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Long Range Planning xxx (xxxx) xxx
Please cite this article as: Sebastian Firk, Long Range Planning, https://doi.org/10.1016/j.lrp.2021.102166
Available online 22 November 2021
0024-6301/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
Top management team characteristics and digital innovation:
Exploring digital knowledge and TMT interfaces
Sebastian Firk
a
,
*
, Yannik Gehrke
b
, Andre Hanelt
c
, Michael Wolff
b
a
Department of Accounting, University of Groningen, Nettelbosje 2, 9747 AE, Groningen, the Netherlands
b
Chair of Management and Control, University of Goettingen, Platz der Goettinger Sieben 3, 37073, Goettingen, Germany
c
Chair of Digital Transformation Management, University of Kassel, Kleine Rosenstraße 3, 34109, Kassel, Germany
ARTICLE INFO
Keywords:
TMT digital knowledge
Digital innovation
TMT interfaces
Chief digital ofcers
TMT hierarchical structure
ABSTRACT
On their journey toward digital transformation, industrial rms need to embrace digital inno-
vation. The top management team (TMT) is expected to set the course for digital innovation,
which is a challenging endeavour given the novel and cross-functional nature of digital innova-
tion. We draw on role theory to make sense of emerging role requirements for the TMT and
combine this view with upper echelon theory to hypothesize on the specic TMT characteristics
that are needed for digital innovation. We rst theorize that rms could benet from TMT digital
knowledge. Second, we argue that the effective utilization of TMT digital knowledge can be
fostered at internal TMT interfaces, such as between the chief executive ofcer (CEO), respec-
tively a chief digital ofcer (CDO), and other top managers. Finally, we consider the TMT hier-
archical structure as a contextual factor in the stimulation of TMT integration processes by
integrative CEOs and CDOs. We employ panel data regressions to a longitudinal dataset of US
industrial rms and nd a positive relation between TMT digital knowledge and digital inno-
vation, on average. We additionally nd evidence for the integrative roles of CEOs and CDOs.
However, our ndings also indicate that the CDO’s integrating role can be hampered by a strong
hierarchical structure in the TMT.
1. Introduction
To deal with the opportunities and threats associated with digital transformation (Verhoef et al., 2021), incumbent rms, even in
industrial contexts, have placed digital innovation at the top of their strategic agendas (Bj¨
orkdahl, 2020; Chanias et al., 2019).
However, many industrial rms struggle to unleash its full potential (Svahn et al., 2017). Especially due to the novel and
cross-functional nature of digital innovation (Bharadwaj et al., 2013; Nambisan et al., 2017; Yoo et al., 2012), the initiation and
implementation of digital innovation is extremely challenging in these rms (Correani et al., 2020). To overcome these challenges,
conceptual and case-based research point to the crucial inuence of the top management team (TMT) (e.g., Chanias et al., 2019; Kohli
and Melville, 2019). In particular, the TMT is key to lay the foundation for digital innovation due to its responsibilities in terms of
recognizing digital innovation’s strategic potentials, articulating its strategic relevance, and allocating resources (Floyd and Lane,
2000; Wrede et al., 2020).
* Corresponding author.
E-mail addresses: s.rk@rug.nl (S. Firk), yannik.gehrke@uni-goettingen.de (Y. Gehrke), hanelt@uni-kassel.de (A. Hanelt), michael.wolff@uni-
goettingen.de (M. Wolff).
Contents lists available at ScienceDirect
Long Range Planning
journal homepage: www.elsevier.com/locate/lrp
https://doi.org/10.1016/j.lrp.2021.102166
Received 11 May 2020; Received in revised form 6 June 2021; Accepted 15 November 2021
Long Range Planning xxx (xxxx) xxx
2
How top managers interpret and execute their roles for digital innovation is of great interest from both, an academic (e.g., Volberda
et al., 2021; Wrede et al., 2020) and practice perspective (e.g., Furr et al., 2019; Westerman et al., 2012). The need for digital
innovation presents a new situational demand that could change the requirements of traditional TMT roles (Nicholson, 1984) and,
thus, be challenging for the TMT. First, top managers need to understand and make sense of digital innovation characteristics (Hanelt
et al., 2021a; Wrede et al., 2020), which, however, require fundamentally different cognitive assumptions than those that are insti-
tutionalized in industrial rms (Henfridsson and Yoo, 2014; Yoo et al., 2012). Second, due to the cross-functional nature of digital
innovation, laying its foundation requires top managers to interpret digital innovation as a shared TMT responsibility and to cope with
blurring boundaries of traditional roles (Bharadwaj et al., 2013; Warner and W¨
ager, 2019). Such novel and potentially conicting role
transitions are known as a key issue in TMT research, as they are linked to cognitive and behavioral difculties for top managers (Floyd
and Lane, 2000).
Given these emerging role requirements for the TMT caused by digital innovation, it is important to understand which charac-
teristics help the TMT to be successful at facilitating digital innovation (Volberda et al., 2021). Especially, the individual characteristics
of top managers are critical in the interpretation and execution of their roles (Ahn et al., 2017; Chapman and Hewitt-Dundas, 2018;
Hambrick and Mason, 1984). Conceptual and case-based research indicates that top managers need to be aware of and support digital
innovation endeavors (Chanias et al., 2019; Hanelt et al., 2021a; Volberda et al., 2021) and thus indicates that top managers may need
to adapt their individual characteristics to the emerging role requirements. However, empirical evidence on the specic characteristics
needed to increase the TMT’s awareness remains scarce. Moreover, research suggests that considering behavioral integration—i.e., the
extent of information exchange, collaborative behavior, and decision-making participation in the TMT (Hambrick, 2007; Simsek et al.,
2005)—is important to understand how individual TMT characteristics actually translate into rm outcomes (Buyl et al., 2011;
Georgakakis et al., 2017; Heyden et al., 2013). Although this research provides valuable insights into TMT processes, it falls short in
accounting for the specic peculiarities of digital innovation.
The purpose of this paper is to explore the inuence of TMT characteristics—i.e., the needed knowledge, roles, and structures in the
TMT—on digital innovation. We draw on role theory to outline transitions in TMT role requirements triggered by digital innovation.
We further combine this view with upper echelon theory to hypothesize on specic TMT characteristics needed for the TMT to act
effectively under these emerging role requirements. We rst predict that TMTs in industrial rms could particularly benet from
digital knowledge, which is understood as individual skills and experiences of TMT members in domains that relate to digital tech-
nologies (i.e., information, computing, communication, and connectivity technologies, Bharadwaj et al., 2013). However, to do so, the
TMT needs to integrate digital knowledge into TMT processes. Here, specic TMT roles, such as the chief executive ofcer (CEO) and
chief digital ofcer (CDO), could be crucial. Specically, we predict that the CEO, respectively the CDO, could establish the needed
integrating mechanisms at their interfaces with other top managers. The hierarchical structure in the TMT could, however, present a
decisive contextual factor as it is closely tied to behavioral expectations (Georgakakis et al., 2019). We argue that a strong hierarchical
structure can create behavioral barriers for the integration processes taking place at the CEO-TMT, respectively CDO-TMT, interfaces.
Fig. 1 summarizes our research framework. To test our predictions, we employ a set of rm xed effects regressions to a longitudinal
dataset of 305 US industrial rms in the period from 2005 to 2016.
Our study contributes to the TMT literature in three major ways. First, our study contributes to research on the TMT’s role and
needed competencies for digital innovation (e.g., Kohli and Melville, 2019; Volberda et al., 2021) by providing large-scale empirical
insights regarding the TMT knowledge and structure needed for digital innovation. Second, our work complements existing literature
on TMT behavioral integration (e.g., Buyl et al., 2011; Georgakakis et al., 2017) by substantiating the crucial role of integrative CEOs
for behavioral integration even in the context of digital innovation. We further highlight how other TMT roles than the CEO (i.e., the
CDO) can be highly benecial for behavioral integration in the context of digital innovation. Third, our study extends the emerging
research on the CDO (e.g., Firk et al., 2021; Kunisch et al., 2020; Singh et al., 2020) by informing the debate on the roles and
effectiveness of CDOs. As such, our study has important practical implications for the composition and design of TMTs.
2. Background
To meet the prevalent digitalization across societies (Tilson et al., 2010), unfolding as digitalized consumer demand and compe-
tition (Verhoef et al., 2021), rms are required to engage in digital innovation. In general, digital innovation can be dened as “the
creation of (and consequent change in) market offerings, business processes, or models that result from the use of digital technology”
(Nambisan et al., 2017, p.224). General Motors’ OnStar provides an example of such a digital innovation. OnStar builds on digital
technologies, such as global positioning systems, mobile technology, entertainment and navigation systems, and on-board micro-
processors, to embed novel digital services in cars, such as emergency services, roadside assistance, and in-vehicle apps, offering a very
different driving experience (Yoo, 2010).
1
However, despite this example, initiating and implementing digital innovation is typically
extremely difcult for industrial rms as it requires fundamental shifts in their innovation trajectories and can relate to a strategic and
organizational change that alters the rms’ value creation logics (Henfridsson and Lindgren, 2005; Henfridsson and Yoo, 2014; Singh
et al., 2020). Hence, industrial rms are especially challenged when embracing digital innovation, with the literature indicating that
two key challenges stand out.
1
For our measurement, we rely on digital patent lings to proxy for digital innovation. Digital patent lings represent an essential foundation in
the development process, but also in ensuring the continued success of digital innovation outcomes as indicated by corporate and business press
regarding the example of General Motors’ OnStar (General Motors, 2009, 2010; Reese, 2016).
S. Firk et al.
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First, many rms struggle to initiate digital innovation (Correani et al., 2020; Kane et al., 2015). Initiating digital innovation means
that the rm has “to identify, assimilate, and apply valuable knowledge from inside and outside the rm regarding opportunities for
digital innovation” (Kohli and Melville, 2019, p.206). This task is particularly challenging due to the fundamentally different traits of
digital innovation. For example, “digital convergence” creates offerings that merge formerly separated customer experiences and
industries (Lyytinen et al., 2016; Yoo et al., 2012, p.1399). Hence, to initiate digital innovation, organizational members of industrial
rms need to adapt key elements of their previous innovation trajectory that are deeply rooted in the historical context of the rm such
as cognitive beliefs about markets, processes, and products (Henfridsson and Yoo, 2014; Kaplan and Tripsas, 2008).
Second, many industrial rms struggle to implement digital innovation (Correani et al., 2020; Morgan, 2019). Specically,
embracing digital innovation requires developing and utilizing digital competencies even in traditional functional units (Yoo et al.,
2010), which implies that industrial rms are required to establish links between existing functional units and digital units (Tumbas
et al., 2018) and to overcome traditional structures (Bharadwaj et al., 2013; Nambisan et al., 2017). However, overcoming these
organizational boundaries can cause major difculties since the underlying digital business logics largely differ in terms of governance
structures, capabilities, collaboration modes and customer interaction (Svahn et al., 2017). Also at General Motors’ OnStar, major
difculties occurred when integrating various computing capabilities into existing car platforms (Henfridsson and Lindgren, 2005;
Yoo, 2010). Consequently, industrial rms face the risk of creating decoupled digital entities that fail to achieve any business impact
(Bj¨
orkdahl, 2020; Morgan, 2019).
Given these challenges in the initiation and implementation of digital innovation, recent literature suggests that the TMT plays a
key role in the rm’s digital innovation endeavors (Chanias et al., 2019; El Sawy et al., 2016; Kohli and Melville, 2019). In particular,
the TMT is required to be aware of digital innovation potentials and threats, not only to react to changes, but also to proactively initiate
these changes by setting the formal context and supporting its implementation (Chanias et al., 2019; Hanelt et al., 2021a; Wrede et al.,
2020). However, in these endeavors, relying on established TMT roles may be insufcient. Rather, an acknowledgment of the tran-
sitioning TMT role requirements may be required as well as an understanding of how top managers should act given these emerging
role requirements (Firk et al., 2021; Volberda et al., 2021). In line with this, debates about existing TMT roles and competencies
(Boyden, 2017; Furr et al., 2019; Klus and Müller, 2021) and even about new TMT roles, such as that of the CDO (Hughes, 2015;
Rickards et al., 2015), are increasing. To make sense of these developments, we rst draw on role theory to outline TMT role transitions
triggered by digital innovation. Second, we combine this view with upper echelon assumptions to hypothesize on specic TMT
characteristics that have become increasingly important in order to fulll these emerging TMT roles for digital innovation.
3. Theory and hypotheses
Role theory concerns an important aspect of organizational life that is characteristic behavior patterns (Biddle, 1986). In general,
role theory is used to describe and classify roles by presuming that people behave differently depending on their position in a social
system (Biddle, 1986; Ren and Guo, 2011). Drawing on role theory can help to outline specic behavioral expectations for the
members of the social system and uncover the foundational building blocks of their interactions, associations, and interdependencies
(Georgakakis et al., 2019; Mathias and Williams, 2017). We focus on the TMT as a social subsystem of the organization and its roles,
namely the CEO role and other TMT roles at or above the level of vice president (Carmeli and Halevi, 2009; Hambrick et al., 2015).
These roles can be described by their identity—for example, the role’s nature, goals, tasks, and requirements—and by their boundaries,
which describe the roles’ interface with the environment, such as with other top managers (Ashforth et al., 2000; Mathias and Wil-
liams, 2017).
TMT roles are delimited by their scope and specicity of responsibility. For example, while CEOs have the nal and overarching
Fig. 1. Research framework.
S. Firk et al.
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4
decision-making responsibility, other TMT roles have a divisional or functional responsibility, such as nance, marketing, operations,
and specic product divisions (Carmeli and Halevi, 2009; Hambrick et al., 2015; Menz, 2012). From a traditional perspective, a clear
divisional or functional role segmentation can benet the TMT because each top manager can focus on his or her specic role identity
(e.g., its goals, tasks) and thereby specialize in specic role requirements (Ashforth et al., 2000; Nicholson, 1984). Given these
specialized and clearly delimited TMT roles, traditional TMT roles can be characterized by rather distinct role boundaries (Ashforth
et al., 2000). Such clear role segmentation can benet the rm at an aggregate level by supporting incremental improvements in terms
of performing each role. Especially in the industrial context, this clear role segmentation has long been benecial due to rms’
relatively stable situational demands and incremental innovation focus (Hill and Rothaermel, 2003).
However, roles are shaped by contextual factors and may change to match new situational demands (e.g., Reay et al., 2006). New
situational demands can trigger transitions in underlying role identities, including self-concepts and the skills of those who take on the
role, and they could lead to redenitions of existing role boundaries (Nicholson, 1984). Especially when initiating and implementing
digital innovation, sticking to traditional TMT role identities and boundaries may be disadvantageous for two reasons. First, the
initiation of digital innovation is more difcult to functionalize. Since digital innovation can unfold in diverse ways, those in a TMT
role must be able to comprehensively recognize and make sense of digital innovation potentials and threats (Bharadwaj et al., 2013;
Hanelt et al., 2021a). Case study evidence suggests that a broad range of TMT members is required to evaluate ideas for digital
innovation (Chanias et al., 2019). As such, digital innovation-related responsibilities increasingly need to be perceived as part of the
identity of each TMT role. Second, the nature of digital innovation implementation is inherently cross functional (Tumbas et al., 2018).
For example, the TMT needs to engage in initiatives that emphasize the relevance of digital innovation, that involve other key
stakeholders, and that lead to organizational structures being redesigned (Wrede et al., 2020). These tasks require TMT members to
consider digital innovation as an interrelated and shared TMT responsibility (Chanias et al., 2019) and require that the rather distinct
boundaries of traditional TMT roles should become increasingly permeable.
Individuals can respond to these emerging role requirements by either adjusting personal attributes, such as mindsets, values, skills,
and behaviors, or by sticking to their existing individual attributes and trying to manipulate the environment to meet their existing
attributes (Bogers et al., 2018; Nicholson, 1984). While the latter will result in sticking to traditional TMT roles and will potentially be
disadvantageous for digital innovation endeavors, it is important to understand which characteristics in the TMT help to adjust to these
emerging TMT role requirements.
3.1. TMT digital knowledge and digital innovation
Each top manager brings his or her own characteristics to meet the specic requirements of his or her role in the TMT. One
important characteristic is the cognitive base (such as knowledge or assumptions) of top managers (Hambrick, 2007; Hambrick and
Mason, 1984). The cognitive base inuences how top managers interpret situational demands, sense opportunities, and evaluate
potential decision-making options (Hambrick and Mason, 1984). Especially in complex and uncertain strategic situations that are not
“objectively knowable but, rather, are merely interpretable,” individual cognitive bases can lead to different notions and evaluations
(Hambrick, 2007, p.334; Hambrick and Mason, 1984; Mischel, 1977). Therefore, the individual knowledge of TMT members could be
crucial in inuencing how top managers make sense of and interpret their role in the TMT.
Prior literature supports the knowledge of top managers as relevant in terms of how they interpret and perform their roles. For
example, prior research indicates that the general and functionally diverse skills of top managers lead to increased innovation out-
comes (Cust´
odio et al., 2019; Haynes and Hillman, 2010; Heyden et al., 2017; Kor, 2006). However, digital knowledge—understood as
skills and experiences in domains that relate to digital technologies (i.e., information, computing, communication, and connectivity
technologies, Bharadwaj et al., 2013)—has largely been neglected. Especially in the industrial context in which digital knowledge
presents skills outside the rm’s focal domain (Hanelt et al., 2021), digital knowledge has not had the greatest relevance in the TMT
and has therefore only been studied to a limited extent. Moreover, there are legitimate concerns about whether digital knowledge as a
specialized, technological source of knowledge is actually needed at the rm’s top level. For example, digitally knowledgeable
managers could fall into the trap of putting in isolated, technology-focused effort that is decoupled from the actual core business and,
hence, they will be less effective in performing their role in favor of digital innovation (Furr et al., 2019).
However, digital knowledge could also be particularly benecial for TMT members to fulll their emerging role requirements. First,
TMT members with digital knowledge may be more likely to interpret their own role in favor of digital innovation. TMT members with
digital knowledge can draw on their experiences in processing, interpreting, and evaluating information related to digital innovation
(Chase and Simon, 1973; Furr et al., 2012; North et al., 2009). Consequently, TMT members with digital knowledge should be better
able to recognize digital innovation opportunities and understand the features and logics underlying digital innovation (Wrede et al.,
2020). Hence, these TMT members will be more likely to take on digital innovation-related responsibilities as part of their role. Second,
top managers with digital knowledge could increasingly encourage and support other top managers to interpret their role in favor of
digital innovation. Given that expert knowledge is attributed to more inuence in decision-making processes (Buyl et al., 2014), top
managers with digital knowledge could motivate other TMT members to engage in digital innovation endeavors. In sum, we suggest
that digital knowledge in the TMT could be benecial for TMT role interpretations in favor of digital innovation, which, in turn, should
translate into increased rm digital innovation:
H1. TMT digital knowledge is positively associated with digital innovation.
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5
3.2. The role of TMT behavioral integration: Integrating interfaces and the hierarchical context
However, even if digital knowledge is present in the TMT, it could still reside in its functional area due to the traditional view on the
subordinated, supporting role of digital knowledge (Bharadwaj et al., 2013). Moreover, traditional role boundaries could hinder other
top managers from perceiving digital innovation as a shared TMT responsibility, and this could even cause tensions due to diverging
perspectives (Chanias et al., 2019) and conicting goals (Svahn et al., 2017). For example, top managers could raise concerns
regarding the prospects of digital innovation success, they could hide relevant information, or they could follow their own strategies
and thereby put cross-functional efforts for digital innovation at risk (Chanias et al., 2019). Hence, the translation of TMT digital
knowledge for digital innovation could especially depend on the behavioral integration in the TMT—the extent of information ex-
change, collaborative behavior, and decision-making participation (Carmeli and Halevi, 2009; Hambrick, 2007; Simsek et al., 2005).
We focus on internal TMT interfaces—understood as the purposive contact points where top managers intersect and potentially
transfer inuence, information, and resources (Simsek et al., 2018)—to explore how the behavioral integration of TMT digital
knowledge for digital innovation could be strengthened.
3.2.1. TMT digital knowledge, integrative CEOs, and digital innovation
As the TMTs’ team leaders, CEOs can substantially shape the extent of integrating various top managers into TMT processes (Buyl
et al., 2011; Chanias et al., 2019; Georgakakis et al., 2017). Especially when translating TMT digital knowledge into digital innovation,
an integrative CEO could be crucial due to the high complexity of the acquisition, interpretation, and understanding of the relevant
information, but also to mediate potential tensions caused by blurred traditional role boundaries (Chanias et al., 2019; Tumbas et al.,
2018). Hence, we understand integrative CEOs as those CEOs who interpret their role in a way that fosters the involvement of top
managers with digital knowledge in TMT processes, on the one hand, and that counteracts potential role conicts in the TMT hindering
the integration of TMT digital knowledge, on the other hand. Based on these mechanisms, we argue that two aspects help CEOs to act in
an integrative way.
First, CEOs need to be aware of the knowledge residing in the TMT. Shared work experiences between CEOs and TMT members
could help CEOs to understand and trust other TMT members and their specic knowledge (e.g., Buyl et al., 2011; Dai et al., 2016). In
turn, CEOs with shared work experiences could also be more aware of the top managers possessing digital knowledge, and hence they
will be more likely to strengthen their involvement in relevant TMT processes. Second, CEOs should be able to mediate and handle
potential conicts in the TMT that could hinder the integration of TMT digital knowledge. Diverse functional experiences could help
CEOs to build a dense understanding of different functional roles (Georgakakis et al., 2017). In contrast to specialized CEOs who may
be inclined to follow opinions from their specialized area of expertise (Georgakakis et al., 2017; Meyer et al., 2015), CEOs with diverse
functional experiences tend to be less “susceptible to functionally grounded biases and stereotypes” (Bunderson and Sutcliffe, 2002;
Buyl et al., 2011, p.155). Consequently, CEOs with diverse functional experiences should be more likely to overcome potential tensions
that may hinder the integration of TMT digital knowledge. Taken together, we argue that CEOs who have shared and diverse work
experiences are more likely to interpret their roles in an integrative way that supports the translation of TMT digital knowledge into
rm digital innovation.
H2. The positive association between TMT digital knowledge and digital innovation is stronger under integrative CEOs (i.e., who
have shared and diverse work experiences).
3.2.2. TMT digital knowledge, CDO existence and digital innovation
Besides the CEO, who is often the focus of research on integrating other TMT members due to his or her powerful role, it is also
possible to create a distinct TMT role dedicated to integrating other TMT members (Menz, 2012). Especially in the context of digital
innovation, the emerging role of CDOs is highlighted for strengthening collaboration across functional boundaries (Haffke et al., 2016;
Singh and Hess, 2017; Tumbas et al., 2017, 2018) and for linking and fostering discussions among intra-organizational key stake-
holders, such as other top managers (Firk et al., 2021; Kunisch et al., 2020; Singh et al., 2020).
Recent case study evidence allows for a more nuanced picture of the CDO’s role in interacting with other top managers. For
example, CDOs work closely with other digital-afne top managers, such as the chief information ofcer (CIO) and chief technology
ofcer (CTO), but also with more general top managers, such as the chief marketing ofcer (CMO) or divisional heads, to align on
crucial requirements for digital innovation, such as technical conditions and customer demands (Haffke et al., 2016; Tumbas et al.,
2018). Moreover, CDOs act as “bridge builders” to foster collaboration and establish links among these top managers and their ac-
tivities (Firk et al., 2021; Tumbas et al., 2018). Complementing this view, the ndings of Singh et al. (2020) indicate that CDOs
combine different formal and informal activities to facilitate information exchange and collaboration within the TMT. For example,
they lead digital steering committees and set up regular events as platforms for information exchange involving other top managers
(Singh et al., 2020). Given these case study insights into these specic CDO activities, the CDO’s role in transcending organizational
boundaries can be particularly valuable for the integration of TMT digital knowledge into TMT processes. In sum, we expect that the
CDO–TMT interface could provide an important platform for integrating digital knowledge in favor of digital innovation:
H3. The positive association between TMT digital knowledge and digital innovation is stronger under the existence of a CDO.
3.2.3. The role of the hierarchical context in TMT interactional processes
The hierarchical structure in the TMT could present a decisive contextual factor for the integration mechanisms taking place at the
CEO–TMT and CDO–TMT interfaces, as it is closely linked to role expectations (Georgakakis et al., 2019). As such, how other TMT
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6
members take part in mutual and collective interaction may be affected by the hierarchical structure in the TMT (Hambrick, 2007;
Hambrick et al., 2015). The hierarchical structure is described by the administrative mechanisms (e.g., hierarchical levels, pay dif-
ferences) arranged in the TMT, and it determines the degree of interdependence, or respectively, the disparity of top managers
(Hambrick et al., 2015).
We argue that a strong hierarchical structure makes it more difcult to effectively drive behavioral integration processes in the
TMT. In particular, a higher degree of hierarchical disparity among top managers could establish behavioral barriers to the stimulation
of integration processes. Accordingly, even if CEOs or CDOs bring top managers together, the top managers could resist engaging in
intensied information exchange or collaborative behavior due to the expectations inherent in their structurally determined roles
(Buyl et al., 2011). For example, in workshops or meetings set up by the CEO or CDO, top managers with digital knowledge could hold
back on giving their opinions in order to avoid any violations of the roles that are structurally conditioned for them and other top
managers. Especially in the context of digital innovation, where collaborative efforts may exceed the top managers’ traditional areas of
responsibility (Svahn et al., 2017), CEOs or CDOs could face difculties when trying to establish integration processes in the TMT
under strong hierarchical structures. Thus, we expect that a strong hierarchical structure will negatively impact the effectiveness of
CEOs and CDOs in integrating TMT digital knowledge for digital innovation:
H4. The moderating effects of an integrative CEO and the existence of a CDO are less pronounced in TMTs with a strong hierarchical
structure.
4. Methodology
4.1. Sample
We focus on a longitudinal sample of industrial rms in the period from 2005 to 2016.
2
We consider the rm years of industrial
rms that have been listed at least once in the S&P 900 Index (i.e., the S&P 500 LargeCap or the S&P 400 MidCap) in the period from
2005 to 2014. Industrial rms are dened as rms in industries that are heavily focused on manufacturing physical products. Similar to
other studies (e.g., Nadkarni and Chen, 2014; Rai et al., 2006), we therefore follow the Standard Industrial Classication (SIC) and only
include rms that belong to the manufacturing division (i.e., SIC 20–39). From this initial sample, we exclude (1) rms related to the
industry group “Computer and Ofce Equipment” (357) due to their familiarity with digital technologies; (2) observations with
missing nancial or other relevant data for regressions; and (3) rms that did not le at least one patent during the period of
observation (Cust´
odio et al., 2019). The resulting sample consists of 305 industrial rms and 2413 rm-year observations.
We decided to focus on this sample for two main reasons. First, embracing digital innovation means coping with a specic type of
strategic change for industrial rms, as their value-creation logics and business scope can be altered (Singh et al., 2020). Therefore,
industrial rms are particularly challenged in setting a new digital innovation course (Hanelt et al., 2021; Svahn et al., 2017). Second,
our focus on industrial rms allows us to concentrate on patenting as a proxy for digital innovation. Industrial rms possess a long
history of patenting (Cohen et al., 2000). Hence, in patent-intensive industries, digital innovation should also be related to digital
patents (Hanelt et al., 2021).
4.2. Dependent variable: Digital innovation
To proxy for the rm’s digital innovation outcomes, we use data on the rm’s digital patent lings. While patent activities generally
allow for insights into the technological prioritization of rms (Griliches, 1990; OECD, 2009), they have also been used in the specic
context of digital innovation (Hanelt et al., 2021). In the context of digital innovation, practical evidence also suggests that patenting
presents a crucial competitive action to build market entry barriers for digital businesses (Parker et al., 2016). Even though digital
business models cannot be patented per se as patent applications need to fall under a patentable subject matter (Marco et al., 2015;
WIPO, 2019), algorithms can be patented and can thus be used to protect the key resources of digital business models. Also in the case
of General Motors’ OnStar, digital patent lings present as an essential foundation in the development process, but they also ensure the
continued success of OnStar services, as indicated by the corporate and business press (General Motors, 2009, 2010; Reese, 2016).
Given these arguments (e.g., Svahn et al., 2017; Yoo et al., 2010), we believe that digital patent lings are a valuable proxy for the
rm’s digital innovation outcomes in the industrial context.
3
Similar to other studies examining patent data (e.g., Balsmeier et al., 2017; Cust´
odio et al., 2019), we use data from the US Patent
and Trademark Ofce (USPTO) (Graham et al., 2015; Marco et al., 2015). We use the raw patent data provided by the USPTO for
2
Our dependent variable (digital innovation) presents a forwarded variable over the next two years. We therefore use observations from 2006 to
2016 to measure our dependent variable. For the measurement of our independent and control variables, we rely on observations from the period
2005 to 2014.
3
We have conducted additional tests to assess the appropriateness of our digital innovation measure based on digital patent lings by examining
whether digital patent lings are associated with more digital market offerings. Specically, we examined the relationship between digital patent
lings and news on digital and non-digital product or service releases (based on data from the Ravenpack database) and also tested the relationship
between non-digital patent lings and news on both digital and non-digital product or service releases (placebo test). Our results indicated a
signicant positive association between digital patent lings and news on digital product or service releases, while the other relationships remained
insignicant, thus supporting the use of digital patent lings as a valuable proxy in the context of digital innovation.
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January 2020. Nevertheless, we needed to restrict our use of this data up to 2016, as later years suffer from truncation bias given that
the time gap between the ling of the patent and its publication can take many years (Graham et al., 2015). To link our sample rms
with the applicant rms in the patent data, we use a name-matching algorithm. Here, we consider that patents may be led by different
corporate entities (i.e., subsidiaries) (Belenzon and Berkovitz, 2010) and that rms may acquire other rms or divest subsidiaries over
time. We further consider abbreviations of the names of our sample companies and check for name changes that occurred within our
period of analysis (Magerman et al., 2006). We further execute an extensive harmonization procedure to harmonize the names of the
patent applicants with the names of our sample companies by cleaning the patent applicants’ names for the most common misspellings
and other irregularities (e.g., spellings of the legal form, punctuation, character irregularities) (Magerman et al., 2006; Peeters et al.,
2010).
4
Afterwards, we employ the matchit command in STATA, which allows for an algorithm-based approximating match between
the names of the patent applicants and the names of our sample companies (Raffo and Lhuillery, 2009). As this matching algorithm is
based on an approximate match and company names may overlap with other companies, we nally check these matches manually for
appropriateness.
After matching patent information to our sample companies, we follow other scholars by focusing on regular, non-provisional
utility lings (Lemley and Sampat, 2008, 2010). We further exclude lings that are not intended to assign a patent, for example,
name changes made to a patent (Graham et al., 2015; Marco et al., 2015). To identify digital patent lings, we only consider specic
technological classes of the US Patent Classication (USPC) scheme that are related to digital technologies. First, we consider the
technological domain of “Communications & Computers,” similar to Hall et al. (2001). Second, we consider technological classes that
were newly created after the initial year of dening this technological domain in 2001 and that are clearly associated with digital
technologies, such as “Data processing: software development, installation, and management.” For example, the patent US8856536B2
led by General Motors in the context of OnStar is classied in the USPC class 713 “Electrical computers and digital processing systems,
” and thereby it was coded as a digital patent ling. Table A1 in the Appendix summarizes the USPC classes used for our oper-
ationalization of digital innovation. Finally, we consider the average number of digital patent llings over the next two years and use,
similar to prior studies (Balsmeier et al., 2017; Cust´
odio et al., 2019; Hanelt et al., 2021a), the natural log transformation for the nal
digital innovation variable. The assignment to a certain year is always based on the ling date.
4.3. Independent variable: TMT digital knowledge
To capture the digital knowledge in the TMT, we use information from the BoardEx database. We use data sources such as
Bloomberg, company press releases, and LinkedIn for manual checks of appropriateness. We start by dening which managers
compose the TMT. We follow Hambrick et al. (2015), who consider managers as TMT members when they hold the title of executive
vice president, senior vice president, and—in cases where a TMT consisted of only ve or fewer members—vice presidents. Since this
denition does not necessarily include important functional roles, such as the CIO, we further include executives with CxO titles under
the restriction that there is no indication in the title for operating at a lower organizational level (i.e., “division” in the role name). We
further exclude the CEO and CDO, as these top managers reect separated variables in our model.
Next, we build our TMT digital knowledge variable by identifying TMT members with experience related to digital technologies in
prior employment. Specically, we consider top managers who have worked in a functional position or industry related to digital
technologies before entering their current position (van Peteghem et al., 2019). We therefore searched through their employment
history and dened positions as related to digital technologies if they included terms such as “CIO,” “CTO,” “information,” “comput,”
“software,” “e-commerce,” “IT,” “technolog,” “digital,” and “CDO.” To ensure that these functional experiences—i.e., the experiences
in a CTO position—are indeed related to digital technologies and not focused on other technologies, we carefully hand-checked for
appropriateness by using additional sources such as Bloomberg, company press releases, and LinkedIn. For experience in
digital-related industries, we use the BoardEx industry classication and consider the “software & computer services,” “telecommu-
nication services,” and “media & entertainment” industries as related to digital technologies. We further perform a textual search on
the company name for digital-related terms such as “digital,” “online,” or “internet,” if the industry classication is unavailable for a
rm (see van Peteghem et al., 2019). Finally, we count the TMT members with experience in either positions or, for at least three years,
in industries related to digital technologies and mean-centered it to build our TMT digital knowledge variable.
4.4. Moderator variable: Integrative CEO
To account for an integrative CEO, we build on prior studies (Buyl et al., 2011; Georgakakis et al., 2017). Specically, we consider
the shared experiences of CEOs with other top managers and the functional diversity of CEOs’ prior work experiences. Again, we use
information from the BoardEx database. First, CEOs’ shared experiences with other top managers is calculated as the pairwise overlap
in tenure between the CEO and the other TMT members (Buyl et al., 2011; Georgakakis et al., 2017). Specically, the calculation is
rooted in Carroll and Harrison’s (1998) formula expressed as 1/n∑
i∕=j
min(ui,uj), where u is the tenure (in years) of each top manager i, j
is the CEO, and n is the number of TMT members. Second, to calculate the CEOs’ functional diversity, we searched through their prior
employment history for functional career experiences. We dene “accounting and nance,” “administration and legal,” “human
4
We also used STATA codes provided by the NBER on name standardization routines under the following link: https://sites.google.com/site/
patentdataproject/Home/posts/namestandardizationroutinesuploaded (last visited: May 3, 2020).
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resources,” “information systems and technology,” “marketing,” “operations,” “research and development,” and “strategy” as relevant
functional areas of experience (Menz, 2012). We count the number of CEOs’ experiences in functional areas (e.g., up to eight areas).
Afterwards, we build a categorical variable, where one indicates that the CEO has experience in more than one functional area, two
indicates that the CEO has experience in more than two functional areas, and three indicates that the CEO has experience in more than
three functional areas. To nally calculate the values for our variable integrative CEO, we standardize the shared experiences of CEOs
with other top managers and the functional diversity of CEOs’ prior work experiences and aggregate the standardized values.
4.5. Moderator variable: CDO existence
To gather information on the existence of a TMT position holding a responsibility for orchestrating and coordinating digital
innovation and/or digital transformation endeavors (Singh and Hess, 2017; Tumbas et al., 2017), we follow prior literature (Firk et al.,
2021; Kunisch et al., 2020) by combining data from multiple sources. First, we examine the BoardEx database for the existence of CDOs
by searching for employment related to the keywords “digital” and “CDO.” Similar to prior literature (Firk et al., 2021; Kunisch et al.,
2020), we exclude employments that does not match our understanding of CDOs by, for example, excluding CDOs representing the role
of a chief diversity ofcer. In a second step, we manually collect further information on the presence of CDOs from sources such as
Bloomberg, company press releases, and LinkedIn by searching for keywords such as “chief digital ofcer,” “digital director,” “digital
ofcer,” and “head of digital.” Here, we check all available descriptions and, for example, exclude CDOs if the description indicates an
operative role that is not linked to frequent interactions with other TMT members.
5
Our nal variable of CDO existence is set at one if
there is a CDO position, otherwise zero.
4.6. Moderator variable: TMT hierarchical structure
To calculate whether there is a strong or at hierarchical structure in the TMT, we calculate an aggregated index composed of two
hierarchy indicators. First, we used BoardEx data and follow Hambrick et al.’s (2015) procedure to measure the vertical levels among
the top managers. This measure reects the aggregated value of (1) the number of distinct hierarchical levels in the TMT by counting
the number of title gradations (i.e., CEO, chief operating ofcer, executive vice president, senior vice president, and possibly vice
presidents), and (2) the presence of a chief operating ofcer, indicating whether there was this additional level in the TMT (Hambrick
et al., 2015). Both components are standardized into one measure. Second, we calculate the disparity of the top managers in terms of
their short-term pay (dened as the sum of salary and bonuses) based on data from the ExecuComp database. Specically, we calculate
the coefcient of variation in the short-term pay among the TMT members included in ExecuComp (Fredrickson et al., 2010; Hambrick
et al., 2015; Hart et al., 2015). Afterwards, we standardize these two indicators, aggregate them, and dene the values above (below)
the median as indicating a strong (at) hierarchical structure.
4.7. Control variables
We include several control variables on the rm, CEO, TMT, and governance levels. Table A1 in the Appendix provides detailed
information on the data sources and calculations. In the following, we explain the reasoning behind our selection. At the rm level, we
include rm size, R&D intensity, and return on assets, as these may inuence the capacities for innovation activities (e.g., Heyden et al.,
2017b). Capital expenditures and leverage are included to account for nancial constraints for innovation endeavors (Balsmeier et al.,
2017; Hanelt et al., 2021). We also include capital intensity to control for the manufacturing intensity and Tobin’s Q to control for
growth opportunities (e.g., Cust´
odio et al., 2019).
On the CEO level, we include CEO educational level as an indicator of generic skills (Georgakakis et al., 2017; Pegels et al., 2000) that
are associated with innovation outcomes (Cust´
odio et al., 2019). We further include CEO equity compensation to control for long-term
incentives that may be related to more digital innovation efforts. Finally, we control for CEO age, for CEO tenure, to capture career
incentives to engage in digital innovation (e.g., Belenzon et al., 2019; Lee et al., 2018), and for CEO duality, to capture the power of
CEOs (Heyden et al., 2017).
On the TMT level, we control for TMT horizontal interdependence, as it may also inuence the interactions taking place in the TMT
(Hambrick et al., 2015). We further capture if there is a chief innovation ofcer in the rm (CINNO existence) that may simultaneously
contribute to behavioral integration. Further, we control for the average TMT educational level, the average TMT age, and the TMT size
(Georgakakis et al., 2017; Simsek et al., 2005) to capture further structural TMT conditions that may be related to TMT digital
knowledge, TMT collaboration, and digital innovation outcomes.
On the governance level, we control for board-related variables by integrating board size, board diversity, and board independence, as
boards may inuence TMT composition and innovation endeavors (e.g., Hillman and Dalziel, 2003). We also include a control variable
for institutional ownership, as it may affect (digital) innovation efforts (Aghion et al., 2013).
5
We also performed an additional test that shows that our results remain robust if we only focus on the CDO observations according to the
BoardEx database.
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4.8. Method of analysis
To analyze our longitudinal sample, we decided to employ rm xed effects regression models similar to prior innovation studies
(Balsmeier et al., 2017; Cust´
odio et al., 2019). The rm xed effects regression considers any time-invariant unobservable rm
characteristics, thereby allowing us to adequately control for unobserved heterogeneity and to mitigate omitted variable concerns
(Wooldridge, 2010). The appropriateness of a rm xed effects model is supported by a Hausman test (comparing a xed effects
regression to a random effects regression). Moreover, to address reverse causality concerns, we forward our dependent variable by
considering digital patent lings in the subsequent two years. Finally, we also include year xed effects to control for economic-wide
shocks as well as truncation biases inherent to patent variables. To operationalize our xed effects models, we employ the xtreg
command in STATA by specifying the within rm xed effects option as well as “robust” or empirical standard errors as otherwise, in
cases of model misspecication or overdispersion, model-based standard errors may be incorrect. To investigate H1, we estimate the
following model:
I. Digital innovationi,(∅t+1,t+2)=
α
+β1(TMT digital knowledge)i,t+γ(Controls)i,t+Tt+Xi+
ε
i,ts
To test H2 and H3, we interact our independent variable of TMT digital knowledge with the moderator variables of integrative CEO,
and respectively, CDO existence. Thus, we estimate the following model:
II Digital innovationi,(∅t+1,t+2)=
α
+β1(TMT digital knowledge)i,t+β2(TMT digital knowledge*Y)i,t+β3(Y)i,t+γ(Controls)i,t+Tt+Xi+
ε
i,t
To examine H4, we analyze the interaction between TMT digital knowledge and each moderator variable in sub-samples of a strong,
and respectively, at hierarchical structure. In all equations, the items besides the dependent, independent, and control variables
comprise year dummies (T
t
), the moderator variables (Y), the constant term (
α
), the rm-specic effects (X
i
), and the error term (
ε
it).
5. Results
5.1. Descriptive statistics
To illustrate the development of digital-related variables in our sample, Table 1 provides an overview of the average digital patent
lings, TMT digital knowledge, and CDOs by year. Table 1 shows that digital patent lings rose to the highest value in the most recent
years. We observe a similar trend in the rm’s TMT digital knowledge. Regarding the existence of CDOs, we nd that 38 of our sample
rms appointed a CDO. Similar to our other digital-related variables, most rms appointed a CDO in recent years, leading to the
highest value of CDO observations in recent years (see Table 1). Taken together, these results support the idea that digital innovation
endeavors are becoming increasingly relevant for industrial rms (e.g., Svahn et al., 2017).
In Table 2, we provide further insights into the data underlying our calculations by showing the summary statistics of our regression
variables. In Table 3, we provide the cross-sectional correlation matrix of all our regression variables. As the cross-sectional corre-
lations between our regression variables are all below critical thresholds, we see no clear indication for multicollinearity from this
analysis. This was further supported by checking the variance ination factors (VIFs) while considering the multiple interaction terms.
The highest individual VIF amounted to 2.65, and the mean VIFs of the regression models were all below 2. The analysis further
alleviated multicollinearity concerns.
Table 1
Means of main digital variables by year.
Year Obs. Digital innovation (t+1 and t+2)
a
TMT digital knowledge
b
CDO existence
2005 218 9.72 0.85 1%
2006 235 9.77 0.89 2%
2007 251 8.49 0.94 2%
2008 258 7.84 0.95 3%
2009 247 8.88 0.99 2%
2010 254 9.69 1.05 3%
2011 246 9.76 1.18 5%
2012 245 9.92 1.20 7%
2013 235 10.78 1.27 9%
2014 224 11.43 1.23 12%
Total 2413 9.59 1.05 5%
a) Average number of digital patent lings in t+1 and t+2. In the regressions, we use a log-transformation of this variable. b) Number of top managers
in the TMT possessing prior experiences in a digital technology-related position or industry. We use the mean-centered values of TMT digital
knowledge in our regression analyses.
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5.2. Regression results
To test our hypotheses, we employ a series of xed effects regression models that are presented in Table 4. Regarding H1, stating
that TMT digital knowledge is positively associated with digital innovation, we nd empirical evidence for this prediction, as Model 1
indicates a signicantly positive effect from TMT digital knowledge (p <.05) and an average increase of 5.4% in digital innovation if
the number of top managers with digital knowledge increases by one.
Model 2 tests H2, which predicts a positive impact of the interplay between TMT digital knowledge and an integrative CEO on
digital innovation. As the effect of the interaction term for TMT digital knowledge and an integrative CEO is signicantly positive on
digital innovation (p < .05), the results of Model 2 support the prediction of H2. One additional top manager with digital knowledge
under a more integrative CEO (mean plus one standard deviation) is associated with an average increase of 9.3% in digital innovation.
Moreover, Model 3 tests H3, which predicts a positive impact of the interplay between TMT digital knowledge and CDO existence on
digital innovation. Given that Model 3 indicates a signicantly positive effect of the interaction term between TMT digital knowledge
and CDO existence on digital innovation (p < .05), our results support H3. One additional top manager with digital knowledge under
the existence of a CDO is associated with an average increase of 19.9% in digital innovation. Figs. 2 and 3 visualize the interaction
effects of TMT digital knowledge and an integrative CEO, and respectively, CDO existence on digital innovation. In addition to that, it
is interesting to see that we nd an insignicant direct effect of CDO existence on digital innovation. Instead, our results show that the
effect of CDO existence on digital innovation depends on the TMT’s digital knowledge (see Model 1 and 2).
We further test H4, stating that the positive impact of the interplay between TMT digital knowledge and an integrative CEO, and
respectively, CDO existence, on digital innovation is less (more) pronounced under a strong (at) hierarchical structure in Model 5
(Model 6). For the effect of the interaction term between TMT digital knowledge and an integrative CEO on digital innovation, Model 5
shows an insignicant effect (p >.10), while Model 6 shows a signicantly positive effect (p <.10). To test the statistical signicance of
the differences in the coefcients of these interaction terms in Model 5 and Model 6, we conduct a Chow test. The Chow test shows that
the interaction effects for TMT digital knowledge and an integrative CEO are not signicantly different in Model 5 and Model 6 (Chi
2
:
0.40, p >.10). For the effect of the interaction term between TMT digital knowledge and CDO existence on digital innovation, Model 5
shows an insignicant effect (p >.10), while Model 6 shows a signicantly positive effect (p <.01). For the interaction effects of TMT
digital knowledge and CDO existence, the Chow test indicates that the effects are indeed signicantly different in Model 5 and Model 6
(Chi
2
: 4.03, p <.05). Specically, under a at hierarchical structure, one additional top manager with digital knowledge under CDO
existence is associated with an average increase of 32.7% in digital innovation compared to an average increase in digital innovation of
4.6% under a strong hierarchical structure. These results partly support H4 by indicating a dependency of the interaction effect be-
tween TMT digital knowledge and CDO existence on the hierarchical structure in the TMT. Taken together, our results, based on xed-
effects regression models, support H1, H2, H3, while we only nd partly support for H4 with regard to the interaction between TMT
digital knowledge and CDO existence.
Table 2
Descriptive statistics of regression variables.
Variables Firm-years Firms Mean Std. Min Median Max
(1) Digital innovation
a
2413 305 0.87 1.25 0.00 0.41 6.35
(2) TMT digital knowledge
b
2413 305 0.00 1.03 −1.05 −0.05 3.95
(3) Integrative CEO
c
2413 305 0.00 1.00 −1.54 −0.21 5.63
(4) CDO existence 2413 305 0.05 0.21 0.00 0.00 1.00
(5) CEO educational level 2413 305 3.13 1.04 1.00 3.22 5.00
(6) CEO equity-based compensation
d
2413 305 0.51 0.37 0.00 0.51 1.00
(7) CEO age
a
2413 305 4.04 0.10 3.71 4.06 4.38
(8) CEO tenure
a
2413 305 1.51 0.75 0.00 1.55 3.99
(9) CEO duality 2413 305 0.62 0.48 0.00 1.00 1.00
(10) CINNO existence 2413 305 0.04 0.21 0.00 0.00 1.00
(11) TMT horizontal interdependence 2413 305 −0.12 0.29 −0.61 −0.15 3.27
(12) TMT educational level 2413 305 2.98 0.44 1.00 3.00 4.67
(13) TMT age
a
2413 305 3.98 0.06 3.71 3.98 4.30
(14) TMT size
a,f
2413 305 2.52 0.41 1.61 2.48 3.47
(15) Board diversity
d
2413 305 0.16 0.10 0.00 0.17 0.60
(16) Board independence
d
2413 305 0.51 0.26 0.00 0.55 1.00
(17) Board size
a,f
2413 305 2.28 0.20 1.79 2.30 2.64
(18) Institutional ownership
d
2413 305 0.47 0.18 0.00 0.49 1.01
(19) Firm size
a,f
2413 305 14.99 1.28 12.15 14.88 18.15
(20) Capital expenditures
e
2413 305 0.04 0.03 0.00 0.03 0.24
(21) R&D intensity
f
2413 305 0.04 0.06 0.00 0.02 0.31
(22) Tobin’s Q
e
2413 305 2.09 1.23 0.58 1.71 9.84
(23) Capital intensity
e
2413 305 0.52 0.32 0.01 0.43 1.76
(24) Return on assets
e
2413 305 0.08 0.08 −0.21 0.08 0.34
(25) Leverage
e
2413 305 0.25 0.15 0.00 0.24 0.84
a) Log-transformed. b) Mean-centered. c) Standardized. d) Measured in percent. e) Winsorized at 0.01 and 0.99 levels. f) Winsorized at 0.03 and 0.97
levels. Notes: Digital innovation captures the average digital patent lings of t+1 and t+2; all other variables measured in t.
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Table 3
Cross-sectional correlation matrix of regression variables.
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25)
(1) Digital innovation
a
1.00
(2) TMT digital knowledge
b
0.36 1.00
(3) Integrative CEO
c
0.00 −0.05 1.00
(4) CDO existence 0.07 0.17 0.07 1.00
(5) CEO educational level 0.07 0.09 −0.05 −0.06 1.00
(6) CEO equity-based comp.
d
0.11 0.16 −0.04 0.09 0.05 1.00
(7) CEO age
a
−0.05 0.01 0.15 −0.01 0.03 0.00 1.00
(8) CEO tenure
a
−0.02 −0.03 0.40 −0.04 0.00 −0.02 0.26 1.00
(9) CEO duality 0.14 0.16 0.03 −0.09 0.05 0.01 0.22 0.22 1.00
(10) CINNO existence 0.02 0.10 −0.04 −0.02 −0.06 0.03 −0.03 0.00 0.03 1.00
(11) TMT horizontal interdep. −0.02 0.02 0.05 −0.02 0.00 0.00 −0.01 0.05 0.02 0.06 1.00
(12) TMT educational level 0.05 0.13 −0.15 −0.04 0.50 0.07 −0.01 −0.04 0.02 −0.01 −0.02 1.00
(13) TMT age
a
−0.03 −0.05 0.24 −0.07 0.02 −0.04 0.45 0.12 0.12 −0.02 0.03 −0.10 1.00
(14) TMT size
a,f
0.27 0.53 −0.07 0.17 0.07 0.18 −0.01 −0.08 0.09 0.15 −0.33 0.13 −0.11 1.00
(15) Board diversity
d
0.05 0.18 0.01 0.13 −0.08 0.08 0.05 −0.05 0.10 0.07 −0.07 −0.06 0.09 0.29 1.00
(16) Board independence
d
0.08 0.08 −0.21 0.03 0.09 −0.03 −0.17 −0.67 −0.12 0.00 −0.08 0.07 −0.04 0.08 0.08 1.00
(17) Board size
a,f
0.26 0.39 −0.07 0.08 0.03 0.14 −0.04 −0.16 0.08 0.11 −0.07 0.04 0.05 0.45 0.29 0.21 1.00
(18) Institutional ownership
d
−0.15 −0.16 −0.08 −0.05 0.01 −0.05 −0.04 0.00 −0.14 −0.06 −0.03 0.03 −0.15 −0.20 −0.14 0.00 −0.29 1.00
(19) Firm size
a,f
0.41 0.41 −0.01 0.08 0.03 0.20 0.08 −0.08 0.21 0.06 −0.10 −0.06 0.17 0.54 0.27 0.11 0.57 −0.42 1.00
(20) Capital expenditures
e
−0.01 −0.01 0.03 −0.05 0.00 −0.02 −0.08 0.05 −0.04 0.01 −0.05 −0.04 0.07 0.02 −0.05 −0.06 0.04 −0.04 0.08 1.00
(21) R&D intensity
f
0.06 0.08 −0.06 −0.06 0.18 0.05 0.00 0.02 −0.01 0.04 0.01 0.39 −0.14 0.15 −0.04 −0.06 −0.07 0.06 −0.16 −0.09 1.00
(22) Tobin’s Q
e
−0.06 −0.05 0.05 0.03 0.09 0.04 −0.06 0.09 −0.02 0.04 0.11 0.15 −0.07 −0.04 −0.01 −0.09 −0.18 −0.10 −0.22 0.08 0.28 1.00
(23) Capital intensity
e
−0.11 0.00 0.06 −0.03 0.02 −0.02 0.02 0.00 0.00 −0.02 −0.05 −0.05 0.22 −0.04 0.05 0.06 0.16 −0.03 0.14 0.55 −0.27 −0.18 1.00
(24) Return on assets
e
0.00 −0.01 0.07 0.00 0.00 0.06 0.02 0.04 0.07 0.02 0.05 −0.01 0.04 −0.05 0.01 −0.02 −0.06 −0.19 0.02 0.10 0.01 0.48 −0.07 1.00
(25) Leverage
e
−0.03 0.02 −0.09 0.02 −0.07 0.04 −0.04 −0.08 0.02 0.03 0.00 −0.05 −0.04 0.11 0.14 0.04 0.17 0.01 0.15 −0.17 −0.09 −0.13 −0.03 −0.16 1.00
a) Log-transformed. b) Mean-centered. c) Standardized. d) Measured in percent. e) Winsorized at 0.01 and 0.99 levels. f) Winsorized at 0.03 and 0.97 levels. Notes: Digital innovation captures the average
digital patent lings in t+1 and t+2; all other variables measured in t. The correlations are based on 2413 rm-year observations of 305 rms.
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5.3. Robustness tests
To test the robustness of our results, we conducted several robustness tests. First, we run the set of xed effects regressions in three
alternative samples. Specically, we restrict the sample to companies (1) that were listed for at least ve years in the S&P900 in our
Table 4
Firm xed effects models estimating the inuence on digital innovation.
Model
1 2 3 4 5 6
DV Digital innovation
Sample Total Total Total Total Strong hierarchy Flat hierarchy
TMT digital knowledge 0.053** 0.052** 0.041 0.041* 0.045 0.017
(2.112) (2.116) (1.637) (1.677) (1.246) (0.492)
TMT digital knowledge * Integrative CEO 0.037** 0.033* 0.022 0.044*
(2.135) (1.887) (1.039) (1.720)
TMT digital knowledge * CDO existence 0.141** 0.127** 0.000 0.265***
(2.298) (2.053) (0.003) (3.246)
Integrative CEO 0.010 0.014 0.008 0.012 0.025 0.022
(0.524) (0.748) (0.439) (0.643) (1.137) (0.772)
CDO existence 0.087 0.074 −0.039 −0.039 0.050 −0.144
(1.009) (0.865) (-0.494) (-0.492) (0.507) (-1.009)
CEO educational level −0.044* −0.043* −0.043* −0.042* −0.056 −0.029
(-1.849) (-1.796) (-1.791) (-1.751) (-1.462) (-1.034)
CEO equity compensation −0.002 0.001 −0.008 −0.005 0.046 −0.043
(-0.048) (0.016) (-0.192) (-0.123) (0.723) (-0.794)
CEO age −0.698*** −0.683*** −0.677*** −0.665*** −0.843** −0.557*
(-2.731) (-2.716) (-2.678) (-2.667) (-2.496) (-1.830)
CEO tenure −0.003 0.000 −0.007 −0.004 −0.022 0.002
(-0.123) (-0.019) (-0.277) (-0.170) (-0.705) (0.068)
Duality 0.058 0.054 0.056 0.053 0.067 −0.007
(1.251) (1.184) (1.211) (1.154) (1.111) (-0.094)
CINNO existence 0.040 0.039 0.045 0.044 −0.055 0.203
(0.464) (0.465) (0.523) (0.520) (-0.628) (1.341)
TMT horizontal interdependence 0.013 0.021 0.015 0.022 0.096 −0.022
(0.215) (0.342) (0.251) (0.361) (0.606) (-0.338)
TMT educational level −0.040 −0.046 −0.043 −0.048 −0.001 −0.078
(-0.759) (-0.865) (-0.823) (-0.912) (-0.019) (-1.101)
TMT average age 0.302 0.355 0.314 0.361 1.137** −0.671
(0.665) (0.799) (0.692) (0.809) (2.051) (-1.054)
TMT size −0.142** −0.137* −0.142** −0.138* −0.007 −0.240***
(-1.994) (-1.947) (-2.006) (-1.962) (-0.053) (-2.785)
Board diversity 0.084 0.085 0.061 0.065 0.004 0.281
(0.389) (0.393) (0.288) (0.302) (0.014) (0.884)
Board independence −0.098 −0.087 −0.106 −0.095 −0.187* −0.006
(-1.249) (-1.113) (-1.370) (-1.234) (-1.837) (-0.054)
Board size −0.113 −0.111 −0.105 −0.104 −0.023 −0.113
(-0.938) (-0.908) (-0.870) (-0.849) (-0.132) (-0.690)
Institutional ownership −0.007 0.004 −0.004 0.006 −0.092 0.123
(-0.051) (0.027) (-0.027) (0.040) (-0.440) (0.772)
Net sales 0.159*** 0.162*** 0.158*** 0.160*** 0.156** 0.154**
(3.294) (3.353) (3.279) (3.331) (2.422) (2.185)
Capital expenditures 0.389 0.308 0.335 0.268 0.957 −0.088
(0.800) (0.649) (0.685) (0.561) (1.119) (-0.194)
R&D intensity −0.584 −0.540 −0.595 −0.555 −0.148 −0.984
(-1.048) (-0.995) (-1.068) (-1.020) (-0.205) (-1.123)
Tobin’s Q 0.015 0.017 0.014 0.016 0.029 0.010
(1.103) (1.253) (1.013) (1.155) (1.299) (0.449)
Capital intensity 0.084 0.092 0.085 0.092 0.064 0.086
(0.965) (1.073) (0.971) (1.066) (0.493) (0.725)
Return on assets −0.177 −0.177 −0.168 −0.168 −0.010 −0.330*
(-1.107) (-1.109) (-1.041) (-1.049) (-0.049) (-1.694)
Leverage −0.111 −0.119 −0.097 −0.106 −0.086 0.033
(-0.869) (-0.933) (-0.758) (-0.825) (-0.379) (0.192)
Firm- and year-xed effects yes yes yes yes yes yes
R
2
0.050 0.055 0.056 0.059 0.094 0.091
F value 2.76*** 2.90*** 2.90*** 3.00*** 1.63** 2.35***
Obs. (rms) 2413 (305) 2413 (305) 2413 (305) 2413 (305) 1207 (262) 1206 (253)
*p <.10; **p <.05; ***p <.01. Robust standard errors clustered at the rm-level. T-values in parentheses. Digital innovation is calculated as the
natural logarithm of one plus the average digital patents led in t+1 and t+2. Effects are estimated by using xed effects regression models.
S. Firk et al.
Long Range Planning xxx (xxxx) xxx
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observation period (Fu et al., 2020); (2) that were actually listed in the S&P900 in the year of observation, and (3) in another test, we
only consider rms that were constituents of the S&P900 in the rst year of our observation period (i.e., 2005). Our results remain
robust in all samples. Second, we test an alternative measure of our dependent variable digital innovation. While our previous proxy
considers patent lings independently of their granting status (e.g., also including patent lings that could be rejected), we exclusively
focus on granted patents. The results also support our ndings. Third, we test the robustness of our results with alternative measures of
our independent variable TMT digital knowledge. We calculate the variable by counting the number of years top managers have worked
in digital technology-related positions or industries instead of counting the number of top managers, and also calculate the variable as
the percentage of top managers with digital knowledge in the TMT. Our results remain robust. Fourth, we test the fourth hypothesis by
testing three-way interaction terms instead of splitting our sample into two sub samples. Our results remain robust.
6. Discussion and conclusion
In this study, we empirically examine the inuence of TMT characteristics on digital innovation in the context of industrial rms.
Thereby, we are among the rst to respond to the calls for more research on the role of the TMT for digital innovation (Kohli and
Melville, 2019; Singh et al., 2020; Volberda et al., 2021). Our results show that, on average, TMT digital knowledge is positively
associated with future digital innovation. We further nd that a more integrative CEO and the presence of a CDO amplies the positive
inuence of TMT digital knowledge on digital innovation. Finally, our results highlight that considering the hierarchical structure in
the TMT as a contextual factor for the stimulation of behavioral integration can be important. Specically, the moderating inuence of
CDOs crucially depends on a at hierarchical structure in the TMT. In contrast, we do not nd that the hierarchical structure creates
such obstacles for integrative CEOs in triggering behavioral integration. The latter nding could be attributed to the powerful role of
CEOs in the TMT. As such, CEOs could better overcome behavioral barriers of strong hierarchical structures in their endeavors for
behavioral integration.
Fig. 3. Interaction of TMT digital knowledge and CDO existence on digital innovation.
Fig. 2. Interaction of TMT digital knowledge and integrative CEO on digital innovation.
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6.1. Contributions to the literature
Our study contributes to research on TMT research in three major ways. First, our study contributes to research on the TMT’s role
and needed competencies for digital innovation (e.g., Hanelt et al., 2021a; Volberda et al., 2021). Specically, case studies and
conceptual research on digital innovation suggest that the top management needs to be aware of and support rm digital innovation
endeavors (Hanelt et al., 2021a; Kohli and Melville, 2019; Wrede et al., 2020), but remain unclear in the specic characteristics needed
in the TMT. Our study adds empirical evidence to this literature by outlining that rm digital innovation can particularly benet from
digital knowledge in the TMT. Thereby, our study also informs the debate on whether rather general managerial or digital compe-
tencies are needed for leadership in the digital era (Furr et al., 2019; Volberda et al., 2021). Specically, we theorize on emerging role
requirements for the TMT and suggest that digital innovation elevates the role of digital knowledge from one of functional importance
to one of more general importance. This empirically supports the notions in recent conceptual works on digital transformation that
digital technology-focused management styles are becoming increasingly important (Dery et al., 2017; Hanelt et al., 2021a).
Second, our work complements existing literature on TMT behavioral integration (e.g., Buyl et al., 2011; Simsek et al., 2005) by
exploring how triggering behavioral integration takes place at internal TMT interfaces in the context of digital innovation. Here, we
substantiate existing research (Buyl et al., 2011; Georgakakis et al., 2017) by outlining that integrative CEOs also have a vital role for
behavioral integration under the specic peculiarities of digital innovation. Moreover, while prior literature mainly focuses on the CEO
as an integrative force in the TMT (e.g., Buyl et al., 2011; Georgakakis et al., 2017), we extend this literature by highlighting how other
specic TMT roles can also be highly benecial for triggering behavioral integration in the TMT. While we focused on CDOs and the
behavioral integration of digital knowledge, our ndings might also be relevant for other TMT roles dedicated to specic phenomena
such as sustainability (e.g., chief sustainability ofcer, Fu et al., 2020). In the case of such specic TMT roles, we highlight the TMT
hierarchical structure as a decisive contextual factor for behavioral integration. We theorize on implicit behavioral expectations in the
TMT that could unfold as barriers for integration processes under a strong hierarchical structure. In sum, our study helps to build a
deeper understanding of how to promote behavioral integration at the interfaces between top managers other than the CEO.
Third, our study contributes to the emerging literature on the CDO (e.g., Kunisch et al., 2020; Singh et al., 2020). In particular, our
work adds to the conceptual discussion of CDO roles. Prior literature points to the CDO as a coordinator, but also emphasizes that CDOs
may act as sole innovators (Bj¨
orkdahl, 2020; Reck and Fliaster, 2019; Tumbas et al., 2017). We inform this literature by showing that
CDOs can have benets as coordinators but are rather limited in their effectiveness if they are viewed solely as digital innovators in a
functional sense. We also add on how the CDO role needs to be embedded in the organization to be effective. While prior research
indicates that rms need to revise organization design parameters at more operative levels to provide supportive structures for the
effectiveness of CDOs (Singh et al., 2020), our research complements this literature by emphasizing the relevance of at hierarchical
structures at the top level to strengthen the coordinative role of the CDO. Hence, our work provides insights into how rms may benet
from the CDO role and thereby complements CDO literature mainly focused on antecedents of CDO presence (Firk et al., 2021; Kunisch
et al., 2020).
6.2. Practical implications
Our study has important implications for managerial practice due to the relevance of digital innovation in industrial rms (e.g.,
Svahn et al., 2017) and the debates on benecial TMT characteristics for setting the digital innovation course (Boyden, 2017; Furr
et al., 2019). First, our work outlines the benets of digital knowledge in the TMT to accomplish the TMT’s tasks in leading the rm
toward digital innovation. In particular, our study suggests that digital knowledge in the TMT is benecial for digital innovation since
the TMT is better able to fulll its emerging tasks for digital innovation, such as recognizing a digital innovation’s potentials and
supporting its implementation. Firms should therefore consider the relevance of digital knowledge in TMT composition processes and
the design of leadership development programs (e.g., training, workshops, etc.).
Second, our work emphasizes the relevance of triggering information exchange and collaborative behavior among top managers to
utilize digital knowledge. Firms should therefore consider the need for information exchange and collaborative behavior in designing
structures and processes at the TMT level that facilitate the rm’s digital innovation endeavors. Here, rms may assess the appro-
priateness of certain CEOs in triggering such information-exchange processes in the TMT as well as the need for creating novel po-
sitions in the TMT. Our study suggests that rms can benet from the CDO position in terms of integrating TMT digital knowledge into
TMT processes for digital innovation. Firms, however, should consider the hierarchical structure in conditioning the effectiveness of
the integration activities triggered by TMT roles. Under strong hierarchical structures, TMT roles can be ineffective in facilitating
information exchange and collaboration within the TMT due to behavioral barriers for other top managers to engage in such infor-
mation exchange and collaboration. Taken together, rms should assess TMT competencies, existing roles, and structural conditions
when preparing to embrace digital innovation.
Finally, our practical implications should be interpreted in light of the unforeseen challenges arising with the COVID-19 pandemic.
First, this pandemic has spurred rms into taking actions for digital innovation (e.g., by providing platforms and technical in-
frastructures for home ofces and more digital customer engagement) and has thus created more awareness of the need for digital
innovation in industrial rms. This increasing awareness may help rms in seeing the need for digital knowledge in the TMT and
increase the willingness to integrate this knowledge. Second, however, the pandemic requires rms to cope with the physical distances
that have occurred due to the physical separation of workplaces. Managers and employees have been challenged to interact and
collaborate in the digital work environment, as communication processes now need to be more explicit than in face-to-face meetings.
This, in turn, creates huge challenges for the behavioral integration processes in the TMT. To prevent the occurrence of separated silos,
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15
specic TMT roles, such as the CDO, may become even more important in establishing interfaces for information exchange to over-
come these physical barriers (Juneja and Sukharevsky, 2020). Thus, our results regarding the crucial role of behavioral integration
should be considered in the heated discussions about digital work environments that likely outlive the COVID-19 pandemic.
6.3. Limitations and future research
Our study has some limitations worth noting. First, we focus on digital innovation by using digital patent lings. Patents present an
established proxy for innovation in industrial rms characterized by a high patent intensity (e.g., Ahuja and Katila, 2001; Balsmeier
et al., 2017; Cust´
odio et al., 2019). However, the focus on patents does not allow us to make explicit statements on the digitally enabled
transformation of the industrial rms’ business models, which may follow on from digital innovation (Nambisan et al., 2017). It would
be interesting for future research to examine which kinds of TMT knowledge and TMT roles might affect the translation of digital
innovation into changes in industrial rms’ business models. In addition, future research could examine how TMT digital knowledge
specically translates into rm digital innovation (e.g., for which concrete decisions and process steps) and it could also examine the
role of TMT digital knowledge in the context of external innovation-related relationships (e.g., open innovation, see Chesbrough,
2003).
Second, we proxy for the TMT’s digital knowledge via the experience that managers have collected in digital technology-related
work positions or industries. While research often uses work experience as a proxy for the knowledge of top managers (e.g., Buyl
et al., 2011; Georgakakis et al., 2017; van Peteghem et al., 2019), future research could consider further sources of knowledge creation
in measuring the TMT’s digital knowledge. For example, future research could account for the technological afnity that is present in
the area in which the workplace is located to capture digital knowledge-creation mechanisms that take place outside of work. In
addition, future research could use more explicit measures of digital knowledge by, for example, using survey-based methods, and it
could also examine further TMT characteristics that may also be relevant in driving the rm’s digital innovation endeavors (e.g.,
personality traits, such as openness).
Third, we investigate the emerging role of the CDO. While we nd that the diffusion of CDOs is comparable to that found in other
studies in the nal years of our panel (Fu et al., 2020), we acknowledge that there may be a limitation in terms of generalizing the
results due to the low number of CDO occurrences. We therefore encourage future research to further examine the consequences of the
CDO role by aiming for even larger datasets. Here, future research could also explore the various facets of the CDO role that go beyond
coordinative tasks for strengthening collaboration. For example, future research could focus on the effects of CDOs in facilitating
customer engagement that relate to a more externally focused role closer to marketing, which is more likely to be seen in
non-manufacturing industries (Horlacher and Hess, 2016; Tumbas et al., 2018).
Fourth, we acknowledge that the decision to add managers with digital knowledge to the TMT is not exogenously determined. To
address the resulting endogeneity problems, we used rm xed effects regressions and forwarded our dependent variable of digital
innovation. Moreover, we focused on industrial rms, as this is a more homogenous group of rms that is challenged by similar in-
dustry developments. However, as with any other study lacking exogenous variation, correlation or causation is up for debate. Hence,
we believe that it could also be fruitful to examine the rm characteristics that drive the decision to add digital knowledge to the TMT.
For example, from an upper echelon perspective, it may be worth exploring the experiences and knowledge of the board, CEO, and
TMT that could lead to increasing TMT digital knowledge.
6.4. Conclusion
We provide insights into benecial TMT characteristics for digital innovation. Our study shows that digital knowledge in the TMT is
positively associated with digital innovation, on average. We also nd that rms can benet from integrative CEOs and the existence of
a CDO in utilizing the TMT digital knowledge for digital innovation. In beneting from the CDO as a TMT integrator, our study outlines
the relevance of a at hierarchical structure. Taken together, our work implies that TMT digital knowledge, even in the industrial
context, is becoming increasingly relevant and rms may need to assess their TMT roles and structures to embrace digital innovation.
Author statement
Sebastian Firk: Conceptualization, Methodology, Software, Investigation, Writing - original draft; Writing - review & editing.
Yannik Gehrke: Conceptualization, Methodology, Data Curation, Software, Investigation, Writing - original draft; Writing - review &
editing. Andre Hanelt: Conceptualization, Writing - original draft; Writing - review & editing. Michael Wolff: Conceptualization;
Resources, Writing - original draft; Writing - review & editing.
Acknowledgements
We thank the Special Issue editors and two anonymous reviewers for their excellent feedback throughout the review process.
Moreover, we are grateful for the valuable comments and suggestions on previous versions of this manuscript made by Jana Oeh-
michen, Michel Avital and the participants in the AOM Workshop “Digital Practices: Unpacking the New Logics of Organizing in a
Digital Age”, as well as the participants at the Annual Meeting of the Academy of Management in Boston (August 2019).
S. Firk et al.
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Appendix
Table A1
Data sources and variable descriptions
Variable Description & Calculation Data source
Digital innovation Calculated as one plus the natural logarithm of the average digital patent lings in year t+1 and t+2. Patent
lings are dened as digital, if they are classied in technological classes that relate to digital technologies
according to the USPC scheme. Specically, we consider the following USPC classes as related to digital
technologies: 178, 333, 340, 342, 343, 358, 367, 370, 375, 379, 385, 455, 341, 380, 382, 395, 700, 701,
702, 704, 705, 706, 707, 708, 709, 710, 712, 713, 714, 345, 347, 360, 365, 369, 711, 715, 716, 717, 718,
719, 720, 725, 726.
USPTO
TMT digital knowledge Measured as the number of top managers who possess experiences in digital technology-related positions or,
for at least three years, in digital technology-related industries. The nal variable is mean-centered.
BoardEx
Integrative CEO Measured by standardizing and averaging the (1) the pairwise overlap in tenure between the CEO and the
other TMT members and (2) the functional diversity in prior employments of the CEO.
BoardEx
CDO existence Dummy variable equaling one if there is a CDO position, otherwise zero. BoardEx & manual
search
TMT hierarchical
structure
Dummy variable equaling one if there is a strong hierarchical structure in the TMT, otherwise zero. The
calculation is based on the median value of an index composed of (1) the vertical interdependence in the
TMT (i.e., the number of hierarchical levels in the TMT and whether there is a COO) and (2) the coefcient
of variation in the short-term pay among the TMT.
BoardEx &
ExecuComp
CEO educational level Coded as one for no academic degree, two for a Bachelor’s degree, three for a Master’s degree, four for an
MBA degree, and ve for a PhD degree or equivalent.
BoardEx
CEO equity compensation Calculated as CEOs’ restricted shares and stock option value divided by CEOs’ total compensation. ExecuComp
CEO age Measured as the natural logarithm of the number of CEO’s years. ExecuComp
CEO tenure Measured as the natural logarithm of the number of CEO’s tenure. ExecuComp
CEO duality Dummy variable equaling one if the CEO is also the chairman of the board. BoardEx
CINNO existence Dummy variable equaling one if there is a chief innovation ofcer in the rm, otherwise zero. BoardEx
TMT horizontal
interdependence
Calculated by standardizing and averaging (1) a dummy variable indicating whether the TMT was based
entirely on functional posts and (2) the number of functional roles in the TMT.
BoardEx
TMT educational level Calculated as the average of the TMT’s educational level, which is coded analogous to CEO educational
level.
BoardEx
TMT age Measured as the natural logarithm of the average number of years of each top manager. BoardEx
TMT size Measured as the natural logarithm of the number of top managers in the TMT. BoardEx
Board diversity Measured as the percentage of women under the non-executive directors. BoardEx
Board independence Measured as the percentage of outside directors who are not appointed by the CEO. BoardEx
Board size Measured as the natural logarithm of the number of non-executive directors. BoardEx
Institutional ownership Calculated as the sum of percentages hold by institutional owners with at least one percent of voting shares. ThomsonOne
Firm size Calculated as the natural logarithm of net sales. Datastream
Capital expenditures Calculated as capital expenditures divided by net sales. Datastream
R&D intensity Calculated as the R&D expenditures divided by net sales. Datastream
Tobin’s Q Calculated as the sum of market capitalization and total assets subtracted by total shareholder’s equity
divided by total assets.
Datastream
Capital intensity Calculated as property, plant, and equipment (gross) divided by total assets. Datastream
Return on assets Calculated as operating income after taxes divided by total assets. Datastream
Leverage Calculated as short-term debt divided by total assets. Datastream
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Sebastian Firk is an Associate Professor at the Accounting Department of the University of Groningen. His research combines insights from corporate governance,
management accounting, and strategy. His research has been published in outlets such as Contemporary Accounting Research, European Journal of Information Systems,
Management Accounting Research, Journal of Management Studies, and Strategic Management Journal.
Yannik Gehrke is a Doctoral Student at the University of Goettingen. His research focuses on the impact of digital innovation on the board and TMT. His research has
been published in the Proceedings of the European Conference on Information Systems.
Andr´
e Hanelt is an Associate Professor at the University of Kassel. His research, which focuses on digital innovation and transformation in established industries, has
been published several times in outlets such as the European Journal of Information Systems, Information Systems Journal, Journal of Management Studies, Journal of
Management Information Systems, and Research Policy.
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Michael Wolff is a Full Professor of Management and Control at the University of Goettingen. His main interests of research are corporate governance structures,
management accounting, and strategic management. His work has been published in, among others, the Contemporary Accounting Research, Human Resource Manage-
ment, Journal of Management Studies, Long Range Planning, and Strategic Management Journal.
S. Firk et al.
