Thesis on exploration in Business Management

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Research Training and Bachelor Thesis

Strategic Exploration Activities and Firm Performance

Team number: 10

Date of submission: 03-06-2014

Thesis Supervisor:

MSc. Tuncdogan, A, Aybars

Course Supervisor:

Dr. Hak, A. Tony

van den Heuvel, F.M.C. Candel, J.D.W.F. Jansen, T.C.J.

ERNA: 400792fh ERNA: 402981jc ERNA: 387463tjTelephone: +31610816198 Telephone: +31625331900 Telephone: +31612882110

“This document is written by Tibbe Jansen, Gijs van den Heuvel, and Jonathan Candel, who declare that

each individual takes responsibility for the full contents of the whole document. We declare that the text

and the work presented in this document is original and that no sources other than mentioned in the text

and its references have been used in creating it. RSM is only responsible for supervision of completion of

the work but not for the contents.”



Contents

List of Tables ............................................................................................................................................ 5

List of Figures ........................................................................................................................................... 5

Abstract ................................................................................................................................................... 6

1. Introduction ......................................................................................................................................... 7

1.1 General Importance ...................................................................................................................... 7

1.2 Theory ............................................................................................................................................ 7

1.3 Current Study ................................................................................................................................ 8

1.4 Thesis Structure ............................................................................................................................. 9

2. Critical Synthesis ................................................................................................................................ 10

2.1 The Structural Process of Selecting Studies ................................................................................ 10

2.2 Critical Evaluation of Studies ....................................................................................................... 10

2.3 Results ......................................................................................................................................... 12

2.3.1 Conclusions from Observed Effect Sizes .............................................................................. 13

2.4 Discussion .................................................................................................................................... 17

2.4.1 Possible Moderating Variables ............................................................................................. 17

2.4.2 Managerial Implications Regarding the Observed Effect Sizes ............................................ 18

2.4.3 Recommendations for Contributing Studies ........................................................................ 19

3. An Attempt at Best Practice Research .............................................................................................. 20

3.1 Introduction ................................................................................................................................. 20

3.2 The Study ..................................................................................................................................... 20

3.2.1 Formulating a ‘Best Practise’ for Generating Empirical Evidence ........................................ 20

3.2.2 The Complications of the Ideal Research Situation .............................................................. 21

3.2.3 Population and Sample ........................................................................................................ 21

3.3 The Structural Process of the Research Design ........................................................................... 22

3.3.1 Specification of the Research Question ............................................................................... 22

3.3.2 Specification of the Research Strategy ................................................................................. 23



3.4 Results ......................................................................................................................................... 27

3.4.1 Effect Size Measures............................................................................................................. 27

3.4.2 Worst Case Response Bias Analysis ...................................................................................... 28

3.5 Advanced Meta-Analyses ............................................................................................................ 28

3.5.1 General Meta-Analysis ......................................................................................................... 28

3.5.2 Geographically Advanced Meta-Analysis ............................................................................. 29

3.6 General Conclusion...................................................................................................................... 30

3.6.1 Managerial Implications ....................................................................................................... 31

3.6.2 Limitations and Recommendations for Future Research ..................................................... 31

3.7 Lessons Learned .......................................................................................................................... 33

4. References ......................................................................................................................................... 35

5. Appendices ........................................................................................................................................ 39



Exploration Activities & Firm Performance5

List of Tables

Table 1. Table with General Information about the Papers .................................................................. 12

Table 2. Geographical and Sector Sample Characteristics of Each of the Studies Included. ................ 15

Table 3. Data Matrix with the Values of the Dependent and the Independent Variable for Each

Individual Case....................................................................................................................................... 25

List of Figures

Figure 1. Number of Cases (N) and Effect Sizes (Correlation Coefficient: r) per Paper ........................ 12

Figure 2. Visual Representation of the Individual Studies that are Included into the Meta-analysis aswell as a 95%-Confidence Interval. ....................................................................................................... 14

Figure 3. Visual Representation of the Individual Studies and the Combined Confidence Interval from

Studies that Regard Non-US Firms. ....................................................................................................... 16

Figure 4. Visual Representation of the Individual Studies and the Combined Confidence Interval from

Studies that Regard US Firms. ............................................................................................................... 16

Figure 5. Visual Representation of the Possible Moderating Variables that are Discussed in Section

2.4.1. ...................................................................................................................................................... 18

Figure 6. Visual Representation of the Meta-analysis including the Studies from the Critical Synthesis

and the Study from the Attempt at Best Practice Research. ................................................................ 29

Figure 7. Visual Representation of the Meta-analysis including All Confidence Intervals from the

Different Geographical Areas. ............................................................................................................... 30




Abstract

In recent years, a lot of research has been conducted on organization’s exploratory activities

and the possible association these activities have with firm performance. James March

(1991) was one of the researchers who introduced the term exploratory activities and

describes it as: “things captured by terms such as search, variation, risk taking,

experimentation, play, flexibility, discovery and innovation.” In the first part of this Bachelor

Thesis, several quantitative studies will be compared that investigate, to some extent, the

possible association between exploratory activities and firm performance. These studies

were first critically analyzed and subsequently formed into a meta-analysis. This analysis

takes into account all the selected studies and predicts a confidence interval for all the

studies combined. The second part of the Thesis will consist of an Attempt at Best Practice

Research to construct a methodologically-sound study that searches for a possible

association between the same variables as used in the meta-analysis: exploratory activities

and firm performance. Combining both parts of this thesis will answer the research question:

Hopefully, the study will also provide substantial insights into the world of exploration and

firm performance, advice with regard to several practical implications and provide

suggestions for future research on the topic.

‘ To what extent are a firm’s exploration activities associated with its performance?’




1. Introduction

1.1 General Importance

The mystery of a firm’s performance has yet to be discovered. Despite the fact that many

researchers have tried to unravel the secret on how to maximize performance. Since the

beginning of the 1990s, companies have to deal with the process of the destruction of

traditional competitive advantages and the building of new set of advantages (D’Aveni,

1994). Traditional advantages no longer guarantee the long-term gains. “It charts the

evolution of industries through a series of competitive moves and countermoves that can be

labeled as dynamic strategic interactions.” (D’Aveni, 1994). As once said by Adam Grant

(2011): “What do we know, what don’t we know, and so what?” What we do not know is towhich extent a firms exploration activities are ‘positively’ associated with firm performance,

while this is valuable to managerial relevance. However, what we do know, is that today’s

firms have to move more quickly and boldly and experiment in ways that do not conform to

the traditional theory (Volberda, 1996). This is due to the rapidly changing and ultra-

competitive markets. “The basic problem confronting an organization is to engage in

sufficient exploitation to ensure its current viability and, at the same time, to devote enough

energy to exploration to ensure its future viability.” (Levinthal and March, 1993). Therefore

there is the need for strategic renewal in today's dynamic markets. According to Hoffman

and Hegarty (1993) the focus on strategic renewal is a growing source of strategic

advantage. One way to do this is to explore new opportunities, also called: exploration (e.g.,

Benner and Tushman, 2003; D’Aveni, 1994; Day, 1994; Jansen et al., 2006; Levinthal and

March, 1993; Volberda, 1996; Young et al., 2001)

1.2 Theory

To give a more specified insight into the relevant theory, the following part will show a

concise but detailed summary of previous studies. Whereas chapter 1.3 will be focused on

the construction of this particular critical synthesis. Regarding the existing theory, studies

have shown according to Alexiev et al. (2010) that “some top management teams (TMTs)

have the ability to recognize distant opportunities and devote organizational resources to

exploratory innovation, while others fail to do so and put their organizations at risk of

becoming obsolete” (Alexiev et al., 2010; Day, 1994; Kaplan et al., 2003; Young et al., 2001).




Exploration (or exploratory activities) includes things captured by terms such as search,

variation, risk taking, experimentation, play, flexibility, discovery and innovation. (March,

1991). Exploration activities are necessary to perform well in today’s rapidly changing

environment. According to March (1991) exploration improves the firm’s performance, hesays that “… exploration involves the acquisition of new information about alternatives and

thus leads to the improvement of future (long-term) performance.” (March, 1991). Also

other researchers like Cao et al. (2004) and Auh & Menguc (2005) discovered this

relationship. In sum, we can say that “exploratory activities build on new knowledge and

require the departure from existing skills and capabilities.” (Benner and Tushman, 2003;

Jansen et al., 2006) “This type of innovation is crucial for organizations operating in more

dynamic environments” (Jansen et al., 2006), “it is also considered to be key to an

organization’s long-term survival” (Levinthal and March, 1993) and “leads to the

improvement of future performance” (March, 1991).

1.3 Current Study

The aim of this paper is to present a critical synthesis of empirical evidence about the impact

of a firm’s engagement in exploration activities related to the firm’s performance.

Subsequently, the critical synthesis itself is aimed at comparing different studies conducted

by different researchers that focus on the same subject. The combined results of the

synthesis will give a clear understanding of what has already been covered by previous

research regarding the research question of this paper. This research question can be

formulated as follows: ‘To what extent are a firm’s exploration activities associated with its

performance? ’ The research question asks for the effect of exploration activities when

looking at a firm’s performance. In other words, when exploration activities are being

practised within a firm, we would like to see the effect of this particular change (positive,

negative or none) on the firm’s performance. The practical relevance of the research is to

help a company’s management to decide whether to pursue exploratory activities. Also

contributing to the practical relevance, the analysis of previous profits (or losses) may be

linked to the way exploratory activities are used in the past. By way of example, an

exploratory activity in the shape of a completely new innovative product should increase the

firm´s performance substantially in order to cover the enormous costs and amounts of time

involved. The mechanism behind this example is that due to exploring new activities (e.g.:




investments) firms can gain competitive advantage (e.g.: higher efficiency, revolutionary

new products, flexibility) and therefore increase profitability. The focal unit used in the

hypothesis is ‘the firm’. The theoretical domain will exist of all firms in the world, in all

economic sectors, in all countries, at all times. In theory, the relationship (mechanism) mightbe causal and positive because it is expected that exploration activities can lead to new

competitive advantages for a firm which will then increase its performance. Summarized, the

above stated can be concluded as: ‘To what extent is the association between firm’s

performance and exploration activities, where the firm can be described as the focal unit,

and it is expected that exploration activities can lead to new competitive advantages for the

theoretical domain that can be described as all firms in the world, in all economic sectors, in

all countries, at all times’.

1.4 Thesis Structure

The overall structure of this thesis is as follows. First is described the critical synthesis. Here

we critically evaluated and synthesized the results of empirical studies and described a

critical evaluation of the empirical evidence regarding a claim about the influence of an

independent variable on a dependent variable. It includes the selection of the studies, the

critical evaluation as well as the results and a discussion. Second, we described an attempt at

best practice by designing and conducting our own study. This part contains an introduction

where we described the aim of the paper. The study section where we described the

methods applied in the study including a description and justification of the sample and how

we measured the variables. Finally we described the learning points. This part contains what

is learned from executing this attempt at best practice.




2. Critical Synthesis

2.1 The Structural Process of Selecting Studies

To start, it was important to look for studies and other existing empirical evidence regardinga possible effect (of any nature) between the dependent and independent variable. The first

approach was to use Google Scholar and use specific keywords that might bring up relevant

studies. Amongst others, keywords that we used are: ‘Firm performance’, ‘Exploration

activities’, ‘Exploration’, ‘Business’ and ‘Research and Development’. Initially, there were

obtained a lot of results from the first attempts of searching. So much results that the search

process needed to be more precise. The keyword ‘Exploration’ also resulted in a lot of

medical papers that could not be used for the critical synthesis. To surpass this problem,

primarily the combination of ‘Exploration’, ‘Activities’ and ‘Business’ was used. Luckily, there

were a couple of meta-analysis papers found that showed in advance which paper could be

of use for the critical synthesis. Furthermore, a lot of papers used the ambiguous term

‘ambidexterity’, which in most cases refers to a mix of exploration and exploitation activities.

Firstly, papers containing this term were perceived to be useful. But during further analysis

the definition of this term seemed too confusing to work with. Additionally, it seemed that

these papers often also used this ‘ambidexterity’ during the evaluation of the results and

during the data analysis. Therefore, papers that used ‘ambidexterity’ were excluded from

the critical synthesis. For this reason, it was needed to exclude two papers from the critical

synthesis that were initially analyzed. See Appendix A for the two excluded papers.

2.2 Critical Evaluation of Studies

In order to critically evaluate the different papers found, the checklist provided in the Course

Book (Hak, 2013) was used. (See Appendix B for a full research evaluation of each of the

studies included in the critical synthesis) Additionally, like stated in the previous chapter, we

evaluated whether there were signs for ‘ambidexterity’ uses and if there were, we excluded

the measurements from the analysis. Consistent with the course book, we critically

evaluated each of the following elements. First, there had to be found out whether the

chosen study contained a causal relationship between the two variables. And if this was the

case, whether the researchers were entitled to make such a claim according to the research

strategy they used. For example, it is not justified to make a causal claim when the




researchers use a cross-sectional research strategy. Second, there was checked whether the

effect size parameter used was consistent with the research strategy. This was the case in all

of the situations. Third, there was checked whether the population from the selected studies

matched was part of the theoretical domain that was set earlier in the critical synthesis.Therefore, the population of each study needed to be exactly specified. Fourth, also

regarding the population, it was important to know whether the population consisted out of

a sample or that it concerned a census population in which all cases were investigated.

Important here was that the study should not contain a ‘convenience sample’, in which cases

are chosen because the researcher prefers them or has easy access to these cases.

Everything was checked carefully and inconveniences were reported whenever necessary.

Fifth, the very important element of ‘non-response’ (i.e.: missing cases) was considered. Like

Tony Hak states in the Course Book: “The problem with this type of bias is that we do not

know how large it is, for the simple reason that the missing cases are missing.” (Hak, 2013)

Therefore, there is carefully checked how the different researchers dealt with non-response.

Sixth, there is also paid a lot of attention to the measurement procedures of the different

papers. Amongst others, there is monitored for the correct validity, inter-item reliability,

accuracy and trustworthiness of the data and the results; any perceived inconvenience was

reported. Additionally, it was determined whether the cases consisted out of ‘respondents’

or ‘informants’. Seventh, the study is not supposed to make claims that reach further than

the studied population. Although this seems obvious, many researchers do not stick with it.

Eighth, there was checked whether the different populations were homogeneous or

heterogeneous. According to Professor Hak in Business Research the populations studied are

almost always heterogeneous, as he states that homogeneity certainly cannot be assumed in

business research since “… it should only be assumed after a series of studies in rather

different populations have all shown more or less similar results”. (Hak, 2013) Subsequently,

there is also checked whether within the different papers, there is referred (regarding for

instance the effect sizes or other results) to other, previously conducted researches. Ninth,

for each study was tried to determine the practical relevance of the observed effect sizes

through the use of confidence intervals. In way of conclusion, the claims made by the

authors were critically evaluated for correctness and accuracy.




2.3 Results

In this concluding part of the critical synthesis there is talked about the similarities and

differences that are observed during the analysis of the different papers. Below there is

made a summary table (Table 1 and Figure 1) that provides information about each paper

separately. The numbers of the different papers correspond with the numbers in the general

meta-analysis only (i.e. the meta-analysis that comprehends all papers from the critical

synthesis).

Table 1. Table with General Information about the Papers

Paper Name Author Effect Size Cases #

1. Balancing Exploration and Exploitation: The

Moderating Role of Competitive Intensity

Seigyoung Auh and

Bulent Menguc

r = 0.70

[0.667 ; 0.731]

980

2. The Influence of Founding Team Company

Affiliations on Firm Behavior

Christine M.

Beckman r = 0.190

[0.024 ; 0.346]

141

3. Unpacking Organizational Ambidexterity:

Dimension, Contingencies, and Synergistic Effects

Cao, Q. Gedajlovic, E.

Hongping, Z.

r = 0.311

[0.139 ; 0.465]

122

4. Exploratory Innovation, Exploitative Innovation,

and Performance: Effects of Organizational

Antecedents and Environmental Moderators302

Jansen, J.J.P. Van

den Bosch, F.A.J.

Volberda, H.W.

r = 0.180

[0.064 ; 0.291]

283

5. Exploitation, Exploration, and Firm Performance:

The Case of Small Manufacturing Firms in Japan

Isobe, T., Makino, S.,

Montgomery, D. B.

r = 0.740

[0.684 ; 0.787]

302

6. Balancing Exploration and Exploitation in

Alliance Forming

Lavie, D and

Rosenkopf, L.

r = 0.050

[-0.034 ; 0.133]

547

7. Alternative Knowledge Strategies, Competitive

Environment, and Organizational Performance in

Small Manufacturing Firms

Bierly, P. E. and Daly,

P. S.

r = 0.253

[0.055 ; 0.432]

98

Figure 1. Number of Cases (N) and Effect Sizes (Correlation Coefficient: r) per Paper




2.3.1 Conclusions from Observed Effect Sizes

First, the geographic dispersion of the populations that are used varies immensely. The only

similarity is that in all papers ‘firms’ are used as the unit of analysis. But amongst others

there are conducted researches that investigate firms in Australia, Japan and Asia. Thismakes it possible that firms from different geographic regions act differently due to for

instance different cultural habits. Therefore, later on in this conclusion, there will be added

an extra part that tries to group different main geographical areas, in order to check

whether there might be geographically-bound differences in effects. Similarly, the different

industries of the different population vary as well. It can be noticed though that mostly the

firms operate in an industrial or high-tech context. Probably this choice is made because the

variety in firm exploration is expected to be best observable in these kinds of industries. For

these meta-analyses only the confidence intervals were used instead of using the combined

correlation coefficient. While the combined correlation coefficient may seem suitable, Hak

(2013) states: "Because the effect sizes are expected to differ between populations, a critical

synthesis of the extant empirical evidence regarding a hypothesis should not focus on

calculating an “average” or overall effect size for the domain. Such an average has hardly any

meaning".

Furthermore, it is important to note the differences in sampling size used in each of the

papers. These vary from 98 to 980 observed cases. This is important because in general

bigger sampling sizes are perceived to be giving more accurate results. A small visual

representation of the different studies with their effects and corresponding CIs can be found

below in order to show the effect visually (figure 2). Furthermore can be concluded from this

meta-analysis in which all the seven studies are compared and analyzed, that the 95%-

confidence interval ranges from 0.001 up to 0.669. This means that there can be stated with

a certain amount of confidence that there is a moderate likelihood of an association

between exploration activities and firm performance in general. The total amount of

subjects that are included in this meta-analysis is 2473.




Figure 2. Visual Representation of the Individual Studies that are Included into the Meta-analysis as well as a

95%-Confidence Interval.

Moreover are most of the studies cross-sectional studies. This kind of method is used often

in real business situations (like in this synthesis) because the data can be obtained relativelyeasy through the use of surveys. But for the quality of the outcomes it would have been

better to use longitudinal research methods. According to the course book, “The internal

validity of the longitudinal study (for a causal claim) is still somewhat higher than the

internal validity of the cross-sectional study because of the chronology of (first) the change

in the value of the independent variable and (next) the change in the value of the dependent

variable (as in the time series study)”. (Hak, 2014) Nevertheless, a renowned study on the

advantages and disadvantages between longitudinal and cross-sectional research quotes:

“Specifically, our research reveals that cross-sectional data is most appropriate for studies

that examine concrete and externally-oriented constructs, sample highly-educated

respondents, employ a diverse array of measurement formats and scales, and are either

descriptive in nature or strongly rooted in theory.” (Rindfleisch et al., 2007) Since in most of

the papers used there is made use of highly-educated respondents and the constructs of

‘exploration activities’ and ‘firm performance’ are embedded deeply in the theory, this

might justify partly for the use of cross-sectional studies in the synthesis.

Some reoccurring characteristic regarding the different studies in the critical synthesis can

be identified as the structural absence of drawing probability samples. This can be

considered to be a big disadvantage. However, there are also very positive features that

characterize the different papers. In almost all of the papers the results are checked

intensively for inter-item reliability, validity and the possible presence of multicollinearity.

Taking these different measures contributes to the final interpretation of the results and

thus can be said that although probability sampling was not common, the results obtained




from these papers can be perceived valid and accurate and can also be used for investigating

the initial hypothesis that was described in the introduction.

The Search for Geographically Bound Effects

Like stated earlier, due to the difference in geographical settings of the package of papers

that is included into the meta-analysis, it is possible that studies that are conducted in a

particular geographic area have differing effects than others. That is why we would like to

add a small section in which we try to divide the different studies into two ‘main groups’ to

see whether the effects might differ. First, the following table (table 2) shows the

geographical and sector background of each of the studies included.

Table 2. Geographical and Sector Sample Characteristics of Each of the Studies Included.

Study Geographical

background of

the data used

Industry / Sector from

which data was derived

1. Balancing Exploration and Exploitation: The

Moderating Role of Competitive Intensity

Australian Manufacturing firms

2. The Influence of Founding Team Company

Affiliations on Firm Behavior

California, United

States

High-technology

industries

3. Unpacking Organizational Ambidexterity:Dimension, Contingencies, and Synergistic Effects

China High-technologyindustries

4. Exploratory Innovation, Exploitative Innovation,

and Performance: Effects of Organizational

Antecedents and Environmental Moderators

Europe Financial services firms

5. Exploitation, Exploration, and Firm Performance:

The Case of Small Manufacturing Firms in Japan

Japan Manufacturing firms

6. Balancing Exploration and Exploitation in Alliance

Forming

United States Software firms

7. Alternative Knowledge Strategies, Competitive

Environment, and Organizational Performance in

Small Manufacturing Firms

Mid-Atlantic

region of the

United States

Manufacturing firms

As can be seen in table 2, far most of the studies are conducted in a manufacturing or high-

technology sector. Additionally there is one software company –that can also be classified

somehow as a ‘technology sector’- and one financial institution, which could be perceived as

something very different. So there is decided to make two groups based on geographical




differences only. Since there are three studies included that are conducted in the United

States there is decided to make two new –smaller – meta-analyses: one containing the

studies from the US and the other one containing the other studies (Rest of the World). The

results of those two sub-meta-analyses can be seen in figure 3 and figure 4.

Figure 3. Visual Representation of the Individual Studies and the Combined Confidence Interval from Studies

that Regard Non-US Firms.

Figure 4. Visual Representation of the Individual Studies and the Combined Confidence Interval from Studies

that Regard US Firms.

As can be seen from the different sub-meta-analyses the results are somewhat different for

US firms compared to the Rest of the World (RoW). In the comparison of the four RoW

studies, there is still a moderate likelihood for an association between the two variables and

the effect measured is even stronger (CI [-0.008 ; 0.826]). On the contrary, the effect

measured earlier on is a lot less secure in the analysis concerning US firms (CI [-0.146 ;

0.409]). Therefore, this grouping is very useful since now there can be concluded that the

effect (if any) will probably be more present in non-US firms and probably be less present in

firms that are based in the US.




2.4 Discussion

The studies that we found and analyzed are the studies that know of to be of good indication

to our own research. There were a lot of ‘hits’ on Google Scholar using the keywords that we

set in advance. Nevertheless, as we are fairly new to this method of selecting and analysing

former studies, there is always the chance for possible biases. For instance, we selected the

studies that we thought of to be applicable. But it is highly possible that studies were not

included because of the fact that the paper did not show the information that was needed

on the first sight, while these might have been of considerable value if investigated with the

help of professionals, or even better, with the authors. This would be preferable to avoid a

possible selection bias. Secondly, we had a strong focus on correlations, disregarding other

facets that were included and might sketch a different portrait that only correlations might

bring up. To improve on the above stated, more time needs to be spend on the analysis and

possible implications of other measures than correlation (regression analyses were checked

for our critical synthesis for possible problems like multicollinearity). As way of conclusion

and perhaps the most important point to bring up is the discussion around ambidexterity.

When clear and mutual understandings might be achieved with a single definition about

what ambidexterity incorporates, a huge amount of studies might be included in this critical

synthesis. As we have chosen not to include ambidexterity, it is possible that we missed

possible relevant data that might give us a different perspective or more weight to the

existing results. Nevertheless, as this is still a problem, ambidexterity was kept out in this

critical synthesis.

2.4.1 Possible Moderating Variables

As can be derived from existing literature, researchers often propose different moderating

variables that can have an effect on the extent to which exploratory activities are associated

with the firm performance (see figure 5). In the papers that are used in the critical synthesis

several of such moderating variables are introduced. Auh and Menguc (2005) introduce the

role of competitive intensity as a possible moderating variable, Cao et al. (2009) introduces

the role of environmental munificence and the size of the organization as a possible

moderator and states: “A firm’s size is strongly indicative of the resources it possesses at its

immediate disposal’’ (Cao et al., 2009). As all of these researches were part of the same




research area, these variables can also be considered as possible moderators in this best

practise research.

Figure 5. Visual Representation of the Possible Moderating Variables that are Discussed in Section 2.4.1.

2.4.2 Managerial Implications Regarding the Observed Effect Sizes

In general, the CI’s in figure 2 show the possibility for a small to moderate association

between exploratory activities and firm performance. From this result can be concluded that

the managerial relevance could be constructed in a way that exploratory activities are very

likely to contribute during firm decision making. It would be a mistake to disregard the effect

that exploratory activities could have on the firm performance as it may lead to competitive

advantages by strengthening the fit with its surrounding environment. However, particular

situations demand a specific insight for the firm in question by which it is recommended to

conduct further research whether the likelihood of an association is in order for that

particular company in that particular time and situation. Furthermore, as can be derived

from the results after all the effect sizes were divided by region, the data provide real and

valid managerial relevance. The most important of which; for managers that operate in US

firms, it would be less attractive to use exploratory activities in order to boost performance

whereas in the rest of the world this would make more sense according to the comparison.

However, it is still important to note that there are only taken into account a limited amount

of studies. Therefore it would be recommended to perform future research with a bigger

amount of studies in order to make even more extensive grouping possible and to reinsure

that the effects observed here are trustworthy.




2.4.3 Recommendations for Contributing Studies

There are some recommendations for contributing studies that are supported by the

literature. These recommendations contribute in order to obtain empirical evidence

regarding a certain effect. First, a further divide between geographical area and industrialsector is necessary to assess the generalizability of the data (Cao et al., 2009; Lavie and

Rosenkopf, 2006). According to Cao et al. (2009) some locations, like high-tech parks, “may

have a resource advantage relative to familiar firms outside of these zones” (Cao et al.,

2009). Also Lavie and Rosenkopf (2006) suggest that outcomes may vary in other industries

and produce different patterns. Therefore it is recommended to set up more studies in

different industrial sectors in a more diversified geographical area. Second, a larger sample is

recommended to increase the reliability of the study. Along this principle, He and Wong

(2004) recommend that “Future research should assemble a larger sample to provide more

fine-grained controls for market and technological environmental factors, and to examine

how the optimal balance between exploration and exploitation may be contingent on such

environmental factors.” (He and Wong, 2004). Therefore it is recommended to maximize the

sample as much as possible. Third, because most of the studies used a cross-sectional

research strategy in which CEOs were asked to provide information, a common method bias

can influence the study’s results due to the use of these so called ‘self-reported measures’.

(Auh and Menguc, 2005) Therefore it is recommended for future research to make more use

of ‘objective data’ such as financial reports in order to be able to better monitor the quality

(i.e.: validity, (inter-item) reliability and measurement accuracy) of the data. Fourth, like

Isobe et al. (2004) state in their research, cross-sectional research “… says little about the

dynamic process of competitive strategy.” Longitudinal research on the contrary is more

useful in the long term determination of the possible presence of a certain association. (Cao

et al., 2009; Jansen et al., 2006; He and Wong, 2004) Especially because the effect of

exploratory activities in most cases is only visible over a longer time period. An additional

advantage of longitudinal research is that it is capable of identifying a possible causal

relationship whereas cross-sectional research cannot do this. From these considerations,

there can be recommended that future research should preferably consist of longitudinal

research only.




3. An Attempt at Best Practice Research

3.1 Introduction

The aim of this part of the thesis is to pursue an Attempt at Best Practice Research ourselves.The best possible research will be conducted taking into account and discussing the different

critics and limitations from the papers in the critical synthesis and the warnings and theory

provided by the course. However, the process should be approached realistically: due to

knowledge constraints and other limitations, we realize that the research most probably will

still have lots of constraints. These constraints will be carefully tracked, analyzed and

reported later. In several papers from the critical synthesis there were displayed limitations

of cross-sectional survey research. (Auh and Menguc, 2005; Cao et al., 2009) And some of

these even suggest a longitudinal approach in future research. (Isobe et al., 2004; Jansen et

al., 2006) However, due to time constraints we are not able to conduct a longitudinal

research. Furthermore, in most of the papers, data were collected from a single source (i.e.;

CEOs) because they are perceived to be the ‘most knowledgeable respondents’. Therefore,

in some of the papers, research limitations suggest future research to be conducted without

making use of ‘self -reported measures’. (Auh and Menguc, 2005) In our research, we will use

objective financial data in order to prevent this single source bias.

3.2 The Study

3.2.1 Formulating a ‘ est Practise’ for Generating Empirical Evidence

In order to conduct a best attempt study, it is desirable to describe what the perfect

situation would be regarding the aforementioned hypothesis. Since the hypothesis is highly

expected to be causal, the ideal situation would exist of a field experiment, which carries a

‘high internal validity’. (Hak, 2014) Furthermore, the experiment should ideally use all

companies in the world, with no missing cases, as a population. Then these companies

would be divided into four groups: An experimental group, a control group and within these

two groups each a group with ‘high’ exploratory activities and one with ‘low’ exploratory

activities. These activities should be consistently measurable, reliable and valid. Then, the

researchers should be able to manipulate the amounts of innovation activities in the

experimental group while the ‘firm performance’ variable is carefully monitored over the

years following (since exploration is expected to affect firm performance in the long run, this




period is preferably three to ten years). Ideally, also the measurements should be low on

biases (i.e.; both random errors and systematic errors (such as measurement errors and

sample design errors)). (McDaniel, 2012) Ideally, from the data derived, clear outcomes in

the form of confidence intervals, regression data and correlations would follow to enable anoptimal interpretation of the research. To enlighten to what extent the above would be

possible, in the following section the feasibility is further discussed.

3.2.2 The Complications of the Ideal Research Situation

As can be imagined, the situation as described above is not feasible at all. First of all,

researchers will never be able to manipulate key organizational factors such as exploration

activities simply because organizations will not allow them to do so. Second, a census

research that involves all companies in the world will not be feasible due to practical reasons

(i.e.; companies start and go bankrupt all the time, especially when a longer period of

observation is necessary this will be impossible). Also, since there are millions of companies

all over the world, only approaching these will probably already taking a man’s live. And

lastly, the statistical analysis of so many data will be too difficult to perform in a bachelor

thesis.

3.2.3 Population and Sample

In an earlier stage of the research design, we decided to approach a company that belonged

to a narrowly defined population. The plan was to survey all managers in the company in

order to derive data. However, partially due to later insights and partially due to advice by

our mentor, we decided that it would be more convenient to use existing data and to use

data from multiple firms from a clearly defined population. Therefore, now there will be

used data from the official ‘European Union Research and Development Scoreboard 2012 &

2013’ to set up a cross-sectional research strategy. The Industrial research and Innovation

‘IRI’ department of the European commission defines the Scoreboard as: ‘’The 2013 EU

Industrial R&D Investment Scoreboard (the Scoreboard) contains economic and financial

data for the world's top 2000 companies ranked by their investments in research and

development (R&D). The sample of the scoreboard consists of 527 companies based in the

EU and 1473 companies based elsewhere. An additional sample comprising the top 1000

R&D investing companies based in the EU is included. The Scoreboard data are drawn from




the latest available companies' accounts, i.e. usually the fiscal year 2011 & 2012’’.

(Hernández et al, 2013)

In the research that is conducted in this part of the thesis the population can be defined as:

“All German businesses with at least 260 employees that are active in either the Technology

Hardware & Equipment sector or in the Software & Computer Services sector and that are

listed in the 2013 EU Industrial R&D Investment Scoreboard (data from 2011 and 2012).” The

EU Industrial R&D Investment Scoreboard “contains economic and financial data for the

world's top 2000 companies ranked by their investments in research and development

(R&D).” (Hernández et al, 2013) The sample was drawn from the R&D Scoreboard using the

clearly defined population parameters. Therefore, the sample consisted of 26 companies

that fit the profile in 2012 and 24 cases in 2011. However, in order to have a more complete

sample, data from one case in 2011 was found in the company’s annual report (USU, 2012).

So in total, there was used data from 25 cases in 2011. Ranging from small to big amounts of

R&D expenditures as well as different sizes of the company regarding the amount of

employees, the sample provides enough variety concerning our research strategy. The list of

included companies can be seen in table 3 ‘Data matrix’.

3.3 The Structural Process of the Research Design

3.3.1 Specification of the Research Question

The focal unit used in the hypothesis is ‘the firm’. The theoretical domain wil l exist of all

firms in the world, in all economic sectors, in all countries, at all times. A firm, in the way we

are using it in our research, “is a ‘black box’ operated so as to meet the relevant marginal

conditions with respect to inputs and outputs, thereby maximizing profits, or more

accurately, present value.” (Jensen and Meckling, 1976) The reason why we are aiming at

firm level, is primarily because of the way we want to see how these firms use explorative

(i.e.; Research and Development) activities to enhance its performance. Other focal units

would be way more difficult partially because firms have the tendency to document

decisions in a structural way, enabling us to get the right information for this research.

Since the study searches for the eff ect of exploration activities on the firm’s performance, it

is clear to say that the exploration activities, e.g. research and development, will be used as

the independent variable. The firm’s performance on the other hand, is being observed as




the dependent variable in our research to see what the influence ‘effect’ of the exploration

activities on the performance may be, if any. Therefore, the firm performance is the

dependent variable is this research. The research question: “A firm’s engagement in

exploration activities is positively associated with its performance” combines the focal unitand the variables in a way that we have a clear vision of what we would like to research and

where we need to focus on.

3.3.2 Specification of the Research Strategy

Like already stated in the introduction of the critical synthesis the relationship between

exploratory activities and firm performance is likely to be causal (if there is an effect

measured in the first place). A causal relation is “a relation between two at tributes (X and Y)

of a focal unit in which a value of X (or its change) results in a value of Y (or in its change).”

(Hak, 2013). According to Hak (2013), when the research question implies causality, the best

research strategy is an experimental study. But in our case the cross-sectional study is more

feasible, even though it is well known that a cross-sectional study cannot generate evidence

in support of the causal direction that is implied in the hypothesis. A cross-sectional research

strategy is only valid for “a study of the simplest type of hypothesis, namely a study with the

aim to assess how much two variables co-vary.” (Hak, 2013). The cross-sectional study ischosen above the experimental study and the longitudinal study because of the earlier

stated impossibility to carry out an experiment and because of time constraints when

carrying out a longitudinal research. However, non-experimental studies have generally a

higher level of ecological validity, because they allow the observation of associations that

actually exist (Hak, 2013). Also other researchers often conduct cross-sectional studies and

point out the limitations. For instance Alexiev et all. (2010) stated that a limitation is the

cross-sectional design of the study, which prevents them from making a firm conclusion

about the direction of causality between the variables. This was also noted by Jansen (2005)

and Cao, et al. (2009).

As mentioned above, the research strategy used here is a cross-sectional research.

According to Dul & Hak (2007) "selection bias in published results exacerbates the risk of

drawing conclusions about the correctness of a theory based on a one-shot confirmation".

For this reason, the 2011 scoreboard data has been added to the data of 2012. Dul & Hak

(2007) stated that a number of replications are needed before someone can generalize a




particular claim. Because this second study (the 2012 scoreboard, data from 2011) is added

to the previous one (the 2013 scoreboard, data from 2012) it is called a serial study. A serial

study is when "one research project consists of a series of replications" (Dul & Hak, 2007).

And because the same research strategies have been used for acquiring the date, it is calleda serial survey (Dul & Hak, 2007). This is used in order to enhance reliability (i.e.: precision).

To organize the data all together, a data matrix is generated which indicates the particular

values of the dependent and the independent variable for each individual case. Additionally,

there is also chosen to provide the standard deviation in order to gain a notion of the

amount of variation there is in each of the variables (Hak, 2013). It is remarkable that

published papers usually not include this matrix in the ‘methods’ section of the paper.

“Adding this matrix to the paper would make the procedures of the study much clearer”

(Hak, 2013). On the next page the data matrix (table 3) is shown for the dependent variable

‘firm performance’ (in the form of 2011 and 2012 profits) and the independent variable

‘innovation activities’ (in the form of 2011 and 2012 R&D expenditures).

The effect sizes that are used, as a result from the data collected below (table 3) will be the

correlation coefficient and the (standardized and unstandardized) regression coefficients.

Since this is a study for detecting the amount of bivariate association, the standardizedregression coefficient will equal the correlation coefficient; so basically there will remain two

main effect sizes. Both have particular advantages and disadvantages. A correlation

coefficient tells more about the theoretical relevance of the results, while regression tells

more about the managerial relevance of the results. As both are indicated, also the

advantages of both can be used to analyse and interpret the results later on. An additional

note here is that these effect sizes will have nothing to do with the possible causality of the

association that is going to be investigated.




Table 3. Data Matrix with the Values of the Dependent and the Independent Variable for

Each Individual Case

Cases Value of R&D

Expenditures

2011

(€ million)

Value of R&D

Expenditures

2012

(€ million)

Value of

Profit 2011

(€ million)

Value of

Profit 2012

(€ million)

1. SAP 1939.00 2253.00 4863.00 4070.00

2. INFINEON TECHNOLOGIES 499.00 455.00 736.00 456.00

3. SOFTWARE 88.08 101.08 270.14 243.84

4.

WINCOR NIXDORF 100.17 90.47 162.36 101.25

5.

AIXTRON 48.39 69.56 115.01 -125.28

6. ADVA 60.45 65.55 13.21 18.83

7. KONTRON 59.72 65.07 33.70 -32.26

8.

LANTIQ 108.52 47.98 -36.29 -29.65

9.

NEMETSCHEK 41.23 45.10 28.61 31.10

10. COMPUGROUP MEDICAL 42.85 37.38 34.85 68.20

11. ELMOS SEMICONDUCTOR 34.90 34.13 26.32 11.75

12.

PSI 16.20 18.17 10.67 12.89

13.

COR&FJA 17.80 14.53 2.07 -19.25

14. P&I PERSONAL & INFORMATIK 13.55 13.85 18.15 24.04

15. MAGIX 7.10 13.18 5.28 2.85

16. FIS KORDOBA N/A 12.13 N/A 3.92

17.

NEXUS 11.82 11.37 4.22 5.4018. MENSCH UND MASCHINE

SOFTWARE

9.45 11.23 11.90 6.18

19.

RIB SOFTWARE 7.54 10.42 6.64 11.96

20. SUSS MICROTEC 10.53 9.77 18.74 13.03

21. BETA SYSTEMS SOFTWARE 5.33 8.53 -6.39 1.43

22. INTERSHOP COMMUNICATIONS 6.22 7.28 2.88 -0.72

23.

USU SOFTWARE 6.49 7.24 3.55 4.67

24.

ATOSS SOFTWARE 6.65 7.11 7.31 7.64

25.

FREENET 10.95 6.81 167.80 203.3926. CENIT SYSTEMHAUS 5.48 6.39 6.28 7.86

N = 25 N = 26 N = 25 N = 26

R&D Exp =

126.30

R&D Exp =

131.63

Profit =

260.24

Profit =

196.11

σR&D Exp =

390.263

σR&D Exp =

441.499

σProfit =

971.352

σProfit =

797.730

As giving insight in our measurement protocol, we used the data from this Scoreboard

because of the fact that it was issued for the European Commission and put together by

some leading companies and researchers. The trustworthiness of this document was thereby




easy to check as everything was well documented like the methods that they used or the

boundaries that they had set to avoid incorrect data. The European Commission states the

report to be seen as: “The Scoreboard is a benchmarking tool which provides reliable up -to-

date information on R&D investment and other economic and financial data, with a uniqueEU-focus. The 2000 companies listed in this year’s Scoreboard account for more than 90% of

worldwide business enterprise expenditure on R&D (BERD). The data in the Scoreboard are

published as a four-year time-series to allow further trend analyses to be carried out, for

instance, to examine links between R&D and business performance” (Hernández et al, 2013).

The report was used to show trends across different industries and sectors, including all data

that was used with their corresponding company name. In our protocol, we tended to use

the data that was most important to our research question and tried to keep all the data

unaltered before using it in our regression analysis.

The population is a part of the theoretical domain. The theoretical domain as described in

the first part of this thesis is defined as: “All firms in the world, in all economic sectors, in all

countries and at all times”. Due to the fact that we have a clear definition of our own

population with corresponding data from all of these companies, we are able to indicate that

most probably the whole population will be studied and thus the research will be a ‘census’research. Therefore, the sample is ‘the whole population’ and a probability sample will not

have to be considered. Reliability is defined by Jan van Dalen as a “the question whether or

not an indicator produces steady measurement results when applied multiple times.” (Van

Dalen, 2014). Due to the nature of our research, the trustworthiness of the results will not

impose problems. The main reason for this is that the data is from the European Commission

and obtained by a strong regulated research.

The variables that need to be defined in our research are both the dependent and

independent variable: exploratory activities and firm performance. Since several researchers

have already defined exploratory activities during the last decades, it is most convenient to

pick the one that suits best to the standards of this thesis. Therefore, we chose the definition

by James March: “Things captured by terms such as search, variation, risk taking,

experimentation, play, flexibility, discovery, innovation” (March, 1991). This definition is also

convenient do to the different concepts that March uses in the definition. The dependent

variable ‘firm performance’ however has been defined too often and in very different




contexts. Therefore, we decided to use the definition that was used in the R&D Scoreboard

from which we used the data for our analysis, partially because this would give us the

opportunity to use it for the operational procedures in the research. The firm performance

construct captures the company’s profit defined by the R&D Scoreboard as following: “Operating profit is calculated as profit (or loss) before taxation, plus net interest cost (or

minus net interest income) minus government grants, less gains (or plus losses) arising from

the sale/disposal of businesses or fixed assets’’ (Hernández et al, 2013). In order to do be

able to measure the effect of the two variables, we collected the data from the European

Commissions’ Scoreboard in the format, as they were included in the database. As R&D and

Profit were both included in the Scoreboard, we simply set the parameters right for our

defined population and took note off all the displayed data for our regression analysis.

3.4 Results

3.4.1 Effect Size Measures

As stated earlier, there was made use of a bivariate linear regression analysis after the

problem of non-linearity was resolved. The regression analysis shows that Profit (set as

variable profit 2011 and profit 2012 with data deducted from the EU Scoreboard) had a

mean of 260.24 million Euros for 2011 and 196.11 million Euros for 2012 with a standard

deviation of 971.35 million Euros and 797.730 million Euros respectively. R&D (set as

variable R&D 2012 with data deducted from the EU Scoreboard) had a mean of 126.30

million Euros for 2011 and 131.63 million Euros for 2012 with a Standard deviation of 390.26

million Euros and 441.499 million Euros respectively. The number of cases (i.e.; N) for 2011 is

25 companies, and 26 companies for the 2012 dataset. But because the sample was not

normally distributed, we had to take the logarithm of all the variables in order to resolve this

problem of non-linearity. Another problem was that there were some negative values

amongst the profits: because a logarithm of a negative number is impossible, in SPSS these

values were automatically excluded. To solve that problem we added a constant value to all

of the profit values. Subsequently, when looking at the results, the correlation (r ) between

R&D 2011 and profit 2011 is 0.533 (i.e.; the Standardized Regression Coefficients ‘Beta’,

when two variables are compared only), and a Unstandardized Regression Coefficient ‘B’ of

0.535, with a R squared of 0.285 and the Adjusted R Square of 0.253. The 95% confidence

interval was given as [0.169 ; 0.901]. The correlation (r ) between R&D 2012 and profit 2012




is 0.324 (i.e.; the Standardized Regression Coefficients ‘Beta’, when two variables are

compared only), and a Unstandardized Regression Coefficient ‘B’ of 0.304, with a R squared

of 0.105 and the Adjusted R Square of 0.068. The 95% confidence interval was given as [-

0.070 ; 0.678]. These results can be considered important as they can be used later on tomake theoretical and practical conclusions.

3.4.2 Worst Case Response Bias Analysis

As stated by Hak (2013): “A worst case is the situation in which the missing information, if

known and added to the matrix, would cause a maximum change in the observed effect.” He

also states that a worst-case response bias analysis is highly recommended when less than

95% of the cases are present. As only one case is missing in this study (i.e.: 96.15% is

present), there can be concluded that there is no need for a worst-case response bias

analysis.

3.5 Advanced Meta Analyses

To enhance managerial relevance it is desirable to include an advanced meta-analysis.

According to Glass (1976) a meta-analysis can be used as "… the statistical analysis of a large

collection of analyses that results from individual studies for the purpose of integrating the

findings." It is used in order to create a link between the critical synthesis and Attempt at

Best Practice Research. To give the best overview, there was decided to use three main

geographical areas namely: Europe, U.S.A. and Rest of the World (RoW). Because the

Attempt at Best Practice Research existed of two studied samples that consisted of German

companies, the German geographical area was included (that consisted of our own study) to

compare with the other effect-sizes.

3.5.1 General Meta Analysis

The first advanced meta-analysis consists of the meta-analysis that was formed in the critical

synthesis with the attempt to best practice included. As described in section 2.3.1, the meta-

analysis contains a method in which all the seven studies can be compared and analyzed for

their combining effect. This provides a 95%-confidence interval that ranges from 0.001 up to

0.669. The total amount of subjects that are included in this meta-analysis is 2473. After

adding the two effect-sizes from the attempt to best practice the results show a 95%-

confidence interval that ranges from 0.079 up to 0.638 (see figure 6). This means that there




can be stated with a certain amount of confidence that there is an even higher moderate

likelihood of an association between exploration activities and firm performance in general.

Figure 6. Visual Representation of the Meta-analysis including the Studies from the Critical Synthesis and the

Study from the Attempt at Best Practice Research.

3.5.2 Geographically Advanced Meta Analysis

To further enhance the view on managerial implication, the divide in geographical areas

when looking at the meta-analysis, might offer vital information to managers in those

specific regions. As previously shown in section 2.3.1, the meta-analysis of the critical

synthesis focussed on two separate regions namely: US and the Rest of the World (non-US).

Now, after the Attempt at Best Practice Research, the study with a sample that consisted of

German companies can be included to enlarge our view on managerial implications when

looking at geographical areas. The geographical areas for this advanced meta-analysis are:

Europe (including Germany), Germany (attempt at best practice on its own), US and the Rest

of the World. The previous meta-analysis on geographical area (from section 2.3.1) shows a

95%-confidence interval of [-0.008 ; 0.826] for non-US firms, whereas US firms show a 95%-

confidence interval of [-0.146 ; 0.409]. When setting all the regions as specified before, the

geographical advanced meta-analysis shows the following results for the following regions.

For Europe the advanced meta-analysis shows a 95%-confidence interval of [-0.211 ; 0.677]

indicating that there is no likelihood for a possible association between the dependent and

independent variable. Secondly for Germany the 95%-confidence interval is [-0.892 ; 0.982]

also indicating that there is no likelihood for a possible association between the dependent

and independent variable. Thirdly the US results show a 95%-confidence interval of [-0.146 ;




0.409]. Whereas this result is less spread, also here the results indicate that there is no

likelihood for a possible association between the dependent and independent variable.

Lastly, for the Rest of the World, the outcome is different. The meta-analysis regarding this

geographical area indicates a 95%-confidence interval of [0.127; 0.868], indicating amoderate likelihood of a possible association between the dependent and independent

variable (see figure 7).

Figure 7. Visual Representation of the Meta-analysis including All Confidence Intervals from the Different

Geographical Areas.

3.6 General Conclusion

In this chapter conclusions are drawn from the results of this study in order to answer the

research question that states: ‘To what extent are a firm’s exploration activities associated

with its performance?’ First, there is discussed whether a general conclusion can be drawn.

Second, to enhance managerial relevance, more detailed conclusions are evaluated.

The general conclusion, that can be derived from the meta-analysis in which all studies are

included, states that there is a small to moderate likelihood of an association between

exploration activities and firm performance (CI [0.079 ; 0.638]). However it is very important

to notice that geographical differences seem to have a large impact on the conclusion

mentioned above. Since the study cannot conclude anything that suggests a possible causal

relationship due to the study’s cross-sectional nature, there can merely be implied that

there is a certain likelihood for an association. Therefore, it is important to watch out for the

level of exploration as the likelihood of an association clearly indicates that in times of

decision-making, this awareness might uphold different outcomes.




3.6.1 Managerial Implications

The above stated effect regarding the geographical importance in the study is most

important in managerial implications. Therefore, several geographical areas were divided

and the Attempt at Best Practice Research was compared with the earlier studies that werecompared in the Critical Synthesis part. From these comparisons, the remarkable conclusion

can be drawn that only in the Rest of the World (RoW) a moderate likelihood of an

association exists when looking at the confidence interval (as this confidence interval did not

hold negative values). Hence, in all the other geographical areas that were included (US,

Europe and Germany) more research is required in order to provide the necessary

information for management decisions.

The Attempt at Best Practice Research suggests that there might be a small to moderate

likelihood of an association between exploration activities and firm performance in

Germany. However, due to the relatively low amount of cases used in this study, this

conclusion does not rule out other possibilities. It is merely an indication for a possible

effect. Furthermore, when looking at the managerial relevance, it is also important that

multinationals might have to take into account that different countries uphold a different

association between the amount of exploration activities and firm performance. Even the

meta-analysis in the critical synthesis pointed this out. To maximise potential, the

management of these multinationals might have to look at each area (and industry)

individually when looking for a suitable reference for an association to be used in their

decision-making. To pose another possible problem, these geographical areas might also

differ when looking at the way that their accounting is done. Each country might have their

own reporting techniques in which they will calculate their profit. (E.g. IFRS, GAAP, or

otherwise known as the generally accepted accounting principles). (Meulen et al, 2007)

These implications are of importance for management decision-making and should be

considered when using possible associations between exploration and firm performance.

Future research might unfold these implications but as of yet, it is not possible to make

these conclusions.

3.6.2 Limitations and Recommendations for Future Research

Unfortunately, there are also some limitations arising from the data collected. Firstly, as the

report describes, it is possible that some companies are not included in the scoreboard. The




report describes that “the Scoreboard relies on disclosure of R&D investment in published

annual reports and accounts. Therefore, companies that do not disclose figures for R&D

investment or that disclose only figures which are not material enough are not included in

the Scoreboard.” (Hernández et al, 2013). As a result, it may be that certain companies, andtherefore some company information, is not included in the Scoreboard and therefore not in

our research. Secondly, the paper states that: “… in some countries, R&D costs are very

often integrated with other operational costs and can therefore not be identified separately.

Because of this, companies from Southern European countries and the new Member States

are under-represented in the Scoreboard. On the other side, UK companies are over-

represented in the Scoreboard.” (Hernández et al, 2013). As our own study only included

German companies, it might be possible that the second limitation is reduced.

As discussed in section 3.6.1, there are several limitations that also pose potential

distortions when looking at the managerial implications. The critical synthesis pointed out

differences between industries and geographical area’s, combined with samples that existed

of many cases. The attempt at best practise did not exist of a sample with many cases what

resulted in a broad confidence interval. As this is not recommendable, it is hard to conclude

possible associations in that specific region. For future research, it would be recommendedto include samples of the specified population that will exist of many cases to be able to

minimise the range of the confidence interval. Also, it would be recommended for future

research to conduct a study that will include multiple geographical areas to enlarge the

compatibility of the results for future references.

The limitations and recommendations mentioned above are all factors that are easily

detectable. Moderating factors on the other hand, are not. As discussed in part 2.4.1.,

competitive intensity, environmental munificence and organisation size are already

identified as possible moderating variables. As firm performance and exploration activities

are dependable on many factors, possible-moderating factors can easily be overlooked while

being of importance. It is therefore recommended for future research that a clear and

structured oversight is to be made with the most important possible moderating variables to

see whether they are indeed of moderating value or not.




3.7 Lessons Learned

While executing the attempt to a best practice research, several learning points could be

identified, which will be discussed in this chapter. First of all, the most important lesson is

that it is not preferable to use p-values at any time. The main reason for this is because of

that the system of Null Hypothesis Significance Testing (NHST) is constructed on the basis of

two existing statistical theories. The first one developed by the Polish mathematician Jerzy

Neyman and statistician Egon Pearson and the second one from Ronald Fisher (Cumming,

2012; Engels, 2014). In the NHST theory, these two theories are used together while they are

absolutely not compatible. Therefore the use of combining p-values with the Cronbach’s

Alpha (α) can be confusing and may even lead to false conclusions. Another reason not to

use p-values is because of the fact that when p-values are obtained, a lot of the data will be

lost. This will be because of the fact that p-values can be derived from confidence intervals

but not the other way around. In this case confidence intervals hold more information and p-

values because of their derived nature will lose a lot of detail.

Second, once again and more explicitly than ever before, we learned the importance of not

generalizing further than the selected population. Even though the temptation to do so is

sometimes present. The results in our research will never be applicable for firms that do notbelong to the narrowly described population. Thus for a researcher, there will always be the

trade-off between a small population for which data could be derived relatively easy and a

larger –more general- population for which data probably is much harder to be obtained.

Third, we perceived it to be very hard to obtain data for our narrowly defined population,

especially because this data should be provided by a reliable source. For a moment the

group thought it would be qualitatively better and more reliable to collect data by

themselves, but soon it became clear that, as professor Hak stated before in his Course

Book, it is preferable to not conduct new measurements. (Hak, 2013) Therefore, we have

learned that it is important to make a well-thought and rational decision while you consider

to use either existing data or to collect primary data by yourself. Both have a lot of

(dis)advantages and it is wise to consider one over another in the design-phase of the

research. Additional to this reliability learning point, we experienced that it could be vague

in some research situations (especially in the presence of a relatively small sample size like

ours) whether or not to exclude a case that can be perceived as a possible statistical outlier.




Fourth, the definition of the different concepts one is using during one’s research is of

utmost important. These definitions should be formally ‘backed-up’ by the existing literature

whereas this is possible. By doing this, there is made sure that definitions are accepted by

the research-community and that –through the reference back to the original literature- thecontent of the definition will be better understood. Subsequently, after having defined

important concepts of the research, the right use of the concepts should be monitored

constantly by every member that is working at the research. Violation of the parameters of

the concept can be absolutely disastrous for the implication of the research and the research

itself will lose its credibility.




4. References

Alexiev, A., Jansen, J., Bosch, van den F. and Volberda, H. (2010) ‘Top Management Team

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Appendix A1. The Antecedents, Consequences, and Mediating Role of

Organizational Ambidexterity [excluded from the meta-analysis]

The article: “The Antecedents, Consequences, and Mediating Role of Organizational

Ambidexterity” by Cristina B. Gibson and Julian Birkinshaw, was one of the articles that we

decided upon, not to use in our meta-analysis. The reason why we did not include this paper

is primarily because of the use of ambidexterity in the paper. Ambidexterity in this paper

was defined as the influence of alignment and adaptation, which was then correlated against

firm performance.

The data for the dependent variable (ambidexterity and performance) and the independent

variable (organization context) was collected using surveys and conducting interviews fromdifferent ‘levels’ within the organization to avoid the same-source bias. All items that were

questioned required a seven-point Likert-style scale. These Likert scales were tested on their

inter-item reliability, resulting in a Cronbach’s Alpha of 0.80 (α = 0.80). (Gibson and

Birkinshaw, 2004) The results show us the association between the performance and

ambidexterity, also stating that the coefficient for ambidexterity was positive (β= 0.47) and

therefor supporting hypothesis 1 in a consistent way with the research strategy. (Gibson and

Birkinshaw, 2004)

While the research itself was set up in a way that was definitely useful for our research, we

decided not to include any papers that use ambidexterity. The problem with ambidexterity is

that there isn’t any conclusive definition of what it incorporates. Many papers use

ambidexterity in their own way, resulting in many different definitions of ambidexterity. This

poses a real problem, not only will it be hard to have a clear definition, it might also be

interpreted in a wrong way by ourselves or other researchers in the future. To ensure that

we did not include this potential error, we left out all papers indicating measurements with

the use of ambidexterity even if they seemed usable in the first place.




Appendix A2. Exploration vs. Exploitation: An Empirical Test of the

Ambidexterity Hypothesis [excluded from the meta-analysis]

The article: ‘Exploration vs. Exploitation: An Empirical Test of the Ambidexterity Hypothesis’

by Zi-Lin He and Poh-Kam Wong, was one of the articles that we decided upon, not to use in

our meta-analysis. The reason why we did not include this paper is primarily because of the

use of a different method for representing the outcome of the research. The method used

here is regression, while correlation is the method used in all the other articles. The

confusing with this article is that the control of the survey is shown using correlation, while

all the hypotheses are represented using regression.

The researchers measured this by using a survey about the innovation behaviour and firmperformance of 2822 manufacturing firms in Singapore and Malaysia. The high Cronbach’s

Alpha for the scale (α = 0.807 and α = 0.752) indicates a good scale construction and precise

measurement. The sampling frame was constructed from the databases provided by the

Economic Development Board of Singapore and Penang Development Corporation.

Questionnaires were sent to the CEOs of these firms. Responses with missing data and

doubtful or contradictory answers that could not be clarified were removed from the

sample. Eventually the valid sample size for this study was 206. Its finds that “…the

interaction between explorative and exploitative innovation strategies is positively related to

sales growth rate.” (He and Wong, 2004) and it also finds that “…the relative imbalance

between explorative and exploitative innovation strategies is negatively related to sales

growth rate.” (He and Wong, 2004).

While the research itself was set up in a way that was definitely useful for our research, we

decided not to include this paper. The method used in this article, regression, does not

correspond to all the other articles which we have found. Hence we do not use this article in

the meta-analysis.




Appendix B1. Balancing Exploration and Exploitation: The Moderating Role of

Competitive Intensity

This article, which is written by Seigyoung Auh and Bulent Menguc and contains a cross-

sectional study on the ‘contingency role that competitive intensity plays in explaining the

relationship between exploration/exploitation and firm performance.’ (Auh and Menguc,

2005) The study is considered to be cross-sectional because 980 sample firms (i.e., their

CEOs) where approached using a questionnaire at one specific point in time. Subsequently,

firm perf ormance can be further subdivided into firm performance’s effectiveness and

efficiency. It is important that within this analysis we will concentrate as much as possible on

(1) exploration activity and (2) effective firm performance. Also, although the paper suggests

different relationships regarding prospectors and defenders, this analysis will be

concentrated only on defenders.

The focal unit used in this hypothesis can be derived as ‘defender’. The paper defines this

term hardly, and readers are only introduced superficially to it. Looking for a better

definition in a paper that is written by the developers of the term, this defines that: ‘The

defender (i.e., its top management) deliberately enacts and maintains an environment for

which a stable form of organization is appropriate.’ (Miles et al., 1978) This definition also

links the defender (which is the focal unit) to its units of analysis: company’s top

management, which is used as well by Auh and Menguc. However, despite the fact that the

term ‘defenders’ seems to be applicable for all defenders in the world, in all sectors and at

all times, only by reading very carefully the papers’ Research Method section and/or the

Limitations and Future Research section can be concluded that the defender are ‘only’

represented by Australian firms that are active in manufacturing industries. This means thatthe domain is considerably smaller than the writers try to insinuate. Therefore, the entire

population in this research would be better defined as: all Australian firms that are operating

in the manufacturing industries. If subsequently is taken a close look to the different

variables used in the research there can be found a clearly stated dependent variable (DV):

‘effective firm performance’, and also an independent variable (IV): ‘exploration’.




Table A1. Copy of Table 2 of the article ‘Balancing Exploration and Exploitation: The Moderating Role of Competitive

Intensity.’ (Auh & Menguc, 2005)

When taken a closer look to the research results that are published in the paper (see table

above), there can be found an effect size concerning our main hypothesis. (Auh and Menguc,

2005) This effect size is denoted as a correlation coefficient ( r ) which carries a value of 0.70

for the association between the two variables. Since the correlation is basically a

standardization of the covariance, it concerns a standardized, but unit-free measurement.

From statistical software that was provided, it was possible to obtain the confidence

intervals (CIs) for this correlation. The lower confidence interval is 0.667 and the upper

confidence interval is 0.731. This suggests that there is a moderate to high likelihood of a

positive association between the two variables. Nevertheless, despite the fact that an effect

size for the hypothesis is provided, the writers do not use it for the evaluation of the results.

Instead they use the t-value and a significance test for the evaluation, something which is

not supported in the literature of the course and which is even defined as a ‘fal lacy of the

slippery slope of (none) significance’. (Hak, 2013: 19; Cumming, 2012: 28-32)

Notably, the writers use the term ‘relation’ in the description of their findings: “We found

that at high levels of competitive intensity exploration was not related to firm performance

(…).” (Auh and Menguc, 2005) Although this could suggest a causal formulation, they do not

explicitly confirm this possible causal nature in the text. And they should not have done this

either. The cross-sectional research strategy is not able to find causality, merely association.

Therefore, they could have better used the term ‘association’ in their hypothesis and




description of the findings. However, the effect size measure is consistent with the cross-

sectional research strategy. Only the verbal formulation of the hypothesis and the

description of the findings could have been described more accurate to avoid

misinterpretation. The difference between our interpretation and the one of the writers isthe measures used. The writers based their conclusions exclusively on significance testing

and interpretation of the t-value.

Since the managerial relevance of this thesis’ general research question concerns the

‘difference in a firm’s performance when or not associated with exploration activities’ (see

the introduction of this synthesis), it is important to take a close look at the population that

is studied and the characteristics of it. This paper contains 980 sample firms which are all

represented through their CEOs. This is well within the theoretical domain of the central

hypothesis. Since this is just a part of the total population it can be concluded that it

concerns a sample study instead of a census study. This sample was drawn from “a masters

list of 1000 firms operating in a variety of manufacturing industries in Australia from a

leading market research/databank company”. (Auh & Menguc, 2005) Despite the fact that

the sample for the pre-test was randomly selected from this masters list, the purchasing of

such a list itself is a sign of that this sample is not a probability sample. The most importantreason for this is that the population behind the sample is not exactly specified. There is only

specified that the masters list is including Australian manufacturing firms. Additionally, also

the possibility of nonresponse was tested: “The likelihood of nonresponse bias was tested by

splitting the total sample into two groups; those received before the second wave of mailing

and those received after the second wave.” (Auh & Menguc, 2005) A comparison of both

groups by using a t-test showed no significant differences.

Furthermore, the validity and reliability of the measurements of both the dependent and the

independent variable should be mentioned. The validity of exploration and firm

effectiveness was measured trough Cronbach’s Alpha, which is a measurement of internal

consistency. For exploration (within the group of defenders) the alpha was 0.88 and for

effective firm performance this was 0.71. According to the rule of thumb, an alpha above

0.70 indicates an internally consistent scale. (Van Dalen, 2014) Thus can be concluded that

both variables are measured validly. Additionally, there is conducted a confirmatory factor




analysis (CFA) that judges both the inter-item reliability and validity of the multi-item scales.

(Auh and Menguc, 2005) Since both variables are quantitative scores, the CFA could be

applied to both exploration and effective firm performance. Also, the average variance

extracted (AVE) results were checked to be at least 0.50 and the writers ran a checkregarding the overlap of different important concepts: a hierarchical moderated regression

analysis to avoid the possible ‘multicollinearity’. (Auh and Menguc, 2005) All results were

perceived to be good and accurate. As to speak of the accuracy of the measurement, both

exploration and firm effectiveness were fixed amounts that could be derived from the

company’s financial reports. Since these reports are externally checked by accountants,

these can assumed to be accurate. The respondents of the surveys, the firms’ CEOs, were

chosen because of the profound knowledge these people have about the company.

However, it is important to note that –despite the fact that the survey results were treated

anonymously- the answers of the CEOs could have been somewhat flawed. They are not able

to give answers that are 100 per cent reliable and objective. However, at least these CEOs

are representative respondents regarding the theoretical domain.

The study by Auh & Menguc does not make any claims beyond the studied population.

When there is referred to the results, these are only implied with regard to the cases

investigated. The writers do well by not generalizing the results to the all firms in all

industries. Since this study is conducted among Australian manufacturing firms and the

researchers did not make use of a series of studies there can be concluded that the

theoretical domain is heterogeneous. Unfortunately, possibly due to the specific selection of

the theoretical domain, the results of the study are not compared with those from other

studies. Due to the research method used (cross-sectional), the effect size does not say

anything about the possible causal nature. Therefore, it has relatively little managerial

relevance. On the other hand, there is found to be a significant correlation which suggests

that there is an association between the two variables.




Appendix B2: The Influence of Founding Team Company Affiliations on Firm

Behavior

This article, which is written by Christine M. Beckman (2006), and contains a study on the

association between shared previous work environments and a company’s engagements in

exploration and exploitation activities. Exploration and exploitations capture a wide variety

of firm actions and behaviors (Beckman, 2006). To fully understand to which extent this is

embedded in an organization, the research opens the question of evaluating managers’

backgrounds to see whether shared working experiences might have an influence on firm

behavior. Especially founding teams, so the collection of ‘managers’ that came up with the

idea to start a business, form the native behavior that will be shared within the whole

organization.

In this study, the Beckman examines groups of early executives that comprise firms’

founding teams and argue that their prior experiences predispose firms to engage in

explorative or exploitative behaviors. In a broader sense, this view suggests that team

composition both informs and constrains later firm action. Affiliations are important because

the past companies in which managers have worked offer employees models for what an

organization should look like and how it should act (Beckman, 2006). The focal units of all of

the hypotheses are the founding team, which is the same as the unit of analysis in the study.

The founding teams were mapped by interviewing and surveying, supplemented with

archival data. To eliminate possible errors in the unit of analysis, the researcher removed the

data ‘companies’ that could give a wrong insight. For example, companies with only one

founder couldn’t give the same information as companies that are founded by teams of two

or more founders.

The independent (IV) and the dependent variable (DV) are formulated in the article itself.

The independent variable is set as the diverse prior company affiliation and the firm

performance as the dependent variable. Furthermore, to add more consistency and

overview, the following control variables were added to the research: Industry, Venture

capital, Firm controls and Team controls

The results were displayed as significant, despite the reduced number of observations (given

a non-exact p-value). Moreover, there were not any confidence intervals portrayed or




calculated, but the effect sizes were displayed in an orderly manner, showing all variables

and their measurements. There are several effect sizes that were used to show a relation.

Some of the non-standardized ESs, portrayed with their mean, SD, minimal and maximum

and corresponding correlation coefficients are Idea change rate, Top management team size,Firm age, Firm growth and Proportion. The variables that we checked for our interest in the

relationship between firm performance and exploration activities are: Exploration strategy

and Venture capital financing (r = 0.19). From statistical software that was provided, it was

possible to obtain the confidence interval (CIs) for this correlation. The lower confidence

interval is 0.024 and the upper confidence interval is 0.346.

Table A2. Copy of Table 1 in the article (Beckman, 2006)

Besides non-standardized, the research also included the Pseudo-R2, which is standardized.

Therefore, both effect size types are used but there are more non-standardized types used

than the standardized types. Unfortunately, there aren’t any confidence intervals presented.

We tried to look at the given p-values to get more information but failed due to the fact that

the p-values are not exact but merely statements. Therefore, we can say that the authors

might tend to use superfluous information in their reasoning. Because these are merely

statements, we cannot conclude what the actual CI’s are with our current understandings of

the CI calculations. The results demonstrate that founding teams whose members have

worked for some of the same prior companies are more likely to pursue an exploitation

strategy and less likely to pursue an exploration strategy, whereas founding team members

from different prior companies are more likely to support an exploration strategy.




Furthermore, the results offer some evidence that firms whose founders have both common

and diverse prior affiliations are more likely to grow (Beckman, 2006).

The interpretation of the effects, show that the researcher concluded that all three of the

hypotheses are supported by the data, but we are skeptical about the fact that they are

giving causal clauses while this research is set up as a cross sectional research and the fact

that significance testing was a big part of this research. The effect sizes themselves were

focused on the correlations, which is consistent with the research strategy. As the results do

show their p-values, we can say that the results depend on their significance ‘value’. Due to

their non-exact nature ‘instead of an exact number’, we cannot say much about these p-

values as the actual p-value might differ in value. We would have liked to see more

estimation or exact data to give a better insight in the overall research. (Cumming, 2012)

The data was drawn from more than 170 young Sillicon Valley companies and the sample

focused on a subset of high-technology industries. To get the information, interviews,

surveys and archival data was collected from a final sample of 141 companies. The

eliminated companies were because of missing data (14 companies) or due to the fact that it

was set up by a single entrepreneur (18 companies). There was no other information

whether or not a probability sample was used or if there was. The dependent and

independent variables were measured using the data from well-known databanks to control

the trustworthiness of the information. Besides the internal control between the variables,

the researchers did say that they used T-tests to check certain relations; they did not use any

method to show the consistency in their data collection. What they did highlight was the

way the data from the interviews was used. They used specific coding procedures to make

sure that they were consistent in the handling of all of the information. As these techniques

are new to us, we could not use them to evaluate whether the techniques being used are

appropriate. (Beckman, 2006). The measurements do rely on informants as they used

questionnaires to get some of the data. They did not portray anything of importance

pointing out the trustworthiness of the respondents. Also, when checking the validity of the

research, we did not find any of the common measures to accompany the construct or

divergent validity. We checked the paper for the CVI-values, the RMSEA, CFI, TLI and the

SRMR. Therefore, we cannot conclude whether the measures are as valid as they are

portrayed.




The inter-item reliability can be checked with the use of the Cronbach’s Alpha or Rho, but

neither of them could be found in the paper. Therefore, as well as the validity, there cannot

be concluded whether the inter-item reliability is according to the rule of thumb ( > 0,7). The

accuracy can be checked by looking at the way they measured/obtained their data. As theywanted to know the effect of founding teams, the way they got their data did not pose a

direct threat to the accuracy. The managers involved are all coming from a company with a

founding team and a manager is a good source to ask for his background and likelihood to

act in certain ways. To back this up, the archival data can check for inconsistencies that

arisen from the data received during interviews or questionnaires.

The author does claim that the results are likely to be applicable beyond the population. The

paper states that there was no control variable for industry, but merely it was used to

maintain consistency with earlier models. The theoretical domain is not specifically

mentioned to be homo- or heterogeneous. But, as the results are compared with prior

research, as mentioned in the contributions part of the report, and the way the report is

saying that the differences in results are because of the ‘unique’ attributes of each research

and the population, we can conclude that they tend to see the population as heterogeneous.

The differences between the measured effect sizes between the different studies are thenused to say something about what actually gives the best result looking at the research

question. The practical relevance is, to our own idea, merely another insight in what might

tend to show the relationship of team affiliation and firm growth. They show that previous

research has put out some initial oversights of what may be the best factor to see if a

company is likely to have a decent firm growth, but add another layer of information trying

to surpass previous research and stating that their findings are the best way to see what is

linked to firm growth and what not.




Appendix B3: Unpacking Organizational Ambidexterity: Dimension,

Contingencies, and Synergistic Effects

This report is about the construct of organizational ambidexterity. Ambidexterity refers to

the ability of an organization to be efficient in its management of today as well as exploring

net opportunities (Duncan, 1976). Organizational ambidexterity is composed of two relative

dimensions: one relating to the balance between exploration and exploitation, the “balance

dimension of ambidexterity” (BD), and the other pertaining to their combined magnitude,

the “combined dimension of ambidexterity” (CD) (Cao et al., 2009). The research strategy is

cross-sectional by using surveys that were sent to 200 small- and medium-sized enterprises

(SMEs) high-tech parks in China in the middle of 2006, of which eventually 122 would

participate in the research and met the criteria.

To use this report for our own research we focus solely on the relationship between

exploration and firm performance. The writers define the concept of exploration according

to an earlier definition by March (1991), and use “exploration involves searching for new

knowledge and opportunities,” to formulate exploration. To measure the firm performance,

Cao et al. (2009) used the scale of Gupta and Govindaran (1986) and “…asked CEOs to rate,

on a 1 –7 Likert scale, their firm’s performance over the last 12 months in terms of sales

growth, profit growth, market share growth, operational efficiency, cash flow from market

operations, and market reputation.” (Cao et al., 2009). The unit of analysis of this paper is

somewhat different from the focal unit of our hypothesis. As described above, this paper is

about small to medium-sized enterprises, while our paper is about all firms in the world, in

all economic sectors, in all countries. Even though this research was conducted in China, the

authors of the article do not insinuate that their findings are limited to this area. Although,

the authors of this article did recommend that the research must be expanded to larger

firms and other geographic areas as well.

When looked at the variables of this paper, there can be concluded that the independent

variable and the dependent variable of this article match with the dependent and

independent variable of the main research hypothesis. Both are about the exploration

strategy and the firm performance of a company.




When taken a closer look to the research results that are published in the paper (see table

below), there can be found an effect size concerning our own research hypothesis. This

effect size is denoted as a correlation coefficient (r ) which carries a value of 0.311 for the

association between our variables. Since the correlation is basically a standardization of thecovariance, it concerns a standardized, but unit-free measurement. From statistical software

that was provided, it was possible to obtain the confidence intervals (CIs) for this correlation.

The lower confidence interval is 0.139 and the upper confidence interval is 0.346. This

suggests that there is a little likelihood of a positive association between the two variables.

Table A3. Copy of Table 2 of the article (Cao et al., 2009).

When we take a look at all the information provided, the report presents all the relevant

information. The analyses and results section gives an effect size using the correlation, what

makes the effect size parameters consistent with the research strategy (Hak, 2013). Notable

is that the report also gives superfluous information. Besides the report gives the effect size,

it also makes some notes about the significance of the results in the robustness test. The

report presents a cross-sectional study. A cross-sectional study consists of a data collection

from a population or (random) sample at one moment in time. But according to the

Research Training and Bachelor Thesis’ course book, a cross-sectional study “…has the

lowest level of internal validity for generating evidence regarding a causal effect” (Hak,

2013). A positive fact is that the writers admit that it is a cross-sectional study, they say:

“…we also note that, given the cross-sectional nature of this study, we have not been able to

explore how a firm’s exploratory and exploitative tendencies, or ambidextrous orientation,

develop over time.“ (Cao et al., 2009). Another positive fact is that the writers (in

contradiction to almost all other researchers), use the term ‘association’ to describe the

effect of exploration on strategic performance. This means that the writers want to indicate




and emphasize that cross-sectional research can absolutely not imply a causal relationship.

And above that, it shows professionalism and accuracy.

The delivered questionnaires to a total of 200 randomly selected firms through park

administrative offices from the high-tech parks. Because not all the high-tech small to

medium-sized enterprises firms from the three economic zones were selected, this study

covers a sample of the population. The population is within our theoretical domain, since we

include all firms in the world, in all economic sectors, in all countries, at all times. The writers

do not give us the information to find out whether the selected companies are from a

complete list of the population or not. According to Hak (2013) such list is do not count as

probability sample. He also mentioned that “…a requirement for the application of

inferential statistics in a data set is that the sample is complete.” (Hak, 2013).

As mentioned above, the researchers delivered questionnaires to a total of 200 randomly

selected firms. The final sample consists of 122 firms. The response rate is therefore

122/200=61%. They also assessed nonresponse bias by searching for differences in early and

late response. When we look at the data measurement method, the respondents from this

research were the CEO and CTO. They selected the CEO as the respondents because “…We

selected the CEOs as the respondents because the CEO is likely to be the most

knowledgeable informant about a firm’s strategy and performance” (Cao et al., 2009). And

to eliminate single-source bias, they also collected the same measures for the CTO. But we

expect a little distorted picture of reality, because they had to assess their own firm.

Furthermore, the validity (inter-item reliability) of the measurements of both the dependent

and the independent variable should be mentioned. The validity is checked with the aid of

the ‘internal reliability’. They calculated the Cronbach’s alpha for each construct, and found

that all of them exceeded the 0.7 level. As mentioned above, the writers used multiple

respondents per firm to eliminate single-source bias and found that the ratings were highly

correlated with each other. But according to Hak (2013) using informants is always a threat

to (inter-item) reliability.

When we look at the conclusion and the claims made in the paper from Cao et al., we can

conclude that the study does not make any claims beyond the studied population. The

writers state that “One boundary condition for our study pertains to the generalizability of

our findings beyond the population from which our sample firms are drawn.” (Cao et al.,




2009). When there is referred to the results, these are only implied with regard to the cases

investigated. Because the researchers did not make use of a series of studies there can be

concluded that the theoretical domain of the study is heterogeneous. Unfortunately, the

results of the study are not compared with those from other studies. The writers state thatthere are different operationalization’s of ambidexterity “… which has made it difficult to

compare results across studies and amass a core set of findings on which to build.” (Cao et

al., 2009). The researchers found a significant correlation which suggests that there is an

association between the two variables, especially for Chinese small- and medium-sized

enterprises. But due to its cross-sectional nature, the research has relatively little managerial

relevance.




Appendix B4: Exploratory Innovation, Exploitative Innovation, and

Performance: Effects of Organizational Antecedents and Environmental

Moderators

This article is written by three researchers from ERIM (Erasmus Research Institute of

Management) and tries to uncover different possible relationships or effects using the

independent variables exploratory innovation and exploitative innovation and the

dependent variable of firm performance. At the same time the article is examining different

moderating variables regarding the effectiveness of the independent variables. It finds that

‘… centralization negatively affects exploratory innovation, whereas formalization positively

influences exploitative innovation.’ (Jansen et al., 2006) And it also finds that “… pursuing

exploitative innovation is more beneficial to a unit’s financial performance in more

competitive environments.” (Jansen et al., 2006) But the most interesting part for us is that

also the relationship between exploratory innovation and firm’s financial performances is

measured. This provides us with possible valuable information. The dependent variable will

be ‘financial performance’ and the independent variable will be ‘exploratory innovation’.

The focal unit in this hypothesis is ‘organization’. Because the research is conducted among

large European financial service firms, this entity matches the focal unit in the hypothesis.

Furthermore, the paper defines exploratory innovation: “Units that engage in exploratory

innovation pursue new knowledge and develop new products and services for emerging

customers or markets.” (Jansen et al., 2006) Due to absence of a good measure for

exploratory innovation in the existing literature, the writers constructed a group of firm

characteristics to measure the variable. The high Cronbach’s Alpha for the scale (α = 0.86)

indicates a good scale construction and precise measurement. Financial performance on the

other hand “… was measured through internal corporate records by a unit’s average

profitability from 2003 up to one year after the measurement of exploratory and

exploitative innovation.“ (Jansen et al., 2006)




Table A4. Copy of Table 1 of the article ‘Exploratory Innovation, Exploitative Innovation, and Performance: Effects of

Organizational Antecedents and Environmental Moderators.’ (Jansen et al., 2006)

As marked in the table above, which is derived from the actual paper, the correlation

coefficient for the association between exploratory innovation and financial firm

performance is r = 0.18. Since the correlation is basically a standardization of the covariance,

it concerns a standardized, but unit-free measurement. The closer this number is to 0 (which

is the case here), the weaker the amount of association is. From statistical software that was

provided, it was possible to obtain the confidence intervals (CIs) for this correlation. The

lower confidence interval is 0.064 and the upper confidence interval is 0.291. There is a

weak to moderate likelihood of a positive association between the two variables.

Finally, it can be concluded that there might be a positive, but very weak association

between exploration innovation and financial firm performance. The writers of the paper did

not explicitly look for this association since they were concentrating mainly on moderating

variables by using multivariate regression analyses. However, they report the figures and

drew conclusions on their own specific hypothesis by using the superfluous p-values instead

of confidence intervals. This can be considered a weakness because a confidence interval

reporting is known to give more specific information. Lastly, the research is cross-sectional

but data was collected “through internal corporate records and the temporary separation of

the independent and dependent measures”. (Jansen et al., 2006) According to the Research

Training and Bachelor Thesis’ course book, “the preferred research strategy for studying an

association is a cross-sectional study” (Hak, 2013) However, the same course book states

that this kind of research strategy absolutely cannot assess the presence of a causal




relationship. So by interpreting this knowledge, this paper can be used only to discover

(linear) association, assuming that not another variable intervenes with this association.

Furthermore is the research conducted among 283 organizational units from 115

autonomous branches of a large European financial service firm. However, this is notimplied. The writers use the terms ‘relation’ several time in the description of their findings.

For the paper’s methodological correctness it would have been better to replace

‘relation(ship)’ with ‘association’.

Since the managerial relevance of this thesis concerns the ‘difference in a firm’s

performance when or not associated with exploration activities’ (see the introduction of this

synthesis), it is of notable importance to take a close look at the population that is studied in

this paper and the characteristics of its population. The paper contains -like stated earlier- a

sample of 283 organizational units from 115 autonomous branches of a large European

financial service firm. These branches were all represented by their respective managers.

Since the 115 branches can be considered separate firms, the sample lays within the

theoretical domain of the central hypothesis. Since this is only a part of the total population

the study regards a sample study instead of a census study. The sample was drawn from a

large European financial services firm and thus can absolutely not be considered as a

random sample. The whole population from which the sample is drawn is not described

clearly in the paper and thus “can the observed effect sizes not be linked to differences in

the characteristics of the population”. (Hak, 2013) Because the possibility of nonresponse is

present, the writers tried to measure this through examining the differences between

respondents and non-respondents for the final sample. (Jansen et al., 2006) The

corresponding t-test did not show significant differences.


and the independent variable should be examined. The validity of exploratory innovation

was measured through Cronbach’s Alpha (0.86), which is well-above the rule of thumb of

0.70 that is discussed earlier. (Van Dalen, 2014) The financial performance was measured by

looking at the corporate records “… from 2003 up to one year after the measurement of

exploratory and exploitative innovation.” (Jansen et al., 2006) These performances were

adjusted to evaluate each organizational unit, by using a unit’s profitability-achievement




rate. (Jansen et al., 2006) After this adjustment the financial performance could be assumed

valid and reliable (because it was directly derived from the corporate financial statements).

However, in order to secure the reliability of exploratory innovation the writers conducted

an exploratory factor analysis and investigated the “correlations between exploratory andexploitative innovation at the branch level.” (Jansen et al., 2006) Both were perc eived

positive. The respondents of the surveys, the managers of different branches, were chosen

because of the profound knowledge these people have about their respective branches.

However, it is important to note that –despite the fact that the survey results were treated

anonymously- the answers of the CEOs could have been somewhat flawed. They are not able

to give answers that are 100 per cent reliable and objective since they can feel the urge to

report positively about their own branches. However, these managers are representative

respondents regarding the theoretical domain in which they operate.

The study by Jansen et al. does not make any claims beyond the studied population.

However, it does make claims to have found ‘relations’ instead of associations, which is

perceived to be a critique since cross-sectional research is never able to imply causality.

When there is referred to the results, these are only implied with regard to the cases

investigated. The writers do well by not generalizing the results to the all firms in allindustries. Since this study is conducted among units from 115 autonomous branches of a

large European financial service firm and the researchers did not make use of a series of

studies there can be concluded that the theoretical domain is heterogeneous. Fortunately,

the results of the researchers are intensely compared with previous studies. Most of the

comparisons made are underscored by the writer’s research (such as the association

between centralization and exploration activities to name an example). The research has

relatively little managerial relevance due to its cross-sectional nature. On the other hand,

there is found to be a significant correlation which suggests that there is an association

between the two variables. It probably also has relatively high managerial relevance for

companies in the banking business because the population used is derived from this

industry.




unit-free measurement. This suggests that there is a moderate to high likelihood of a

positive association between the two variables.

Table A5. Copy of Table 1 of the article ‘Exploitation, Exploration, and Firm Performance: The Case of Small

Manufacturing Firms in Japan.’ (Isobe et al., 2004)

A very positive fact is that the writers also use exclusively these Pearson correlation

coefficients for the interpretation of their results. This is noteworthy because in most papers

the researchers tend to rely on superfluous p-values. However, these values –used in

significance testing- are not used for the interpretation in the results here. So the writers of

the paper interpret the findings in the same way as is done in this critical synthesis. Another

positive fact is that the writers (in contradiction to almost all other researchers), use the

term ‘association’ to describe the effect of exploration on strategic performance. This means

that the writers want to indicate and emphasize that cross-sectional research can absolutely

not imply a causal relationship. And above that, it shows professionalism and accuracy. They

write the following: “These results strongly support Hypotheses 1 and 2, which predict

positive associations between exploitation and operational efficiency (Hypothesis 1) and

between exploration and strategic performance (Hypothesis 2).” (Isobe et al., 2004)

Additionally, also in the hypothesis the writers describe only a possible effect but no causal

claim is made. The research strategy with its corresponding effect size (i.e.; Pearson’s

correlation coefficient) is not able to insinuate such a claim and thus did the researchers

make the right decisions.




Since the managerial relevance in this critical synthesis’ general research question concerns

the ‘difference in a firm’s performance when or not associated with exploration activities’

(see the introduction of this synthesis), it is important to take a close look at the population

that is studied and the characteristics it has within this paper. For the analysis in the paperthere were used 302 small to medium-sized manufacturing firms which were all represented

through their presidents. This is well within the theoretical domain of the central hypothesis.

Since these firms are taken from a large population of manufacturing firms within an

association, it concerns a sample study rather than a census study. Since the sample was

taken from a larger amount of possible cases that was derived from the Osaka Industrial

Association (OIA), there can be concluded that the selection of the cases from the

population was not formed through probability sampling. (Isobe et al., 2004) The population

in this case could possibly be described as: all Japanese small to medium-sized

manufacturing firms. However, this is not specified in the paper. This suggests that that

there are also a lot of manufacturing firms that are not a member of the OIA and therefore –

if the population had been described precisely- the list with members could would have

been the ‘sampling frame’. However, when the population would have been described as:

‘all Japanese small to medium-sized manufacturing firms that are a member of the OIA’, the

sample would have been valid. Regarding the possibility for nonresponse, the writers used

the ‘nonresponse bias detection method’ by Armstrong and Overton, with “comparisons

between several key variables for the earlier and later respondents in our sample were

made”. (Isobe et al., 2004) The corresponding t-tests indicated that there were no significant

differences between the groups and therefore the nonresponse bias can be ignored. (Isobe

et al., 2004)


and the independent variable should be mentioned. The validity, which is equal to ‘internal

reliability’ was checked as follows: “To assess internal reliability, we calculated the

Cronbach’s alpha for each construct, and found that all of them exceeded the 0.7 level that

is recommended by Nunnally (1978).” (Isobe et al., 2004) The alpha’s for both exploration

(independent variable; alpha = 0.84) and for strategic performance (dependent variable;

alpha = 0.79) are considered to be very consistent. Subsequently, the hypotheses were

checked for reliability by the use of a ‘complementary factor analysis’. The writers state:




“This method allowed us to identify the effects of both exploitation and exploration on firm

performance.” (Isobe et al.,2004) The results from this analysis conclude an ‘excellent fit’

and were further analyzed by using statistical software called LISREL. (Isobe et al., 2004)

Therefore, the results are reliable.

The respondents, as stated earlier, were the respective presidents of the selected firms.

These people were chosen because of the profound knowledge about the firm they possess.

As stated by the writers: “Small to medium-sized firms tend to have a relatively limited

number of core products or technologies, and thus managers are likely to have a good

understanding of the key technologies of the firm and their impact on the firm’s core

competencies, which thus enhances the accuracy of the responses.” (Isobe et al., 2004)

However, it remains important to note that –like in previous analyses of cross-sectional

research using surveys- the answers of the presidents could have been somewhat flawed.

They are not able to give answers that are 100 per cent reliable and objective due to the fact

that they are not objectively observing the firm’s activities. However, at least these

presidents are representative respondents regarding the theoretical domain and contain the

knowledge that is required to provide the information that is needed.

The paper by Isobe et al. does not make any claims beyond the studied population. A

noteworthy fact is that the writers even stressed this by putting in the papers’ title that the

research concerns only Japanese Manufacturing firms. When there is referred to the results,

these are only implied with regard to the cases investigated. Another compliment towards

the writers is that they explicitly refer to ‘association’ instead of effect (or even worse

‘relation’). The writers do well by not generalizing the results to the all firms in all industries.

Since this study is conducted among units from 302 small to medium-sized manufacturing

firms in Japan and the researchers did not make use of a series of studies there can be

concluded that the theoretical domain is heterogeneous. Fortunately, also here the results

of the researchers are compared with previous researches regarding different aspects and

concepts. The research has relatively little managerial relevance due to its cross-sectional

nature. It would be especially relevant for Japanese manufacturing firms. On the other hand,

there is found to be a significant correlation which suggests that there is an association

between the two variables.




Looking at the results, the effect size that we will be using is denoted as a correlation

coefficient as they used these as well for their own ‘IV’ and ‘DV’. As they have multiple

indicators for firm performance and exploration, we mainly used firm profitability (8) and

Function exploration experience (11) (r = 0.050). As this is a standardized measurement, wecan suggest that there is a very small likelihood of a positive association between these two

variables. From statistical software that was provided, it was possible to obtain the

confidence intervals (CIs) for this correlation. The lower confidence interval is -0.034 and the

upper confidence interval is 0.133.

Table A6. Copy of Table 2 of the article ‘Balancing Exploration and Exploitation in Alliance Forming.’ (Lavie and

Rosenkopf, 2006)

The researchers did however use p-values and are also using them while interpreting the

results. Therefore the writers might tend to rely too much on superfluous information. As a

convenience for ourselves, the researchers also stated the relation between function

exploration (11) and profitability (8) themselves, stating that there is a positive relation (r =

0.05) which confirms our own findings in their effect sizes. (Lavie and Rosenkopf, 2006) This

was helpful as their hypotheses were not aimed at this at all. The general hypotheses thatthey set up do have a certain causal claim in it, but as this research is set up as a timed series

analysis, it is possible to do so. As the correlations show, over a time of several years, they

provide detailed data to support their claims. As the effect sizes are measured on the basis

of their respected years, these measures are consistent with their research strategy.

The population consisted of U.S. bases Software companies. They initially selected 2,777

publicly traded partners that accounted for more than 60% of the alliances to limit potential

biases. (Lavie and Rosenkopf, 2006) All firms that were not engaged in an alliance were kept




out of the research to enlarge the measurements validity. Concluding, the population

consisted of a well-defined sample of which the ‘missing cases’ were selected and kept out

of the research. We have not found any information indicating the use of a probability

sample, or if there were cases of non-response and how they treated these. Theindependent and dependent variables were measured using the data from highly trusted

sources as mentioned earlier. There wasn’t any information whether information was being

passed on through people that may give their own twist to certain data. On a personal basis

that might have brought in more risk than wanted. Therefore we can conclude that the way

the information was obtained was trustworthy due to the face that they executed an

extensive archival research using trustful data archives.

To back up the trustworthiness of the data, the data was collected during a time span of 15

years, including extra research on a 5 year period from 1985 till 1990 to add the extra data

that was missing as some of the databases did not exist or were not used for the firms being

researched. Thus, there is provided a lot of historical and current data to enable accurate

measurements. (Lavie and Rosenkopf, 2006) The validity of the measures was not reported

with the CVI-values, the RMSEA, CFI, TLI or the SRMR values, by which we cannot conclude

whether the acclaimed validity of their research is valid. They did however stated that theyused the same random-effects models that were used in prior research, thus enlarging the

validity as these measures are likely to be checked already. For the inter-item reliability we

could not find the use of Cronbach’s Alpha or Rho to which we also have to conclude that

the inter-item reliability of the research remains something that we need to be skeptical

about. (Lavie and Rosenkopf, 2006) The claims made in the report remained within the

boundaries of the studied population. As they used the majority of large U.S. software firms,

that are representative for their whole population, they do not extend their findings beyond

the studied population.

The researchers ran multiple tests to check the sub-samples for their independence

assumption. (Lavie and Rosenkopf, 2006) As for the assumptions regarding the theoretical

domain, the researchers state that there are found similar results in other studies. As Hak

(2013) states in the Course Book: “Homogeneity of the domain is the exception and should

only be assumed after a series of studies in rather different populations have all shown more

or less similar results’’. As the above stated is not the case in this research, there can be




assumed that the theoretical domain is heterogeneous. They state that they found similar

results and adding that because of the fact that they were using multiple indicators for their

hypotheses, it is likely that there were more aspects that were not prior measured that has

an effect on the measured variables. E.g. more R&D alliances as used in this research. (Lavieand Rosenkopf, 2006) The practical relevance of the observed effect are somewhat limited

to our own research as the report uses multiple measures on different hypotheses than our

own. We therefore tend to use a limited number of the reported effect-sized for our own

meta-analysis as they do give us more information about the relation between firm

performance and exploration activities. (Lavie and Rosenkopf, 2006)




Appendix B7. Alternative Knowledge Strategies, Competitive Environment,

and Organizational Performance in Small Manufacturing Firms

This article, which is written by Paul E. Bierly and Paula S. Daly, examines the relationship

between “knowledge strategy (exploration or exploitation) and performance, and the

possible role of external environment variables.”(Bierly and Daly, 2007). The research

strategy is cross-sectional by using surveys that were sent to 250 small to medium-sized

manufacturing firms in the mid-Atlantic region of the United States, of which eventually 98

would participate in the research and met the criteria. The writers define the concept of

exploration according to an earlier definition by March which states that: “exploration

strives to develop capabilities to excel at the creation or acquisition of new knowledge and

where exploitation develop capabilities to excel at the ability to leverage existing knowledge

to rapidly create new organizational products and processes.” (Bierly and Daly, 2007) March

(1991) also argued that “… the exploration of radical new knowledge is more likely maximize

long-term firm success.” In this article, the firm’s success is measured using the firm

performance and is measured with “… the extent to which the firm has excelled in the areas

of financial performance and growth over the three previous years.” (Bierly and Daly, 2007).

In the search for an association between the dependent variable ‘performance’ and the

independent variable ‘exploration’, none of the papers’ hypothesis exactly matches our

hypothesis. Instead of looking at one specific hypothesis from this paper, we only specify on

the relationship between exploration and firm performance, which is also measured in this

article. The unit of analysis of this paper is somewhat different from the focal unit of our

hypothesis. As described above, this paper is about small to medium-sized manufacturing

firms, while our paper is about all firms in the world, in all economic sectors, in all countries.

Even though this research was conducted in the United States, and mainly in the state of

Virginia, the authors of the article do not insinuate that their findings are limited to this area.

The authors of this article also recommend that the research must be expanded to larger

firms as well.

When examining the variables of this paper, there can be concluded that the independent

variable and the dependent variable of this article match with the independent variable and

the dependent variable of the main hypothesis. Both are about the exploration strategy and

the firm performance of a company. When taken a closer look to the research results that




are published in the paper (see table below), there can be found an effect size concerning

our own research hypothesis. This effect size is denoted as a correlation coefficient (r ) which

carries a value of 0.253 for the association between our variables. Since the correlation is

basically a standardization of the covariance, it concerns a standardized, but unit-freemeasurement. This suggests that there is a little likelihood of a positive association between

the two variables. From statistical software that was provided, it was possible to obtain the

confidence intervals (CIs) for this correlation. The lower confidence interval is 0.055 and the

upper confidence interval is 0.432. A very positive and notable fact is that the writers

mention that the impact is less than expected because they only focus on small firms. A very

positive fact is that the writers exclusively use these Pearson correlation coefficients for the

interpretation of their results, but for the other hypotheses it uses some regression models.

Table A7. Copy of Table 2 of the article ‘Alternative Knowledge Strategies, Competitive Environment, and Organizational

Performance in Small Manufacturing Firms’ (Bierly and Daly, 2007).

As mentioned above, this research presents a cross-sectional study. The paper provides

correlation and regression for the other hypotheses, what makes the effect size parameters

consistent with the research strategy (Hak, 2013). A cross-sectional study consists of data

collection from a population or sample at one moment in time. This can be concluded from

the way the writers describe the following effect supporting hypothesis 3: “The exploitation

and the exploitation-squared terms are significant, indicating a concave, nonlinear

relationship between exploitation and performance that supports hypothesis 3. Thus,

exploitation is positively correlated with performance up to a point, but then is negatively

correlated.” (Bierly and Daly, 2007). But according to the Research Training and Bachelor




Thesis’ course book, a cross-sectional study “…has the lowest level of internal validity for

generating evidence regarding a causal effect” (Hak, 2013).

The population of this study are small to medium-sized manufacturing firms in the mid-

Atlantic region of the United States. These participants were provided by the “…state Small

Business Development Centres, which identified potential participants and provided

company names and contacts.” (Bierly and Daly, 2007). Because not all the small to medium-

sized manufacturing firms in the mid-Atlantic region of the United States were selected, this

study covers a sample of the population. The population is within our theoretical domain,

since we include all firms in the world, in all economic sectors, in all countries, at all times.

All the companies are from a list which is known by the Business Development Centres. The

writers do not give us the information to find out whether this is a complete list of the

population or not. According to Hak (2013) such list is do not count as probability sample. He

also mentioned that “…a requirement for the application of inferential statistics in a data set

is that the sample is complete.” (Hak, 2013). From the sample of 250 companies, only 98

returned a complete questionnaire. Also, the paper did not mention anything about non-

response. According to Hak (2013), this study can be labelled as a “failed study” because of

the incorrect sampling method and non-response.

When we look at the data measurement method, the respondents from this research were

“… three individuals working in different positions within the company, with preference

given to top management, human resources, and production positions.” (Bierly and Daly,

2007). They did this to eliminate single-source bias. However, the answers of the employees

could have been somewhat flawed. Furthermore, the validity and reliability of the

measurements should be mentioned. The validity is checked with the aid of the intra-class

correlation coefficient (ICC). The ICC can be used to “… quantify the degree of agreement

between two (or more) repeatedly measured values.” (Geloven and Bossuyt, 2009) and

indicate a moderate to high level of inter-rater agreement. Also “…a factor analysis of the

eight items in the exploration and exploitation scales was used to establish independence of

these constructs.” (Bierly and Daly, 2007). And therefore one item was eliminated. As

mentioned above, the writers used multiple respondents per firm to eliminate single-source

bias. But according to Hak (2013) using informants is always a threat to reliability.



When we look at the conclusion and the claims made in the paper from Bierly and Daly, we

can conclude that the study does not make any claims beyond the studied population. When

there is referred to the results, these are only implied with regard to the cases investigated.

Because the researchers did not make use of a series of studies there can be concluded thatthe theoretical domain is heterogeneous. Unfortunately, the results of the study are not

compared with those from other studies. A positive aspect is that the researchers found a

significant correlation which suggests that there is an association between the two variables,

especially for small to medium-sized manufacturing firms in the mid-Atlantic region of the

United States. But due to its cross-sectional nature, the research has relatively little

managerial relevance. They state that “One limitation of our study is that we measure

performance over a 3-year period, which does not capture the long-term effects of

exploration.” (Bierly and Daly, 2007).

Thesis on exploration in Business Management

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