Topic choice

How to find a topic that creates momentum?

Andy Weeger

Neu-Ulm University of Applied Sciences

August 18, 2025

Motivation

I believe that the choice of research problem—choosing the phenomena we wish to explain or predict—is the most important decision we make as a researcher. We can learn research method. Albeit with greater difficulty, we can also learn theory-building skills. With some tutoring and experience, we can also learn to carve out large numbers of problems that we might research. Unfortunately, teasing out deep, substantive research problems is another matter. It remains a dark art. Weber (2003)

Introduction

What is research?

Weber (2003) characterizes doing research in terms of three activities:

1. Describing some phenomena that we perceive in the world
2. articulating a theory to account for the phenomena; and
3. testing how well the theory accounts for the phenomena.

In this course, we will not focus on the skills required for good research design or theory-building, rather we focus on communicating the results of research, that is telling a relevant story.

The relevance of your story is dependent on the phenomena that wish to explain or predict.

Thus, we start with discussing what a good topic is and outline some criteria and tools that may help you to find and chose a phenomena that creates momentum and is worth to devote your scarce resources.

Effective research topics

Exercise

Have a look at the first four pages of the MISQ editorial of Weber (2003).

What do you learn about the challenge of choosing a topic for your research?

Take approx. 20 minutes for that task.

Focus areas

To find an effective topic you need to identify (Recker, 2021, p. 36)

1. a specific domain with an
2. important phenomenon that deserves attention from academia and that relates to a
3. problem with the existing knowledge about this type of phenomenon.

Criteria for effective topics

Colquitt & George (2011) put forth five distinct criteria for effective topics in information systems (IS) and management science:

Significance, novelty, curiosity, scope, and actionability

Significance

Taking on “grand challenges”

• Identify and confront or contribute to a grand challenge¹
• Tackle such problems in a particular area if inquiry (i.e., slicing the elephant)
• Aim at a contribution that has theoretical usefulness and from which individual and societal benefit may accrue
• Take a bold and unconventional way that leaps beyond existing explanations (e.g., going behind conventional explanations)
• Articulate clearly how the study solves a piece of a larger puzzle

Example: Ferlie et al. (2005) took on a grand challenge in asking why evidence-based innovations failed to spread in the health care industry

Discussion

What challenges are
you interested in?

In which area if inquiry could (your) research contribute to it?

Novelty

Changing the conversation

• Consider whether your study might change the conversation that is already taking place in a given literature²
• Aim at either adding momentum to a conversation or giving it a new direction
• Think of how you can generate new and creative solutions by exploring new domains and not to prefer the familiar, the mature and perspectives that are near to existing approaches (i.e., avoid the familiarity, maturity, and nearness traps)

Example: Agarwal et al. (2004) focused on a new, under researched area by investigating how knowledge capabilities of industry incumbents affected the generation, development, and performance of “spin-outs”

Common traps when finding a topic

Familiarity and nearness trap

Picking a topic that is too familiar and/or too close to the existing literature and, thus, might may only offer contributions that are not sufficiently different from existing perspectives.

Maturity trap

Selecting a topic that is too mature and, thus, might only offer redundant contributions.

Exercise

What is required to
identify a novel topic?

What can you do to identify a topic that is not too mature and or too close to existing literature?

Curiosity

Catching and holding attention

• Topics are interesting when their propositions counter a reader’s taken-for-granted assumptions (e.g., showing a seemingly good phenomenon to be bad)
• Try to identify and use surprising findings that cannot be explained by methodological issues or existing explanations (i.e., “breakdowns” that signal the potential existence of a mystery such as inconsistent findings)³
• Try to reframe, reformulate or solve the “mystery”

Example: Van Kleef et al. (2009) strived to solve the mystery of contradictory findings about the effects of leader displays of emotion. They studied whether team performance would be facilitated by leaders displaying happiness or by leaders displaying anger.

Exercise

What are “mysteries” you have identified in your literature work?

Scope

Casting a wider net (not easily applicable for your master thesis)

• Even the best topic ideas can be undermined if the resulting study is too small
• Studies cannot tackle grand challenges if they are not ambitious in scope
• Aim to fully and comprehensively sample the landscape in a given domain (i.e., relevant constructs, mechanisms, and perspectives)
• You may may even include constructs and mechanisms derived by using multiple lenses

Example: Seibert et al. (2001) examine the effect of social capital on career success. They include all three theoretical perspectives on social capital that can explain why and how the characteristics of a personal social network can impact career success

Actionability

Offer insights for managerial or organizational practice

McGahan (2007) outline five major ways that management studies can be actionable:

• Offering counter-intuitive insights
• Highlighting the effect of new and important practices
• Showing inconsistencies in and consequences of practices
• Suggesting a specific theory to explain an interesting and current situation
• Identifying an iconic phenomenon that opens new areas of inquiry and practice

Exercise

What insights for practice do you expect from your study?

Conclusion

An effective topic …

• allows researchers to tackle a grand challenge in a literature,
• pursue a novel direction that arouses,
• maintains curiosity,
• builds a study with ambitious scope, and
• uncovers actionable insights.

Research question

Importance

Once you have identified a problem domain with an important phenomenon that deserves attention and relates to an problem with existing knowledge (i.e., an effective topic), you need to formulate and develop your own research questions and propose a plan to address them.

This challenge is much more difficult than learning methods and theories, largely because it is not as structured but is undefined and highly contextual (Recker, 2021).

However, the research question(s) is/are the fundamental cornerstone that around which your whole research project revolves and evolves.

Indications of bad research questions

Recker (2021) outlines for main problems indicating that research questions should be revised:

The “monologuing” problem, the “so what” problem, the “solving the world” problem, and the “multitude” problem.

• The “monologuing” problem: you cannot tell what research question your are tackling unless you engage in a five-minute monologue—you have not grasped the essence of the problem yet.
• The “so what” problem: You cannot tell why answering the research question matters to anyone.
• The “solving the world” problem: Your question has value, but cannot be answered given your resource constraints.
• The “multitude” problem: You ask (too) many questions instead of one.

Further categories of inappropriate research questions are (Recker, 2021):

• Obvious questions: “Are there challenges in using information technology?” Of course there are. Obvious questions have answers to which everyone would agree.
• Irrelevant questions: “What is the influence of weather on the salaries of technology professionals?” There is no reason to believe that there is any influence whatsoever.
• Absurd questions: “Is the earth flat after all?” Absurd questions have answers to which everyone would agree, although I read that almost two percent of people still believe the earth is flat.
• Definitional questions: “Is technology conflict characterised by disagreement?” The answer is simply a matter of creating a concept that says it does. A definition is a form of description, not research.
• Affirmation questions: “Can a decision-support tool be developed to facilitate decision-making for senior retail executives?” Yes.

Good research questions

A good research question should be:

• Clear and focused: the question should clearly state what you need to do.
• Not too broad and not too narrow: it must be possible to answer the question within the constraints of your research.
• Not too easy to answer: the answers should not be obvious or affirmative.
• Researchable: you must have access to data required to answer the question.
• Analytical rather than descriptive the question should allow you to produce an analysis of the problem rather than a simple description of it.

Once you have identified an important phenomenon, you need to engage with literature to identify problems with the existing knowledge. This helps you to narrow down your topic and create a good research question.

Research questions are typically one of two types based on the issues they address (Recker, 2021):

1. “What,” “who,” and “where” questions tend to focus on issues we seek to explore or describe because little knowledge exists about them.
2. “How” and “why” questions are explanatory as they seek to answer questions about the causal mechanisms that are at work in a particular phenomenon.

Guiding questions

Recker (2021, pp. 35–36) proposes a number of guiding questions can help you find a good research question, e.g.:

• Do you know in which field of research your research questions reside?
• Do you have a firm understanding of the body of knowledge in the field?
• What are important open research questions or unsolved problems in the field that scientists agree on?
• What areas need further exploration?
• Could your study fill an important gap in knowledge?
• Has your proposed study been done before? If so, is there room for improvement or expansion?
• Is the timing right for the question to be answered?
• Who would care about obtaining an answer to the question?

Exercise

Check following research questions — do they meet the criteria presented?

• What are consumer- and product-related antecedents to differences in the valuation of human- vs. algorithm-created digital products? (Rix et al., 2025)
• Whether and how do conversational style and reciprocation triggers influence user information disclosure? (Adam & Benlian, 2024)
• How do daily congruence and incongruence in the team-enacted versus individually needed use of agile practices affectdeveloper well-being? (Benlian et al., 2025)
• How does the introduction of a substitutive decision-making AI system affect employees’ professional role identity? And how do employees adapt their professional role identity in response to these AI systems? (Strich et al., 2021)
• How canIT support be provided for reducing the problem-solving complexity of monitoring the structural anddynamic complexity of IT architectures in the context of a digital business strategy? (Widjaja & Gregory, 2020)

What are consumer- and product-related antecedents to differences in the valuation of human- vs. algorithm-created digital products? (Rix et al., 2025)

• Significance: Very high — taps into AI/consumer behavior, timely for digital markets.
• Novelty: Yes — “algorithm discount” is an emerging phenomenon; adds new conceptual ground.
• Curiosity: Clear puzzle (why do consumers devalue algorithmic output?).
• Scope: Ambitious but focused — restricted to antecedents, not all possible outcomes.
• Actionability: Strong — firms can adjust design/marketing strategies.
• Clarity: Excellent — concise, no ambiguity.
• Answerability: Yes — lends itself to experiments/surveys.
• Analytical focus: Clear independent (consumer/product antecedents) and dependent (valuation differences) variables.

Whether and how do conversational style and reciprocation triggers influence user information disclosure? (Adam & Benlian, 2024) * Significance: High — relates to privacy, chatbot/UX design. * Novelty: Yes — adds nuance (stylistic & social triggers) beyond generic “chatbot vs. form.” * Curiosity: Medium–high — “whether and how” signals uncertainty and depth. * Scope: Appropriately narrow — focuses on two levers affecting one key outcome. * Actionability: Clear — design implications for conversational agents. * Clarity: Minor flaw: “Whether and how” could be split into two clearer sub-RQs. * Answerability: Yes — testable in lab/field experiments. * Analytical focus: Strong — well-specified factors and outcome.

How do daily congruence and incongruence in the team-enacted versus individually needed use of agile practices affect developer well-being? (Benlian et al., 2025)

• Significance: High — agile use/misalignment is a core IS management issue.
• Novelty: Yes — fine-grained “daily congruence” adds new perspective.
• Curiosity: High — counterintuitive potential (incongruence may harm/help).
• Scope: Nicely bounded — one outcome (well-being), one context (agile).
• Actionability: Very strong — managers can adapt agile routines.
• Clarity: Long sentence but precise; terms are operationalizable.
• Answerability: Yes — daily diary studies or panel data possible.
• Analytical focus: Excellent — congruence/incongruence as predictor, well-being as outcome.

How does the introduction of a substitutive decision-making AI system affect employees’ professional role identity? And how do employees adapt their professional role identity in response to these AI systems? (Strich et al., 2021)

• Significance: Very high — addresses identity threats in the future of work.
• Novelty: Strong — few studies directly link AI substitution with identity adaptation.
• Curiosity: Very strong — open question with human and organizational stakes.
• Scope: Appropriately bounded to identity, not all socio-technical effects.
• Actionability: Medium — insights more conceptual/theoretical than prescriptive.
• Clarity: Excellent — two tightly worded sub-RQs.
• Answerability: Qualitative/longitudinal research feasible.
• Analytical focus: Good — causal mechanisms implied (AI introduction → identity shifts/adaptation).

How can IT support be provided for reducing the problem-solving complexity of monitoring the structural and dynamic complexity of IT architectures in the context of a digital business strategy? (Widjaja & Gregory, 2020)

• Significance: High — enterprise architecture complexity is a major IS challenge.
• Novelty: Moderate–high — adds a DSR angle to monitoring complexity.
• Curiosity: Solid — “reducing problem-solving complexity” is compelling.
• Scope: Quite broad — multiple forms of complexity, strategic context.
• Actionability: Strong — artifact-oriented, prescriptive.
• Clarity: Weaker — long and abstract phrasing; could be split into clearer parts.
• Answerability: Yes — via artifact design + evaluation.
• Analytical focus: Weaker than others — less explicit on variables/mechanisms.

Motivation of RQs

Recker (2021, p. 36 ff) proposes a systematic four-step approach for motivating research questions that ensures logical flow from broad context to specific inquiry.

Problem domain statement + specific phenomenon + knowledge gap or problem + research question

First, establish a problem domain statement that highlights an important business or organizational context, emphasizing the scale and significance of the domain while including quantifiable evidence when possible, such as dollar amounts invested or lost, percentages of organizations affected, or market size metrics.

Second, identify a specific phenomenon by narrowing down from the broad domain to a particular issue or challenge, focusing on what makes this phenomenon especially important or problematic within the larger context.

Third, articulate the knowledge gap or problem by clearly stating what is wrong with current knowledge—this could be a gap where we don’t know enough about a topic, inconsistencies where studies show conflicting results, outdated assumptions where current theories rest on unrealistic premises, or limited scope where existing research only covers part of the phenomenon.

Finally, formulate the research question as a logical conclusion flowing from the previous three steps, ensuring it directly addresses the identified knowledge problem and is positioned to fill the gap or resolve the inconsistency identified in the literature.

Homework

The research question is the fundamental cornerstone that around which your whole research project and your writing revolves and evolves.

Think about the phenomenon, go to literature, identify a relevant problem and draft candidate research question(s).

Q&A

Literature

Adam, M., & Benlian, A. (2024). From web forms to chatbots: The roles of consistency and reciprocity for user information disclosure. Information Systems Journal, 34(4), 1175–1216.

Agarwal, R., Echambadi, R., Franco, A. M., & Sarkar, M. B. (2004). Knowledge transfer through inheritance: Spin-out generation, development, and survival. Academy of Management Journal, 47(4), 501–522.

Benlian, A., Pinski, M., & Adam, M. (2025). Team-enacted use versus developer-needed use of agile practices: How perceptual (in-) congruence and team feedback-seeking shape developer well-being. Information Systems Research.

Colquitt, J. A., & George, G. (2011). Publishing in AMJ—part 1: Topic choice. In Academy of Management Journal (3; Vol. 54, pp. 432–435). Academy of Management Briarcliff Manor, NY.

Ferlie, E., Fitzgerald, L., Wood, M., & Hawkins, C. (2005). The nonspread of innovations: The mediating role of professionals. Academy of Management Journal, 48(1), 117–134.

McGahan, A. M. (2007). Academic research that matters to managers: On zebras, dogs, lemmings, hammers, and turnips. Academy of Management Journal, 50(4), 748–753.

Recker, J. (2021). Scientific research in information systems: A beginner’s guide. Springer Nature.

Rix, J., Berger, B., Hess, T., & Rzepka, C. (2025). The algorithm discount: Explaining consumers’ valuation of human-versus algorithm-created digital products. Journal of Management Information Systems, 42(2), 633–668.

Seibert, S. E., Kraimer, M. L., & Liden, R. C. (2001). A social capital theory of career success. Academy of Management Journal, 44(2), 219–237.

Strich, F., Mayer, A.-S., & Fiedler, M. (2021). What do i do in a world of artificial intelligence? Investigating the impact of substitutive decision-making AI systems on employees’ professional role identity. Journal of the Association for Information Systems, 22(2), 9.

Van Kleef, G. A., Homan, A. C., Beersma, B., Van Knippenberg, D., Van Knippenberg, B., & Damen, F. (2009). Searing sentiment or cold calculation? The effects of leader emotional displays on team performance depend on follower epistemic motivation. Academy of Management Journal, 52(3), 562–580.

Weber, R. (2003). Editor’s comments: The problem of the problem. MIS Quarterly, 27(1), 7.

Widjaja, T., & Gregory, R. W. (2020). Monitoring the complexity of IT architectures: Design principles and an IT artifact. Journal of the Association for Information Systems, 21(3), 4.

Footnotes

1. The grand challenges are large, important unsolved problem. Current grand challenges might be reflected by the United Nations Millennium Development Goals to eradicate global poverty, disease, and hunger.

2. Adding to a conversation requires to know the conversation. Research topics (and research questions), thus, do not come out of the air, you need to read a lot.

3. According to Colquitt & George (2011) such topics arouse more interest than the more typical “gap-spotting” approach to generating research questions