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International Business Research > Definitions > Flashcards

Definitions Flashcards

Learn abstract theory

1
Q

Business Research

A

a series of well thought out activities and carefully executed data analysis that help a manager avoid, solve or minimize a problem

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2
Q

Applied Research

A

to solve a current problem that demands a timely solution. Applies to a specific company, within firms or research agencies

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3
Q

Fundamental Research

A

generate a body of knowledge by trying to understand how certain problems that occur in organizations can be solved. Research done to make a contribution to existing knowledge. (Teaching us something we didn’t know before, mainly done in universities and knowledge institutes)

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4
Q

Internal Research Benefits

A

More chance of being accepted, Less time needed to understand the structure of the organization, Less costly

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5
Q

Internal Research Disadvantages

A

Might be stereotyped, not perceived as experts by the staff, less objective findings

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6
Q

External Research Benefits

A

Has experience in working with different types of organizations, more knowledge

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7
Q

External Research Disadvantages

A

High cost and time, might not be accepted by Staff

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8
Q

The Hallmarks of Good Research

A

Purposiveness, Rigor, Objectivity, Parsimony, Replicability, Generalizability

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9
Q

Purposiveness

A

A definite aim or purpose, knowing the ‘‘why’’ of your research

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10
Q

Rigor

A

Ensuring a good theoretical base and a good methodological design adds rigor to a purposive study (implies carefulness)

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11
Q

Objectivity

A

Drawing conclusions based on facts rather than on subjective ideas

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12
Q

Parsimony

A

Shaving away unnecessary details, explaining a lot with a little

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13
Q

Replicability

A

Finding the same results if the research is repeated in similar circumstances

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14
Q

Generalizability

A

Being able to apply the research findings in a wide variety of different settings

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15
Q

Deductive Research

A

Theory to data, testing theory

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16
Q

Inductive Research

A

data to theory, building theory

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17
Q

Seven-step Deductive Research Process

A
  1. Define the Business problem.
  2. Formulate the problem statement.
  3. Develop theoretical framework
  4. Choose a research design
  5. Collect data
  6. Analyze data
  7. Write-up
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18
Q

Seven-step Inductive Research Process

A
  1. Define the business problem
  2. Formulate the problem statement.
  3. Provide a conceptual background
  4. Choose a research design
  5. Collect data
  6. Analyze data
  7. Develop theory
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19
Q

Primary data

A

Information that the researcher gathers first hand through instruments such as surveys

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20
Q

Secondary data

A

Data that already exists and doesn’t have to be gathered by the researcher

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21
Q

Business Problem

A

Gap between actual and desired situation (state)

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22
Q

What makes a good business problem?

A

Feasibility and relevance

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23
Q

Feasibility

A

Is it doable?

  • Is the problem demarcated? (Make smaller if it is too big)
  • Can the problem be expressed in variables?
  • Are you able to gather the required data?
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24
Q

Relevance

A

Is it worthwhile?

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25
Q

Managerial relevance

A

Who benefits from having my problem solved?

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26
Q

Academic relevance

A

Has the problem not already been solved in prior research?

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27
Q

Completely new topic (academic relevance)

A

No research available at all, although the topic is important

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28
Q

New context (academic relevance)

A

Prior research is available but not in the same context

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29
Q

Integrate scattered research (academic relevance)

A

e.g., different studies have focused on different IVs/moderators; consequently, their relative importance is not clear

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30
Q

Reconcile contradictory research (academic relevance)

A

Solve the contradictions through introducing one or more moderators

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31
Q

A good problem statement is….

A
  • Formulated in terms of variables and relations
  • Open-ended question
  • Stated clearly/ unambiguously
  • Managerially and academically relevant
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32
Q

Good research questions…

A
  • Should collectively address the problem statement; one problem statement is translated into multiple research questions
  • First theoretical, then practical research questions
  • Stated clearly/ unambiguously
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33
Q

Theoretical Research questions

A

Context questions, conceptualization questions, relationship questions

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34
Q

Practical Research Questions

A

Relationship Questions, implication Questions

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35
Q

Relationship Questions (Practical)

A

To what extent does X affect Y?

What is the (relative) magnitude of the relations?

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36
Q

Implication Questions (Practical)

A

How can practitioners implement your results?

Open question

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37
Q

3 Types of Research Questions

A
  1. Exploratory research question
  2. Descriptive research question
  3. Causal research question
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38
Q

Exploratory Research Questions

A

Often relies on qualitative approaches to data gathering (not much is known)

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39
Q

Descriptive Research Questions

A

Obtain data that describes the topic of interest

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40
Q

Causal Research Questions

A

Studies whether or not one variable causes another variable to change

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41
Q

A theoretical framework consists of…

A

Variable definitions
Conceptual model
Hypotheses

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42
Q

Variables

A

Anything that can take on varying values

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43
Q

Dependent Variable

A

The phenomena that you are trying to understand (measuring variable)

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44
Q

Independent Variable

A

Influences the dependent variable in a positive or negative way (manipulated variable)

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45
Q

Mediating variable

A

A variable that explains the mechanisms at work between X and Y

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46
Q

Full mediation

A

X only has effect on Y through the mediating variable

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47
Q

Partial mediation

A

X has an indirect effect on Y through the mediating variable, but also has a direct effect on Y

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48
Q

Moderating variable

A

A variable that alters the strength and sometimes even the direction of the relationship between X and Y

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49
Q

Quasi moderation

A

Moderating variable moderates the relationship between X and Y, but it also has a direct effect on Y

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50
Q

Pure moderation

A

Moderating variable moderates the relationship between X and Y, but it has no direct effect on Y

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51
Q

The 4 conditions for causality (to establish a change is n the IV causes a change in the DV)

A
  1. X and Y co-occur (covary)
  2. A logical explanation for the effect of X on Y is needed
  3. X proceeds Y in time
  4. No other cause (Z) explains the co-occurrence of X and Y
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52
Q

Omitted variable bias

A

Lack of important variables in the model

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53
Q

Hypotheses

A

A tentative statement about the coherence between two or more variables

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54
Q

Directional (one-sided) hypotheses

A

Direction of the relationship is indicated. Terms such as positive, negative, more than, less than are used

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55
Q

Unidirectional (two-sided) hypotheses

A

They postulate a relationship or difference but offer no indication of the direction

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56
Q

Null hypotheses

A

Expresses NO relationship or difference between groups and is set up to be rejected (almost never presented in research reports)

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57
Q

Alternate hypotheses

A

Express their relationship or difference between groups; research hypothesis

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58
Q

Negative case method

A

To test the hypothesis, the researcher should look for data to refute it. When you find data that does not support the hypothesis, the theory needs revision.

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59
Q

Research Design / Plan

A

Plan for collection, measurement, and analysis of data

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60
Q

Causal Research: Experiment

A

A data collection method in which one or more IV’s are manipulated to measure the effect of this manipulation on the DV

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61
Q

Critical research design decisions

A
  • Choosing between deductive research strategies
  • Choosing between statistical techniques
  • Choosing between sampling designs
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62
Q

Causal Research Experiment: Lab experiments

A
  • Explore cause and effect relationship in artificial environment
  • One or more IV’s manipulated after which the effect on the DV is measured
  • High degree of control by researcher
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63
Q

Causal Research Experiment: Field experiments

A
  • An experiment is carried out in the natural environment (work/life goes on as usual)
  • Manipulation/ interference possible
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64
Q

Deductive research strategies

A

Lab experiments, field experiments

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65
Q

Correlation Research

A

Descriptive Research

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66
Q

Archival Research (Correlation research)

A
  • Research based on data that already exists
  • External: data gathered by sources outside of the firm
  • Internal: existing company data
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67
Q

Survey Research (Correlation research)

A

Research based on questionnaire to which respondents record their answers, typically with closely defined alternatives

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68
Q

Contrived Settings

A

Artificial environment (lab experiment)

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69
Q

Non-contrived settings

A

Natural environment (field study)

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70
Q

Unit of Analysis

A

Individual, Dyad (two person interaction), Group, Organization, Culture

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71
Q

What determines the unit of analysis?

A

The research question

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72
Q

Cross sectional (time horizon)

A

Data gathered just once, one shot studies

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73
Q

Longitudinal (time horizon)

A

Study phenomena at more than one point in time (more time and effort, more expensive)

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74
Q

Mixed method research

A

Research question cannot be answered by qualitative or quantitative approach alone

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75
Q

Is more data better?

A

Raw data means nothing without the proper tools to analyse or interpret them

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76
Q

Descriptive statistics

A

Methods of summarizing the data in an informative way

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77
Q

Types of measures for descriptive statistics

A

Measures of central tendency: mean, mode, median

Measures of dispersion: range, standard deviation, variance and interquartile range

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78
Q

Inferential statistics

A

Methods to draw conclusions

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79
Q

Methods to draw conclusions (inferential stats)

A

Mean difference test, chi square test, ANOVA, regression analysis, logit analysis etc.

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80
Q

4 Types of Measurement Scales

A
  1. Nominal
  2. Ordinal
  3. Interval
  4. Ratio
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81
Q

Nominal (Types of measurement scales)

A

No logical order (ethnicity, social security number, gender)

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82
Q

Ordinal (Types of measurement Scales)

A

Ranked and ordered (not only categorizes but also rank order them in a meaningful way) (clothing sizes, ranking)

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83
Q

Interval (Types of measurement scales)

A

Meaningful differences between values, but no natural zero point (the difference between any two values on the scale is identical to the difference between any other two neighbouring values on the scale is identical to the difference between any other two neighbouring values on the scale (e.g., thermometer, time on a 12-h clock))

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84
Q

Ratio (Types of measurement scales)

A

Meaningful differences and ratios between values due to a natural zero point i.e., income, weight, money, blood pressure etc.

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85
Q

IV: Nominal/ordinal; DV: Nominal/ Ordinal; Statistical technique:….

A

Chi-square test

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86
Q

IV: Nominal/ ordinal; DV: interval/ ratio; Statistical technique..

A

T-test, ANOVA

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87
Q

IV: Interval/ratio; DV: Nominal/ ordinal; Statistical technique:….

A

Logit analysis

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88
Q

IV: Interval/ratio; DV: Interval/ ratio; Statistical technique:…

A

Regression analysis

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89
Q

Popular rating scales in business research

A

Likert Scale, Semantic Differential (both treated as interval scales)

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90
Q

Sample

A

Subset of the population of interest

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91
Q

Sampling

A

Procedure where a given number of members from a population are selected as representative subjects of that population

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92
Q

The sampling process (4 steps)

A
  1. Define the target population
  2. Determine the sampling frame
  3. Determine the sampling design
  4. Determine the sampling size
93
Q

Target population

A

Defining in terms of elements, geographical boundaries and time (Who are you targeting as a sample during this study?)

94
Q

Sampling frame

A

Physical representation of the target population

95
Q

Coverage error

A

When sampling frame does not match the population

96
Q

Sampling design

A

Probability sampling vs. Non-probability sampling

97
Q

Probability sampling

A

Each element of the population has a known chance of being selected as a subject

98
Q

Types of probability sampling (4)

A
  1. Simple random sampling
  2. Systematic sampling
  3. Stratified sampling
  4. Cluster sampling
99
Q

Simple random sampling (probability sampling)

A

Least bias, most generalizable since every element has an equal change of being chosen

100
Q

Systematic sampling (probability sampling)

A

Select random starting point and then pick every I-th element (low generalizability)

101
Q

Stratified sampling (probability sampling)

A

Divide population into groups, then apply SRS within each group

102
Q

Cluster sampling (probability sampling)

A

Divide the population into heterogeneous groups, randomly select a number of groups and select each member within this group

103
Q

Non probability sampling

A

The elements of the population do not have a known chance of being selected as a subject

104
Q

Four types of non-probability sampling

A
  1. Convenience Sampling
  2. Quota Sampling
  3. Judgement Sampling
  4. Snowball Sampling
105
Q

Convenience Sampling (Non-probability sampling)

A

Select subjects who are conveniently available (lowest generalizability)

106
Q

Quota Sampling (Non-probability sampling)

A

Fix a quota for each subgroup, select on the basis of specific criteria

107
Q

Judgement Sampling (Non-probability sampling)

A

Select subjects based on their knowledge/ professional judgement

108
Q

Snowball Sampling (Non-probability sampling)

A

Do you know people who..?

109
Q

A larger sample size leads to….

A

a lower sampling error

110
Q

Rules of thumb for sampling

A
  1. Sample size >75 and <500 is appropriate
  2. Multivariate search: >10x parameters to be estimated
  3. Subsamples: >20-30 per subsample
111
Q

Survey Research

A

Research based on a questionnaire to which respondents record their answers, typically with closely defined alternatives

112
Q

When to use surveys

A
  • Interest in quantitative descriptors

- Want to say something about the population, but you can’t measure the whole population

113
Q

Categories of Questions

A

Open-ended
Closed-ended
Single-item Measures
Multi-item Measures

114
Q

Open-ended Questions

A

Allows respondents to answer a question in any way they choose

115
Q

Closed-ended

A

Asks the respondents to make choices among a set of alternatives

116
Q

Single-item measures

A

When concrete singular object/attribute is used (What is your marital status? What is your profession? NOT ‘’ How diverse is the workforce of your company etc.)

117
Q

Multi-item measures

A

In all other cases

118
Q

Item-response scales

A

Comparative scales, non-comparative scales

119
Q

Comparative scales ( item-response scales)

A
  1. Paired comparison
  2. Contact sum
  3. Rank ordering
120
Q

Paired comparison (comparative scales)

A

Compare pairs of shampoo brands etc.

121
Q

Contact sum (comparative scales)

A

Divide 100 points among the following etc.

122
Q

Rank ordering (comparative scales)

A

Rank brands, rank companies etc.

123
Q

Non-Comparative scales

A

Continuous rating scale
Likert scale
Semantic differentials

124
Q

Continuous rating scale (non-comparative)

A

Rate department score from 0 to 100 etc.

125
Q

Likert scale (non-comparative)

A

Disagree/agree, 5- or 7- point scale

126
Q

Semantic differentials (non-comparative)

A

Good or bad, powerful or weak, modern or old-fashioned

127
Q

Nominal scale measured questions…

A

should be mutually exclusive: only 1 answer applies

should be collectively exhaustive: the answer possibilities cover the entire realm of possible answers

128
Q

Tailor Design Method

A
  1. Pre-notification
  2. Questionnaire (package contains: personalized cover letter, questionnaire, token of appreciation, free return envelope or reply e-mail button)
  3. Thank you/ reminder
  4. Replacement questionnaire
  5. Final contact
129
Q

Validity measures..

A

Provide precedence, but provide sound logic to support that considerable conceptual overlap exists between measurement/proxy and construct

130
Q

Social Desirability Bias

A

Respondents may not always be willing to communicate their true response in case of sensitive issues; to minimize socially desirable responding, use deliberately leading and or/loading questions to make the sensitive normal

131
Q

Reliability of survey measures…

A

For multi-item measures use Cronbach’s Alpha which measure to what extent a set of items are inter-related; when highly inter-related = high reliability

132
Q

Cronbach’s Alpha

A

Outcome is between 0 an 1, values >0.7 are considered acceptable

133
Q

Experimental Research

A

Data collection method where one or more IVs are manipulated to measure the effect on the DV, and where you control for other causes

134
Q

Two main objectives of experimental studies…

A
  1. To draw valid conclusions about the effect ov IVs on DV

2. To make valid generalizations towards a broader group/population

135
Q

Threats to internal validity

A
  1. History effects
  2. Maturation
  3. Testing
  4. Instrumentation
  5. Statistical regression
  6. Mortality
136
Q

History effects (threats to internal validity)

A

Events outside the experiment have an impact on the DV during the experiment

137
Q

Maturation (threats to internal validity)

A

Biological changes over time

138
Q

Testing (threats to internal validity)

A

Prior testing affects the DV

139
Q

Instrumentation (threats to internal validity)

A

The observed effect is due to a change in measurement

140
Q

Statistical regression (threats to internal validity)

A

Extreme scores in the beginning are less extreme in the end

141
Q

Selection bias (threats to internal validity)

A

Incorrect selection of respondents

142
Q

Mortality (threats to internal validity)

A

Drop out of respondents during the experiment

143
Q

Increase internal validity by…

A

Randomization of participants
Design control: extra group, control group
Statistical control: measure extraneous variables, and include these in the statistical analysis

144
Q

How to solve selection bias, instrumentation, history or mortality threats to validity

A

Randomization of participants

145
Q

Measuring reliability

A

Cronbach’s Alpha

146
Q

Lab experiment

A

Artificial setting to have as much control as possible over the manipulations

147
Q

Field experiment

A

Natural environment where manipulation is possible

148
Q

Lab experiment has high or low internal/ external validity?

A

High internal validity

149
Q

Field experiment has high or low internal/ external validity?

A

High external validity

150
Q

In field experiments, ideally…

A

Participants are (a) unaware that they are taking part in a study and (b) unaware of the different manipulations

151
Q

Field experiments external validity…

A

high as it generalizes results to real-world behaviour

152
Q

Advantages of Field experiments

A
  1. Real world behaviours = Real world results
  2. Authenticity
  3. Novel insights
153
Q

Authenticity (advantages of field experiments)

A

Field experiments provide authentic (a) context, (b) treatments, (c) participants, and (d) outcomes measures

154
Q

Novel insights (advantages of field experiments)

A

Field experiments enable (a) to answer questions that cannot be answered in the lab, (b) to check if lab results hold in real-world situations, and (c) to capture second-order and long-term effects

155
Q

Disadvantages of field experiments

A
  1. Time consuming
  2. Challenging to implement
  3. Focus on observed behaviour
  4. High degree of noise
  5. Ethical consideration
156
Q

Time consuming (disadvantages of field experiments)

A

Need to identify potential partners, convince key stakeholders, legal considerations etc.

157
Q

Challenging to implement (disadvantages of field experiments)

A

Need to monitor procedure; address organization-specific infrastructure

158
Q

Focus on observed behaviour (disadvantages of field experiments)

A

Focus on field experiments is limited on behaviour that can be observed, low ability to investigate underlying psychological processes

159
Q

High degree of noise (disadvantages of field experiments)

A

Limited control over experimental procedure; several potential influences that threaten the validity of the results

160
Q

Ethical consideration (disadvantages of field experiments)

A

Need to consider if field experiment is ethically correct in context of study

161
Q

Internal validity threats experimental research

A

1: Poor timing and unexpected situational factors
2. Failure to randomize
3. Non-compliance/ Failure to treat
4. Spill overs & side-effects
5. Insufficient sample size

162
Q

Poor timing & unexpected situational factors (Internal validity threats experimental research)

A

Changes in the environment unrelated to the study i.e., weather, technology, news, politics

163
Q

Failure to randomize (internal validity threats experimental research)

A

No randomization of participants in groups (due to targeting, technical failures etc.)

164
Q

Non-compliance / Failure to treat (internal validity threats experimental research)

A

Subjects that are supposed to receive the treatment do not receive it

165
Q

Spill overs & side-effects (internal validity threats experimental research)

A

One participant is affected by the treatment of other participants; no consideration of unexpected side effects

166
Q

Insufficient sample size (internal validity threats experimental research)

A

Insufficient power to detect effects (main and interaction)

167
Q

A/B Testing

A

randomized field experiment with two variants, A and B. It includes application of statistical hypothesis testing or “two-sample hypothesis testing”

168
Q

Archival Data

A

Data gathered from existing sources (secondary rather than primary data) collected for another purpose than that of the current study

169
Q

Archival based research

A

Research that capitalized on data that are already in existence (rather than new primary data)

170
Q

Internal Archival Data

A

Company records and archives

171
Q

External Archival Data

A

Commercially available data sets, publicly available data sets

172
Q

Archival data is cheap, true or false?

A

FALSE, fata bases have to be paid for at time which costs more than 50k a year!

173
Q

Archival data is quick, true or false?

A

FALSE, archival data often consists of piecing together multiple data sets, which can be more time intensive than the analysis itself

174
Q

Why use archival research? (5 reasons)

A
  1. Tap into industry wisdom
  2. Power
  3. Examining effects across time
  4. Examining effects across countries
  5. Examining socially sensitive phenomena
175
Q

Tap into industry wisdom (reasons to use archival research)

A

Learn from past successes and failures in the industry when you cannot rely on your own experience

176
Q

Power (reasons to use archival research)

A

High likelihood of rejecting H0 when H0 is false = low likelihood of missing a real effect

177
Q

Examining effects across time (reasons to use archival research)

A

Examine whether a phenomenon changes over time, or examine the duration of an effect

178
Q

Examining effects across countries (reasons to use archival research)

A

Primary international research is expensive and cumbersome

179
Q

Examining socially sensitive phenomena (reasons to use archival research)

A

Archival data= unobtrusive (what people do rather than say, minimize the opportunity of distorted responses)

180
Q

Sources of measurement unreliability in survey research

A
  1. Missing observations
  2. Inaccurately recorded observations
  3. Fake observations
181
Q

Big Data

A

Data sets that are so big and complex that traditional data processing software are inadequate to deal with them

182
Q

10 Characteristics of Big Data

A 
Good for research:
1. Big
2. Always-on
3. Nonreactive
Bad for research:
4. Incomplete
5. Inaccessible
6. Non-representative 
7. Drifting
8. Algorithmically conformed
9. Dirty
10. Sensitive
183
Q

Big (advantage Big Data characteristic)

A

Rare events, different reactions across units, heterogeneity, small effects

184
Q

Always on (advantage big data characteristic)

A

Real-time estimates of economic activity

185
Q

Non reactive (advantage big data characteristic)

A

Measurement in big data sources is less likely to change behaviour

186
Q

Incomplete (disadvantage big data characteristic)

A

Leaves out missing information (demographics, behaviour on other platforms etc.)

187
Q

Inaccessible (disadvantage big data characteristic)

A

Legal, business or ethical barriers to giving outside researchers access to data

188
Q

Non-representative (disadvantage big data characteristic)

A

Can’t make inferences about population based on sample

189
Q

Drifting (disadvantage big data characteristic)

A

User can change, usage changes, platform changes

190
Q

Algorithmically confounded (disadvantage big data characteristic)

A

Platform design can influence behaviour, introducing bias and noise to study

191
Q

Dirty (disadvantage big data characteristic)

A

Can be loaded with junk or spam (bots and trolls)

192
Q

Sensitive (disadvantage big data characteristic)

A

Can be damaging when made public, data re-identification: matching anonymous data with publicly available data in order to identify an individual

193
Q

The 4 V’s of Big data

A
  1. Volume
  2. Velocity
  3. Variety
  4. Veracity
194
Q

Volume (4Vs big data)

A

n*p data matrix

195
Q

Velocity (4Vs big data)

A

Speed of data processing, big data usually are sparse so most elements are zero or missing

196
Q

Variety (4Vs big data)

A

Number of types of data

197
Q

Veracity (4Vs big data)

A

Uncertainty of data

198
Q

Three ways to learn from big data

A
  1. Measuring (includes counting)
  2. Prediction
  3. Approximating experiments
199
Q

Measuring (ways to learn from big data)

A

What brand compete more closely with each other (example) & perceptual maps: marketing tool to display perceptions of customers about competing brands, collecting via surveys etc.

200
Q

Prediction (ways to learn from big data)

A

Can Google predict the flu? Trends in searches, traditional data has gaps whereas big data is always on, now casting = predicting the present

201
Q

Approximating experiments (ways to learn from big data)

A

Big data have so many observations, they can be matched

Create pairs of observations who are the same in every way, except the variable you want to study

202
Q

Big Data Ethics

A

Just because it’s mathematical doesn’t make it objective or fair; Audit the algorithm

203
Q

Supervised learning paradigm

A

Model variation; don’t trust anyone who says they have a good learning algorithm unless you see results of careful cross-validation, because flexible models can lead to overfitting (= following errors too closely)

204
Q

Spurious correlations

A

Fit well in the beginning, worse in the end

205
Q

Construct Equivalent

A

Are we studying the same phenomena in different countries?

206
Q

Measurement equivalent

A

Are the phenomena that we study measured in the same way in terms of: wording; (translation equivalence), scaling (metric equivalence)

207
Q

Obtaining metric equivalence

A

Pre-data collection through pictorious response scale, post data collection through standardized variable (Z)

208
Q

Response style bias

A

Extreme responding and Socially desired responding

209
Q

Egoistic response tendency

A

Superhero, masculine countries

210
Q

Moralistic Response Tendency

A

Saint, feminine and collectivist countries

211
Q

Sampling Equivalence

A

Achieve representative and comparable samples

212
Q

Exploratory research

A

To require an in-depth understanding when prior theory is absent, often based on qualitative data (words rather than numbers used to build theory, phenomena of interest involve words and language)

213
Q

Fundamental Characteristics of Qualitative Data

A
  1. Open-ended
  2. Concrete and vivid
  3. Rich and nuanced
214
Q

Open-ended (characteristics qualitative data)

A

No need to predetermine precise constructs; flexible and exploratory

215
Q

Concrete and vivid (characteristics qualitative data)

A

See the world through the eyes of the subjects

216
Q

Rich and nuanced (characteristics qualitative data)

A

Capture details

217
Q

Sources of qualitative data

A
  1. Primary: field research, interviews

2. Secondary: desk research, annual reports and other company records, blogs, websites etc.

218
Q

When NOT to use exploratory research

A

When results are to be generalised to the total population

When numbers are needed to make a decision

219
Q

Research strategies of exploratory research

A
  1. In-Depth interviews
  2. Focus Groups
  3. Observation
220
Q

In-Depth interviews

A

A conversation where the researcher asks questions and listens to the respondents answers

221
Q

Focus groups

A

An interview on a group basis of 8 to 10 participants, chosen based on their familiarity with the topic; discussion is facilitated by the moderator

222
Q

Observation

A

The watching and analysis of the behaviour of employees, consumers, investors etc.

223
Q

Reliability Exploratory Research

A

Interjudge reliability

224
Q

Interjudge reliability

A

Degree of agreement among raters/judges

225
Q

Validity Exploratory Research

A

Interview biases, interviewee biases

226
Q

Interview/interviewee biases

A

Loaded questions, expressing one’s own opinion and judging whilst asking questions

227
Q

Selective perception

A

Hearing what you want to hear from the interviewer, observing what you want to observe

228
Q

Obedience

A

Desire to please the interviewer

229
Q

Conformity

A

Do/think what the majority does or thinks (normative social influence)

International Business Research (2 decks)

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