Literature review means the systematic study, summary, and critical evaluation of the existing published works (books, journal articles, theses, reports, and other sources) that are relevant to a particular research topic.

In simple terms, it is a review of what earlier researchers and scholars have already written and found on the subject, done before carrying out one's own research. Its main purposes are:

• To know what has already been studied and what gaps remain.
• To avoid unnecessary duplication of past research.
• To develop a theoretical and conceptual base for the present study.
• To identify suitable methods, variables, and tools used by previous researchers.

Thus, a literature review provides the foundation and direction for a new research study.

A case study is an in-depth, detailed analysis of a single person, group, event, organization, or situation, studied intensively over a period of time to understand its behaviour, causes, and outcomes in a real-life context. It uses multiple sources of evidence (observation, interviews, documents, records) and is commonly applied in management, marketing, and social research to draw practical conclusions.

APA book reference — example:

Author, A. A. (Year). Title of the book (Edition). Publisher.

Example:

Robbins, S. P., & Coulter, M. (2018). Management (13th ed.). Pearson Education.

Structure to remember:

• Author's surname first, followed by initials — Robbins, S. P.
• Year of publication in parentheses — (2018)
• Book title in italics, sentence case — Management
• Edition in parentheses after the title — (13th ed.)
• Publisher name — Pearson Education
• Hanging indent and alphabetical order in the reference list.

The main objectives of research are:

1. To discover new facts and truths — to gain new knowledge about a phenomenon that was previously unknown or poorly understood.
2. To verify and test existing facts — to confirm or reject old theories, hypotheses, and findings through systematic investigation.
3. To analyse cause-and-effect relationships — to find out why and how one variable affects another.
4. To develop new theories and principles — to build a systematic body of knowledge that explains and predicts events.
5. To solve practical problems — to provide reliable information and solutions for business, social, and policy decisions.
6. To enable prediction and control — to forecast future trends and events so that outcomes can be controlled or planned.

Moderating Variable

A moderating variable is a third variable that affects the strength or direction of the relationship between an independent variable and a dependent variable. It interacts with the independent variable, so the effect of the independent variable on the dependent variable changes at different levels of the moderator. In other words, the relationship between X and Y is not the same for all values of the moderator; it is conditional on it.

For example, if we study the effect of advertising (independent variable) on sales (dependent variable), the income level of customers can act as a moderating variable. Advertising may increase sales strongly for high-income customers but only weakly for low-income customers. Here, income moderates the advertising–sales relationship. Similarly, gender, age, education, and organizational culture often serve as moderating variables in business research.

Reliability and Validity in Attitude Scales

An attitude scale is a measurement instrument used to quantify a person's feelings, beliefs, or predispositions toward an object, person, or issue. Since attitudes are abstract and cannot be observed directly, the quality of the scale itself determines whether the conclusions drawn from it are trustworthy. Reliability and validity are the two most fundamental criteria for judging that quality.

Reliability

Reliability refers to the consistency and stability of a measuring instrument — the extent to which a scale produces the same or similar results when applied repeatedly under the same conditions. A reliable attitude scale yields dependable, reproducible scores.

Why it matters:

1. Consistency of measurement — If the same respondent is measured twice with the same scale and the scores differ widely, the scale is unreliable and the measured "attitude" is really just random error. High reliability means the observed score reflects the true attitude rather than chance fluctuation.
2. Reduction of measurement error — Reliability is expressed as the ratio of true score variance to total variance. A reliable scale keeps error variance low, so differences between respondents reflect genuine differences in attitude, not noise.
3. Comparability across time and groups — Only a reliable scale can be used to compare attitudes before and after a campaign, or across different districts or demographic groups. Without consistency, such comparisons are meaningless.
4. Foundation for validity — A scale cannot be valid unless it is first reliable. If scores jump around randomly, they cannot consistently measure what they are supposed to measure.

Reliability is commonly assessed through test–retest reliability (same scale administered twice), split-half reliability (correlating two halves of the items), internal consistency (Cronbach's alpha, KR-20), and inter-rater reliability (agreement between judges).

Validity

Validity refers to the extent to which a scale measures what it is intended to measure — nothing more and nothing less. A valid attitude scale captures the true attitude construct and not some unrelated variable.

Why it matters:

1. Accuracy of inference — A reliable scale may consistently measure the wrong thing. For example, a scale intended to measure "attitude toward family planning" might actually be measuring "attitude toward government health services." Without validity, research conclusions about attitudes are simply wrong.
2. Construct coverage — Validity ensures the items adequately represent all dimensions of the attitude being studied (content validity) and that the scale behaves as the underlying theory predicts (construct validity).
3. Predictive usefulness — A valid attitude scale should relate to behaviour or outcomes as expected. For instance, a valid scale of "attitude toward entrepreneurship" should predict intention to start a business. This is criterion-related validity.
4. Policy and programme decisions — Governments, NGOs, and marketers design interventions based on attitude surveys. Invalid scales lead to misdirected policies and wasted resources.

Validity is assessed through face validity, content validity (expert judgement), criterion validity (concurrent and predictive), and construct validity (convergent and discriminant).

Relationship Between the Two

Reliability and validity are related but distinct. Reliability is a necessary but not sufficient condition for validity:

• A scale can be reliable but not valid — it consistently measures the wrong construct.
• A scale cannot be valid but unreliable — inconsistent scores cannot accurately reflect a stable attitude.
• Ideally, a scale should be both reliable and valid.

Conclusion

Reliability ensures that an attitude scale gives consistent, stable, and error-free measurements, while validity ensures that it measures the correct attitude construct accurately. Without reliability, results are unpredictable; without validity, results are misleading. Together they form the foundation on which the credibility of any attitude measurement — and the research, policy, or marketing decisions based on it — ultimately rests. Hence both are indispensable in the construction and evaluation of an attitude scale.

Various Research Designs

A research design is the overall plan or blueprint that a researcher prepares to collect, measure and analyse the required data in order to answer the research question. It specifies the sources of data, the methods of collection, the sampling plan and the tools of analysis. Research designs are broadly classified into three main categories:

1. Exploratory (Formulative) Research Design

This design is used when the problem is not clearly defined and the researcher has little prior knowledge about it. Its main purpose is to gain familiarity with the phenomenon, generate hypotheses and gain insight.

• Methods used: literature survey, experience survey, and study of insight-stimulating cases.
• Nature of study: flexible and unstructured; no rigid hypotheses.
• Example: A study exploring why customers in Kathmandu prefer mobile banking over branch banking.

2. Descriptive Research Design

This design is used to describe the characteristics of a population or phenomenon as it exists at present. It answers the questions who, what, when, where and how, but not why.

• Types: survey method (census and sample surveys) and observational method.
• Nature of study: rigid, structured, with clearly defined research questions and hypotheses.
• Example: A survey describing the saving habits of BBS students in Kathmandu Valley.

3. Causal (Experimental) Research Design

This design is used to establish cause-and-effect relationships between variables. The researcher manipulates the independent variable and observes its effect on the dependent variable while controlling other variables.

• Types: before-and-after without control, after-only with control, before-and-after with control, and completely randomised / randomised block designs.
• Nature of study: highly structured, with control, manipulation and randomisation.
• Example: Testing whether a new advertisement increases sales of a product.

Other Common Classifications

• Exploratory vs. Conclusive: exploratory designs generate ideas; conclusive designs (descriptive and causal) test them.
• Cross-sectional vs. Longitudinal: cross-sectional studies collect data from many units at one point of time; longitudinal studies study the same units repeatedly over time.
• Experimental vs. Non-experimental: experimental designs involve manipulation of variables; non-experimental designs (survey, case study, correlation) do not.
• Quantitative vs. Qualitative: quantitative designs use numerical data and statistical tools; qualitative designs use words, meanings and in-depth understanding.

Conclusion: The choice of a research design depends on the nature of the research problem, the objectives of the study, the availability of time and resources, and the degree of accuracy required. A proper design ensures that the study is systematic, economical and free from bias.

Principles of Questionnaire Writing (Design and Components)

A questionnaire is a structured set of questions used to collect primary data from respondents. Its quality directly affects the reliability and validity of the research findings. The principles of questionnaire writing can be discussed under two broad bases: design and components.

A. Principles Based on Design

1. Clarity and simplicity — Questions should be short, simple and easily understood by respondents. Technical jargon, complex wording and double meanings should be avoided.
2. Relevance — Every question must relate directly to the research objectives. Irrelevant questions waste time and irritate respondents.
3. Logical sequence — Questions should flow from general to specific, and from easy to difficult. Personal or sensitive questions should come at the end.
4. Objectivity — Questions should be neutral and free from bias. Leading, loaded or suggestive questions must be avoided.
5. Precision — Questions should ask for exact information (e.g., "How many times did you visit last month?") rather than vague responses.
6. Appropriateness to respondents — The language, tone and content must suit the education level, culture and background of the target respondents.
7. Avoidance of double-barrelled questions — Two questions should not be combined into one (e.g., "Are you satisfied with the price and quality?").
8. Pre-testing (pilot testing) — The draft questionnaire should be tested on a small sample before final use to detect weaknesses.
9. Attractive and neat format — Proper spacing, numbering and printing make the questionnaire easy to read and fill.
10. Confidentiality assurance — Respondents should be assured that their identity and responses will be kept confidential.

B. Principles Based on Components

A questionnaire consists of several components, and each must be carefully designed:

1. Covering letter / Introduction — States the purpose of the study, the researcher's identity, assurance of confidentiality and instructions for filling the form. It builds trust and encourages response.
2. Identification / Background information — Includes name (if required), age, sex, education, occupation, income, etc., to classify respondents.
3. Instructional part — Clear directions on how to answer (tick, rank, write, skip, etc.) so that responses are uniform.
4. Main body / Question items — The core section containing questions on the research topic. It should include:
   • Open-ended questions — allow free answers; useful for opinions and suggestions.
   • Closed-ended questions — offer fixed alternatives (dichotomous, multiple choice, rating, ranking scales); easy to code and analyse.
   • Contingency questions — asked only if applicable to certain respondents.
5. Classification / Filter questions — Help group respondents (e.g., users vs. non-users) and direct them to relevant sections.
6. Closing / Ending section — Thanks the respondents and may ask for additional comments or suggestions.

Conclusion

A well-designed questionnaire must be clear, relevant, logically ordered, unbiased and suited to its respondents. Its components — covering letter, instructions, background items, main questions and closing — should be arranged systematically so that accurate and complete data can be collected with minimum effort and error.

a. Ethical concerns in research

Research ethics refers to the moral principles and professional standards that guide researchers in the conduct of scientific inquiry. Ethical concerns arise at every stage of the research process — from selecting the problem to reporting the findings — and they exist to protect the rights, dignity and welfare of the people involved, as well as to preserve the integrity of knowledge itself.

The major ethical concerns in research are as follows:

1. Informed consent. Participants must be told, in clear language, the purpose of the study, the procedures involved, the possible risks and benefits, and their right to withdraw at any time without penalty. Consent must be voluntary and free from coercion or undue inducement.

2. Protection from harm. Researchers must ensure that respondents suffer no physical, psychological, social or economic harm. Sensitive topics such as income, caste, sexuality or trauma require special care in questioning and in the handling of data.

3. Confidentiality and anonymity. Personal identities and the information supplied by respondents should not be disclosed without permission. Anonymity means the researcher cannot link a response to a person; confidentiality means the link exists but is never revealed. This is particularly important in business research where data may be commercially sensitive.

4. Honesty and objectivity. Data must not be fabricated, falsified or selectively reported. Findings that contradict the researcher's expectations or the sponsor's interest must still be reported truthfully. Plagiarism — presenting another's work as one's own — is a serious violation.

5. Avoidance of deception. Concealing the true purpose of a study or using confederates is generally unacceptable unless the deception is minor, necessary, and followed by a full debriefing.

6. Right to privacy. Intrusion into the private life of respondents, including the use of hidden cameras or unauthorised access to records, is unethical.

7. No exploitation of vulnerable groups. Children, the poor, the illiterate, prisoners and other vulnerable persons need additional safeguards and, where required, the consent of guardians or institutional review boards.

8. Proper acknowledgement and fair use of resources. Sponsors' funds must be used for the stated purpose, and all contributors, including research assistants and funding agencies, should be duly acknowledged.

Institutional safeguards such as ethics review committees, codes of conduct and professional guidelines help enforce these concerns. In the Nepalese context, university research committees and the Nepal Health Research Council perform this role. Ethical conduct is not merely a legal requirement; it is the foundation of public trust in research. Without it, findings lose credibility and the research community loses the cooperation of the very people it studies.

Characteristics of Scientific Research and the Process of Scientific Research

1. Characteristics of Scientific Research

Scientific research is a systematic, controlled, and objective investigation aimed at discovering, verifying, or developing knowledge. Its major characteristics are:

(a) Systematic and Ordered Research follows a logical sequence of steps — problem identification, hypothesis, design, data collection, analysis, and conclusion. Nothing is left to chance.

(b) Objective and Impartial The researcher must remain free from personal bias, prejudice, and preconceived notions. Findings are based on facts and evidence, not on the researcher's beliefs or desires.

(c) Empirical Research is based on direct observation and experience rather than on speculation or imagination. Conclusions are drawn from data collected from the real world.

(d) Controlled Variables other than the one being studied are kept constant or neutralized so that the effect of the independent variable on the dependent variable can be measured accurately.

(e) Verifiable and Replicable The methods and results are recorded in such a way that another researcher can repeat the study and obtain similar results. This makes the findings reliable.

(f) Logical and Rational Research uses reasoning — both inductive (from particular to general) and deductive (from general to particular). Conclusions must follow logically from the evidence.

(g) Generalizable Findings are not limited to a single case; they can be applied to a wider population or similar situations, within stated limits.

(h) Original and Creative Research seeks to discover something new or to add new knowledge, or to test existing knowledge in a new context. It is not mere repetition.

(i) Ethical The researcher must respect honesty, confidentiality, informed consent, and avoid fabrication, falsification, and plagiarism.

(j) Cyclical / Self-Correcting Research is a never-ending process. Every conclusion opens new questions, and errors in earlier work are corrected by later studies.

2. Process of Scientific Research

The research process is the systematic series of steps through which a research study is carried out. The main steps are:

Step 1: Formulating the Research Problem

The researcher identifies a broad area of interest and narrows it down into a clear, specific, and researchable problem. The problem should be stated in the form of a question or statement that defines what is to be studied and why.

Step 2: Reviewing the Literature

Existing books, journals, reports, theses, and previous studies related to the problem are reviewed. This helps to:

• know what has already been done,
• identify gaps in knowledge,
• sharpen the research problem, and
• develop a theoretical framework.

Step 3: Formulating Objectives and Hypotheses

Clear objectives state what the study intends to achieve. A hypothesis is a tentative, testable statement about the relationship between variables. It guides the collection and analysis of data. In exploratory studies, a research question may replace the hypothesis.

Step 4: Preparing the Research Design

The research design is the blueprint of the study. It specifies:

• the type of research (descriptive, exploratory, causal, etc.),
• the population and sampling method,
• the sources of data (primary/secondary),
• the tools of data collection (questionnaire, interview, observation, etc.),
• the methods of analysis, and
• the time and cost budget.

Step 5: Determining the Sample Design

The researcher defines the population (universe) and selects a representative sample using probability or non-probability sampling techniques. Sample size is decided on the basis of the required precision, confidence level, and resources available.

Step 6: Collecting the Data

Data are gathered from respondents or records using the selected tools. Primary data may be collected through questionnaires, interviews, observation, or experiments; secondary data from published reports, databases, and records. Care is taken to ensure accuracy, completeness, and reliability.

Step 7: Editing, Coding, and Tabulating the Data

• Editing: checking for errors, omissions, and inconsistencies.
• Coding: assigning numerical or symbolic codes to responses.
• Tabulation: arranging data in tables for easy analysis.

Step 8: Analyzing and Interpreting the Data

Statistical tools such as percentages, averages, correlation, regression, chi-square, t-test, and ANOVA are applied. The results are interpreted in the light of the objectives and hypotheses to draw meaningful conclusions.

Step 9: Testing the Hypotheses

Each hypothesis is accepted or rejected on the basis of the analyzed data. Statistical significance is checked at a chosen level (usually 5% or 1%).

Step 10: Generalization and Interpretation

The findings are generalized to the population within the limits of the study. The researcher explains what the results mean in theoretical and practical terms.

Step 11: Preparing the Research Report

The final step is writing the report, which includes the introduction, literature review, methodology, findings, conclusions, and recommendations. The report must be clear, accurate, and properly referenced.

Step 12: Follow-up / Further Research

The conclusions suggest new problems for future research. Thus the process is cyclical — the end of one study becomes the beginning of another.

Conclusion Scientific research is distinguished by its systematic, objective, empirical, controlled, and verifiable nature. Its process moves from problem identification through literature review, design, sampling, data collection, analysis, hypothesis testing, and generalization to report writing — a logical chain that ensures the findings are reliable, valid, and useful for knowledge and practice.

Purposes of a Research Proposal

A research proposal is a written plan of the study the researcher intends to carry out. Its main purposes are:

1. To define the research problem — it states clearly what is to be studied, why it matters, and what gap in existing knowledge it addresses.
2. To set the objectives — it specifies the general and specific objectives the study seeks to achieve.
3. To outline the methodology — it explains the research design, population, sample, data collection tools, and analysis techniques to be used.
4. To justify the study — it shows the significance, scope, and expected contribution of the research to theory, policy, or practice.
5. To plan resources and time — it presents the budget, manpower, and work schedule (timeline) required to complete the study.
6. To obtain approval and funding — it serves as the formal document submitted to the research committee, university, or funding agency for acceptance, registration, and financial support.
7. To provide a basis for evaluation — it acts as a benchmark against which the progress and final report of the study can be assessed.
8. To anticipate problems — it helps the researcher foresee difficulties (data access, cost, time) and plan alternatives in advance.

Format of a Research Proposal

Although formats vary slightly by institution, a standard research proposal contains the following sections:

1. Title page / Preliminary section

• Title of the study (clear, specific, and self-explanatory)
• Name of the researcher, institution, and date of submission

2. Introduction / Background of the study

• General background of the topic
• Statement of the problem
• Objectives of the study
• Research questions or hypotheses
• Significance/rationale of the study
• Scope and limitations of the study
• Definition of key terms

3. Review of literature

• Review of related theories, previous studies, and reports
• Identification of the research gap

4. Research methodology

• Research design (descriptive, exploratory, causal, etc.)
• Population and sampling technique
• Sources of data (primary and secondary)
• Data collection instruments (questionnaire, interview, observation)
• Data analysis techniques (statistical tools, tables, charts)
• Reliability and validity measures

5. Chapter plan / Organization of the study

• Brief outline of the chapters the final report will contain

6. Work schedule (timeline)

• Activities and the time allotted to each, often shown in a Gantt chart

7. Budget

• Estimated cost of the study under headings such as stationery, travel, data processing, and miscellaneous

8. References / Bibliography

• List of sources cited, in the prescribed style (APA, etc.)

9. Appendices (if any)

• Questionnaire, interview schedule, and other supporting documents

In short, a research proposal states what is to be studied, why it is to be studied, how it will be studied, when it will be completed, and at what cost — thereby serving as the blueprint of the research.

Scientific research is a systematic, objective and logical process of inquiry in which a researcher identifies a problem, formulates a hypothesis, collects and analyses relevant data using scientific methods, and draws valid conclusions to discover new knowledge or to verify and refine existing knowledge.

In short, it is the organised search for truth carried out through observation, experimentation and reasoning rather than through guesswork or personal opinion.

Ethnography is a qualitative research method in which the researcher studies a social group, culture, or community by observing it closely and often living among its members over a long period. It relies mainly on participant observation and in-depth interviews to describe the group's behaviour, customs, and way of life in its natural setting.

Deductive approach is a method of reasoning that moves from the general to the specific. It begins with a general premise, theory, or established principle and draws a specific conclusion from it through logical reasoning. It is also called reasoning from general to particular or the top-down approach.

Example: A general principle states that "all humans are mortal." Since Ram is a human, we conclude specifically that "Ram is mortal."

Theoretical framework is the structure of concepts, theories, and assumptions that a researcher uses to guide a study. It explains the existing theories on which the research is based and shows how the variables under study are related according to those theories. It provides the foundation for formulating hypotheses, interpreting findings, and justifying the research problem.

Sampling error is the difference between a sample statistic (such as the sample mean) and the corresponding true population parameter, arising purely because the data come from only a part of the population rather than the whole population. It occurs due to chance variation in the selection of sample items, not due to mistakes in data collection. It can be reduced by increasing the sample size or by using a better (more representative) sampling method.

Research Problem

A research problem is a clear, specific statement of a question or issue that a researcher intends to study, along with the gap in existing knowledge that the study seeks to fill. It defines what is to be investigated, why it matters, and within what scope the inquiry will be carried out. In other words, it is the perceived difficulty or unanswered question that motivates the research and guides its objectives, hypotheses, and methodology. A well-stated research problem tells the reader what is not yet known, why it is worth knowing, and how the study will contribute to knowledge.

A research problem is not merely a topic. "Employee motivation" is a topic; "the effect of non-financial incentives on the job satisfaction of commercial bank employees in Kathmandu Valley" is a research problem. The former is broad and open-ended, while the latter specifies the variables, the population, and the setting.

Characteristics of a Good Research Problem

• Clear and specific — the variables and population are identifiable.
• Researchable — data can actually be collected and analysed within available time, cost, and skill.
• Original or gap-filling — it addresses something not already settled by existing studies.
• Significant — it has theoretical or practical value for policy, business, or society.
• Feasible in scope — neither too broad nor too narrow.
• Ethical — it can be studied without harming participants.

Identification of the Research Problem

Identifying the research problem is the first and most critical step of the research process. It is a systematic narrowing-down exercise that moves from a broad area of interest to a precise, testable statement. The following steps are involved.

1. Selection of a broad field or subject area. The researcher begins with a general area of interest — for example, marketing, human resource management, or consumer behaviour — guided by academic background, personal interest, and the availability of literature and data.

2. Review of existing literature. The researcher reads books, journal articles, theses, reports, and government publications related to the field. This reveals what has already been studied, which theories and methods have been used, and — most importantly — what remains unanswered. The gap so revealed is the seed of the research problem.

3. Identification of the research gap. By comparing existing studies, the researcher locates contradictions in findings, neglected variables, unstudied populations or regions, or outdated results. For example, if many studies examine job satisfaction in banking but none examine it after the merger of banks, that gap becomes a candidate problem.

4. Consultation with experts and stakeholders. Discussion with supervisors, professors, practitioners, and concerned agencies helps test whether the problem is genuine, relevant, and feasible. Experts can also point out practical difficulties and suggest sharper formulations.

5. Observation of real-world problems and issues. Field visits, work experience, news reports, and social or organisational problems often suggest researchable questions. A manager noticing high employee turnover, for instance, may identify it as a problem worth investigating.

6. Narrowing the problem to a specific statement. The broad area is progressively narrowed by specifying the unit of analysis (individuals, firms, households), the variables (independent and dependent), the setting (industry, district, time period), and the relationship to be examined. This produces a precise problem statement.

7. Formulation of research objectives and questions. The identified problem is converted into clear objectives (what the study aims to achieve) and research questions or hypotheses (what is to be tested). This confirms that the problem is researchable.

8. Evaluation of feasibility and significance. Finally, the researcher checks the problem against the criteria of time, cost, data availability, ethical clearance, and contribution to knowledge or practice. Only a problem that passes this test is finally selected.

Example

Broad interest: Employee motivation. Literature review: studies exist on financial incentives in manufacturing, but few on non-financial incentives in Nepali commercial banks. Narrowed problem: What is the effect of non-financial incentives on the job satisfaction of employees of commercial banks in Kathmandu Valley? Objective: To examine the relationship between non-financial incentives and job satisfaction. Hypothesis: Non-financial incentives have a significant positive effect on job satisfaction.

Conclusion

A research problem is the foundation on which the entire research is built; a faulty problem leads to wasted effort. Its identification is therefore a deliberate, stepwise process — moving from a broad field, through literature review and gap analysis, to a sharply defined statement with clear objectives and hypotheses — tested at every stage for feasibility, originality, and significance.

Concept of Management Research

Management research is the systematic, objective and organised process of collecting, analysing and interpreting information in order to solve managerial problems and support decision-making in organisations. It applies the principles of scientific inquiry to the study of management phenomena — such as planning, organising, staffing, leading, controlling, marketing, finance and human resource practices — so that managers can make decisions based on evidence rather than intuition or guesswork.

Its main features are:

• Systematic — it follows a defined sequence of steps (problem definition → hypothesis → design → data collection → analysis → conclusion).
• Objective — findings are based on facts and verifiable evidence, not personal bias.
• Purposeful — it is directed at solving a specific managerial problem or answering a specific question.
• Replicable — the procedure can be repeated by others to test the reliability of results.
• Decision-oriented — its ultimate aim is to provide a rational basis for managerial decisions.

In short, management research is the systematic search for truth about managerial problems, providing the knowledge and evidence a manager needs to decide wisely.

Applying Scientific Thinking to Management Problems

Scientific thinking means approaching a problem in a logical, systematic and evidence-based manner — observing facts, forming hypotheses, testing them against data, and drawing conclusions that can be verified. It is applied to management problems through the following steps:

1. Identifying and defining the problem The manager first senses a difficulty (e.g., falling sales, high employee turnover). The problem is then clearly defined in specific, measurable terms — for example, "sales in Region A declined by 15% in the last two quarters."

2. Reviewing existing knowledge Relevant literature, past records, company reports and prior studies are reviewed to understand what is already known and to avoid repeating mistakes.

3. Formulating hypotheses Tentative explanations are stated as testable propositions, e.g., "The decline in sales is due to increased competition from a new entrant," or "High turnover is caused by low salary relative to the market."

4. Designing the research A plan is prepared specifying the type of research (exploratory, descriptive or causal), the sources of data (primary/secondary), the sampling method, and the tools of data collection (questionnaire, interview, observation).

5. Collecting data Relevant facts are gathered systematically from employees, customers, records or the market, using appropriate instruments so that the data are reliable and valid.

6. Analysing and interpreting data Statistical and analytical tools (percentages, averages, correlation, regression, chi-square, etc.) are used to test the hypotheses and to identify relationships and causes.

7. Drawing conclusions and making decisions On the basis of the analysis, the manager accepts or rejects the hypotheses and selects the best course of action — for example, revising the pricing policy or improving the compensation package.

8. Implementing and evaluating The decision is put into practice, and its results are monitored. If the outcome differs from expectation, the process is repeated — making scientific thinking a continuous, self-correcting cycle.

Conclusion

Scientific thinking converts management from an art based on intuition into a discipline based on evidence. By following the systematic steps of problem definition, hypothesis, data collection, analysis and verification, managers can diagnose the real causes of problems, evaluate alternatives objectively, reduce the risk of wrong decisions, and continuously improve organisational performance.

Basic Principles of Research Design

A research design is the overall plan or blueprint that guides the collection, measurement and analysis of data. It is the logical and systematic arrangement of conditions for carrying out a study so that the research objectives are achieved with minimum effort, time and cost. The basic principles underlying a sound research design are as follows:

1. Principle of purpose clarity. The design must clearly state the objectives, research questions or hypotheses of the study. Every element of the design — sampling, tools, analysis — should flow logically from these objectives. A design without a clear purpose cannot produce meaningful conclusions.

2. Principle of objectivity. The design should be free from the personal bias, prejudice and preconceptions of the researcher. Procedures for selecting respondents, collecting data and interpreting results must be neutral and verifiable, so that another researcher repeating the study would reach similar conclusions.

3. Principle of reliability. The design must be capable of yielding consistent and dependable results when the study is repeated under similar conditions. This requires carefully standardized instruments, clear instructions and controlled conditions of observation.

4. Principle of validity. The design should measure what it is actually intended to measure. Internal validity (correctness of causal inferences within the study) and external validity (generalizability of findings to the wider population) must both be safeguarded through appropriate sampling and control techniques.

5. Principle of flexibility. Although a design provides a systematic framework, it should not be so rigid that it cannot be adjusted when unexpected situations arise in the field. Flexibility is particularly important in exploratory and social research.

6. Principle of economy. The design should make the most efficient use of available resources — money, time, manpower and materials. An unnecessarily complex or costly design is a poor design.

7. Principle of reliability of data sources and proper sampling. The design must specify appropriate sources of data (primary and secondary) and a scientifically sound sampling technique so that findings are representative of the population.

8. Principle of ethical consideration. The design must respect the rights, privacy, dignity and consent of respondents and must avoid causing harm to any individual or group.

9. Principle of feasibility. The design should be practical and workable within the researcher's competence, resources and access to the study area.

Criteria of a Good Research Design

A good research design is judged by the following criteria:

1. Clearly defined objectives and hypotheses — it must specify what is to be studied and what is expected to be found.
2. Appropriateness — the design must suit the nature of the problem, the type of data required and the purpose of the study (descriptive, exploratory, explanatory or experimental).
3. Reliability — it should produce consistent results on repeated application.
4. Validity — it should measure the intended variables accurately and allow generalizable conclusions.
5. Objectivity — it should eliminate researcher bias in data collection and interpretation.
6. Representativeness — the sample and data sources should truly represent the population under study.
7. Flexibility — it should permit modification when field conditions demand it.
8. Economy — it should be cost-effective in terms of time, money and effort.
9. Feasibility — it must be practically executable within the researcher's capacity and resources.
10. Ethical soundness — it must protect the rights and welfare of respondents.
11. Simplicity — it should be simple enough to be understood and applied without confusion.
12. Control of variables — it should provide adequate means to control extraneous and confounding variables.

Conclusion. A good research design is thus one that is clear in purpose, objective in approach, reliable and valid in measurement, economical and feasible in operation, and ethical in conduct. Such a design serves as a dependable roadmap from the statement of the problem to the final conclusions of the study.

a. Importance of Review of Literature

Review of literature is the systematic study and evaluation of existing published works—books, journals, theses, reports and articles—that are relevant to the research problem. It is done before formulating the research design and serves as the foundation of a good research study.

Importance:

1. Identification of the research gap: It shows what has already been studied and what remains unexplored, helping the researcher select a problem that adds new knowledge instead of repeating old work.
2. Clarity of the research problem: By reading related studies, the researcher sharpens the objectives, scope and boundaries of the study.
3. Theoretical and conceptual base: It provides the theories, models and concepts on which the study is built, and helps in defining key variables.
4. Guidance for methodology: Previous studies suggest suitable research designs, sampling techniques, data collection tools and methods of analysis.
5. Avoidance of duplication: It prevents the researcher from unknowingly repeating work already done, saving time, effort and cost.
6. Source of hypotheses: Reviewing past studies helps in formulating testable hypotheses and research questions.
7. Comparison and interpretation of results: Findings of the present study can be compared with earlier findings, which helps in drawing valid conclusions and generalisations.
8. Justification of the study: It enables the researcher to convince readers, supervisors and funding agencies about the relevance and need of the research.

In short, a good review of literature gives direction, depth and credibility to the entire research process.

Research Report

A research report is a formal, written document that presents the findings of a systematic investigation in an organised and objective manner. It communicates the problem studied, the methods used, the data collected and analysed, and the conclusions or recommendations drawn, so that the reader can understand and, if necessary, verify or act upon the results. It is the final output of the research process and serves as a permanent record of the study.

Structure of Writing a Research Report

A standard research report follows a fixed sequence of parts, usually divided into three broad sections: preliminary (front) matter, the main body, and the supplementary (back) matter.

A. Preliminary Parts

1. Title Page — Title of the study, name of the researcher, name of the institution, and date of submission.
2. Letter of Transmittal / Certificate — A brief note submitting the report to the concerned authority, or a certificate of approval by the supervisor.
3. Acknowledgements — Thanks to persons and organisations who helped in the study.
4. Table of Contents — List of chapters, sections and sub-sections with page numbers.
5. List of Tables and Figures — Titles of tables, charts and diagrams with page numbers.
6. Abstract / Executive Summary — A short summary of the objectives, methods, major findings and conclusions.

B. Main Body

1. Introduction

   • Background of the study
   • Statement of the problem
   • Objectives of the study
   • Research questions / hypotheses
   • Significance (rationale) of the study
   • Limitations of the study
   • Organisation of the report

2. Literature Review

   • Review of related theories, concepts and previous studies
   • Identification of the research gap

3. Research Methodology

   • Research design (descriptive, exploratory, causal, etc.)
   • Population and sample, sampling technique
   • Sources and methods of data collection (primary/secondary)
   • Research instruments (questionnaire, interview, observation)
   • Data analysis tools (percentage, mean, correlation, regression, chi-square, etc.)

4. Data Presentation and Analysis

   • Presentation of data in tables, graphs and charts
   • Analysis and interpretation of results in line with the objectives
   • Testing of hypotheses

5. Summary, Conclusions and Recommendations

   • Summary of major findings
   • Conclusions drawn from the findings
   • Recommendations and suggestions for improvement
   • Scope for further research

C. Supplementary Parts

1. Bibliography / References — List of books, journals and reports consulted, in a standard citation style (APA, MLA, etc.).
2. Appendices — Questionnaire, interview schedule, raw data, calculations and other supporting material.

Conclusion

Thus, a research report is a structured document that moves logically from the statement of the problem, through methodology and analysis, to conclusions and recommendations. Following this standard structure makes the report clear, complete, verifiable and useful to its readers.

Sampling

Sampling is the statistical process of selecting a representative subset of individuals, items or observations (called a sample) from a larger group (called the population or universe) in order to study the characteristics of the whole population. Instead of conducting a complete enumeration (census) of every unit, the researcher examines only a part of the population and draws inferences about the whole. Sampling is widely used because a census is costly, time-consuming, difficult and sometimes impossible, while a well-drawn sample gives reliable results at low cost and in less time.

The main advantages of sampling are: lower cost, less time, greater accuracy (fewer non-sampling errors), suitability for large and infinite populations, and feasibility where the units are destroyed in testing. Its main limitations are sampling error and the need for expert knowledge and a proper sampling frame.

Sampling techniques are broadly classified into two types: probability sampling and non-probability sampling.

1. Probability (Random) Sampling

In probability sampling, every unit of the population has a known, non-zero and equal (or known) chance of being selected. Selection is made by a random mechanism, so personal bias is eliminated and the reliability of the estimate can be measured. The main techniques are:

(a) Simple Random Sampling Every unit has an equal probability of selection. The sample may be drawn by the lottery method (drawing chits) or by using a random number table / computer-generated random numbers. It is simple and unbiased, but difficult when the population is large and widely scattered.

(b) Systematic (Quasi-Random) Sampling The population is arranged in some order, a random start is chosen, and then every kth unit is selected, where k = N/n (sampling interval). For example, selecting every 10th household from a list. It is easy and quick, but may give a biased sample if the list has a periodic pattern.

(c) Stratified Random Sampling The population is divided into homogeneous groups called strata (e.g., by sex, income, region), and a random sample is drawn from each stratum. It may be proportional (sample size of each stratum proportional to its size) or disproportional. It gives greater precision and ensures representation of every group.

(d) Cluster Sampling The population is divided into groups called clusters (e.g., wards, villages, schools), a few clusters are selected at random, and all units within the selected clusters are studied. It is economical when the population is widely spread, though less precise than simple random sampling.

(e) Multistage Sampling Sampling is done in successive stages: first large units (primary units), then smaller units within them (secondary units), and so on. For example, selecting districts, then wards, then households. It is convenient for large-scale national surveys.

(f) Probability Proportional to Size (PPS) Sampling Units are selected with probability proportional to their size (e.g., population of a village), often used in the first stage of multistage sampling.

2. Non-Probability (Non-Random) Sampling

In non-probability sampling, the chance of selection of a unit is not known; the investigator selects units on the basis of convenience, judgement or quota. It is cheaper and quicker but subject to personal bias, and the sampling error cannot be measured. The main techniques are:

(a) Convenience (Accidental) Sampling Units are selected because they are easily available — for example, interviewing shoppers in a nearby market or students in one's own college. It is the easiest and cheapest method but highly unrepresentative.

(b) Purposive (Judgement) Sampling The investigator uses his own judgement to select units believed to be representative or suitable for the study — for example, selecting a few expert farmers to study improved farming practices. It is useful for special or small studies but depends on the investigator's skill.

(c) Quota Sampling The population is divided into groups and a fixed quota (number) is assigned to each group; the interviewer then fills the quota at his convenience. It is widely used in market and opinion surveys but suffers from interviewer bias.

(d) Snowball Sampling Existing respondents are asked to identify further respondents from their contacts. It is useful for studying hidden or rare populations (e.g., drug users, migrant workers) but the sample is not representative.

(e) Purposive/Voluntary Sampling Respondents themselves volunteer to take part (e.g., listeners phoning a radio programme). It is highly biased.

Comparison of Probability and Non-Probability Sampling

Conclusion

Sampling is the scientific method of studying a part in order to know the whole. Probability sampling, being based on random selection, is more reliable and is preferred when the results must be generalised to the population and the margin of error must be known. Non-probability sampling is convenient, cheap and useful in exploratory or specialised studies, but it suffers from bias and its results cannot be safely generalised. The choice of technique therefore depends on the nature of the population, the objective of the study, the availability of a sampling frame, and the time and money at the researcher's disposal.

The common sources of error in research design are:

1. Sampling error — error arising because the study is conducted on a sample rather than the whole population.
2. Measurement error — error caused by faulty or imprecise measuring instruments, scales, or questionnaires.
3. Data collection error — error due to biased questions, faulty interviewing, or mistakes in recording responses.
4. Respondent error — error from inaccurate, incomplete, or deliberately false information given by respondents.
5. Sampling frame error — error when the list of population elements used for sampling is incomplete or outdated.
6. Non-response error — error resulting when selected respondents fail to respond, making the sample unrepresentative.
7. Processing and analysis error — error in coding, editing, tabulating, or applying wrong statistical techniques.
8. Researcher bias — error from the personal prejudice, faulty judgment, or carelessness of the researcher.

Primary vs. secondary data

In short, primary data is original and first-hand, while secondary data is second-hand and already available.

Role of Pilot Study

A pilot study is a small-scale preliminary investigation conducted before the main research to test the research design, tools, and procedures. Its roles are:

1. Testing research tools — It checks the reliability and validity of questionnaires, interview schedules, or observation forms, and helps identify unclear or ambiguous questions.
2. Assessing feasibility — It verifies whether the planned design, sampling method, and time and cost estimates are practical.
3. Detecting weaknesses — It reveals problems in the research plan so they can be corrected before the full study, saving time, money, and effort.
4. Providing guidance — The experience and results of the pilot study guide the researcher in refining objectives, hypotheses, sampling, and data analysis techniques.

In short, a pilot study acts as a "trial run" that increases the accuracy and validity of the main research.

Non-random (non-probability) sampling methods are:

1. Judgement (purposive) sampling – the researcher selects units based on personal judgement.
2. Quota sampling – a fixed number (quota) of units is selected from each group.
3. Convenience sampling – units are chosen because they are easily available.
4. Snowball sampling – existing respondents refer further respondents.
5. Voluntary sampling – units themselves come forward to be included.

Dependent variable: The variable that is being explained, predicted, or measured in a study. Its value depends on changes in another variable. It is also called the explained or criterion variable.

Independent variable: The variable that is manipulated, controlled, or used to explain or predict changes in the dependent variable. It is also called the explanatory or predictor variable.

Example: In a study of the effect of study hours on exam marks, study hours is the independent variable and exam marks is the dependent variable.

Secondary Data and Its Sources

Meaning of Secondary Data

Secondary data refers to data that has already been collected, compiled, and published by some other person or organisation for a purpose other than the present investigation. Such data are not gathered firsthand by the researcher; they are simply obtained from existing records, reports, publications, or databases and reused for the current study.

For example, if a researcher studying the saving habits of Nepalese households uses the household survey reports published by the Nepal Rastra Bank or the Central Bureau of Statistics, that information is secondary data for the researcher, even though it was primary data for the agency that collected it.

Secondary data may be published (available in books, journals, government reports, websites) or unpublished (available in the internal records, files, and registers of an organisation).

Sources of Collecting Secondary Data

Secondary data can be collected from two broad sources:

1. Published Sources

• Government publications: Economic Survey, Statistical Yearbook, census reports, budget speeches, and reports of ministries — mainly from the Central Bureau of Statistics (CBS), Nepal Rastra Bank (NRB), and the Ministry of Finance.
• International publications: Reports of the World Bank, IMF, Asian Development Bank, WHO, ILO, and UNESCO.
• Semi-government and private publications: Annual reports of corporations, banks, insurance companies, and cooperatives; publications of chambers of commerce and industry associations.
• Journals and periodicals: Economic and management journals, newspapers, magazines, and research bulletins.
• Books and research reports: Textbooks, reference books, dissertations, and research papers of universities and research institutions.
• Electronic sources: Websites, online databases, e-journals, and digital libraries.

Sources of Unpublished Data

• Internal records of organisations: Sales records, purchase records, personnel files, financial statements, minutes of meetings, and customer databases.
• Reports of earlier research: Unpublished theses, dissertations, and project reports available in university libraries.
• Records of government offices: Registers, files, and reports maintained by local bodies and government agencies that are not formally published.

Precautions in Using Secondary Data

Before using secondary data, the researcher should check its reliability, accuracy, adequacy, and relevance by examining who collected it, when and how it was collected, the method of sampling used, and whether it suits the objectives of the present study.

Structure of a Research Proposal

A research proposal is a written plan that outlines what the researcher intends to study, why it is worth studying, and how it will be carried out. In the BBS context it is prepared before conducting a study (e.g., a term paper or project report) and submitted for approval. Although formats vary slightly between institutions, a standard research proposal contains the following components:

1. Title Page / Cover Page

Contains the title of the study (clear, specific and self-explanatory), the name of the researcher, the name of the institution/faculty, the degree for which it is submitted, the name of the supervisor, and the date of submission. A good title indicates the variables, the setting and the nature of the study.

2. Introduction / Background of the Study

Introduces the topic and its context. It presents the theoretical and practical background, a brief review of related past studies (literature review), the research gap, and the rationale or justification for selecting the topic. It ends by clearly stating the problem.

3. Statement of the Problem

States precisely what is unknown or unresolved. It is often expressed through research questions such as What is the relationship between X and Y? or What are the factors affecting Z? This section defines the boundaries of the problem and shows why the study is needed.

4. Objectives of the Study

Lists the specific purposes the study intends to achieve, usually in measurable terms. The objectives should be consistent with the problem statement and research questions. For example: "To examine the effect of advertising on sales of XYZ Company."

5. Research Questions / Hypotheses

• Research Questions: The questions the study seeks to answer.
• Hypotheses: Testable statements of expected relationships between variables (e.g., H₀: There is no significant relationship between advertising and sales). Hypotheses are used mainly in quantitative studies.

6. Significance / Rationale of the Study

Explains who will benefit from the findings — managers, policy makers, students, researchers, or the organization studied — and how the study contributes to existing knowledge.

7. Scope and Limitations of the Study

• Scope (Delimitations): The area, population, time period, and variables covered by the study.
• Limitations: Constraints such as limited time, budget, sample size, data availability, or methodological weaknesses that may affect the results.

8. Research Methodology

The most important technical section. It includes:

• Research Design: Descriptive, exploratory, or causal-comparative design.
• Population and Sample: Target population, sampling technique (probability/non-probability), and sample size.
• Data Collection Methods and Instruments: Primary data (questionnaire, interview, observation) and secondary data (annual reports, journals, websites).
• Data Analysis Tools: Descriptive statistics (mean, percentage, standard deviation) and inferential statistics (correlation, regression, chi-square, t-test).
• Reliability and Validity: How the instruments were tested for consistency and accuracy.

9. Organization of the Study / Chapter Plan

Briefly describes how the report will be arranged, e.g., Chapter I: Introduction; Chapter II: Literature Review; Chapter III: Research Methodology; Chapter IV: Data Presentation and Analysis; Chapter V: Summary, Conclusion and Recommendations.

10. Work Schedule / Time Frame

A timetable (often a Gantt chart) showing the sequence and duration of activities — proposal writing, data collection, analysis, and report writing.

11. Budget / Cost Estimate

An estimate of the funds required for stationery, printing, travel, data collection, and other expenses.

12. References / Bibliography

A list of all sources cited in the proposal, presented in a standard style (APA, MLA, or the format prescribed by the institution).

13. Appendices (if any)

Supporting materials such as the questionnaire, interview schedule, and letters of request.

Conclusion: A well-structured research proposal moves logically from the problem (introduction, statement of problem, objectives, hypotheses) to the plan of action (methodology, schedule, budget) and finally to the supporting details (references, appendices). This systematic structure helps the researcher conduct the study systematically and enables the supervisor to evaluate its feasibility and academic value.

Answer

Meaning of Research

The word "research" is derived from the French word rechercher, meaning "to search again" — that is, to investigate thoroughly. Research is a systematic, objective and organised effort to discover, interpret or revise facts, theories and applications. It is a careful inquiry or investigation aimed at discovering new knowledge or verifying existing knowledge.

In the words of Fred Kerlinger, "Research is a systematic, controlled, empirical and critical investigation of hypothetical propositions about the presumed relations among natural phenomena."

Thus, research is not mere collection of information; it is a planned, methodical search for truth using reliable methods and verifiable evidence.

Scientific Research Process

Scientific research follows a logical, step-by-step sequence. The main steps are:

1. Identification and Formulation of Research Problem The researcher identifies a gap in knowledge or a practical difficulty. The problem is then narrowed down and stated clearly in the form of a question or objective. A well-defined problem tells what is to be studied and why.

2. Review of Literature The researcher studies existing books, journals, reports and previous research related to the problem. This helps in understanding what is already known, avoiding duplication, sharpening the objectives and developing a theoretical base.

3. Formulation of Hypothesis A hypothesis is a tentative, testable statement about the expected relationship between variables. For example: "Higher advertising expenditure leads to higher sales." It gives direction to the study and can be accepted or rejected on the basis of evidence.

4. Preparation of Research Design The research design is the blueprint of the study. It specifies the type of research (descriptive, exploratory, causal), the population, sampling method and sample size, sources of data (primary/secondary), instruments of data collection (questionnaire, interview, observation) and methods of analysis.

5. Collection of Data Data are gathered from the field or from records using the selected tools. Primary data may be collected through questionnaires, interviews or observation; secondary data from publications, websites and reports. Care is taken to ensure accuracy, reliability and validity.

6. Processing and Analysis of Data The collected raw data are edited, coded, classified and tabulated. Statistical tools such as percentages, averages, correlation, regression, chi-square and t-test are applied to test the hypothesis and draw meaningful conclusions.

7. Interpretation and Generalisation The results are interpreted in the light of the objectives and hypothesis. The researcher explains what the findings mean, compares them with earlier studies and states the generalisations that can be drawn.

8. Preparation of Research Report Finally, the findings are presented in a written report containing introduction, objectives, methodology, findings, conclusions and recommendations. The report communicates the results to concerned parties and serves as a basis for decision-making and further research.

Conclusion

Research is a systematic and scientific search for knowledge. The scientific research process moves from problem identification to report writing in a logical order. Each step is interdependent, and following this process ensures that the findings are objective, reliable and useful for theory building and practical decision-making.

a. Importance of Review of Literature in Research

Review of literature is the systematic examination of existing published and unpublished works—books, journal articles, theses, reports and government documents—that are relevant to the research problem. It is carried out mainly at the beginning of the study, though it continues throughout. Its importance can be stated as follows:

1. Identification of the research gap. It shows what has already been studied and what remains unexplored, helping the researcher select a problem that adds something new instead of repeating completed work.

2. Formulation of objectives and hypotheses. Findings and conclusions of earlier studies guide the researcher in framing clear objectives and testable hypotheses.

3. Development of a theoretical framework. It provides the concepts, theories and models on which the study is built, and helps define key variables and terms.

4. Selection of appropriate methodology. By reviewing how others designed their studies, the researcher can choose suitable research design, sampling technique, data collection tools and methods of analysis, and can avoid the mistakes made earlier.

5. Avoidance of duplication. It prevents unnecessary repetition of research already done, thereby saving time, money and effort.

6. Interpretation and comparison of results. The researcher can compare his or her findings with those of previous studies, explain similarities and differences, and draw more reliable conclusions.

7. Widening the researcher's knowledge. It deepens the researcher's understanding of the subject and increases confidence in carrying out the study.

8. Justification of the study. It supplies evidence and authority to convince supervisors, funding agencies and readers that the research is worthwhile.

In short, review of literature is the foundation of sound research: it guides the researcher from selection of the problem to interpretation of findings, and gives the study direction, depth and credibility.

Research Report

Meaning

A research report is a formal, written document that presents the findings of a systematic investigation in an organised and objective manner. It records the problem studied, the methods used, the data collected and analysed, and the conclusions and recommendations drawn from them. It is the final output of the research process, written for a specific audience (supervisors, management, funding agencies or the academic community) so that the reader can understand what was studied, how it was studied, what was found, and what it means.

In simple words, a research report is the written statement of research activities and their results. It converts raw data and analysis into meaningful, readable information that supports decision-making and adds to the body of knowledge.

Structure of a Research Report

A standard research report (especially a thesis, dissertation or business research report) has three broad parts — preliminary (preliminary pages), main body (main text) and supplementary (end matter) — divided into the following sections:

A. Preliminary Parts

1. Title Page — Contains the title of the study, the name of the researcher, the name of the institution, the degree/programme for which it is submitted, and the date of submission. The title should be clear, specific and self-explanatory.

2. Certificate of Approval / Recommendation — A statement signed by the research supervisor and/or the research committee certifying that the report has been carried out and submitted under their supervision and is recommended for acceptance.

3. Declaration — A statement by the researcher declaring that the report is his/her own original work and has not been submitted elsewhere for any degree or purpose.

4. Acknowledgements — A note of thanks to the supervisor, respondents, institution and others who helped in completing the study.

5. Table of Contents — A list of chapters, sections and sub-sections with page numbers, so the reader can locate any part easily.

6. List of Tables and Figures — A list of tables, charts, graphs and diagrams used in the report, with their page numbers.

7. Abbreviations / Acronyms — Full forms of short forms used in the report (e.g., BBS, SPSS, GDP).

8. Abstract / Executive Summary — A brief summary (usually 150–300 words) of the objectives, methods, major findings and conclusions of the study. It gives the reader a quick overview of the whole report.

B. Main Body

9. Chapter 1: Introduction — Introduces the research problem. It generally includes:

   • Background of the study
   • Statement of the problem
   • Objectives of the study
   • Research questions / hypotheses
   • Significance (rationale) of the study
   • Limitations of the study
   • Organisation of the report

10. Chapter 2: Literature Review — Reviews the theoretical concepts and previous studies related to the topic. It shows what is already known and identifies the gap the present study fills. It may also present the conceptual/theoretical framework.

11. Chapter 3: Research Methodology — Explains how the research was conducted. It includes:

    • Research design (descriptive, exploratory, causal, etc.)
    • Population and sample (sampling technique and size)
    • Sources of data (primary and secondary)
    • Data collection instruments (questionnaire, interview, observation)
    • Data analysis tools and techniques (percentage, mean, correlation, regression, chi-square, etc.)

12. Chapter 4: Data Presentation and Analysis — The core of the report. Collected data are presented in tables, graphs and figures, and analysed using appropriate statistical tools. Each table/figure is interpreted and linked to the objectives and hypotheses of the study.

13. Chapter 5: Summary, Conclusions and Recommendations — Summarises the whole study, states the major conclusions drawn from the analysis, and gives practical recommendations. It also points out the scope for future research.

C. Supplementary Parts

14. Bibliography / References — A list of all books, journals, reports and websites cited in the report, arranged in a standard format (APA, MLA, etc.).

15. Appendices — Supporting materials such as the questionnaire, interview schedule, raw data tables, letters and detailed calculations, placed at the end.

Conclusion

Thus, a research report is a systematic and well-structured document that communicates the entire research work from problem identification to recommendations. Its structure — preliminary pages, main body and supplementary parts — makes the report logical, complete and easy for the reader to follow.

Research Design

Definition: Research design is the overall plan, structure or blueprint according to which a research study is conducted. It specifies the methods and procedures for collecting, measuring and analysing the required information. In the words of Kerlinger, "Research design is the plan, structure and strategy of investigation conceived so as to obtain answers to research questions and to control variance." Thus, it is a blueprint that guides the researcher from the formulation of the problem to the final conclusions.

Types of Research Designs

Research designs are broadly classified into the following types:

1. Exploratory Research Design This design is used when the problem is not clearly defined and the researcher has little prior knowledge. Its main purpose is to explore the situation, gain familiarity and generate hypotheses or insights. It is flexible and unstructured, and relies on methods such as literature review, expert interviews, focus group discussions and case studies. For example, a study exploring why consumers in Kathmandu prefer online shopping.

2. Descriptive Research Design This design is used to describe the characteristics of a population or phenomenon as it exists. It answers the questions who, what, when, where and how, but not why. It is based on a clear research problem and uses surveys, observation and case studies. For example, a study describing the saving habits of Nepalese households.

3. Causal (Explanatory) Research Design This design is used to establish cause-and-effect relationships between variables. It attempts to explain why things happen by testing hypotheses. It requires control over variables and is usually conducted through experiments or quasi-experiments. For example, a study examining the effect of advertising expenditure on sales volume.

4. Experimental Research Design In this design, the researcher manipulates one or more independent variables and measures their effect on the dependent variable, keeping other factors constant. It provides the strongest evidence of causation. It may be laboratory or field experimentation. For example, testing the effect of a new packaging design on consumer purchase intention.

5. Quasi-Experimental Research Design This design is similar to experimental research but lacks random assignment of subjects to groups. It is used when randomisation is not possible or ethical. For example, comparing the performance of two classes taught by different methods where classes already exist.

6. Correlational Research Design This design studies the relationship between two or more variables without manipulating them. It measures the degree and direction of association using tools such as the coefficient of correlation. For example, a study of the relationship between study hours and academic achievement.

7. Ex Post Facto Research Design In this design, the effect is studied after it has already occurred, and the researcher attempts to trace back the causes. The independent variable is not manipulated but selected as it exists. For example, studying the causes of business failure of firms that have already closed down.

8. Historical Research Design This design is used to study past events, developments and trends to understand the present and predict the future. It relies on primary and secondary historical sources. For example, a study of the evolution of the banking system in Nepal.

9. Case Study Research Design This design involves an in-depth, intensive study of a single unit — an individual, group, organisation or event — over a period of time. It provides detailed insight into the unit under study. For example, a detailed study of the marketing practices of a particular company.

10. Longitudinal and Cross-Sectional Research Design

• Longitudinal design studies the same subjects repeatedly over a long period to observe changes over time.
• Cross-sectional design studies different subjects at a single point in time to compare groups.

Conclusion

The choice of a research design depends on the nature of the research problem, objectives, available resources and time. A proper research design ensures that the study is systematic, objective and capable of producing reliable and valid results.