Research Problems, Questions and Objectives

Introduction

A dissertation begins before data are collected, software is opened, or questionnaires are distributed. It begins with a disciplined decision about what is worth investigating and how the investigation will be bounded. In accounting and finance, this decision is especially important because apparently simple issues—such as declining profitability, weak internal controls, earnings volatility, or low investor confidence—may have several possible causes and may be interpreted differently by managers, auditors, regulators, investors, employees, and customers.

The central task of this chapter is to transform a broad practical or academic concern into a coherent research architecture. This architecture normally contains a problem statement, research questions, objectives, hypotheses where appropriate, scope boundaries, and an explanation of the study’s expected contribution. These elements must fit together logically. A question that asks about causal effects requires evidence capable of examining causality; an objective that promises comparison requires clearly defined groups or periods; and a hypothesis requires variables that can be observed or measured.

A well-designed research problem is not merely a topic. “Working capital management in manufacturing firms” is a topic. “The relationship between cash-conversion-cycle efficiency and profitability among listed manufacturing firms” is a more focused area. A complete problem statement explains why the issue matters, what is not yet known, who or what is affected, and what the proposed study will investigate.

Learning Objectives

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

1. Distinguish a research topic, issue, problem, gap, purpose, question, objective, and hypothesis.
2. Develop a clear and evidence-based accounting or finance problem statement.
3. Formulate aligned descriptive, comparative, relational, explanatory, and evaluative research questions.
4. Write specific, measurable, achievable, relevant, and time-bounded research objectives.
5. Decide when hypotheses are appropriate and express them in testable form.
6. Define the population, setting, period, variables, and exclusions that establish study scope.
7. Explain expected theoretical, empirical, practical, professional, and policy contributions.
8. Identify common weaknesses, ethical risks, and limitations in early research design.

1. From a Broad Concern to a Researchable Problem

1.1 Topic, issue, problem and gap

A topic is a general area of interest. Examples include audit quality, financial inclusion, taxation, corporate governance, sustainability reporting, and investment decision-making.

An issue is a condition or development that attracts attention. For example, small enterprises may report difficulty obtaining bank finance, or a company may experience repeated inventory write-downs.

A research problem is a clearly articulated uncertainty, inconsistency, deficiency, or unexplained condition that can be investigated systematically. It identifies what is not adequately understood or what requires evidence-based improvement.

A research gap is the specific limitation in existing knowledge or practice that the proposed study addresses. Gaps may be:

• Contextual: evidence exists elsewhere but not in the selected country, industry, firm size, or institutional setting.
• Conceptual: important concepts or relationships have been defined inconsistently.
• Empirical: previous findings are limited, contradictory, outdated, or based on weak evidence.
• Methodological: earlier studies used methods that did not adequately capture the phenomenon.
• Practical or policy-related: existing knowledge does not provide useful guidance for decisions or regulation.

A gap is not simply the statement “few studies have been conducted.” A convincing gap explains why the absence, weakness, or inconsistency of evidence matters.

1.2 Sources of research problems

Research problems may originate from:

• financial statements, annual reports, audit reports, and regulatory disclosures;
• operational performance data, such as receivables days, inventory turnover, or budget variances;
• professional experience and observations made during employment or placement;
• contradictory findings in accounting and finance literature;
• changes in accounting standards, tax rules, financial technology, or capital-market regulation;
• stakeholder concerns, including investor protection, access to credit, and reporting transparency;
• theory that makes predictions not yet adequately tested in a particular setting.

A practical concern becomes a research problem only after it is converted into an answerable question. For instance, “the company has poor cash flow” is a managerial observation. A researchable formulation might ask whether delayed customer payments, excessive inventory, or short-term borrowing costs are associated with the firm’s cash-flow difficulty.

1.3 The problem statement

A strong problem statement usually performs five functions:

1. Establishes the context and significance of the issue.
2. Presents evidence that the issue exists.
3. Identifies what is unknown, contested, or inadequately addressed.
4. Explains the consequences of leaving the problem unresolved.
5. Leads directly to the purpose and questions of the study.

A useful structure is:

Despite [known condition or intervention], [specific problem] persists in [population and setting]. Existing evidence is limited or inconsistent regarding [knowledge gap]. This matters because [consequence]. Therefore, this study will investigate [focused purpose].

This is a planning structure, not a formula that should produce artificial prose. Claims should be supported by credible sources, such as peer-reviewed research, official statistics, company reports, professional bodies, and legislation. A problem statement should avoid unsupported adjectives such as “serious,” “huge,” or “very important.”

2. Research Questions

2.1 Purpose of research questions

Research questions translate the problem into answerable inquiries. They determine what data are relevant, what analysis is appropriate, and what conclusions can legitimately be drawn. They should be clear, focused, feasible, and aligned with the study’s purpose.

A question is not strong merely because it ends with a question mark. “What is the effect of accounting?” is too broad because the concepts, population, mechanism, and outcome are undefined. A stronger question specifies the relationship, setting, unit of analysis, and period where relevant.

2.2 Major types of questions

Causal wording requires particular care. A cross-sectional survey may identify an association, but it usually cannot establish that one variable causes another because timing, omitted variables, and reverse causality may remain unresolved. Unless the design supports causal inference, “associated with,” “related to,” or “predicts” may be more defensible than “causes.”

2.3 Main and subsidiary questions

A study normally has one main question supported by subsidiary questions. The main question expresses the central inquiry; subsidiary questions divide it into manageable parts. For example:

Main question: How is working-capital management associated with profitability among listed manufacturing companies in Country X from 2019 to 2023?

Subsidiary questions:

1. What were the average receivables days, inventory days, and payables days during the period?
2. What was the level and trend of profitability?
3. What association existed between working-capital indicators and return on assets?
4. Did the association remain after controlling for firm size and leverage?

A question should not contain too many independent inquiries joined by “and.” If a proposed study asks about determinants, consequences, employee perceptions, regulatory effects, and investor responses, it may actually represent several dissertations.

3. Research Objectives

3.1 Meaning and role

Objectives state what the researcher intends to accomplish. They are not a list of activities. “To distribute 100 questionnaires” is an activity; “to assess the relationship between financial literacy and retirement-planning behaviour” is an objective.

A well-written objective normally begins with an action verb and identifies the object, population, setting, and, where useful, period. Appropriate verbs include describe, measure, compare, examine, assess, estimate, evaluate, explore, determine, test, develop, and interpret. Avoid vague verbs such as “understand,” “look at,” or “study” unless they are further specified.

3.2 SMART objectives

Objectives are often assessed using the SMART principle:

• Specific: identifies the precise phenomenon and population.
• Measurable or assessable: can be addressed through evidence.
• Achievable: fits available time, skills, access, and resources.
• Relevant: responds to the research problem.
• Time-bounded: specifies a period where time is analytically important.

For example, “To examine the influence of digital banking on financial inclusion among university students in City Y during 2025” is more useful than “To study digital banking.”

3.3 Alignment matrix

An alignment matrix exposes inconsistency before data collection begins.

Every major question should have at least one corresponding objective, and every objective should be answerable with the proposed evidence. The matrix also prevents a common error: collecting data about variables that never appear in the questions or objectives.

4. Hypotheses and Conceptual Reasoning

4.1 When hypotheses are appropriate

A hypothesis is a provisional, logically derived statement about an expected relationship or difference. Hypotheses are common in quantitative studies that test relationships among measurable variables. They are not compulsory in exploratory qualitative research, where open questions and inductive analysis may be more suitable.

A hypothesis should identify the variables, direction where theory justifies it, and relevant population. For example:

• H₁: Among listed manufacturing firms, a longer cash conversion cycle is associated with lower return on assets, controlling for firm size and leverage.
• H₀: Among listed manufacturing firms, cash conversion cycle is not associated with return on assets after controlling for firm size and leverage.

The null hypothesis is a statistical testing convention; it does not mean that the researcher believes the relationship is impossible. Hypotheses should not be invented after examining results merely to make findings appear predictable.

4.2 Variables and operational definitions

A variable is a characteristic that can take different values. A dependent variable is the outcome of interest; an independent or explanatory variable is a proposed predictor; a control variable is included to reduce confounding; and a moderator changes the strength or direction of a relationship.

Operational definitions state how concepts will be observed. For example, profitability may be represented by return on assets:

Here, net income is the accounting profit for the period and average total assets may be calculated as beginning total assets plus ending total assets, divided by two. The measure is appropriate only when the accounting basis, period, and asset definitions are consistent across observations. It is a performance proxy, not a complete measure of economic success.

For a cash conversion cycle, a common expression is:

where CCC is the cash conversion cycle in days, DIO is days inventory outstanding, DSO is days sales outstanding, and DPO is days payable outstanding. The formula assumes compatible annual or period data and consistent treatment of credit sales, inventory, and trade payables. It indicates the approximate time between cash paid for operating inputs and cash received from customers; it does not by itself establish financial health or causality.

Operational definitions should specify the numerator, denominator, unit, period, data source, and treatment of missing or unusual observations.

5. Scope and Delimitations

Scope explains what the study covers. Delimitations are boundaries deliberately chosen by the researcher; limitations are constraints that may affect the study despite the researcher’s efforts.

Scope may specify:

• Population: for example, listed commercial banks rather than all financial institutions.
• Unit of analysis: firm, branch, employee, transaction, household, or country-year.
• Geographical setting: one city, region, or country.
• Time period: such as financial years 2019–2023.
• Variables or concepts: for example, audit committee independence and disclosure quality.
• Methodological boundary: archival data, survey responses, interviews, or mixed methods.
• Exclusions: private firms, non-bank financial institutions, or non-financial disclosures.

Boundaries improve feasibility and interpretive accuracy. A study of ten listed firms should not claim to represent every enterprise. Similarly, evidence from a single institution may provide contextual insight without supporting national generalisation.

6. Expected Contributions

A contribution explains what the study adds, rather than repeating that the topic is important. Possible contributions include:

1. Theoretical contribution: clarifies, extends, or tests a theory such as agency theory, signalling theory, stakeholder theory, or the resource-based view.
2. Empirical contribution: provides evidence for a population, period, or relationship not adequately represented in existing research.
3. Methodological contribution: demonstrates the usefulness of a measurement approach, dataset, or mixed-method design.
4. Professional contribution: helps accountants, auditors, analysts, managers, or investors make better decisions.
5. Policy contribution: informs regulators, standard setters, tax authorities, or financial-inclusion programmes.
6. Educational contribution: develops a clearer framework or instrument for future research and professional training.

Contributions must be proportionate to the design. A bachelor’s dissertation may make a valuable local empirical contribution without claiming to resolve an international theoretical debate. A contribution may also be a carefully documented null or contradictory finding, especially when it challenges an assumption or identifies conditions under which a relationship does not hold.

7. A Framework for Coherent Design

A useful chain of reasoning is:

Figure: Alignment from the research problem to the expected contribution, with findings feeding back into interpretation of the original problem.

The framework is iterative rather than strictly linear. Reading literature may refine the problem; feasibility checks may narrow the scope; and ethical review may require changes to the questions. Nevertheless, the final proposal should show a visible line of reasoning from problem to contribution.

8. Illustrative Case: Mobile Payments and Small-Business Cash Management

This is a fictional illustrative case.

Background

A national payment provider observes that many small retailers have adopted mobile-payment services, but their reported cash-flow stability has not improved consistently. Managers claim that digital receipts make transactions easier to record, while some owners continue to mix personal and business funds.

Problem

The existence of mobile-payment access does not demonstrate improved cash management. It is unclear whether adoption is associated with better cash-flow visibility and whether bookkeeping capability conditions that relationship.

Questions and objectives

The main question is: How is mobile-payment use associated with cash-flow management among small retailers in District A? Subsidiary questions ask how frequently retailers reconcile digital receipts, whether adoption differs by business size, and whether bookkeeping capability changes the association.

Corresponding objectives are to measure mobile-payment use, assess cash-flow-management practices, compare adopters and non-adopters, and examine whether bookkeeping capability moderates the relationship.

A possible hypothesis is: Retailers using mobile payments more intensively report more timely cash reconciliation, controlling for business age and number of employees. The study must define “intensively,” “timely,” and “cash reconciliation” before measurement.

Approach and evidence

A cross-sectional survey could provide descriptive and relational evidence, while interviews could explain why owners do or do not reconcile transactions. The design should avoid claiming that mobile payments cause improved performance unless temporal evidence and appropriate controls support that inference.

Expected outcome and learning points

The study might find a positive association, no association, or a relationship that depends on bookkeeping capability. Each outcome could be useful. The case demonstrates that technology adoption is not itself the research problem; the research problem concerns an unresolved relationship, its conditions, and its practical significance.

9. Common Errors, Ethics and Governance

Common errors include choosing a topic that is too broad, writing a problem without evidence, confusing objectives with tasks, using causal language for correlational evidence, listing too many questions, and promising recommendations before analysing the problem. Another error is treating a convenient sample as representative without justification.

Ethical considerations begin at problem formulation. Researchers should avoid questions that expose individuals or firms to unnecessary reputational, employment, legal, or financial risk. Financial records may contain confidential personal or commercial information. Access permissions, informed consent, secure storage, anonymisation, and retention rules should be considered before data collection. Researchers must not alter financial data, selectively report favourable results, fabricate responses, or present AI-generated text or analysis as independently verified scholarship.

AI tools may assist with brainstorming, coding support, language editing, or document classification, but the researcher remains responsible for source verification, confidentiality, bias assessment, and intellectual ownership. Sensitive company data should not be uploaded to an uncontrolled system. Any institutional policy on AI disclosure must be followed.

10. Practical Checklist for a Proposal

Before submitting a research proposal, ask:

• Is the problem specific, important, and supported by evidence?
• Is the knowledge or practice gap explicit?
• Can the population, setting, unit, and period be identified?
• Does each question correspond to an objective?
• Are variables or qualitative concepts operationally clear?
• Does the proposed evidence answer the questions?
• Are causal claims justified by the design?
• Are the scope and exclusions realistic?
• Are expected contributions proportionate to a bachelor’s project?
• Have confidentiality, consent, data protection, conflicts of interest, and AI use been addressed?

Chapter Summary

A successful accounting or finance dissertation begins with a focused research problem rather than a broad topic. The researcher should distinguish the issue from the gap, explain why the gap matters, and formulate a purpose that can be investigated with available evidence. Research questions specify what must be answered, while objectives state what the study will accomplish. Questions may be descriptive, comparative, relational, explanatory, evaluative, or exploratory, and their wording must reflect the strength of the proposed evidence.

Hypotheses are appropriate when theory and prior evidence support testable statements about measurable relationships or differences. Concepts must be operationally defined, and quantitative measures must be interpreted as proxies rather than complete representations of complex phenomena. Scope boundaries protect feasibility and prevent overgeneralisation. Finally, a credible project identifies realistic theoretical, empirical, professional, or policy contributions and addresses ethical, governance, confidentiality, and technology-related responsibilities.

Key Terms

Research topic: A broad area of academic or professional interest.

Research issue: A condition or development that attracts concern or attention.

Research problem: A focused uncertainty, deficiency, inconsistency, or unexplained condition requiring systematic investigation.

Research gap: A limitation or absence in existing knowledge, evidence, method, or practice.

Problem statement: A concise explanation of the issue, gap, significance, and intended investigation.

Research question: An answerable inquiry that directs the study.

Research objective: A specific statement of what the researcher intends to accomplish.

Hypothesis: A provisional, testable statement about a relationship or difference.

Null hypothesis: A formal statement that a specified relationship or difference is absent in the population or model.

Variable: A measurable characteristic that can take different values.

Dependent variable: The outcome that the study seeks to explain or assess.

Independent variable: A proposed explanatory or predictor variable.

Control variable: A variable included to account for alternative explanations.

Operational definition: A statement of how an abstract concept will be observed or measured.

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

Scope: The population, setting, period, concepts, and methods covered by a study.

Delimitation: A boundary deliberately set by the researcher.

Limitation: A constraint that may affect the study or interpretation of findings.

Contribution: The new knowledge, evidence, method, practice, or policy insight added by a study.

Alignment: Logical consistency among the problem, questions, objectives, evidence, and conclusions.

Review Questions

Short Answer Questions

1. Distinguish a research topic from a research problem.
2. What is a research gap, and why must it be justified?
3. State two differences between a research question and a research objective.
4. When is causal wording inappropriate in a research question?
5. Identify four elements that may be used to define study scope.

Long Answer Questions

1. Explain the functions of a strong problem statement and illustrate them with an accounting example.
2. Compare descriptive, comparative, relational, explanatory, evaluative, and exploratory research questions.
3. Discuss how an alignment matrix improves the quality of a bachelor’s dissertation proposal.
4. Explain operational definitions and discuss why they are necessary in accounting and finance research.
5. Evaluate the importance of realistic expected contributions and ethical boundaries in research design.

Analytical / Application Questions

1. A listed retailer has experienced falling profitability and rising inventory. Develop a problem statement, one main research question, three objectives, and two possible hypotheses.
2. A student proposes to study “the impact of financial technology on the economy.” Critique the proposal and redesign it by specifying a population, setting, period, variables, and feasible questions.
3. A survey finds that firms with stronger internal controls report fewer detected fraud cases. Explain at least three alternative interpretations and state what additional evidence would be needed before claiming that stronger controls reduce fraud.

Multiple Choice Questions with Answers

Question 1

Which statement best describes a research problem?

A. A broad area such as auditing

B. A focused uncertainty or deficiency that can be investigated systematically

C. A list of tasks required to complete a dissertation

D. A prediction made after reviewing the results

Correct Answer: B. A focused uncertainty or deficiency that can be investigated systematically

Question 2

Which question is primarily descriptive?

A. Does leverage cause earnings volatility?

B. Why do auditors experience deadline pressure?

C. What internal-control procedures are used by small retailers?

D. Do family firms pay higher audit fees than non-family firms?

Correct Answer: C. What internal-control procedures are used by small retailers?

Question 3

Which objective is written most appropriately?

A. To look at banking

B. To distribute 100 questionnaires

C. To understand everything about finance

D. To examine the association between financial literacy and saving behaviour among university students

Correct Answer: D. To examine the association between financial literacy and saving behaviour among university students

Question 4

A cross-sectional survey most directly supports which type of conclusion?

A. A guaranteed causal effect

B. An association measured at one point in time

C. A complete historical explanation

D. A universal conclusion about all countries

Correct Answer: B. An association measured at one point in time

Question 5

What is the main purpose of an operational definition?

A. To identify the dissertation supervisor

B. To state how an abstract concept will be observed or measured

C. To replace the literature review

D. To guarantee statistically significant results

Correct Answer: B. To state how an abstract concept will be observed or measured

Question 6

In the formula CCC = DIO + DSO − DPO, what does DSO represent?

A. Days sales outstanding

B. Debt service obligation

C. Declared sales output

D. Daily stock ordering

Correct Answer: A. Days sales outstanding

Question 7

Which is a delimitation rather than a limitation?

A. Some respondents fail to answer sensitive questions

B. The researcher deliberately excludes private firms from the population

C. Financial records contain missing observations

D. The sample response rate is lower than expected

Correct Answer: B. The researcher deliberately excludes private firms from the population

Question 8

Which contribution is most directly concerned with helping regulators or standard setters?

A. Policy contribution

B. Decorative contribution

C. Personal contribution

D. Sampling contribution

Correct Answer: A. Policy contribution

Question 9

Which hypothesis is most testable?

A. Accounting is important

B. Better firms are better managed

C. Among listed manufacturers, a longer cash conversion cycle is associated with lower return on assets, controlling for firm size

D. Finance affects many things

Correct Answer: C. Among listed manufacturers, a longer cash conversion cycle is associated with lower return on assets, controlling for firm size

Question 10

What should a researcher do when the proposed evidence supports association but not causal inference?

A. Use causal language to make the study stronger

B. Remove all variables from the study

C. Use more cautious wording such as 'associated with'

D. Report only statistically significant findings

Correct Answer: C. Use more cautious wording such as 'associated with'