2. Research Problems, Questions and Contributions

Introduction and Context

A doctoral study begins long before data are collected. It begins with an intellectually defensible account of what is not adequately understood, why that lack of understanding matters, and how systematic inquiry can improve knowledge and practice. The central task of this chapter is therefore not simply to generate a topic, but to transform an organisational concern into a researchable problem and then into a coherent set of questions, objectives, propositions or hypotheses, units of analysis, and anticipated contributions.

Business research is often initiated by an apparent practical difficulty: declining customer retention, ineffective transformation, weak innovation performance, ethical misconduct, or persistent supply-chain disruption. Such difficulties are important, but they are not automatically research problems. A practical problem concerns an undesirable condition requiring action. A research problem concerns a significant uncertainty, contradiction, unexplained relationship, boundary condition, or inadequacy in existing knowledge. The two may overlap, but they are not identical. A firm may know that employee turnover is high while still lacking knowledge about the mechanisms through which leadership practices affect turnover in a particular institutional context.

At doctoral level, the researcher must demonstrate more than topical interest. The study must establish a credible chain of reasoning: a phenomenon exists; existing explanations are incomplete or contested; the gap is consequential; the proposed questions can be answered with appropriate evidence; and the findings will contribute to theory, method, management, policy, or some combination of these. Research design is strengthened when this chain is explicit from the beginning.

Learning Objectives

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

1. Distinguish practical problems, research problems, research gaps, and research opportunities.

2. Diagnose complex organisational and industry problems using evidence and systems thinking.

3. Formulate aligned research questions, objectives, propositions, and hypotheses.

4. Define the unit of analysis and distinguish it from the level at which variables are measured or conclusions are drawn.

5. Evaluate the quality, feasibility, originality, and significance of a proposed research problem.

6. Articulate theoretical, methodological, managerial, organisational, and societal contributions.

7. Recognise ethical, political, and methodological risks in problem formulation.

8. Apply a structured process to develop a defensible doctoral research proposal.

From Organisational Concern to Research Problem

A practical problem is a gap between a desired state and an existing state. For example, a logistics company may seek same-day delivery but experience repeated delays. The practical response might involve redesigning routes, adding distribution centres, or changing supplier contracts. A research problem emerges when the organisation or scholarly community lacks a reliable explanation of why the problem occurs, under which conditions it occurs, or which intervention is likely to work.

A useful distinction is:

• Practical problem: a condition experienced by managers, employees, customers, regulators, or communities as undesirable or unresolved.
• Research problem: a focused statement of insufficient, inconsistent, or contested knowledge that can be investigated systematically.
• Research gap: a specific deficiency in the existing scholarly record, such as an untested relationship, neglected context, weak explanation, inconsistent findings, or methodological limitation.
• Research opportunity: a promising possibility for extending knowledge, often created by technological, institutional, social, or theoretical change.

A topic such as “artificial intelligence in human resource management” is too broad to constitute a research problem. A stronger formulation might be: “Existing studies explain the adoption of AI-based recruitment largely through efficiency and perceived usefulness, but provide limited evidence about how transparency concerns and professional identity shape adoption decisions among public-sector HR professionals.” This formulation identifies a phenomenon, a population, an explanatory limitation, and a possible theoretical direction.

Diagnosing Complex Problems

Organisational problems are rarely caused by one variable. They are usually embedded in systems of incentives, routines, technologies, power relations, regulations, cultures, and historical decisions. A doctoral researcher should resist premature simplification. Several diagnostic questions are useful:

1. What precisely is the undesirable condition, and how is it evidenced?

2. Who defines it as a problem, and whose interests may be absent?

3. What are its symptoms, underlying mechanisms, and consequences?

4. Is the problem located at the individual, team, organisational, inter-organisational, industry, or institutional level?

5. Which explanations already exist, and where do they disagree?

6. What contextual conditions may alter the relationships under investigation?

7. What evidence would distinguish among competing explanations?

Problem diagnosis benefits from combining documentary evidence, exploratory interviews, industry reports, archival records, observational material, and scholarly literature. These sources serve different purposes. Industry reports may establish practical significance, while peer-reviewed studies establish the state of scholarly knowledge. Interviews may reveal mechanisms not visible in performance metrics, but interview accounts also reflect perception and position. Triangulation does not eliminate bias, but it helps the researcher avoid defining the problem from a single viewpoint.

A systems perspective is particularly valuable. For example, high sales-staff turnover may be attributed to compensation, yet the underlying system may include unrealistic targets, algorithmic monitoring, weak supervisory support, customer aggression, and limited career mobility. Treating compensation as the sole cause may generate a technically precise but practically ineffective study.

Locating and Evaluating the Research Gap

A research gap is not merely a sentence claiming that “few studies have examined” a topic. It must be demonstrated through a critical synthesis of literature. The researcher should identify what is known, how it has been studied, what remains uncertain, and why the uncertainty matters.

Common forms of gap include:

• Descriptive gap: insufficient knowledge about the nature, prevalence, or development of a phenomenon.
• Explanatory gap: inadequate understanding of causes, mechanisms, or processes.
• Theoretical gap: an existing theory does not adequately explain the phenomenon, or competing theories make different predictions.
• Contextual gap: evidence is concentrated in particular countries, industries, organisational forms, or populations.
• Methodological gap: dominant methods cannot capture important temporal, relational, qualitative, or causal features.
• Contradictory evidence gap: prior studies produce inconsistent findings that require boundary conditions or reconciliation.
• Practical or implementation gap: knowledge exists in principle, but organisations lack evidence about how to apply it effectively and ethically.

A strong gap statement specifies the relevant literature rather than treating the literature as a catalogue of sources. It should explain the pattern of findings and expose an unresolved issue. The researcher should also avoid the “novelty fallacy”: a topic is not important merely because it has not previously been studied. Originality must be connected to significance and feasibility.

One useful test is the so what? test. If the gap were filled, who would learn something important? Would a theory be refined, a managerial decision improved, a policy reconsidered, or a methodological problem addressed? A second test is the why now? test. Has a technological, regulatory, economic, or social development changed the relevance of the question? A third is the why this setting? test. Does the selected context reveal a mechanism, boundary condition, or institutional feature that matters beyond the case itself?

Constructing Research Questions and Objectives

Research questions provide the intellectual architecture of a study. They should be answerable through evidence, sufficiently focused for the available resources, and open to an outcome that is not predetermined. A question is not necessarily strong because it is phrased as “how” or “why.” Its quality depends on conceptual precision and researchability.

A broad doctoral question may be divided into a primary question and supporting subquestions. For example:

Primary question: How do algorithmic performance-management systems influence employee autonomy in platform-based work?

Subquestions:

1. How do workers interpret the system’s measurement and feedback practices?

2. Through what mechanisms do these practices affect perceived autonomy?

3. How do employment status and local regulation moderate these effects?

4. What organisational practices reduce harmful consequences without eliminating useful coordination?

The questions imply a possible mixed or multi-method design. The first two may require qualitative inquiry, the third may support comparative or quantitative analysis, and the fourth may involve design-oriented or intervention research. Questions should therefore be formulated with the eventual evidence strategy in mind, without allowing available methods to dictate the problem prematurely.

Research objectives translate questions into intended scholarly actions. Strong objectives use observable verbs such as examine, compare, explain, evaluate, develop, test, interpret, or theorise. “To understand leadership” is too vague. “To explain how middle managers interpret and enact competing strategic priorities during digital transformation” is more precise.

The relationship can be represented as follows:

Problem → Purpose → Research questions → Objectives → Evidence requirements → Contribution

Misalignment at any point weakens the study. A project that asks how employees experience organisational change but uses only a cross-sectional numerical survey may fail to capture process and meaning. Conversely, a study claiming to test causal hypotheses requires stronger temporal, comparative, or quasi-experimental evidence than a one-time perception survey normally provides.

Hypotheses, Propositions, and Exploratory Questions

A hypothesis is a testable statement about an expected relationship between constructs. It should identify the direction or form of the relationship where theory permits. For example: “Perceived procedural justice is positively associated with employee willingness to adopt an algorithmic scheduling system, controlling for digital literacy and tenure.” A hypothesis is not a prediction selected simply because it seems plausible; it should follow from a theoretical argument.

A proposition is commonly used in conceptual, qualitative, or theory-building research to express an anticipated relationship or process without requiring statistical testing in the same form. For example: “When algorithmic decisions are opaque, employees are more likely to interpret efficiency initiatives as threats to professional identity.”

Exploratory questions are appropriate where constructs, mechanisms, or meanings are insufficiently understood. Qualitative inquiry may begin with “How do...” or “What meanings do...” rather than imposing a complete set of prior variables. Exploration, however, does not mean absence of intellectual direction. The researcher still needs a conceptual sensitising framework and explicit criteria for interpreting evidence.

Units, Levels, and Boundaries of Analysis

The unit of analysis is the primary entity about which a study makes claims. It may be an individual employee, team, project, organisation, business relationship, industry, policy, event, or process. The unit must be distinguished from the unit of observation, which is the entity from which data are obtained. A study may collect data from individual employees but analyse teams, provided individual responses are appropriately aggregated and the aggregation is justified.

The level of analysis refers to the analytical scale at which concepts and relationships are examined: individual, group, organisational, inter-organisational, industry, or institutional. Confusing levels creates serious validity problems. For example, finding that organisations with stronger learning cultures have higher innovation does not prove that every employee in those organisations is more innovative. That inference would commit an ecological fallacy. The reverse error—inferring organisational patterns from individual responses—is an atomistic fallacy.

Multilevel research requires explicit theoretical reasoning about cross-level relationships. A team climate may influence individual behaviour; organisational policies may shape team practices; institutional regulation may constrain organisational choices. In such studies, the researcher must define whether variables are measured at one level and whether they are aggregated, disaggregated, or modelled hierarchically.

Boundaries are equally important. A study should specify its population, geographical or institutional setting, time period, relevant industry segment, and conceptual scope. Boundaries are not weaknesses; they make claims more credible. A carefully bounded study can identify where its findings are likely to transfer and where caution is required.

The Architecture of Research Contributions

A contribution is not simply a finding. It is the difference the study makes to what scholars or practitioners can reasonably claim, explain, do, or investigate next.

Theoretical Contribution

A theoretical contribution may extend a theory to a new context, identify a mechanism, specify a boundary condition, reconcile competing explanations, refine constructs, or develop a new conceptual model. Claiming that a study “adds to the literature” is insufficient. The researcher should state what changes in the theory because of the findings.

For example, a study may show that a technology-adoption model requires an additional construct—perceived identity threat—or that the effect of perceived usefulness depends on professional autonomy. The contribution lies in the revised explanation, not merely in reporting a new correlation.

Methodological Contribution

A methodological contribution may involve a new measurement strategy, a longitudinal design in a field dominated by cross-sectional studies, integration of qualitative and quantitative evidence, a novel use of archival or digital trace data, or a procedure for studying sensitive populations. Methodological novelty should solve a substantive problem. A sophisticated method used without a clear reason is not automatically a contribution.

Managerial and Organisational Contribution

Managerial contributions should specify decisions, practices, or capabilities affected by the evidence. Instead of stating that findings are “useful to managers,” a study might show how firms can redesign algorithmic feedback, sequence change communication, or identify conditions under which employee participation improves implementation. Recommendations should follow from evidence and acknowledge trade-offs.

Societal, Policy, and Professional Contribution

Business research can affect regulators, professional bodies, workers, consumers, communities, and public institutions. A study of workplace surveillance, for example, may inform governance standards concerning consent, proportionality, transparency, and data protection. Such contributions require attention to distributional consequences: who benefits, who bears risk, and whose voice is excluded.

Contributions can also be negative or boundary-setting. Demonstrating that a widely recommended practice fails under certain conditions is valuable if the evidence is credible. Similarly, showing that a measurement instrument performs poorly across cultural groups may prevent misleading comparisons.

A Practical Framework for Proposal Development

A doctoral researcher can use a problem-to-contribution matrix to test alignment:

| Element | Diagnostic question |

|---|---|

| Phenomenon | What is happening, and how is it evidenced? |

| Significance | Why does the issue matter academically and practically? |

| Existing knowledge | What explanations and findings already exist? |

| Gap | What remains uncertain, contradictory, or underdeveloped? |

| Purpose | What will the study accomplish? |

| Questions | What must be answered to fulfil the purpose? |

| Constructs or sensitising concepts | What ideas organise the inquiry? |

| Unit and level | About whom or what will claims be made? |

| Evidence | What data could answer the questions credibly? |

| Contribution | What will change in theory, method, or practice? |

The matrix should be treated as iterative rather than mechanical. Early field contact may modify the problem; literature analysis may narrow the questions; feasibility constraints may require a change in population or design. Such refinement is not a failure of planning. It is a normal part of responsible research, provided the final logic remains transparent.

Illustrative Case: Digital Transformation in a Regional Hospital Network

The following is a fictional illustrative case designed to demonstrate problem formulation.

Background

A regional hospital network introduced an integrated electronic care platform intended to improve coordination among physicians, nurses, laboratories, and administrative units. After implementation, senior management observed that use varied substantially across departments. Some units adopted the platform quickly, while others developed workarounds and continued using informal communication channels.

Situation and Problem

Management initially defined the issue as inadequate employee training. Training attendance was high, yet usage remained inconsistent. A practical problem therefore existed: the expected coordination benefits were not being realised. The research problem was more specific: existing explanations based on technology acceptance did not adequately explain how professional autonomy, workflow interdependence, and trust in data governance shaped adoption across departments.

Analysis

Preliminary interviews indicated three patterns. First, clinicians accepted the platform when it reduced duplication but resisted features perceived as managerial surveillance. Second, nurses viewed data entry as valuable for continuity of care but burdensome when staffing was inadequate. Third, departments differed in whether local leaders translated the system into meaningful work practices. These observations suggested that adoption was not an individual attitude alone; it was a multilevel process involving professionals, teams, and organisational governance.

Research Design Direction

A possible primary question became: “How do professional autonomy and local implementation leadership shape sustained use of an integrated care platform?” Subquestions addressed employee interpretations, departmental routines, governance trust, and differences across units. The unit of analysis was the hospital department, while individual staff members supplied observational and interview data. A comparative case design with longitudinal observation could examine implementation processes, supplemented by system-use records and a survey of perceived autonomy and trust.

Anticipated Contributions

The theoretical contribution might be a model showing how technology acceptance is conditioned by professional identity and local enactment. The methodological contribution could involve integrating digital trace data with longitudinal interviews rather than relying on post-implementation perceptions. The managerial contribution would identify implementation practices that preserve clinical autonomy while improving data coordination.

Learning Points

The case illustrates that training may be a symptom-level response to a deeper organisational problem. It also demonstrates why unit of analysis, level of analysis, and contribution claims must be specified together. Finally, it shows how a practical concern can be transformed into a researchable problem without reducing the phenomenon to a single variable.

Common Errors, Risks, and Limitations

Several recurring mistakes weaken doctoral research proposals. The first is the “topic disguised as a problem,” in which a broad subject is presented without evidence of uncertainty or significance. The second is the “gap by absence” claim, which assumes that little research automatically means important research. The third is question inflation: a project asks many questions across multiple levels and populations that cannot be studied adequately within the available time.

Other risks include:

• treating correlation as proof of causation;
• selecting a unit of analysis after data collection has begun;
• using constructs inconsistently across the problem statement and measures;
• promising managerial recommendations not supported by the design;
• confusing novelty of context with theoretical contribution;
• ignoring negative cases or evidence that challenges the preferred explanation;
• assuming access to proprietary organisational data without a formal agreement;
• defining the problem solely from senior management’s perspective;
• overlooking historical, cultural, institutional, or power-related explanations.

Feasibility must be assessed early. A theoretically important question may be impossible if access is restricted, the population is too small, the time frame is inadequate, or the required data are unreliable. Narrowing the question is often a sign of scholarly maturity rather than reduced ambition.

Ethical and Governance Considerations

Problem formulation itself has ethical consequences. Labels such as “resistant employees,” “low-performing units,” or “unproductive teams” can stigmatise groups and conceal structural causes. Researchers should ask who benefits from a particular framing and whether participation may expose individuals to retaliation.

Confidentiality is especially important in organisational research. Even when names are removed, combinations of role, department, tenure, and quotation may identify participants. Data governance should address informed consent, access control, retention, secondary use, and secure destruction. Digital trace data require particular caution because the existence of a record does not necessarily imply consent for research use.

Researchers should also manage conflicts of interest. A sponsor may expect findings that justify a predetermined intervention. Academic independence requires transparent disclosure of funding, negotiated publication rights, and clear separation between organisational consultancy and scholarly inquiry. Where research involves vulnerable workers, precarious employment, or unequal power relationships, voluntary participation must be assessed substantively rather than assumed from a signed form.

Technology, Analytics, and Emerging Research Problems

Digitalisation is expanding the range of organisational phenomena available for study. Enterprise systems, collaboration platforms, sensors, customer analytics, and generative AI can provide behavioural traces at scale. These data may support longitudinal analysis, network analysis, process mining, and near-real-time evaluation. However, data abundance does not remove the need for theory. A platform log may show what happened, but not necessarily why it happened or what participants believed it meant.

Generative AI can assist with literature mapping, coding suggestions, question refinement, and the identification of conceptual relationships. Its use must remain accountable. Researchers should verify sources, protect confidential information, document substantial assistance, and retain responsibility for interpretation. Automated text analysis can reproduce linguistic and cultural bias, while predictive models may mistake historical organisational inequality for legitimate performance differences.

Emerging research problems increasingly concern the governance of algorithmic decisions, human–AI collaboration, platform labour, synthetic data, cyber-resilience, digital exhaustion, and the consequences of automation for professional expertise. These areas demand careful attention to causality, transparency, fairness, and the changing meaning of organisational boundaries.

Best Practices for Doctoral Researchers

A defensible problem statement normally does the following:

1. Begins with a consequential phenomenon rather than a fashionable label.

2. Uses credible evidence to establish that the phenomenon exists.

3. Reviews literature analytically, identifying patterns and tensions.

4. States a precise gap and explains why filling it matters.

5. Aligns purpose, questions, objectives, methods, and contribution claims.

6. Defines the unit, level, population, and temporal boundaries.

7. Anticipates alternative explanations and contradictory evidence.

8. Limits claims to what the design can support.

9. Includes the perspectives of affected stakeholders, not only sponsors.

10. Revises the formulation as evidence and scholarly debate develop.

Chapter Summary

A doctoral research project gains credibility when it moves logically from a consequential phenomenon to a clearly demonstrated research problem. Practical problems concern undesirable conditions; research problems concern incomplete or contested knowledge about those conditions. Research gaps may be descriptive, explanatory, theoretical, contextual, methodological, contradictory, or implementation-oriented, but they must be demonstrated through critical literature synthesis.

Strong research questions are focused, answerable, and aligned with the intended evidence. Objectives express the actions required to answer them, while hypotheses and propositions translate theoretical reasoning into testable or examinable relationships. The unit and level of analysis determine what claims can legitimately be made and help prevent ecological and atomistic fallacies. Contributions should specify what changes in theory, method, managerial practice, policy, or professional understanding. Throughout the process, feasibility, ethics, governance, digital data risks, and stakeholder perspectives must be addressed. The result is not merely an interesting topic but a coherent, significant, and defensible research design foundation.

Key Terms

Practical problem: An undesirable condition requiring organisational, managerial, policy, or social action.

Research problem: A significant uncertainty, contradiction, or inadequacy in knowledge that can be investigated systematically.

Research gap: A specific deficiency or unresolved issue in existing scholarly knowledge.

Research opportunity: A possibility for extending knowledge created by a new context, development, method, or theoretical tension.

Research question: A focused inquiry that directs the collection and interpretation of evidence.

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

Hypothesis: A theoretically grounded, testable statement about a relationship among constructs.

Proposition: A theoretically informed statement about a relationship or process, often used in conceptual or qualitative research.

Construct: An abstract concept formulated for theoretical reasoning and, where appropriate, empirical measurement.

Unit of analysis: The primary entity about which a study makes claims.

Unit of observation: The entity or source from which data are obtained.

Level of analysis: The analytical scale, such as individual, team, organisation, or institution, at which relationships are examined.

Ecological fallacy: Incorrectly inferring individual characteristics or relationships from aggregate-level evidence.

Atomistic fallacy: Incorrectly inferring aggregate-level patterns from individual-level evidence.

Theoretical contribution: An extension, refinement, challenge, or development of conceptual knowledge.

Methodological contribution: An improvement or innovation in research design, measurement, data, or analytical procedure.

Managerial contribution: Evidence-based implications for organisational decisions, practices, or capabilities.

Boundary condition: A circumstance that specifies when, where, or for whom a relationship is expected to hold.

Triangulation: The deliberate use of multiple sources, methods, perspectives, or theories to strengthen interpretation.

Research feasibility: The practical possibility of completing a study with available access, time, competence, resources, and ethical approval.

Review Questions

Short Answer Questions

1. How does a research problem differ from a practical problem?

2. What is meant by a contextual research gap?

3. Distinguish a unit of analysis from a unit of observation.

4. What makes a hypothesis theoretically grounded?

5. Why should a doctoral researcher define the boundaries of a study?

Long Answer Questions

1. Explain the process of transforming an organisational concern into a defensible doctoral research problem.

2. Discuss the major types of research gaps and explain how each may generate a different contribution.

3. Explain the alignment among research problems, questions, objectives, methods, and contributions.

4. Analyse the importance of distinguishing units and levels of analysis in organisational research.

5. Compare theoretical, methodological, managerial, and societal contributions, using suitable examples.

Analytical / Application Questions

1. A manufacturing firm reports low innovation despite substantial investment in training. Formulate a research problem, one primary research question, three objectives, and a possible unit of analysis.

2. A study collects individual employee surveys but claims that organisational culture causes firm-level performance. Identify the analytical risks and propose a more defensible design.

3. An organisation asks a researcher to prove that remote work reduces productivity. Reframe the request ethically and academically, including alternative explanations and a suitable contribution claim.

22. Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes a research problem from a practical problem?

A. A research problem is always more important than a practical problem.

B. A research problem identifies a significant gap or uncertainty in knowledge that can be systematically investigated.

C. A research problem is the same as an organisation's preferred solution.

D. A research problem must always be expressed as a statistical hypothesis.

Correct Answer: B. A research problem identifies a significant gap or uncertainty in knowledge that can be systematically investigated.

Question 2

A literature review finds inconsistent evidence about whether employee autonomy improves technology adoption, with effects varying by professional context. This most directly indicates which type of gap?

A. Descriptive gap

B. Contradictory evidence and boundary-condition gap

C. Sampling frame gap only

D. Measurement-free gap

Correct Answer: B. Contradictory evidence and boundary-condition gap

Question 3

Researchers survey individual employees but draw conclusions about organisational culture without justifying aggregation. What is the principal concern?

A. An ecological fallacy caused by using too many organisations

B. An atomistic fallacy caused by inferring organisational properties from individual data

C. A failure to formulate any research question

D. A violation of experimental control

Correct Answer: B. An atomistic fallacy caused by inferring organisational properties from individual data

Question 4

Which objective is most suitable for a doctoral study on algorithmic performance management?

A. To understand algorithms generally

B. To investigate everything about employee performance

C. To explain how perceived algorithmic transparency shapes employee trust across different work contexts

D. To prove that managers are wrong about technology

Correct Answer: C. To explain how perceived algorithmic transparency shapes employee trust across different work contexts

Question 5

What is the strongest reason for using a longitudinal design when studying organisational change?

A. It guarantees causal proof.

B. It allows the researcher to examine processes and changes over time rather than relying only on a single snapshot.

C. It eliminates the need for theory.

D. It makes sampling irrelevant.

Correct Answer: B. It allows the researcher to examine processes and changes over time rather than relying only on a single snapshot.

Question 6

Which statement is most likely to represent a theoretical contribution?

A. The study interviewed 40 managers.

B. The findings were collected from a new company.

C. The study shows that professional identity conditions the relationship between perceived usefulness and technology adoption.

D. The study produced a longer questionnaire.

Correct Answer: C. The study shows that professional identity conditions the relationship between perceived usefulness and technology adoption.

Question 7

A researcher collects system-use logs from employees without identifying whether those data may expose individuals to employment consequences. Which issue is most directly raised?

A. Ethical and data-governance risk

B. A descriptive research gap

C. The atomistic fallacy

D. The need for a larger title

Correct Answer: A. Ethical and data-governance risk

Question 8

Why is the statement “few studies have examined this topic” insufficient as a research gap?

A. Research gaps must always concern technology.

B. Lack of prior studies does not by itself demonstrate significance, uncertainty, or a useful contribution.

C. A gap can only be identified through interviews.

D. Doctoral research cannot study new topics.

Correct Answer: B. Lack of prior studies does not by itself demonstrate significance, uncertainty, or a useful contribution.

Question 9

In the illustrative hospital case, why was inadequate training treated as a possible symptom rather than the complete research problem?

A. Training can never affect technology adoption.

B. High training attendance coexisted with variation linked to autonomy, governance trust, and local implementation leadership.

C. Hospitals cannot be studied quantitatively.

D. The platform had no technical features.

Correct Answer: B. High training attendance coexisted with variation linked to autonomy, governance trust, and local implementation leadership.

Question 10

Which sequence best represents a coherent research-development logic?

A. Method → data access → topic → contribution → problem

B. Contribution → hypothesis → practical complaint → literature search

C. Phenomenon and problem → purpose → questions → evidence requirements → contribution

D. Questionnaire → findings → theory → population → problem

Correct Answer: C. Phenomenon and problem → purpose → questions → evidence requirements → contribution