Business Research

Methodology
Prof: Jaya Shankar Singh
 MODULE 1
Prof: Jaya Shankar Singh
Recommended Book
● Research Methodology, New Age International,
Kothari & Garg, ISBN: 9788122436235
What is Research?
● Finding answers to the questions.
● Systematic search for truth (Wuhan Lab)
● New and original information, ideas about the world we live in, are
obtained (Can we make vaccine for RNAs)
● Search for knowledge (Stock Market).
● Scientific and systematic search for information on a specific topic.
What is Research?
● An art of scientific investigation.
● The Advanced Learner’s Dictionary of Current English lays down the
meaning of research as “a careful investigation or inquiry specially
through search for new facts in any branch of knowledge.”.
Define Research
● Research refers to a search for knowledge.
● Redman and Mory define research as a “systematized effort to gain
new knowledge.”
● Research contributes to the generation of new knowledge or the
validation and refinement of existing knowledge.
● According to Clifford Woody research comprises defining and redefining
problems, formulating hypothesis or suggested solutions; collecting,
organising and evaluating data; making deductions and reaching
conclusions; and at last carefully testing the conclusions to determine
whether they fit the formulating hypothesis.
Business Research
Definition and Scope:
● Academic Focus: The term 'business research, refers to academic research on topics relevant to the field of business and
management. This research has a strong orientation towards social sciences.
● Purpose: It aims to address questions that are significant within the business and management domain.

Areas of Business Research:

● Organizational Behaviour: Research on how individuals and groups behave within organizations, focusing on improving
organizational effectiveness and employee well-being.
● Marketing: Research on understanding consumer behavior, market dynamics, and strategies to effectively promote and sell products
or services.
● Accounting: Research on financial reporting, auditing, and management accounting practices, focusing on improving transparency
and efficiency in financial management.
● Human Resource Management (HRM): Research on effective management of an organization’s workforce, including recruitment,
training, performance management, and employee relations.
● Strategy: Research on the development and implementation of business strategies to achieve competitive advantage and
organizational goals.

Social Science Orientation:

● Conceptual and Theoretical Inspiration: Business research draws on the social sciences for its conceptual frameworks and
theoretical underpinnings. This interdisciplinary approach enriches the research by incorporating diverse perspectives and
methodologies.

By understanding these aspects of business research, you can appreciate the breadth and depth of the field and how it integrates social
science principles to address complex business challenges.
Which of these can be classified as research?
● Pushpa prepared a paper on “computer usage in secondary schools” after
reviewing literature on the subject available in his university library.
● Kattappa says that he has researched and completed a document which
gives information about the age of his students, their results, their
parents income and distance of their schools from the District Office.
● Rocky Bhai participated in a workshop on curriculum development and
prepared what he calls, a report on the curriculum for Engineers.
Research (example)
● A general manager of a car producing company was concerned with the
complaints received from the car users that the car they produce have some
problems with sound at the dashboard and the rear passenger seat after few
thousand kilometers of driving.
● She obtained information from the company workers to identify the various
factors influencing the problem.
● She then formulated the problem and generated guesses/solutions
(hypotheses).
● She constructed a checklist and obtained requisite information from a
representative sample of cars.
● She analyzed the collected data, interpreted the results in the light of her
hypotheses and reached conclusions.
Objectives
● Exploratory or Formulative: Understand a phenomenon or to achieve a
new insights into it
● Descriptive: Aimed at describing the characteristics of a population or
phenomenon being studied.
● Diagnostic: Aimed at identifying the causes or factors contributing to
specific conditions, behaviors, or phenomena
● Hypothesis Testing: is a statistical method used to make decisions or
inferences about a population based on sample data. It involves testing
an assumption (hypothesis) about a population parameter.
Motivation
● Personal
● Professional
● Societal
Motivation
● Personal
○ Curiosity and Desire for Knowledge
○ Problem-Solving
○ Career Advancement
○ Intellectual Challenge
● Professional
○ Advancing a Field of Study
○ Innovation and Development
○ Funding and Resources
○ Meeting Regulatory and Compliance Requirements
● Societal
○ Solving Societal Problems
○ Informing Policy and Practice
○ Improving Quality of Life
○ Fostering Social Justice and Equity
Business Research Methodology vs RM
Why is it Important to Study Research Methods?
1. Sensitization to Research Choices:

● Diverse Methodologies: Training in research methods sensitizes you to the wide range of
methodologies available to business and management researchers, helping you understand
the strengths and limitations of each approach.
● Informed Decision-Making: This knowledge enables you to select the most appropriate
research method for your specific research question, enhancing the accuracy and relevance of
your findings.

2. Awareness of Best Practices:

● Guidelines and Standards: Training provides you with an awareness of the ‘dos’ and ‘don’ts’
when employing a particular approach to collecting or analyzing data, ensuring ethical and
effective research practices.
● Avoiding Pitfalls: By understanding common mistakes and pitfalls, you can avoid them,
thereby improving the validity and reliability of your research outcomes.
Why is it Important to Study Research Methods?
3. Insights into the Research Process:

● Holistic Understanding: Research methods training offers insights into the overall research
process, from the formulation of a research question to data collection, analysis, and
interpretation.
● Process Orientation: This comprehensive understanding helps you grasp the
interconnectedness of different research stages and how they contribute to the final outcome.

4. Critical Awareness of Research Quality:

● Evaluating Research: Training provides you with an awareness of what constitutes good and
poor research. This critical awareness is essential for evaluating the quality of research you
encounter in academic literature and professional reports.
● Understanding Limitations: It also helps you understand the limits and limitations of various
research methods, enabling you to critically assess the findings and conclusions of studies.
Why is it Important to Study Research Methods?
5. Transferable Skills:

● Versatile Application: The skills acquired through research methods training, such as
sampling, designing questionnaires, conducting interviews, and facilitating focus groups, are
highly transferable across different contexts.
● Broader Relevance: These skills are relevant not only in academic research but also in practical
settings such as corporate firms, public-sector organizations, and non-profits, enhancing your
versatility and employability.
What research Can do !
https://www.youtube.com/watch?v=HtPCyQaMeu8
Types of Research
Basic vs Applied
1. Basic/Fundamental Research
● Purpose: To increase the understanding of fundamental principles.
● Nature: Theoretical.
● Application: Conducted without a specific practical application in mind.
● Example: Studying the molecular structure of a new compound.
2. Applied Research
● Purpose: To solve practical problems.
● Nature: Practical.
● Application: Directly applied to real-world issues.
● Example: Developing a new drug to treat a disease.
Comparison: Basic research aims to expand knowledge without immediate
application, while applied research aims to solve specific, practical problems.
Descriptive vs Analytical
3. Descriptive Research

● Purpose: To describe characteristics of a population or phenomenon.

● Nature: Observational.
● Application: Collecting data to describe occurrences or behaviors.
● Example: Conducting a survey to find out the average age of customers in a store. Most ex post
facto research projects are used for descriptive studies for example, frequency of shopping,
preferences of people, or similar data.

4. Analytical Research

● Purpose: To understand relationships and test hypotheses.

● Nature:
● .
● Application: Involves the analysis of information already available.
● Example: Analyzing the effect of advertising on sales.

Comparison: Descriptive research focuses on describing characteristics, while analytical research

seeks to understand relationships and causations.
Quantitative vs Qualitative
5. Quantitative Research

● Purpose: To quantify data and generalize results from a sample to a population.

● Nature: Statistical.
● Application: Uses numerical data.
● Example: Measuring the percentage of people who prefer a certain product.

6. Qualitative Research

● Purpose: To gain insights and understand motivations.

● Nature: Interpretive.
● Application: Uses non-numerical data.
● Example: Conducting interviews to understand customer satisfaction.

Comparison: Quantitative research uses numerical data and statistical methods, while
qualitative research focuses on understanding concepts and experiences through
non-numerical data.
Conceptual vs Empirical
7. Conceptual Research

● Purpose: To develop and refine theories or concepts.

● Nature: Theoretical.
● Application: Examines and develops ideas and frameworks.
● Example: Exploring new theories on leadership styles.

8. Empirical Research

● Purpose: To gather data through observation or experimentation.

● Nature: Observational or experimental.
● Application: Collects data to validate or refute theories.
● Example: Conducting a controlled experiment to test the impact of a new teaching
method on student performance.

Comparison: Conceptual research focuses on developing and refining theories and ideas,
while empirical research involves collecting data to test and validate those theories.
Examples
● Aristotle, Copernicus, Galileo, Newton and Einstein: these famous philosophers and
scientists relied heavily on conceptual research to develop insight and theories about
the way the world works.
● They established concepts to explain common occurrences by observing their
surroundings and compiling, studying and summarizing existing information.
● Plato asked conceptual questions such as "What is justice?" as the basis of philosophy.
The statements, "That action is wrong," or, "Knowledge is justified true belief," are
conceptual claims.
● Empirical research, on the other hand, is based not on theory, but on
experimentation. Using either a quantitative or qualitative methodology, researchers
gather data that can be measured according to a certain population, place and/or
time. They then use this information to make meaningful, fact-based conclusions.
Others
● One-time research or Longitudinal research.
● Field-setting research or Laboratory research
● Clinical or Diagnostic research
● Historical research is that which utilizes historical sources like
documents, remains, etc.
● Experimental Research vs Non-experimental Research
Criterias of Good Research
● Purpose clearly defined.
● Research process detailed.
● Research design thoroughly planned.
● High ethical standards applied.
● Limitations honestly revealed.
● Adequate analysis for decision maker’s needs.
● Findings presented clearly.
● Conclusions justified.
● Researcher’s experience reflected.
Criterias of Good Research
Purpose clearly defined:

● Good Practice: A study aiming to evaluate the effectiveness of a new

teaching method in improving student performance clearly states its
purpose as "To determine the impact of XYZ teaching method on the
math scores of 8th-grade students."
● Not Followed: A research paper discusses various teaching methods
without a clear purpose, making it unclear whether it aims to compare
methods, evaluate one method, or explore a different aspect of teaching.
Criterias of Good Research
Research process detailed:

● Good Practice: A clinical trial for a new medication provides a

step-by-step explanation of the research process, including participant
selection, dosage administration, and data collection methods.
● Not Followed: A report on a new drug simply states that "various tests
were conducted" without detailing the methodology, making it difficult to
replicate the study or understand how the conclusions were reached.
Criterias of Good Research
Research design thoroughly planned:

● Good Practice: An environmental study on pollution includes a detailed

research design outlining the sampling methods, control measures, and
statistical tests to be used.
● Not Followed: A survey on public opinion regarding climate change fails
to specify the sample size, sampling method, or how the data will be
analyzed, resulting in potentially biased and unreliable findings
Criterias of Good Research
High ethical standards applied:

● Good Practice: A psychological study ensures informed consent,

confidentiality, and the right to withdraw at any time, adhering to ethical
guidelines.
● Not Followed: A market research study collects personal data without
participants' consent and fails to protect their anonymity, violating ethical
standards.
Criterias of Good Research
Limitations honestly revealed:

● Good Practice: A study on the effectiveness of a new educational app

acknowledges limitations such as a small sample size and short study
duration.
● Not Followed: A research paper claims broad applicability of its findings
without acknowledging that the sample was limited to a specific
demographic, thus overstating the generalizability of the results.
Criterias of Good Research
Adequate analysis for decision maker’s needs:

● Good Practice: A business research report analyzing customer

satisfaction uses appropriate statistical methods to provide actionable
insights for decision makers.
● Not Followed: A report provides raw data without analysis, leaving
decision makers without clear conclusions or recommendations.
Criterias of Good Research
Findings presented clearly:

● Good Practice: A health study uses graphs, tables, and clear language to
present its findings, making them accessible to both experts and the
general public.
● Not Followed: A technical research paper uses overly complex jargon and
lacks visual aids, making it difficult for readers to understand the key
findings.
Criterias of Good Research
Conclusions justified:

● Good Practice: A marketing study justifies its conclusions with solid data
analysis, showing a clear link between advertising strategies and sales
increases.
● Not Followed: A report on employee productivity concludes that a new
policy is effective without providing sufficient data or analysis to support
the claim.
Criterias of Good Research
Researcher’s experience reflected:

● Good Practice: A seasoned economist conducts a study on market

trends, bringing in-depth knowledge and expertise to ensure robust
methodology and insightful analysis.
● Not Followed: A novice researcher conducts a complex economic
analysis without sufficient background or guidance, leading to
questionable methods and conclusions.
Case Study 1: Declining Sales of a Smartphone Brand
Problem: A leading smartphone brand is experiencing a decline in sales
despite introducing new models with advanced features.
Research Questions:
● What are the primary reasons for the decline in sales?
● What are the customer perceptions about the brand and its competitors?
● How can the company regain its market share?
Potential Research Methods:
● Surveys
● Focus groups
● Secondary data analysis (market research reports, social media analysis)
Case Study 2: Launch of a New Electric Vehicle (EV)
Case Study 2: Launch of a New Electric Vehicle (EV)
Problem: An automobile company is planning to launch a new EV model and
needs to assess market potential and consumer preferences.
Research Questions:
● What are the key factors influencing EV purchase decisions?
● What is the target market for the new EV model?
● What pricing strategy would be optimal for the new EV?
Potential Research Methods:
● Surveys
● Experimental research (test marketing)
● Secondary data analysis (industry reports, government data)
Case Study 3: Employee Turnover in a Tech Startup
Case Study 3: Employee Turnover in a Tech Startup
Problem: A tech startup is facing high employee turnover rates, affecting
productivity and morale.
Research Questions:
● What are the primary reasons for employee turnover?
● How does employee satisfaction relate to turnover?
● What strategies can be implemented to reduce turnover?
Potential Research Methods:
● Employee surveys
● Exit interviews
● Observational research
Case Study 4: Customer Satisfaction in a Retail Store
Case Study 4: Customer Satisfaction in a Retail Store
Problem: A retail store is experiencing declining customer satisfaction scores.

Research Questions:
● What factors contribute to customer satisfaction or dissatisfaction?
● How does customer satisfaction relate to customer loyalty?
● What improvements can be made to enhance customer experience?

Potential Research Methods:

● Customer surveys
● Mystery shopping
● Social media analysis
Key Considerations for Case Study Selection:
● Relevance: The case should be relatable and interesting to students.
● Complexity: It should be challenging enough to require critical thinking
and problem-solving skills.
● Data Availability: Sufficient data should be available for analysis.
● Ethical Considerations: The case should not involve sensitive or
confidential information.
Significance of Research
Driving Progress and Innovation:

● Inspiration Through Inquiry: "All progress is born of inquiry. Doubt is often better than
overconfidence, for it leads to inquiry, and inquiry leads to invention." - Hudson Maxim. This
quote highlights how questioning and investigating drive innovation and advancements.

Development of Scientific Thinking:

● Logical Habits: Research inculcates scientific and inductive thinking, promoting the
development of logical habits of thought and organization. This systematic approach is crucial
for solving complex problems.

Complex Problem-Solving:

● Operational Solutions: The increasingly complex nature of business and government has
focused attention on research as a tool for solving operational problems. It helps in formulating
strategies to address these complexities effectively.
Significance of Research
Policy Formation:

● Economic Policy: Research provides the basis for nearly all government policies in our
economic system. For instance, government budgets rely on research to analyze the needs and
desires of the people, and to estimate the availability of revenues to meet these needs.
Equating the cost of needs with probable revenues is an area where research is indispensable.

Business and Industry:

● Operational and Planning Problems: Research plays a critical role in solving various
operational and planning problems in business and industry. It helps in decision-making,
improving processes, and strategic planning.

Social Science Research:

● Studying Social Relationships: Research is equally important for social scientists studying
social relationships and seeking answers to various social problems. It helps in understanding
societal dynamics and addressing social issues.
Other Significance of Research
Academic Achievement:
● Thesis and Careers: For students writing a master’s or Ph.D. thesis, research is a means to achieve career
goals and attain high positions in the social structure.

Professional Livelihood:
● Research Methodology Experts: For professionals in research methodology, research is a source of
livelihood, providing opportunities for employment and career advancement.

Philosophical and Intellectual Growth:

● New Ideas: For philosophers and thinkers, research is an outlet for generating new ideas and insights,
contributing to intellectual growth and innovation.

Creative Development:
● Literary Contributions: For literary men and women, research fosters the development of new styles
and creative works, enriching the cultural and literary landscape.

Theoretical Advancements:
● Generalizing Theories: For analysts and intellectuals, research is crucial for developing and generalizing
new theories, advancing knowledge in various fields.
Qualities of Good Research
1. Systematic:

● Structured Approach: Good research follows a structured approach with specified

steps to be taken in a specified sequence according to a well-defined set of rules.
● Creativity within Structure: While it does not rule out creative thinking, systematic
research rejects the use of guessing and intuition in arriving at conclusions.

Example: A market research study to understand consumer preferences for a new product
follows a structured approach. The researcher first defines the research question, then
designs a survey, selects a representative sample, collects data, analyzes the data using
statistical methods, and finally interprets the results according to pre-defined steps.
Qualities of Good Research
2. Logical:

● Guided by Reasoning: Good research is guided by the rules of logical reasoning. The
processes of induction (reasoning from specific instances to general conclusions) and
deduction (reasoning from general premises to specific conclusions) are valuable in
conducting research.
● Decision-Making Value: Logical reasoning makes research more meaningful and
reliable in the context of decision-making.

Example: A study investigating the relationship between employee satisfaction and

productivity employs logical reasoning. Researchers collect data on employee satisfaction
(through surveys) and productivity (through performance metrics), and use statistical
methods to analyze whether higher satisfaction levels lead to increased productivity.
Qualities of Good Research
3. Empirical:
● Real-World Relevance: Good research is empirical, meaning it is related
to one or more aspects of a real situation and deals with concrete data.
● External Validity: This empirical nature provides a basis for the external
validity of research results, ensuring they are applicable and relevant to
real-world contexts.
Example: A healthcare study examining the effectiveness of a new drug for
treating hypertension involves collecting and analyzing patient data from
clinical trials. The researchers measure blood pressure levels before and after
administering the drug.
Qualities of Good Research
4. Replicable:
● Verification: Good research is replicable, meaning its results can be
verified by replicating the study.
● Sound Decision Basis: Replicability builds a sound basis for decisions, as
consistent results across multiple studies enhance the reliability and
credibility of the findings.
Example: A psychological experiment testing the impact of sleep deprivation
on cognitive performance is designed to be replicable. The researchers detail
the procedures for sleep deprivation, the cognitive tests used, and the
statistical methods for analyzing the data.
Problems Encountered by Researchers in India
Lack of Scientific Training:
● Example: Researchers often lack formal training in research methodologies, leading to
improper research designs and flawed data analysis. For instance, a researcher might not be
familiar with advanced statistical methods, resulting in incorrect interpretation of data.

Insufficient Interaction Between Entities:

● Example: University research departments and business establishments or government
departments do not interact sufficiently. This results in valuable primary data remaining
unused due to a lack of collaboration and proper contacts. For example, data on consumer
behavior collected by a government agency may not be utilized by university researchers
working on market trends.

Need for Data Confidentiality Assurance:

● Example: Business units may hesitate to share data with researchers due to fear of misuse. This
lack of trust hinders the availability of valuable data for research purposes. A company might
be reluctant to provide sales data fearing it could be leaked to competitors.
Problems Encountered by Researchers in India
Duplication of Research Studies:

● Example: Overlapping research studies often occur due to inadequate information sharing,
leading to resource wastage. For example, multiple studies on the same topic, like agricultural
productivity in a specific region, may be conducted independently without knowledge of each
other’s work.

Inadequate Secretarial and Computer Assistance:

● Example: Researchers face delays due to the lack of timely secretarial support and computer
facilities. A researcher might struggle with data entry and analysis because of the absence of
adequate computing resources and support staff.

Poor Library Management:

● Example: Inefficient library management results in researchers spending excessive time
locating books, journals, and reports rather than utilizing the content. For instance, a
researcher might waste hours searching for a specific journal issue due to poor cataloging and
organization.
Problems Encountered by Researchers in India
Delayed Access to Government Publications:
● Example: Libraries often fail to acquire old and new Acts, rules, reports, and other
government publications promptly. This delay hampers researchers who need the
latest regulatory information for their studies. A researcher might face difficulties
accessing the latest economic survey data needed for policy analysis.
Delayed Availability of Published Data:
● Example: Published data from government and other agencies are not made available
in a timely manner, causing setbacks in research progress. For example, census data
might be released late, delaying demographic studies that depend on it.
Problems with Conceptualization and Data Collection:
● Example: Researchers may face difficulties in conceptualizing their studies and
collecting relevant data. This can lead to incomplete or biased data collection. For
instance, a researcher might struggle to frame the right survey questions or face
challenges in reaching the target population for data collection.
Research Process
What is a research problem?
● A research problem, in general, refers to some difficulty which a
researcher experiences in the context of either a theoretical or practical
situation and wants to obtain a solution for the same.
● What is the importance of Research Problem?
● “Well begun is half done”-Aristotle
Components of a Research Problem
Presence of a Difficulty or Problem:
● Description: There must be an individual or a group facing a specific difficulty or
problem.
● Example: A company experiencing declining sales in a particular product line.
Objective(s) to be Attained:
● Description: There must be clear objectives that need to be achieved. Without
objectives, there is no problem to solve.
● Example: The company's objective might be to increase sales by 20% over the next
year.
Alternative Means for Achieving Objectives:
● Description: There must be at least two possible ways or courses of action to achieve
the objectives. If there are no alternatives, there is no problem to solve.
● Example: The company could either invest in a new marketing campaign or develop a
new product feature to attract customers.
Components of a Research Problem
Doubt About the Selection of Alternatives:
● Description: There must be uncertainty or doubt in the researcher's mind
about which alternative is the best. Research aims to resolve this
uncertainty.
● Example: The company is uncertain whether the new marketing campaign
or the new product feature will be more effective in increasing sales.
Relevant Environment:
● Description: There must be a specific environment or context to which the
difficulty or problem pertains.
● Example: The competitive market environment where the company
operates, including customer preferences, competitor actions, and
market trends.
Criteria for Selecting Research Problem
Avoid Overdone Subjects:

● Description: Subjects that have been extensively researched should be avoided as it is challenging to
provide new insights.
● Example: Researching the effects of caffeine on alertness might not yield new findings due to the
extensive existing literature.

Avoid Controversial Subjects for Average Researchers:

● Description: Controversial topics should be avoided by average researchers as they can be difficult to
handle and might lead to biased results.
● Example: Political issues or sensitive social topics might be best left to experienced researchers.

Avoid Too Narrow or Vague Problems:

● Description: Problems that are too specific or too broad should be avoided to ensure that the research is
manageable and meaningful.
● Example: Studying the effect of a specific type of leaf on a rare species of insect might be too narrow,
while exploring the impact of social media on human behavior might be too broad.
Criteria for Selecting Research Problem
Select Familiar and Feasible Subjects:

● Description: Choose a subject that is familiar and feasible, with accessible research material or sources.
● Example: A marketing professional might select a problem related to consumer behavior in digital
marketing, leveraging their expertise and available resources.

Consider Importance, Qualifications, Costs, and Time:

● Description: Evaluate the significance of the subject, the researcher’s qualifications and training, the
costs involved, and the time required.
● Example: Before selecting a problem, a researcher should consider if they have the necessary
background, if the study is financially viable, and if they have enough time to complete it.

Assess Feasibility and Preliminary Study:

● Description: Conduct a preliminary study to assess the feasibility of the research, especially if the field of
inquiry is new or lacks well-developed techniques.
● Example: Before embarking on research into a new technology, a feasibility study can help determine the
availability of resources and the potential for successful outcomes.
Questions to Consider Before Final Selection:
Is the researcher well-equipped in terms of background to carry out the
research?
● Example: A researcher with a background in psychology might not be
well-equipped to conduct a study on advanced biochemical processes.
Does the study fall within the budget the researcher can afford?
● Example: A study requiring expensive equipment or travel might be unfeasible
for a researcher with limited funding.
Can necessary cooperation be obtained from those who must participate in
the research as subjects?
● Example: Research involving interviews with high-level executives might be
challenging if they are not willing to participate.
Literature Review
● Review concepts and theories
● Review previous research finding
The process of conducting research begins with formulating a problem and
writing a brief summary.
For Ph.D. students, a synopsis must be submitted for approval. Extensive
literature research is then required, using sources like abstracting journals,
bibliographies, academic journals, conference proceedings, government
reports, and books. It's important to note that one source can lead to others.
Previous studies related to the current research should be carefully examined.
A well-equipped library is a valuable resource during this stage.
Formulate Hypothesis
A working hypothesis is a tentative assumption made to test its logical or empirical
consequences. The development of these hypotheses is crucial, as they guide the research by
focusing attention on key aspects of the problem, influencing the quality of data needed, and
determining the methods of data analysis.
To develop working hypotheses, researchers can:
● Engage in discussions with colleagues and experts about the problem, its origins, and
the objectives of finding a solution.
● Examine available data and records for trends and peculiarities.
● Review similar studies on the topic or related problems.
● Conduct exploratory personal investigations, such as field interviews, to gain practical
insights.
Working hypotheses are most effective when stated in precise and clearly defined terms.
While not all research requires hypotheses—especially in exploratory studies—formulating
them is generally a critical step in the research process.
Research Design
A research design is a blueprint for conducting a marketing research project. It
details the procedures necessary for obtaining the required information.
Formulating the research design involves the following steps:

● Definition of the information needed,

● Secondary Data analysis,
● Qualitative research,
● Methods of collecting quantitative data (survey etc.),
● Measurement and scaling procedures,
● Questionnaire design,
● Sampling process and sample size,
● Plan of data analysis.
Research Design
Consideration when designing:

● The means of obtaining the information,

● The availability and skills of the researcher and his staff (if any);
● Explanation of the way in which selected means of obtaining information
will be organised and the reasoning leading to the selection;
● The time available for research; and
● The cost factor relating to research
Sample Design
● In any field of inquiry, the total set of items under consideration is called a
'universe' or 'population.' A complete enumeration of all items in this
population is known as a census inquiry, which theoretically leaves no
element of chance and achieves the highest accuracy.
● However, in practice, even a small bias can be magnified with larger
observations, and detecting such bias often requires a resurvey or
sample checks.
● Additionally, census inquiries are time-consuming, costly, and sometimes
impractical, such as in blood testing, which is typically done on a sample
basis.
Sample Design
● Therefore, researchers often select a subset of items from the population,
known as a sample.
● The method for selecting this sample is referred to as the sample design,
a plan determined before data collection.
● Sample designs can be based on either probability or non-probability
sampling methods.
● In probability sampling, each element has a known chance of being
included, using methods like simple random sampling, systematic
sampling, stratified sampling, or cluster sampling. Non-probability
sampling, on the other hand, does not allow for the calculation of
inclusion probability and includes techniques like convenience sampling,
judgment sampling, and quota sampling.
Sample Design
● Deliberate (Purposive) Sampling: A non-probability sampling method where
specific units are selected intentionally to represent the entire population. This
method includes:
○ Convenience Sampling: Selection based on ease of access, which can lead to biased results if
the population is not homogeneous.
○ Judgment Sampling: The researcher selects items that they believe are representative of the
population, often used in qualitative research.
● Simple Random Sampling: A probability sampling method where every item in
the population has an equal chance of being selected. This can be done through
methods like lotteries or random number tables.
● Systematic Sampling: Involves selecting every nth item from a list after a
random starting point. This method is practical when a sampling frame is
available.
● Stratified Sampling: Used when the population is not homogeneous. The
population is divided into strata, and samples are taken from each stratum,
often using simple random sampling within each group.
Sample Design
● Quota Sampling: A non-probability method similar to stratified sampling,
but the selection within strata is left to the interviewer's judgment rather
than random selection.
● Cluster Sampling and Area Sampling: Involves grouping the population
into clusters and then randomly selecting clusters rather than individual
elements. Area sampling is similar but applies to geographical regions.
● Multi-Stage Sampling: An extension of cluster sampling where the
selection process occurs in multiple stages, such as selecting states, then
districts, and so on.
● Sequential Sampling: A complex sampling design where the sample size
is not fixed in advance but determined as the survey progresses, typically
used in statistical quality control.
Data Collection
When addressing real-life problems, researchers often find that existing data
is inadequate, requiring the collection of new, appropriate data. There are
various methods to collect data, each varying in cost, time, and resources.

Primary data can be gathered through experiments or surveys:

● Experiment: Experiments are designed to test specific hypotheses by

manipulating variables in a controlled environment. The goal is to observe
the effects of these manipulations and determine causal relationships
between variables.
● Surveys: Are designed to gather information about opinions, behaviors,
experiences, or characteristics of a population. They do not manipulate
variables but rather collect data as it naturally occurs.
Data Collection
Experiment: The researcher conducts quantitative measurements to test the
validity of a hypothesis.

● Data is typically quantitative, collected through observations,

measurements, or instruments during the experiment.
● Example: A researcher might conduct an experiment to test the effect of
a new drug on blood pressure by administering the drug to one group
and a placebo to another, then measuring the changes in blood pressure.
Data Collection
Survey: Data can be collected through several methods:
● Observation: Collecting data by observing current events without
interviewing respondents. This method is costly and limited in scope,
making it unsuitable for large samples.
● Personal Interviews: The researcher follows a structured procedure to ask
pre-determined questions. The quality of data largely depends on the
interviewer's skills.
● Telephone Interviews: Involves contacting respondents via phone, useful
in industrial surveys where time is limited.
● Mailing Questionnaires: Questionnaires are sent to respondents to fill out
and return. This is a common method in economic and business surveys,
often preceded by a pilot study to refine the questionnaire.
● Schedules: Trained enumerators collect data by filling out schedules based
on respondents' answers. The quality of data depends on the enumerators'
abilities, and occasional field checks are recommended to ensure accuracy.
Execution of Project
● Execution of the project is a critical step in the research process, as it directly
impacts the adequacy and reliability of the data collected. To ensure success,
the project must be executed systematically and on time.
● If using structured questionnaires, data can be efficiently machine-processed by
coding questions and answers.
● For data collection through interviews, proper selection and training of
interviewers are essential, often supported by instruction manuals.
● Regular field checks should be conducted to ensure interviewers perform their
tasks sincerely and effectively.
● It's also important to monitor for unexpected factors to maintain the survey's
realism and ensure it remains statistically controlled, adhering to predefined
accuracy standards.
● If respondents are uncooperative, strategies should be developed to address
this, such as creating a list of non-respondents and making additional efforts,
possibly with expert assistance, to secure their participation.
Data Analysis
After data collection, the researcher moves on to data analysis, which involves
several key operations.
● First, the raw data is categorized, coded, and tabulated to make it manageable
for analysis. Coding transforms data into symbols that can be easily counted
and tabulated, often with the help of computers, especially in large studies. This
process saves time and allows for the simultaneous analysis of multiple
variables.
● Following tabulation, analysis typically involves calculating percentages,
coefficients, and other statistical measures. The researcher then tests
relationships or differences between variables to determine if they support or
contradict the original hypotheses. Statistical tests, like tests of significance,
help establish whether observed differences are real or due to random chance.
○ For example, comparing mean values from different samples can reveal whether they come from
different populations or not.
Data Analysis :Hypothesis Testing
● Apply statistical tests to determine if the data supports the hypotheses.
This involves using tests such as Chi-square, t-tests, or F-tests to evaluate
the validity of the hypotheses based on the data.
● The hypotheses may be tested through the use of one or more of such
tests, depending upon the nature and object of research inquiry
Generalization and Interpretation
● If a hypothesis is consistently upheld through multiple tests, it may lead to
generalizations and the development of a theory.
● The ultimate value of research often lies in its capacity to establish such
generalizations.
● If no initial hypothesis was formulated, the researcher might interpret the
findings based on existing theories. This interpretation can lead to new
questions and potentially spark further research.
Reporting : Structure
Preliminary Pages

● Title and date

● Acknowledgments
● Foreword
● Table of contents
● List of tables
● List of figures/charts
Reporting : Structure
Main Text

● Introduction
○ Clear statement of the research objective
○ Explanation of methodology
○ Scope of the study
○ Limitations
● Summary of Findings and Recommendations
○ Concise, non-technical summary
● Main Report
○ Logical sequence
○ Identifiable sections
● Conclusion
○ Clear and precise summary of results
Reporting : Structure
End Matter

● Appendices (technical data)

● Bibliography (list of references)
● Index (for published reports)
Reporting : Structure
Writing Style and Guidelines
● Concise and Objective: Use simple language, avoid vague expressions.
● Charts and Illustrations: Use tables, charts, and graphs to present data
clearly and support your findings. Use only if they enhance clarity.
● Legends and Captions: Provide explanations for all visuals to ensure they
are easily understood.
● Formatting: Follow the specific formatting guidelines provided by your
institution or publisher, including font size, margins, and citation style.
● Confidence Limits: Mention calculated confidence limits.
● Constraints: Acknowledge any limitations or challenges faced during the
research.
● Revisions: Review and revise the document multiple times to correct
errors, improve clarity, and ensure consistency.
Management Decision Problem vs Marketing Research Problem

In business research, distinguishing between management decision problems and

marketing research problems is crucial. Here's a detailed explanation:
Management Decision Problems:
● These are broad, strategic questions that require decisions from managers.
● They focus on what the decision should be.
Examples:
● Should a new product be introduced?
● Should the advertising campaign be changed?
● Should the price of the brand be increased?
Management Decision Problem vs Marketing Research Problem
Marketing Research Problems:
● These are specific questions aimed at gathering the necessary data to solve the
management decision problems.
● They focus on understanding the "why" and "how" behind the decisions.

Examples:
● To determine consumer preferences and purchase intentions for the proposed new
product.
○ Research Example: Conducting surveys or focus groups to understand what features consumers want in a
new product, their willingness to pay, and their likelihood of purchasing.
● To determine the effectiveness of the current advertising campaign.
○ Research Example: Analyzing data on customer engagement, brand awareness, and sales figures before
and after the campaign to measure its impact.
● To determine the price elasticity of demand and the impact on sales and profits of
various levels of price changes.
○ Research Example: Using statistical models to simulate how different price points affect consumer
demand and profitability, and running A/B tests to observe actual consumer responses to price changes.
Thank You