College aantekeningen
International Business Research Lecture Notes Summary
- Instelling
- Tilburg University (UVT)
Full summary of the lecture notes covering the entire semester
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Online modules IBR: From LAIK SARAH, for more DM Whatsapp +33 609840925MODULE 1: The research process
Business research:
- Is a systematic process → consists of several distinct but highly interrelated stages + systematic bc these stages
are universally agreed upon (=universellement recenonnues)
- Tests hypotheses
- Entails collecting & analyzing data → BR is empirical, data collected through surveys, experiments, companies’
internal databases, interviews, government databases
- Is meant to help managers make better decisions → better decisions are evidence-based decisions
Managerial intuition:
→ Managers are better off when they base their decisions on research rather than intuition
Example of Coca-Cola C2: why it failed
◊ Coca-Cola C2: mid-calorie soda but with a full flavor of a regular coke with no carbs
◊ Failed bc this product was solely based on the decision maker’s intuition, but NOT on research → ppl were not looking
for this type of product
→ Intuition should never be a substitute for research
Why can managers’ intuition be so wrong?
Manager (like all humans) are prone to cognitive biases = unconscious thinking errors → an attempt of our brain to
simplify the complex work and speed up decision-making → lead managers to misinterpret information → negatively
affect the rationality & accuracy of their decisions
3 types of cognitive biases:
- Confirmation bias = tendency only to consider info that agrees with (“confirms”) our preexisting beliefs → only
look for evidence that supports what we are already thinking and disregard the rest
◊ business example: investors investing in a specific sector may only absorb good news and ignore bad news
regarding that sector
◊ everyday example: pessimists ppl who focus on the negatives while ignoring the positives
- Availability bias = we decide based on readily available information, even though it may not be the best info to
inform our decision → leads to overestimating some events
◊ business example: availability bias amplifies stock market volatility bc investors tend to overreact to the latest
news & buzz
◊ everyday example: overestimating the % of shark attacks that occur each year due to recent news report abt a
particular shark attack / overestimating the % of delayed flights each year based on recent news reports abt delays
on Schiphol airport
- What You See Is All There Is (WYSIATI) bias = when evaluating whether there is a relationship btw an event & an
outcome → we tend to notice what is ‘present’, but we often forget to consider what is ‘absent’ → in that case,
managers make decisions without examining all the data
◊ business example: managers tend to notice the times when both a decision & the desired outcome are present
but are less likely to notice the times when they didn’t make that decision, but the outcome was still present (they
didn’t run an advertising campaign for a brand and brand sales increased anyway)
◊ everyday example: failing to consider all the times you went to the airport and didn’t see any delayed flights
How to evaluate research evidence?
1° Judging academic-journal quality: while some journals are legitimate, others are predatory → their sole purpose is to
make money. To avoid this, you can check:
- If the articles in the journal are peer-reviewed (if they are not, they are likely to be predatory)
- Look up the impact factor (journal w an impact factor of at least 1.à is less likely to be predatory)
- In field of business, can consult the list of quality journals complied by TISEM
,2° Judging popular-press articles: science journalists translate the finding of academic articles for a wider audience →
most science journalists do a good job, but some are unreliable bc it’s based on flawed academic research (it doesn’t
describe the underlying academic article accurately: it doesn’t get the story night)
SUMMARY: we should be able to:
- Evaluate business research: judge to what extent academic & popular press articles can be trusted as a basis for
your decisions
- Delegate business research: interact effectively with your research department that will conduct the research
studies for you
- Perform business research: perform research studies yourself to solve smaller problems that you’ll encounter in
your future jobs
Deductive VS Inductive research:
• Inductive research: researchers first collect data → next, they try to
find a pattern in these data, after which they develop a theoretical
framework based on this pattern (building theory)
• Deductive research: researchers first hypothesize relationships btw
variable based on theory → these hypotheses are then tested using
data (testing theory)
→ Sometimes used in combination, within a single research study
The 7-step deductive research process:
MODULE 2: Research questions
STEP 1: A demarcated (aka narrowed) business problem:
→ Sometimes, a developer doesn’t know precisely what he would like the market research agency to investigate: many
research studies encounter this problem at the start → it’s unclear what precisely should be investigated → therefore,
the 1st step of the research process is demarcate the business problem
A business problem occurs when a company encounters an opportunity (a situation that might be improved) or a threat
(a difficulty to be eliminated)
→ business problem MUST be demarcated (or narrowed) before it can be researched
Example: the business problem = Pfizer would like to increase its profits
If not well demarcated, there are hundreds of paths that Pfizer could consider to increase its profit (new product
introduction, acquisition, change in selling strategy)
An example of a demarcated business problem is: “Pfizer would like to know whether soft-selling new drugs to doctors
leads to more prescriptions than hard-selling”
Problem relevance:
→ why only business problems that are academically and managerially relevant should be researched?
2 types of relevance:
• Academic relevance: researching the problem contributes to the current state of knowledge
,4 major ways in which a study can contribute to existing knowledge:
→ New topic: no prior research exists, although the topic is important (how consumers evaluate the usage of drones to
deliver their grocery parcels) BUT if topic is unimportant, the, it’s not academically relevant
→ New context: prior research exists but in a different context (existing research abt what makes private labels successful
in PHYSICAL grocery stores → another context: in ONLINE stores)
→ Integrate scattered findings: prior studies focus on different variables in isolation, hence the relative impact of these
variables is unclear, so research into a particular topic may be scattered across many articles if different studies focus on
different drivers of success. We may have learned that each driver is important, but we don’t know the relative importance
of these drivers (all 3 types of advertising TV, Print & Mobile contribute positively to a brand sale, BUT we don’t know
which type is most effective → a new study is academically relevant bc it increases our knowledge abt the relative
important of the different types of advertising)
→ Reconcile conflicting findings: prior studies report different findings (small VS large effect, positive VS negative), and
the conditions under which these findings hold are unclear → study 1: mobile advertising = larger positive effect on sales,
study 2: small positive effect on sales, study 3: negative effect on sales SO study academically relevant = how does the
effect of mobile advertising on sales depend on a third factor (e.g. personalization of the ad)?
• Managerial relevance: one or more parties benefit from having the problem researched
The parties that may benefit:
→ Managers: the results of most research studies in the business domain are aimed at helping managers improve their
decision making (brand managers, accountants, supply chain managers, financial directors)
Some studies are only relevant to managers of 1 particular company (Albert Heinj managers and not Jumbo managers)
Some studies are relevant for all managers operating in 1 particular industry (all supply chain managers operating in the
grocery industry and not just for AH managers)
Some studies are relevant to managers in multiple industries (all customer support managers operating in a service
oriented, not just the banking industry but also the travel, entertainment, healthcare industry etc.)
→ End users: managerial relevant when it offers useful advice to end users (e.g. consumers, investors, taxpayers)
→ Public policy makers: governments, EU (study on the impact of soft drink advertising on soft drink consumption could
be relevant for the Dutch government in its attempt to increase its citizens health / study on the impact of mergers &
acquisitions on competition could be relevant to the EU’s competition Commission)
→ Or a combination of those
SUMMARY:
• A business problem has to be academically relevant if you are to research it: the same problem should not have
been researched before.
• A business problem has to be managerially relevant if you are to research it: researching the problem needs to
benefit managers, consumers, and/or public policymakers.
Research questions
STEP 2: from business problem to formulate research questions:
A good problem statement is:
1. An open-ended question: cannot be answered simply by yes/no → use of “what”, “how” & “to what extent”
What is the relationship btw … and …?
How are … and … associated?
To what extent does … relate to …?
2. That identifies the study’s unit of analysis: the unit of analysis = the subjects, the entity that the study wishes to
say something abt, the focus of the study (individuals = consumers, investors, CEOs; firms = multinationals, groups
= board of directors, alliances, industries; things = products, brands, shares; geographical units = cities, countries)
A study’s unit of analysis can be at a lower or a higher level of aggregation:
Study based on comparing students’ exam grades: unit if analysis is the individual student
, Study based on comparing the noise level btw 20 different lecture halls full of students: unit of analysis is the
lecture hall (the collective group of students in each hall)
Study compares the average exam grade btw several universities: unit of analysis is the university
→ university level is a higher aggregation level than the lecture-hall level, which is a higher aggregation level than
the student level ??????????????????????
3. That is expressed in terms of variables & relationships:
• In terms of variable:
A variable must have at least 2 values/levels in a study (variable ‘price’ = might go from 0,01 cents to whatever the
price; variable ‘loneliness’ = might range from 1 to 7; variable ‘job turnover’ = 2 levels: quit VS stay)
→ Variables can vary in 3 ways:
- across subjects (e.g. persons, products, firms, industries, countries) at the same point in time → test whether
advertising is more effective for firms in a service VS a product-based industry → the variable “service-based VS
product-based industry” varies across subjects (firms in that case): whether a firm operates in a service, or a
product-based industry doesn’t change over time
- over time, within the same subject (person, firm, industry, product) → study the economic factors that affect
firms’ R&D spending over the last 10 years -_> variable “R&D spending” varies over time
- across subjects & over time → measure your and classmates’ satisfaction with the IBR course every é weeks
during the semester → “satisfaction” is variable that varies across subjects (students) & over time (two-weekly
periods)
→ when something that could potentially vary ONLY HAS 1 LEVEL in the study → it’s not a variable, it’s a constant (in a
study on the relationship btw intangible assets & firm profitability for publicly-listed firms: “public-listed firms” is a
constant bc the study only considers publicly listed firms WHILE a study on the relationship btw the public listing of forms
& those firms’ profitability: “public listing” is a variable with 2 levels: publicly listed VS private firm)
• In terms of relationships:
A problem statement expresses the relationship btw at least 2 variables → ‘What % of companies pay dividends? Does
NOT qualify as a problem statement bc it only involves 1 variable (dividend payment) WHILE ‘How are (i) package size, (ii)
package shape, (iii) package color related to young children’s consumption of sweets?’
Example of a good problem statement:
What is the relationship between entrepreneurs' finance and marketing skills on their firms' profitability?
• This is an open-ended question -- it cannot be answered by a simple yes or no
• The problem statement is expressed in terms of three variables: (1) finance skills, (2) marketing skills, and (3)
profitability. All three variables vary across subjects (entrepreneurs)
• The problem statement is expressed in terms of two relationships: (1) the relationship between finance skills and
profitability, and (2) the relationship between marketing skills and profitability
• The unit of analysis is clear: the entrepreneur
The relationship btw 2 variables depend on a third variable, a so-called moderating effect
Instead of formulating a single overly complex question (the problem statement), you can formulate multiple sub
questions
◊ Use correlational rather than causal language to phrase the problem statement
→ Associational/correlational claims include:
• X is related to Y
• X is associated with Y
• Y increases/decreases when X increases
While causal claims make stronger statements: they go beyond a simple association btw variables and suggest that one
variable causes the other such as: X impacts Y, X affects Y, X causes Y, X decreases Y, X enhances Y
SUMMARY :
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