This summary covers:
- Chapters: 1,2,3,5,6,7,8,9,10,11,12,13 of the book: Research Methods
For Business - A Skill Building Approach By Sekeran and Bougie (7th
press)
- The relevant papers about Archival Proxies, Conducting experiments
and Google flu.
- The notes from l...
Business Research Techniques for Pre-master_320087-B-6_2021
Business Research Techniques for Pre-master_320087-B-6
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Business Research Techniques
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Business Research Techniques (BRT)
Chapter 1: Introduction to research
Research: The process of finding solutions to a problem after a thorough study and analysis of the situational
factors.
There are 2 reasons to do research:
- Applied Research: To solve a current problem faced by the manager in the work setting, demanding a
timely solution.
- Basic/Fundamental/pure research: Generate a body of knowledge by trying to comprehend how
certain problems that occur in organisations can be solved.
(read examples page 6 – 8)
Having the knowledge of research gives a manager a distinct advantage. Being knowledgeable about research
helps the manager with:
- Identifying and effectively solve minor problems, knowing how to discriminate between good or bad
research, appreciate and be constantly aware of the multiple influences and effects, take calculated
risks in decision making, prevent possible vested interests, related to the researchers/consultants and
combine experience with scientific knowledge while making decisions.
Managers often need to hire a consultant to study the more complex and time consuming problems within a
firm. In order to make this work, manager should do the following:
- Make the roles and expectations of both parties made explicit
- Communicate relevant philosophies and/or value systems of the organisation (with constraints)
- Stimulate employees to cooperate with the researchers to bring a good report.
Some firms have internal consultants who are on the firms payroll to research problems within the business.
Others have external consultants
(Read (dis)advantages page 10 – 12)
Making the right decisions is greatly facilitated by research knowledge. It heightens the sensitivity to detect
internal and external factors, helps to facilitate effective interactions with the consultants, and helps to
comprehend the nuances in research.
Ethics: refers to a code of conduct or expected societal norms of behaviour while conducting research. Ethics
are relevant within the entire organisation. Ethical research should be done in good faith, while paying attention
to the what the results indicate, surrendering ego and to pursue the companies interests rather than your own
and confidential information should be safeguarded. Because of this the entire research process pervades in
every step.
Chapter 2: The scientific approach and alternative approaches to investigation.
In chapter 1 research was defined as:
‘an organised, systematic, databased, critical, objective inquiry into a specific problem that needs a solution’. It
is important that research is carried out in a scientific way. Scientific means:
Solving problems by pursuing a step-by-step, logical, organised and rigorous method to identify the problem(s),
gather data, analyse the data and to draw valid conclusions.
The scientific approach is therefore not based on: Hunches, experience and intuition. Scientific research is
always objective and applies to both applied- and basic research.
The important hallmarks, when it comes to scientific research are:
- Purposiveness: knowing the ‘why’ of your research. The purpose of the research (i.e. the reason for
doing research)
- Rigour: Sound theoretical base and sound methodological design
- (Testability: (applies to hypotheses): The hypothesis has to be testable)
- Replicability: The possibility to redo the research with the same/or other data that will lead to the
same conclusions.
- Precision: the closeness of the findings to ‘reality’ based on a sample
Confidence: the probability that the estimation is correct.
- Objectivity: Draw conclusions based on facts rather than on values or opinions.
- Generalisability: if possible the findings should be applicable to more than 1 setting
The hypothetico-deductive method is a step by step guide how to do scientific research:
1) Identify a broad problem area
2) Define the problem statement: encompasses a general objective and research questions
3) Develop hypothesises: to ascertain the contribution of variables. Hypothesises should be falsifiable and
testable.
4) Determine measures: Determine the measurements of variables (operationalisation).
, 5) Data collection: Data is gathered that is relevant for the research
6) Data analysis: Data is statistically analysed to see if the hypotheses are supported.
7) Interpretation of data: by interpreting the meaning of the results we determine if the hypotheses are
supported.
Deductive reasoning: to test a theory by doing observations
Inductive reasoning: to observe and then formulate a theory
Chapter 3: Defining and refining the problem
A ‘problem’ can refer to two situations:
- The current situation has something that is seriously wrong
- The current situation is not seriously wrong, but can be improved.
A broad management problem needs to be narrowed down to a feasible topic for research preliminary
information gathering (or research) will help do that. The figure below shows this.
Step 1: ID’ing the broad Step 2: Preliminary Step 3: Definition of the
management problem Research research problem
Preliminary research helps the research to answer questions as: What is the problem? Why does the problem
exist? Is the problem important? And What are the benefits of solving the problem? These questions can be
classified as information on the organisation and/or environment (contextual factors) and information on the
topic of interest. The contextual factors give some background like: Origin/history of the company, Size, Charter,
Location, Resources, Relationships, Financial Position, Structural factors and management philosophy (read
through page 37). Data can be obtained from various sources.
- Primary Data: Data that was found first hand and in line with the current study
- Secondary Data: Data that has already been collected by others for another purpose other than the
current study
Important criteria for secondary data are: Timeliness, Accuracy, Relevance and costs.
To get to know more about the topic, literature about the problem can help.
After narrowing down the original broad management problem with preliminary research, the problem
statement must be defined. Important criteria are: unambiguous, specific, focused and addressed from
academic perspective.
The problem statement includes both research objectives and research questions (read examples pag. 40).
Once the purpose of the research has been formulated (objectives) the questions can be determined. The
problem statement addresses the ‘why’ and ‘what’ of the research. Three criteria determine the quality of the
problem statement:
- Problem statement is relevant and has managerial value or academic value (read pag. 42)
- The problem statement is feasible
- The problem statement is interesting to you
There are 3 basic types of questions that can be used within research:
- Exploratory Research Questions: are questions that heavily rely on qualitive research where: not much
is known, existing research is unclear or is limited, the topic is highly complex or not enough theory is
available.
- Descriptive research questions: describe a topic of interest and typically describe characteristics of
objects (persons, organisations, products or brands, events or situations. The data used can be both
qualitive and quantitative.
- Causal research questions: test whether or not one variable causes another variable to change.
(Read: page 45 – 48)
Chapter 5: The theoretical framework and hypothesis development
Theoretical framework: represents how certain phenomena (or variables/concepts) are related to each other
and an explanation why you believe these variables are associated with each other.
The process of building a theoretical framework includes:
1. Introducing definitions of concepts or variables in you model
2. Developing a conceptual model that provides a descriptive representation of your theory
3. Coming up with a theory that provides an explanation for relationships between the variables of your model
The testable hypotheses can be tested by statistical analysis, therefore a sound theoretic framework is really
important
Variable: anything that can differ or vary in value. There are 4 types of variables:
, - The dependent variable (criterion variable): is the variable that is influenced by the independent
variable.
- The independent variable (predictor variable): influences the dependent variable.
To establish whether a independent variable influences a dependent variable the next 4
conditions should be met: Both variables covary (change of one should be associated with
the change of the other), Independent variables should precede dependent variables, no
other factor should be responsible for the change, a logical explanation is needed to explain
the change.
- The moderating variable: has an effect on the relationship between the independent and dependent
variable.
- The mediating variable: Is one that surfaces in the relation between the dependent and independent
variable.
Variables can be either discrete (separable like female/male and 1,2,3,4) or continuous (like the age)
(read 81 – 83)
After having a sound framework, a hypothesis (or hypotheses) must be formulated. Data and statistical analysis
will eventually either confirm or falsify the hypothesis.
Hypothesis: A tentative, yet testable, statement that predicts what you expect to find in the (empirical) data.
Hypotheses are usually defined in a certain format. This is called a “If-then statement”.
Examples of hypotheses are:
Directional: show a relationship between two variables (the direction of variable A influences the
direction of variable B)
Nondirectional: Show that the direction of a variable doesn’t say anything about the direction of other
variables
Null: a hypothesis set up to be rejected in order to support an alternate hypothesis
Alternate: statement expressing a relationship between two variables or indicating differences
between groups.
(read page 85 – 88)
Chapter 6: Elements of research design
After doing the steps from chapters 1-5 you have to design a research in a way that the data can be gathered
and analysed to answer the research questions.
Research design: a blueprint/plan for the collection, measurement, and analysis of data to answer the research
questions.
Elements of Research Design are:
- Research Strategy: a plan to meet your research objectives and to answer the research questions.
o Experiments: are used to study a causal relationship where the researcher manipulates the
independent variable. This can be done either in a lab-setting or by doing field experiments.
o A survey: is a system for collecting information from or about people to describe, compare or
explain their knowledge, attitudes or behaviour. It allows to gather qualitative and
quantitative research
- Extent of interference with the study: the interference of the researcher explains whether the study is
correlational (minimal interference) or causal (maximal interference).
- Study setting: The study of exploratory or descriptive nature is often done in a natural setting (non-
contrived) and are called field studies, causal studies however are likely to be performed in a contrived
or artificial environment which often is a lab experiment. A mix of these two is a field experiment,
where variables are manipulated in a non-contrived (or field) setting.
- Unit of analysis: Individuals, dyads (couples), groups, organisations, cultures:
The unit of analysis refers to the level of gathering of data. Does the data reflect an individual
or a group?
- Time horizon:
o Cross-sectional studies: studies where data is gathered once (over a period of days, weeks or
months)
o Longitudinal studies: Studies where data is gathered at multiple moments in time.
Experiments are often regarded as longitudinal studies (because they measure before and after the treatment).
Mixed methods: aims to answer research questions that cannot be answered by quantitative or qualitative
approaches alone. Data of both qualitative and quantitative nature is used within a single study. This approach
is heavily advocated for in business research because it helps to research different aspects and combines both
inductive and deductive reasoning.
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