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Appendix to Managerial attention to exploitation versus exploration

Authors:
Bob Walrave at Eindhoven University of Technology
Bob Walrave
Georges Romme at Eindhoven University of Technology
Georges Romme
Kim E. Van Oorschot at BI Norwegian Business School
Kim E. Van Oorschot
Fred Langerak at Eindhoven University of Technology
Fred Langerak
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Abstract

Data appendix to the article "Managerial attention to exploitation versus exploration"
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1
Appendix to:
Managerial attention to exploitation versus exploration:
Toward a dynamic perspective on ambidexterity
This appendix reports the geographical distribution of the sample and several robustness tests
conducted to assess the reliability of the results in “Managerial attention to exploitation versus
exploration: toward a dynamic perspective on ambidexterity.” (This appendix is made available
to the reviewers. In case the paper is published, the document will be available from the authors
as well as made accessible via a public website.)
Geographical distribution of the final sample
Country
Number of firms
United States of America
66
Germany
7
France
3
Great Britain
3
The Netherlands
3
Norway
1
Sweden
1
Spain
1
Swiss
1
SUM
86
The length of the letters to shareholders
The length of the letters can possibly influence the independent variable; for example, shorter
letters can be expected to exhibit more extreme exploitation-exploration (EE) ratios. That is,
finding one additional keyword in a shorter text, that is likely to contain a relatively small
number of keywords, may have a greater influence on the EE ratio than finding one additional
keyword in a longer text. To test for this possible confounding effect, we took the absolute value
of 0.5 (the mean of EE ratio scale), subtracted the EE ratio and correlated the result with the
number of characters appearing in a letter. This procedure effectively tests whether fewer
characters result in more extreme EE ratios. This robustness check produced a non-significant
2
relationship (r = 0.010, p > .1); therefore, the length of the letters in our sample appears to have
no significant effect on the EE ratio distilled.
Robustness tests with respect to the analysis
We also assessed the robustness of the key findings. More specifically, we tested the model:
(a) by varying the number of instruments (Tables I and II);
(b) with a two and six quarters delay in how managerial attention affects firm performance
(rather than four quarters) (Table III);
(c) with return on assets as dependent variable (rather than net profit) (Table IV);
(d) with a quarterly varying measure of the EE ratio (i.e., a moving average over 3
observations) (Table V);
(e) by means of competing pooled OLS and random-effects GLS regression (allowing for
time-invariant variables) (Table VI).
Overall, these tests serve to demonstrate sufficient levels of robustness of our main findings. The
outcomes of these tests are reported in the remainder of this appendix.
3
a. Varying the number of instruments
Tables I and II denote the results of these robustness tests. All tests conducted confirm the
robustness of the results.
Table I.
Robustness tests with respect to the number of lags (instrument matrix) (Part 1)
Dependent variable:
Profit margin
1–1 lag (min)
1–2 lags
1–4 lags
Coeff.
(SE) b
Coeff.
(SE) b
Coeff.
(SE) b
b1 – EE ratio t – 4
3.36
(1.63)*
3.67
(1.50)**
3.36
(1.35)**
b2 – (EE ratio)2 t – 4
–2.28
(1.17)*
–2.51
(1.08)*
–2.31
(.96)**
b3 – Recovery dummy
1.35
(.70)*
1.46
(.65)*
1.10
(.51)*
b4 – EE ratio t – 4 × Recovery dummy
–4.06
(2.29)*
–4.39
(2.09)*
–4.09
(1.84)*
b5 – (EE ratio)2 t – 4 × Recovery dummy
2.81
(1.76)+
3.04
(1.61)*
2.80
(1.44)*
b6 – Profit margin t – 1 a
.16
(.08)*
.15
(.09)*
.18
(.09)*
b7 – Net income a
.70
(.24)**
.70
(.24)**
.67
(.22)**
b8 – Total assets a
–.55
(.34)+
–.55
(.31)*
–.46
(.25)*
b9 – R&D–Sales ratio a
–.01
(.10)
.01
(.10)
.01
(.08)
b10 – R&D missing dummy
.16
(.27)
.17
(.24)
.33
(.20)+
b11 – Firm size a
.04
(.27)
.04
(.24)
–.03
(.19)
b12 – Firm age a
–.14
(.20)
–.18
(.13)+
–.16
(.09)*
b13 – GICS 4510 dummy
.14
(.16)
.16
(.15)
.11
(.14)
b14 – GICS 4520 dummy
.14
(.15)
.13
(.13)
.13
(.13)
b15 – US location dummy
–.09
(.15)
–.11
(.14)
–.09
(.12)
b16 – Constant
–1.14
(.52)*
–1.23
(.50)**
–1.13
(.47)**
Hansen test of over-identification
1.00
1.00
1.00
Arellano Bond AR(1) c
–2.87
**
–2.91
**
–2.94
**
Arellano Bond AR(2) c
–.03
–.05
.08
a Standardized value; b The standard errors are robust to heteroskedasticity and arbitrary patterns of autocorrelation within agents
(Roodman 2009a); c z values reported; + p < .10; * p < .05; ** p < .01; *** p < .001. Time dummy variables were included in all
models but are omitted from these results. One–tailed significance levels are reported.
4
Table II.
Robustness tests with respect to the number of lags (instrument matrix) (Part 2)
1–16 lags
1–20 lags (max)
All endogenous
Coeff.
(SE) b
Coeff.
(SE) b
Coeff.
(SE) b
3.16
(1.27)**
3.12
(1.26)**
3.35
(1.43)**
–2.18
(.91)**
–2.16
(.90)**
–2.28
(1.01)*
1.44
(.55)**
1.14
(.51)*
1.27
(.53)**
–3.62
(1.67)*
–3.52
(1.66)*
–3.86
(1.80)*
2.52
(1.31)*
2.46
(1.31)*
2.70
(1.38)*
.21
(.09)**
.21
(.09)**
–.22
(.09)**
.60
(.19)**
.60
(.19)**
.57
(.19)**
–.39
(.19)*
–.38
(.19)*
–.36
(.18)*
–.02
(.07)
–.02
(.08)
–.02
(.07)
.02
(.10)
.01
(.10)
.06
(.08)
–.04
(.13)
–.04
(.12)
–.03
(.12)
–.11
(.05)*
–.10
(.05)*
–.08
(.05)+
0.17
(.12)+
.17
(.12)+
.14
(.11)+
0.13
(.11)
.13
(.11)
.12
(.11)
–.08
(.10)
–.08
(.10)
–.05
(.10)
–1.32
(.47)**
–1.03
(.44)*
–1.42
(.54)**
1.00
1.00
–
–3.09
**
–3.10
**
–3.15
**
.26
.28
.33
a Standardized value; b The standard errors are robust to heteroskedasticity and arbitrary patterns of autocorrelation within agents
(Roodman 2009a); c z values reported; + p < .10; * p < .05; ** p < .01; *** p < .001. Time dummy variables were included in all
models but are omitted from these results. One-tailed significance levels are reported.
5
b. Varying the delay in the effect of managerial attention on firm performance
In the paper, we assumed the effect of senior management’s attention to exploitation-exploration
on the profit margin involves a delay of four quarters. To test for robustness, we also ran the
model with delays of two quarters and six quarters. The results reported in table III are similar,
especially with a delay of two quarters. This also supports our key assumption that, in the IT
industry, performance effects of different EE ratios arise over a relatively short time span (e.g.,
compared to the electronics or pharmaceutical industry).
Table III.
Robustness tests with respect to lagging the EE ratio (8/20 lags used)
Dependent variable:
Profit margin
EE ratio lagged 2
quarters
EE ratio lagged 4
quarters (presented)
EE ratio lagged 6
quarters
Coeff.
(SE) b
Coeff.
(SE) b
Coeff.
(SE) b
b1 – EE ratio t – x
2.36
(1.19)*
3.39
(1.33)**
.84
(.75)
b2 – (EE ratio)2 t – x
–1.57
(.87)*
–2.35
(.95)**
–.59
(.59)
b3 – Recovery dummy
.63
(.51)
1.56
(.61)**
.39
(.55)
b4 – EE ratio t – x × Recovery dummy
–2.46
(1.55)+
–3.93
(1.82)*
–.92
(1.72)
b5 – (EE ratio)2 t – x × Recovery dummy
1.51
(1.19)
2.72
(1.41)*
.95
(1.34)
b6 – Profit margin t – 1 a
.20
(.09)*
.20
(.09)*
.20
(.09)*
b7 – Net income a
.62
(.20)**
.62
(.20)**
.60
(.20)**
b8 – Total assets a
–.39
(.21)*
–.40
(.22)*
–.39
(.21)*
b9 – R&D–Sales ratio a
–.01
(.07)
–.00
(.07)
–.03
(.07)
b10 – R&D missing dummy
.15
(.14)
.17
(.17)
.16
(.13)
b11 – Firm size a
–.04
(.15)
–.04
(.16)
–.03
(.15)
b12 – Firm age a
–.13
(.06)*
–.14
(.07)*
–.12
(.06)*
b13 – GICS 4510 dummy
.14
(.13)
.15
(.13)
.10
(.12)
b14 – GICS 4520 dummy
.10
(.12)
.12
(.12)
.08
(.11)
b15 – US location dummy
–.09
(.11)
–.09
(.11)
–.07
(.11)
b16 – Constant
–.77
(.41)*
–1.39
(.49)**
–.50
(.28)*
Hansen test of over-identification
1.00
1.00
1.00
Arellano Bond AR(1) c
–2.90
**
–3.03
**
–2.90
**
Arellano Bond AR(2) c
.43
.18
.42
a Standardized value; b The standard errors are robust to heteroskedasticity and arbitrary patterns of autocorrelation within agents
(Roodman 2009a); c z values reported; + p < .10; * p < .05; ** p < .01; *** p < .001. Time dummy variables were included in all
models, but are omitted from these results. One-tailed significance levels are reported.
6
c. ROA and Tobin’s Q as dependent variable
The model was also tested with return on assets (ROA). Table IV shows the results, which are
highly similar to the main model.
Table IV.
Robustness tests regarding the dependent variable (8/20 lags used)
Dependent variable:
Profit margin
(presented)
ROA
Coeff.
(SE) b
Coeff.
(SE) b
b1 – EE ratio t – x
3.39
(1.33)**
3.85
(1.63)**
b2 – (EE ratio)2 t – x
–2.35
(.95)**
–2.68
(1.17)*
b3 – Recovery dummy
1.56
(.61)**
1.24
(.60)*
b4 – EE ratio t – x × Recovery dummy
–3.93
(1.82)*
–4.41
(1.96)*
b5 – (EE ratio)2 t – x × Recovery dummy
2.72
(1.41)*
3.00
(1.49)*
b6 – Autocorrelation t – 1 a
.20
(.09)*
.14
(.05)**
b7 – Net income a
.62
(.20)**
.60
(.19)***
b8 – Total assets a
–.40
(.22)*
-
-
b9 – R&D–Sales ratio a
–.00
(.07)
–.07
(.07)
b10 – R&D missing dummy
.17
(.17)
.29
(.21)
b11 – Firm size a
–.04
(.16)
–.38
(.11)***
b12 – Firm age a
–.14
(.07)*
–.18
(.06)**
b13 – GICS 4510 dummy
.15
(.13)
.11
(.14)
b14 – GICS 4520 dummy
.12
(.12)
.03
(.13)
b15 – US location dummy
–.09
(.11)
–.22
(.12)*
b16 – Constant
–1.39
(.49)**
–1.26
(.59)*
Hansen test of over-identification
1.00
1.00
Arellano Bond AR(1) c
–3.03
**
–2.79
**
Arellano Bond AR(2) c
–.18
–.36
a Standardized value; b The standard errors are robust to heteroskedasticity and arbitrary patterns of
autocorrelation within agents (Roodman 2009a); c z values reported; + p < .10; * p < .05; ** p < .01; *** p
< .001. Time dummy variables were included in all models but are omitted from these results. One–tailed
significance levels are reported.
7
d. Quarterly varying measure of attention to exploitation-exploration
As a robustness test, we developed and tested another measure of managerial attention to
exploitation-exploration, one that varies quarterly, by taking the moving average of the annual
EE ratio (over three t’s). The results of the model run with this alternative EE ratio are highly
similar with our main findings (see Table V).
Table V.
Robustness tests regarding yearly versus quarterly
operationalization of the EE ratio (8/20 lags used)
EE ratio (yearly)
(presented)
EE ratio as moving
average
Coeff.
(SE) b
Coeff.
(SE) b
3.39
(1.33)**
4.92
(1.90)**
–2.35
(.95)**
-3.56
(1.41)**
1.56
(.61)**
1.98
(.77)**
–3.93
(1.82)*
-5.60
(2.41)*
2.72
(1.41)*
4.06
(1.89)*
.20
(.09)*
.22
(.10)*
.62
(.20)**
.66
(.21)**
–.40
(.22)*
-.38
(.25)+
–.00
(.07)
.02
(.08)
.17
(.17)
.04
(.14)
–.04
(.16)
-.05
(.19)
–.14
(.07)*
-.13
(.06)*
.15
(.13)
.20
(.14)+
.12
(.12)
.17
(.13)+
–.09
(.11)
-.12
(.11)
–1.39
(.49)**
-1.85
(.68)**
1.00
1.00
–3.03
**
-2.83
**
–.18
.54
a Standardized value; b The standard errors are robust to heteroskedasticity and arbitrary patterns of
autocorrelation within agents (Roodman 2009a); c z values reported; + p < .10; * p < .05; ** p < .01; *** p
< .001. Time dummy variables were included in all models but are omitted from these results. One-tailed
significance levels are reported.
8
e. Competing pooled OLS and random-effects GLS regression
Table VI denotes the results of competing pooled OLS and random-effects GLS regression
model (bootstrapped 100 iterations). Once more, the results are highly comparable to our main
findings.
Table VI.
Robustness test with respect to method
Dependent variable:
Profit margin
Pooled OLS
Random-effects
GLS
(bootstrapped;
100 iterations)
Coeff.
(SE) b
Coeff.
(SE)
b1 – EE ratio t – 4
2.61
(.79)***
2.61
(1.52)*
b2 – (EE ratio)2 t – 4
-1.82
(.60)**
-1.82
(1.07)*
b3 – Recovery dummy
1.10
(.41)**
1.10
(.53)*
b4 – EE ratio t – 4 × Recovery dummy
-3.28
(1.45)*
-3.28
(1.69)*
b5 – (EE ratio)2 t – 4 × Recovery dummy
2.19
(1.18)*
2.19
(1.29)+
b6 – Profit margin t – 1 a
.24
(.07)***
.24
(.10)**
b7 – Net income a
.62
(.14)***
.62
(.27)**
b8 – Total assets a
-.39
(.12)**
-.39
(.22)*
b9 – R&D–Sales ratio a
-.01
(.04)+
-.01
(.12)
b10 – R&D missing dummy
.06
(.04)
.06
(.09)
b11 – Firm size a
-.03
(.05)
-.03
(.15)
b12 – Firm age a
-.04
(.03)+
-.04
(.04)
b13 – GICS 4510 dummy
.13
(.07)*
.13
(.13)
b14 – GICS 4520 dummy
.14
(.08)*
.14
(.15)
b15 – US location dummy
-.05
(.06)
-.05
(.11)
b16 – Constant
-.94
(.28)***
-.94
(.55)**
a Standardized value; + p < .10; * p < .05; ** p < .01; *** p < .001. One-tailed significance levels
are reported. b Robust standard errors.

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... We argue that it is reasonable to think that SMEs' ability to anticipate demand shocks and deploy countercyclical strategies as the best way to face crises may rely more on their exploration than on their natural tendency to exploit. In this sense, Walrave et al. (2012) argue that exploration implies that managers engage in discovery actions and in detecting business opportunities. More importantly, they will be more focused on the future rather than on the past or present environment, that is, detecting changes in the environment and adapting the company to the requirements imposed by the recession. ...
... Explorative firms are also better prepared than non-explorative firms, who do not replace customers lost during the first phase of the crisis with new customers by offering new products or adapting to meet their needs. As Walrave et al. (2012) argue, exploration enables firms to uncover opportunities that could be relatively scarce during a recession. In this respect, in an economic downturn, the decreased level of munificence in terms of less readily available finance means that business opportunities are less common (Sidhu et al., 2004), and hence, for non-explorative SMEs more difficult to exploit. ...
... Exploration may serve to both slow down the pre-crisis growth of firms and accelerate their growth in the recession phase so that they can emerge from the recession with better results. In terms of the recession phase, our results fit with those of Walrave et al. (2012). Their findings from a sample of 86 companies (66 in the United States and 20 in Europe) and 1,720 valid observations over 20 quarters suggest that more attention to exploration during the recessionary period is linked to better performance outcomes. ...
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Managerial attention to exploitation versus exploration: Toward a dynamic perspective on ambidexterity
Article
November 2017
Bob Walrave · Georges Romme · Kim E. Van Oorschot · Fred Langerak