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Lecture (week 1-6) summary BRM I €5,29
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Lecture (week 1-6) summary BRM I

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Lecture notes for BRM I at the VU Amsterdam

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  • 9 oktober 2022
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Lecture 1 Introduction
Business Research
 is a series of well-thought-out and carefully executed activites that enable the manager to
know how organizational problems can be solved, or at least considerably minimized.

What should business researchers do for a good business research?
- They specify the information necessary to address these issues
- They design the method for collecting information
- They manage and implement the data collection process
- They analyze the results
- They communicate the findings and their implications

Why should future managers know about research?
 To be able to perform business research
e.g. undertake research yourself to solve the smaller problems you encounter
 To be able to steer business research
e.g. interact effectively with researchers/research agencies
 To be able to evaluate business research
e.g. to discriminate between good and bad research proposals of researchers/research
agencies

Myths - Researches are NOT:
“There is no need to study business research for a future manager”
“Business research ends up in the bottom drawer”
“Business research is only for the wealthiest organizations”
“Business research is only useful when you have a major decision to make”
“There is just one best way of researching a business problem”

Business research
“A series of well-thought-out and carefully executed activities that enable the manager to
know how organizational problems can be solved, or at least considerable minimized”.
- Business researcher specifies the info necessary to address the issue
- Designs the method for collecting info
- Manages and implements the data collection process
- Analyses the results
- Communicates the findings and their implications

HALLMARKS of scientific research
Purposiveness: knowing “the why” of your research (e.g. dissatisfied employees, weak stock
market, traffic to our website is low)
Rigor: Ensuring a sound theoretical base and methodological design
Testability: being able to test logically developed ideas based on data
Replicability: finding the same results if the research is repeated in similar circumstances
Precision & confidence: drawing accurate conclusion with a high degree of confidence
Objectivity: drawing conclusions based on facts (rather than on subjective ideas)
Generalizability: being able to apply research findings in a wide variety of different settings
Parsimony: shaving away unnecessary details, explaining a lot with a little

Research process

,Stage 1: Problem definition; identify problem area, define problem statement
Problem definition through the eyes of manager or researcher
- Manager is more focused on decision problem: A and B have merged. Several
former employees of B have taken an unmotivated attitude and have become less
productive
- Researcher more focused on research problem: To what extent does executive
communication impact the productivity of the former employees of B through
increasing their morale, and does this depend on employee skills?
Stage 2: Research approach development; theoretical framework, hypotheses, and model
 theoretical framework consists of:
- Description of all relevant variables and their definitions (motivate why these are important)
- Hypotheses – expected relationships between variables (based on existing theory, testable, and
unambiguous – provide a logical justification based on theory)
- Conceptual model (e.g. a graphical representation – covers all variables and relationships)
Stage 3: Research design development; determine nature of research, measures, sampling..
It details the procedures necessary for obtaining the information needed to structure or solve
research problems (two types: exploratory and conclusive design) see below
Stage 4: Field work or data collection; data collection
Stage 5: Data integrity and analysis; data preparation and data analysis
Stage 6: Communicate research findings; data interpretation


EXPLORATORY DESIGN – Qualitative and Quantitvative exploration
 a design flexible and evolving approach to understand phenomena that are inherently
difficult to measure. This design is often required when prior theory is absent and an in-depth
understanding is required. Aim: develop new theory. So results bring us new theory.
Qualitative exploration Quantitative exploration
Smaller sample (10 respondents) Larger sample (1000 respondents)
One-to-one interviews (when interaction Group discussion (when interaction helps,
hurts, i.e. by sensitive topics) i.e. when people can build on each others
answers)
In-depth interviews (when detailed answers Broader interviews (when respondents can
are needed, like for complex topics and say what is relevant in less than 10 minutes)
expert respondents)

CONCLUSIVE DESIGN – Descriptive and causal research
 a conclusive design is characterized by clearly defined phenomena that can be measured
by means of quantitative data. So well-known theory brings us new results!
Descriptive research: testing the correlation relationship between two or more variables
Causal research: testing the causal relationship between two or more variables

Exploratory vs Conclusive research

,Research design
“A framework or plan for conducting a … research project. It details the procedures
necessary for obtaining the information needed to structure or solve … research problems.”
 Define the information needed
 Decide on nature of research
 Decide on techniques and measurement
 Construct and pre-test the research
 Decide on sampling process and sample size
 Develop a data analysis plan

Lecture 2 Measurement, scales and survey design
Sources of error (do not discuss non-response errors)

, Response errors: error that arises from participants who do respond but who give inaccurate
answers or whose answers are mis-recorded or mis-analyzed.

Response errors
Researcher errors Interviewer errors Respondent errors
Surrogate information Respondent selection Inability
Measurement Questioning Unwillingness
Population definition Recording
Sampling frame Cheating
Data analysis

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