research viewpoint

How To Write Viewpoint in Case Study (With Examples)

Your goal in writing a case study is to analyze and provide solutions to a problem that a business or organization tackles.

To achieve this, your analysis must be framed from the perspective of someone who can solve your case study’s problem, such as the firm’s Chief Executive Officer, a department director, or a shop manager. The Viewpoint of a Case Study is the portion where you put yourself in the shoes of that handpicked individual. 

In this article, we will present and elaborate on the steps in writing the viewpoint of the case study so you can select whose perspective best suits your case study’s problem.

Table of Contents

What is viewpoint in a case study, what is the importance of the viewpoint of the case study, 1. start by reviewing your case study’s problem, 2. identify the person(s) that you think has the ability and authority to decide and solve your case study’s problem, 3. justify why you have selected that person(s) as your case study’s point of view (optional), tips and warnings, 1. what point of view should a case study be written in.

The Viewpoint or Point of View in a case study indicates the person who has the authority, ability, and expertise to recommend and decide how to solve your case study’s problem. Once you have identified this person, you will assume his/her role in analyzing the problem. Basically, this portion tells the readers that you are viewing the given case in the eyes of your selected person. 

Suppose that your case study involves recruitment issues in a firm. In this case, you can use the HR manager’s perspective as your case study’s viewpoint. The HR manager has the appropriate skill sets and knowledge about the firm’s recruitment process, making him/her qualified to decide regarding this issue.

Most case studies state the individual’s name whose perspective serves as the Viewpoint of the Case Study (e.g., Mr. Juan Dela Cruz ). However, there are also those that just indicate this specific person’s title or position in the company (e.g., Chief Executive Officer ). 

Some case studies use the term “Point of View” or “Protagonist of Case Study 1 ” instead of Viewpoint. 

The Viewpoint is located at the beginning of the case study, usually after the “Statement of the Facts” portion.

People have different perspectives about a particular issue of an organization. For this reason, it is challenging to concentrate or focus our analysis since it can be viewed from multiple points of view. 

For instance, a finance officer may attribute the decline in sales to insufficient funds disbursed to the marketing department. Meanwhile, a sales officer may attribute the same problem to the increasing market competition. This variation in perspectives makes it more challenging to develop the most appropriate approach to analyze the problem.

Limiting the perspective of your case study to the most suitable person makes your analysis more concise and straightforward. There is no need to capture everyone’s perspective since the viewpoint of your selected individual is the only relevant and valuable one.

How To Write a Viewpoint in Case Study?

Writing your case study’s viewpoint is pretty straightforward. All you have to do is to follow the given steps below:

Review and identify in which “field” or “category” your case study’s problem belongs. Say your case study’s issue is about the declining satisfaction level of the firm’s customer service department, as reflected by their recent survey. The category which this problem falls under is apparently “customer service”.

Another example: if your case study’s problem is rooted in how a firm drew flak from the public for its waste disposal mechanisms that have degraded the natural environment around a certain community, then this problem falls under “environment and waste disposal”

Shortlist people who you think can qualify as the viewpoint. Ensure the candidates are involved in the “category” or “field” where your problem belongs. Afterward, determine who is the most qualified to decide for your case. 

Using our previous example, you may select from the following individuals the viewpoint for the problem of the declining satisfaction level of the firm’s customer service:

• The firm’s CEO
• The Director of the firm’s customer service department
• A customer service employee 

All of them can be a suitable viewpoint since they are all involved in the particular category where the problem belongs. However, you should only select one person. If we analyze each individual:

• The firm’s CEO – Although he/she has the highest authority in the firm, he/she might have no specialized knowledge about customer service. Thus, we cannot select him/her as a viewpoint.
• The Director of the firm’s customer service department – This person supervises the entire customer service arm of the company so he/she knows everything about it, including its nitty-gritty processes. This person is the best viewpoint for our problem.
• A customer service employee – Although this person has skill and experience in handling customers’ concerns and queries, he/she has no authority to change something in the firm’s current customer service system. Thus, we cannot select him/her as a viewpoint.

Upon analysis, the most suitable perspective to use for this case study’s problem is that of the Director of the customer service department. 

Once you have figured out the Viewpoint of your case study, you may now explicitly state in your manuscript his/her name and title or position in the firm. 

Explain why you have selected this person by stating his/her role, experiences, and contributions to the firm or organization. 

Some published case studies do not put justification for their selected viewpoint since it is optional. However, it is advised to include one to make this portion more detailed.

Examples of Viewpoint of Case Study

To help you further understand how to create the viewpoint of a case study, we have provided you with some examples that you may use as a reference.

Case Study Problem : The popularity and momentum of Netflix Inc. start to wane as it loses around 200,000 subscribers in the first quarter of 2022, resulting in lower investor confidence in the firm. 

Viewpoint: Reed Hastings, Chief Executive Officer (CEO) and co-founder of Netflix. 

The CEO has the highest authority to decide how to solve Netflix’s problem. He also oversees the entire operation, making him qualified to deal with the problem.

The straightforward example above simply states the name of the selected viewpoint and then his position or title in the company. However, although the example above already satisfies what a viewpoint is, the format used in this example lacks justification. Let’s look at the next example which includes reasons or justification for the selected viewpoint.

Case Study problem: The marketing and publicity department of Solstice Clothing Line decided to concentrate their marketing efforts in the digital realm during the height of the COVID-19 pandemic, which drove their reach and engagement upward in the early quarters of 2019. However, in the last quarter of the same year, the business experienced a gradual decline in its overall engagement levels on its social media handles. 

Viewpoint: The problem needs the expertise of Celine Garcia, the Marketing and Publicity Director of Solstice Clothing Line. She is in charge of assessing the performance of the clothing line’s marketing officers, approving digital marketing content, and evaluating the level of engagement with digital publicity materials. 

This example mentions the name of the chosen person together with her position in the firm. It also explains the reason why she was selected as a viewpoint by stating her role in the business .

Case Study Problem: Paws Corner is one of the largest pet shops in the National Capital Region. It serves as a haven for different types of pet animals until a willing person adopts them. In 2017, the Paws Corner experienced a continuous decline in profit due to the increasing rental and operating costs. For this reason, the business is faced with the dilemma of whether to continue its operations or to close it indefinitely.

Viewpoint: Mr. Lito Cruz, the manager, and owner of Paws Corner, has led the operations of Paws Corner since 2015. He manages the day-to-day transactions of Paws Corner and keeps track of its revenues and expenses. He is a certified animal lover and an entrepreneur at heart. 

The example above stated the most appropriate perspective that must be used for the case study (which is its manager and owner). There’s also an explanation to justify why he was selected as the viewpoint.

• Narrow down your case study’s viewpoint to a single individual . Although a case study can be approached through multiple perspectives, it’s better to limit your case study’s Viewpoint to a single person who can best decide on the problem. This will also make your analysis of the case easier and less complex. 
• If the case being analyzed involves a certain department of an organization, the best viewpoint of the case study is the department head. For example, if your case involves the financial management of an organization, you can select the Director of Finance of that organization as your viewpoint.

Frequently Asked Questions

Since a case study is a form of formal writing, it is usually written in the third-person perspective. Hence, pronouns such as “He”, “She”, “They”, and “It” are generally used in case studies.

• Schweitzer, K. (2019). Writing Business Case Studies for Class. Retrieved 23 May 2022, from https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

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The Four Types of Research Paradigms: A Comprehensive Guide

5-minute read

• 22nd January 2023

In this guide, you’ll learn all about the four research paradigms and how to choose the right one for your research.

Introduction to Research Paradigms

A paradigm is a system of beliefs, ideas, values, or habits that form the basis for a way of thinking about the world. Therefore, a research paradigm is an approach, model, or framework from which to conduct research. The research paradigm helps you to form a research philosophy, which in turn informs your research methodology.

Your research methodology is essentially the “how” of your research – how you design your study to not only accomplish your research’s aims and objectives but also to ensure your results are reliable and valid. Choosing the correct research paradigm is crucial because it provides a logical structure for conducting your research and improves the quality of your work, assuming it’s followed correctly.

Three Pillars: Ontology, Epistemology, and Methodology

Before we jump into the four types of research paradigms, we need to consider the three pillars of a research paradigm.

Ontology addresses the question, “What is reality?” It’s the study of being. This pillar is about finding out what you seek to research. What do you aim to examine?

Epistemology is the study of knowledge. It asks, “How is knowledge gathered and from what sources?”

Methodology involves the system in which you choose to investigate, measure, and analyze your research’s aims and objectives. It answers the “how” questions.

Let’s now take a look at the different research paradigms.

1.   Positivist Research Paradigm

The positivist research paradigm assumes that there is one objective reality, and people can know this reality and accurately describe and explain it. Positivists rely on their observations through their senses to gain knowledge of their surroundings.

In this singular objective reality, researchers can compare their claims and ascertain the truth. This means researchers are limited to data collection and interpretations from an objective viewpoint. As a result, positivists usually use quantitative methodologies in their research (e.g., statistics, social surveys, and structured questionnaires).

This research paradigm is mostly used in natural sciences, physical sciences, or whenever large sample sizes are being used.

2.   Interpretivist Research Paradigm

Interpretivists believe that different people in society experience and understand reality in different ways – while there may be only “one” reality, everyone interprets it according to their own view. They also believe that all research is influenced and shaped by researchers’ worldviews and theories.

As a result, interpretivists use qualitative methods and techniques to conduct their research. This includes interviews, focus groups, observations of a phenomenon, or collecting documentation on a phenomenon (e.g., newspaper articles, reports, or information from websites).

3.   Critical Theory Research Paradigm

The critical theory paradigm asserts that social science can never be 100% objective or value-free. This paradigm is focused on enacting social change through scientific investigation. Critical theorists question knowledge and procedures and acknowledge how power is used (or abused) in the phenomena or systems they’re investigating.

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Researchers using this paradigm are more often than not aiming to create a more just, egalitarian society in which individual and collective freedoms are secure. Both quantitative and qualitative methods can be used with this paradigm.

4.   Constructivist Research Paradigm

Constructivism asserts that reality is a construct of our minds ; therefore, reality is subjective. Constructivists believe that all knowledge comes from our experiences and reflections on those experiences and oppose the idea that there is a single methodology to generate knowledge.

This paradigm is mostly associated with qualitative research approaches due to its focus on experiences and subjectivity. The researcher focuses on participants’ experiences as well as their own.

Choosing the Right Research Paradigm for Your Study

Once you have a comprehensive understanding of each paradigm, you’re faced with a big question: which paradigm should you choose? The answer to this will set the course of your research and determine its success, findings, and results.

To start, you need to identify your research problem, research objectives , and hypothesis . This will help you to establish what you want to accomplish or understand from your research and the path you need to take to achieve this.

You can begin this process by asking yourself some questions:

• What is the nature of your research problem (i.e., quantitative or qualitative)?
• How can you acquire the knowledge you need and communicate it to others? For example, is this knowledge already available in other forms (e.g., documents) and do you need to gain it by gathering or observing other people’s experiences or by experiencing it personally?
• What is the nature of the reality that you want to study? Is it objective or subjective?

Depending on the problem and objective, other questions may arise during this process that lead you to a suitable paradigm. Ultimately, you must be able to state, explain, and justify the research paradigm you select for your research and be prepared to include this in your dissertation’s methodology and design section.

Using Two Paradigms

If the nature of your research problem and objectives involves both quantitative and qualitative aspects, then you might consider using two paradigms or a mixed methods approach . In this, one paradigm is used to frame the qualitative aspects of the study and another for the quantitative aspects. This is acceptable, although you will be tasked with explaining your rationale for using both of these paradigms in your research.

Choosing the right research paradigm for your research can seem like an insurmountable task. It requires you to:

●  Have a comprehensive understanding of the paradigms,

●  Identify your research problem, objectives, and hypothesis, and

●  Be able to state, explain, and justify the paradigm you select in your methodology and design section.

Although conducting your research and putting your dissertation together is no easy task, proofreading it can be! Our experts are here to make your writing shine. Your first 500 words are free !

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Research Types and Viewpoints

Haritha Sudaraka Abeysundara

What is a Research?

Research is the process of discovering new knowledge which can be development of new concepts or advancement of new knowledge, leading to a new understanding. This process involves collection, organization and analysis of information to achieve understanding of a topic or an issue.

“Research is a systematic investigation (i.e., the gathering and analysis of information) designed to develop or contribute to generalizable knowledge” - Code of Federal Regulations -

Viewpoints of a Research

Researches can be divided based on their viewpoints. There are mainly three viewpoints for categorization of researches.

Application Perspective

Objectives perspective, enquiry mode perspective.

A Research belongs to one type of research in each perspective.

For example, A Research can be an Applied Research in Application perspective. That same research can be a Correlational Research in Objectives perspective and a Qualitative Research in Enquiry Mode perspective at the same time.

In Application perspective, research types are based on the purpose or the final outcome of a research. Means that the research type is determined based on the final outcome of the research. Researches can be divided into two types under the Application perspective.

Pure Research

Applied research.

This is also called as Basic Research or Theoretical Research . This research type focuses on generating new knowledge or new theory. A researcher cannot say that “this is a pure research” unless they have proof to confirm the new theory.

For example, in Isaac Newtons 3rd law is a Pure Research. Because that research introduced a new theory or knowledge about physics.

This research type is widely used in the STEM (Science, Technology, Engineering, Medical) fields. This research type focuses on applying Pure Research outcomes (Theories) into the real world situations. In applied research, there are two further perspectives.

• Technological applied research (Use some technology to apply that pure research theory)
• Scientific applied research (Predicting future issues based on the data and the pure research theory)

In Objectives perspective, research types are based on the depth of the final outcome of the scope. Scope means that how far you do researches or how much deep you do the research on a specific domain. In objectives perspective, researches can be divided into four types.

Descriptive Research

Correlational research, explanatory research, exploratory research.

Descriptive researches are focused on the out layer of an issue or topic. When someone doing a research without investigating the origin or the base knowledge of the research topic or issue, that is called as a Descriptive Research. Those researches answer questions about “What” the characteristics occurred than how/when/why. Descriptive Researches are mainly used for short period researches.

For example, In these days, Sri Lanka is facing lack of fuel problem. The origin of that problem lies within the fuel producing countries. But, If someone is starting a research on that issue in Sri Lankan perspective without investigating the origin problem, that research is called as a Descriptive Research.

This is a non-experimental type of research method. Correlational researches can identify the relationship between two or more variables. Which means a researcher measures two variables, understands and assesses the statistical relationship between them with no influence from any extraneous variable. There are two types of Correlational Research.

Positive correlation - When an increase in one variable leads to a rise in the other variable and decrease in one variable will see a reduction in the other variable.

Negative correlation - If there is an increase in one variable, the second variable will show a decrease and vice versa.

This is the most common type of research method. Explanatory researches are focused on predicting the future effects on current issues. Which means that studying what will happen on future according to the current situations or the issues.

For example, Sri Lanka has a natural harbor which is valuable because it is located in the middle of the ocean. A researcher can do a research and determine, In the future what may happen if the government assign that harbor to another country. That kind of research can be called as Explanatory Research.

Exploratory researches are focused on the areas that is not well understood or sufficiently done research. If some researcher starting to do a research on an area that is not yet discovered properly or not understood yet, that research is called as an Exploratory Research.

For example, Even if there are some theories that says there is water on other planets, there are no much researches or proofs on that theory. So, NASA has started a research on that topic for further proof and knowledge. Therefore, that research can be called as an Exploratory Research.

For a research, it’s compulsory to collect data before begin. In Enquiry mode perspective, research types are determined based on the data type that collected for the research. Based on that data type, researches can be divided into three types.

Quantitative Research

Qualitative research, mixed method research.

Quantitative research is focused on collecting and analyzing numerical data and find averages, patterns and make predictions. (e.g., Age, Temperature, Number of people) Quantitative research is widely used in the natural and social sciences. (e.g., Economics, Marketing, Biology, Chemistry)

For example, “ How has the average temperature changed globally over the last century” is a quantitative research. Because, it requires temperature data which is numerical type of data to determine the final outcome.

Qualitative research is the opposite of the Quantitative research method. Which means that Qualitative research requires non-numerical data. (e.g., text, video, or audio) With qualitative research method we can understand concepts, opinions or experiences. Qualitative research is widely used in the humanities and social sciences. (e.g., Education, Health sciences, History, etc.)

For example, “ What factors influence employee retention in a large organization?” is a qualitative research because as it seems it require non-numerical data to process the final outcome.

Mixed method research is a combination of Qualitative and Quantitative research methods. Mixed method research can gain more complete outcome rather than standalone qualitative or quantitative research methods as it contains benefits of both methods. Mixed methods research is widely used in the health, social sciences and societal research.

For example, “ How do student perceptions of their school environment (qualitative) relate to differences in test scores (quantitative)?”

Written by Haritha Sudaraka Abeysundara

Text to speech

Academic Writing: Point of View

If you’re sitting down to write an analytical or research essay (common in the humanities), use the third-person point of view: Achebe argues … or Carter describes her experiences as…

Scientists (including social scientists) tend to use third-person point of view as well, because they depend largely on quantitative research to present their findings or support their opinions: The results indicated…

Occasionally, social scientists and writers in the humanities will use first person to discuss their own experiences while doing research or if writing part of a personal narrative as evidence: After spending a year living with the Upendi, I came to the conclusion that… or Every Christmas we went to the same place, as if our memories could be rekindled…

About Writing: A Guide Copyright © 2015 by Robin Jeffrey is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Through Whose Eyes Are You Observing the Phenomena? The Critical Yet Latent Concept of Researcher Perspective

1. introduction, 2. theory relating to researcher perspective, 2.1 the concept of researcher perspective.

A Researcher Perspective is the viewpoint of a particular stakeholder in the relevant domain, which is adopted by a researcher as the, or a, viewpoint from which to observe phenomena during the conduct of a research project

Figure 1: Conceptual Model of the Research Process

2.2 stakeholders, 2.3 the significance of stakeholders' perspectives for research, table 1: alternative research questions from different perspectives, 2.4 dimensions, table 2: researcher perspectives within dimensions and levels of abstraction, 2.5 single-perspective research.

"What proportion of social media users need to authorise the provider to exploit their data to ensure that advertising-based business models are viable?"

"What techniques and tools are available to social media users to enable them to obfuscate, subvert or falsify their identities and locations, in order to prevent the provider from exploiting their data?".

2.6 Dual-Perspective and Multi-Perspective Research

3. the modest empirical base, table 3: summary of findings, 4. discussion, 5. conclusions, acknowledgements, author affiliations.

Created: 17 November 2016 - Last Amended: 1 August 2018 by Roger Clarke - Site Last Verified: 15 February 2009 This document is at www.rogerclarke.com/SOS/RP8.html Mail to Webmaster    -    © Xamax Consultancy Pty Ltd, 1995-2022    -    Privacy Policy

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Search catalog, critical thinking and academic research: point of view.

• Information
• Point of View
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Understand Point of View

No one is completely objective or unbiased. Every researcher has a particular point of view, as does every outside source.

As you work through the research process, think carefully about your beliefs, assumptions, and potential biases. How might these things influence your interpretation of outside sources? How does your point of view affect whether you agree or disagree with a certain argument? You should try to be reasonably objective in your research, looking at multiple points of view, not just those sources that agree with what you already think. However, you should also be aware of your point of view and how it influences your thinking.

Likewise, you should look for the beliefs, assumptions, and biases that influence other points of view. And you should be wary of authors who do not acknowledge their own biases or the existence of other points of view.

Critical Questions

• How am I looking at this research topic or question?
• What are my potential biases?
• What are some other possible points of view on this issue? Have I considered them?
• Why does this author view the issue in this way?
• What are the author's potential biases?
• Does this source acknowledge other perspectives?
• What interests are served by adopting this point of view?
• Who or what is responsible for this content/publication? A corporation? A political organization?
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Home > Journals > AIS Journals > JAIS > Vol. 21 > Iss. 2 (2020)

Research Perspectives: Through Whose Eyes? The Critical Concept of Researcher Perspective

Roger Clarke , University of New South Wales / Australian National University Follow Robert M. Davison , City University of Hong Kong Follow

In this article, we explore the notion of “researcher perspective,” by which we mean the viewpoint from which the researcher observes phenomena in any specific research context. Inevitably, the adoption of a particular viewpoint means that the researcher privileges the interests of one or more stakeholders while downplaying the interests of other stakeholders. Preliminary empirical analysis of a corpus of 659 articles published in three separate years in the AIS Basket of Eight journals, undertaken in preparation for the present paper, revealed that around 90% of articles (1) adopted a single-perspective approach, (2) were committed solely to the interests of the entity central to the research design, and (3) considered only economic aspects of the phenomena investigated in the research. Taken together, we argue that these three characteristics are unhealthy for the discipline and are likely to lead to the neglect of important research opportunities. We suggest that the principle of triangulation be applied not only to data sources and research methods, but also to researcher perspectives and that a consequent broadening of the IS discipline’s scope is essential. We conclude the article with prescriptive recommendations for the practice of research that is relevant to multiple stakeholders.

Recommended Citation

Clarke, Roger and Davison, Robert M. (2020) "Research Perspectives: Through Whose Eyes? The Critical Concept of Researcher Perspective," Journal of the Association for Information Systems , 21(2) , . DOI: 10.17705/1jais.00609 Available at: https://aisel.aisnet.org/jais/vol21/iss2/1

10.17705/1jais.00609

Since April 02, 2020

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Viewpoint: Information systems research strategy

This article 1 , 2 has two aligned aims: (i) to espouse the value of a strategic research orientation for the Information Systems Discipline; and (ii) to facilitate such a strategic orientation by recognising the value of programmatic research and promoting the publication of such work. It commences from the viewpoint that Information Systems (IS) research benefits from being strategic at every level, from individual researcher, to research program, to research discipline and beyond. It particularly advocates for more coordinated programs of research emphasising real-world impact, while recognising that vibrant, individual-driven and small-team research within broad areas of promise, is expected to continue forming the core of the IS research ecosystem. Thus, the overarching aim is the amplification of strategic thinking in IS research – the further leveraging of an orientation natural to the JSIS community, with emphasis on research programs as a main strategic lever, and further considering how JSIS can be instrumental in this aim.

Introduction

This article is motivated by the viewpoint that Information Systems (IS) research (and all research) benefits from being strategic 3 at every level, from individual researcher, to research program, to research discipline and beyond. 4 I first argue the merits of a strategy orientation on the Information Systems discipline (the discipline level) and introduce “IS discipline (ISd) strategy” as a new theme of Strategic Information Systems (SIS) research. My focus is more specifically on ISd research strategy. An important mechanism of ISd research strategy is research programs (the program level). There are strong motivations for research to become more programmatic. Programmatic research (defined in Section ‘(E) ISd-Research Strategic Programs’) is necessarily strategic, entailing larger and longer-term investment and risk, and concomitantly, increased oversight and direction. For scope reasons, I do not focus on the potential value from individuals being strategic in their research activity (the individual level), an important topic warranting separate attention. It is hoped the discussion herein will engender a longer-term and more strategic view of IS research activity, perhaps promoting coalitions, engagement and impact.

The viewpoint proceeds as follows. I next present background to the challenges currently faced by disciplines and briefly explore their contemporary strategic context. I then introduce the notion of Information Systems discipline (ISd) research strategy, identifying five valuable levers or strategy mechanisms: A) strategy theory and its intersection with the nature of the discipline; B) strategic governance; C) strategic methods; D) strategic foci; and E) strategic programs. Having argued the value of adopting a strategy lens on ISd-research, in part four I consider possible paper submissions to JSIS and elsewhere that address any of the five ISd research strategy mechanisms, with an emphasis on strategic programs. The viewpoint concludes with discussion on contributions, limitations and future directions.

Like all disciplines, ISd faces many challenges. Changes in the Higher Education sector worldwide “ have meant a growth in the strength and number of forces acting on academic cultures ” ( Becher & Trowler, 2001, p. xiii ). In recent decades, the way in which knowledge is produced has been changing ( Gibbons et al., 1994 ) and these changes are impacting ISd. Gibbons et al. describe two modes of knowledge production: “ By contrast with traditional knowledge, which we will call Mode 1, generated within a disciplinary, primarily cognitive, context, Mode 2 knowledge is created in broader, transdisciplinary social and economic contexts .” (p.2) Manathunga and Brew (2012, p. 45) contrast Mode 1 as “ a situation where knowledge was principally defined within universities by academics within well-defined disciplinary domains ” with Mode 2 in which “ governments intervene in dictating research agendas through, for example, their funding and evaluation mechanisms requiring researchers to focus on short-term clearly defined project outcomes that have economic benefits ”, “ there is critical questioning by an informed public of the practical and ethical implications of particular discoveries and programmes ” [i] , and there are “ new triple helix linkages between universities, the state and industry ”.

In parts of the world, the shift towards Mode 2 knowledge production has been long-standing ( Trowler, 2012 ). Funding agencies such as governments are playing an important role in this shift, using drivers such as performance-based research funding systems (e.g. ERA in Australia and REF in the UK), funding models that encourage researchers to partner with industry and to achieve commercial outcomes (e.g., co-operative research centres and centres of excellence) and that emphasise research aimed at defined regional and national objectives ( Turpin & Garrett-Jones, 2000 ). While this shift has been less pronounced and less orchestrated in the U.S. and places where universities are comparatively more autonomous and less reliant on government funding ( Lepori et al., 2019 ), it is happening, and has the potential to mushroom as the world transitions to a new state post-COVID-19 5 .

The Information Systems discipline has from its inception needed to adapt rapidly to changing technologies and environments. Merali et al. (2012) consider that the discipline’s “ adaptive characteristics” and “the coexistence of stability (and relatively smooth evolution) at the meta-level with diversity and churn at lower levels suggest the kind of ambidexterity” that will allow “the co-existence of both ‘Mode 1’ research emphasising the creation of a rigorous body of knowledge and establishment of identity of the field and its researchers in academia, and ‘Mode 2’ research having a trans-disciplinary character, working across boundaries with heterogeneous stakeholders and real world problems” (p. 131).

Accelerating social and technological change ( Krishnan, 2009 ) and the de-professionalization of academia have also been impacting disciplines. De-professionalization is evidenced by the number of academics in non-tenured positions ( Mazurek, 2012 ) 6 ; by the “ the multiplicity of managerial responsibilities” being added to the workload of academics ( i.e. additional “ responsibility without power”) ( Demailly & de la Broise, 2009 ); and by “ requirements to meet externally imposed performance criteria” and “ demands to demonstrate the relevance of their work in relation to new institutional mission statements ” ( Beck & Young, 2005, p. 184 ). In 2016 with colleagues, I wrote ( Gable et al., 2016, p. 693 ), “ The challenge today is from de-professionalization, a challenge facing all academic disciplines to varying degrees. We sense a growing tension between the disciplines and institutions that seek increased allegiance from individuals in the face of increasingly demanding organizational KPIs and new directions. That strong institutional pull demands that disciplines better communicate their value proposition and reconsider opportunities for reinforcing and strengthening the values, beliefs, and codes that have underpinned IS research. This is a complex, recent, and potent development demanding further research scrutiny .” [ii]

Articles discussing the state of the Information Systems discipline are many and varied and opinions often conflict. One key debate has centered on what forms the core and scope of IS. This debate generated a series of eleven articles published in the Communications of the Association for Information Systems (CAIS) in 2003 (e.g. Alter, 2003 , Myers, 2003 ) and continues today (e.g. Petter et al., 2018 ). Another debate concerns the nature of the IS discipline; this debate is exemplified by the eight articles in CAIS in 2018 that debated whether information systems is a science (e.g. Dennis et al., 2018 , McBride, 2018 ). These are important debates, as the degree of convergence of a discipline can have political implications. “ Convergent communities are favourably placed to advance their collective interests since they know what their collective interests are, and enjoy a clear sense of unity in promoting them ” ( Becher 1989, p.160 ).

The IS discipline too is varied worldwide. From our 2006 multi-country case study of the state of the IS discipline in Pacific-Asia ( Gable, 2007 ) we observed many differences; for example, there were stark differences in levels of engagement with practice across countries surveyed. That study's theoretical framework ( Ridley, 2006 ) was partly guided by Whitley’s Theory of Scientific Change (1984a) . Scientific fields are seen as “ reputational systems of work organization and control ” ( Whitley, 1984b, p.776 ) and Whitley proposes that there is an inverse relationship between the impact of local contingencies and a discipline’s degree of professionalism and maturity. With reference to Taiwan and Singapore I wrote “ These two cases alone – where local contingencies have much influence on IS in universities, yet where IS in the universities is strong and of high status - suggest a need to revisit the theory, and cast doubt on the theoretical proposition that a mature discipline should be uniform internationally, and relatively uninfluenced by local contingencies ” ( Gable, 2007, p. 17 ).

Many disciplines have questioned whether they will continue existing, for example Sociology ( McLaughlin, 2005 ), Accounting ( Fogarty and Markarian, 2007 ), and Strategic Management ( Jarzabkowski and Whittington, 2008 ). Less apocalyptic prognosticators have over the years suggested various ways of responding and strengthening their discipline (e.g. Keith, 2000 , Kitchin and Sidaway, 2006 , Somero, 2000 , van Gigch, 1989 ). IS researchers have made several recommendations. Thatcher et al. (2018, p. 191) propose three: “ (1) adopt an entrepreneurial model of scholarship, (2) engage with practice, and (3) double down on the intertwining of the social context and IT” . Hirschheim and Klein (2012, p. 193) suggest there is a need for “ continuous effort to define and redefine IS to reflect the evolving boundaries of the field ”. Other prominent researchers have suggested there is value in re-examining current methods and understandings, for example Gregor (2018, p. 114) suggests “ there should be ongoing questioning of our epistemological foundations” . Nunamaker et al. (2017, p. 335) believe that how we conduct research can make a difference and that “ IS researchers can produce higher-impact contributions by developing long-term research programs around major real-world issues, as opposed to ad hoc projects addressing a small piece of a large problem ” [iii] . Perhaps the challenges disciplines have been facing will pale in comparison to new challenges that emerge post-COVID-19, or perhaps COVID-19 will in the main, exacerbate existing forces. 7

In summary, ISd is amidst major change due to accelerating social and technological change, de-professionalization, the inexorable shift towards Mode 2 knowledge production, and COVID-19. A variety of responses has been proposed, all of which imply value from a longer-term strategic orientation and closer proximity to practice.

Information Systems Discipline (ISd) Research Strategy

Through an archival analysis of 316 Journal of Strategic Information Systems (JSIS) research papers, from the journal’s inception through to the end 2009 ( Gable, 2010 ), I discerned a high-level three category classification of Strategic IS (SIS) research: (i) IS for strategic decision making, (ii) Strategic use of IS, and (iii) Strategies for IS issues. As implied in the preceding section, disciplines are facing challenges and discipline-level strategizing is warranted. Herein I propose a fourth category of SIS research; namely, (iv) IS Discipline Strategy. Fig. 1 depicts the relationship between these four nodes or research themes and SIS (wherein SIS is represented as one of several top-level domains of IS research). It is notable that the primary referent or focal phenomenon in (i) and (ii) is the “information system”. In (iii), the primary referent is “IS management” or the “IS function” (whether a formal or informal function; whether central or diffuse). With the addition of (iv) the fourth theme of SIS, I introduce a further primary referent, this time the “IS discipline” (ISd). Of course ISd, like other entities, may also benefit from IS for strategic decision making, strategic use of IS (e.g. the Association for Information Systems (AIS) web presence) and strategies for IS issues (e.g. information management of the AIS eLibrary), thus research in the original three categories may have direct or analogous relevance for ISd as well.

A Strategic Information Systems (SIS) Research Taxonomy (adapted from Gable, 2010 ).

While proactive IS discipline strategizing [iv] may initially seem insular or self-serving and perhaps counter to the general conception of academe as open, collegial and cooperative, I nonetheless believe that many disciplines as traditionally constituted are under some threat and there is value in being proactive rather than passive in responding. All disciplines will benefit from pertinent reflection and positioning (many envisaged findings regarding ISd would have direct, parallel relevance and value in other disciplines).

It is important to note that representing ISd Strategy in Fig. 1 as a fourth theme exaggerates its pertinence in SIS research. This amplification of prominence serves the purposes of this article, the main of which is to promote attention to strategy in IS research. Many alternative classifications of SIS research are possible, and while the suggested four categories of SIS research are not entirely mutually exclusive, nor strictly at the same level of abstraction, they are useful (perhaps a taxonomy rather than a classification).

With reference to Fig. 1 , it is useful to attempt a single overarching research question that captures much of the research represented by each node of such a hierarchy; questions at higher levels being inherently broad, becoming more specific at lower levels (each node can be expanded into a set of underpinning sub-nodes and research questions; such a detailed research agenda was outside scope). The question being asked at the new fourth SIS node is “What is the ISd Vision and how should ISd strategize?” (Recognising that the vision today will likely be much different from 10 or even five years ago). Referring back to the original three categories (themes in Fig. 1 ) identified in ( Gable, 2010 ), in retrospect I suggest the following questions: (i) IS for Strategic Decision Making – How can IS support strategic decision making? (ii) Strategic Use of IS - How can IS be used strategically? (iii) Strategies for IS Issues - What strategies support the IS management/function? (While it is difficult to divine a sufficiently broad yet adequately focused question at the highest SIS node, I suggest “How can IS be strategic? (where IS = Information Systems, IS function, IS discipline).

While much of the discussion herein has relevance to ISd broadly, the focus of this article is on ISd-Research strategy, which is differentiated in Fig. 2 as a sub-theme of ISd strategy (other possible sub-themes might be, for example, Teaching & Learning and Professional Service – in Australia many universities evaluate academic performance in these three areas). Following I argue the relevance of five mechanisms of ISd-Research Strategy: (A) ISd-Research Strategy Theory, (B) ISd-Research Strategic Governance, (C) ISd-Research Strategic Methods, (D) ISd-Research Strategic Foci, and (E) ISd-Research Strategic Programs. The dictionary definition of mechanism serves our purposes here; “ An Instrument or process, physical or mental, by which something is done or comes into being ” ( The Free Dictionary by Farlex ). A definition more specific to this article might be “a means of being strategic”. Thus, ISd research is strategically facilitated through these mechanisms. The five mechanisms in Fig. 2 can usefully be considered as pertaining to ISd-Research Strategy practices: (A) how we define ourselves (as a discipline), (B) how we govern (as a discipline), (C) what we do (as a discipline), (D) how we do it (as a discipline), and (E) how we organise (as a discipline). I next discuss each of these five mechanisms (A) through (E) in turn, thereby also further explicating Fig. 2 .

Information Systems Discipline (ISd) Research Strategy: a Research Taxonomy.

(A) ISd-Research Strategy Theory

There is merit in exploring relevant research strategy theory for ISd. The IS discipline can influence perceptions, conceptions and directions of IS research through theorising IS research strategy. While parallels may exist between disciplines and other entities that have been the focus of past strategy research (e.g., commercial enterprises), disciplines, and ISd more specifically, have distinctions of strategic consequence. There is thus value in critically revisiting extant strategy theory with a view to its relevance in this context. 8

In a seminal work on strategy, Mintzberg (1987) presented and discussed several interrelated definitions of strategy: a plan (a consciously intended course of action), a pattern (consistency in behaviour, whether or not intended), a position (locating the organization in the environment), and a perspective (individuals linked by common thinking and behaviour). Mintzberg particularly emphasised the significance of collective perception and action in any strategy, stating “ strategy is not just a notion of how to deal with an enemy or a set of competitors or a market … It also draws us into some of the most fundamental issues about organizations as instruments for collective perception and action ”. Thus, the process of ISd-research strategizing offers value irrespective of the outputs.

The literature on strategy as practice might also aid our understanding. Peppard et al. (2014, p.1) suggest “ In addressing strategy as practice, the focus of research is on strategy praxis, strategy practitioners and strategy practices […] – the work, workers and tools of strategy in other words .” Strategy as practice takes the view that “ we must focus on the actual practices that constitute strategy and strategizing while at the same time reflecting on our own positions, perspectives and practices as researchers ” ( Golsorkhi et al., 2010, p. 2 ). How does the discipline strategize both consciously and unconsciously (e.g. by our patterns of behaviour)? In essence, the five mechanisms of ISd-Research strategy in Fig. 2 represent research practices of the ISd.

The strategy as action approach has been discussed in relation to research on information systems strategy (e.g. Whittington, 2014 ) but has yet to be applied to ISd-research strategy. A novel paper that broaches ISd-research strategy theory is by Merali et al. (2012) which conceptualises the IS research domain as a complex adaptive system. The authors suggest that the field of IS research is relatively diversified and dynamic, with new topics rapidly emerging, thus the landscape of IS research is constantly changing with scholars frequently shifting attention to investigate new IS and IS phenomena. They ultimately argue that the IS domain has sufficient adaptive capacity to evolve in the emerging competitive landscape, responding to the turbulence, uncertainty and dynamism of IS research (a complex adaptive system).

Banville and Landry (1989) had an alternative view of IS research. They characterised MIS [v] as a fragmented adhocracy (which suggested a lack of strategic direction). A few years later, Hirschheim and Klein lamented “ That our current status remains a ‘fragmented adhocracy’ suggests the field may indeed be in crisis or headed for a crisis ” stating that the disconnects within the IS research community arise “ from the lack of communication among the numerous research sub-communities ” (p.254). Hirscheim and Klein recommended addressing the communication deficit through the development of a body of knowledge in information systems. Taylor et al. (2010) were more optimistic: they believed they demonstrated that “ over the 20-year period from 1986 to 2005, the discipline has shifted from fragmented adhocracy to a polycentric state, which is particularly appropriate to an applied discipline such as IS that must address the dual demands of focus and diversity in a rapidly changing technological context ” (p. 647). These dual demands have also been recognised by other IS scholars. For example, Tarafdar and Davison (2018, p. 543) envision IS as “ a flexibly stable discipline that has both (1) a consolidated deep structure (through the Single and Home Disciplinary contributions); and (2) a periphery of flux (through the Cross Disciplinary and Interdisciplinary contributions )”.

What other theory might be relevant to ISd research strategy? In light of the Hirschheim and Klein (2003) call for the development of a body of knowledge in information systems, an example might be analogizing “research knowledge assets” with knowledge assets broadly, and drawing on knowledge management theory. James (2005) usefully bridges knowledge management and strategic management in ways that lend readily to the notion of research knowledge assets. He found that “ existing knowledge assets form the basis of strategies” (p.165). Theories and strategies from other analogous spheres could be adapted and applied to IS research practice. An example might be analogizing research practices with consulting practices and borrowing from Maister (1993) to develop what I call a Research Practices Lifecycle View, which I elaborate further in later discussion on “ISd-Research Strategic Programs”.

(B) ISd-Research Strategic Governance

For our purposes, strategic governance mechanisms are simply defined as mechanisms of alignment of research practice with ISd-Research Strategy . The IS discipline has ability to influence research strategy through introducing and adjusting various governance levers [vi] . High-level examples in Fig. 2 include the Association for Information Systems ( https://aisnet.org ), doctoral consortia, and conferences. The AIS is a primary ISd-Research Strategic Governance Mechanism for many. Reverse-engineering the AIS website would yield, for example, the AIS Faculty Directory (valuable in expanding and tracking membership), the eLibrary (establishing what is in, and what is not), AISWorld Listserv (communicating core ideas), Awards & Recognitions (signalling what is valued), AIS journal viewpoints and review/fit policy e.g. JAIS/CAIS, and so on [vii] . At a more local level, using Australia as an example (being Australian, I use several Australian examples), strategic governance mechanisms include: the AAIS (the Australasian regional branch of the AIS), Australasian Conference on Information Systems (ACIS), Australian Council of Professors and Heads of Information Systems (ACPHIS), IS Heads of Discipline listserve (ISHoDs) and various initiatives of AAIS and ACPHIS. Example initiatives of AAIS and ACPHIS include their lobbying for IS representation on Australian Research Council (ARC) panels, their Journal and Conference rankings [viii] and awards (e.g. ACPHIS Best Australian IS PhD Medal), and representation in the Australian Computer Society, etc.

“Research on strategy broadly suggests that professional organizations use signalling mechanisms such as awards to direct attention to high-level priorities” ( Ghobadi & Robey, 2017, p. 360 ). Best publication awards signal the core values and priorities of the field. 9 The characteristics of papers/theses that receive awards form patterns that influence what is researched and how [ix] . For example, Ghobadi and Robey found that “ most of the award winning IS articles rate high on theory building” and suggested that this is to position IS as a “ distinct discipline” rather than one that mostly borrows theory . The discipline needs to be aware of the patterns of research that awards are solidifying and take note of evolving trends that may necessitate the development of newer more advantageous patterns [x] . Strategic action in this regard is important “ to support the continuous development of the field ” ( Ghobadi and Robey, 2017 ).

As a further example, my co-authors and I ( Gable et al., 2016 ) explored the strategic importance of doctoral consortia as an ISd-Research strategic governance mechanism. We differentiated four levels of consortia: (i) International (ICIS), (ii) Regional (e.g. PACIS), (iii) National (e.g. ACIS) and (iv) Local (e.g. the School of Information Systems Doctoral Consortium at Queensland University of Technology) and considered their relative roles and influence. We argue that “ Consortia are strategic: a centrally important mechanism for sustainment, rejuvenation and defence of the discipline” (p. 691) . Closing the article, we further wrote “ we return to the advent of the 1st ICIS Doctoral Consortium in 1980, seen against a background at that time of questioning the status of Information Systems as a legitimate academic discipline. In some sense, the adversary then was other disciplines, and their perceived higher standards of professionalism. Today there is a different adversary, and again we see the Consortium as a possible vehicle of discipline reinforcement.”

Thus, the strategic influence of existing and potential governance mechanisms is readily apparent. That said, more transformational governance is possible ( Rosemann, 2017 ).

(C) ISd-Research Strategic Methods

The IS discipline has the ability to influence the kinds of questions being asked and research being pursued through the development, promotion and endorsement of contemporary research methods. Grover and Lyytinen (2015) argue the need to “ critically examine and debate the negative impacts of the field's dominant epistemic scripts and relax them by permitting IS scholarship that more fluidly accommodates alternative forms of knowledge production .” Given the IS research phenomenon of interest (the nexus of people and technology) and our proximity to rapidly changing technology, and the dynamism and generativity of these developments, we need as a discipline to explore new and improved ways of researching ( Rai, 2018 ). Moreover, though the outputs and outcomes of strategic methods may offer potential for research broadly, they offer particular advantage to the IS discipline. This is one of several places in the viewpoint where I advocate that ISd “practice what it preaches” by deploying within ISd, research methods developed by ISd, 10 the implicit question being “What new research methods does ISd require in order to remain relevant?” Put differently, the question might be, “What new research methods would be to the advantage of ISd research?”

As an example Galliers (2011, p. 300) suggests that, what previously might have been considered opposing research traditions, “ might unite to provide a more holistic, and ultimately more edifying, research agenda: one that is inclusive of various research approaches, and thereby better able to provide fresh, and more plausible insights into the complex phenomena we study ”. Tarafdar and Davison (2018) warn of the risk of asking narrow research questions in an attempt to reduce their complexity. They observe a “ lack of multiple IS subdisciplines in a single paper [suggesting] that IS phenomena are perhaps not being investigated in their full richness and complexity, reinforcing the concern about narrowly conceptualized research questions” (p.539). More nuanced, creative and relevant questions may demand new methods.

IS is particularly well placed to progress new and strategic research methods by leveraging what we already know, have and do [xi] . I first argue there is merit in exploring the potential from employing various IS representation techniques in the redescription (or reverse engineering) of existing research methods (e.g. Zhang and Gable, 2017 , Leist and Rosemann, 2011 ). In addition to facilitating improved understanding and combining of well-trod methods, such “redescribing the old” is expected to better-inform design of new research methods, particularly those needed by IS given the dynamism of our focal research phenomena. I further subscribe to the views of March and Smith, 1995 , Venable and Baskerville, 2012 who advocate the merits of a design science research (DSR) approach to the design of new research methods: research methods here conceived as designed artifacts, and DSR originating out of IS. Thus, we see that IS is both well placed to contribute to the design of improved and new research methods and to benefit as a discipline from the adoption of such improved and new methods. 11

ISd has not been idle with innovating new methods, in example the extensive methodological thought around Design Science Research (e.g. Vaishnavi et al., 2019 ). As further example, with net-nography (an online research method originating from ethnography employed to study social interaction in digital communication), we observe a phenomenon through the very technologies that we study (thanks to a panellist here). Grover et al. (2020) discuss the dramatic shift occurring to big data research (stating 16% of papers in top IS outlets employed this approach in 2018). They offer several conjectures that suggest possible unanticipated consequences of these approaches: the implication being these approaches have not been researched adequately. Thus, while I do not advocate epistemological anarchy ( Treiblmaier et al., 2018 ), I believe much more is possible. Furthermore, the focus of the information systems discipline on new phenomena, in combination with our position straddling the sciences of the real and artificial, arguably places us in a position of strategic advantage with regard to epistemology. Put simply, if we investigate new phenomena first, then we are well placed for providing leadership in how to investigate it.

(D) ISd-Research Strategic Foci

I here suggest that an important mechanism of ISd-Research Strategy is “ISd-Research Strategic Foci”, the overarching question being “What new research foci are strategic for ISd?” The IS discipline has ability to influence the direction of research in IS through identifying, recognising and promoting promising research foci.

It is apparent that the notions of ISd-Research Strategy and ISd-Research Strategic Foci imply centrally coordinated strategizing and decision-making. While this does occur, I think increasingly (e.g. by AIS in relation to the international discipline; by ACPHIS in relation to the Australian IS discipline; by universities, faculties and schools in relation to the local IS presence), it is acknowledged that historically, developments have been more distributed and organic. Thanks to a panellist who suggested “ The history of the discipline suggests that the strategic foci (at a disciplinary level) are plastic, emergent, and to a considerable extent, responsive to changes in technology and its role and uses. Studies examining the “core of the discipline” have frequently been bottom up” and have identified changes over time. Arguably, this has affected the ability of IS researchers at various levels to articulate the strategic foci of their work and how it aligns with broader institutional or national research strategy .” Such developments have been driven by individual preferences, capabilities, efforts and circumstances, and only indirectly shaped by various governance mechanisms (e.g. doctoral consortia and conference panels, editor and reviewer feedback and other institutional and discipline-cultural influences).

Though the shift to performance-based funding systems which emphasise impact and economic benefits is anticipated to amplify efforts to centrally coordinate research activity (e.g. in Programs – to be discussed), individual-driven and small-team research within broad areas of promise is expected to continue forming the core of the research ecosystem. Such broad areas of promise, or foci, may evolve organically, inductively, from the bottom-up. Blue-sky research is acknowledged to form “ a vital part of scientific discovery ” ( Linden, 2008, p. 10 ). Alternatively, such foci might be promoted top-down, by the AIS or other authorities (international, regional or local) - promoted, but not coordinated. Many recognise that such highly distributed inductively selected research activity is foundational to science. Wu et al. (2019) analysed 65 million papers, patents and software products, spanning 1954–2014, concluding that smaller teams have tended to disrupt science and technology with new ideas and opportunities, ultimately concluding, “ both small and large teams are essential to a flourishing ecology of science and technology ”.

While, I believe that in IS individual-driven and small-team research in IS has predominated and should continue to be strongly encouraged and supported, I later argue that to achieve a flourishing ecology, we also need to promote larger, more programmatic research initiatives [xii, xiii] . I recognise that a dichotomy of small and large initiatives is coarse and accept there is value in considering more of a continuum. As suggested by one senior scholar, “ sometimes people proactively/serendipitously reach out, pool resources if working on the same topic and cumulatively build the research projects. This can be an organic and agile way of doing things rather than the traditional funding way. A combination of programmatic and individual if you will – perhaps a ‘third’ way that could capture the best of both worlds? ”

A journal too can be strategic (in fact all are at some level) and a vehicle for promoting strategic foci, and JSIS is a prime example. In the first issue of JSIS Bob Galliers reported “ In business schools around the world it is often the case that IT, IT strategy and information management are considered at best as optional topics unworthy of being included in the core programme” and “ discussion of information and IT issues is not integrated into other business topics ” ( Galliers, 1991, p. 3 ). Galliers' championing of JSIS in the late 1980s was a strategic move to address this concern and to claim related research for the IS discipline, by promoting an outlet with this focus.

In closing this section, I argue there is value in promoting strategic research foci [xiv] , as distinct from strategic research programs (to be discussed). I believe there is merit in promoting, for example, Digital Innovation as a research focus of individual researchers and small teams (important research questions demanding attention being a key motivation for a focus), while possibly in parallel orchestrating larger more programmatic oversight (e.g. a Digital Innovation research program). Further, not all research foci lend themselves to a programmatic approach or warrant the size of a program.

(E) ISd-Research Strategic Programs

I suggest that an important mechanism of ISd-Research Strategy is strategic ISd Research Programs, or “ISd-Research Strategic Programs”, the overarching question being “What research programs are strategic for ISd?” [xv, xvi] . As mentioned above, differentiating strategic programs from strategic foci highlights the possibly more proactive role of the AIS or other coalitions within the IS discipline in strategically coordinating targeted research. For my purposes herein, research programs are simply “organisations for conducting programmatic research.” I intentionally define programs broadly and loosely to encompass both less, and more formal organisations or orchestrations of research.

The notion of programmatic research is not well established. In Communication, Benoit and Holbert (2008, p. 615) suggest programmatic research “ systematically investigates an aspect of communication with a series of related studies conducted across contexts or with multiple methods .” In Education, Berninger (2009, p. 69) suggests “ Programmatic research is designed to answer questions systematically, with the results of one study informing the research questions and design of the next study in a line of research that seeks comprehensive understanding of a phenomenon .” In Management Information Systems, Martin (1995, p. 28) defines programmatic research as “ a structured plan for conducting studies systematically across the entire span of a chosen field of enquiry .” While all of these definitions overlap and complement, none explicitly addresses the extension of research strategy through to implementation in practice, with feedback, a notion we endorse and depict in Fig. 3 .

Relevance Realisation Lifecycle Model [adapted from Moeini et al, 2019 ].

For the purposes of this paper I define programmatic research as the systematic and holistic investigation of strategically significant phenomenon through multiple coordinated and interrelated projects by a team of researchers, with the joint aims of knowledge contribution and positive impact on the world. An assumption is that programmatic research is in accord with strategy; there can never be reason to initiate research on the scale of a program that is not in accord with strategy. And though a basic-research program is conceivable, herein there is the assumed joint intention of positive impact as well as knowledge contribution. The program itself may extend to impact and outcomes or it may be extensible to impact, and what is meant by holistic must be locally defined. 12

Programmatic research demands closer interaction and cooperation amongst the program members. They rely more closely on each other’s ideas and outputs, the related ownership of which is more complex and nuanced. A program’s scale facilitates specialisation of function, which introduces complexity around the allocation of roles and rewards. The relative longevity of programs suggests the possible need to conceive of intermediate outputs and outcomes of value to the program, where the value to individuals isn’t clear and needs to be managed or orchestrated. Terms often used to differentiate programs (from projects) are larger, longer, more complex, less well defined, with more stakeholders, evolutionary, holding a systems view, exhibiting a plurality of goals, and being more strategic. More specific to research, other terms include multidisciplinary, trans -disciplinary, lifecycle-wide, and multi-paradigmatic (of course these terms do not pertain strictly to either projects or programs).

The Project Management Institute (2008, p. 9) defines a program as “ a group of related projects managed in a coordinated way to obtain benefits and control not available from managing them individually. Programs may include elements of related work outside the scope of the discrete projects in the program. A project may or may not be part of a program but a program will always have projects. ” Thus, there may be outputs/outcomes that pertain to the program but not directly to any of its component projects. This has profound implications for the planning, identification and recognition of value that derives from the research program. Clarke and Davison (2020) argue that what they call “multi-perspective research”, while feasible within individual research projects, may be more readily achieved through research programmes.

A conceptual framework I've found valuable for interrelating and accommodating diversity in programmatic research is what I refer to as a “research practices lifecycle view”. I analogize research practices with consulting practices and borrow from Maister (1993) who differentiates three types of consulting practices: the “big brains” practice which employs considerable raw brain power to solve frontier (unique, bleeding edge, new) problems; the “grey hair” practice which has prior experience of similar situations; and procedural practices which use developed procedures and systems to handle specific problems efficiently. Choo (1995) describes these as background knowledge framework, practical know-how and rule-based procedures. I loosely refer to analogous research practices as Expertise, Experience and Efficiency respectively, and suggest that these are not discrete, but rather positions on a continuum; and that practices tend to start from strong emphasis on Expertise (e.g. novel, basic research), and with the passage of time and through experience gained, move along the continuum towards Efficiency (e.g. incremental replication studies) and relatively more applied research. In a large research group, or through collaboration, such practices can co-exist and complement, with more basic research findings generating more applied, possibly practice-based testing and extension studies (perhaps extending to commercialisation). Each practice area benefits from quite different capabilities and team member motivations, thus accommodating diversity. The Research Practices Lifecycle View framework further demonstrates the value to be gained from a broader and longer-term view of research; it represents a higher level of abstraction in research design/strategy, that spans and interrelates multiple, otherwise disjoint zero-base initiatives.

Ultimately, I chose to amplify the value of programs as distinct strategic mechanisms. Such mechanisms may be higher-order and formal, for example, the AIS Bright ICT Initiative ( Lee, 2015 , Lee et al., 2018 , Lee et al., 2020 ); a key project of this initiative aimed to develop the framework for “ a new and safer Internet platform ” ( Lee, 2015, p. iii ). Programs can vary in size, scope and formality, from substantial and long-lived programs such as AVATAR (Automated Virtual Agent for Truth Assessments in Real-Time), an almost 20 year partnership of eight universities led by the University of Arizona in conjunction with more than ten Government departments ( Nunamaker et al., 2017 ), to more local research concentrations (e.g. the continuing trend in Australia towards concentrating people and resources locally – e.g. within a “Centre” – in areas of perceived strength, promise and advantage 13 ). Though I imply herein that such programs are at this stage often more implicit than explicit (and vary internationally in terms of their prevalence), it is felt that all such forms can be usefully considered under the umbrella of research programs [xvii] .

ISd-Research Strategy Publications

This viewpoint set out with two main aims: (i) to argue the importance of a strategic research orientation for the Information Systems Discipline (discipline level); and (ii) to facilitate such a strategic orientation by trumpeting the value of related research and promoting its publication. Having in Section ‘Information Systems Discipline (ISd) Research Strategy’ above addressed (i), in this section I further address (ii) aiming to partially clear-the-way 14 for manuscript submissions to JSIS and elsewhere, in attention to any node of the ISd-Research Strategy branch in Fig. 2 , but with particular emphasis on research programs (program level).

Thus, a key aim of this viewpoint is to facilitate manuscript submissions that align with any of the five mechanisms in Fig. 2 ; namely, ISd-Research Strategy … (A) theory, (B) governance, (C) methods, (D) foci, and (E) programs. Papers on (A) theory and (B) governance, though important, are anticipated to be rare, with papers on (C) strategic methods again anticipated to be occasional only. Papers arguing for a new strategic research focus (D) would entail a general call for the IS discipline to pursue research in relation to some new or emerging phenomenon of perceived consequence. The expectations of such papers and the standards for acceptance will be demanding. Such papers will tend to encompass a comprehensive review of the pertinent literature (akin to a review or curation), but rather than emphasising theory development, will include greater detail on the research agenda into the future; they should amplify discussion of potential impact and how the focus is strategic for IS [xviii] (in many ways this current paper is such an article, without the rigorous attention to the literature). Thus far in this section, having very briefly accounted for (A) through (D) of Fig. 2 , I now turn my more detailed attention to (E) Programs.

Papers arguing for a new strategic program would detail either a proposed or an in-progress program of research, evidencing to the extent possible the value of and potential from a programmatic approach. There would be strong emphasis on potential future research, as well as on how the proposed future research is strategic for the IS discipline. Note that articles explicating research Programs, much like foci articles, will often (not always) build on an established track record of work across and beyond the discipline, and perhaps parallel or subsume a curation (e.g. https://misq.org/research-curations/ ) or major structured literature review (e.g. see Rivard 2014 ), while bringing a more strategic lens to the analysis and interpretation, and having greater emphasis on strategic prescription. It is useful to differentiate explicitly the design of these larger programmatic initiatives as “research strategy” as compared to “research design” at a more individual project level. Employing this language, I consider the Tarafdar et al. (2018) suggestion for more longitudinal studies [xix] , to be a research strategy, more at the collective program level.

I am not so naïve as to assume I can here offer comprehensive guidance on what to attend to in the design and reporting of research programs [xx] . Rather I constrain my focus to engendering and promulgating the relevance of such research (here I largely equate relevance with impact which I more specifically define in the next section – ( Davison and Bjørn-Andersen, 2019 ) refer to 'societal impact'), for which purpose I draw on Moeini et al. (2019) . Their work suggests several valuable mechanisms of programmatic research including: (i) the value from adopting a multilevel view in research design (I herein usefully employ this view by differentiating between the individual/project level, the program level, and the discipline level), and (ii) the value of a longer, multi-phase view of research design from knowledge-potential-generation through to realised-impact (a more programmatic view). Though their focus is constrained to relevance, which is not the sole intent of programmatic research, the relevance intent demands a consequential and valuable longer-term, more strategic and holistic view [xxi, xxii] .

A Relevance-Centric Approach to Programmatic Research

Fig. 3 depicts the four phases of research relevance realisation differentiated by Moeini et al. (2019) (Potential > Perceived > Used > Realised). Key variations herein to that work are (i) the alternative conception of their four “dimensions” of relevance potential as “stages” of relevance-potential generation, 15 and (ii) the combining of those stages with their phases of relevance realisation in a process view including feedback loops. I believe this lifecycle view represents a more readily actionable and systematic approach to designing relevance into a larger, more strategic research program. Moreover, though the stages of relevance-Potential generation in Fig. 3 (the left-hand side) tend to be associated with research, and the latter three phases of relevance realisation (the right-hand side … Perceived, Used, Realised) tend to be associated with practice, the stages and phases can be tightly intertwined. For example, in action research or action design science research, or through other larger, longer-term more programmatic research that extends to the later phases of relevance realisation, there can be useful iteration back to earlier phases, thus the feedback loops.

My use of the term “impact” throughout this viewpoint has a different connotation from many. I am not referring to impact-factors and citations, but rather I am concerned with influence on practice and policy (societal value). However, unlike many definitions of “impact on practice and policy”, I am not solely focused on the impact of research outside of academia, as some impact frameworks are (e.g. https://re.ukri.org/research/ref-impact/ ); I also value research that impacts the practice of research. I note that the Strategic Methods node C in Fig. 2 is all about research that impacts research practice.

To more clearly position and bound their study, Moeini et al. (2019) (i) differentiate their focus on “potential practical relevance” from other conceptions of relevance, (ii) differentiate dimensions of relevance, (iii) differentiate stakeholders of relevance, and (iv) differentiate levels of analysis of relevance. These distinctions all have value in clarifying discussion on research relevance, each addressing a different question - What type (phase) of relevance realisation? Which dimension (or stage)? For whom (what goal)? And, at what level (e.g. individual project, Program, research area)? I now turn briefly to (iv) differentiating the levels of analysis of relevance.

As stated at the outset, it is my view there is value in a strategic orientation on research at every level, from individual to program to discipline and beyond. Such a multilevel view of research also recognises that the value from research can be at different levels: individual project, program, domain/field (research area); or emergent. As Moeini et al. indicate, “ while each individual article does not necessarily need to have a high potential for practical relevance … a research area needs to provide a degree of potential practical relevance”. 16 The value of some research may be more local e.g. relevant to the program but not to practice broadly. If such an intermediate output is intended, then perhaps there is a greater need to explicitly position the pertinent sub-study within the larger program. To the extent that individuals have some sense of how their individual projects are part of a larger, multilevel undertaking, they will be better placed to understand and explain the merits of their work to peers, research groups, examiners, reviewers, editors, funding bodies and so on. I believe this multilevel view of research strategy worthy of further attention.

In summary, in this section I advocate for more relevant IS research; an underutilized strategic option. I strongly endorse focusing a relevance-lens on programmatic research, my discussion further amplifying the value of the Moeini et al. (2019) phases of relevance-realisation. I believe such a lens, and approach ( Fig. 3 ) is valuable in the design and reporting of research programs. I encourage researchers to consider this view in crafting future-oriented research programs strategic for the Information Systems Discipline.

Rigour in programmatic research strategy

In the preceding section, I suggested the beginnings of a relevance-centric approach to programmatic research strategy and reporting, my aim being to partially clear-the-way for larger, more relevant, impactful and strategic research and publications. I am not alone in calling for such work. Yet the elephant-in-the-room whenever such calls are made is the unbending expectation of rigour ( i.e. the appropriate and adequate application of methods).

With regards rigour, I differentiate three possible units of analysis: the project, the program (design and execution), and reporting on the program (description and evaluation). My emphasis in this section is on rigour of reporting. I earlier differentiated projects and programs, suggesting that research project design/execution rigour has received much valuable attention, and that further such attention can be strategic for the discipline (and thus would align with node C in Fig. 2 ). In the preceding section I cautiously offered sample guidance on a relevance-centric approach to research program design (or strategy), acknowledging that research programs are large, complex and highly varied.

The question thus becomes how does one rigorously report on, or describe, and perhaps evaluate, a research program? [xxiii] Pawson (2013) recommends that such description and evaluation should be motivated by rigorous attention to the existing knowledge base and possibly by the development of a conceptual framework or platform. This part of a proposed program report, would parallel in many ways a review article, some such articles going further than others, to advocate for a research agenda. 17 To the extent that such a research agenda is new, such a review article comes very close to a research foci article, as described earlier (mechanism D in Fig. 2 ); such a research foci article promoting widespread, organic, individual projects that attend to argued areas of research need and value that are strategic for the discipline. A paper reporting on a program however, must go further, to address the coalition of resources and stakeholders, the range of constituent projects and their logic (the program “meta-logic”), and the strategic value and importance of the coalition for all stakeholders (the coalition, the discipline, the world). Moreover, while individual projects should, to the extent possible in their design and reporting, attend to outcomes, the opportunity to elaborate such potential is greater and thus essential with research program reports.

Given that Research Programs tend to be large, complex and unique, any guidance here on their description, can be high-level and exemplary only. It is possible that a design science research approach may be appropriate (e.g. treating the Program as a designed artefact), or Action Design Research or Action Research 18 (e.g. evaluating Program implementation with theoretical implications). Perhaps appropriate too are evaluation research approaches, which are varied and employed widely in the evaluation of both practice- and research-based programs. While the term “Evaluation Research” implies ex post evaluation, which is perhaps characteristic of much if not most such work, anticipation of such evaluation can usefully inform ex ante program strategy and description.

In closing this section, I acknowledge merit in documenting programs at various stages, with some provisos. While I appreciate the value of documenting each stage of a program of research (proposed, in-progress, and completed) including its planning, implementation and operation, and with attention being given to predicted, intermediate or final outputs and outcomes, I anticipate that being compelling in such writing will be easier where a program has at least commenced and there is some empirical evidence of its progress. Where the program is planned, but yet to commence, its publication might be considered a form of research-in-progress. Where the program is in progress or completed, some form of program evaluation may be appropriate.

This viewpoint argues the merit of focusing a strategy lens on the Information Systems Discipline. I introduced IS Discipline (ISd) strategy as a new theme of Strategic IS (SIS) research, subsequently focusing more specifically on ISd research strategy. I have advocated the value of a strategic orientation at every level, from individual, to Program, to Discipline and beyond. Research programs in particular, can be a valuable mechanism of research strategy. Though I acknowledge variation across regions and countries (and no doubt within countries), there are strong motivations for research to become more programmatic. Programmatic research is necessarily strategic, entailing larger and longer-term investment and risk, and concomitantly, increased oversight and direction.

Careful consideration was given to the fit of this Viewpoint with JSIS. Details of that thinking are included as Appendix A (I encourage all authors of submissions to JSIS to undergo similar reflection). Ultimately though, all rationales are peripheral to the central aim of the article, which is the amplification of strategic thinking in IS research – the further leveraging of an orientation natural to the JSIS community, with emphasis on research programs as a main strategic lever, and further considering how JSIS can be instrumental in this aim.

This article is a response to the ongoing call for increased attention to relevance and impact in research (e.g., Rai, 2017a , Tarafdar et al., 2018 ). Moeini et al. (2019, p. 210) include a table of suggested “ opportunities for improving potential practical relevance” , with recommendations for authors, reviewers and journals. Table 1 lists the six recommendations that apply to journals [xxiv] . The promotion herein of research pertaining to any of the mechanisms of ISd-Research Strategy in Fig. 2 , and of programmatic research broadly, addresses much in Table 1 , perhaps with some emphasis on opportunities (1)–(4) in that table. One Senior Editor of JSIS has been explicitly advocating for (5), more linked publications. More engagement on social media (6) has admittedly only just appeared on our radar. I am with this viewpoint attuning JSIS editors, reviewers and Board members to all of these priorities.

Suggestions to Journals for Improving Potential Relevance (based on Moeini et al., 2019, p. 210 ) [xxvi, xxvii] .

In some sense this viewpoint is also a response to the Tarafdar and Davison (2018, p. 537) call for increased intra- and inter-disciplinary research in IS and to the Galliers (2003) call for trans-disciplinary/inter-disciplinary research. 19 Tarafdar and Davison suggest, amongst other things, that IS journal editors “ consider editorial policies such as sections especially devoted to the interdisciplinary contributions .” That recommendation overlaps several of the six suggestions in Table 1 [xxv] .

Tarafdar et al. (2018) , rather than focusing on kinds of research or publications, refer to kinds of researchers required to achieve impact and engagement. Specifically, they argue the need for “public intellectuals” in the Information Systems Discipline. They identify three challenges to the production of impactful research: (i) researchers don’t know how to do such work, (ii) journals lack strategies for disseminating such work, and (iii) academic leaders lack strategies and processes to encourage such work. I hope Section ‘ISd-Research Strategy Publications’ above offers some guidance on how to amplify relevance and impact in research. As regards the Tarafdar et al. (2018) second challenge, a key aim herein has been to influence traditional outlets to be more receptive to such work by both attuning them to the need and value, and exploring (if not explicating outright) criteria for the evaluation of such work. Gill and Bhattacherjee (2009) suggest journals might introduce portfolio targets. JSIS, like other journals, is keen to promote more impactful research broadly; I invite both research that is “designed for impact”, and research that while not designed for impact from the outset, offers compelling interpretation pointing to potential future impactful outputs and outcomes. I hope too that this article engenders more programmatic research as described herein, a response to the Tarafdar et al. (2018) third challenge.

I believe the article offers ideas for several potential readers. For discipline leaders, those who are integrally involved in the oversight and nurturing of the IS discipline, there is little new here; perhaps the discussion suggests a framework around which current thinking might coalesce. For research quality gate-keepers – editors, reviewers, examiners – there is a strong plea to accommodate work pertaining to any of the five mechanisms of Fig. 2 , again with particular emphasis on strategic research programs. At the same time, I am acutely aware, and have been reminded by more than one of the senior scholars, that this plea is not new, and that the obstacles are several and complex (see endnotes).

For program leaders and aspirants to research program-leadership, the article raises many more issues than it solves. That said, it offers some argument for the creation of new, and sustenance of existing research programs, and I hope points the way to important thinking needed to promote improved programmatic research. For many Deans and Heads of School, and for research administrators in universities and governments whose policies strongly influence the course and focus of research, the call herein for more impactful research and the endorsement of research programs as a vehicle, will I expect be welcome.

For individual junior researchers, I apologize for only peripheral consideration of implications. Let it suffice here to suggest that individuals should carefully consider their personal and their projects' fit within the landscape of foci and consider their possible role(s) in programs. It is never too soon to be thinking strategically about who you seek to be: what you aspire to as a researcher. This should be a question asked of every commencing PhD student. Such a strategic orientation on career is all the more important in these uncertain times. For individual seasoned researchers the single message is, consider how in the current and projected climate you align with the other levels. Of course, many seasoned researchers do these things naturally.

Limitations

The emphasis throughout the discussion herein has been on research strategy, without explicitly extending this discussion to teaching and learning strategy. One might question whether it is possible to separate ISd Research Strategy from other discipline strategy, as they are to some degree intertwined. While inattention herein to those linkages may be considered a limitation, I believe ISd research strategy is sufficiently conceptually distinct to warrant separate consideration. “ Academic legitimacy comes with the salience of the subjects studied, the strength of the results obtained, and the plasticity of the field in responding to new challenges” ( King and Lyytinen, 2006 ). That being said, much of what is discussed here has broad relevance to the full discipline. There is merit in future work extending the discourse herein and shining a light on IS teaching and learning, viewing them through a discipline strategy lens [xxviii, xxix, xxx] .

A limitation of the Relevance Realisation Lifecycle view ( Fig. 3 ) is its inattention to what should be the focus across the lifecycle. Yes, relevance, but what more specifically? Rai (2019) argues that the focus across the phases should be problem formulation (which leads to the research question), thereby increasing chances of successfully addressing the dual objectives of scholarly and broa1er impact; and avoiding type III errors (addressing problems that don't matter) ( Rai, 2017a ). He goes further ( Rai, 2017b ) to suggest ways of abstracting the immediate problem to an archetypal problem (so the immediate problem is not over-problematized) and illuminating the distinctive characteristics of the immediate problem to challenge the archetypal problem (so the archetypal problem is not under-problematized). This guidance complements well the ideas of Fig. 3 . In an 'engaged scholarship' approach, engagement with practice is through the processes of problem formulation (which leads to the research question), theory development, research design, and solution assessment. One can argue that by engaging with practice in each of these phases (and doing other things to avoid Type III errors), the dual objectives of scholarly impact and broader impact are more likely to be realized.

I must accept the article is somewhat discipline-centric, with inadequate consideration of the university perspective, universities being the main vehicle of IS discipline research. Academics are members of both a discipline and a university. 20 Disciplines establish and enforce standards of research quality and research value, and universities encourage and facilitate research that meets those standards and delivers value. Historically, the main value sought has been contribution to knowledge. A key gauge of a university researcher's worth is their aggregate contributions to knowledge that meet the discipline standards. A key gauge of a university's research worth is the aggregate of its researchers' contributions to knowledge. Disciplinary involvement promotes training and growth in research standards and methods of their achievement, as well as breadth and depth in the discipline knowledge domain (involvement in the discipline has many other benefits, such as networking with other researchers). Regardless of individual university reward systems, stature within the discipline is perhaps the academic's main credential for advancement. There is however a “new normal”. Universities and disciplines have worked in tandem for over a century and their alignment needs attention.

This article makes no claim to inventorying the extensive, excellent disciplinary strategic advice to date from within the IS discipline. Fig. 2 offers the beginnings of a framework for usefully interrelating that work. Comprehensive inventorying, and interrelating that knowledge base within a coherent framework, is useful further research. The Strategic Methods node would undoubtedly benefit from a comprehensive revisiting of the IS literature (and beyond) to inventory and harmonize to the extent possible, extant methodological thought.

Though this article does not probe the merits and costs of research program involvement for individuals (a theme in the suggested follow-on work), implicit in the arguments is a strong belief in the efficiency and effectiveness of programmatic research; I think the potential for “a larger pie” is there. What isn't obvious is “how that pie gets shared”, which opens up much complexity. I think a general limitation of this article is that it generates expectations of answers while ultimately, in the main, only raising questions. Contrary, I think, to the view of one senior scholar, the article ends up being more descriptive than prescriptive. Lastly, I acknowledge I should have paid greater heed to the Hirschheim (2008) guidelines for the critical reviewing of conceptual papers, but practical realities intervened.

Future directions

The aims of this viewpoint are to espouse the merits of focusing a strategy lens on ISd-research, to facilitate related research and to call for future research in this direction. I believe a multilevel view of research strategy has the potential to be highly revealing, and is worthy of close future research attention. Reflecting back on the mechanistic view in Fig. 2 , I lament how the figure is inexplicit regarding the levels of research implied throughout the article. The discipline-level is clear as a sub-theme (ISd-Research strategy). Programs are explicitly accounted for as a mechanism (ISd-Research programs). Individuals however are only indirectly represented through projects or foci (ISd-Research Foci), and there is no explicit indication of multi-/inter-/trans-disciplinarity beyond disciplines. Shifting to a more multilevel view, I arrived at Table 2 , which cross-references levels of research with the five strategy mechanisms from Fig. 2 .

A Research Level and Strategy Mechanism Cross-Reference.

(a) I here adopt the British spelling where I refer more specifically to programmes as a 'level' as opposed to a mechanism, these being somewhat conflated prior.

It became clear that one can usefully consider the five mechanisms at each level, from: (i) individual, to (ii) programme, to (iii) discipline, iv) to x-disciplinary research (multi-/trans-/inter-disiciplinarity); perhaps worthwhile extending further to a higher 5th level – the ecology or society of research (e.g. Kuhn, 2012 ). I note also that programs are both a mechanism and a level (where levels are in essence groupings of people as in multilevel analysis – e.g. see ( Zhang and Gable, 2017 ).

I introduce Table 2 as an afterthought and suggest much interesting and valuable research could be pursued in attention to each of its cells. Such work, in the first instance, might entail inventorying pertinent thought to date. Such effort would likely be multidisciplinary, drawing from many disciplines. Rather than elaborating the individual cells of Table 2 and its full potential for encouraging further research and interrelating extant research, I make several selective observations. First, I note that extant methodological guidance pertains primarily to the individual project level. There is particular need for methods guidance at the program level; I recognise that multi-method and mixed-method guidance has some pertinence here, but much more is required.

Second, while I suggest the nexus of the individual level and governance pertains to “how individual research is governed”, I am of the view that individual research is largely self-governed. That said, both “how individuals govern their research” and “how their research is governed” are of interest to research organisers (e.g. program leaders/administrators). As mentioned, this article gives short shrift to the individual level, as it does to projects as a mechanism. While this scoping decision has to some extent been because, that level has to date received greater attention (e.g. in terms of methods guidance and our understanding of research projects), Table 2 reveals much need and further potential for attention to the individual level (e.g. How do researchers self-govern their research strategy?).

Beyond Table 2 , other areas of valuable further research are suggested herein. As a methodology for the design and execution of both research projects and programmatic research, there is value in employing the Relevance Realisation Lifecycle Model in Fig. 3 , while reflecting on and reporting on its merits (perhaps making a methodological contribution – mechanism C in Fig. 2 ).

As suggested earlier, with the swing to Mode 2 knowledge and growing emphasis on impact and engagement, discipline-university alignment requires attention. To the extent that discipline measures of stature and value are misaligned with university research goals and reward systems, academics and administrators experience conflict and productivity is compromised (some would argue university reward systems haven't evolved in lock-step with university and society aspirations for impact, thereby further exacerbating alignment). Disciplines and ISd have lagged in establishing measures of quality and value that promote impact. Thus, there is merit in assessing this alignment. Attention to this problem has been a major aim herein. More focused attention to ISd – University alignment is warranted, as are measures of rigour that encourage impact.

Further stretching the conception of “research as practice” there may be merit in analogizing other business concepts from industry (e.g. Business Models ( Fielt, 2014 )) to disciplines and research programs. I am herein talking about value and stakeholders. If programmatic research is to be strategic, it must consider the competition and its consumers and investors. Perhaps analogous with the notion of business model is 'research strategy model' (another example of reverse-cross-fertilization). Big data/data analytics is undoubtedly disrupting traditional (explicit and implicit) research strategy models. Some would argue that much of the contemporary data-driven research is too myopic and piecemeal, suggesting value from a more programmatic view; a new research strategy model.

It was argued earlier that discipline – university alignment is in need of attention. A premise of this argument is that disciplines continue to have value. There will be those who advocate that academics sole-allegiance should be to the university; a view I believe to be short-sighted and narrow. Discipline research standards are not static; rather they are constantly evolving, with new methods needed given new research phenomenon and technologies, a key role of the disciplines being to facilitate (and subsequently enforce) the evolution of new standards in lock step with the changing context, foci and priorities. Thus, any assumption that standards are established and the disciplines have served their role is misplaced.

Consistent with the above, a senior scholar espoused the value of engaged scholarship as argued in Rai (2017a) , but qualified that view commenting, “ I would state the tension a bit differently – at least in US institutions, particularly business schools. As an applied discipline, there is a challenge to create interventions for practice, but these necessarily inhibit us from meeting the institutional demands ( e.g. , tenure requirements). The middle ground of “engaged scholarship” which involves problemitization of our research helps in this regard, but the tension remains. Juxtaposition this with the demands from federal funding agencies that create layers of investment demands on the individual to tackle broad societal, interdisciplinary problems with cross-disciplinary teams. On top of this, our journals are increasingly sensitive to enforcing some kind of disciplinary boundary (i.e., is this IS research?).”

In summary, the main contributions of this viewpoint are twofold. First, the viewpoint argues the need for the IS discipline to think strategically in order to amplify value and contribution, and to thrive. Second, this viewpoint aims to smooth-the-way for manuscript submissions to JSIS and elsewhere, in attention to any of the nodes of the IS Discipline Research Strategy branch in Fig. 2 . It is hoped that discussion herein will engender a longer-term and more strategic view of IS research activity, perhaps promoting coalitions, engagement and impact.

Disciplines, and in some sense the sector, are under threat. Though the threats are several and some nuanced, the aftermath of COVID-19 will be a quite different world; as of this writing it has been reported that rationalisation of the university sector in Australia due to constrained finances and new delivery models following from COVID-19, will entail the loss of 21,000 academic positions. It is time to revisit strategy at all levels.

Acknowledgements

The shape of ideas presented herein has been much improved through the editorial panel review process, with key new ideas introduced. Many thanks to Bob Galliers, Suzanne Rivard, Monideepa Tarafdar and Mary Tate. The literature review and clarity of the paper owes much to the assistance and input of my senior research assistant Karen Stark. I must acknowledge the significant influence on my thinking from breakfast conversations with Alison Gable. The development of ideas proposed has been much facilitated from involvement in the Australian Research Council Discovery grant DP150101022 “Towards Engineering Behavioural Research Design Systems” core team members of which include Arun Rai, Wasana Bandara, Meng Zhang and Mary Tate. Further, I am much appreciative of additional incisive feedback from senior scholars who read the paper subsequent to editorial panel acceptance, which I believe promoted important balance in several areas (though not all will agree I have achieved this).

1 This Viewpoint was invited by Bob Galliers 18 December 2015.

2 In addition to central editorial panel review, the paper was further read by several generous, senior scholars (I use the term descriptively here, though most were Senior Scholars https://aisnet.org/general/custom.asp?page=SeniorScholars ), whose additional feedback was often challenging. Their anonymous contributions are acknowledged in several places, and quotations included, mainly in endnotes (referenced using roman numerals within square brackets). I further observe that many of these comments pertain to known limitations of the paper and complexities with implementing its explicit or implicit recommendations, thereby reflecting a form of rebuttal.

3 By strategic I mean pertaining to, marked by, or important to a long-term strategy. By long-term I suggest 5+ years. I acknowledge both, that short-term and opportunistic initiatives may align with strategy, and where they do not, they may or may not be prudent given strategy.

4 … to transdisciplinary, or inter-disciplinary, or what ( Galliers, 2003 ) refers to as Fields of Study, recognising that disciplines themselves can be more or less transdisciplinary; more or less inclusive and pluralistic.

5 This article was commenced 2015 and largely completed pre-COVID-19. References to COVID-19 are last minute and few, the potential implications of COVID-19 nonetheless believed to amplify many ideas herein.

6 In addition Crimmins (2017) reports that " Bryson identified that approximately 50% of teaching is undertaken by casual academics in the UK, France, Germany and Japan ( Bryson, 2013 ), and in the US casual appointments constitute 70% of faculty teaching positions ( Kezar and Maxey, 2015 ) ".

7 The antithesis of this large viewpoint article is the incisive ( Watson et al., 2020 ) guest editorial in MISQ Executive “Practice-oriented Research Contributions in the COVID-19 Forged New Normal” which in three pages offers four trends greatly accelerated by COVID-19, a list of priority areas for CIOs, and suggestions to scholars on research questions needing rigorous attention.

8 Here we have an instance of what I might refer to as reverse-cross-fertilization, where theory for practice that originated from academe, is being mirrored back onto research practice. There are several other instances of such reverse-cross-fertilization herein.

9 For example, rigor values (what standards of rigor are valued). With regards foci priorities, thesis and conference paper awards will be more current than journal paper awards; journal articles taking longer to see publication.

10 This is not new; much valuable methodological guidance has been targeted at IS researchers (e.g., Weber, 2003 , Weber, 2012 ; Lee, 1989 ). Thus, I advocate more of the same, as well as inventorying and interrelating such effort.

11 It is acknowledged that IS has a chequered track-record adopting good methodological advice. Here again, ISd-Research Governance must play a role in advocating and maximizing access to new methods on offer.

12 Again I mention the value of balance in the IS research ecosystem; small and large initiatives; bottom-up and top-down; basic and applied. While there is clear emphasis herein on applied research, at the discipline level this should not be at the sake of basic research. To quote George Porter, “To feed applied science by starving basic science is like economizing on the foundations of a building so that it may be built higher. It is only a matter of time before the whole edifice crumbles” ( Porter, 1986, p. 16 ).

13 Two local examples of substantive research programs at my home insititution are YAWL www.yawlfoundation.org and the Centre for Future Enterprise https://www.qut.edu.au/research/centre-for-future-enterprise . YAWL (Yet Another Workflow Language) is an established research program that combines technology innovation (e.g., business process modelling language) and managerial application (e.g., knowledge of service and business process design in organizations). YAWL has produced a large body of knowledge through impactful collaboration with industry over the past two decades. YAWL has evolved bottom-up, organically. The CFE is a commencing research program, initially centrally funded by QUT commencing in early 2020. The CFE program seeks to develop knowledge that can address critical challenges facing organizations in the new digital age.

14 I say 'partially clear-the-way' as I anticipate continuing complexity in both the crafting of such papers, and with their equitable evaluation.

15 Moeini et al. (2019) go further to identify factors of relevance-potential generation associated with each of the four stages: 17 factors in total. I encourage readers to further explore the Moeini et al. (2019) 17 factors and consider their value in both near-term research design, and longer-term career and research program strategy.

16 Though I advocate a strategic orientation in research at all levels, from individual to program to discipline, our dual emphasis herein has been primarily on discipline and programs; I've given little attention to the need for individuals to be strategic in their research careers, an important topic for further discussion. Let it suffice here to suggest that individuals should carefully consider their personal, and their projects' fit within the landscape of foci and consider their possible role(s) in programs.

17 E.g. Vial’s (2019) article “Understanding digital transformation: a review and research agenda” ultimately advocates for two main streams of research into the future: (i) How dynamic capabilities contribute to digital transformation, and (ii) The strategic relevance of ethics in digital transformation.

18 Some reference is made to Action Research 'Programs' in Avison et al (2018) .

19 In fact this Viewpoint is consistent with the very first JSIS editorial ( Galliers, 1991:3 ) in which Galliers states " Recognising both the strategic importance and potential of information, [JSIS] will present papers from both the academic and business worlds which will draw attention to these key issues, and provide practical lessons in dealing with them from both state-of-the-art research and current experience. "

20 Our focus herein is on those universities having a substantive research mission. We recognize universities are not homogenous and that there are other tertiary education and private sector and semi-public institutions of relevance. In addition, universities in the U.S. and elsewhere are more autonomous than in Australia and the UK, an important consideration for the generalizability of results.

Appendix A. Article fit with JSIS

As with all submissions, careful consideration was given to the fit of this Viewpoint with JSIS. In this regard, it is useful to consider both JSIS policy on Viewpoint articles and current JSIS Scope and Aims. JSIS policy is that “All Viewpoint articles are by invitation only. Viewpoints may or may not involve empirical evidence and are often provocative or introduce an interesting new line of enquiry. Regardless, Viewpoint articles must be well-referenced and rigorous in their logic and arguments, and are subject to careful review, over multiple rounds by an appointed panel, including at least one member of the editorial team ” (JSIS Policy).

The current abridged Aims & Scope (A&S) state that JSIS “ focuses on the strategic management, business and organizational issues associated with the introduction and utilization of information systems, and considers these issues in a global context. The emphasis is on the incorporation of IT into organizations' strategic thinking, strategy alignment, organizational arrangements and management of change issues. The journal publishes research from around the world […] A transdisciplinary, critical approach/perspective is welcome .”

As stated, viewpoints “ are often provocative or introduce an interesting new line of enquiry ”. Thus the role of review with viewpoints is less to achieve unanimous agreement with the view expressed, and more to assess whether the viewpoint is provocative or interesting and “ well-referenced and rigorous in their logic and arguments ”. Second, though not specifically or solely about “ the introduction and utilisation of information systems ”, conceiving the IS discipline (ISd) as an organisational entity warranting a strategy orientation does open discussion on the strategic value of IS for ISd (with several examples alluded to). Third, this article represents IS research methods as strategic mechanisms of ISd-Research Strategy. The author subscribes to the view that methods are valued design science research artifacts and that ISd should “practice what it preaches” by deploying its own contemporary methods in the conduct of more impactful research. Fig. 3 process view of the stages and phases of research relevance realization depicts and advocates for the integration of research and practice. In this view, research methods pertain to the earlier stages of an IT value production continuum, informing later information systems.

Ultimately though, all of these arguments on fit of the article with JSIS, are peripheral to the central aim of the article, which is the amplification of strategic thinking in IS research – the further leveraging of an orientation natural to the JSIS community, with emphasis on research programs as a main strategic lever, and further considering how JSIS can be instrumental in this aim.

While the Aims & Scope of JSIS are periodically reviewed, given their centrality to the journal identity there is healthy resistance to change without strong rationale ( Gable, 2020 ). Based on arguments espoused herein, it is believed there is good fit of the article with JSIS; it is not believed the addition of ISd-Research Strategy as a fourth theme of SIS demands a change to the Aims & Scope. That said, early in my tenure as the new Editor-in-Chief of JSIS I initiated discussion on the continuing suitability of the existing Aims & Scope; that discussion is continuing.

Comments from senior scholars

• [i] A related senior scholar comment was “ This seems like an interpretation of Gibbons et al which I’d see as view of Mode 2 which is more rigid (e.g. government involvement) which may or may not be what Gibbons et al mean when they say Mode 2 .” I recognise not all Mode 2 research is dependent on government intervention. As suggested by another senior scholar, “ Individual academic researchers from different disciplines can come together to work on a project of mutual and interdisciplinary interest, irrespective of government pressure (or lack thereof)”
• [ii] An anonymous senior scholar contrarily wrote, “ the issue for US universities is ‘money’ – where does it come from given that students from overseas are drying up, funding from government is declining, and the competition from a myriad of sources for student dollars is increasing at a rapid rate. ” Yet, I don't see these views as divergent. Universities will seek to rationalise, through cost-cutting and further leveraging existing resources (mainly people), while also promoting new sources of revenue (e.g. collaborative research income).
• [iii] One senior scholar commented “ Possibly, but what is missing here is indeed the individual perspective. Why would individual researchers want to conform to such a process [long-term research programs]? How does it help their careers? Their cvs? I know that you don’t plan to deal with the individual perspective in this article, but individual authors/researchers will simply ignore any policies that they don’t like.”
• [iv] One senior scholar questioned whether a discipline can be an actor; can have agency. “ I would think of discipline almost like a religion. So let’s take Christianity as the “discipline”. Churches are certainly actors as are various church ministers/staff and individual church members (and for Catholics, the papacy). But does it make sense to talk of Christianity as an actor and strategize at the level of Christianity itself? Well, in fact, some might say “yes”. Christianity (or Judaism or Islam, whatever) needs a coherent identity guided by mission (similar to strategy, I suppose) to maintain its influence in the world, some would argue. So maybe one can see a religion, or a discipline, as an actual entity with an identity. My own analogy is making me think that perhaps I was wrong in my dismissal of the IS discipline as an actor. AIS is certainly an actor, as are senior scholars and EICs even the Editorial boards. These are representatives of the discipline in the same way that the papal institution, for example, is a representation of Catholicism (it is not actually the embodiment of it because the faith preceded the institution much like IS research preceded AIS and the journals etc.). So yes for me the question really gets to – is the discipline (or a religion) an actor that needs a strategy/mission or is the discipline some nebulous concept used to describe various individuals and organizations that are connected by a shared identity/mission.”
• [v] While I agree with the senior scholar who observed “MIS and IS research are not necessarily the same thing […] IS research in 2020 and IS research in 1989 are not the same thing ”, I am not here attempting to theoretically define IS research, but rather simply highlighting the value from such efforts.
• [vi] One senior scholar commented, “ Here again you have the ‘Mintzberg’ issue whether ISd research strategy is planned or emerging – I think very much the latter. I also do not see how the field of IS could be ‘governed by any type of governor’. It is clearly liberalistic, but of course there are (and I very much think there should be) influences from funding agencies.”
• [vii] One senior scholar wrote, “ Is this a reverse engineering of a strategy, or a simple summary of activities and outcomes hoping to find a pattern leading to an (implicit) strategy?”
• [viii] Vidgen et al. (2019) discuss the importance of journal ranking lists saying, “Journal lists are a yet stronger and more potent form of strategic signalling in research. Organizations, such as the Financial Times, signal which journals matter, for example, through the FT50, and business schools in turn enrol these lists in order to communicate [research] values and priorities to their academic staff .“
• [ix] A senior scholar wrote, “ Sounds more like an inductive strategy development, ie authors can still pick their directions and an entity selects best examples to communicate what is desired .”
• [x] One senior scholar wrote, “ If we thought our research mattered, ICIS would be the premier outlet. If we do find anything of relevance, our journals take years to publish it and practice has moved on.”
• [xi] A senior scholar wrote, “ Is this sufficient? Do these methods help us to develop artefacts in the speed needed? To deal with new scales of empirical evidence? To understand the co-existence of theory and data-inferred insights?”
• [xii] To which one senior scholar commented, “ I can accept the legitimacy of this argument, but … most IS researchers don’t particularly care whether the ecology is flourishing or not.”
• [xiii] A senior scholar wrote, “ Isn’t this exactly where strategy is needed? A discipline has the strategic option to collectively focus on grand challenges (eg, medicine) or value a more democratic, bottom-up growth of the research portfolio. Not making a clear statement of which of these options is valued is probably a sign of a lack of strategy, and maybe a sign of where ISd is in terms of its stategizing, or lack of?”
• [xiv] A senior scholar asked “ Who would decide what these should be?” As mentioned earlier, these directions are often shaped by a range of governance mechanisms and gatekeepers at various levels (e.g. Editors, reviewers, examiners, grant proposal panel members …). They are sometimes more overtly advocated (e.g. this article being a case in point). In the main, historically, they have emerged bottom-up (see 2 nd footnote prior).
• [xv] Another senior scholar commented “ The section […] advocates for research programs (which I agree with), but leaves open the question of where these programs should come from? They could be advocated through funding centers, professional associations (like AIS), institutional structures (like research centers in the US) or just through individual agency. While the paper indicates that all programs are good…. there are tradeoffs that can make certain sources of these programs as counterproductive. There has to be a blending of societal importance, resources, value systems and incentive systems for research programs to work .”
• [xvi] A further senior scholar commented, “A top down approach to drive research strategy in a discipline, whilst a noble pursuit perhaps, I think it is a bit of an 'oxymoron'. Ultimately research is driven by problems in a changing world and any research program not plugged properly in the world is doomed i.e. a bottom up view is a key to success (how to reconcile with the nobleness of a top down view- frankly, I am not sure, but I think this is a challenge for research administrators who hold purses, and perhaps to chief editors, and research leaders at various levels..).”
• [xvii] A senior scholar commented, “ I wonder how difficult it would be to set up such programmes? Who would initiate them? Who would give them ‘credibility’? How would we ‘enforce’ compliance?”
• [xviii] A senior scholar commented “ I was SE for […] and I found it difficult to get reviewers to review such papers. They didn’t follow the ‘traditional’ template and hence there was difficulty in getting ‘serious’ reviews. I even wrote an article on how to undertake a critical review of such papers. But it is not an easy fix .”
• [xix] To which one senior scholar commented, “ I support this, but such studies take time and given tenure and promotion clocks, I find researchers don’t embark on such projects .”
• [xx] A senior scholar commented, “ I have tried to publish articles that might change the field. For example, we argued we should balance the attention given to explanatory science by some attention to intervention science so we go beyond theories based on associations found in data to field tests of a theory’s efficacy. This and other challenges to the status quo failed. Though, when I present these views the response is positive. People know we have a problem but there is no external force compelling change.”
• [xxi] One senior scholar commented, “ I personally find it unethical, if research is not driven by some type of relationship to ‘contribution of ‘societal value’. This is something we have forgotten and neglected in our frantic race for publishing more and getting a higher h-index … Without contribution to attainment of societal values, the ISd field does not have any meaning, and should be eradicated .”
• [xxii] One senior scholar wrote, “ Quite simply, an applied field that does not produce applicable knowledge in a timely manner is failing society .”
• [xxiii] One senior scholar commented, “ I think ‘rigor’ is problematic because of the way the field views ‘rigor’ […] I firmly believe this is the root of most of the problem. Rigor is too narrowly conceived.”
• [xxiv] A senior scholar – “ Most people will agree with Table 1 , but few will do anything to make it happen.”
• [xxv] A senior scholar commented, “ I like the “opening up” of JSIS to introspective work that advances the discipline. Proposal of new research programs has traditionally been handled organically (i.e., borrowed theory like TRA creates TAM which then creates a platform for incremental research) or initiated through special issues that are usually aligned with emergent phenomena. So, in advocating papers that develop new foci and research programs, it is important to provide some idea of scope. For instance, is a call for papers on a new phenomenon (e.g. domestic robots) the type of papers that JSIS would like to attract? Is a research program at the level of design science vs. behavior science vs. economics (which could fundamentally change the nature of the discipline itself) or should it be more narrowly scoped? ”
• [xxvi] One senior scholar commented, “ I am a bit concerned by the aggressive push toward practice as described in Table 1, if it comes at the cost of theory. In fact, theory bashing has now become fashionable in our field, with many articles (e.g., Dennis, Hirschheim, special issue in JIT) arguing against theory. This has emboldened big data research that take data sets and applies sophisticated analytics to predict very tactical corporate questions (since the data is low level digital trace data). These papers often don’t even make the pretence of theorizing…and usually address questions that companies (with their data and analytical resources) can do better. I think this is dangerous for our field – we need to improve our theorizing and engage with practice – but not abdicate theory altogether ”.
• [xxvii] One senior scholar commented, “ I shifted my approach towards tackling problems that mattered for CIOs or society with the goal of producing applicable knowledge. This view is somewhat echoed in Table 1, except I think many articles should be about implications for practitioners rather than a perfunctory afterthought section at the end of the paper. Creating applicable knowledge should dominate the research question, design, and findings. Maybe we could add a paragraph on implications for theory.”
• [xxviii] One senior scholar wrote “ I am on board with what is suggested but also mindful that our relevance is linked to our enrolments and our linkages with the broader institutional environment to ensure our enrolments. I think the simple equation is ‘No enrolments, no research’, so it might be worth mentioning that somewhere in the article ... [with reference to Figure 1] I think one area that AIS should link to are accreditation agencies (AACSB, Seoul Accord etc.) to ensure that our research continues to get reflected in what is taught … AIS should also have linkages with various bodies such as SFIA that develop ‘bodies of knowledge’ that include IS-related content.”
• [xxix] They went on to comment “While there can be no disagreement with this claim, I think a broader source of legitimacy for disciplines is the number of students enrolled in the discipline.
• Strategically, as a discipline, we also need to pay attention to how we can ensure a continuing increase in enrolments in our discipline … We have seen often enough in business schools that when enrolments in IS drop, hiring of junior faculty drops. When faced with such an existential crisis, it is difficult to see how we can expect commitment to programmatic research … The reality is that over 90% of revenue for business schools comes from teaching. Undergraduate teaching in a majority of business schools contributes a bulk of this revenue. With the continuing casualisation of teaching, esp undergraduate teaching, it is easy to see how the discipline could go into a downward spiral.
• [xxx] And … “I think that there are a couple of strategic linkages with our institutional environment that we need to work on as a discipline. One is with accreditation bodies (esp AACSB, EQUIS and AMBA) to ensure that the coverage of IS/digital issues continues to increase in what is expected by these bodies of business school graduates ... The other linkage is with bodies such as the Seoul Accord and SFIA to ensure that we continue to have a say in how the IS curriculum evolves to reflect our research programmes … Perhaps, this is something the AIS should aim for, if it is not already doing so.
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Understanding different research perspectives

Introduction

In this free course, Understanding different research perspectives , you will explore the development of the research process and focus on the steps you need to follow in order to plan and design a HR research project.

The course comprises three parts:

• The first part (Sections 1 and 2) discusses the different perspectives from which an issue or phenomenon can be investigated and outlines how each of these perspectives generates different kinds of knowledge about the issue. It is thus concerned with what it means to ‘know’ in research terms.
• The second part (Sections 3 to 7) identifies the different elements (e.g. methodologies, ethics) of a business research project. In order to produce an effective project, these different elements need to be integrated into a research strategy. The research strategy is your plan of action and will include choices regarding research perspectives and methodologies.
• The third part (Sections 8 to 10) highlights the main methodologies that can be used to investigate a business issue.

This overview of the research perspectives will enable you to take the first steps to develop a work-based project – namely, identifying a research problem and developing the research question(s) you want to investigate.

By the end of this course you should have developed a clear idea of what you want to investigate; in which context you want to do this (e.g. in your organisation, in another organisation, with workers from different organisations); and what are the specific questions you want to address.

This OpenLearn course is an adapted extract from the Open University course B865 Managing research in the workplace .

Learning outcomes

After studying this course, you should be able to:

understand the different perspectives from which a problem can be investigated

consider the researcher’s involvement in the research process as insider and/or outsider

reflect on the importance of ethical processes in research

understand the development of a research strategy and how this is translated into a research design

identify a research problem to be investigated.

1 Objective and subjective research perspectives

Research in social science requires the collection of data in order to understand a phenomenon. This can be done in a number of ways, and will depend on the state of existing knowledge of the topic area. The researcher can:

• Explore a little known issue. The researcher has an idea or has observed something and seeks to understand more about it (exploratory research).
• Connect ideas to understand the relationships between the different aspects of an issue, i.e. explain what is going on (explanatory research).
• Describe what is happening in more detail and expand the initial understanding (explicatory or descriptive research).

Exploratory research is often done through observation and other methods such as interviews or surveys that allow the researcher to gather preliminary information.

Explanatory research, on the other hand, generally tests hypotheses about cause and effect relationships. Hypotheses are statements developed by the researcher that will be tested during the research. The distinction between exploratory and explanatory research is linked to the distinction between inductive and deductive research. Explanatory research tends to be deductive and exploratory research tends to be inductive. This is not always the case but, for simplicity, we shall not explore the exceptions here.

Descriptive research may support an explanatory or exploratory study. On its own, descriptive research is not sufficient for an academic project. Academic research is aimed at progressing current knowledge.

The perspective taken by the researcher also depends on whether the researcher believes that there is an objective world out there that can be objectively known; for example, profit can be viewed as an objective measure of business performance. Alternatively the researcher may believe that concepts such as ‘culture’, ‘motivation’, ‘leadership’, ‘performance’ result from human categorisation of the world and that their ‘meaning’ can change depending on the circumstances. For example, performance can mean different things to different people. For one it may refer to a hard measure such as levels of sales. For another it may include good relationships with customers. According to this latter view, a researcher can only take a subjective perspective because the nature of these concepts is the result of human processes. Subjective research generally refers to the subjective experiences of research participants and to the fact that the researcher’s perspective is embedded within the research process, rather than seen as fully detached from it.

On the other hand, objective research claims to describe a true and correct reality, which is independent of those involved in the research process. Although this is a simplified view of the way in which research can be approached, it is an important distinction to think about. Whether you think about your research topic in objective or subjective terms will determine the development of the research questions, the type of data collected, the methods of data collection and analysis you adopt and the conclusions that you draw. This is why it is important to consider your own perspective when planning your project.

Subjective research is generally referred to as phenomenological research. This is because it is concerned with the study of experiences from the perspective of an individual, and emphasises the importance of personal perspectives and interpretations. Subjective research is generally based on data derived from observations of events as they take place or from unstructured or semi-structured interviews. In unstructured interviews the questions emerged from the discussion between the interviewer and the interviewee. In semi-structured interviews the interviewer prepares an outline of the interview topics or general questions, adding more as needs emerged during the interview. Structured interviews include the full list of questions. Interviewers do not deviate from this list. Subjective research can also be based on examinations of documents. The researcher will attribute personal interpretations of the experiences and phenomena during the process of both collecting and analysing data. This approach is also referred to as interpretivist research. Interpretivists believe that in order to understand and explain specific management and HR situations, one needs to focus on the viewpoints, experiences, feelings and interpretations of the people involved in the specific situation.

Conversely, objective research tends to be modelled on the methods of the natural sciences such as experiments or large scale surveys. Objective research seeks to establish law-like generalisations which can be applied to the same phenomenon in different contexts. This perspective, which privileges objectivity, is called positivism and is based on data that can be subject to statistical analysis and generalisation. Positivist researchers use quantitative methodologies, which are based on measurement and numbers, to collect and analyse data. Interpretivists are more concerned with language and other forms of qualitative data, which are based on words or images. Having said that, researchers using objectivist and positivist assumptions sometimes use qualitative data while interpretivists sometimes use quantitative data. (Quantitative and qualitative methodologies will be discussed in more detail in the final part of this course.) The key is to understand the perspective you intend to adopt and realise the limitations and opportunities it offers. Table 1 compares and contrasts the perspectives of positivism and interpretivism.

Some textbooks include the realist perspective or discuss constructivism, but, for the purpose of your work-based project, you do not need to engage with these other perspectives. This course keeps the discussion of research perspectives to a basic level.

Search and identify two articles that are based on your research topic. Ideally you may want to identify one article based on quantitative and one based on qualitative methodologies.

Now answer the following questions:

• In what ways are the two studies different (excluding the research focus)?
• Which research perspective do the author/s in article 1 take in their study (i.e. subjective or objective or in other words, phenomenological/interpretivist or positivist)?
• What elements (e.g. specific words, sentences, research questions) in the introduction reveal the approach taken by the authors?
• Which research perspective do the author/s in article 2 take in their study (i.e. subjective or objective, phenomenological/interpretivist or positivist)?
• What elements (e.g. specific words, sentences, research questions) in the introduction and research questions sections reveal the approach taken by the authors?

This activity has helped you to distinguish between objective and subjective research by recognising the type of language and the different ways in which objectivists/positivists and subjectivists/interpretivists may formulate their research aims. It should also support the development of your personal preference on objective or subjective research.

2 The researcher as an outsider or an insider

The researcher’s perspective is not only related to philosophical questions of subjectivity and objectivity but also to the researcher’s position with respect to the subject researched. This is particularly relevant for work-based projects where researchers are looking at their own organisation, group or community. In relation to the researcher’s position, s/he can be an insider or an outsider. Here the term ‘insider’ will include the semi-insider position, and the term ‘outsider’ will include the semi-outsider position. If you belong to the group you want to study, you become an ‘insider-researcher’. For example, if you want to conduct your research project with HR managers and you are a HR manager yourself you will have a common language and a common understanding of the issues associated with doing the same job. While, on one hand, the insider perspective allows special sensitivity, empathy and understanding of the matters, which may not be so clear to an outsider, it may also lead to greater bias or to a research direction that is more important to the researcher. On the other hand, an outsider-researcher would be more detached, less personal, but also less well-informed.

Rabe (2003) suggests that once outsider and insider perspectives in research are examined, three concepts can lead to a better understanding. First, the outsider and insider can be understood by considering the concept of power : there is power involved in the relationship between the researcher and the people and organisations participating in the research. As researchers are gathering data from the research participants, they have the power to represent those participants in any way they choose. The research participants have less power, although they can choose what to say to the researchers. This has different implications for insiders and outsiders. It is obvious that in the case of work-based projects conducted in your organisation, the ways in which you choose to represent your colleagues and your organisation places you in a position of power.

Second, insider and outsider perspectives can be understood in the context of knowledge : the insider has inside knowledge that the outsider does not have. If you conduct research in your organisation, institution or profession you will have access to inside knowledge that an outsider will not be able to gain.

The third way in which the insider/outsider concept can be understood is by considering the role of the researcher in the field of anthropology . In fact, anthropologists approach those being studied (e.g. remote cultures, tribes, social groups) as outsiders. As researchers experience the life of those studied by living with them, they acquire an insider’s perspective. The goal is to obtain both insider and outsider knowledge and to maintain the appropriate detachment. This approach applies to participatory research in general, not only anthropology.

Figure 1 reports the various stages you are expected to follow in order to complete and write up your research report.

3 Deciding what to research

Selecting an appropriate research topic is the first step towards a satisfactory project. For some people, choosing the topic is easy because they have a very specific interest in an area. For example, you might be interested in studying training and development systems in multinational companies (MNCs), or you might have a pressing work issue you want to address, like why the career progression of women in the construction industry is slower than that of men in the same industry. Alternatively, your sponsoring organisation might like you to carry out a specific project that will benefit the organisation itself, for example, it might need a specific HR policy to be developed.

While you may be among those who already have clear ideas, for other learners the process of choosing the research topic can be daunting and frustrating. So how can you generate research ideas? Where should you start? It is easiest to start from your organisational context, if you are currently in employment or if you are volunteering. Is there anything that is bothering you, your colleagues or your department? Is there a HR issue that could be addressed or further developed? Talking about your project with colleagues and family may also help as they may have suggestions that you have not considered.

The following case study describes a process that might help you to identify opportunities in your daily activities that could lead to a suitable topic.

Lucy works as a HR assistant manager for a large manufacturer of confectionery that operates at a national level. The company has three factories and a head office. While the company has a centralised HR function based at the head office, Lucy is based at one of the factories (the largest of the three) that employs 176 workers. The HR department at the factory comprises three HR experts: the manager and two assistants. Their focus is mainly concerned with the training and development of the on-site staff, with the recruitment of factory workers, grievances, disciplinary and day-to-day HR management. It excludes general issues such as salaries, benefits, pensions, recruitment and development of managerial staff and more centralised aspects that are managed by the HR function at head office level.

For some time, Lucy has received feedback through the appraisal system and exit interviews that shop floor workers are dissatisfied by the lack of progression to supervisory level positions within the company. In fact, the company had no career progression plans nor a structured assessment of training needs for factory workers, who make up the majority of employees. Training was provided on-site by supervisors, managers and HR managers and off-site by external consultants when a specific skill or knowledge was needed. Equally employees could request to attend a course by choosing from a list of courses provided on a yearly basis. However, although the company was keen for employees to attend training courses, these were not systematically recorded on the employee file nor did they fit in a wider career plan.

Lucy is doing a part-time postgraduate diploma in HRM at The Open University and she has to complete a research project in order to gain CIPD membership. She has fully considered this issue and thought that it could become a good project. It did not come to her mind immediately but was the result of talks with her HR colleagues and her partner who helped her to see an opportunity where she could not see it.

Lucy talked to supervisors and factory workers and she analysed the organisational documents (appraisal records and exit interview records). Having searched the literature on blue-collar worker career development and training she compiled a loose structure (semi-structured) for interviews to be conducted with shop floor employees. At the end of her course she submitted a project which included the development of an online programme that managed a record system for each employee. The system brought together all the training courses completed as well as the performance records of each employee. It became much easier for managers and supervisors to identify the training courses attended by each employee as well as future training needs.

When people are employed in a job, or on a placement, and are undertaking research in the employing organisation they can be defined as (insider) practitioner–researchers. While the position of insider brings advantages in terms of knowledge and access to information and resources, there may be political issues to consider in undertaking and writing up the project. For example, a controversial or sensitive issue might emerge in the collection of data and the insider researcher has to consider carefully how to present it in their writing. Furthermore, while the organisation or some of its members may initially be willing to collaborate, resistance to full participation may be experienced during some stages of the project. If you are carrying out research in your own organisation it is therefore important to consider, in addition to its feasibility, any political issues likely to emerge and whether your status may affect the process of undertaking the research (Anderson, 2013).

Another way that can help you to identify a topic of investigation is through reading a HR magazine (such as People Management , Personnel Today or HR Magazine ), a journal article or even a newspaper. What topics do they include? Are you interested in any of them? Would the topic appeal to your organisation? Could it be developed into a feasible project?

If you develop an idea, however rudimentary, it is worth writing down the topic and a short sentence that captures some aspects of the topic. For example if the topic is ‘diversity in organisations’, you might want to write a note such as ‘relationship between diversity policy and practices’. From here you can start to develop a map of ideas that will help to identify keywords that can be used to do a more in-depth search of the literature. Figure 2 shows the first step in developing the idea.

The figure shows three general approaches that you might take in developing a research project on diversity in organisations. You may decide to focus on one of these aspects and you could brainstorm it and add elements such as relationships with the various other aspects, national and organisational context, organisational sector, legislation, and aspects of diversity (e.g. age, gender, sexuality, race, etc.). Obviously this is an example but you can apply this process to any topic.

If you have an idea of a topic for your project, take five minutes to think about the possible perspectives from which it can be investigated. Having done this, take a sheet of paper and write the topic in the middle of the page. Alternatively, if you are still uncertain about the topic you want to investigate, you might want to think about the role of the HR practitioner and the various activities associated with this role. As you consider the various aspects of the role of HR practitioner, you may realise that one of these can become your chosen topic.

Starting from this core idea, now draw a mind map or a spider plan focusing on your chosen topic.

Mind mapping gives you a way to illustrate the various elements of an issue or topic and helps to clarify how these elements are linked to each other. A map is a good way to visualise, structure and organise ideas.

Now that you have identified your research topic you can move on to work on the research focus. Activity 3 will help you with this.

Develop a research statement of approximately 600 words explaining your chosen topic, the research problem and how you are thinking of investigating it.

You could start by using the mind map you developed in Activity 2. If you wished, you could do some online research around your topic to get a clearer focus on your own project. You might also read a few sources such as newspaper or magazine articles that are particularly relevant to your topic. These sources may yield some additional aspects for you to investigate. If your topic has been widely researched, try not to feel overwhelmed by the amount of existing information. Focus on one or two articles that appear more closely related to your topic and try to identify the specific area you want to focus on.

Once you have researched the topic, consider the research problem and how this can be investigated. Would you need to interview specific people? Would a questionnaire be more appropriate if you need to access a large number of individuals? Would you need to observe work practices in a specific organisation?

Your statement is a draft research proposal and should include:

• an overview of the topic area with an explanation of why you think this needs to be researched
• the focus of the research and the problem it addresses (and possibly the research questions if you have a clear idea of them at this stage, however, this is not essential if you are still developing the focus of your research)
• how the problem can be investigated (i.e. questionnaire, interviews, secondary data).

You may want to show this proposal to colleagues and other contacts who may be interested in order to gain their feedback about the focus and feasibility.

There is no feedback on this activity.

The next section discusses the ethical issues that need to be considered when doing a research project.

4 Research ethics

Ethics is a fundamental aspect of research and of professional work. Ethics refers to the science of morals and rules of behaviour. It is concerned with the concept of right and wrong conduct in all stages of doing research. However, while the idea of right and wrong conduct may seem straightforward, on reflection you will realise how complex ethics is. Ethics is obviously applied to many aspects of life, not just research, and, in business, topics such as ethics and social responsibility and ethical trading are often brought to people’s attention by the media.

As the meaning of what is ethical behaviour is often subjective and may have controversial elements, think about the following questions and make some notes:

• What does ethics in research mean for you?
• Why is ethical behaviour important for you?
• Why should ethics matter in research?

Research ethics is concerned with the prevention of any harm which may occur during the course of research. This is particularly important if your research involves human participants. Harm refers to psychological as well as physical harm. Human rights and the law must be respected by researchers with regard to the safety and wellbeing of their participants at all times. Research ethics is also concerned with identifying high standards of research conduct and putting them into practice. Cameron and Price (2009) suggest that researcher conduct is guided by a number of different obligations:

• Legal obligations which apply not only to the country in which researchers conduct the project, but also where they collect and store data.
• Professional obligations which are established by professional bodies (e.g. British Psychological Society, The Law Society, CIPD) to guide the conduct of its members.
• Cultural obligations which refer to informal rules regulating the behaviours of people within the society in which they live.
• Personal obligations which include the behavioural choices that individuals make of their own will.

In planning and carrying out a research project researchers should consider their responsibilities to the participants and respondents, to those sponsoring the research, and to the wider research community (Cameron and Price, 2009, p. 121). Before embarking on a research project it is worth identifying all stakeholders and considering your responsibilities towards them.

Generally universities and professional bodies have a list of principles or a code of ethics conduct that governs the research process. The principles to be followed in conducting research with human participants, and which you must follow when collecting data for your research project, are outlined below:

• Informed consent : Potential participants should always be informed in advance, and in understandable terms, of any potential benefits, risks, inconvenience or obligations associated with the research that might reasonably be expected to influence their willingness to participate. This should normally involve the use of an information sheet about the research and what participation will involve, and a signed consent form. Sufficient time shall be allowed for a potential participant to consider their decision from receiving the information sheet to giving their consent. In the case of children (individuals under 16 years of age) informed consent should be given by parents or guardians. An incentive to participate (e.g. a prize or a small payment) should be offered only after consent has been given. Participants should be informed clearly that they have a right to withdraw their consent at any time, that any data that they have provided will be destroyed if they so request and that there will be no resultant adverse consequences.
• Openness and integrity : Researchers should be open and honest about the purpose and content of their research and behave in a professional manner at all times. Covert collection of data should only take place where it is essential to achieve the research results required, where the research objective has strong scientific merit and where there is an appropriate risk management and harm alleviation strategy. Participants should be given opportunities to access the outcomes of research in which they have participated and debriefed, if appropriate, after they have provided data.
• Protection from harm : Researchers must make every effort to minimise the risks of any harm, either physical or psychological. Researchers shall comply with the requirements of the UK Data Protection Act 1998, the Freedom of Information Act 2000 and any other relevant legal frameworks governing the management of personal information in the UK or in any other country in which the research may be conducted. Where research involves children or other vulnerable groups, an appropriate level of disclosure should be obtained from the Disclosure and Barring Service for all researchers in contact with participants.
• Confidentiality : Except where explicit written consent is given, researchers should respect and preserve the confidentiality of participants’ identities and data at all times. The procedures by which this is to be achieved should be specified in the research protocol (an outline of the research topic and strategy).
• Professional codes of practice and ethics : Where the subject of a research project falls within the domain of a professional body with a published code of practice and ethical guidelines, researchers should explicitly state their intention to comply with the code and guidelines in the project protocol. Research within the UK NHS should always be conducted in compliance with an ethical protocol approved by the appropriate NHS Research Ethics Committee.

This guidance has been adapted from, and reflects the principles of, the Open University Research Ethics Guideline. The CIPD’s Code of Professional Conduct provides more information on professional standards in the field of HR.

Look at your research topic mind map and the research statement you wrote for Activity 3. Did you consider ethics? Regardless of whether or not you included ethics in your earlier outline of your research topic, consider what ethical factors could prevent you from conducting a research project on the chosen topic. Write your notes in the space provided below.

Describe at least two types of risks that could be encountered in HR research.

What is informed consent? What factors would you want to know before agreeing to participate in a research study? What should be included in an informed consent form?

This activity added the ethical dimension to the research topic, which was likely to be missed out in a previous outline of the project topic and aim(s). It also compels you to reflect on the specific ethical risks of HR research. There are risks associated with most HR research (e.g. stress can be induced by an interview or questionnaire questions) and it is important to consider them before finalising the research proposal. The activity also encouraged you to consider the elements that should be included in the informed consent form.

The next section considers the research question.

5 Developing research questions

By now you should have a clear direction for your research project. It is now necessary to think about the sorts of questions that you need to formulate in order to define your research project. Will they be ‘what’, ‘how’ or ‘why’ type questions? An important point to bear in mind, as discussed above, is that the wording of a question can be central in defining the scope and direction of the study, including the methodology. Your research question(s) do not necessarily have to be expressed as question(s); they can be statements of purpose. The research question is so called because it is a problem or issue that needs to be solved or addressed. Here are some examples of research questions that focus on different areas of HR. They are expressed as questions but they can easily be changed to research statements if the researcher prefers to present them in that way.

• How do the personal experiences and stories of career development processes among HR professionals in the UK and in Romania differ?
• To what extent do NHS managers engage with age diversity?
• How are organisational recruitment and selection practices influenced by the size of the organisation (e.g. small and medium-sized organisations, large national companies, multinational corporations)?
• In what ways, and for what reasons, does the internal perception of the organisational culture vary in relation to the culture that the organisation portrays in official documents?
• How are absenteeism levels linked to employees’ performance?

The process of producing clearly defined and focused research questions is likely to take some time. You will continue to tweak your research questions in the next few weeks even after you have written your research proposal, read the relevant literature and processed the information. The research questions above are very different but they also have common elements. The next activity invites you to think about research questions, both those in the examples above and your own, which may still be very tentative.

Make notes on the following questions:

• What constitutes a research question?
• What are the main pieces of information that a research question needs to contain?

This activity helped you to focus further on your research aims. What you wrote will guide you towards the development of your research questions. Figure 4 shows that research questions can contain a number of elements.

You should now note down possible research questions for your project. You might have only one question or possibly two. You do not need too many questions because you need to be realistic about what you can achieve in the project’s time frame. At the end of this course you will come back to your question(s) and finalise them.

6 Research strategy

A research strategy introduces the main components of a research project such as the research topic area and focus, the research perspective (see Sections 1 and 2), the research design, and the research methods (these are discussed below). It refers to how you propose to answer the research questions set and how you will implement the methodology.

In the first part of this course, you started to identify your research topic, to develop your research statement and you thought about possible research question(s). While you might already have clear research questions or objectives, it is possible that, at this stage, you are uncertain about the most appropriate strategy to implement in order to address those questions. This section looks briefly at a few research strategies you are likely to adopt.

Figure 5 shows the four main types of research strategy: case study, qualitative interviews, quantitative survey and action-oriented research. It is likely that you will use one of the first three; you are less likely to use action-oriented research.

Here is what each of these strategies entails:

• Case Study : This focuses on an in-depth investigation of a single case (e.g. one organisation) or a small number of cases. In case study research generally, information is sought from different sources and through the use of different types of data such as observations, survey, interviews and analysis of documents. Data can be qualitative, quantitative or a mix of both. Case study research allows a composite and multifaceted investigation of the issue or problem.
• Qualitative interviews : There are different types of qualitative interviews (e.g. structured, semi-structured, unstructured) and this is the most widely used method for gathering data. Interviews allow access to rich information. They require extensive planning concerning the development of the structure, decisions about who to interview and how, whether to conduct individual or group interviews, and how to record and analyse them. Interviewees need a wide range of skills, including good social skills, listening skills and communication skills. Interviews are also time-consuming to conduct and they are prone to problems and biases that need to be minimised during the design stage.
• Quantitative survey : This is a widely used method in business research and allows access to significantly high numbers of participants. The availability of online sites enables the wide and cheap distribution of surveys and the organisation of the responses. Although the development of questions may appear easy, to develop a meaningful questionnaire that allows the answering of research questions is difficult. Questionnaires need to appeal to respondents, cannot be too long, too intrusive or too difficult to understand. They also need to measure accurately the issue under investigation. For these reasons it is also advisable, when possible, to use questionnaires that are available on the market and have already been thoroughly validated. This is highly recommended for projects such as the one you need to carry out for this course. When using questionnaires decisions have to be made about the size of the sample and whether and when this is representative of the whole population studied. Surveys can be administered to the whole population (census), for example to all employees of a specific organisation.
• Action-oriented research : This refers to practical business research which is directed towards a change or the production of recommendations for change. Action-oriented research is a participatory process which brings together theory and practice, action and reflection. The project is often carried out by insiders. This is because it is grounded in the need to actively involve participants in order for them to develop ownership of the project. After the project, participants will have to implement the change.

Action-oriented research is not exactly action research, even though they are both grounded in the same assumptions (e.g. to produce change). Action research is a highly complex approach to research, reflection and change which is not always achievable in practice (Cameron and Price, 2009). Furthermore action researchers have to be highly skilled and it is unlikely that for this specific project you will be involved in action research. For these reasons this overview focuses on the less pure action-oriented research strategy. If you are interested in exploring this strategy and action research further, you might want to read Chapter 14 of Cameron and Price (2009).

It is possible for you to choose a strategy that includes the use of secondary data. Secondary data is data that has been collected by other people (e.g. employee surveys, market research data, census). Using secondary data for your research project needs to be justified in that it meets the requirements of the research questions. The use of secondary data has obvious benefits in terms of saving money and time. However, it is important to ascertain the quality of the data and how it was collected; for example, data collected by government agencies would be good quality but it may not necessary meet the needs of your project.

It is important to note that there should be consistency between the perspective (subjective or objective) and the methodology employed. This means that the type of strategy adopted needs to be coherent and that its various elements need to fit in with each other, whether the research is grounded on primary or secondary data.

Now watch this video clip in which Dr Rebecca Hewett, Prof Mark Saunders, Prof Gillian Symon and Prof David Guest discuss the importance of setting the right research question, what strategy they adopted to come up with specific research questions for their projects, and how they refined these initial research questions to focus their research.

Make notes on how you might apply some of these strategies to develop your own research question.

7 Research design

In planning your project you need to think about how you will design and conduct the study as well as how you will present and write up the findings. The design is highly dependent upon the research strategy. It refers to the practical choices regarding how the strategy is implemented in practice. You need to think about what type of data (evidence) would best address the research questions; for example, when considering case study research, questions of design will address the choice of the specific methods of data collection, e.g. if observation, what to observe and how to record it? For how long? Which department or work environment to observe? If interviews are chosen, you need to ask yourself what type? How many? With whom? How long should they be? How will I record them? Where will they be conducted?

The following list (adapted from Cameron and Price, 2009) shows some of the different types of data, or sources of evidence, available to draw on:

• observations
• conversations
• statistics (e.g. government)
• focus groups
• organisational records
• documents (e.g. organisational policies)
• secondary data.

8 Research methodology

The most important methodological choice researchers make is based on the distinction between qualitative and quantitative data. As mentioned previously, qualitative data takes the form of descriptions based on language or images, while quantitative data takes the form of numbers.

Qualitative data is richer and is generally grounded in a subjective and interpretivist perspective. However, while this is generally the case, it is not always so. Qualitative research supports an in-depth understanding of the situation investigated and, due to time constraints, it generally involves a small sample of participants. For this reason the findings are limited to the sample studied and cannot be generalised to other contexts or to the wider population. Popular methods based on qualitative data include semi-structured or unstructured interviews, participant observations and document analysis. Qualitative analysis is generally more time-consuming than quantitative analysis.

Quantitative data, on the other hand, might be easier to collect and analyse and it is based on a large sample of participants. Quantitative methods are based on data that can be ‘objectively’ measured with numbers. The data is analysed through numerical comparisons and statistical analysis. For this reason it appears more ‘scientific’ and may appeal to people who seek clear answers to specific causal questions. Quantitative analysis is often quicker to carry out as it involves the use of software. Owing to the large number of respondents it allows generalisation to a wider group than the research sample. Popular methods based on quantitative data include questionnaires and organisational statistical records among others.

The choice of which methodology to use will depend on your research questions, the formulation of which is consequently informed by your research perspective. Generally, unstructured or semi-structured interviews produce qualitative data and questionnaires produce quantitative data, but such a distinction is not always applicable. In fact, language-based data can often be translated into numbers; for example, by reporting the frequency of certain key words. Questionnaires can produce quantitative as well as qualitative data; for example, multiple choice questions produce quantitative data, while open questions produce qualitative data.

Go back to the two papers you started reading in Activity 1 and read the methodology sections (they may be called methods or something similar) of both papers.

Now answer the following questions.

• What type of method(s) have the author/s in article 1 used to collect data?
• What method of analysis have these author/s used?
• What type of method(s) have the author/s in article 2 used to collect data?
• What methods of analysis have these author/s used?
• How do you think the methods used in both papers address the initial research aims or questions?
• Why do you think the methods used in both papers are appropriate to address these initial research aims or questions?

If you have chosen two papers based on different methodologies, you should reflect on the link between the ways in which the purposes of the studies were developed and the specific methods that the authors chose to address those questions. In the articles there should be a fit between research questions and methodology for collecting and analysing the data. The activity should have helped you to familiarise yourself with processes of planning a research methodology that fits the research question.

This course so far has given you an overview of the research strategy, design and possible methodologies for collecting data. What you have learned should be enough for you to have developed a clear idea of the general research strategy you want to adopt before you move on to develop your methodology for collecting data and review the methods in detail so that you are clear about the benefits and limitations of each before you collect your data.

9 Further development of your research questions

Before you embark on collecting data for your project, it is necessary to have a clear and specific focus for your research – i.e. the research aim, purpose or question(s). Section 5 gave you some examples of research questions. As a research question does not necessarily need to be expressed as a question (it is called a question because it is a problem to be solved), a study can have one general research aim or question and a few secondary aims or questions. It may also have only one research aim or question. In the case of quantitative studies, a few hypotheses might be developed. These will emerge from gaps in the literature but would have to derive from, and be linked to, an overall research purpose. Section 10 discusses research hypotheses.

The clear development of one or more research questions will guide the development of your data collection process and the tool(s) or instrument(s) you will use. Your research question should emerge from a specific need to acquire greater knowledge about a phenomenon or a situation. Such need may be a personal one as well as a contextual and organisational need. Box 1 gives an example of this.

Box 1 An example of how to develop a research question

As a consequence of government cuts, your arts organisation has to re-structure and this is causing stress and tension among staff. You are involved in the planning of the change initiative and want to develop an organisational change programme that minimises stress and conflict. In order to do so you need to know more about people’s views, at the various organisational levels.

What type of questions would help you to:

• understand the context
• demonstrate to the various research stakeholders (e.g. organisational members and research participants or supervisor, etc.) what you intend to do.

Perhaps you would like to make some notes of your initial ideas and think about how you could apply this process to developing your own research question.

Reading around the topic will help you to achieve greater focus, as will discussing your initial questions with colleagues or supervisor. In the example above, assuming the literature has been searched and several articles on change management and business restructuring have been read, you are likely to have developed clearer ideas about what you want to investigate and how you want to investigate it. Figure 6 shows what the main research question and the sub-questions or objectives might be:

This example is very well developed and would constitute a much larger project than the work-based project you might be doing. However the development of a general question and more specific questions focusing on different aspects should give you an idea of the relationship between the main question and the sub-questions.

In developing your research questions you also need to be concerned with issues of feasibility in terms of access and time. You do not need to be over ambitious but you need to realistically evaluate how difficult it would be to get the data you are planning in the time you have available before submitting the project at the end of the course. You need to plan a project that is neither too broad nor too narrow in scope and one that can be carried out in the available time.

Revisit your research topic and look back at the notes you have made about the topic (including the mind map you developed earlier in the course). Expand and amend where you need to.

Now think about what you need to do to re-write your statement as a research question and write the question in the space provided below.

Write a maximum of four sub-questions or research objectives that will help you to answer the main research question given above. In formulating the sub-questions make sure you consider the scope and the feasibility of the project. Write your questions in the space provided below.

10 Research hypotheses

For quantitative studies a research question can be further focused into a hypothesis. This is not universally the case – especially in exploratory research when little is known and so it is difficult to develop hypotheses – however it is generally the case in explanatory projects. A hypothesis usually makes a short statement concerning the relationship between two or more aspects or variables; the research thus aims to verify the hypothesis through investigation. According to Verma and Beard (1981, p. 184) ‘in many cases hypotheses are hunches that the researcher has about the existence of relationships between variables’. A hypothesis differs from a research question in several ways. The main difference is that a question is specific and asks about the relationship between different aspects of a problem or issue, whereas a hypothesis suggests a possible answer to the problem, which can then be tested empirically. You will now see how a research question (RQ) may be formulated as a research hypothesis (RH).

• RQ: Does motivation affect employees’ performance?

This is a well-defined research question; it explores the contribution of motivation to the work performance of employees. The question omits other possible causes, such as organisational resources and market conditions. This question could be turned into a research hypothesis by simply changing the emphasis:

• RH: Work motivation is positively related to employees’ performance.

The key elements of a hypothesis are:

• The variables used in a hypothesis must all be empirically measurable (e.g. you need to be able to measure motivation and performance objectively).
• A hypothesis should provide an answer (albeit tentatively) to the question raised by the problem statement.
• A hypothesis should be as simple as possible.

If you are planning to do an explanatory quantitative study, you will need to develop hypotheses. You will develop your hypotheses once you have read and reviewed the literature and have become familiar with previous knowledge about the topic.

In this free course, Understanding different research perspectives , the various perspectives (subjective/objective and interpretivist/positivist) that a researcher can take in investigating a problem have been discussed, as well as the issues that need to be considered in planning the project (ethics, research design, research strategy and research methodology). This fits with the first two stages of the overall research process as shown in Table 2.

The activities in this course have guided you through these first two processes.

Acknowledgements

This free course was written by Cinzia Priola.

Except for third party materials and otherwise stated (see terms and conditions ), this content is made available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Licence .

The material acknowledged below is Proprietary and used under licence (not subject to Creative Commons Licence). Grateful acknowledgement is made to the following sources for permission to reproduce material in this free course:

Course Image: © Kristian Sekulic/iStockphoto.com

Figure 2: © Yuri/iStockphoto.com

4. Research Ethics: CIPD’s Code of Professional Conduct: courtesy of Chartered Institute of Personnel and Development (CIPD)

7. Research and Design: extract adapted from: Cameron, S. and Price, D. (2009) Business Research Methods: A Practical Approach , London, CIPD

Every effort has been made to contact copyright owners. If any have been inadvertently overlooked, the publishers will be pleased to make the necessary arrangements at the first opportunity.

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There are a couple of different ways you can use Opposing Viewpoints to choose a topic.

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So what is a Viewpoints essay, and how is it different from other things you'll find in this database?

Viewpoints essays argue about a topic from a clear standpoint or side of the issue. Usually you'll find two of them relating the same issue, but arguing from two different points of  view. In this way, the purpose of Viewpoints are to give a balanced perspective to controversial topics :

So if you're writing a persuasive/argument paper, you could use one Viewpoint essay to provide support for your side of the argument, and the counterpoint Viewpoint essay for ideas on refuting opposing arguments.

Viewpoints essays are not periodicals like magazine, newspaper, or journal articles that are published regularly for a general or scholarly audience.

Viewpoints essays are written or compiled by the staff at Gale, the company that publishes the book series and the database. Sometimes they are reprints of articles or reports:

Viewpoints in the database are the same as chapters you'd find in the Opposing Viewpoints book series.  We have nearly 500 of them across all three libraries , so you may have run across them before. If you find a book in our library catalog that's part of the Opposing Viewpoints series, and the book is checked out or missing, you could try to find the same content in the database.  

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A word of caution: some professors think Viewpoints are OK to use as sources for research papers, and some professors do not. Always check your assignment and with your professor to make sure you know what sources will be accepted for your own research paper.

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Open Access

Peer-reviewed

Research Article

The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

Contributed equally to this work with: Laure Perrier, Erik Blondal, Heather MacDonald

Roles Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft

* E-mail: [email protected]

Affiliation University of Toronto Libraries, University of Toronto, Toronto, Ontario, Canada

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – review & editing

Affiliation Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada

Roles Data curation, Formal analysis, Investigation, Writing – review & editing

Affiliation MacOdrum Library, Carleton University, Ottawa, Ontario, Canada

• Laure Perrier, 
• Erik Blondal, 
• Heather MacDonald

• Published: February 27, 2020
• https://doi.org/10.1371/journal.pone.0229182
• Peer Review
• Reader Comments

2 Jun 2020: The PLOS ONE Staff (2020) Correction: The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLOS ONE 15(6): e0234275. https://doi.org/10.1371/journal.pone.0234275 View correction

Funding agencies and research journals are increasingly demanding that researchers share their data in public repositories. Despite these requirements, researchers still withhold data, refuse to share, and deposit data that lacks annotation. We conducted a meta-synthesis to examine the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data.

We searched the published and unpublished literature for studies on data sharing by researchers in academic institutions. Two independent reviewers screened citations and abstracts, then full-text articles. Data abstraction was performed independently by two investigators. The abstracted data was read and reread in order to generate codes. Key concepts were identified and thematic analysis was used for data synthesis.

We reviewed 2005 records and included 45 studies along with 3 companion reports. The studies were published between 2003 and 2018 and most were conducted in North America (60%) or Europe (17%). The four major themes that emerged were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Researchers lack time, resources, and skills to effectively share their data in public repositories. Data quality is affected by this, along with subjective decisions around what is considered to be worth sharing. Deficits in infrastructure also impede the availability of research data. Incentives for sharing data are lacking.

Researchers lack skills to share data in a manner that is efficient and effective. Improved infrastructure support would allow them to make data available quickly and seamlessly. The lack of incentives for sharing research data with regards to academic appointment, promotion, recognition, and rewards need to be addressed.

Citation: Perrier L, Blondal E, MacDonald H (2020) The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis. PLoS ONE 15(2): e0229182. https://doi.org/10.1371/journal.pone.0229182

Editor: Pablo Dorta-González, Universidad de las Palmas de Gran Canaria, SPAIN

Received: July 9, 2019; Accepted: February 2, 2020; Published: February 27, 2020

Copyright: © 2020 Perrier et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data are available from the Zenodo Repository, DOI: doi.org/10.5281/zenodo.3258850 .

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Research communities, including funding agencies and scholarly journals, have moved towards greater access to data through the development of policies that promote data sharing [ 1 – 4 ]. Examples include the development of data sharing requirements for clinical trials by the International Committee of Medical Journal Editors [ 5 ], the creation of a data repository for all researchers working towards a solution to the Zika virus so that all data is published as soon as it becomes available [ 6 ], and large funding bodies such as the Bill & Melinda Gates Foundation implementing strong open data policies [ 7 ].

These global developments require more researchers to share their data and make it available for reuse. Proponents for open data maintain that it offers the opportunity for others to freely reuse data, makes research more reproducible, uses public funds more effectively, and expands the potential to combine data sets for increased statistical power or creating new knowledge [ 8 ]. Sharing data is routine and embedded into the research process for some disciplines such as genomics and astronomy [ 9 – 10 ]. However, in many fields data produced by researchers has traditionally only been shared at the discretion of the principal investigator upon request, and otherwise kept in filing cabinets or on hard drives. These global shifts around research data have left some feeling uneasy and argue that those who generate the data own the data, certain studies (e.g., those with human subjects) require protection that may be difficult to assure with open data, and data sharing puts an increased administrative burden upon researchers [ 11 ]. There are also concerns of the inequity of a career built on data reuse versus the hard work of writing grants, being ‘scooped’, or being falsely discredited [ 11 – 12 ].

Although funding agencies, institutions, and journals have implemented policies on data sharing and archiving, these practices have not produced the anticipated results. Researchers still withhold data [ 13 ], refuse to share data upon request [ 14 – 15 ], publish without data availability statements [ 16 ], and fail to put their data into repositories [ 16 ] even after agreeing to share their data when publishing a journal article. Problems encountered when data is retrieved from repositories include inadequate annotation [ 17 ], limited structured data (Marwick), and incomplete specifications for data processing and analysis [ 17 ]. To gain insights on these behaviors, it is important to understand researchers’ perspectives. In this study, we report on researchers’ views and experiences on data sharing and reuse.

Our metasynthesis focuses on the individuals conducting research, and synthesizes the available qualitative literature that examines academic researchers and data sharing. This study addresses the question: what are the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data?

Materials and methods

A protocol was developed and is available upon request to the authors. Although the PRISMA statement has not been modified for meta-syntheses, it was used to guide the reporting of this review and can be viewed in S1 Appendix .

Types of studies

This is a metasynthesis of qualitative primary studies. Qualitative research seeks to discover how people perceive and experience the world around them [ 18 ]. Direct communication (e.g., interviews, focus groups) or observation are used to explore people’s perceptions. Data is explored using qualitiatve analytical methods and findings are then presented narratively using thick description rather than through numbers [ 19 ]. Thick description presents the findings as they were interpreted or explained by the authors as opposed to simply providing descriptive summaries of each study [ 20 ]. This provides the opportunity to translate the findings into a richer, more complete understanding of a phenomenon [ 21 ]. We included studies that reported qualitative methodologies and utilized qualitative methods for data analysis. Studies that collected data using qualitative methods but did not use qualitative analysis (e.g., surveys with open-ended questions that used descriptive statistics) were excluded. Mixed methods studies were included if it was possible to retrieve findings exclusively from the qualitative research.

Identification of studies.

The studies used for our meta-synthesis were derived from two sources. The first source was a dataset [ 22 ] generated from a scoping review on research data management in academic institutions [ 23 ] which provided records from inception to April 2016. The purpose of the scoping review was to describe the volume, topics, and methodological nature of the existing research literature on research data management as it specifically related to academic institutions. The search strategy included the terms data sharing, sharing research, data reuse, and research reuse, along with spelling variations and wildcards to ensure all relevant records were captured. The second source for data came from re-running the literature searches from the scoping review with the addition of a validated qualitative search filter [ 24 ] in order to retrieve current records from April 2016 to October 2018. When the searches were conducted for the update, four of the original literature databases were unavailable and were replaced with comparable platforms upon consultation with subject matter specialists. Both the original and the updated search included a total of 40 literature databases representing a wide range of disciplines. The search strategy for MEDLINE can be found in S2 Appendix and the full search strategies for the other databases can be obtained by contacting the author. S3 Appendix lists all the literature databases searched. As well, the grey literature, conference proceedings, and a search of the reference list of all included studies were completed. No restrictions were placed on publication date or language. Thus, a comprehensive search of the literature was conducted.

Eligibility criteria.

We aimed to identify all studies that investigated the views, perspectives, and experiences of academic researchers with data sharing and reuse. Studies were included if they were original research and reported qualitative methodologies, specifically focus groups or interviews. Studies had to include researchers (full- or part-time) conducting studies in academic institutions. We defined an academic institution as a higher education degree-granting organization dedicated to education and research. If studies included a mixed population, 50% or more of the total sample had to be researchers from academic institutions in order to be eligible for inclusion. Data sharing is defined as the practice of making data available for reuse [ 25 ]; reuse is defined as the use of content outside of its original intention [ 25 ]. Examples of this include depositing data into a digital repository or publishing raw data. Mixed methods studies that used both qualitative and quantitative methods within the same study were eligible if the qualitative portion met our inclusion criteria.

Study selection.

Two investigators independently screened all records from the scoping review dataset in order to identify qualitative studies that met the eligibility criteria. The records from the updated search were assessed for eligibility by two investigators independently at level 1 (title and abstract) and level 2 (full-text) screening. Discrepancies were resolved by discussion or by a third investigator at every phase of study selection.

Quality appraisal.

The CASP (Critical Appraisal Skills Programme) Qualitative Checklist [ 26 ] was used for quality appraisal of all included studies. The CASP Qualitative Checklist is a 10-item checklist that examines three domains: validity of results, reporting of the results, and value of the research ( S4 Appendix ). Each study was assessed independently by two investigators and discrepancies were resolved by discussion.

Data abstraction and analysis.

The authors read and reread all articles to become familiar with each study [ 27 – 28 ]. A data abstraction sheet was developed that included the study characteristics (e.g., year study conducted), participant characteristics (e.g., sample size), and key concepts. Key concepts or interpretations included all findings along with associated quotes from study participants [ 29 ]. Thematic analysis using constant comparison was used for data synthesis [ 30 ]. An initial set of 10 studies were coded independently by two investigators. These codes were then compared and refined in order to create an initial set of codes that were used going forward. Two investigators continued to independently code the remainder of the studies in duplicate and the team met regularly to discuss and iteratively refine the codes. All discrepancies were resolved through discussion or by a third member of the team. Finally, analytical themes were generated that offered a higher level interpretation beyond a descriptive synthesis [ 27 , 31 ]. The codes were grouped and categorized by making comparisons across articles in order to ensure that we appropriately captured similar themes from multiple studies. Meetings were used to review all constructs and resolve discrepancies, resulting in a refinement of the analytical themes.

Forty-five studies and three companion reports were included in the review ( Fig 1 ). Included studies are listed in S5 Appendix .

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• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0229182.g001

Study characteristics

The studies included in the review were published during a 15 year period between 2003 and 2018. The most studies were published in 2014 (11 out of 45) and the method for data collection were interviews (37), a combination of interviews/focus groups (5), and focus groups (4). Over half of the studies (27 out of 45) were conducted in the United States. Table 1 provides a summary of study characteristics.

https://doi.org/10.1371/journal.pone.0229182.t001

We identified four major themes and several sub-themes. The four major themes were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Themes and sub-themes along with illustrative quotes are summarized in Table 2 . Each theme is described below and details are provided for each sub-theme.

https://doi.org/10.1371/journal.pone.0229182.t002

Data integrity.

The theme data integrity addresses researcher’s perspectives on data that are available from repositories and their expectations around the prospects of reusing this data based on the quality, documentation available, and what individual researchers deemed as worthy of sharing.

• Data quality: Researchers acknowledged that although there may be interest or willingness to consider using open data, there would always be people that would not trust the quality of a dataset unless they collected it themselves. They recognized the need to manage expectations with some suggesting that lowering their standards related to data quality may help with increasing the likelihood of being able to use another researcher’s dataset. Nuances around the conditions, context, or materials that are not normally recorded were identified as one of the challenges to data quality. One example was offered in the field of engineering where equipment may not be anchored solidly and produce a variation that would impact the quality of data outputs [ 42 ]. The range of skill levels of researchers (e.g., junior versus senior researchers) was flagged as potentially affecting the quality of data collected and it was noted that there was no way of knowing this when reusing a dataset. Similarly, the quality of datasets may also vary depending on the person’s intentions or purpose when collecting data. It was felt that data collected with the intention of reporting only to people within their own discipline may look different from data collected for external groups, (i.e., these datasets may include more details).
• Data documentation: For a dataset to be truly reusable, researchers indicated adequate documentation was necessary including a significant amount of detail and metadata. The importance of contextual information was noted as providing layers of information that offered necessary insight. Providing this was seen as a time- and energy-intensive endeavor for the researcher collecting the data and making it available for reuse. Comprehensive documentation signaled a reliable dataset to researchers that were looking for datasets to reuse.
• What is worth sharing: When considering their data, researchers varied on what was worth sharing. It spanned from believing the preservation of datasets to be a top priority, to complete lack of interest. For those that felt their data was not worth sharing, they believed their data would be irrelevant after a period of time and nothing more than a “historical curiosity” if it was offered for reuse [ 56 ]. Others thought their data had the potential to be useful but had clear views on what was worth sharing and felt it had to have ‘scholarly value’. As an example, researchers described biospecimens they had collected as valuable since it provided the opportunity to look at the development of a disease [ 64 ].

Responsible conduct of research.

The responsible conduct of research emerged as a theme that encompassed the professional standards, ethical principles, and tacit norms that researchers described when considering data sharing. The five sub-themes under this theme are the misuse of data, protecting one’s own work/intellectual property, privacy/confidentiality/ethics, control of data, and work culture.

• Misuse of data: Researchers expressed concern about the potential for the inappropriate use of their data. This included what was termed as ‘fishing expeditions’ which involved dredging data with no particular research question in the hopes of stumbling upon possible relationships that could be presented as convincing results. The potential for data to be misunderstood and thus produce inappropriate or misguided conclusions was also considered a possibility, even if researchers reused datasets with a focused research question. The people reusing data were given names such as ‘free riders’ [ 42 ] and it was believed that misunderstood data could lead to false conclusions and ultimately threaten the original work related to the data.
• Protecting one’s own work/intellectual property: Clarity around who owns data, along with intellectual property rights, were raised as issues when sharing data. Collaborations were mentioned as making it difficult to determine ownership since multiple people and institutions were involved. Licensing data was seen as both a potential solution, as well as a potential barrier (e.g., the cost could mean it would not be accessible to all) for providing access to research data. Since data had publication value, this was considered a major deterrent to sharing data. This obvious connection between publications and data made researchers feel that data needed to be protected. Publications were seen as a key research output with a relationship between this and future funding.
• Privacy/confidentiality/ethics: Privacy and confidentiality were taken seriously when researchers considered their data, particularly when it came to human subjects. When data was shared between institutions, the precautions undertaken were complex. This included considerations such as de-identification, re-identification risks (along with the potential for this to happen unintentionally), consent (e.g., whether re-consent was necessary), and the challenge of future use if the purpose for future use was not pre-defined. Precautions implemented included each institution independently obtaining ethics approval before data was exchanged. Views were divergent around whether data should be freely accessible with some favoring restrictions on the re-use of data and others indicating it should be freely accessible.
• Control of data: It was acknowledged that the relationship between research being publicly funded and making data available for public benefit had merit. However, some felt they would like to know who was using their data and for what purpose. It was considered important to have a relationship with the person who wished to reuse data. As well, access should be controlled and only given to those that could be identified as a research professional who were qualified to do research. The level of control ranged from wanting systems in place that would allow them to monitor data sharing with people who were not known to them personally (although this was acknowledged as labor-intensive), to simply wanting to have a list of who was using their data and for what purpose as a minimum level of communication. The need to protect data until publication was considered a deterrent to sharing data and it was necessary to have control over data until this was completed.
• Work culture: Work culture highlights the beliefs of how research should be conducted that are influenced by shared attitudes, views, and written/unwritten rules developed over time. These normative values were described as being taught to junior researchers by senior academics. In the past, the cultural norm was to rely on informal processes, such as personal relationships, for sharing data. Usually, these were people who were known and trusted either through direct contact or by reputation. As new requirements were introduced by funding agencies and journals, researchers observed changes in their practice of sharing data over time. There was an acknowledgement that a shift in culture that favored a more open view of data was needed. It was also noted that even if researchers were to understand the benefits of sharing data, this transition would not be immediate and that incentives must be identified for researchers in this process.

Feasibility of sharing data.

The feasibility of sharing data examines the ease with which researchers can make their data available to others, along with the related barriers and facilitators. Infrastructure, time/work required, and skills are the three sub-themes that emerged and are described below.

• Infrastructure: Researchers described the structures and supports that would help ensure data sharing. Infrastructure support included data storage, file migration, and funding for making data available. These challenges involved both local (e.g., individual research labs) and institution-wide settings. Data handling was a fragmented activity managed by researchers who devised their own independent strategies that generally lacked sustainability. One example was the number of people (i.e., students, staff) that rotated through a research lab where everyone was responsible for their own data [ 62 ]. Although the lab manager encouraged best practices (e.g., including sufficient documentation), this did not necessarily translate into being adopted and applied. As a result, it was not guaranteed that datasets could be easily shared or be accessible in the future. Appropriate data storage infrastructure and support were associated with good data management, which in turn laid the groundwork for data sharing. An institution-level policy to support data sharing, along with resources, were identified as important to ensure good quality data being deposited and made available.
• Time/work required: The effort to prepare data for sharing was seen as time-consuming, expensive, and labor-intensive. Barriers included the lack of time to organize the necessary documentation, challenges with repository interfaces, and the lack of resources. For those that chose to offer their data upon request, the administrative aspect of filling requests for data was considered an added burden.
• Skills: For many disciplines, data sharing was a new activity that was typically imposed by funding agencies or journals. As a result, researchers were looking for services or resources that would help with this task. The lack of technical skills and knowledge included how to anonymize data, how to create metadata, and unfamiliarity with depositing data into repositories. It was felt that providing open access to data was complex. Providing adequate support may not be feasible given that each discipline had a variety of data types, different amount of data being generated, disparities in what is considered data, and varying norms in research culture.

Value of sharing data.

The value of sharing data theme describes researchers’ views on the importance placed on making data available to others. While the sub-theme promote future discovery identifies a benefit to society with sharing data, researchers’ perspectives focused on the benefits to researchers themselves.

• Promote future discovery: The importance of making data accessible for possible use in the future was understood as a benefit. Those that described proactively sharing data (before they were required to) also noted the importance of sharing computer code as well. There was recognition that research funded by public money should be open and available. It was felt that taxpayers provided an investment and the public deserved a return on their investment. In some instances, researchers were able to identify examples of data sharing that helped promote scientific progress, such as the development of a new drug or containment of a disease. It was felt that data sharing had the potential to move a field forward by closing knowledge gaps and further opening new avenues of investigation.
• Researchers’ perspectives: Data was identified as a research product that helped achieve a goal such as completing a publication and there was the recognition that amongst researchers that data sharing would provide greater accountability and transparency. For those that were already reusing data, its value was recognized as helpful for writing proposals and training students. The importance of providing incentives for sharing data was emphasized with researchers unable to identify significant benefits. Suggestions included creating grants that focused specifically on the reuse of data generated from earlier grants.

Quality assessment.

The CASP tool, used to assess the quality of studies, identified 27 (out of 45) studies that had seven out of the ten items present. Most studies adequately addressed the methods (41 out of 45 studies) and aims (40 out of 45 studies) ( Fig 2 ). Author reflexivity, which asked if the relationship between the researcher and participants was adequately considered, was not apparent in any of the studies. No studies were excluded due to a low score as this may have eliminated those with relevant and insightful results [ 77 – 78 ].

https://doi.org/10.1371/journal.pone.0229182.g002

We conducted a comprehensive review that included 45 studies along with 3 companion reports on the views, perspectives, and experiences of academic researchers on sharing their research data. The National Institutes of Health (NIH) in the United States were one of the first funding agencies to introduce a policy on sharing research data in 2001 [ 4 ]. This aligns with beginning to see research published on this topic starting in 2003, along with over half of the studies being conducted in the United States. Our results show that some of the themes and sub-themes offer positive support for sharing data however, most highlight areas of discomfort for researchers. In particular, researchers identified concern with issues related to data quality, misuse of data, protecting data, lack of time and skills, and deficiencies in infrastructure and support.

By default, researchers believed the quality of datasets available for reuse were poor and there is support for this in the literature. Studies assessing data available in public repositories have found incomplete datasets, saved in a way that compromised reuse [ 79 – 80 ]. Researchers who felt their data had value describe using a tacit set of criteria to determine if it had ‘scholarly value’ [ 38 ]. These criteria are based on discretion and would vary from person to person thus adding further to factors that affect the quality of datasets. For researchers who felt their data was not worth sharing, this may be reflected in how they prepare their data for depositing into repositories (e.g., providing poor documentation) and ultimately its final quality.

The lack of supportive infrastructure, lack of time, and skills deficit had an influence on data quality as well as data availability. Researchers indicated that the lack of time and skills impacted the production of sufficient data documentation, creation of suitable metadata, and appropriately anonymized data. They also lacked skills in navigating repository interfaces in order to deposit data. While training and education may address these issues [ 81 ], a more effective pathway is to focus energy and resources on creating user-friendly interfaces that allow users to accomplish their goal of depositing datasets as quickly and easily as possible [ 82 ]. At an institutional level, the lack of procedures, policies, and guidance contributed to challenges in sharing data. This was particularly true for sensitive data that requires more vetting and scrutiny before sharing. Solutions for this include using a trusted party regulated by an ethics board that manages requests and maintains the de-identified records and original identifiers [ 55 ].

Our results show that a major concern of researchers is the possibility of misuse or misinterpretation of their data, and this is reported as well in surveys [ 69 , 83 – 84 ]. Traditionally, research data has been shared through professional networks and by personal request [ 32 , 36 , 38 ]. These ‘traditions’ were incorporated into research processes as early-career researchers were indoctrinated by mentors and senior researchers [ 52 ]. This approach allowed those who owned datasets to scrutinize requests and all aspects of the requestor, including the reputation of their institution, their publications, and any other factors they felt important. Data producers had a hand in assuring their work and intellectual property were protected, privacy and confidentiality were safe, and it allowed them to exercise caution if there were any concerns around the misuse of their data, including the option to decline the request to share. Currently, funding agencies and journals are moving researchers in the direction of sharing data which is not embraced by all members of the research community [ 12 , 40 , 69 , 83 – 84 ]. In a recent paper, Campbell and colleagues [ 85 ] identified senior researchers as less likely to support data sharing while their early-career colleagues were more willing to make their data available for reuse. Researchers describe shifting to a culture of open data as a gradual transition in our findings, and stage of career may contribute to this need for a gradual shift.

Incentives were also identified as necessary for researchers within the research process that promoted open data [ 47 , 51 , 86 ]. In 2016, more than 500 researchers that received grants from the Wellcome Trust ( welcome.ac.uk ) in the United Kingdom were surveyed and although over half indicated that they made their data available for reuse, few reported direct benefits [ 69 ]. The lack of benefits appeared in our results and were identified as necessary yet lacking in the realm of data sharing. Suggestions for incentives included offering research grants that focused specifically on the reuse of data generated from earlier grants [ 51 ], and creating systems that ensure credit is awarded to data generators [ 87 – 90 ]. In one example, Pierce and colleagues [ 88 ] proposed creating enduring links between those who generate data and any time it was used in the future. This would involve linking persistent identifier (PIDs) to all datasets and provide infrastructure to link the identifiers to publications. In this strategy, data authorship would be listed on curriculum vitae, considered in academic institutions promotions criteria, and be considered by granting agencies as an element for review for funding.

There is a global movement towards openness in research that includes open data. Data sharing and reuse is a key part of this movement and anticipated benefits include promoting research transparency, verification of findings, and gaining new insights from re-analysis [ 8 ]. Despite this, it has not become a common practice [ 13 – 16 ]. Investing in strategies that improve skills amongst researchers that focus on improving data integrity in repositories and identifying incentives that provide motivation for data sharing are essential.

Limitations

Quality assessment indicated that some items in the CASP tool were addressed poorly in the studies. This included author reflexivity, analysis, and ethical issues. Limitations set by journals (i.e., word counts) may restrict authors from providing rich data and thick descriptions which are characteristic of qualitative studies. Studies based on low reporting quality were not excluded as this may have eliminated those with highly relevant and insightful results [ 77 ] and were used to judge the relative contribution in developing explanations in the study findings.

The qualitative data collected for this review originates from multiple disciplines and each may use a variety of data collection methods and research processes. However, when examining the studies by discipline, over a third of the studies in our review are listed as ‘combined’ (37% or 17 out of 45) [ 91 ] (i.e., participants came from multiple disciplines) yet none of the authors reported this as an issue in their analysis or impacting their results ( S6 Appendix ). Similarly, one of the study authors (LP) conducted focus groups with academic researchers in the area of research data management (including data sharing) and found that data saturation was reached after conducting four focus groups despite collecting data from discrete disciplines (i.e., health science, humanities, natural science) [ 81 ]. While diverse tools and methods may be employed by researchers in distinct disciplines to conduct their studies, issues related to research data management were identified as a commonality [ 32 , 38 , 39 , 44 – 45 , 47 , 52 – 54 , 56 , 58 , 59 – 60 , 65 , 70 – 71 , 75 , 81 ]

Most of the studies accepted into our review are interviews (82% or 37 out of 45 studies). While the group setting of a focus group may prompt ideas and memories from group members by listening to other participants [ 92 ], interviews provide the opportunity to go deeper into a topic and gather in-depth information [ 93 ]. When Guest and colleagues [ 93 ] performed a randomized controlled trial comparing focus groups and interviews, they found that individual interviews were more effective at generating a broad range of items at an individual level [ 93 ].

Conclusions

Misuse and misinterpretation of data is a significant concern amongst researchers when sharing their data. Preparation of data so that it is truly reusable requires an investment in time and resources as well as skills that researchers indicate they lacked. Deficiencies in infrastructure may hamper sharing data effectively, particularly sensitive data. The availability of data is marked by researchers’ decision making around what they determine is worth sharing. Currently, there is a lack of incentives for researchers to share their data with regards to academic appointment, promotion, recognition, and rewards. As such, enhancements need to be considered that focus on providing direct benefits to researchers who share their data. Identifying appropriate incentives may help improve motivation to share data and enhance the integrity of data put into repositories.

Supporting information

S1 appendix. prisma checklist..

https://doi.org/10.1371/journal.pone.0229182.s001

S2 Appendix. MEDLINE search strategy.

https://doi.org/10.1371/journal.pone.0229182.s002

S3 Appendix. Literature databases searched.

https://doi.org/10.1371/journal.pone.0229182.s003

S4 Appendix. CASP (critical appraisal skills programme): Qualitative checklist.

https://doi.org/10.1371/journal.pone.0229182.s004

S5 Appendix. Included studies.

https://doi.org/10.1371/journal.pone.0229182.s005

S6 Appendix. Included studies by discipline.

https://doi.org/10.1371/journal.pone.0229182.s006

Acknowledgments

We thank Jordan Raghunandan for collating information after the data was coded.

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Computer Science > Robotics

Title: rovi-aug: robot and viewpoint augmentation for cross-embodiment robot learning.

Abstract: Scaling up robot learning requires large and diverse datasets, and how to efficiently reuse collected data and transfer policies to new embodiments remains an open question. Emerging research such as the Open-X Embodiment (OXE) project has shown promise in leveraging skills by combining datasets including different robots. However, imbalances in the distribution of robot types and camera angles in many datasets make policies prone to overfit. To mitigate this issue, we propose RoVi-Aug, which leverages state-of-the-art image-to-image generative models to augment robot data by synthesizing demonstrations with different robots and camera views. Through extensive physical experiments, we show that, by training on robot- and viewpoint-augmented data, RoVi-Aug can zero-shot deploy on an unseen robot with significantly different camera angles. Compared to test-time adaptation algorithms such as Mirage, RoVi-Aug requires no extra processing at test time, does not assume known camera angles, and allows policy fine-tuning. Moreover, by co-training on both the original and augmented robot datasets, RoVi-Aug can learn multi-robot and multi-task policies, enabling more efficient transfer between robots and skills and improving success rates by up to 30%.

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