Business Research Methods 2078 Board Question Paper

Definition of Population and Sample

• Population (Universe): The entire collection, aggregate, or totality of all elements, individuals, events, or units having common observable characteristics from which a researcher intends to draw inferences (e.g., all active retail banking customers in Kathmandu Valley).

• Sample: A representative, manageable subset or subgroup of elements deliberately selected from the target population for actual measurement and empirical investigation (e.g., 400 randomly chosen retail banking customers in Kathmandu).

Meaning of Sampling Error

Sampling error is the statistical discrepancy or divergence between a sample statistic (e.g., sample mean $\bar{X}$) and its true population parameter (e.g., population mean $\mu$), resulting solely from the fact that an empirical study inspects only a fraction rather than the entire census population.

• Key Characteristics:
  1. It is an inherent mathematical property of random sampling.
  2. It varies inversely with sample size ($n$); increasing the sample size reduces sampling error.
  3. Calculated as: $\text{Sampling Error} = |\text{Sample Statistic} - \text{Population Parameter}|$.

Difference between Primary and Secondary Data

Four Types of Variables in Management Research

A variable is any measurable characteristic, attribute, or property that can assume different numerical values or categories across subjects. Four fundamental types are:

1. Independent Variable (IV): The antecedent or predictor variable presumed to cause, influence, or explain changes in another variable (e.g., employee training hours).
2. Dependent Variable (DV): The primary outcome or criterion variable whose variation the researcher seeks to understand, explain, or predict (e.g., employee productivity).
3. Moderating Variable (MV): A qualitative or quantitative variable that affects the strength, direction, or nature of the relationship between the IV and DV (e.g., employee experience).
4. Mediating (Intervening) Variable: A variable that surfaces between the operation of the IV and the DV, explaining the internal causal mechanism through which the IV influences the DV (e.g., employee motivation).

Definition of Scientific Research

Scientific research is a systematic, controlled, empirical, objective, and critical investigation of hypothetical propositions about the presumed relations among natural or organizational phenomena.

• Key Hallmarks:
  • Purposiveness: Focused on a clearly defined goal or problem.
  • Testability: Propositions can be evaluated empirically with observational data.
  • Replicability: Identical methodological steps yield consistent results when repeated.
  • Objectivity: Conclusions are anchored purely in factual evidence rather than subjective bias.

Sampling and Probability Sampling Procedures

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I. Meaning of Sampling

Sampling is the systematic process of selecting a predetermined number of representative units (individuals, households, firms, transactions) from an entire target population so that, by studying the sample, the researcher can draw valid, generalizable inferences regarding the whole universe.

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II. Types of Probability Sampling Procedures

In probability sampling, every element in the target population possesses a known, non-zero probability of being selected. It ensures objectivity and permits the statistical estimation of sampling error and confidence intervals.

1. Simple Random Sampling (SRS)

• Concept: Every individual element in the sampling frame has an equal and independent chance of selection.
• Procedures:
  • Lottery method (drawing chits from an urn).
  • Table of random numbers or computerized pseudo-random number generators.
• Merits: Maximum objectivity, free from human classification bias.
• Limitations: Requires an exhaustive, up-to-date sampling frame; geographically dispersed samples increase travel costs.

2. Systematic Random Sampling

• Concept: Elements are selected at regular, predetermined sampling intervals ($k$) after picking a random starting point between $1$ and $k$.
• Formula: $\text{Sampling Interval } (k) = \frac{N}{n}$ (where $N$ = Population size, $n$ = Sample size).
• Example: If $N = 10,000$ and $n = 500$, $k = 20$. If a random start selects unit 7, subsequent selections are 27, 47, 67, and so on.
• Merits: Simpler and faster to administer than SRS in physical record searches.
• Limitation: Susceptible to severe bias if the population list contains hidden periodicity coinciding with interval $k$.

3. Stratified Random Sampling

• Concept: The heterogeneous population is divided into mutually exclusive, exhaustive sub-populations called strata based on relevant characteristics (e.g., bank size, industry sector, geographical zone). Independent random samples are then drawn from each stratum.
• Approaches:
  • Proportionate Stratified Sampling: Sample size drawn from each stratum is proportional to the stratum’s weight in the total population.
  • Disproportionate Stratified Sampling: Strata exhibiting greater variance receive disproportionately larger sample allocations.
• Merits: Ensures representation of minority or critical subgroups; dramatically increases statistical precision (reduces variance).
• Limitation: Requires accurate prior knowledge of population stratification parameters.

4. Cluster Sampling

• Concept: The target population is partitioned into heterogeneous, naturally occurring, mutually exclusive groups or clusters (e.g., municipal wards, school districts, bank branches). A random sample of clusters is chosen, and all units (single-stage) or a sample of units (two-stage) within selected clusters are studied.
• Merits: Economically advantageous; eliminates the need for an exhaustive individual sampling frame when geographic dispersal is vast.
• Limitation: Higher sampling error compared to SRS because units within natural clusters tend to be internally homogeneous.

5. Multi-Stage Sampling

• Concept: An extension of cluster sampling where sampling is conducted in successive hierarchical phases (e.g., Province $\rightarrow$ District $\rightarrow$ Municipality $\rightarrow$ Household).
• Application: Extensively utilized in large-scale national census surveys and nationwide consumer behavior studies across Nepal.

The Process of Scientific Research

The process of scientific research is an organized, cyclical, and multi-stage sequence of intellectual and operational activities undertaken to investigate management questions systematically.

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Key Stages in the Scientific Research Process

1. Identification and Formulation of the Research Problem

• Broad management dilemmas (e.g., declining retail sales) are diagnosed and crystallized into a sharp, researchable problem statement.
• Establishes the boundaries, specific research questions, and primary objectives of the study.

2. Extensive Review of Literature

• Involves thorough examination of theoretical books, peer-reviewed journals, previous dissertations, and institutional reports.
• Helps determine what is already known, prevents unwitting duplication, clarifies conceptual definitions, and highlights empirical gaps.

3. Development of Theoretical Framework and Hypothesis Formulation

• Synthesizes relevant theoretical models connecting independent, dependent, and intervening variables into a conceptual schema.
• Formulates clear, falsifiable hypotheses ($H_0$ and $H_1$) that predict relationships between variables.

4. Formulation of Research Design

• Develops the comprehensive methodological blueprint: exploratory, descriptive, causal-comparative, or experimental.
• Specifies the study setting (field vs. laboratory), time horizon (cross-sectional vs. longitudinal), and unit of analysis (individual, department, firm).

5. Sample Design and Measurement Scaling

• Defines the target population, sampling frame, sample size determination, and selection technique (probability vs. non-probability).
• Operationalizes abstract constructs into measurable indicators using validated scales (nominal, ordinal, interval, or ratio).

6. Data Collection

• Execution of fieldwork using reliable instruments: self-administered surveys, structured interviews, controlled observations, or secondary financial datasets.
• Involves pilot testing the instrument beforehand to identify ambiguities and verify internal consistency.

7. Data Processing and Analysis

• Data Preparation: Editing questionnaires for completeness, coding responses into digital matrices, and screening for outliers and missing values.
• Statistical Analysis: Running descriptive statistics (means, standard deviations) followed by inferential statistical tests (t-tests, ANOVA, Chi-square, correlation, and multiple regression).

8. Hypothesis Testing and Interpretation

• Assessing whether empirical statistical evidence supports or refutes the stated null hypotheses ($p$-value approach vs. critical value approach).
• Interpreting findings in relation to existing literature and theoretical models.

9. Conclusion, Reporting, and Dissemination

• Documenting findings into a structured research report adhering to academic conventions (APA style).
• Providing actionable recommendations for managerial practice and suggesting directions for future research.

Research Design: Concept and Key Features

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I. Meaning of Research Design

A research design is the master architectural plan, blueprint, and operational strategy that specifies the methods and procedures for collecting, measuring, and analyzing empirical data to address research questions efficiently and accurately.

It acts as the structural glue holding the entire investigation together, answering:

• What is the study about?
• Where will the study be conducted?
• What types of data are required, and from where?
• How will the sample be selected, measured, and analyzed?

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II. Essential Features of an Effective Research Design

1. Objectivity and Neutrality

• The instruments and measurement methods must ensure that empirical observations and analytical interpretations remain completely unbiased and free from researcher preconceptions.

2. High Reliability

• The design must guarantee that the measuring instruments yield consistent, stable, and dependable results when repeated measurements are conducted under comparable settings.

3. Internal and External Validity

• Internal Validity: The design isolates and controls for confounding extraneous variables, ensuring that observed changes in the dependent variable are genuinely attributable to the independent variable.
• External Validity: The findings possess generalizability, meaning they can be credibly extrapolated to other settings, organizations, and populations.

4. Generalizability

• Enables researchers to draw broader conclusions about the population based on sample findings through rigorous probability sampling and representative designs.

5. Operational Feasibility and Economy

• A pragmatic research design balances methodological rigor against real-world constraints such as budget, time horizons, access to data sources, and researcher capabilities.

6. Flexibility and Adaptability

• While maintaining methodological discipline, the design should permit necessary adjustments if unexpected field conditions or emergent qualitative insights arise during investigation.

7. Ethical Compliance

• Integrates protections for human subjects, including informed consent, participant confidentiality, voluntary participation, and prevention of harm.

Comprehensive Notes on Both Options

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Option (a): Validity in Research

Validity refers to the degree to which a measurement instrument or research study accurately measures what it is intended to measure, rather than capturing extraneous noise or unintended constructs.

• Primary Types of Measurement Validity:
  1. Content / Face Validity: Ensures that the scale items adequately cover all relevant facets and domains of the construct being measured.
  2. Criterion-Related Validity: Evaluates how well the measure correlates with external benchmarks or established criteria, subdivided into:
     • Concurrent Validity: Agreement with an existing benchmark measured simultaneously.
     • Predictive Validity: Ability to accurately forecast a future outcome (e.g., GMAT scores predicting MBA academic success).
  3. Construct Validity: Confirms that the instrument truly assesses the theoretical construct under investigation. Examined through:
     • Convergent Validity: High correlation between two different measures assessing the same construct.
     • Discriminant Validity: Low correlation between measures of theoretically distinct constructs.

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Option (b): Research Problem

A research problem is a clear, specific, and systematically formulated statement identifying a knowledge gap, theoretical contradiction, or practical managerial challenge that necessitates empirical investigation.

• Core Dimensions:
  • Identification: Arises from practical workplace observations, literature reviews, contradictory empirical findings, or changing socioeconomic dynamics in Nepal.
  • Criteria of a Good Research Problem:
    1. Originality & Significance: Offers meaningful intellectual or practical value to the discipline of management.
    2. Feasibility: Can be realistically investigated within accessible resource, time, and data constraints.
    3. Clarity: Formulated in unambiguous language, defining the target population, context, and core operational variables.
    4. Testability: Amenable to empirical testing and evidence-based verification.

Techniques of Primary Data Collection and Sample Questionnaire Design

Primary data is original data collected directly from respondents by the investigator for the specific purpose of the research project.

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I. Techniques of Primary Data Collection

1. Questionnaire / Survey Method

• Involves administering a structured set of questions printed on paper or distributed electronically (e.g., Google Forms, Qualtrics).
• Advantages: Cost-effective for large geographically dispersed samples, guarantees respondent anonymity, eliminates interviewer bias.
• Limitations: Lower response rates, lack of opportunity to clarify misunderstood questions.

2. Personal and Telephone Interviews

• Personal In-Depth Interviews: Direct, face-to-face qualitative or quantitative dialogue between the researcher and respondent. Allows probing, non-verbal observation, and detailed feedback.
• Telephone / Computer-Assisted Interviewing (CATI): Rapid, wide geographical reach, lower logistical cost compared to field travel.

3. Observational Method

• Systematically watching, recording, and analyzing behavioral patterns of individuals, organizational operations, or retail customer traffic without relying on direct verbal communication.
• Approaches include participant observation (researcher joins the group) and non-participant observation (objective bystander).

4. Focus Group Discussions (FGD)

• Moderated, interactive discussions involving a small, homogeneous group (6–10 participants) led by a trained facilitator to explore consumer attitudes, product perceptions, or organizational culture.

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II. Set of Questionnaire for Data Collection

Research Topic: Impact of Digital Banking Services on Customer Satisfaction in Commercial Banks of Nepal

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Customer Satisfaction and Digital Banking Survey

Dear Respondent, You are cordially invited to participate in an academic study exploring digital banking usage in Nepal. Your responses will be kept strictly confidential and used solely for academic research purposes.

Part I: Demographic Background (Please tick [✓] the appropriate box)

1. Gender: [ ] Male [ ] Female [ ] Other

2. Age Group: [ ] 18–25 years [ ] 26–35 years [ ] 36–50 years [ ] Above 50 years

3. Primary Digital Banking Channel Used: [ ] Mobile Banking App [ ] Internet Banking (Web) [ ] Digital Wallets (ConnectIPS / eSewa)

4. Frequency of Usage: [ ] Daily [ ] 2–3 times a week [ ] Weekly [ ] Monthly

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Part II: Digital Service Quality and Customer Satisfaction (Please rate your agreement with the following statements using the 5-point Likert scale: 1 = Strongly Disagree [SD], 2 = Disagree [D], 3 = Neutral [N], 4 = Agree [A], 5 = Strongly Agree [SA])

Part III: Open-Ended Feedback 8. What primary improvement would you suggest to enhance your digital banking experience?

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Thank you for your valuable time and cooperation!

Comprehensive Format of a Research Report

A standard academic and institutional research report (adhering to Tribhuvan University guidelines and international APA standards) is structured into three distinct structural sections: Preliminary Section, Main Body of the Report, and End Matter (Supplementary Section).

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I. Preliminary Section (Front Matter)

The preliminary section sets the formal context and guides the reader through the report.

1. Title Page: Contains the formal title of the study, author’s full name, registration/roll numbers, institutional submission statement, faculty, university name, and date of submission.
2. Declaration of Authenticity: Formal statement signed by the researcher certifying the originality of the empirical work.
3. Supervisor Recommendation & Acceptance Letter: Signed certification by academic advisors validating the report for final defense.
4. Viva-Voce Evaluation Sheet: Official evaluation record signed by internal and external examiners.
5. Acknowledgements: Professional gratitude acknowledging academic advisors, participating organizations, survey respondents, and funding bodies.
6. Table of Contents: Hierarchical listing of chapter titles, primary headings, and sub-headings with corresponding page numbers.
7. List of Tables and List of Figures: Comprehensive list of statistical tables and graphical illustrations with exact titles and page locations.
8. Executive Summary / Abstract: A self-contained, 250–350 word summary stating the background, problem statement, methodology, major empirical results, and key recommendations.

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II. Main Body of the Report (Core Text)

The core text contains the theoretical, empirical, and analytical substance, systematically divided into distinct chapters:

Chapter I: Introduction

• Background of the Study: Theoretical and contextual setting of the research.
• Statement of the Problem: Articulation of the research dilemma, gap, and specific research questions.
• Objectives of the Study: General and specific operational goals.
• Significance / Importance: Theoretical, practical, and managerial utility of the research.
• Limitations of the Study: Methodological, budgetary, geographical, and time constraints.
• Chapter Organization: Brief roadmap of remaining chapters.

Chapter II: Literature Review and Theoretical Framework

• Conceptual Review: Critical review of fundamental theories, models, and concepts.
• Review of Empirical Studies: Chronological and thematic synthesis of previous empirical journal articles, theses, and findings.
• Theoretical / Conceptual Framework: Visual schema outlining relationships between independent, dependent, and intervening variables.
• Research Hypotheses: Explicit, falsifiable null and alternative hypotheses.

Chapter III: Research Methodology

• Research Design: Methodological paradigm (descriptive, causal, exploratory).
• Population and Sampling Design: Target population, sampling frame, sample size determination, and sampling techniques.
• Data Sources and Instrumentation: Description of primary instruments (questionnaire, interview schedules) and secondary databases.
• Reliability and Validity: Cronbach’s alpha values, pilot testing results, and content validation.
• Data Analysis Tools: Descriptive and inferential statistical techniques (correlation, multiple regression, ANOVA).

Chapter IV: Results and Discussion

• Data Presentation: Systematic presentation of empirical data in tables and figures.
• Descriptive Analysis: Means, standard deviations, percentage distributions.
• Inferential Analysis & Hypothesis Testing: Correlation matrices, regression outputs, and hypothesis test decisions.
• Discussion of Findings: Critical comparison of empirical findings with previous literature cited in Chapter II.

Chapter V: Summary, Conclusions, and Recommendations

• Summary of Findings: Concise recap of empirical results addressing each research objective.
• Conclusions: Logical inferences and generalizations drawn directly from the findings.
• Recommendations: Actionable, practical managerial recommendations and strategic suggestions.
• Directions for Future Research: Unresolved areas and suggested topics for future scholars.

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III. End Matter (Supplementary Section)

The end matter contains documentation and supporting evidence supporting the main report:

1. References / Bibliography: Complete, alphabetical list of all referenced academic sources strictly styled under APA 7th Edition formatting conventions.
2. Appendices:
   • Appendix A: Copy of the questionnaire or interview schedule.
   • Appendix B: Institutional approval letters and consent forms.
   • Appendix C: Supplementary statistical outputs (SPSS/R regression tables, normality tests).