CEO research orientation, organizational context, and innovation in the pharmaceutical industry

Abstract

This study develops and tests a comprehensive framework that explains what, when, and how CEO characteristics influence firms’ innovation outcomes in R&D‐intensive industries. Empirical evidence from 109 CEOs from 87 U.S.‐based pharmaceutical firms over the period 2001–2013 reveals that research‐oriented CEOs – those with ability and motivation for science and technology – increase their firms’ innovation outcomes. The results indicate that the CEO–innovation relationship strongly depends on the extent of CEOs’ managerial discretion, which is shaped by the organizational context. We contribute to a more comprehensive understanding of the role of CEOs in firms´ innovation performance differentials.

Full text

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd 239
CEO research orientation,
organizational context, and
innovation in the pharmaceutical
industry
Nino van de Wal1, Christophe Boone1,
Victor Gilsing1,2 and Bob Walrave3
1 Department of Management,University of Antwerp,ACED, Antwerp, Belgium. nino.vandewal@
uantwerpen.be, christophe.boone@uantwerpen.be
2 School of Business and Economics,VU Amsterdam, Amsterdam, The Netherlands. victor.gilsing@
uantwerpen.be
3 School of Industrial Engineering,Eindhoven University of Technology, Eindhoven, The Netherlands.
b.walrave@tue.nl
This study develops and tests a comprehensive framework that explains what, when, and
how CEO characteristics influence firms’ innovation outcomes in R&D-intensive indus-
tries. Empirical evidence from 109 CEOs from 87 U.S.-based pharmaceutical firms over the
period 2001–2013 reveals that research-oriented CEOs – those with ability and motivation
for science and technology – increase their firms’ innovation outcomes. The results indicate
that the CEO–innovation relationship strongly depends on the extent of CEOs’ managerial
discretion, which is shaped by the organizational context. We contribute to a more compre-
hensive understanding of the role of CEOs in firms´ innovation performance differentials.
1. Introduction
Understanding how CEOs influence firm strat-
egies and outcomes have received growing
research interest recently (Burgelman et al., 2018;
Liu et al., 2018). A rise in the importance of CEOs’
general managerial skills has been observed as one
of the most striking trends in the past half cen-
tury (Crossland et al., 2014), and stemming from
this observation, recent research has shown that
S&P 1500 firms with ‘generalist’ CEOs exhibit
higher innovation outcomes (Custódio et al., 2017).
Simultaneously, however, many firms have shifted
from a strategic orientation of ‘R&D as a driver of
growth’ to ‘R&D as an expense’. This is exemplified
by a steady decline in R&D intensity and reduced
innovation outcomes (Cummings and Knott, 2018).
This decrease in innovation may be explained by a
lack of CEOs with context-specific skills (Simsek
et al., 2015; Cummings and Knott, 2018) such as
technological domain expertise in R&D-intensive
industries (Felix and Bistrova, 2015).
Indeed, the pharmaceutical industry has experi-
enced a decline in innovation outcomes (Scannell
et al., 2012), even though the exploration of cut-
ting-edge technology is critical to a firm’s sustained
profits and even existence (Roberts, 1999). The inno-
vation literature has emphasized innovation from a
competence perspective, by attributing performance
differentials to differences in firms’ underlying
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© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
Nino van de Wal, Christophe Boone, Victor Gilsing and Bob Walrave
240 R&D Management 50, 2, 2020
innovation capabilities (Henderson and Cockburn,
1994; Ahuja et al., 2008). This literature offers no
explanation, however, why firms make different
innovation strategy choices in the first place (Ahuja
and Lampert, 2001). In this respect, the innovation
literature misses out on the role of critical strategic
and organizational conditions in which such inno-
vation capabilities reside (Talke et al., 2010). As a
result, we lack a comprehensive understanding of the
strategic choices and processes through which CEOs
influence firms’ innovation outcomes.
To address this issue, we study what, when, and
how CEO characteristics impact firms’ innovation
outcomes in the pharmaceutical industry. Drawing
on the upper echelons theory (Hambrick and Mason,
1984; Hambrick, 2007), we first argue that CEOs
exhibit considerable heterogeneity in their ability
and motivation in relation to science and technology.
This aids us in explaining the differences between
firms in innovation outcomes. A comparison of for-
mer Genentech CEO Arthur Levinson, who enjoyed
a distinguished career in scientific research, and
Robert Hugin, an MBA graduate who is specialized
in corporate finance in emerging technologies before
becoming Celgene’s CEO, illustrates the diver-
sity between CEOs. To capture such heterogeneity
among CEOs and show what CEO characteristics
impact firms’ innovation outcomes in R&D-intensive
industries, we introduce the notion of CEO research
orientation, which we define as the array of career
experiences in the domains of research, science, and
technology that an executive had, prior to becoming
a CEO.
To assess when CEOs with a research orientation
influence firms’ innovation outcomes, we argue
that the organizational context in which CEOs
operate, determines their managerial discretion
or latitude for action (Hambrick and Finkelstein,
1987; Busenbark et al., 2016). Specifically, we
examine whether CEOs’ structural power as a board
chairperson, the availability of slack resources
for experimentation, and the presence of inertial
forces in older firms affect the extent to which a
CEO’s research orientation spurs a firm’s inno-
vation. To detail one mediating mechanism how
research-oriented CEOs influence innovation, we
argue that those CEOs who strategically choose an
R&D-intensive investment strategy and who have
a better understanding of how to effectively imple-
ment such a strategy, positivelyaffect their firms’
innovation outcomes (Bromiley and Rau, 2016; Liu
et al., 2018). Taken together, this study proposes
a comprehensive framework that indicates that
through their research orientation and operating in
a given context, CEOs shape their firms’ strategic
choice of R&D resource allocation and attendant
innovation outcomes. We find considerable support
for our model by utilizing a panel dataset involving
109 CEOs from 87 U.S. pharmaceutical firms over
the period 2001 to 2013.
This study adds to the upper echelons theory by
explaining and testing the complex causal chain
between CEOs and firms’ innovation outcomes.
This study moves beyond merely observing a direct
relationship between CEO characteristics and inno-
vation (Balsmeier and Buchwald, 2014; Custódio
et al., 2017; Cummings and Knott, 2018). Instead,
it explicitly examines when and how CEO strate-
gic choices fuel subsequent processes that result in
firms’ innovation outcomes. We thereby respond to
recent calls by developing and testing comprehen-
sive models that examine CEO influence on firm
outcomes such as innovation (Busenbark et al.,
2016; Liu et al., 2018). This study also contributes
to the innovation literature by studying the role
of the CEO as an important yet often overlooked
factor that influences firms’ innovation outcomes
(Talke et al., 2010).
2. Theory and hypotheses
2.1. CEO research orientation and firms’
innovation outcomes
People’s career paths provide much insight into with
respect to their ability and motivation in general as
well as with regard to their preferences, beliefs,
skill sets, values, goals, and search for meaning in
particular (Judge et al., 1995; Sullivan and Baruch,
2009). In a similar vein, CEOs’ careers reflect
their unique abilities and motivations (Crossland
et al., 2014; Busenbark et al., 2016). The core of
the upper echelons theory holds that, faced with
competing tasks and limited attentional resources
(Cyert and March, 1963), CEOs make strategic
choices through highly personalized lenses that
arise from their experiences, motives, and person-
alities (Hambrick and Mason, 1984; Hambrick,
2007). Much research has subsequently shown that
the specific ‘orientations’ that CEOs bring to the
firm shape strategic decision making and organi-
zational outcomes (Finkelstein et al., 2009, p. 49).
In this study, we argue that CEOs have a certain
level of research orientation – that is, ability and
motivation for science and technology. Such orien-
tation is revealed by their career experiences in the
domains of research, science, and technology prior
to their becoming a CEO. Specifically, we postu-
late that CEOs who (1) have a PhD degree in sci-
ence or engineering, (2) have academic experience,

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
CEO research orientation, organizational context, and innovation in the pharmaceutical industry
R&D Management 50, 2, 2020 241
(3) have R&D experience, and (4) hold patents are
likely to exhibit higher ability and motivation in
relation to science and technology.
Three mechanisms explain why research-oriented
CEOs are more likely to create a supportive organi-
zational context for innovation. First, their experi-
ence in research, science, and technology provides
them with complex ‘cognitive schemas’ that enable
them to develop a more comprehensive awareness
of new opportunities and to better understand new
technologies at earlier stages of development and
in times of technological uncertainty (Nadkarni
and Chen, 2014). As a result, CEOs with a research
orientation are better able to discover and compre-
hend technological opportunities and subsequently
develop a clearer vision for technological advance-
ment (Kaplan and Tripsas, 2008).
Second, recent studies show that CEO personality
and strategic leadership behaviors influence innovation
through the development of a socio-cultural context
(Elenkov and Manev, 2005; O’Reilly et al., 2014). Here,
research-oriented CEOs are likely to create a context of
shared norms, values, and beliefs that are supportive
of an innovation culture (Berson et al., 2008; Giberson
et al., 2009). For instance, they can create and foster
such a culture through leadership activities, standard
operating procedures, reward systems, and evaluation
criteria, all of which can be used to steer, reward, and
control innovation activities (Wu et al., 2005).
Finally, research-oriented CEOs are likely to hire and
attract like-minded personnel who share their technolog-
ical vision and skills. As human capital is a key element
of innovation, the selection and retention of research-ori-
ented people are paramount (Zucker et al., 1998). The
resulting innovation culture not only stimulates inno-
vation activity (as discussed) but also strengthens the
attraction, selection, and departure of organizational
members on the basis of their fit with a socio-cultural
context focused on innovation (Elenkov and Manev,
2005; Berson et al., 2008). Combining these three
mechanisms explains why research-oriented CEOs are
more likely to enable firms’ innovation outcomes, which
results in the following hypothesis:
Hypothesis 1: CEO research orientation is posi-
tively related to firms’ innovation outcomes.
2.2. Organizational context, managerial
discretion, and CEO influence
The extent to which CEOs are able to influence their
firms’ strategies and outcomes depends on the organi-
zational context in which CEOs operate (Busenbark
et al., 2016). That context is shaped by firm-level fac-
tors related to organizational structures, resources,
and routines that enhance or hinder CEOs’ manage-
rial discretion or latitude for action (Hambrick and
Finkelstein, 1987; Liu et al., 2018). Therefore, we
consider three contextual factors that determine man-
agerial discretion and thus explain when CEOs can
influence firms’ innovation outcomes: CEO formal
power, availability of slack resources, and organiza-
tional age.
First, the amount of formal power that a CEO
has, is an important predictor of a CEO’s influence
on organizational activities and outcomes. Here,
the CEO’s structural position relative to the board
effectively reflects such structural (i.e., formal)
power (Finkelstein et al., 2009; Chin et al., 2013).
Specifically, CEO-chair duality has been consis-
tently found to raise such CEO power (Krause et al.,
2014). It effectively determines how (un)constrained
a CEO is in shaping strategy. Hence, we expect that
research-oriented CEOs who also chair the board are
more able to formulate an innovation strategy that is
in line with personal aspirations.
Hypothesis 2a: CEO duality positively moderates
the positive relationship between CEO research ori-
entation and firms’ innovation outcomes.
Second, slack resources may offer CEOs more
leeway in steering the organization in line with per-
sonal preferences (Jensen and Meckling, 1976). In
this respect, slack resources may free up manage-
rial resources (e.g., time, effort, and attention). This
may facilitate experimentation and increase the abil-
ity to pursue risky innovation projects (Chen, 2008;
Nohria and Gulati, 1996). Slack may also decrease
the pressure for short-term performance from share-
holders (Walrave et al., 2011). As a result, CEOs with
a research orientation and slack resources are able
to better pursue their innovative ideas and research.
We, therefore, expect that the relation between CEO
research orientation and firms’ innovation outcomes
is positively influenced by the availability of slack
resources.
Hypothesis 2b: Firm slack resources positively
moderate the positive relationship between CEO
research orientation and firms’ innovation outcomes.
Finally, firm age affects the influence that CEOs
have on their firms’ activities and outcomes. As firms
develop specific routines, competences, and norms
over time (Hannan and Freeman, 1984), R&D pro-
cesses, incentive systems, and resource-allocation
processes tend to become increasingly routinized and
therefore difficult to change (Kapoor and Klueter,
2015) – even by the CEO. An older firm’s innova-
tion activities, such as the search for new technolog-
ical knowledge, are therefore often constrained by its

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
Nino van de Wal, Christophe Boone, Victor Gilsing and Bob Walrave
242 R&D Management 50, 2, 2020
own imprinted processes, cultures, and capabilities
(Sorensen and Stuart, 2000). In addition, research
shows that the influence that the CEO can exert over
firm behavior and subsequent outcomes decreases with
firm age (Beckman and Burton, 2008). In this respect,
a CEO’s prior career experiences are found to have a
particularly strong influence on explorative activities
when a firm is founded (Beckman, 2006). Compared
to younger firms, older firms have more inert routines,
competences, and norms that may reduce the poten-
tial impact CEOs have on their firms. Therefore, we
expect that the influence of CEO research orientation
on innovation outcomes will be weaker for older firms.
Hypothesis 2c: Firm age negatively moderates the
positive relationship between CEO research orienta-
tion and firms’ innovation outcomes.
2.3. CEO strategic choices and allocation
of resources to R&D
To examine in more detail how research-oriented
CEOs’ strategic influence fuels subsequent organiza-
tional processes and ultimately innovation outcomes,
we argue here that resource allocation to R&D par-
tially mediates the CEO–innovation relationship.
CEOs have significant control over their firms’ strat-
egy formulation processes and R&D resource-al-
location processes (Bromiley and Rau, 2016). Prior
research shows that CEOs who have advanced sci-
ence-related degrees and extensive experience in
engineering and technology spend more R&D dollars
per employee (Barker and Mueller, 2002). CEOs with
technological domain expertise have a tendency to use
R&D and science as a universal response to organiza-
tional failure and to achieve corporate growth objec-
tives (Cummings and Knott, 2018). They are also
more likely to be inclined to employ more people with
a high level of education and technical background
(Zucker et al., 1998). This may lead research-oriented
CEOs to choose an R&D-intensive investment strat-
egy to pursue their technological vision for innovation.
While investment in R&D is not a guarantee
of innovation success, it is certainly an important
input. It helps firms to attract, train, and retain R&D
employees as well as to acquire other R&D resources
that are required to develop an absorptive capac-
ity and innovation capability (Zucker et al., 1998).
Especially in technology-intensive industries, R&D
investments are a primary source of innovation out-
put (Hagedoorn and Cloodt, 2003). Studies show that
R&D intensity positively impacts patent output or
related types of innovation outcomes (e.g., Griliches,
1990). Hence, we argue that one of the mechanisms
through which research-oriented CEOs influence
firms’ innovation outcomes is through intensifying
R&D investments. Specifically, we hypothesize that
CEO research orientation increases firms’ innovation
outcomes because they increase firms’ investments
in R&D, which increases innovation outcomes.
Hypothesis 3: Firm R&D intensity partially medi-
ates the positive relationship between CEO research
orientation and firms’ innovation outcomes.
2.4. The CEO’s role in the implementation
of an R&D-intensive investment
strategy
Another explanation for how research-oriented
CEOs spur firms’ innovation outcomes is through
effective strategy implementation. Successful imple-
mentation of a firm’s strategy through daily lead-
ership activities is one of the CEO’s primary tasks
(Burgelman et al., 2018; Simsek et al., 2015). Prior
research shows that R&D-intensive firms achieve
higher productivity and economic returns when they
are managed by CEOs with technical expertise and a
propensity for innovation (Beal and Yasai-Ardekani,
2000; Pan, 2015). In this respect, research-oriented
CEOs can effectively steer the implementation of
an R&D-intensive strategy. They are able to process
more technical information and they are motivated
to solve complex research-related problems, which
facilitates fast strategic decision making (Barker
and Mueller, 2002; Katila et al., 2017). Given their
strong attentional focus on the present and the future
(Nadkarni and Chen, 2014), they are also more likely
to detect links among technological developments
over time (Brown and Eisenhardt, 1997). Research-
oriented CEOs are therefore more capable to strate-
gically steer for the detection and development of
new technologies and achieve a higher frequency of
innovation outcomes for each R&D dollar invested.
As such, we hypothesize that CEO research orienta-
tion positively moderates the relation between R&D
intensity and innovation outcomes.
Hypothesis 4: CEO research orientation positively
moderates the positive relationship between a firm’s
R&D intensity and its innovation outcomes.
3. Method
3.1. Sample and data
We studied U.S. research-based pharmaceutical
firms to test our hypotheses (Standard Industrial
Classification [SIC] codes: 2833, 2834, 2835, and
2836) (Caner et al., 2017). Especially in R&D-intensive

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
CEO research orientation, organizational context, and innovation in the pharmaceutical industry
R&D Management 50, 2, 2020 243
environments, CEO characteristics become reflected
in organizational outcomes because these environ-
ments demand action of a more strategic kind, offer
a wide range of strategic options, and provide CEOs
with more discretion (Wu et al., 2005; Gerstner et al.,
2013). We limited our scope to publicly list U.S. firms
that were among the hundred largest employers, as
recorded by Compustat, at any time during the period
between 2001 and 2013. We identified the CEOs for
these firms and years in BoardEx and Execucomp and
applied four selection criteria to increase our study’s
internal validity. First, a CEO’s first year in office
was the first year in which he or she served for more
than half the calendar year. Second, we included only
CEOs who served at least two full years, to observe
at least a minimally potential effect of their research
orientation on corporate strategy and innovation (Chin
et al., 2013). Third, we excluded CEOs who had pre-
viously served as CEO somewhere else, as our mea-
sure of CEO research orientation was based on each
CEO’s ‘pre-CEO’ experiences (Chin et al., 2013).
Fourth, we excluded firms that focused only on gener-
ics or reformulations and did not actively engage in
pharmaceutical innovation to maintain our focus on
research-intensive firms (Kapoor and Klueter, 2015).
This procedure resulted in a sample consisting of 109
CEOs at 87 firms and 711 firm-year observations.
Although the sample of firms is not large in an
absolute sense, it represents the vast majority of the
population of U.S. research-based pharmaceutical
companies over a period not studied before. It is also
larger than the samples used in related studies of
this industry (e.g., Gerstner et al., 2013). In addition
to increasing the study’s internal validity, the study
design resulted in a handcrafted sample that enabled
detailed measurement of heterogeneity among
CEOs’ research orientation and extensive data col-
lection using a variety of data sources. Data on CEO
characteristics and experiences were collected using
the BoardEx and Execucomp databases. Where infor-
mation was missing, we consulted numerous other
sources, such as SEC filings, corporate websites,
press releases, Thomson Reuters Eikon, Lexis Nexis,
and Marquis Who’s Who, and several other online
directories containing information on executive
backgrounds (e.g., Bloomberg Executive Profile and
Biography, The Wall Street Transcript, and Equilar)
to make our database complete. The accounting data
were obtained from Compustat and the patent data
were collected from the U.S. Patent and Trademark
Office (USPTO). We used USPTO data because they
match with our sample of U.S. publicly listed firms.
Since the USPTO assigns patents to both parent firms
and their subsidiaries, we constructed detailed fam-
ily trees that included historical company names of
all firms using Securities and Exchange Commission
(SEC) 10-K filings and company websites. We
matched company names, legal forms, and country
data to USPTO assignees on patent applications.
Eventually, all 9,953 patents of subsidiaries were
aggregated at the parent-company level, which
resulted in a total of 37,086 patent applications for
the 87 firms under observation.
3.2. Dependent variables
We measured each firm’s yearly innovation outcomes
by counting the number of patent applications per
year (dated by patent application date). Especially
in the pharmaceutical industry, where firms have a
strong incentive to file for patents, patent activity is
commonly used for studies on innovation (Hagedoorn
and Cloodt, 2003; Grigoriou and Rothaermel, 2014;
Caner et al., 2017). We used USPTO patent data
because the USPTO recently made datasets available
on patents granted from 1976 onward and on patent
applications published from 2001 onward (i.e., pat-
ent applications with a pending instead of granted
status). This allows us to count all patent applications
(i.e., granted and pending applications)of the firms
in our sample, filed for during the period ofstudy.
We adopted patent applications as opposed to
granted patents, as each application represents a
valuable kind of technology that results from the
innovation process (Balsmeier and Buchwald, 2014).
Notably, patent applications are especially suited to
study a CEO’s most immediate impact on a firm’s
technological innovation outcomes. Whether a patent
is filed for a given invention is determined mainly by
the firm’s own decision making, whereas the grant
of a patent is dependent on the substantially delayed
outcome of a third party’s appraisal of the invention,
the patent office. This choice implies that we forgo
the potential of granted patents to proxy invention
quality at the benefit of a more direct causal chain
(Grigoriou and Rothaermel, 2014) – in both time
and space. This approach follows recent work (e.g.,
Balsmeier and Buchwald, 2014). Additional analyses
using granted patents, as a robustness test, resulted in
highly similar results (see Online Appendix A).
To test the mediation effect, we measured R&D
intensity as a firm’s R&D expenditure divided by the
number of employees. Dollars spent by the firm on
R&D were converted to the year 2000 U.S. dollars
using the consumer price index published by the
U.S. Bureau of Labor Statistics (Barker and Mueller,
2002). In upper echelons studies, the amount of R&D
dollars invested per employee is the standard and
most robust measure of a firm’s investments in intel-
lectual human capital for research and innovation

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
Nino van de Wal, Christophe Boone, Victor Gilsing and Bob Walrave
244 R&D Management 50, 2, 2020
(Baysinger, Kosnik, and Turk, 1991; Barker and
Mueller, 2002). A firm’s R&D investments relative to
its number of employees are a strong indicator of the
strategic importance of innovation for a firm because
human capital is critical to firms’ capabilities neces-
sary to innovate (Zucker et al., 1998; Kor, 2006).
3.3. Independent variable
We coded CEO research orientation using four indi-
cators that are indicative of a CEO’s ability and moti-
vation in relation to science and technology. More
specifically, based on a person’s career experiences
prior to becoming CEO, we coded whether: (1) the
CEO holds a PhD degree in science or engineering;
(2) the CEO has academic experience; (3) the CEO’s
dominant functional experience is in R&D; and (4) the
CEO holds any patents. We specifically looked at those
experiences because they effectively reflect abilities
and motivations that are of particular relevance to stra-
tegic leadership in technological innovation (Judge et
al., 1995; Zucker et al., 1998). Reliability analysis and
factor analysis showed that the four indicators reflect
a latent construct that we call CEO research orienta-
tion (see Online Appendix B). Subsequently, we cal-
culated the main independent variable CEO research
orientation, per CEO, as the sum of the mentioned
indicators because the indicators had similar means
and variances, and all varied between 0 and 1. The
resulting variable ranges between 0=no research ori-
entation and 4=high research orientation. We deter-
mined a CEO’s research orientation prior to he or she
becoming CEO of the focal firm, to make sure that
our operationalization was consistent with the causal
logic of our arguments (Chin et al., 2013).
3.4. Moderator variables
Three contextual factors that determine CEOs’ man-
agerial discretion served as moderator variables to
test Hypotheses 2a–c. CEO duality was measured as
a dummy variable, indicating that whether the CEO
was also board chairperson for each year (Chin et al.,
2013). We measured slack resources as each firm’s
financial slack by dividing its current assets by its
current liabilities (Nohria and Gulati, 1996). Firm age
was measured as the difference between the observa-
tion year and the year of firm incorporation (Wu et al.,
2005). These variables were lagged by one year in all
regressions compared to the dependent variable.
3.5. Control variables
To control for potentially confounding factors, we
included multiple managerial and firm variables
that have been widely used in research on upper
echelons and innovation. Based on prior studies
that relate firm differences in R&D investments and
firm patenting to CEO characteristics (Carpenter
et al., 2004; Cummings and Knott, 2018), we con-
trolled for CEO tenure (the total number of years
a CEO had held office), founder (dummy vari-
able indicating whether a CEO was a founder of
the focal firm), CEO ownership (the percentage
of stock owned by the CEO), and insider (dummy
variable indicating whether the current CEO had
been hired from inside the company rather than
from outside of it). We controlled for the board
independence (the number of inside directors to the
total number of directors on the board) because of
inside directors’ influence through their monitor-
ing and advising roles (Walrave et al., 2014). At
the firm level, we controlled for firm size (the log-
arithm of the number of employees) and financial
performance (return on assets; net income divided
by total assets) because these may influence a
firm’s innovation potential (Ahuja and Lampert,
2001). As a firm’s ownership influences CEO
behavior and innovation (Baysinger et al., 1991),
we included institutional ownership (the total per-
centage of shares owned by external nonmanage-
ment shareholders who individually owned at least
five percent of company shares).
All independent variables were lagged one year in
all regressions compared to the dependent variable.
We also included SIC and year dummy variables in
all models to account for industry heterogeneity, mac-
roeconomic conditions, and unobserved time effects.
3.6. Analysis
We used the generalized estimating equations’
(GEEs) regression method because our investiga-
tion focuses on CEOs who are clustered within
firms by analyzing longitudinal data with nonnormal
response variables (Liang and Zeger, 1986). This
method accounts for firm heterogeneity and autocor-
relation by estimating the within-subject correlation
of repeated responses on dependent variables. GEE
regressions also offer the flexibility to cope with the
different distributions of the two dependent variables
in our models. In the models predicting patent appli-
cations, we specified a negative binomial distribution
and used a log link function to calculate the variance
because the number of patents takes on nonnegative
integer values and is zero for some firms in some
years. The goodness-of-fit and likelihood-ratio tests
confirm that the distribution of this patent count vari-
able shows overdispersion. In the models predicting
R&D intensity (see Models 7 and 8 in Table 4), we

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
CEO research orientation, organizational context, and innovation in the pharmaceutical industry
R&D Management 50, 2, 2020 245
specified a Gaussian distribution and used an iden-
tity link function to calculate the variance because
this variable is a ratio measure, and thus of a nondis-
crete nature. We chose an exchangeable correlation
structure as our study design resulted in unbalanced
observations with unequal spacing. Finally, we used
Huber–White–sandwich standard errors to correct
for heteroscedasticity.
We constructed a presample patent stock variable
as the accumulated number of granted patents of each
focal firm and its subsidiaries from the year 1975 until
a firm’s first observation year using a 15% annual
depreciation rate of knowledge. We also included a
dummy that indicates when a firm has zero presam-
ple patent applications. In contrast to the presample
patent-stock variable, this presample dummy vari-
able controls for the fact that some firms do not have
a history of filing patents. This approach enables us
to use the full variation in the sample (Balsmeier and
Buchwald, 2014) and to control for the possibility
that firms may enter the sample with inherently dif-
ferent innovation-generating capabilities (Ahuja and
Lampert, 2001). This ensures that our estimates are
consistent despite the hierarchical panel-data structure
(i.e., repeated observations of CEOs nested in firms).
This presample variable approach also limits the
threat of endogeneity. Furthermore, we tried to con-
trol for endogeneity by closely following the approach
of recent upper echelons studies (e.g., Chin et al.,
2013; Gerstner et al., 2013). As the results of these
efforts were unsatisfactory (see Online Appendix
C), we decided to include a rich and fine-grained
set of controls to limit the threat of endogeneity.
Further concerns for endogeneity were addressed by
constructing the CEO research-orientation variable
based on pre-CEO appointment experiences only,
by accounting for a possible dynamic process of the
innovation activity by including the lagged values of
patent applications as a control, and by lagging all
explanatory variables by a one-year period to reduce
possible simultaneity biases as well as to allow for
the influence of the explanatory variables to become
observable in a firm’s innovation outcomes.
4. Results
Tables 1 and 2 report the means, standard deviations,
and correlations among all variables. The mean vari-
ance inflation factor (VIF) of 1.69 is well below 3,
and the VIF of each variable is far below 10, indicat-
ing very limited multicollinearity. The observed firms
have a median size of 525 employees and are 15years
old. Regarding the distribution of CEO research ori-
entation, 12 CEOs score 4, 17 CEOs score 3, 4 CEOs
score 2, 11 CEOs score 1, and 65 CEOs score 0. For
illustration, and providing face validity for this con-
struct, Genentech’s Arthur Levinson has a research
orientation of 4 and Celgene’s Robert Hugin received
a score of 0. These descriptive statistics indicate that
CEOs exhibit considerable heterogeneity in their
research orientation.
Models 1 to 6 in Table 3 provide the results for
H1 (i.e., the what question) and H2a–c (i.e., the
when questions), while Models 7 to 12 in Table 4
provide the results related to H3 and H4 (i.e., the
how questions). We report Wald chi-square sta-
tistics to test the overall model significance and
further include the quasi-likelihood under the inde-
pendence model (QIC) criterion to compare models
(Cui and Qian, 2007). Models 1 and 7 only include
the control variables and the lagged dependent vari-
able. Model 1 shows that the firm size is positively
related to firm patent applications and firm age is
negatively related to them. Model 7 illustrates that
R&D intensity is significantly and positively influ-
enced by financial slack and board independence,
while firm size has a significant negative effect.
Model 2 shows that CEO research orientation is
significantly and positively associated with a firm’s
number of patent applications (β=0.159, P = .002).
This translates into an average change in firms’ pat-
ent applications of 17 percent when CEO research
orientation increases by one unit. This supports
Hypothesis 1’s postulation that increasing CEO
research orientation increases firms’ innovation out-
comes. Models 3 to 5 indicate that CEO influence
depends on the organizational context. In these mod-
els, the coefficient of the main effect (H1) becomes
insignificant (except in Model 5), suggesting that
moderation is present. More specifically, the effect of
CEO research orientation increases, as anticipated,
in the case of CEO duality (Model 3), when more
financial slack is present (Model 4), or when the firm
is younger (Model 5). However, when we simulta-
neously include all moderation effects in Model 6,
the interaction effect of firm age becomes insignifi-
cant. Similar findings are obtained for Models 10 and
12, which also include R&D intensity as a mediator.
Thus, we find strong support for H2a and H2b, and
moderate support for H2c.
We further investigated Hypothesis 2a and 2b by
plotting their marginal effects and calculating the pre-
dictive margins. Figure 1 shows that when a CEO with
a research orientation of 4 is also board chairperson, the
number of patent applications increases by 39 percent.
Figure 2 illustrates that an increase of financial slack by
one standard deviation (5.69) from the mean (5.24), for
firms with a research-oriented CEO (score 4), results in
a15 percent increase in patent applications.

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
Nino van de Wal, Christophe Boone, Victor Gilsing and Bob Walrave
246 R&D Management 50, 2, 2020
Hypothesis 3 predicts that firm R&D intensity
partially mediates the relationship between CEO
research orientation and firms’ innovation out-
comes. To test for mediation, we first followed
Baron and Kenny (1986). Their procedure indi-
cates that there might indeed be mediation of R&D
intensity because: (1) Model 8 reveals that CEO
research orientation is significantly related to R&D
intensity, (2) Model 9 shows that R&D intensity,
in turn, is significantly related to firms’ patents,
and (3) the relationship between CEO research
orientation and innovation drops in strength (from
β=0.157 in Model 2 to β=0.153 in Model 9). Yet,
such a relatively small drop in the effect size indi-
cates that R&D intensity only partially mediates
the CEO–innovation relation. To further assess the
significance of this mediation effect we applied the
Sobel test to the focal coefficients and their stan-
dard errors, which shows a marginally significant
mediation effect (z-value=1.72, P=.085). These
findings offer moderate support for Hypothesis 3.
In contrast to Hypothesis 4, Models 11 and 12
reveal a significantly negative coefficient of the
interaction term of R&D intensity and CEO research
orientation. Figure 3 illustrates the marginal effects
of this moderation. It can be observed that the R&D
intensity-innovation relationship becomes less strong
and even changes sign when a CEO has a research
orientation of 3 or 4. For firms with a CEO with a
research orientation of 1, increasing R&D intensity
by one standard deviation (163.17) from the mean
(151.28) increases patent applications by 46 percent.
For firms with a CEO with a research orientation
of 4, increasing R&D intensity from low to high
decreases patent applications by 17%. However, it is
important to note that the patent output of firms led
by more research-oriented CEOs is always higher for
equal levels of R&D intensity.
4.1. Additional analyses
It might be the case that CEOs with a higher research
orientation steer their organization toward devel-
oping patents of a higher quality, emphasizing less
the actual number of patents. To investigate this,
we conducted additional analyses using alternative
dependent variables that account for such quality:
(1) the granted patent count and (2) a forward cita-
tion-weighted patent count (using a 2001 to 2010
sample1
). With these dependent variables, the mod-
eration effect of CEO research orientation on the
R&D-innovation relationship turns insignificant.
CEOs with a low research orientation achieve higher
R&D productivity (i.e., number of patents for each
invested R&D dollar) as far as number of patents is
concerned, but this productivity advantage disappears
when accounting for the quality of the patents. The
other results of these additional analyses are highly
similar to the ones reported in the paper, which lends
confidence to our main conclusions. We also ran a
robustness test with respect to the operationalization
of CEO research orientation and lag structure in our
models. The results of these analyses are consistent
with the reported findings in our main analyses.
Table 1. Descriptive statistics
Mean SD Min Median Max
1Innovation outcomes 36.67 89.18 0.00 8.00 715.00
2 R&D intensity 149.76 160.67 1.28 108.74 1967.55
3 CEO research orientation 1.14 1.51 0.00 0.00 4.00
4 Firm size1 8,911.56 22,798.33 11.00 525.00 122,200.00
5 Firm age 19.58 15.45 2.00 15.00 62.00
6 Financial slack 5.22 5.71 0.49 3.57 64.14
7 Financial performance −0.10 0.29 −3.17 −0.02 0.76
8 Institutional ownership 29.31 18.61 0.00 28.93 92.38
9 Board independence 0.22 0.12 0.00 0.20 0.88
10 CEO tenure 8.05 7.10 0.00 5.81 36.02
11 Founder 0.31 0.46 0.00 0.00 1.00
12 CEO duality 0.58 0.49 0.00 1.00 1.00
13 CEO ownership 0.85 2.34 0.00 0.11 27.39
14 Insider 0.52 0.50 0.00 1.00 1.00
15 Presample patent stock 194.18 487.20 0.00 20.79 3178.40
16 Presample dummy 0.03 0.17 0.00 0.00 1.00
711 observations.
1Log transformed variable but original values reported here.

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
CEO research orientation, organizational context, and innovation in the pharmaceutical industry
R&D Management 50, 2, 2020 247
Table 2. Correlation matrix
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1Innovation outcomes
2 R&D intensity −0.16
3 CEO research
orientation
−0.11 0.23
4 Firm size 0.62 −0.43 −0.23
5 Firm age 0.56 −0.25 −0.24 0.72
6 Financial slack −0.14 0.22 0.22 −0.33 −0.28
7 Financial performance 0.21 −0.39 −0.18 0.46 0.37 −0.08
8 Institutional ownership −0.38 0.23 0.27 −0.46 −0.46 0.13 −0.25
9 Board independence −0.04 −0.09 0.05 −0.10 −0.14 0.10 −0.02 0.11
10 CEO tenure −0.10 0.00 0.16 −0.07 −0.04 0.10 0.01 0.05 0.03
11 Founder −0.17 0.04 0.38 −0.24 −0.33 0.24 −0.17 0.15 0.07 0.45
12 CEO duality 0.25 −0.21 0.00 0.39 0.27 −0.10 0.17 −0.26 0.07 0.19 0.11
13 CEO ownership −0.11 −0.10 0.02 −0.16 −0.13 0.05 −0.03 −0.06 0.13 0.31 0.33 0.02
14 Insider 0.22 −0.14 −0.02 0.26 0.27 −0.12 0.22 −0.14 −0.03 −0.36 −0.33 0.17 −0.22
15 Presample patent stock 0.83 −0.15 −0.13 0.64 0.68 −0.17 0.22 −0.37 −0.04 −0.16 −0.20 0.21 −0.12 0.25
16 Presample dummy −0.07 −0.04 0.01 −0.09 −0.13 −0.06 −0.05 0.18 −0.09 −0.07 −0.11 −0.06 −0.04 0.10 −0.07
Correlations <.07 are significant at P<.05 and those <.10 are significant at P<.01.

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
Nino van de Wal, Christophe Boone, Victor Gilsing and Bob Walrave
248 R&D Management 50, 2, 2020
5. Discussion
For a long time, scholars have been interested in
understanding why firms differ in their innovation
performance (Ahuja et al., 2008). Whereas prior
research has predominantly emphasized a firm’s
capability to develop successful innovations, this
study’s focus is on the role of a firm’s CEO as a key
antecedent of variation in a firm’s innovation strat-
egy and its innovation outcomes. We introduce the
Table 3. Results of when CEO research orientation affects innovation
Dependent variable
Innovation outcomes
1 2 3 4 5 6
CEO research orientation  0.16*** 0.04 0.09 0.26*** 0.02
 (0.05) (0.07) (0.06) (0.07) (0.08)
CEO RO*CEO duality   0.16**   0.19***
  (0.07)   (0.07)
CEO RO*Financial slack    0.01***  0.01***
   (0.00)  (0.00)
CEO RO*Firm age     −0.00** −0.00
    (0.00) (0.00)
Firm size 0.48*** 0.51*** 0.52*** 0.52*** 0.51*** 0.52***
(0.07) (0.07) (0.06) (0.06) (0.07) (0.05)
Firm age −0.02** −0.02* −0.02* −0.02** −0.01 −0.02*
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Financial slack 0.01* 0.01 0.01 −0.02* 0.01 −0.02**
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Financial performance −0.10 −0.15 −0.16 −0.11 −0.14 −0.12
(0.12) (0.13) (0.14) (0.13) (0.14) (0.13)
Institutional ownership 0.00 0.00 0.00 0.00 0.00 0.00
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
CEO tenure 0.00 0.00 0.01 −0.00 0.00 0.01
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Founder 0.13 −0.15 −0.18 −0.19 −0.22 −0.27
(0.14) (0.16) (0.16) (0.17) (0.17) (0.17)
CEO duality −0.02 −0.14 −0.32** −0.23** −0.15 −0.46***
(0.10) (0.10) (0.13) (0.10) (0.11) (0.12)
CEO ownership 0.01 0.01 0.00 −0.00 0.01 −0.01
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
Insider −0.07 −0.12 −0.10 −0.21* −0.16 −0.20*
(0.11) (0.11) (0.11) (0.12) (0.11) (0.12)
Board independence −0.12 −0.15 −0.18 −0.30 −0.21 −0.35
(0.39) (0.38) (0.36) (0.39) (0.36) (0.37)
Presample patent stock 0.00** 0.00* 0.00 0.00* 0.00* 0.00
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Presample dummy −1.97*** −2.04*** −2.17*** −2.15*** −2.10*** −2.32***
(0.38) (0.35) (0.34) (0.38) (0.36) (0.37)
Lagged dependent variable
(patents)
0.01*** 0.01*** 0.01*** 0.01*** 0.01*** 0.01***
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Constant −1.23** −1.42*** −1.42*** −1.05** −1.41*** −1.05**
(0.52) (0.51) (0.51) (0.50) (0.50) (0.49)
Observations 711 711 711 711 711 711
QIC 5178.4 5118.0 5105.9 5116.3 5119.1 5103.3
Wald chi-square 546.0*** 586.7*** 589.9*** 794.9*** 619.6*** 799.3***
Robust standard errors in parentheses. All models include SIC and time dummies. Significance levels of two-tailed tests:
*P < .10;**P < .05;***P < .01.

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
CEO research orientation, organizational context, and innovation in the pharmaceutical industry
R&D Management 50, 2, 2020 249
Table 4. Results of how CEO research orientation affects innovation
R&D intensity Innovation outcomes
Dependent variable 7 8 9 10 11 12
CEO research orientation  8.62** 0.16*** 0.03 0.25*** 0.11
 (4.27) (0.05) (0.08) (0.05) (0.08)
R&D intensity   0.00*** 0.00*** 0.00*** 0.00***
  (0.00) (0.00) (0.00) (0.00)
CEO RO*CEO duality    0.18**  0.18***
   (0.07)  (0.07)
CEO RO*Financial slack    0.01***  0.01***
   (0.00)  (0.00)
CEO RO*Firm age    −0.00*  −0.00
   (0.00)  (0.00)
R&D intensity*CEO RO     −0.00*** −0.00***
    (0.00) (0.00)
Firm size −23.53*** −22.07*** 0.56*** 0.56*** 0.56*** 0.56***
(5.64) (5.42) (0.07) (0.06) (0.07) (0.06)
Firm age 0.83 0.83 −0.02** −0.02* −0.01* −0.01*
(0.54) (0.53) (0.01) (0.01) (0.01) (0.01)
Financial slack 1.64** 1.45** 0.01 −0.02** 0.01 −0.02**
(0.68) (0.71) (0.01) (0.01) (0.01) (0.01)
Financial performance 29.72* 30.56* −0.08 −0.05 −0.06 −0.03
(16.85) (16.86) (0.13) (0.14) (0.12) (0.13)
Institutional ownership 0.44** 0.38** 0.00 0.00 0.00 0.00
(0.19) (0.19) (0.00) (0.00) (0.00) (0.00)
CEO tenure −0.52 −0.58 0.00 0.01 0.01 0.01
(0.86) (0.81) (0.01) (0.01) (0.01) (0.01)
CEO founder 7.51 −3.55 −0.13 −0.26 −0.07 −0.19
(11.03) (10.25) (0.16) (0.17) (0.14) (0.15)
CEO duality −9.02 −10.29 −0.15 −0.47*** −0.16 −0.45***
(9.66) (9.53) (0.10) (0.12) (0.09) (0.11)
CEO ownership −1.54 −1.55 0.01 −0.01 0.00 −0.01
(0.94) (0.97) (0.01) (0.01) (0.01) (0.01)
Insider 6.88 2.05 −0.14 −0.22* −0.08 −0.16
(12.96) (12.33) (0.11) (0.13) (0.10) (0.11)
Board independence −30.43 −29.32 −0.17 −0.37 −0.23 −0.40
(30.69) (31.26) (0.38) (0.36) (0.37) (0.36)
Presample patent stock 0.02* 0.02 0.00 0.00 0.00 0.00
(0.01) (0.01) (0.00) (0.00) (0.00) (0.00)
Presample dummy −41.55* −43.61** −1.93*** −2.21*** −1.80*** −2.09***
(23.50) (19.04) (0.38) (0.39) (0.37) (0.37)
Lagged dependent variable
(R&D)
0.43*** 0.43***    
(0.05) (0.05)    
Lagged dependent variable
(patents)
  0.01*** 0.01*** 0.01*** 0.01***
  (0.00) (0.00) (0.00) (0.00)
Constant 133.59*** 126.83*** −1.68*** −1.31** −1.88*** −1.48***
(39.10) (36.57) (0.54) (0.52) (0.54) (0.51)
Observations 711 711 711 711 711 711
QIC 5277543.5 5118918.3 5083.8 5073.8 5072.0 5061.6
Wald chi-square 1698*** 2119*** 586.7*** 812.7*** 689.6*** 966.3***
Robust standard errors in parentheses. All models include SIC and time dummies. Significance levels of two-tailed tests:
*P < .10;**P < .05;***P < .01.

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
Nino van de Wal, Christophe Boone, Victor Gilsing and Bob Walrave
250 R&D Management 50, 2, 2020
concept of CEO research orientation, which reflects
CEOs’ ability and motivation for science and tech-
nology, to examine what CEO characteristics relate
to firm heterogeneity in firms’ innovation outcomes.
Using a longitudinal sample of U.S. pharmaceutical
firms, we found that CEO research orientation is pos-
itively associated with firms’ innovation outcomes.
Moreover, the extent to which CEO research orienta-
tion influences innovation outcomesincreases when
(1) the CEO is also the chair of the board and (2) slack
resources are available to the firm. We also found that
R&D intensity partially explains how research-ori-
ented CEOs achieve higher levels of innovation: they
invest more in R&D compared to CEOs with a low
research orientation. This might indicate that CEOs
with a low research orientation achieve higher R&D
productivity based on the number of patents per
R&D dollar but not when accounting for the quality
of those patents.
These findings have several important implica-
tions on the upper echelons and innovation litera-
ture. First, we show that research-oriented CEOs
are associated with higher innovation outcomes for
firms. This insight into contributes to the long-stand-
ing debate on whether and how much CEOs impact
their firms’ outcomes (Hambrick and Mason, 1984;
Quigley and Hambrick, 2015). There is growing evi-
dence that CEOs are important to their firms’ adap-
tation to technological discontinuities (Eggers and
Kaplan, 2009; Gerstner et al., 2013). We extend this
line of research by highlighting that CEO human
capital not only underlies the dynamic capabilities
for organizational adaptation and strategic change
(Helfat and Martin, 2015) but also initiates the ‘evo-
lution of technology’ by directing the firm’s inno-
vation development toward promising new research
areas (Kaplan and Tripsas, 2008). Thus, some CEOs
are more inclined to the development of new technol-
ogies and the creation of new products (Nadkarni and
Chen, 2014), while others have a stronger orientation
toward the commercialization of existing technolo-
gies and products.
Second, when CEOs influence innovation out-
comes strongly depends on the organizational con-
text. In this study, we found that a CEO’s structural
power and a firm’s slack resources determine to
what extent the CEO’s research orientation becomes
reflected in innovation outcomes. By the integrating
aspects of CEO characteristics and organizational
context, we contribute to a more comprehensive
understanding of CEO effects on firm performance
(Busenbark et al., 2016; Liu et al., 2018). We show
that CEOs’ unique characteristics, notably their
research orientation, might not be enough on their
own to stimulate innovation; CEOs may also need
power and resources to influence firm strategy and
outcomes. Given sufficient managerial discretion
(Hambrick and Finkelstein, 1987), research-oriented
CEOs may shape R&D resource-allocation processes
and support organizational processes that steer their
firms toward groundbreaking research and technol-
ogy development.
Figure 1. Moderation impact of CEO duality on the marginal
effect of CEO research orientation on innovation.
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1R&(2'XDOLW\ <HV&(2'XDOLW\
Figure 2. Moderation impact of financial slack on the marginal
effect of CEO research orientation on innovation.
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3UHGLFWHGLQQRYDWLRQRXWFRPHV
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Figure 3. Moderation impact of CEO research orientation on the
marginal effect of R&D intensity on innovation.

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
CEO research orientation, organizational context, and innovation in the pharmaceutical industry
R&D Management 50, 2, 2020 251
Third, this study also contributes to the literature
by shedding more light on a hitherto key source of
unobserved heterogeneity that affects a firm’s inno-
vation capabilities. We show that a CEO consti-
tutes through his or her research orientation, a key
antecedent of variation in a firm’s innovation-strat-
egy choices and its attendant performance conse-
quences. Whereas the dominant emphasis in the
literature has been on the ability to innovate (Ahuja
and Lampert, 2001), this study’s focus on the role of
a CEO’s research orientation is also indicative of the
firm’s motivation to innovate (Ahuja et al., 2008). In
this way, the findings suggest that firms also differ in
their motivation to pursue innovation because some
CEOs are more research oriented and allocate more
resources to R&D in comparison to their counter-
parts at other firms.
However, there are also potential downsides to
highly research-oriented CEOs. This insight into
comes from the surprising finding of a negative
moderation effect of CEO research orientation on
the R&D-innovation relationship. In this respect, a
research-oriented CEO may be less effective in the
implementation of substantial R&D investments
which could be the result of micromanagement by
the CEO, which stifles innovation, or of inexpe-
rience due to a CEO’s substantial technical train-
ing and experience having come at the cost of that
CEO having less developed managerial skills. As
a result, a research-oriented CEO might overinvest
in R&D, despite increases in innovation outcomes,
only to position himself or herself as the inventor
of a new technology or because of personal inter-
ests. However, the productivity differences in R&D
investments related to a CEO’s research orientation
seem to disappear when accounting for the quality
of patent applications. Gaining more insight into this
pattern of findings constitutes an interesting avenue
for further research.
5.1. Managerial implications
This study also has important managerial implica-
tions. For CEOs and executive teams, our results
confirm the idea that top managers may enable
innovation and influence firm performance, through
R&D investments. Furthermore, the results pre-
sented in this paper may help to identify CEOs
who are most likely to focus on innovation as well
as create the conditions under which they operate
best. More specifically, executives with a techni-
cal background are more likely to spur innovation,
especially when the organizational context increases
their managerial discretion and provides them with
slack resources.
Our findings are important to supervisory boards,
as agents that safeguard shareholder value, and there-
fore hire and fire executive team members (incl. the
CEO). Assigning a research oriented CEO in combi-
nation with allowing for an innovation friendly con-
text may serve long-term shareholder value through
innovation. Also, firms facing times of discontinu-
ities, that require exploration and adaptation, might
benefit from a research oriented CEO as an enabler
of innovation (Walrave et al., 2017). Such a strategy,
however, requires strong boards, which can with-
stand the substantial short-term pressure for results
generated by some (large) investors.
Board directors should, of course, remain cau-
tious about the possibility of CEOs aiming for tech-
nological success over commercial success. In this
respect, our findings suggest that a research oriented
CEO should ideally be linked to an executive (e.g.,
Chief Operational Officer) with strong organiza-
tional and implementation skills. This idea resonates
with the organizational structure of SpaceX where
Elon Musk, as visionary and research oriented CEO,
is linked to Gwynne Shotwell – president and COO
at SpaceX – who is tasked with the execution of such
ideas.
Finally, these findings are also relevant to inves-
tors as CEO characteristics, in relation to organiza-
tional context, may serve as an important indicator for
innovation and thereby long-term firm performance.
Especially for investors active in the pharmaceutical
industry, our results allow for better informed (long-
term) investment portfolio choices.
5.2. Limitations and future research
This study has a number of limitations, which pro-
vide directions for future research. First, although
this study’s findings may be generalizable to other
R&D-intensive industries such as semiconduc-
tors and chemicals, future research should examine
whether our proposed theory is transferable to other
industries. The strategic process of increasing firms’
innovation outcomes through R&D investments
might even be stronger in other industries, compared
to the weak mediation effect we found, because
R&D investment intensity is ‘the norm’ for research-
based firms in the pharmaceutical industry. Second,
although this is one of the first studies that explicitly
tests for a potential mediating mechanism that links
CEO characteristics to firms’ innovation outcomes,
we only focused on one underlying mechanism and,
therefore, could only partially open the black box
of this relationship. The finding of a weak partial
mediation effect suggests the plausibility of addi-
tional mechanisms that could be examined and tested

© 2019 The Authors. R&D Management published by RADMA and John Wiley & Sons Ltd
Nino van de Wal, Christophe Boone, Victor Gilsing and Bob Walrave
252 R&D Management 50, 2, 2020
empirically. Third, while we controlled for potential
endogeneity in all ways available to us, it remains a
central issue to upper echelons studies in general. We
avoided problems with the causality of the studied
relationship by incorporating different lag structures
that ensure that our antecedent variables temporally
precede the dependent variable, by constructing the
CEO research orientation variable based on pre-CEO
appointment experiences only, by including rich and
fine-grained control variables, and by adding a pre-
sample patent-stock control variable. Moreover, the
theoretical mechanisms introduced and tested by the
complex interaction effect are difficult to explain
by reverse causality logic. Thus, the statistical tech-
niques employed here confirm the hypothesis that
the degree of CEO research orientation is positively
associated with innovation. A further limitation
arises from the sample size. While our sample size
is perhaps small in the absolute sense, it does rep-
resent a substantial part of the U.S. research-based
pharmaceutical companies over the studied period.
In this respect, our sample size approaches the popu-
lation size for this particular industry. This approach
follows recent work studying the same industrial
context (e.g., Gerstner et al., 2013). Moreover, the
coefficients of our controls resonate strongly with
previous work on this sector (Caner et al., 2017), fur-
ther increasing our confidence in both analyses and
results. Future research may gather data from a dif-
ferent industry with a larger population (or combine
data from several industries) to replicate and further
uncover the influence of CEO research orientation on
innovation and firm performance.
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Note
1. Given that we collected the patent data at the beginning
of 2016, we used a sample that covers the years 2001
to 2010 to correct for the two years that elapse on av-
erage between the patent application date and the grant
date and to allow for a five-year lag period between pat-
ent activity and other observations in order to reduce
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2001).
Nino van de Wal is a consultant in EY’s strategy
and operations practice. Nino holds a PhD degree in
economics and an MSc degree (cum laude) in man-
agement. His work on organizing for innovation has
been published in academic journals and featured in
business outlets.
Christophe Boone is a Professor of Organization
Theory and Behavior at the Faculty of Business and
Economics (University of Antwerp, Belgium). His
research interests focus on the dynamics of orga-
nizational populations in local communities, the
antecedents and consequences of team and organi-
zational diversity, CEO values and cognition, and
the neuroeconomics of decision making. He pub-
lished in journals such as Academy of Management
Journal, Academy of Management Review, Strategic
Management Journal, Management Science, Orga-
nization Science, Journal of Management, Journal
of Management Studies, American Sociological
Review, American Journal of Sociology, Journal of
Conflict Resolution, Nature Human Behavior, Hor-
mones and Behavior, and Brain and Cognition.
Victor Gilsing (1969) is a full Professor of
Strategic Entrepreneurship and Innovation at Free
University Amsterdam. He is also a part-time
professor at the Faculty of Applied Economics at
Antwerp University. His academic research has
been published in ISI journals such as Journal of
Management, Strategic Organization, Journal of
Management Studies, Research Policy, Journal of
Product Innovation Management, Technovation,
R&D Management, and others. For the period
of 2015 to 2020, he has received a prestigious
Odysseus research grant from the Flanders Science
Foundation (FWO) for a research program on the
role of Top Managers and the Board of Directors in
the building of an innovative organization and the
creation of breakthrough innovations.
Bob Walrave is an Associate Professor of Modeling
Innovation Systems at the Eindhoven University
of Technology. He has a background Industrial
Engineering (MSc, cum laude) and Strategic
Innovation Management (PhD). His research inter-
ests are centered around strategic decision making
in dynamically complex situations in the context of
innovation management and entrepreneurship. His
work has been published in journals such as Journal
of Management Studies, Research Policy, Long
Range Planning, R&D Management, Industrial and
Corporate Change, and Technological Forecasting
and Social Change.
Supporting Information
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