This summary contains all information of the lectures of Business Research Techniques. Applicable for all premaster studies at TiSEM 2019 (Tilburg University). As preparation for the exam in December 2019.
Business Research Techniques for premasters TiSEM 2019
Summary for exam 6th of December 2019
Lecture 1 – Setting the scene
1. What is business research?
A series of well-thought out activities and carefully executed data analyses that help a manager to
avoid, solve or minimize a problem.
Example 1: the death of Coke C2
Why should future managers know about business research?
Managerial decisions based on:
- the results of ‘’good’’ research tend to be more effective.
- hunches, intuition, and past experiences are likely to be wrong.
1. To be able to perform business research.
2. To be able to steer business research.
3. To be able to evaluate business research. Example 2: the wet mop flop
2. Myths about business research
a. The ‘’managers are from Mars’’, ‘’researchers are from Venus’’ myth:
- Myth: there is no need to study business research for a future manager.
- Reality: managers with knowledge about research have an advantage over
those without.
Example 3: IKEA goes USA
b. The ‘’most research is not read’’ myth:
- Myth: business research ends up in the bottom drawer.
- Reality: for knowledgeable manager, research need not to be intimidating.
c. The ‘’big bucks’’ myth:
- Myth: business research is only for the wealthiest.
- Reality: business research is very diverse – it can be cheap, it can be expensive.
d. The ‘’universal truth’’ myth:
- Myth: there is just one best way of researching a business problem.
- Reality: there is no such thing as an absolute truth in business (however, it does not imply that any approach is good).
3. Hallmarks of good business research
1. Purposiveness: knowing ‘’the why’’ of your research.
2. Rigor: ensuring a sound theoretical base and methodological design (representative sample, unbiased Qs).
3. Objectivity: drawing conclusions based on facts (rather than on subjective ideas).
4. Parsimony: shaving away unnecessary details, explaining a lot with a little.
5. Replicability: finding the same results if the research is repeated in similar circumstances (research design in detail).
6. Generalizability: being able to apply the research findings in a wide variety of different settings
Fundamental vs. applied research
4. The seven-step research process
The seven-step deductive research process
,Quantitative in nature. Theoretically driven, first you use theory to develop hypotheses, afterwards
you test these hypotheses by using data.
The seven-step inductive research process
Qualitative in nature. Exploratory driven, it uses data as a starting point, seeks explanation for these
data and ends up with developing a theory.
Examples: deductive or inductive reasoning?
- If it rains, everything outside becomes wet. - The first duck in the park is brown.
- It rains. - The second duck in the park is brown.
- The car is outside. - The third duck in the park is brown.
------------------------------------------------------------ ----------------------------------------------------
The car will become wet = deductive Every duck in the park is brown = inductive
Example: deductive research process
, Lecture 2 – The research process
1. The seven-step deductive research process
Quantitative in nature. Theoretically driven, first you use theory to develop
hypotheses, afterwards you test these hypotheses by using data.
2. What makes a good business problem?
When does a business problem occur?
A business problem occurs when there is a gap between the actual status of a company and the
desired status of a company. There are two types of business problems:
1. The actual situation is seriously wrong and needs to be solved asap
2. The actual situation is not seriously wrong, but can be improved
What makes a good business problem?
- Feasibility – is it doable / is it possible?
- Relevance – is it worthwhile / is it important?
Checklist – Feasibility
Is the problem demarcated? (Is the problem not too broad?)
Can the problem be expressed in variables?
Are you able to gather the required data?
O Existing data (data that the company owns e.g. sales/profit data, or purchased data)
O New data (e.g. through surveys or interviews)
Examples of an infeasible business problems
(1) Philips would like to know whether running price promotions on its products can reduce the
profitability of its competitors’ new products.
→ Problem is demarcated.
→ Variables are correctly expressed.
→ However, you will not be able to gather the required data, since there is no profit information
available on products of competitors.
Examples of feasible business problems
(1) Philips would like to know whether running price promotions on its products can reduce the sales
of its competitors’ new products.
→ Sales data can be purchased from market research agencies (existing data).
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