), (2), and (3) are 0.0965, 0.0861, and 0.1329, respectively, and all results are significantly positive. This indicates that, after excluding possible endogenous disturbances associated with bidirectional causality, the enterprises’ digital transformation still has a significant upward effect on innovativeness. In the regression results in column (4), the estimated coefficient of the main independent variable—digital transformation—is still positive but is not significant, suggesting that possible bilateral causality overestimates the impact of digital transformation on firms’ design patents. This also suggests that, for transforming firms represented by listed A-shares, digital transformation technology still mainly affects the output of patents with technological content (i.e. it mainly improves innovation capability when there is a higher level of technological content).

In summary, this paper considers different sources of potential confounding factors in the research process and applies different approaches to target processing. The results confirm the robustness of the empirical findings of this paper, as well as the reliability of the theoretical analysis. Building on the above robust results, this paper conducts a further analysis on this basis.

Further research

Mechanisms analysis.

In the theoretical mechanism analysis section, this paper hypothesizes that digital transformation can improve enterprises’ innovation capability in two ways: by reducing agency costs and by reducing enterprises’ risk-bearing levels. This study uses the stepwise regression method put forth by Wen and Ye 92 to test these two mechanisms. Following the work of Singh, Davidson and Suchard 86 , Xiao and Chen 87 , and Xu and Zhang 1 , total asset turnover (ATO) is selected as a proxy variable to measure the value of the trustworthy representation of enterprises. The construction of corporate risk (Risk) follows the practice of Yu, Li and Pan 88 and Song, Wen, Wang and Shen 63 , using the volatility of corporate profits to represent risk.

The results of the regression test for the agency cost mechanism are presented in column (1) and column (2) of Table 10 . The estimated coefficient of the main independent variable in column (1) is 0.0327, which is a significant positive value at the 1% significance level, indicating that there is a significant positive relationship between digital transformation and total corporate asset turnover. Digital transformation can increase the total asset turnover of the firm (i.e. reduce the enterprises’ agency costs). The estimated ATO coefficient of total asset turnover in column (2) is 0.2587 and is significantly positive at the 1% significance level, indicating that, the lower the agency cost, the higher the level of innovativeness. The regression results confirm the agency cost mediation test. That is, the higher the enterprise’s digital transformation, the higher the turnover rate and the lower the agency costs, which ultimately increases the enterprises innovativeness, confirming hypothesis H2a.

The regression results used to test the risk level mechanism are shown in column (3) and column (4) of Table 10 . The estimated coefficient of the main independent variable in column (3) is 2.6374, which is significantly positive at the 1% significance level, indicating that digital transformation significantly increases the risk-bearing levels of enterprises. The estimated coefficient of the variable risk in column (4) is 0.0057, which is a significant positive value at the 1% significance level. Hence, digital transformation can increase the innovation capability of enterprises by enhancing their risk-bearing levels. That is, the greater the digital transformation, the higher the volatility of an enterprise’s profitability, thus increasing the enterprise’s risk-bearing level and ultimately enhancing its innovation capability. The risk-bearing level mechanism developed in hypothesis H2b is thus confirmed.

Heterogeneity analysis

The relationship between digital transformation and an enterprise’s innovation capability may vary depending on the type of enterprise. Therefore, this paper compares the regression results of the SME and GEM sample with the full benchmark sample in order to better clarify the characteristics of the role that digital transformation plays, and to provide an evidence base for further optimizing the digital management of companies. Table 11 shows the regressions for all A-share companies in columns (1) and (2), the regression results for the GEM sample in columns (3) and (4), and the regression results for the SME version in columns (5) and (6). The following conclusions are drawn from the comparative analysis.

First, digital transformation has a significant impact on the innovativeness of listed firms in different sectors, with the estimated coefficients of the main independent variables in the respective columns of the results all being significantly positive. The impact on the total number of R&D patents and invention patents is even more significant, demonstrating the universality of the effect of digital transformation.

Second, due to the fact that the main independent variables in this study are transformed into natural logarithms, the estimated coefficients of the independent variables are influence elasticities, which in turn allow us to compare the strength of the influence of each group. If we take patents on inventions as an observational benchmark, the influence of digital transformation on the innovation capability of GEM firms is higher than that of SMEs (0.1170% versus 0.0798%), mainly because GEM firms generally have clear technological advantages, both in terms of their stock of technological resources and their experience in R&D management in general, which are better than those of SMEs. Therefore, controlling for other influencing factors, the same digital transformation has a stronger effect on the innovation capability of GEM firms. Second, the impact of digital transformation on innovation capability is lower in both the GEM and SME board samples than in the overall sample, implying that the greater benefit of digital transformation still accrues to the larger companies in the main board.

Analysis of the moderation effects

The empirical results obtained in the previous section confirm the impact of digital transformation on enterprises’ innovation capabilities, but the specific mechanism at work here requires further analysis. According to the theoretical analysis in the previous section, the degree of influence of digital transformation on enterprises’ innovativeness may vary according to the change in certain internal and external factors (e.g. internal factors such as enterprise characteristics and degree of openness and external factors such as institutional investors’ participation ratio in enterprise capital and industry concentration). Identifying the overlapping influence of these factors is useful in terms of optimizing enterprise management and maximizing enterprises’ innovativeness. The results of the regression test of the moderating effect are shown in Table 12 . The estimated coefficients of the main independent variables and the cross-sectional multipliers in column (1) are significantly positive at the 1% significance level, indicating that, when controlling for other influencing factors, digital transformation can induce SOEs to perform more R&D than private enterprises at the same intensity. The impact of digital transformation on the innovation capability of SOEs is stronger than that of private enterprises, which supports hypothesis H3a.

Column (2) of Table 12 shows that the estimated coefficients of the core explanatory variables and the cross-multiplier terms are significantly positive at the 1% significance level. This suggests that institutional investor shareholding positively moderates the relationship between digital transformation intensity and enterprises’ innovation capability (i.e., the higher the institutional investor shareholding, the stronger the enterprises’ innovation capability). When the shareholding ratio of institutional investors is higher, not only will the institution itself pay more attention to the daily production and operation of the enterprise, but these circumstances will also help the enterprise gain the attention of other institutions and retail investors through publishing research reports and performance forecasts. This external attention creates moderate pressure on the enterprises to further improve their performance by strengthening their management, thus reinforcing the role of digital transformation in enhancing innovation capacity, proving hypothesis H3b.

Column (3) of Table 12 shows that the estimated coefficients of the core explanatory variables and the cross-multiplier terms are all significantly positive. In this paper, the openness level of enterprises is measured by the proportion of enterprises’ overseas business revenue. A higher level of overseas business revenue implies a higher degree of enterprises’ openness to the outside world, indicating that the degree of openness to the outside world positively moderates the relationship between the intensity of digital transformation and enterprises’ innovation ability. Through internationalization, enterprises can produce a reverse technology spillover effect on the one hand and borrow advanced management concepts from abroad on the other hand, which in turn can strengthen the effect of digital transformation on the enhancement of the enterprise’s innovation ability. Hypothesis H3c is thus verified.

As shown in column (4) of Table 12 , the estimated coefficients of the core explanatory variables and the cross-multiplier terms are opposing (i.e., the higher the industry concentration, the lower the effect of digital transformation on the enhancement of enterprises’ innovation capabilities, all other influencing factors being consistent). This paper uses the Herfindahl Index (HHI) to measure industry concentration. The larger the HHI value, the more monopolized the industry in which the enterprise is located. For industries with strong monopoly power, the lack of competition will lead to insufficient incentives for enterprises to reform and innovate, and thus the effect of digital transformation is greatly reduced. Therefore, the conclusions of this paper are consistent with hypothesis H3d, which states that reducing the monopoly power of the industry and improving the level of competition will help to fully absorb the effect of digital transformation on the enhancement of the innovation ability of enterprises.

Conclusion and discussion

Research conclusion.

This paper investigates the mechanism and impact of digital transformation on enterprises’ innovation capability using the data of Chinese listed A-share enterprises from 2007 to 2021. The results of the study show that, first, digital transformation has a positive effect on enterprises’ innovation capability. The results of the data regression show that more intensive digital transformation can motivate an enterprise to achieve greater innovation output. In this paper, benchmarking regressions on innovation capability using groups of variables enriches the variable setting of existing studies, as well as the theoretical mechanisms. At the same time, digital transformation can help companies to break through, innovate, and re-innovate in today’s digital economy, thereby improving their core competitiveness and strengthening their position in the digital economy market. Second, agency costs and the extent of risk-bearing level mediate the relationship between digital transformation and enterprises’ innovation capability.

Theoretical significance

Most scholars use innovation application 93 , 94 and R&D input ratio 95 as proxy variables to measure corporate innovation, but digital transformation is a dynamic process that creates a complex environment. In this paper, we expand existing research to encompass cumulative innovation application, which reflects dynamic capability theory. We constructed a form of digital transformation using text mining technology to replace the dummy variable used in other papers. The conclusions drawn in this study creatively complement and extend the mechanisms of the impact of digital transformation on enterprises’ innovative capabilities. On the one hand, digital transformation lowers the costs of an enterprise’s headmaster agent while accounting for the fact that lowering the costs of an enterprise’s headmaster agent can increase its innovativeness. On the other hand, this paper argues that digital transformation can effectively reduce the volatility of corporate profits in order to reduce risk-bearing level; doing so provides more opportunities to help companies improve their innovation capabilities. By linking the influence mechanisms of digital transformation and corporate innovation capability to agent costs and risk-bearing levels, respectively, this paper creatively proposes and demonstrates the mediating role of agency costs and risk-bearing level. Third, the impact of digital transformation on the innovation capability of enterprises of different natures and sectors is heterogeneous, which has strong theoretical guiding significance. In particular, digital transformation has a stronger impact on the innovation potential of SOEs than on that of private enterprises. Additionally, an increase in the share of shares owned by institutional investors, a greater opening of companies to the outside world, and a reduction in industry concentration can all increase the impact of digital transformation on the innovation capabilities of enterprises. This analysis shows that, digital transformation has a general impact on the innovativeness of listed companies across all sectors. Further analysis shows that digital transformation has a stronger impact on the innovative capabilities of large companies than on that of small and medium-sized companies. Many advantages of large listed companies, such as a sufficient capital chain and the ability to manage resources, mean that large listed companies are able to benefit more from digital transformation.

Practical implications

Based on the above findings, this chapter makes the following recommendations.

At the enterprise level, corporate managers should appropriately lead their organizations in making and implementing sustained and beneficial decisions for long-term digital transformation, with a focus on the impact of big data on the enterprise’s digital infrastructure and development. First, the use of digital technologies by enterprises can change the basic form and function of their products, increase product acceptance, and achieve the basic goals of product innovation. Second, digitalization can change the internal governance structure of enterprises, focusing on the strategic changes of enterprises at different stages of transformation. Enterprises should make full use of integrated and shared digital resources to improve the efficiency of traditional configuration, optimize the internal management structure, and explore effective ways to reduce the cost of the entrusted agency of the enterprise and improve the enterprise’s risk-bearing, so as to realize the sustainable and maximum release of the value of the enterprise’s digital transformation. Finally, enterprises should take digital transformation as a way to make breakthroughs in innovation, accelerate the flow of resources between internal and external enterprises, and promote the realization of internal and external collaborative innovation. Enterprises must be brave enough to bear the various risks and challenges on the road of transformation, so as to promote the realization of high-quality development.

At the government level, the government should actively create a digital environment and provide policy support. Meanwhile, based on the discussion in the heterogeneity section of this paper, the digital transformation of enterprises in different industries has different degrees of impact on innovation capability. The government should introduce special policies for enterprises in different industries and regions. In this way, the government can assist enterprises in establishing public digital platforms, create a better open environment in which enterprises can implement digitalization, and lower their digitalization thresholds. Through the flexible application of financial subsidies, tax exemptions, and other targeted policies, the government can support some enterprises to carry out digital transformation and reduce the monopoly present in the industry. Relevant government departments should also improve the supporting regulatory and governance systems for digital transformation, in order to further standardize the development of the digital economy.

Research limitations

As the research samples are from Chinese enterprises among different types of industries, the findings of this study cannot necessarily be generalized to other countries. Additionally, our understanding of the mechanism at work in regard to digital transformation and corporate innovation still needs to be enriched in the future.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Xu, Z. Y. & Zhang, L. S. How digital economy development affects firms’ agency costs—Evidence from Chinese manufacturing firms. Secur. Mark. Hrld. 2 , 25–35 (2022).

Google Scholar  

Yang, D. M. & Liu, Y. W. Why can Internet plus increase performance. Chin. Ind. Econ. 5 , 80–98 (2018).

China Academy of Information and Communications Technology. White Paper on the Development of China’s Digital Economy. http://www.caict.ac.cn/kxyj/qwfb/bps/202104/t20210423_374626.htm (2021).

Wu, F., Chang, X. & Ren, X. Y. Government-driven innovation: Fiscal science and technology expenditures and firms’ digital transformation. Pub. Financ. Res. 1 , 102–115 (2021).

Frynas, J. G., Mol, M. J. & Mellahi, K. Management innovation made in China, Haier’s rendanheyi. Calif. Manag. Rev. 61 (1), 71–93 (2018).

Article   Google Scholar  

Hanelt, A., Bohnsack, R., Marz, D. & Marante, C. A. A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. J. Manag. Stud. 58 (5), 1159–1197 (2020).

Negroponte, N. Being Digital (ed. Knopf, A.) (New York, 1995).

Hess, T., Matt, C., Benlian, A. & Wiesböck, F. Options for formulating a digital transformation strategy. MIS Q. Exec. 15 (2), 123–139 (2016).

Wei, Y. Y., Gong, X. Y. & Liu, C. Does digital transformation improve firms’ export resilience. J. Int. Trade 10 , 56–72 (2022).

Vial, G. Understanding digital transformation: A review and a research agenda. J. Strategic Inf. Syst. 28 (2), 118–144 (2019).

Nambisan, S. Information technology and product/service innovation: A brief assessment and some suggestions for future research. J. Assoc. Inf. Syst. 14 (4), 215–226 (2013).

Hinings, B., Gegenhuber, T. & Greenwood, R. Digital innovation and transformation, an institutional perspective. Inf. Org. 28 (1), 52–61 (2018).

Zhang, W. D., Luo, G. M. & Tao, Y. Y. Research on shareholder’s wealth effect of employee stock option plan in listed companies: Evidence from China’s stock market. J. Beijing Tech. Bus. Univ. Soc. Sci. 31 (2), 61–70 (2016).

Huang, D. Y., Xie, H. B., Meng, X. Y. & Zhang, Q. Y. Digital transformation and enterprise value—Empirical evidence based on text analysis methods. Economy 12 , 41–51 (2021).

Qi, Y. D. & Xiao, X. Transformation of enterprise management in the era of digital economy. Manag. World 6 , 135–152 (2020).

Li, C. L., Gao, L. M. & An, G. The nature and evolution of digital platform organization: Based on perspective of division of labor. Ind. Econ. Rev. 12 (06), 134–147 (2021).

CAS   Google Scholar  

Dosi, G., Marengo, L. & Pasquali, C. How much should society fuel the greed of innovators? On the relations between appropriability, opportunities and rates of innovation. Res. Pol. 35 (8), 1110–1121 (2006).

Hall, B. H. & Harhoff, D. Recent research on the economics of patents. Annu. Rev. Econ. 4 (1), 541–565 (2012).

Tong, T. W., He, W., He, Z. & Lu, J. Patent regime shift and firm innovation: evidence from the second amendment to China’s patent law. Acad. Manag. Proc. 2014 (1), 14174 (2014).

Tan, Y. X., Tian, X., Zhang, X. D. & Zhao, H. L. Privatization and innovation: Evidence from a quasi-natural experiment in China. Kelley Sch. Bus. Res. Paper https://doi.org/10.2139/ssrn.2433824 (2014).

Fishman, R. & Svensson, J. Are corruption and taxation really harmful to growth? Firm level evidence. J. Dev. Econ. 83 (1), 63–75 (2007).

An, X. P. Reconstruction: Logic of Digital Transformation (Publishing House of Electronics Industry, 2019).

Schumpter, J. The Theory of Economic Development (Havard University, 1912).

Acemoglu, D. Labor and capital augmenting technical change. J. Eur. Econ. Assoc. 1 (1), 1–37 (2003).

Article   MathSciNet   Google Scholar  

Fang, V. W., Tian, X. & Tice, S. Does stock liquidity enhance or impede firm innovation?. J. Financ. 69 (5), 2085–2125 (2014).

Tian, X. & Wang, T. Y. Tolerance for failure and corporate innovation. Rev. Financ. Stud. 27 (1), 211–255 (2011).

Loebbecke, C. & Picot, A. Reflections on societal and business model transformation arising from digitization and big data analytics. J. Strategic Inf. Syst. 24 , 149–157 (2015).

Li, X. S., Dang, L. & Zhao, C. Y. Digital transformation, global innovation network and innovation performance. Chin. Ind. Econ. 10 , 43–61 (2022).

Dong, J. Q. & Yang, C. H. Information technology and organizational learning in knowledge alliances and networks: Evidence from US pharmaceutical industry. Inf. Manag. 52 (1), 111–122 (2015).

Autio, E., Nambisan, S., Thomas, L. D. W. & Wright, M. Digital affordances, spatial affordances, and the genesis of entrepreneurial ecosystems. Strategic Entrep. J. 12 (1), 72–95 (2018).

Hair, J., Hult, G. T. M., Ringle, C. & Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (Sage Publications, 2016).

Li, W. J. & Zheng, M. N. Is it substantive innovation or strategic innovation?—Impact of macroeconomic policies on micro-enterprises’ innovation. Econ. Res. J. 4 , 60–73 (2016).

Miao, W. L., He, D. X. & Zhou, C. Innovation heterogeneity and the effect of government technological innovation expenditure. Econ. Res. J. 1 , 85–99 (2019).

Long, X. N., Yi, W. & Lin, Z. F. How valuable is intellectual property right protection?: Empirical evidence from patent data of China listed companies. J. Financ. Res. 8 , 120–136 (2018).

Quan, X. F. & Yin, H. Y. Chinese short selling mechanism and corporate innovation: A natural experiment from Chinese margin trading program. Manag. World 1 , 128–144 (2017).

Wang, Y. Z., Luo, N. S. & Liu, W. B. What leverage is beneficial to firm innovation. Chin. Ind. Econ. 3 , 138–155 (2019).

He, Y., Yu, W. L., Dai, Y. C. & Wang, Y. Y. Career experience of executives and enterprise innovation. Manag. World 11 , 174–192 (2019).

Zhao, Z. L. & Lin, J. H. Marine culture and innovation: An empirical study based on three merchant groups on the southeast coast. Econ. Res. J. 54 (2), 68–83 (2019).

Liu, H. W., Zheng, S. L. & Wang, Y. F. Ownership types, technological innovation, and enterprise performance. Chin. Soft. Sci. 3 , 28–40 (2015).

Wang, Y. N. & Dai, W. T. Does internal control inhibit or promote corporate innovation? The logic of China. J. Audit. Econ. 6 , 19–32 (2019).

Li, Y. H. Corporate governance and heterogeneity R&D innovation: The empirical research based on listed companies from strategic emerging industries in ChiNext. Secur. Mark. Hrld. 12 , 26–31 (2014).

Li, S. & Wang, Y. H. Enterprise digital transformation and enterprise innovation—Empirical evidence from electronic manufacturing industry. J. Ind. Tech. Econ. 08 , 19–20 (2022).

Barnea, A., Haugen, R. A. & Senbet, L. W. A. Rationale for debt maturity structure and call provision in the agency theoretical framework. J. Financ. 35 (5), 1223–1234 (1980).

Holmstrom, B. Moral hazard and observability. Bell J. Econ. 10 , 74–91 (1979).

Jensen, M. C. & Meckling, W. H. Theory of the firm: Managerial behavior, agency costs and ownership structure. J. Financ. Econ. 3 (4), 305–360 (1976).

Wu, Y. B. The dual efficiency losses in Chinese state-owned enterprises. Econ. Res. J. 3 , 15–27 (2012).

La Porta, R., Lopez-de-Silanes, F. & Shleifer, A. Corporate ownership around the world. J. Financ. 54 , 471–517 (1999).

Wang, H. D., Wu, H. Y. & Yue, H. Type II agency problem and firm R&D investment—An empirical analysis on listed manufacturing companies in China. J. Ind. Tech. Econ. 6 , 45–53 (2020).

Ye, K. T., Zhu, J. G., Lu, Z. F. & Zhang, R. The independence of independent directors: Evidence from board voting behavior. Econ. Res. J. 1 , 126–139 (2011).

Bharadwaj, A., Sawy, O. A. E., Pavlou, P. A. & Venkatraman, N. Digital business strategy: Toward a next generation of insights. MIS Q. 37 (2), 471–482 (2013).

Liu, F. How digital transformation improve manufacturing’s productivity: Based on three influencing mechanisms of digital transformation. Financ. Econ. 10 , 93–107 (2020).

Matt, C., Hess, T. & Benlian, A. Digital transformation strategies. Bus. Inf. Syst. Eng. 57 (5), 339–343 (2015).

Song, X. B. & Liu, X. Analysis of the effect of controlling shareholders agency on technology innovation investment. J. Manag. Sci. 1 , 59–63 (2007).

Peng, Z. Y. & Luo, G. Q. Shareholder network improving innovation performance: The mediating effect of two types of agency costs. J. Bus. Econ. 5 , 28–45 (2022).

Tang, Y. J. & Zuo, J. J. Ownership nature, large shareholder governance and corporate innovation. J. Financ. Res. 6 , 177–192 (2014).

Boubakri, N., Cosset, J. C. & Saffar, W. The role of state and foreign owners in corporate risk taking, evidence from privatization. J. Financ. Econ. 108 (3), 641–658 (2013).

Wei, Z. L., Xie, P. K. & Zhang, L. Q. Research on the influence of digital innovation characteristics on market performance from the perspective of innovation diffusion. Sci. Manag. 40 (2), 1–9 (2020).

Teece, D. J. Explicating dynamic capabilities: The nature and micro foundations of (sustainable)enterprise performance. Strateg. Manag. J. 28 (13), 1319–1350 (2007).

Garcia-Granero, A., Llopis, Ó., FernÁndez-Mesa, A. & Alegre, J. Unravelling the link between managerial risk-taking and innovation: The mediating role of a risk-taking climate. J. Bus. Res. 68 (5), 1094–1104 (2015).

Chanias, S., Myers, M. D. & Hess, T. Digital transformation strategy making in pre-digital organizations: The case of a financial services provider. J. Strategic Inf. Syst. 28 (1), 17–33 (2019).

Guo, J. T. & Yao, J. C. Digital economy and corporate risk taking: The moderating effects of managerial discretion. J. Hohai Univ. (Philos. Soc. Sci.) 1 , 83–91 (2022).

Zhang, S. S., Hu, H. G., Sun, L. & Xia, M. L. Supply chain digitization and supply chain security and stability—A quasi-natural experiment. Chin. Soft. Sci. 12 , 21–30+40 (2021).

Song, J. B., Wen, W., Wang, D. H. & Shen, W. Managerial power, internal and external motoring and corporate risk-taking. Econ. Theor. Bus. Manag. 6 , 96–112 (2017).

Lewis, W. A. The Theory of Economic Growing (CRC Press, 1955).

Tan, Z. D., Zhao, X., Pan, J. & Tan, J. H. The value of digital transformation: From the perspective of corporate cash holdings. J. Financ. Econ. 48 (3), 64–78 (2022).

Shu, W., Cao, J., Cao, J. & Wang, F. J. Can enterprise informatization investment restrain surplus management? Empirical evidence from Chinese A-share listed firms. J. Soochow Univ. (Philos. Soc. Sci. Edit.) 42 (5), 115–127 (2021).

Brown, L. & Osborne, S. P. Risk and innovation. Pub. Manag. Rev. 2 , 186–208 (2013).

Wrede, M., Velamuri, V. K. & Dauth, T. Top managers in the digital age: exploring the role and practices of top managers in firms’ digital transformation. Manag. Decis. Econ. 41 (8), 1549–1567 (2020).

Solberg, E., Traavik, L. E. M. & Wong, S. I. Digital mindsets: recognizing and leveraging individual beliefs for digital transformation. Calif. Manag. Rev. 62 (4), 105–124 (2020).

Zhou, Y. D. & Yu, H. Resarch on relationship between state power and industrial performance of SOEs. Chin. Ind. Econ. 6 , 31–43 (2012).

Wu, Y. B. Innovation capabilities of different ownership enterprises. Ind. Econ. Res. 2 , 53–64 (2014).

Zhong, Y. J., Zhang, C. Y. & Chen, D. Q. Privatization and innovation efficiency: Promotion and suppression?. J. Financ. Econ. 42 (7), 4–15 (2016).

Brochet, F., Loumioti, M. & Serafeim, G. Speaking of the short term: Disclosure horizon and managerial myopia. Rev. Acct. Stud. 3 , 1122–1163 (2015).

Manso, G. Motivating innovation. J. Financ. 66 , 823–1860 (2011).

Zhou, M. L., Jiang, K. Q. & Guo, W. Can enterprise internationalization strategy and digital transformation achieve win-win results?—Empirical evidence from the belt and road initiative. Contemp. Econ. Manag. 45 (9), 28–46 (2023).

Wu, Z., Fan, Y. C., Chen, Y. T. & Huang, Y. The reverse knowledge spill over effect of FDI in emerging economies—An empirical test of China’s of FDI in “one belt and one road”. Chin. J. Manag. Sci. 23 (51), 690–695 (2015).

Zhao, F., Wang, T. N. & Wang, Y. External technology acquisition in open innovation and product diversification: The moderating effect of dynamic capabilities. Manag. Rev. 28 (6), 78–87 (2016).

Li, Z. Q. & Ji, L. J. Market structure and technological innovation. Chin. Soft. Sci. 10 , 29–33 (2001).

Zhang, J., Zheng, W. P. & Zhai, F. X. How does competition affect innovation: Evidence from China. Chin. Ind. Econ. 11 , 56–68 (2014).

Arrow, K. Economic Welfare and the Allocation of Resources for Invention 609–626 (Princeton University, 1962).

Li, D. H., Wu, R. H. & Chen, D. How does coopetition influence innovation: A tracking study of Chinese manufacturing firms’ domestic vs. cross-border coopetition. Manag. World 2 , 161–181 (2020).

Li, J., Xue, H. R. & Pan, Z. Product market competition of enterprise in manufacturing industry, organizational slack and enterprise technology innovation. Chin. Econ. Stud. 2 , 112–125 (2016).

Wu, Y. H., Huang, P. P., Chen, W. & Wu, S. N. Product market competition advantage, capital structure and trade credit supply: Empirical evidence from Chinese listed firms. J. Manag. Sci. Chin. 20 (5), 51–65 (2017).

Feng, G. F., Zhang, Y. C. & Wen, J. Comparative analysis on technological innovation capability of listed companies on Main board, SME board and GEM board. Mod. Econ. Sci. 35 (6), 109–114 (2013).

Luo, J. H. & Wu, Y. L. Level of digital operation and real earnings management. J. Manag. Sci. 34 (4), 3–18 (2021).

MathSciNet   Google Scholar  

Singh, M., Davidson, W. N. & Suchard, J. A. Corporate diversification strategies and capital structure. Quart. Rev. Econ. Financ. 43 (1), 147–167 (2003).

Xiao, Z. P. & Chen, D. S. The effect of corporate governance structure on agency costs—Empirical evidence from Chinese listed companies. Financ. Trade. Econ. 12 , 29–35 (2006).

Yu, M. G., Li, W. G. & Pan, H. B. Managerial overconfidence and corporate risk taking. J. Financ. Res. 1 , 149–163 (2013).

Chemmanur, T., Loutskina, E. & Tian, X. Corporate venture capital, value creation and innovation. Rev. Financ. Stu. 27 (8), 2434–2473 (2014).

Chen, S., He, W. L. & Zhang, R. Venture capital and firm innovation: Impact and potential mechanisms*. Manag. World 1 , 158–169 (2017).

Xiao, T. S., Sun, R. Q., Yuan, C. & Sun, J. Digital transformation, human capital structure adjustment and labor income share. Manag. World 38 (12), 220–235 (2022).

Wen, Z. L. & Ye, B. J. Mediation effect analysis, methodology and model development. Adv. Psychol. Sci. 22 (5), 731–745 (2014).

Liu, J. Q., Liu, C. Y. & Feng, S. Impact of digital transformation on accelerating enterprise innovation—Evidence from the data of Chinese listed companies. Dis. Dyn. Nat. Soc. 2023 , 1–17 (2023).

Li, X. Z., Wang, D. & Zhao, Q. Y. How does digital transformation affect corporate innovation? Evidence from Chinese listed corporations. SSRN https://ssrn.com/abstract=4641949 (2023).

Ji, Z. Y., Zhou, T. Y. & Zhang, Q. The impact of digital transformation on corporate sustainability: Evidence from listed companies in China. Sustainability 15 , 1–19 (2023).

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Acknowledgements

The authors would like to thank all of the people who participated in the studies.

This work was funded by Research on the Dynamic Innovation of Economic Management Teaching Content Integrating China's "New Development Pattern", JG22DB240, National Social Science Fund of China, 19AGL013 and Fundamental Research Funds for the Central Universities, 2020CDJSK02TD03.

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Xu Zhao & Qi-an Chen

Surrey International Institution, Dongbei University of Finance and Economics, Dalian, 116025, Liaoning, China

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Xu Zhao, Xiaoshu Yuan and Yannan Yu: contributed to conceptualization, methodology, analysis, and writing; Haitao Zhang: contributed to validation and resources; Xu Zhao, Qi-an Chen and Haitao Zhang: contributed to experiment design, and data collection; Qi-an Chen and Xu Zhao: contributed to investigation, supervision, and review editing. All authors have read and agreed to the published version of the manuscript.

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Zhao, X., Chen, Qa., Yuan, X. et al. Study on the impact of digital transformation on the innovation potential based on evidence from Chinese listed companies. Sci Rep 14 , 6183 (2024). https://doi.org/10.1038/s41598-024-56345-2

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ORIGINAL RESEARCH article

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Immersive Media in Connected Health - Volume II

Normative Visual Attention Process Performance Results from Neurotypical Children Assessed Within a Virtual Reality Classroom Provisionally Accepted

  • 1 Cognitive Leap Inc., United States
  • 2 Institute for Creative Technologies, University of Southern California, United States

The final, formatted version of the article will be published soon.

Virtual Reality (VR) is revolutionizing healthcare research and practice by offering innovative methodologies across various clinical conditions. Advances in VR technology enable the creation of controllable, multisensory 3D environments, making it an appealing tool for capturing and quantifying behavior in realistic scenarios. This paper details the application of VR as a tool for neurocognitive evaluation, specifically in attention process assessment, an area of relevance for informing the diagnosis of childhood health conditions such as Attention Deficit Hyperactivity Disorder (ADHD). The data presented focuses on attention performance results from a large sample (n=837) of neurotypical male and female children (ages 6-13) tested on a visual continuous performance task, administered within an immersive VR classroom environment. The results indicate systematic improvements on most metrics across the age span and sex differences are noted on key variables thought to reflect differential measures of hyperactivity and inattention in children with ADHD. This data was collected to create a normative baseline database for use to inform comparisons with the performances of children with ADHD to support diagnostic decision-making in this area. The results indicate that VR technology can provide a safe and viable option for testing attention processes in children under stimulus conditions that closely mimic ecologically relevant challenges found in everyday life. In response to these stimulus conditions, VR can support advanced methods for capturing and quantifying users' behavioral responses. VR offers a more systematic and objective approach for clinical assessment and intervention, and provides conceptual support for its use in a wide variety of healthcare contexts.

Keywords: virtual reality, ADHD, Attention Processes, assessment, virtual classroom

Received: 07 Oct 2023; Accepted: 12 Mar 2024.

Copyright: © 2024 Goh, Ma and Rizzo. 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) or licensor 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: Dr. Crystal Goh, Cognitive Leap Inc., Los Angeles, CA., United States Dr. Albert Rizzo, Institute for Creative Technologies, University of Southern California, Los Angeles, United States

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Analysis of the evolution characteristics of infrared energy of coal samples under composite disturbance of dynamic and static loads

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  • Volume 83 , article number  191 , ( 2024 )

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  • Peng-Fei Shan 1 , 2 ,
  • Yi-Wei Shi 1 , 2 ,
  • Xing-Ping Lai 1 , 2 ,
  • Wei Li 1 , 2 ,
  • Tong Yang 1 , 2 ,
  • Chen-Wei Li 1 , 2 &
  • Pan Yang 1 , 2  

Coal deformation and damage are the fundamental causes of mining disasters. This paper proposes an intelligent sensing method of infrared thermal imaging applied to the characterization analysis of deformation and damage of coal mass during loading. The thermal infrared imager and crack monitoring equipment were used to jointly monitor the average infrared radiation temperature (AIRT) and damage pattern of coal samples under the combined disturbance of dynamic and static loads during uniaxial loading. The time–frequency characteristics of infrared radiation during the destruction of coal samples were analyzed; a crack recognition model based on Mask R-CNN was constructed to identify and detect different crack shapes after the destruction of coal samples. The results show that the uniaxial compressive strength of the coal sample under static load is greater than the combined disturbance of dynamic and static loads, the degree of deformation and damage of the coal sample is more severe, and the cracks are more obvious; the uniaxial compressive strength of the coal sample under the combined disturbance of low-frequency dynamic and static loads is higher than that of the high-frequency composite disturbance of dynamic and static loads. Composite disturbance of frequency, dynamic and static loads; a DST analysis method for coal sample fracture and a new index of coal sample damage degree are proposed. The research results can provide reference for early warning of coal and rock deformation and damage.

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Alexander QG, Hoskere V, Narazaki Y et al (2022) Fusion of thermal and RGB images for automated deep learning based crack detection in civil infrastructure. AI Civ Eng 1:3. https://doi.org/10.1007/s43503-022-00002-y

Article   Google Scholar  

Chen KQ, Zhu ZL, Deng XM et al (2021) Deep learning for multi-scale object detection: a survey. J Softw 32(04):1201–1227. https://doi.org/10.13328/j.cnki.jos.006166 . ( in Chinese )

Dai LP, Pan YS, Li ZG et al (2021) Quantitative mechanism of roadway rock bursts in deep extra-thick coal seams: theory and case histories. Tunn Undergr Space Technol 111:103861

Du YY, Sun H, Ma LQ et al (2022) Characteristics of infrared radiation response during coal damage evolution. Coal Sci Technol 50(09):67–74. https://doi.org/10.13199/j.cnki.cst.2020-1600 . ( in Chinese )

Feng C, Wang Z, Wang J et al (2023a) Damage characteristics analysis and constitutive model establishment for deep rock considering pre-static loads and frequent dynamic disturbance. Environ Earth Sci 82:559. https://doi.org/10.1007/s12665-023-11220-7

Article   ADS   Google Scholar  

Feng XJ, Shen YX, Zhou D et al (2023b) Multi-scale distribution of coal fractures based on CT digital core deep learning. Coal Sci Technol 51(08):97–104. https://doi.org/10.13199/j.cnki.cst.2022-0530 . ( in Chinese )

Gu K, Ning Z, Kang Y (2023) Analysis about water influence and mechanism on mechanical behavior of Longmaxi outcrop shale through uniaxial/triaxial compressive test and SCB test. Environ Earth Sci 82:539. https://doi.org/10.1007/s12665-023-11248-9

Huang J, Zhang G (2020) Survey of object detection algorithms for deep convolutional neural networks. Comput Eng Appl 56(17):12–23 ( in Chinese )

Google Scholar  

Jie X, Quan J, Li SJ, Chen PF, Zhao HR (2023) Fracturing and energy evolution of rock around prefabricated rectangular and circular tunnels under shearing load: a comparative analysis. Rock Mech Rock Eng. https://doi.org/10.1007/s00603-023-03532-8

Jing S, Wen Z, Jiang Y et al (2024) Mechanical behaviors and failure characteristics of coal-rock combination under quasi-static and dynamic disturbance loading: a case based on a new equipment. Geomech Geophys Geo Energ Geo Resour 10:2. https://doi.org/10.1007/s40948-023-00717-x

Lai XP, Xu HC, Shan PF, Hu QX, Ding WX et al (2024) Research on mechanism of rock burst induced by mined coal-rock linkage of sharply inclined coal seams. Int J Miner Metall Mater. https://doi.org/10.1007/s12613-024-2833-8

Li DY, Wan QR, Zhu QQ et al (2021) Experimental study on mechanical properties and failure behave-our of fractured rocks under different loading methods. J Min Saf Eng 38(05):1025–1035. https://doi.org/10.13545/j.cnki.jmse.2021.0187 . ( in Chinese )

Liu SJ, Wei JL, Huang JW et al (2015) Quantitative analysis methods of infrared radiation temperature field variation in rock loading process. Chin J Rock Mech Eng 34(S1):2968–2976. https://doi.org/10.13722/j.cnki.jrme.2014.0656 . ( in Chinese )

Liu Y, Liu HY, Fan JL et al (2020) A survey of research and application of small object detection based on deep learning. Acta Electr Sin 48(03):590–601 ( in Chinese )

Ma QQ, Ma K (2020) Experimental study on influence of infrared thermal effect on pore structure of coal. Coal Technol 39(06):113–116. https://doi.org/10.13301/j.cnki.ct.2020.06.034 . ( in Chinese )

Ma LQ, Zhang Y, Sun H et al (2017) Experimental study on dependence of infrared radiation on stress for coal fracturing process. J China Coal Soc 42(01):140–147. https://doi.org/10.13225/j.cnki.jccs.2016.5003 . ( in Chinese )

Ren ZJ, Lin SZ, Li DW et al (2019) Mask R-CNN object detection method based on improved feature pyramid. Laser Optoelectron Prog 56(04):174–179 ( in Chinese )

Salami Y, Dano C, Hicher PY (2017) Infrared thermography of rock fracture. Géotech Lett. 7(1):36–40

Tian F, Sun H, Ma LQ et al (2022) Characteristics of temperature field variation during sandstone crack development. Min Saf Environm Protect 49(01):14–19. https://doi.org/10.19835/j.issn.1008-4495.2022.01.003 . ( in Chinese )

Wang Y, Liu R, Ji H et al (2023) Correlating mechanical properties to fractal dimensions of shales under uniaxial compression tests. Environ Earth Sci 82:2. https://doi.org/10.1007/s12665-022-10642-z

Wu XZ, Gao X, Zhao K et al (2016) Abnormality of transient infrared temperature field (ITF) in the process of rock failure. Chin J Rock Mech Eng 35(08):1578–1594. https://doi.org/10.13722/j.cnki.jrme.2015.1052 . ( in Chinese )

Wu LX, Mao WF, Liu SJ et al (2018) Mechanisms of altering infrared-microwave radiation from stressed rock and key issues on crust stress remote sensing. J Remote Sens 22(S1):146–161 ( in Chinese )

Xie G, Suo YL, Liu L et al (2023) Pore characteristics of sulfate-activated coal gasification slag cement paste backfill for mining. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-30554-0

Xie G, Liu L, Suo YL et al (2024) High-value utilization of modified magnesium slag solid waste and its application as a low-carbon cement admixture. J Environ Manage 349:119551. https://doi.org/10.1016/j.jenvman.2023.119551

Article   CAS   PubMed   Google Scholar  

Xu DG, Wang L, Li F (2021) Review of typical object detection algorithms for deep learning. Comput Eng Appl 57(08):10–25 ( in Chinese )

CAS   Google Scholar  

Xue DJ, Tang QC, Wang A et al (2019) FCN-based intelligent identification of crack geometry in rock or concrete. Chin J Rock Mech Eng 38(S2):3393–3403. https://doi.org/10.13722/j.cnki.jrme.2019.0010 . ( in Chinese )

Yang XB, Cheng HM, Pei YY et al (2020) Study on the evolution characteristics of rock deformation and post-peak energy under different loading methods. Chin J Rock Mech Eng 39(S2):3229–3236. https://doi.org/10.13722/j.cnki.jrme.2019.1076 . ( in Chinese )

Article   CAS   Google Scholar  

Yuan H, Sun Q, Geng J et al (2023) Thermal acoustic emission characteristics and damage evolution of granite under cyclic thermal shock. Environ Earth Sci 82:388. https://doi.org/10.1007/s12665-023-11075-y

Zhang YB, Wu WR, Yao XL et al (2020) Acoustic emission, infrared characteristics and damage evolution of granite under uniaxial compression. Rock Soil Mech 41(S1):139–146. https://doi.org/10.16285/j.rsm.2019.0305 . ( in Chinese )

Zhang QH, Chen C, Yuan L et al (2022a) Early and intelligent recognition of dynamic cracks during damage of complex fractured rock masses based on DIC and YOLO algorithms. J China Coal Soc 47(03):1208–1219. https://doi.org/10.13722/j.cnki.jrme.2019.1076 . ( in Chinese )

Zhang DX, Guo WY, Zhao TB et al (2022b) Experimental study on directional propagation of rock type-I crack. Rock Soil Mech 43(S2):231–244. https://doi.org/10.16285/j.rsm.2021.2188 . ( in Chinese )

Zhao YX, Jiang YD (2010) Acoustic emission and thermal infrared precursors associated with bump-prone coal failure. Int J Coal Geol 83(1):11–20

Article   MathSciNet   CAS   Google Scholar  

Zhou ZL, Liu Y, Cai X et al (2022) Infrared radiation characteristics of sandstone exposed to impact loading. J Cent S Univ Sci Technol 53(07):2555–2562

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Acknowledgements

The authors thank for the Key Laboratory of Western Mine Exploration and Hazard Prevention, Ministry of Education, Xi'an University of Science and Technology.

We thank the project supported by the Major Program of the National Natural Science Foundation of China (52394191), National Natural Science Foundation of China (52274138), Innovation Capability Support Program of Shaanxi (2022KJXX-58) and Yulin High-tech Zone Science and Technology Plan Project (ZD-2021–01) for its support of this study. We would sincerely want to thank the peoples who are supported to do this work and reviewing committee for their estimable feedbacks.

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Peng-Fei Shan, Yi-Wei Shi, Xing-Ping Lai, Wei Li, Tong Yang, Chen-Wei Li & Pan Yang

Key Laboratory of Western Mine Exploration and Hazard Prevention, Ministry of Education, Xi’an University of Science and Technology, Xi’an, 710054, Shaanxi, China

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Contributions

Peng-Fei Shan and Xing-Ping Lai guided the writing of the article, Yi-Wei Shi wrote the draft. Wei Li and Pan Yang provided the ideas and collected the data to test. Tong Yang and Chen-Wei Li assisted in carrying out the test.

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Correspondence to Yi-Wei Shi .

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SHAN Peng-fei, SHI Yi-wei, LAI Xing-ping, LI Wei, YANG Tong, LI Chen-wei and YANG Pan declare that they have no conflict of interest.

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Shan, PF., Shi, YW., Lai, XP. et al. Analysis of the evolution characteristics of infrared energy of coal samples under composite disturbance of dynamic and static loads. Environ Earth Sci 83 , 191 (2024). https://doi.org/10.1007/s12665-024-11511-7

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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