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Research Paradigms: Explanation and Examples

4-minute read

• 1st March 2022

Are you planning a research project? If so, you’ll need a research paradigm. But what exactly is a research paradigm, and why is it important? This blog post will cover the following:

●  The definition of a research paradigm

●  Why research paradigms are important

●  Common examples of research paradigms

●  Merging research paradigms

●  Expert editing and proofreading

Read on to find out more or learn about research paradigms in the video below!

The Definition of a Research Paradigm

A research paradigm is a philosophical framework that your research is based on. It offers a pattern of beliefs and understandings from which the theories and practices of your research project operate.

A research paradigm consists of ontology, epistemology, and research methodology .

●  Ontology answers the question: “What is reality?” That is, does a single reality exist within your research? An example of an ontological question would be: “Does God exist?” There are two possible realities (or ontologies) in response to this question: “Yes, God exists,” or “No, God does not exist.”

●  Epistemology is the study of knowledge. It answers the question: “How is it possible to know reality?” Epistemology incorporates the validity, parameters, and methods of acquiring knowledge. An example of an epistemological question would be: “How is it possible to know whether God exists or not?”

●  Research Methodology answers the question: “How do we go about discovering the answer or reality?” This includes the process of data collection and analysis. Research methodology should outline how you conduct your research and demonstrate that the findings are valid.

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Together, ontology and epistemology comprise research philosophy.

Research philosophy combined with research methodology comprises a research paradigm.

Why Are Research Paradigms Important?

Research paradigms are important because they form the philosophical basis of a research project. Research paradigms influence how different schools of learning (such as the sciences versus the humanities) undertake their research. Once a research philosophy has been determined, an appropriate methodology can be chosen.

Furthermore, a knowledge of the philosophical foundation of your research will increase its quality and improve your performance in any analysis you may have to undergo!

Common Examples of Research Paradigms

1. Positivism

Positivists believe that there’s a single reality that’s possible to measure and understand. Because of this, they’re most likely to use quantitative methods in their research. Typically, positivists propose a hypothesis that can be proved or disproved using statistical data analysis. Positivism tends to investigate the existence of a relationship between two variables rather than the reason behind it.

2. Constructivism

Constructivists believe that there’s no single reality or truth, but rather multiple realities. They devote themselves to understanding and interpreting the meaning attached to an action. For this reason, constructivists tend to use qualitative research methods , such as interviews or case studies, which focus on providing different perspectives. Constructivism aims to provide the answer to “why.” For example, asking “Why do 25% of the employees of an organization regularly arrive late to work?” rather than merely establishing the relationship between two variables (e.g., time of arrival at work and availability of nearby parking).

3. Pragmatists

Pragmatists believe that reality is continually interpreted and renegotiated against the backdrop of new and unpredictable situations. Because of this, the philosophy they apply in research depends on the research question itself. Pragmatists often combine positivist and constructivist principles in the same research project, using both qualitative and quantitative methods to investigate different components of a research problem. They believe that the optimal research methods are those that most successfully answer the research question.

Merging Research Paradigms

While most social science research operates from either a positivist (experimental) or constructivist paradigm, it’s possible to combine both, as the field of psychology often does. Quantitative and qualitative methodology are frequently used together in psychology, illustrating the subject’s footing in multiple research paradigms (positivist and constructivist).

Expert Editing and Proofreading

If you’re writing a research proposal or paper , you’ll want to ensure that your writing is error-free, fluent, and precise. Although re-reading your own work is valuable, it can be very helpful to get another opinion on your writing. We offer a free trial of proofreading and editing services when you submit your first document. Find out more today!

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Qualitative Methodologies in Organization Studies pp 1–31 Cite as

• Marta Strumińska-Kutra 4 &
• Izabela Koładkiewicz 5  
• First Online: 14 December 2017

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The main aim of the chapter is to discuss the case study method. We shall begin by confronting its definition. It is quite a challenge, as researchers representing various paradigms embark on this type of research project. These paradigms define the way we perceive the explored reality, our chances of understanding/cognizing it, and the acceptable research methods. As a consequence, not only is the case study subject to various definitions, but it is also employed to achieve manifold goals). Despite these differences, we can point out a number of characteristics that distinguish case study method; they shall be the focus of our discussion. As much as possible, we shall take into account the variety of perspectives in case study-based research, or recommend to readers the sources where they can find more detailed information on a particular issue. In this chapter, the presentation of premises and types of case studies will be followed by a manual, guiding readers in their endeavor to design their own research using the method discussed.

• Selection of cases
• Case based generalizations
• Theory and cases
• Types of case study
• Single case study
• Multiple case study

Author’s Note:

This chapter is substantially revised version of a chapter published in Jemielniak, D. ed. (2012) Badania jakościowe, PWN: Warszawa.

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The concept of strategy, as referred to by Robert Yin ( 2003a , b ), Norman Denzin and Yvonne Lincoln ( 2005 ), means research process design. Here, we shall use it interchangeably with two other terms: approach (Creswell 2007 ) and method. The latter is understood broadly as a set of directives and rules based on ontological and epistemological assumptions, indicating certain ways of conducting research. “Strategy ” and “method” are also referred to as synonyms of the case study methodology (Mills et al. 2010 ).

Even if the starting point of our research is interest in a particular case, we need to bolster our case with a theoretical framework, which will serve as a point of reference for research results.

We must remember that sampling should also involve documents, articles, posts on Internet fora, place and time of observation, and so on.

Here, “case” refers rather to a happening, an expression, or a statement that does not match the emerging pattern, and not to “case” understood as a bounded system/phenomenon.

In fact, the analysis is far less structured and multistage. It comprises abundant feedback and requires the researcher to revert to theoretical reflection; there are periods of “creative impotence” and the process is affected by the other publications read by researchers during the process.

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Strumińska-Kutra, M., Koładkiewicz, I. (2018). Case Study. In: Ciesielska, M., Jemielniak, D. (eds) Qualitative Methodologies in Organization Studies. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-65442-3_1

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Case study: a bridge across the paradigms

Affiliation.

• 1 School of Nursing, College of Health and Science, University of Western Sydney, New South Wales [corrected] Australia. [email protected]
• PMID: 16700753
• DOI: 10.1111/j.1440-1800.2006.00309.x

Case study as a teaching and research tool has an extensive history in health and social sciences. Despite its suitability for many of the research questions that face nurses, nurses have not fully embraced case study as a comprehensive approach for research. The vagaries of the real-life clinical setting can confound methodologically purist researchers. Case study provides a milieu in which nurse researchers can respond to these vagaries and move towards a paradigmatic openness. In this paper, we argue that case study offers, as yet, under-explored and under-utilised potential as a bridge across the traditional research paradigms. We argue that case study has broad research application and epistemological, ontological and methodological flexibility. When used as a research approach, case study is both the process and end product of research. It provides a delineated boundary for inquiry, and a structural process within which any methods appropriate to investigating a research area can be applied.

Publication types

• Attitude of Health Personnel
• Data Collection
• Data Interpretation, Statistical
• Models, Nursing
• Nursing Records*
• Nursing Research / organization & administration*
• Nursing Theory
• Philosophy, Nursing*
• Reproducibility of Results
• Research Design / standards*
• Research Personnel / psychology

Research Paradigms and Methodologies Case Study

There are two types of research paradigms: positivism and interpretivism. While the first one is grounded in the belief that social reality is not influenced by research, the second one is quite the opposite. According to interpretivism, the investigation has a great impact on social reality (Collis & Hussey 2013). The two articles under analysis employ the interpretive paradigm since they both admit the influence made on social reality by the investigation. Saini and Budhwar (2008) analyze the issues of people management, and Şendoğdu, Kocabacak, and Güven (2013) investigate the connection between human resource management and organizational commitment.

The methodology used in Saini’s and Budhwar’s (2008) study is a case study. This method has several benefits. First of all, it allows researchers to perform a thorough investigation of the chosen issue. Also, case studies may promote new research. Finally, this method suggests new insight into the analyzed phenomena (Kin 2013). However, case studies also have some limitations. The major disadvantage of case studies is that they cannot be repeated. Thus, there is no possibility to compare results at different points. Another problem that may occur is researcher bias. Also, case studies do not present the opportunity to create a classification since their sample size is usually too small (Kin 2013).

The method employed in the article by Şendoğdu, Kocabacak, and Güven (2013) is a questionnaire. The strengths of such a method are numerous. Questionnaires are practical because they allow researchers to collect much data from many participants within a short time. Also, the questionnaires are not expensive. Another benefit is that the analysis of data gathered with the help of this method is relatively easy and inexpensive (Ang 2014). It is a good idea to use questionnaires because once the results have been analyzed, they may be compared and contrasted with the findings of other studies to assess the divergences. Finally, data obtained through questionnaires may be employed to build new theories and methods (Ang 2014). However, along with numerous advantages, questionnaires have some serious limitations. The most serious one is that such a method does not provide enough validity. Also, there is no possibility to check whether the participants are being honest while answering the questions. Another problem is that different people may understand the questions, not in the same way, which results in the inconsistency of findings. Finally, there is a danger of researchers’ bias when creating the questions since they may have some assumptions which can lead them to neglect some crucial issues (Ang 2014).

Since my research is also focused on human resource management, I could employ some of the approaches used by the authors of the analyzed articles. I think that a questionnaire is the most suitable method for my future research because it is the easiest way of finding out the opinions of many people within a short period, which will give me much information for analysis. However, I will need to be cautious regarding the ethical considerations of research (Anderson 2013). Also, I will have to think carefully about my study’s feasibility. I think that employing the questionnaire method is much easier in both organizational and financial aspects than the method of the case study. Therefore, I would rather choose a questionnaire because it has many advantages, and the limitations may be avoided if I plan the study thoroughly.

Reference List

Anderson, V 2013, Research methods in human resource management , 3rd edn, London, UK: Chartered Institute of Personnel and Development.

Ang, SH 2014, Research design for business and management , Thousand Oaks, CA: SAGE.

Collis, J & Hussey, R 2013, Business research: a practical guide for undergraduate and postgraduate students , 4th edn, Palgrave-MacMillan, London.

Kin, RY 2013, Case study research: design and methods , 5th edn, Thousand Oaks, CA: SAGE.

Saini, DS & Budhwar, PS 2008, ‘Managing the human resource in Indian SMEs: the role of indigenous realities’, Journal of World Business , vol. 43, no. 4, pp. 417-434.

Şendoğdu, AA, Kocabacak, A & Güven, Ş 2013, ‘The relationship between human resource management practices and organizational commitment: a field study’, Social and Behavioral Sciences , no. 99, pp. 818-827.

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Where is customer care in 2024?

Customer care leaders are facing their greatest challenge in decades. They must prepare their organizations for an AI-enabled future while simultaneously meeting tough commercial targets and rising customer expectations. Our latest global survey suggests that many companies are struggling on all these fronts.

About the authors

This article is a collaborative effort by Eric Buesing , Maximilian Haug, Paul Hurst, Vivian Lai, Subhrajyoti Mukhopadhyay, and Julian Raabe , representing views from McKinsey’s Operations Practice.

Major disruptions are always painful, and the transition from a care paradigm dominated by human agents to one steered by AI technologies may be the biggest disruption in the history of customer service. Can organizations find a route to hyperefficient, digitized customer care while retaining the personal contact and responsiveness that customers require?

Right now, many customer care leaders feel trapped in no-man’s-land. Technology has enabled them to evolve their operations significantly, and the traditional call center environment is rapidly becoming a thing of the past. Yet when these digitally enabled models underperform—and they often do—companies need to master entirely new approaches to performance improvement alongside their traditional tool kits.

Customer care in the spotlight

The key findings in this article are based on McKinsey’s fourth global survey of customer care executives. This survey was our largest yet, gathering the views of more than 340 leaders at the director, senior director, vice president, and C-suite levels. Respondents came from companies with annual revenues of $100 million to $10 billion-plus, representing every major industry segment.

The majority of respondents said that the companies they worked for were headquartered in North America (just over 50 percent) or Western Europe (almost 25 percent), with 10 percent headquartered in India and 4 percent in China. Most respondents said their organizations operated in multiple regions: 75 percent reported operating in North America, 58 percent in Europe, 57 percent in Asia–Pacifc, 39 percent in the Middle East and Africa, and 37 percent in Latin America. We plan to expand future research to include more organizations headquartered outside North America and Western Europe.

To make matters worse, executives say that most of the challenges highlighted in our last survey  are still present today (see sidebar, “Customer care in the spotlight”). Those challenges include rising call volumes, high levels of employee attrition, and persistent talent shortages. Meanwhile, some of the largest consumer-facing technology organizations in the world have become exceptional at digitally enabled customer care, which is lifting customer expectations everywhere, piling further pressure onto customer care staff and leadership at other companies.

Our survey reveals three major themes that are top of mind for customer care leaders. First, their priorities are shifting, from an overwhelming focus on customer experience to a multidimensional approach that also emphasizes revenue goals and technology transformation. Second, they are working hard to build future-ready AI-enabled ecosystems for their operations. Finally, they are boosting their capabilities by investing in employee upskilling programs and building stronger outsourcing relationships.

Would you like to learn more about our Operations Practice ?

Reprioritizing core operations.

When we began monitoring the sentiment of customer care leaders in 2016, their priorities were clear. Customer experience came first, followed at a distance by operational improvement, technology transformation, and revenue generation—in that order.

Over the past seven years, those priorities have converged (Exhibit 1). Revenue generation, which was mentioned by about one in 20 customer care leaders in our first survey, has been rising steadily in importance ever since. It is now a priority for a third of customer care leaders. But over the past two years, technology enhancements and operational improvements have seen the fastest increases. The expectation that customer care functions can do it all and do it well has never been higher.

Leaders also understand that they need to engage with their customers to delight them. Currently, only 11 percent of respondents say reducing contact volume is important to them, a 20-percentage-point drop over 12 months. Indeed, 57 percent of leaders expect call volumes to increase by as much as one-fifth over the next one or two years.

Separate research suggests that these leaders are right to stay focused on direct personal interaction, even when many of their customers are young digital natives. In a recent McKinsey survey of 3,500 consumers, respondents of all ages said that live phone conversations were among their most preferred methods of contacting companies for help and support. That finding held true even among 18- to 28-year-old Gen Z consumers, a cohort that favors text and social messaging for interpersonal communications.

There’s also evidence that younger consumers are getting tired of the digital self-service paradigm. One financial-services company reports that its Gen Z customers are 30 to 40 percent more likely to call than millennials, and they use the phone as often as baby boomers. Premium-segment customers of all ages also prefer the phone, with many saying that live phone support is part of the premium service they are paying for.

These findings don’t point to a future of phone-only customers, however. While customers of all generations prioritize support from a real person, they also want the flexibility to use different channels according to their needs. Digital-chat services have achieved a high level of acceptance across generations, and email remains important, especially for older consumers (Exhibit 2).

The need to excel in service across multiple channels creates extra challenges for customer care leaders, especially when budgets are tight. And 37 percent of respondents in our survey say that cost is still a key priority. This tension is driving companies to look for ways to control the customer care costs that go beyond call volume reduction, with automation and outsourcing the most frequently cited levers.

Creating a future-ready AI ecosystem

The tensions in modern customer care are clearly seen in companies’ approaches to advanced digital technologies. Our survey demonstrates that digital has already become a decisive differentiator. Among respondents who report that their operations are delivering better-than-expected performance, more than half have high levels of digital integration. Banking, telecommunications, and travel and logistics are among the leading industries in this regard.

Those high performers are in the minority, however. Only 8 percent of respondents from North America report greater-than-expected satisfaction with their customer performance. In Africa, Europe, and the Middle East, the figure is 5 percent. Among organizations reporting that performance was in line with or lower than expected, more than 80 percent also say their levels of digital integration are partial or low.

Leaders agree that they need to get digital right. More than half of the respondents to our survey expect the share of inbound contacts that take place through digital channels to exceed 40 percent in the next three years.

Artificial intelligence will play a decisive role in future customer care ecosystems. Respondents to our survey are already deploying AI tools in a variety of applications, including chatbots and automated email response systems, training and support for call center agents, back-office analytics, and decision making.

Over the past 12 months, the availability of powerful generative AI (gen AI) tools, especially large language models (LLMs) that can parse and respond to unstructured text or speech, has opened new possibilities for technology in customer care. More than 80 percent of respondents are already investing in gen AI, or expect to do so in the coming months, with leaders highlighting a wide range of potential applications.

One European subsidiary of a global bank replaced its well-established rules-based customer chatbot with a new system based on gen AI technology. Seven weeks after launch, the AI chatbot was 20 percent more effective at successfully answering customer queries than the old tool. The bank has already identified a road map of improvements that could double its performance in the coming months.

Early adopters are extremely ambitious about the potential of gen AI. The executive in charge of customer care at one major global organization told us that they expect 100 percent of customer interactions to be AI-enabled in the coming years, using a combination of technologies including new virtual assistants, agent-assist tools, and AI-powered voice analytics.

For most companies, however, the gen AI customer care revolution is still in its early stages. Leaders highlight multiple issues that are making it hard for them to integrate these technologies into their existing processes and workflows. The issues include technical challenges regarding deployment and scaling; concerns about safety, security, and governance; and difficulties in defining the desired outcomes from, or business case for, gen AI investments (Exhibit 3).

Learn more about Customer Care

Rethinking skills.

Today, customer care organizations lack many of the critical skills they need to deliver excellent service and navigate the transition to a digitally mediated, AI-enabled world. In part, that’s because customer care leaders have been running to stand still. Record levels of staff attrition following the COVID-19 pandemic meant that supervisors spent much of their time interviewing and bringing new staff up to speed. They spent less time mentoring their established teams, a problem exacerbated by the introduction of hybrid and remote working arrangements. Some agents and team leaders have spent years working with little interaction or coaching from their managers.

Staff turnover has now slowed, and two in three leaders in our latest survey say upskilling and reskilling are critical priorities. Companies highlight a range of benefits that accrue from effective upskilling and reskilling programs, including improvements to employee morale, increased productivity, and faster adoption of new technologies and working methods. Meanwhile, technology is changing upskilling programs. Twenty-one percent of leaders tell us that they are already using AI-based tools to train and support their customer care staff.

AI-based agent support systems are already becoming a key tool for companies seeking to offer extremely effective personal service to demanding customers. These systems can help agents resolve complex queries the first time, simultaneously reducing care costs and boosting customer experience.

One global construction equipment company, for example, uses a gen AI system to help its call center staff navigate thousands of pages of technical-support documentation. The system selects the appropriate steps to resolve a customer’s problem in seconds, based on free text questions entered by the agent and background information such as the serial numbers of vehicles and parts. The tool has cut average call resolution times from around 125 minutes to a few seconds, and it is currently saving customers €150,000 to €300,000 per day in reduced asset downtime.

Elsewhere, companies are using AI to transform the way they manage and support their customer care agents. New AI-based tools can optimize call volume forecasting, for example. This approach helped one company improve forecast accuracy by seven percentage points, while halving the work required to manage team capacities and schedules. The change improved customer service levels by more than 10 percent, while cutting staffing and overtime costs by more than 5 percent.

Companies are also looking outside their organizations for innovative ways to fill capability gaps. Outsourcing, once viewed primarily as a way to reduce costs, is increasingly seen as an effective source of additional skilled capacity and innovation capabilities. Fifty-five percent of the companies in our survey currently outsource part of their customer care operations, and 47 percent of those organizations expect to increase their outsourcing over the next two years.

Outsourcing relationships are becoming deeper too, with respondents telling us that they are now using their business process outsourcing for a range of activities that extends far beyond traditional call and email handling. They include content management and digital-marketing services, payments handling, and the development of AI-based customer care tools. Following the blueprint established by major players in the industrial products, medical device, software, and e-commerce sectors, some companies are now working with outsourcing partners to set up global innovation hubs that will drive the development of next-generation customer care technologies.

Our survey suggests that customer care organizations are running at two different speeds. In the fast lane, top performers have seized the opportunities presented by advances in digital technologies. With ruthless prioritization, they are investing capital to drive efficiency and service excellence across the customer journey. The best have already reshaped their organizations around highly integrated digital platforms. One high-performing company with more than 5,000 service agents is on track to deliver 75 digital-experience improvements this year, for example.

Other companies are still in the slow lane, struggling to fit a patchwork of digital point solutions into legacy care ecosystems. Unsure where to put their dollars, they are trapped in a cycle of continual system adaptation with no clear destination or road map.

In 2024, both types of organizations may need to shift their positions on the road. Gen AI is raising the bar for performance, productivity, and personalization in customer care, and tomorrow’s fully AI-enabled care organizations will operate very differently from those of today. It’s time for companies to look at their care ecosystems with fresh eyes. They should formulate an independent perspective on the changing expectations of their customers and the role of advanced AI in their organization. The future of customer care is calling. Leaders should answer with a bold vision and an aggressive time line for change.

Eric Buesing is a partner in McKinsey’s Charlotte office, where Paul Hurst is an associate partner; Maximilian Haug is an associate partner in the Boston office; Vivian Lai is a consultant in the New York office; Subhrajyoti Mukhopadhyay is an expert in the Chicago office; and Julian Raabe  is a partner in the Munich office.

The authors wish to thank Jorge Amar, Brian Blackader, Marcela Guaqueta, Suryansha Gupta, and Josh Wolff for their contributions to this article.

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