Chapter 2: Research Problems, Questions, and Contributions

1. Introduction

A doctoral study begins not with a preferred method or a fashionable technology, but with a significant research problem. In information technology (IT), where technologies, organizations, and social practices change rapidly, the ability to identify a meaningful problem and convert it into answerable research questions is a central scholarly competence. A well-designed problem provides direction for the literature review, research design, data collection, analysis, and interpretation. It also establishes why the study deserves attention from researchers, practitioners, policy makers, or society.

A research problem is more than a general concern such as cybersecurity, artificial intelligence, cloud computing, or digital transformation. It is a clearly bounded situation in which an important phenomenon is insufficiently understood, inadequately explained, poorly designed, or ineffectively addressed. The doctoral researcher must demonstrate that the problem is real, significant, researchable, and not already resolved by existing knowledge.

This chapter explains how to identify research gaps, formulate research problems and questions, develop hypotheses and objectives, define scope and feasibility, and establish theoretical and practical contributions. It emphasizes alignment: the problem, questions, methodology, evidence, and contribution must form a coherent whole.

2. Learning Objectives

By the end of this chapter, the reader should be able to:

1. Distinguish a research topic, issue, problem, gap, question, hypothesis, and objective.

2. Identify and evaluate research gaps in IT literature and practice.

3. Formulate significant, precise, feasible, and ethical research problems.

4. Develop aligned research questions, hypotheses, and objectives.

5. Define the scope, assumptions, delimitations, and feasibility of a doctoral study.

6. Explain theoretical, methodological, empirical, technological, and practical contributions.

7. Apply structured frameworks to assess originality, significance, and contribution.

8. Evaluate research proposals for coherence, rigor, and managerial relevance.

3. Meaning and Concept

A research topic is a broad area of interest, such as blockchain adoption or software quality. A research issue is a recognized concern within that topic, such as inconsistent blockchain adoption across public agencies. A research problem is a specific and consequential deficiency in knowledge, explanation, design, practice, or policy that warrants systematic investigation.

A useful problem statement normally contains four elements: the context, the undesirable condition or uncertainty, evidence that the condition matters, and the gap that the proposed study will address. For example, organizations may deploy AI-based recruitment systems, yet little is known about how transparency mechanisms influence applicant trust across culturally diverse environments. This statement identifies a context, an uncertainty, and a potential knowledge gap.

A research gap is the difference between what is known or available and what should be known or available for a particular purpose. Gaps may be theoretical, empirical, methodological, contextual, technological, or practical. A gap is not simply a topic that has received little attention. Lack of research may indicate that a question is unimportant, infeasible, or already addressed indirectly. The researcher must justify why filling the gap matters.

A research question expresses what the study seeks to answer. A hypothesis is a testable proposition about relationships, differences, or effects. A research objective states what the study will accomplish. These elements must be aligned. For example, a question about whether perceived transparency predicts trust requires variables, measurement, and an analytical design capable of examining prediction. A qualitative question about how employees interpret transparency requires a different form of inquiry.

A research contribution is the defensible addition made by a study. Contributions may extend theory, introduce evidence, improve methods, create or evaluate an artifact, inform professional practice, or influence policy. A contribution is not merely the completion of a project or the use of a new tool; it is what the scholarly and professional community can learn or do because of the study.

4. Fundamental Principles

4.1 Significance

The problem should matter. Significance may arise from theoretical uncertainty, substantial organizational cost, public risk, inequality, safety concerns, or an opportunity for important improvement. A technically interesting problem is not necessarily a significant doctoral problem.

4.2 Clarity and precision

A problem statement should avoid vague terms such as “many,” “effective,” “better,” or “modern” unless they are defined. It should identify the population, setting, phenomenon, and relevant time or technological conditions.

4.3 Evidence-based justification

Claims about the existence and importance of a problem should be supported by scholarly studies, industry reports, incident data, policy documents, or organizational evidence. Assertions unsupported by evidence weaken the proposal.

4.4 Originality

Doctoral research must offer an original contribution. Originality can involve a new theory, an extension of an existing model, an underexamined context, a novel dataset, a new method, a new artifact, or a new explanation of contradictory findings. Novelty alone is insufficient; the work must also be valuable and credible.

4.5 Researchability

The problem must be investigable using available data, participants, tools, skills, time, and ethical permissions. A problem may be important but unsuitable if evidence cannot be accessed or outcomes cannot be evaluated.

4.6 Alignment

The problem, questions, objectives, hypotheses, methods, data, analysis, and claims must be logically consistent. Misalignment occurs, for example, when a study claims causal effects but uses only descriptive data, or when a qualitative design is paired with hypotheses requiring statistical testing.

4.7 Appropriate scope

A doctoral study should be ambitious in contribution but bounded in execution. Scope defines what is included and excluded. Delimitation is a deliberate boundary chosen by the researcher; limitation is a constraint that may affect the study.

4.8 Ethical responsibility

Problem formulation can affect vulnerable groups, organizations, and public interests. Researchers should avoid framing communities as deficient, collecting unnecessary personal data, or promising solutions that evidence cannot support.

5. Major Components / Elements

5.1 Context and unit of analysis

The context may be hospitals, universities, software firms, government agencies, or online communities. The unit of analysis may be an individual, team, organization, system, transaction, document, or country. Confusing the unit of analysis produces invalid conclusions; data collected from employees, for example, cannot automatically justify claims about entire organizations.

5.2 Phenomenon and constructs

The phenomenon is what is being studied, such as algorithmic trust or DevOps capability. Constructs are abstract concepts represented through indicators. Constructs should be conceptually defined before they are operationalized.

5.3 Research gap

The gap should specify what prior studies have established, where they disagree or remain incomplete, and how the proposed study addresses the deficiency. A literature matrix can compare theories, contexts, samples, methods, findings, and limitations across studies.

5.4 Research questions

Descriptive questions ask what exists or occurs. Explanatory questions ask why or how relationships arise. Predictive questions ask whether outcomes can be estimated. Evaluative questions examine value or effectiveness. Design questions address how an artifact or intervention can be created and assessed. Exploratory questions investigate poorly understood phenomena.

5.5 Hypotheses and propositions

A hypothesis should identify expected relationships and, where appropriate, direction. For example: “Perceived explainability positively influences user trust in clinical decision-support systems.” A null hypothesis may state that no significant relationship exists. In qualitative or interpretive research, propositions or sensitizing concepts may be more appropriate than statistical hypotheses.

5.6 Objectives

The general objective expresses the overall purpose. Specific objectives divide it into observable tasks, such as reviewing theory, developing a model, collecting evidence, testing relationships, designing an artifact, and evaluating outcomes. Objectives should use precise verbs: identify, compare, explain, develop, validate, evaluate, or characterize.

5.7 Expected contribution

A proposal should state who benefits, what changes in knowledge or practice, and how the contribution will be demonstrated. Contribution claims should be proportional to the evidence.

6. Process / Framework / Methodology

A practical problem-to-contribution process contains the following stages.

Stage 1: Explore a domain. Begin with sustained reading, professional observation, discussions with stakeholders, and examination of current incidents or policy developments. Maintain a research diary to record recurring concerns and assumptions.

Stage 2: Map the literature. Search systematically across scholarly databases. Classify studies by theory, context, method, variables, findings, and limitations. A scoping review, systematic review, bibliometric analysis, or concept matrix can reveal patterns that casual reading misses.

Stage 3: Diagnose the gap. Ask whether the gap concerns absence of evidence, conflicting evidence, weak explanation, limited generalizability, methodological weakness, or inadequate design. Distinguish a genuine gap from a merely unstudied niche.

Stage 4: Test significance. Assess the consequences of leaving the problem unresolved. Consult practitioners and potential users. Consider theoretical importance, economic impact, social consequences, risk, and policy relevance.

Stage 5: Formulate the problem statement. Write a concise statement that identifies the setting, condition, evidence, gap, and need for investigation. Revise it until a reader can identify exactly what requires explanation or improvement.

Stage 6: Choose a research logic. Decide whether the study is exploratory, descriptive, explanatory, predictive, evaluative, or design-oriented. This decision guides the questions and method.

Stage 7: Develop questions and objectives. Create one central question and a manageable set of subquestions. Each objective should correspond to a question and a planned source of evidence.

Stage 8: Develop hypotheses or propositions where appropriate. Ground each expected relationship in theory and prior findings. Avoid introducing hypotheses merely to make a study appear quantitative.

Stage 9: Establish scope and feasibility. Specify population, setting, technologies, period, variables, and exclusions. Conduct a feasibility assessment covering access, budget, skills, data quality, computing resources, and ethics.

Stage 10: Specify the contribution and evaluation criteria. State how the contribution will be judged. A predictive model might be assessed by accuracy, fairness, robustness, and usefulness; a design artifact by functionality, utility, usability, and rigor.

Stage 11: Verify alignment. Use a design matrix connecting problem elements, questions, objectives, data, methods, analysis, and anticipated contribution. Seek feedback from supervisors, domain experts, and intended users.

7. Important Theories and Models

The falsification perspective associated with Karl Popper emphasizes that scientific claims should be exposed to possible refutation. In IT research, a hypothesis should imply observable conditions under which it could be rejected.

The design science research paradigm treats the creation and evaluation of artifacts—such as methods, models, architectures, or software—as a legitimate research contribution. It requires a relevant problem, rigorous construction, systematic evaluation, and communication of results.

The technology–organization–environment framework explains technology adoption through technological characteristics, organizational conditions, and environmental pressures. It can help researchers move from a broad adoption topic to questions about specific mechanisms and contexts.

The technology acceptance model links perceived usefulness and perceived ease of use to technology acceptance. Researchers should not apply it mechanically; a doctoral contribution may involve extending, testing, or challenging its assumptions in a new setting.

The task–technology fit perspective proposes that performance improves when technology capabilities match task requirements. It is useful for formulating questions about whether a digital system actually supports work rather than merely being accepted by users.

The socio-technical perspective views IT outcomes as products of interactions among people, processes, technology, and institutional structures. It discourages purely technical problem definitions when organizational or human factors are central.

The contribution typology distinguishes theory contribution, empirical contribution, methodological contribution, artifact contribution, and practical or policy contribution. A strong dissertation may provide several forms, but each must be explicitly demonstrated.

8. Practical Applications

Research problem formulation is useful in cybersecurity, where a broad concern about ransomware can become a study of how incident-response teams prioritize recovery decisions under incomplete information. In software engineering, “poor quality” can become an investigation of whether automated code-review explanations improve defect detection without increasing developer workload. In digital health, “low adoption” can become a study of how interoperability barriers and perceived clinical responsibility affect use of electronic decision-support tools.

In managerial research, a company may report that employees resist a data platform. Research can distinguish resistance caused by usability, surveillance concerns, inadequate training, weak governance, or misalignment with work practices. The resulting questions are more useful than a generic question about whether the platform is successful.

9. Examples

A weak topic is: “Artificial intelligence in education.” A stronger problem is: “Although AI tutoring systems provide adaptive feedback, evidence is inconsistent regarding whether explanation transparency improves student reliance without reducing independent problem-solving.” Possible questions concern the mechanism of reliance, differences among learners, and evaluation of an explanation design.

A weak objective is: “To study cybersecurity.” A stronger objective is: “To develop and evaluate a behavioral model explaining how security fatigue influences employees’ compliance with multi-factor authentication procedures in medium-sized financial firms.”

A weak hypothesis is: “Blockchain improves supply chains.” A stronger hypothesis is: “Perceived data integrity mediates the relationship between blockchain traceability and interorganizational trust among pharmaceutical distributors.” The latter identifies constructs and a mechanism that can be examined.

10. Technology and Digital Applications

Digital tools can support problem formulation when used critically. Scholarly databases and citation-management systems assist literature mapping. Bibliometric software can reveal influential themes, collaboration patterns, and emerging topics. Qualitative analysis platforms support coding of interviews, documents, and field notes. Statistical and causal-analysis tools help examine relationships after constructs and designs have been justified.

Machine-learning tools may assist with document classification, duplicate detection, topic clustering, and preliminary evidence mapping. However, automated summaries can omit limitations, reproduce publication bias, or confuse correlation with causation. Generative AI may support brainstorming or language revision, but researchers remain responsible for source verification, originality, confidentiality, and accurate representation of evidence.

Requirements repositories, issue trackers, system logs, and privacy-preserving data platforms can reveal practical problems. Digital-twin or simulation environments may help assess feasibility before deployment. Technology should serve the research logic, not determine the research problem merely because it is available.

11. Strategic / Managerial Importance

A precise research problem improves investment decisions by clarifying what evidence is needed before an organization adopts or scales a technology. It prevents managers from treating symptoms—such as low system usage—as causes. It also supports prioritization: a problem involving safety, legal exposure, customer harm, or strategic capability may deserve greater attention than a minor usability issue.

For executives, a contribution statement translates research into decisions, capabilities, controls, or performance improvements. For research managers, it supports portfolio selection and reduces duplication. For policy makers, it clarifies which assumptions require regulation or public investment. Importantly, managerial relevance does not replace scholarly rigor; practical claims must still be supported by an appropriate design and credible evidence.

12. Detailed Case Study

Background

The following fictional case is illustrative. Meridian Care Network operates eight hospitals and has introduced an AI system that predicts patient deterioration. The system provides risk scores and recommended monitoring priorities.

Problem

Although the system is technically accurate in retrospective testing, nurses use its recommendations inconsistently. Management initially defines the problem as insufficient training. Preliminary interviews suggest a more complex issue: nurses do not understand why the system produces particular alerts and are uncertain about accountability when an alert is ignored.

Analysis

A literature review finds extensive research on predictive accuracy but limited evidence about explainability, professional responsibility, and alert use in multi-hospital environments. Existing studies also rely heavily on laboratory tasks rather than real clinical workflows. The gap is therefore contextual, explanatory, and practical. The unit of analysis includes nurses interacting with alerts, while the organizational context includes hospital governance and workflow design.

Strategy / Solution

The research team formulates the central question: “How do explanation transparency and perceived accountability influence nurses’ appropriate use of AI deterioration alerts?” Subquestions address how nurses interpret explanations, whether accountability concerns mediate use, and how workflow conditions moderate the relationship. The team uses a mixed-method design: interviews and observation identify mechanisms; a survey tests relationships; and a controlled prototype evaluation compares alternative explanation formats.

The scope excludes diagnosis and focuses on alert interpretation. Objectives include developing a conceptual model, measuring relevant constructs, designing an explanation interface, and evaluating appropriate use rather than simple acceptance. Ethical safeguards include clinician consent, patient-data de-identification, independent oversight, and a rule that the prototype cannot replace clinical judgment.

Outcome

The illustrative evaluation finds that concise explanations linked to observable patient indicators improve appropriate use only when accountability procedures are clear. Longer explanations increase cognitive burden. The contribution includes a refined socio-technical model, empirical evidence from hospital workflows, design principles for explanation interfaces, and governance recommendations.

Learning Points

The initial symptom—low use—was not the complete research problem. A precise diagnosis revealed interacting technical, human, and organizational factors. The case also demonstrates that accuracy alone does not establish usefulness, and that a contribution may combine theory, artifact design, empirical evidence, and practice guidance.

13. Advantages / Benefits

Careful problem formulation improves research focus, reduces irrelevant data collection, and makes methodological choices defensible. It helps identify appropriate participants, measures, and evaluation criteria. It increases the likelihood that findings will be useful beyond the immediate project. It also enables transparent assessment of originality and reduces the risk of claiming contributions that the evidence cannot support.

14. Limitations / Challenges

Research gaps may be difficult to establish because publications are unevenly distributed across disciplines and negative results are underreported. Access to organizational data may be restricted, and rapidly changing technologies can make a problem unstable. Constructs such as trust, fairness, or digital maturity may be difficult to measure consistently. Researchers may also overstate novelty, select questions that are too broad, or allow sponsor interests to distort the problem definition.

Feasibility can change during a doctoral project. Participants may withdraw, systems may be replaced, or regulations may change. A well-designed study therefore includes contingencies and distinguishes essential objectives from optional extensions.

15. Ethical and Governance Considerations

Researchers should assess privacy, informed consent, data minimization, security, fairness, explainability, and potential harm. Research questions involving algorithmic systems should consider whether errors affect groups differently and whether the study may legitimize harmful practices. Organizational access agreements should specify ownership, confidentiality, publication rights, and responsibilities for discovered vulnerabilities.

Governance also concerns the integrity of the research process. Researchers should disclose conflicts of interest, preregister hypotheses where appropriate, preserve an audit trail, report null findings, and avoid fabricating certainty. When using AI-assisted research tools, they should verify sources, protect confidential information, document material assistance, and comply with institutional policies.

16. Current and Emerging Trends

Research problems increasingly address trustworthy AI, algorithmic accountability, privacy-preserving analytics, cyber resilience, quantum-safe security, digital inequality, and sustainability of computing infrastructure. The growth of platform ecosystems creates questions about data ownership, interoperability, labor, and governance across organizational boundaries.

Emerging research is also moving from isolated technical performance toward real-world evaluation. Researchers increasingly examine robustness, fairness, energy use, maintainability, human oversight, and long-term institutional effects. Open science, reproducible workflows, synthetic data, and responsible research assessment are changing how contributions are demonstrated.

17. Best Practices

Begin with a problem, not a method. Read across disciplines and include contradictory findings. Define the unit of analysis and key constructs early. Use a literature matrix and maintain an evidence trail for claims. Consult stakeholders without allowing sponsors to predetermine conclusions. Write one central problem statement and a small number of answerable questions. Match questions to design, data, and analysis. State exclusions and feasibility assumptions explicitly. Define contribution in terms of what changes in knowledge or action. Pilot instruments and revise questions when evidence shows that assumptions are wrong. Finally, invite critical review from people who may disagree with the proposed explanation.

18. Future Scope

Future research can develop stronger methods for identifying meaningful gaps in fast-changing technical fields. Automated evidence mapping may become more capable, but human judgment will remain necessary for evaluating relevance and quality. Longitudinal and cross-cultural studies are needed to determine whether IT theories remain valid as technologies and institutions evolve. There is also scope for integrated evaluation frameworks that assess technical performance together with fairness, sustainability, organizational value, and public legitimacy.

Doctoral researchers may increasingly produce portfolios of related contributions: a conceptual model, an open dataset or benchmark, an evaluated artifact, and implementation guidance. Such portfolios require especially careful boundary setting so that breadth does not compromise depth.

19. Chapter Summary

A doctoral IT study is founded on a significant and researchable problem. Researchers must distinguish broad topics from specific problems and demonstrate a meaningful gap through critical engagement with literature and practice. Effective questions, hypotheses, and objectives are precise, feasible, ethical, and aligned with the research design. Scope and feasibility prevent overreach, while contribution statements explain the value of the work. Contributions may be theoretical, empirical, methodological, technological, practical, or policy-oriented. The strongest studies connect rigorous inquiry with real-world importance without allowing managerial preferences or technological novelty to replace scholarly reasoning.

20. Key Terms

1. Research topic: A broad area of scholarly interest.

2. Research issue: A recognized concern within a topic.

3. Research problem: A significant deficiency in knowledge, explanation, design, practice, or policy.

4. Research gap: An important difference between existing knowledge and what is needed.

5. Research question: A precise question that guides investigation.

6. Hypothesis: A testable proposition about a relationship or difference.

7. Proposition: A theoretically informed statement, often used in qualitative or conceptual research.

8. Research objective: A specific statement of what a study intends to accomplish.

9. Construct: An abstract concept represented through observable indicators.

10. Unit of analysis: The entity about which conclusions are drawn.

11. Scope: The boundaries of a study.

12. Delimitation: A boundary deliberately established by the researcher.

13. Limitation: A constraint that may affect the study or its findings.

14. Originality: The degree to which research adds something new and valuable.

15. Theoretical contribution: An extension, refinement, test, or challenge to theory.

16. Empirical contribution: New evidence about a phenomenon or relationship.

17. Methodological contribution: An improvement or innovation in research method.

18. Artifact contribution: A rigorously designed and evaluated technological or conceptual artifact.

19. Feasibility: The practical possibility of completing a study with available resources.

20. Alignment: Logical consistency among problem, questions, methods, evidence, and claims.

21. Review Questions

Short Answer Questions

1. What distinguishes a research problem from a research topic?

2. Why is a research gap not automatically a justification for doctoral research?

3. What is the difference between a delimitation and a limitation?

4. Name four possible types of research contribution.

5. Why must the unit of analysis be identified explicitly?

Long Answer Questions

1. Explain the process of moving from a broad IT topic to a defensible doctoral research problem.

2. Discuss how research questions, hypotheses, and objectives should be aligned with methodology.

3. Compare theoretical, empirical, methodological, artifact, and practical contributions.

4. Explain how significance, originality, feasibility, scope, and ethics should be evaluated together.

5. Discuss the value and risks of using digital tools and generative AI during problem formulation.

Analytical / Application Questions

1. An organization reports that employees are not adopting a new cybersecurity platform. Develop a problem statement, three research questions, and corresponding objectives without assuming that training is the cause.

2. A proposed study claims that an AI model improves managerial decisions but plans to collect only user satisfaction ratings. Analyze the alignment problems and recommend a revised design.

3. Select an emerging IT issue and construct a contribution framework showing its potential theoretical, empirical, methodological, and practical contributions.

22. Multiple Choice Questions with Answers

Question 1

Which statement best defines a research problem?

A. A broad area of interest such as cloud computing

B. A significant and researchable deficiency in knowledge, practice, design, or policy

C. A list of technologies available to the researcher

D. A preferred statistical technique

Correct Answer: B. A significant and researchable deficiency in knowledge, practice, design, or policy

Question 2

Which activity most directly helps establish a genuine research gap?

A. Selecting a method before reviewing literature

B. Counting only the most recent publications

C. Comparing prior studies by theory, context, methods, findings, and limitations

D. Choosing a topic with no prior publications

Correct Answer: C. Comparing prior studies by theory, context, methods, findings, and limitations

Question 3

A question asking whether explainability predicts user trust is primarily what type of question?

A. Predictive or explanatory

B. Historical

C. Purely descriptive

D. Administrative

Correct Answer: A. Predictive or explanatory

Question 4

Which is the strongest hypothesis?

A. Technology is important

B. Blockchain is good for organizations

C. Perceived data integrity mediates the relationship between traceability and interorganizational trust

D. Organizations should use artificial intelligence

Correct Answer: C. Perceived data integrity mediates the relationship between traceability and interorganizational trust

Question 5

What is the principal purpose of defining scope?

A. To guarantee statistically significant results

B. To establish manageable boundaries for the study

C. To eliminate the need for ethical review

D. To replace the literature review

Correct Answer: B. To establish manageable boundaries for the study

Question 6

Which example represents a theoretical contribution?

A. Providing a new explanation that extends an existing adoption model

B. Installing a software package in an organization

C. Increasing a project's budget

D. Reporting the number of study participants

Correct Answer: A. Providing a new explanation that extends an existing adoption model

Question 7

Why is alignment important in research design?

A. It ensures that every study uses quantitative methods

B. It connects the problem, questions, methods, evidence, and claims logically

C. It prevents researchers from changing their ideas

D. It guarantees that hypotheses will be supported

Correct Answer: B. It connects the problem, questions, methods, evidence, and claims logically

Question 8

In the illustrative hospital case, why was accuracy alone insufficient to establish usefulness?

A. Accuracy is unrelated to all IT systems

B. Usefulness also depended on explanation, accountability, workflow, and professional interpretation

C. The system had no technical components

D. Nurses were not part of the research context

Correct Answer: B. Usefulness also depended on explanation, accountability, workflow, and professional interpretation

Question 9

Which is an example of a delimitation rather than a limitation?

A. Participants withdraw unexpectedly

B. The research team lacks computing resources

C. The researcher deliberately excludes diagnosis and studies alert interpretation only

D. The organization refuses access to historical data

Correct Answer: C. The researcher deliberately excludes diagnosis and studies alert interpretation only

Question 10

Which practice is most appropriate when using generative AI during research formulation?

A. Accepting generated references without checking them

B. Uploading confidential participant data for convenience

C. Using it for assistance while verifying sources and protecting confidentiality

D. Allowing it to determine the research contribution

Correct Answer: C. Using it for assistance while verifying sources and protecting confidentiality