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Research-in-Progress Papers
ECIS 2020 Proceedings
6-15-2020
Chief Digital Officer's Characteristics and Digital Transformation
Activities: Does Industry Digitalization Play a Role?
Anna Moker
Technical University of Munich, anna.moker@tum.de
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Digitalization Play a Role?" (2020). Research-in-Progress Papers. 81.
https://aisel.aisnet.org/ecis2020_rip/81
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CHIEF DIGITAL OFFICER’S CHARACTERISTICS AND
DIGITAL TRANSFORMATION ACTIVITIES:
DOES INDUSTRY DIGITALIZATION PLAY A ROLE?
Research in Progress
Moker, Anna, Technical University of Munich, Munich, Germany, anna.moker@tum.de
Abstract
To navigate digital transformation successfully, firms in many industries are starting to implement a
new executive position of a Chief Digital Officer (CDO). Despite the growing interest of scholars in
the CDO position, existing research on this topic has failed to examine, which impact the characteristics of the CDO could have on the digital transformation activities of a firm. Relying on upper echelons theory, we argue that whereas the task of all CDOs is to promote digital transformation of a firm,
some CDOs will pursue more digital transformation activities than others based on their education,
functional tracks, and career experiences. Additionally, we investigate whether the strength of the relationship between the CDO’s characteristics and the amount of digital transformation activities is
dependent on the industry’s digitalization degree. To test our hypotheses, we use a sample of the largest German firms between 2011 and 2019. Our study aims at both extending research on the CDO and
upper echelons and integrating them. We further aim at providing recommendations to practitioners
on whether they should appoint a certain type of the CDO based on their industry and the intended
digital transformation intensity.
Keywords: Chief Digital Officer, Upper Echelon Characteristics, Upper Echelons Theory, Digital
Transformation Activities, Industry Digitalization.
1 Introduction
Digital transformation of companies is currently the centre of attention of researchers and practitioners
(Hess et al., 2016; Vial, 2019). Digital transformation is defined as the process that is directed at improving an organization by triggering significant changes at its different levels through the combination of new digital technologies such as social media, mobile, analytics, Internet of things, cloud, and
platforms (Sebastian et al., 2017; Vial, 2019). To realize improvements, connected to digital transformation, such as improved organizational efficiency and performance, enhanced relationship with customers and suppliers, and new value propositions, firms comprehensively transform their processes,
products, structures, and entire business models (Chanias et al., 2019; Fitzgerald et al., 2013; Vial,
2019). To undergo this complex transformation process successfully, firms require new mind-sets and
skills, and thus, leadership (Fitzgerald et al., 2013; Singh and Hess, 2017). Hence, many firms in different industries are introducing a new executive position: the Chief Digital Officer (CDO) (Singh and
Hess, 2017; Singh et al., 2019).
The CDO is a company’s digital transformation specialist (Singh and Hess, 2017), whose task is to
help the company to drive business value from the digital technologies (Tumbas et al., 2017). In doing
so, the CDO needs to make digital transformation a strategic priority for the own company (Singh and
Hess, 2017). Thus, the CDO is responsible for supporting top management in formulating and implementing a digital transformation strategy and infusing it into all business areas, coordinating and orchestrating digital transformation activities, as well as fostering digital innovation within a firm
(Drechsler et al., 2018; Hansen and Siew Kien, 2015; Singh and Hess, 2017; Tumbas et al., 2017).
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CDO’s Characteristics and Digital Transformation
Despite the growing interest of scholars in this new managerial position, research on the CDO is still
in its infancy (Horlacher and Hess, 2016; Singh et al., 2019). This research has primarily addressed the
tasks, responsibilities and role types of the CDOs (e.g., Haffke et al., 2016; Horlacher and Hess, 2016;
Singh and Hess, 2017; Tumbas et al., 2017) as well as organization design parameters, surrounding the
CDOs in their role (e.g., Horlacher et al., 2016; Singh et al., 2019). However, this research has mostly
neglected the role of the CDOs in the digital transformation activities (Singh et al., 2019). Particularly,
it has failed to consider, which impact the CDO’s characteristics could have on the digital transformation activities of a firm. This research gap is of major importance, because according to upper echelons theory (Hambrick and Mason, 1984), top executives’ characteristics such as functional background, career experiences, and education are likely to predict their strategic choices (Hambrick,
2007). This proposition is supported by a large body of literature both from the area of strategic management (e.g., Belenzon et al., 2019; Helfat and Martin, 2015; Kish-Gephart and Campbell, 2015), and
information systems (e.g., Leidner et al., 2010; Masli et al., 2016).
We address this research gap by studying the impact of the CDO’s characteristics on firm’s digital
transformation activities. Our research question is: How do the CDO’s characteristics influence a
firm’s digital transformation activities, pursued by this CDO? In answering our research question, we
also aim at investigating, whether the strength of this relationship varies dependent on the digitalization degree of the firm’s industry, which is likely to be related to a different effort that is required to
initiate digital transformation projects (Maedche, 2016). To enable this analysis, we use panel data
from the largest German firms, listed on the stock index HDAX, between 2011 and 2019.
With this study, we plan to contribute to the existing literature in three ways. First, we aim to extend
the scarce research on the CDO (e.g., Tumbas et al., 2017) by exploring new organizational outcomes,
which might be influenced by the CDO, and investigating the consequences, bound to the appointment
of this executive in more depth. Second, we aim at extending the upper echelons research (Bromiley
and Rau, 2016) by proposing industry digitalization as a new contextual factor that might influence the
strength of the relationship between managerial characteristics and their strategic outcomes. Third, we
aim to integrate the upper echelons research (e.g., Belenzon et al., 2019) with the research on the CDO
(e.g., Singh and Hess, 2017) by applying upper echelons theory to this newly emerged digital executive position and investigating, how the upper echelons characteristics impact new strategic choices,
which are brought along by the digital age.
Further, our study is also important for practitioners, because we investigate how the characteristics of
the appointed CDO might influence a firm’s digital transformation depending on the current digitalization status of its industry. In doing so, we aim to draw the attention of practitioners to the CDO’s characteristics, which might be relevant for the digital transformation outcomes and provide recommendations to practitioners on whether firms should appoint a certain type of the CDO based on their industry and the intended digital transformation intensity.
2 Theoretical Background
To investigate the impact of the CDO’s characteristics on a firm’s digital transformation activities, we
rely on upper echelons theory (Hambrick and Mason, 1984; Hambrick, 2007). This theory is appropriate for our study, because it helps to explain the influence of the personal characteristics of top executives on their strategic choices (Hambrick, 2007), and can be applied not only to top (Leidner et al.,
2010), but also to senior (Masli et al., 2016), and middle (Leidner et al., 2017) management. This is
particularly important, as the position of the CDO might be established not only at the level of top
management (Singh and Hess, 2017), but also some levels below it (Singh et al., 2019). According to
upper echelons theory (Hambrick and Mason, 1984), top executives perceive situations, evaluate alternatives and make strategic decisions through their individual lenses, which are shaped by their personal attributes (Hambrick, 2007). These attributes include both unobservable characteristics, such as
personalities or values, and observable characteristics, such as functional or educational background
(Hambrick and Mason, 1984). As CDOs are mainly hired based on their professional skills and experiences, which might be useful for digitally transforming a firm (Kane et al., 2015), we consider the
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appropriate observable characteristics from upper echelons theory in terms of education, functional
tracks, and other career experiences in this study (Hambrick and Mason, 1984). In the following, we
argue that based on these characteristics, some CDOs will pursue more digital transformation activities
than others (Hambrick and Mason, 1984; Hambrick, 2007).
First, education indicates the CDO’s knowledge and skill base (Hambrick and Mason, 1984). A higher
amount of formal education, received by the CDO, provides this executive with a larger knowledge
and skill base to navigate digital transformation (Hambrick and Mason, 1984). This is essential, as the
CDO’s task focus is broad and aims at transforming a company as a whole across its different areas
(Singh and Hess, 2017). Further, a higher amount of formal education also endows the CDO with a
higher openness towards digital innovation (Hambrick and Mason, 1984), which presents an inherent
part of the CDO’s job (Tumbas et al., 2018) due to being required for digital transformation (Hinings
et al., 2018). Accordingly, we hypothesize:
H1: CDOs with a higher amount of formal education will pursue more digital transformation
activities than CDOs with a lower amount of higher education.
Second, functional tracks refer to the CDO’s experience in a certain functional area (Hambrick and
Mason, 1984) and reveal, whether the CDO can be considered as a narrow functional specialist or a
broad generalist from a general management background (Kish-Gephart and Campbell, 2015). Such a
background, as reflected in a general management course of studies and functional background, provides the CDO with cross-functional knowledge and change management skills (Kish-Gephart and
Campbell, 2015), which are required due to the broad task scope involving cross-department authority
for digital initiatives (Singh and Hess, 2017) and the necessity to promote digital change (Helfat and
Martin, 2015). In line with this, anecdotal evidence shows that many CDOs come from the general
management background (Kane et al., 2015). Hence, we hypothesize:
H2: CDOs a) with the general management course of studies, b) from the general management
functional background will purse more digital transformation activities than their counterparts.
Third, the CDO’s career experiences other than functional track might also influence the course of
action, chosen by the CDO (Hambrick and Mason, 1984). For instance, the diversity of career experiences, as reflected by firm outsiderness and the number of prior employments provide the CDO with a
broader knowledge and experience, needed to figure out how to digitally transform a company (Kane
et al., 2015; Singh and Hess, 2017; Wade and Obwegeser, 2019). In line with this, research has shown
that many CDOs are hired from outside the firm (Kane et al., 2015; Wade and Obwegeser, 2019). Further, a large network size provides executives with an access to valuable resources and connections,
which the CDO can rely on during the interdisciplinary digital transformation projects, leading to a
perceived risk reduction (Kish-Gephart and Campbell, 2015). This leads us to our next hypothesis:
H3: CDOs a) who come from outside the company, b) with a higher number of prior employments, c) with a larger network size will purse more digital transformation activities than their
counterparts.
Besides that, career experiences from the information technology (IT) field are important for the CDO
(Kane et al., 2015). Primarily, the CDO needs experience in IT in order to understand IT applications
and their underlying infrastructure as well as the possibilities to change them, formulate IT requirements, and develop new digital products and services (Singh and Hess, 2017). Further, a prior employment by a digital pioneer boosts the digital pioneering skills of the CDO (Singh and Hess, 2017)
through broader knowledge and experience concerning digital transformation, e.g., in the form of best
practices on how to leverage employees’ digital innovativeness (Ciriello and Richter, 2015), and
makes the CDO more open-minded and innovation-affine. Accordingly, we hypothesize:
H4: CDOs with a) a higher experience in IT, b) a prior employment by a digital pioneer will
pursue more digital transformation activities than their counterparts.
The intensity of digital transformation changes, which a firm is underdoing under the CDO’s direction,
is likely to differ among industries (Chanias et al., 2019; Sebastian et al., 2017). For instance, whereas
some industries such as finance are already advanced in implementing digital initiatives, others such
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as health care are rather at the beginning of their digital transformation path (Barrett et al., 2015; Eden
et al., 2019; Gomber et al., 2018). Initiating digital transformation in firms from less digitalized industries requires the CDO to first figure out a suitable digital transformation path, then define an according strategy, and make huge efforts in implementing this strategy, potentially facing resistance from
within the firm (Eden et al., 2019; Tumbas et al., 2017). Vice versa, CDOs, who are well-equipped for
digital transformation based on their education, functional tracks, and career experiences, might experience less challenges in leading a firm’s digital transformation and initiate even more activities in
case of a high digitalization degree of the industry. Hence, we formulate the following hypothesis:
H5. The digitalization degree of the firm’s industry moderates the relationship between the
CDO’s a) amount of formal education, b) general management course of studies, c) general
management functional background, d) firm outsiderness, e) number of prior employments, f)
network size, g) experience in IT, h) prior employment by a digital pioneer, and the amount of
the pursued digital transformation activities, such as the positive effect of the CDO’s characteristics on the amount of the digital transformation activities will be the stronger the higher the
digitalization degree of the industry.
Our research model is presented in Figure 1.
Figure 1.
Research model.
3 Methods
3.1
Sample and data sources
To test our hypotheses, we will analyse panel data of the German HDAX firms. This index encompasses 110 (from 2018: 102) stock-listed companies with the highest market capitalization in Germany
(Hutzschenreuter and Horstkotte, 2013). These companies belong to different industries and have different sizes, from the largest enterprises via mid-sized companies to smaller technology firms
(Tuschke et al., 2014). Thus, HDAX has been widely utilized by prior research to study organizational
phenomena (e.g., Flickinger et al., 2016; Hutzschenreuter and Horstkotte, 2013; Tuschke et al., 2014).
Our sample covers HDAX firms, who had a CDO in the period of our study. With the first CDO in
HDAX being appointed in 2011 (Von Boeselager, 2018), our sample will comprise the data from 2011
to 2019. To construct our sample, we in a first step retrieve the list of firms, which were listed at
HDAX, at the end of each year (as of December, 30th or another date, for which the latest composition
report was available) from STOXX Ltd., a part of Deutsche Börse Group. For each year, we account
for the changes in the index composition, resulting in unbalanced panel data.
In a second step, we identify for each of the HDAX member firms, whether a firm had a CDO in the
respective year. To collect this information, we use firms’ web pages, their annual reports as well as
LinkedIn accounts of executives, and perform an internet-based search via the search engine Google to
verify the results. In doing so, we code as CDOs such persons, who occupied either a position with the
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title “Chief Digital Officer” or “CDO” or a board position with responsibility for digital transformation topics. We identify the latter based on the word root “digit*” appearing in either a position or a
function description of a board member (Moker et al., 2020). Hereby, we include only CDOs of the
groups, which were listed on HDAX in the respective year, but not of their affiliated companies. However, if a group had a CDO, who was responsible only for one group area, such CDOs are included.
Due to using panel data, we assign only one CDO to each firm. Hence, in cases when we can identify
more than one person, fitting our search criteria, we decide as follows. First, if there was both a CDO
and a board member, responsible for digital transformation topics, than we select the CDO, because
our research primarily targets executives in this position. Second, if a CDO change took place in the
considered year, than we selected the CDO, who occupied the position for most of the year, as we expect such CDOs to have more time to initiative digital transformation activities. Third, if there were
multiple persons, who were CDOs during the year, than we select the person, who occupied a position
at a higher hierarchical level, because we expect such persons to have more power and thus, better
possibilities to influence firm strategic decisions such as digital transformation.
To collect the remaining data for our study, we use the following data sources. First, we obtain executives’ personal data from the databases Orbis (Albino-Pimentel et al., 2018) and BoardEx (Tang et al.,
2018), companies’ homepages, LinkedIn accounts, newspaper articles with biography, Hoppenstedt
Aktienführer and Munzinger Online (Flickinger et al., 2016). Second, we gather data on digital transformation activities from the database Nexis (former Lexis-Nexis), which provides daily newspaper
articles and press announcements (Drechsler et al., 2019). Third, we collect firm data from the database Wordscope (Thomson One Banker) (Hutzschenreuter and Horstkotte, 2013).
3.2
Measures
3.2.1
Independent variables
To measure the amount of formal education, received by a CDO, we create dummy variables for the
highest academic degrees, possessed by CDOs: PhD (or higher) degree and diploma (master or comparable) degree. Hereby, we take the bachelor degree as the baseline. We use a dummy variable to
measure CDO’s general management course of studies, which is coded one if a CDO had studied
business administration while attaining the first university degree, and zero otherwise. Following
Kish-Gephart and Campbell (2015), we measure CDO’s functional background by coding it into one
of the following four categories: throughput functions, output functions, peripheral functions, and general management background (Hambrick and Mason, 1984). Throughput functions involve operations,
R&D and engineering; output functions comprise marketing and sales; peripheral functions cover finance, accounting, and law (Hambrick and Mason, 1984; Kish-Gephart and Campbell, 2015). If the
background does not fit into one of these three categories, we code it as general management background (Bednar, 2012; Kish-Gephart and Campbell, 2015). We use peripheral background as the baseline (Kish-Gephart and Campbell, 2015) and create dummy variables for the other three categories.
Further, we consider a CDO to be a firm outsider, if this CDO had been employed by another company before taking on the current role. We code a corresponding dummy variable one in this case, and
zero otherwise. To measure the number of prior employments, we consider only jobs, and not internships, in companies outside the group, which employed the CDO to the studied point of time. Hereby,
if a CDO had been employed by the same firm more than once, we count this firm only once. We
measure the CDO’s network size using the respective variable from executives’ individual profiles on
BoardEx, which is calculated as the number of overlaps to other individuals through education, employment, and other activities (Liu, 2014). We measure CDO’s experience in IT as the total number of
years, which a CDO spent working in the IT industry or in an IT position (Drechsler et al., 2019). We
operationalize CDO’s prior employment by a digital pioneer as a dummy variable, which is coded one
if a CDO had worked for one of the firms listed on the Forbes Top 100 Digital companies list (Forbes,
2019) and zero otherwise (Grover et al., 2018).
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To measure industry digitalization, we use data from the German “DIGITAL Economy Index”, which
is collected on behalf of the Federal Ministry of Economic Affairs and Energy on an annual basis and
considered as representative for the German economy. This index displays the digitalization status of
German companies on a scale between zero and 100 points and contains digitalization indices for different industries (Federal Ministry of Economic Affairs and Energy, 2018a; 2018b).
3.2.2
Dependent variables
We define digital transformation activities as practices directed at navigating and implementing digital
transformation (Chanias et al., 2019; Johnson et al., 2003). To measure the amount of digital transformation activities, pursued by CDOs, we rely on newspaper articles and press announcements. We consider media announcements (Gamache and McNamara, 2019) as a useful representation of the major
digital transformation activities, pursued by CDOs, for three main reasons. First, media announcements have been largely utilized by existing research as the main legitimate source of information on
firm’s strategic actions, such as strategic change (Graf-Vlachy et al., 2020; Graffin et al., 2011; Kiss
and Barr, 2015), which also includes digital transformation (Singh et al., 2019). Second, HDAX companies as the German largest stock-listed companies are usually the center of public attention. In line
with this, prior research has shown that firms with a large size in terms of market capitalization and
the number of employees as well as a membership on a prominent stock index have a comprehensive
media coverage (Bushee et al., 2010; Fang and Peress, 2009; Graf-Vlachy et al., 2020; Hillert et al.,
2014). Third, subjects, related to digital transformation and CDOs, are of current interest and high topicality (Singh et al., 2019; Wade and Obwegeser, 2019). Therefore, it is highly probable that media
will report about major digital transformation activities, pursued by the CDOs of the HDAX firms.
We retrieve newspaper articles and press announcements from the database Nexis (Drechsler et al.,
2019). To construct the measure of digital transformation activities, we proceed as follows. First, to
find the articles, relevant for the considered company, we search for the company name in the area
“News”, and set a filter to get results in English language. Second, to identify the articles, related to
the CDOs from our list, we search within the displayed results for the name of the CDO. Third, to
make sure that the found press releases deal with digital transformation activities, we filter the results
based on whether they contain the word “digital” and select only those, which do. Fourth, to obtain the
measurement of digital transformation activities for each year in our sample, we limit the period of
time to cover only the respective year (from January, 1st to December, the 31st). Subsequently, we record all results, generated by applying this search strategy, and download the texts of the announcements. Finally, in order to avoid double counts, we read these announcements and filter those, which
address the same digital transformation activity, out of the sample (Gamache and McNamara, 2019).
Hence, we receive a count of digital transformation activities, pursued by a CDO in a certain year.
3.2.3
Control variables
We control for factors, which might influence CDO’s decisions on digital transformation activities, on
the level of the CDO, position, top management team (TMT), firm and industry (Kish-Gephart and
Campbell, 2015). First, we control for CDO’s age, as the risk-taking propensity might vary with age
(Kish-Gephart and Campbell, 2015), as well as CDO’s gender, by creating a dummy variable female
CDO. Further, we include CDO’s tenure in a current position. For board members with digital responsibilities, we calculate tenure in a current position beginning from the year, in which they took over
digital responsibilities. To control for the CDO’s power and decision enforcement possibilities, we
include a dummy variable, indicating whether a CDO was a board member or not. This variable is
coded one if the CDO was a board member, and zero otherwise.
Regarding CDO’s position, we consider whether a CDO was responsible only for digital transformation topics, or whether a CDO had further responsibilities. For this purpose, we create a dummy
variable CDO’s focus on digital responsibility, which is coded one, if only digital responsibility was
indicated in CDO’s position title, and zero otherwise. Additionally, we control for the presence of a
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Co-CDO through incorporating a dummy variable, which is coded one, if there was a second person
with digital responsibilities, employed by the same firm in the same year, and zero otherwise.
To account for the potential influence of TMT members on digital transformation activities, we control for TMT size, an average TMT age, and the number of female TMT members (Helfat and Martin,
2015). Further, we control for firm characteristics in terms of firm size, measured as the natural logarithm of the number of firm’s employees (Chen et al., 2011) and firm past performance, measured as
Return on Assets (ROA) in a given year (Guo et al., 2017). To capture the overall economic situation
in a given industry, we include industry performance in our model, which we operationalize as the
average ROA values of all firms operating in the firm's industry (Tanriverdi, 2006) according to the
Industrial Classification Benchmark (Cincera and Veugelers, 2014). Finally, to account for any macroeconomic trends or shocks, we include year fixed effects in our model (Calabrò et al., 2018).
3.3
Planned analysis strategy
To estimate the effect of the CDO’s characteristics on digital transformation activities, we will conduct a panel data analysis in Stata 14.1. We will calculate a fixed effects model, which utilizes a within-firm variation in variables and allows for arbitrary correlation between the independent variables
and the unobserved effect (Certo et al., 2017; Wooldridge, 2012). A fixed effects model will allow us
to control for any unobserved firm-specific heterogeneity, which could influence digital transformation outcomes (Belderbos et al., 2014; Wooldridge, 2012). We prefer a fixed effects model to estimate ceteris paribus effects, because our sample has not been randomly drawn from a large population
of firms (Wooldridge, 2012). We will allow for unobserved firm effects in our data by using robust
standard errors to control for any kind of serial correlation and heteroscedasticity (Wooldridge, 2012).
To conduct this panel data analysis, we will use the command xtreg, fe cluster(id) in Stata. Further, we
will calculate simple slopes of the interaction between the CDO’s characteristics and the digitalization
degree of the industry on digital transformation activities using the margins, dydx command in Stata.
4 First Descriptive Statistics
Currently, we are at the stage of data collection. We have already collected the HDAX composition
lists, annual reports and firm data for the years 2011 to 2018. Further, we have already collected the
list of firms’ CDOs for the year 2018 and started to gather data on the CDO’s personal characteristics.
To prove that our sample can generate variance with respect to the CDO’s characteristics, we present
the first descriptive statistics for some of our variables for the year 2018 in Table 1. In the year 2018,
our sample comprised 32 CDOs. As the first descriptive statistics illustrate, our sample can be expected to generate enough variance with respect to the considered CDO’s characteristics.
Variables
1. Age
Obs.
22
Mean
49.23
SD
6.18
Min
32
Max
61
2. Female CDO
32
0.16
0.37
0
1
3. Tenure in a current position
32
2.22
1.29
1
7
4. Tenure in a company
32
6.22
5.81
1
28
5. Focus on digital responsibility
32
0.59
0.50
0
1
6. Presence of a Co-CDO
32
0.16
0.37
0
1
7. CDO’s experience in IT
32
7.53
7.16
1
29
8. PhD degree
31
0.29
0.46
0
1
9. CDO’s general management course of studies
31
0.42
0.50
0
1
10. Firm outsiderness
32
0.50
0.51
0
1
Table 1.
Descriptive statistics for some of the CDO’s characteristics in 2018.
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5
Discussion
In a next step, we will collect the data as outlined above and perform the analyses to investigate the
impact of the CDO’s education, functional tracks, and career experiences on the amount of digital
transformation activities, while accounting for the potential moderating role of industry digitalization.
With this study, we plan to contribute to the existing literature in three ways. First, we aim to extend
the scarce research on the CDO (e.g., Tumbas et al., 2017) by exploring new organizational outcomes,
which might be influenced by the CDO, and investigating the consequences of this executive’s appointment in more depth. In doing so, we also respond to the call of existing studies to investigate the
CDO’s role in execution of digital transformation activities in a longitudinal study (Singh et al., 2019).
Second, we aim at extending the upper echelons research (Belenzon et al., 2019), which has considered different environmental and industry-related moderators (Bromiley and Rau, 2016), by proposing
industry digitalization as a new contextual factor that might influence the strength of the relationship
between managerial characteristics and their strategic outcomes. Third, we aim to integrate the upper
echelons research (e.g., Belenzon et al., 2019) with the research on the CDO (Singh et al., 2019) by
applying upper echelons theory to this new digital executive position and investigating, how the upper
echelons characteristics impact new strategic choices, which are brought along by the digital age.
Our study has four main limitations. First, although the use of panel data contributes to establishing
causality in our results (Wooldridge, 2012), they might still be subject to endogeneity (Hambrick,
2007). Hence, we will search for appropriate instrumental variables that could be used in our study
(Hambrick, 2007). Second, we measure the amount of digital transformation activities based on press
releases. Even though, as explained above, we expect the press announcements to encompass the major digital transformation activities, conducted by the CDO, there might still be some less visible activities, which are not covered in the news. Accordingly, future research might measure digital transformation activities using other data sources. Third, our measurement of prior employment by a digital
pioneer will reflect only the CDO’s experiences in a private sector. Therefore, future research might
utilize a different measure that would also include the CDO’s public sector experiences. Fourth, we
will use data only from Germany. As the executive effects might differ depending on national systems
(Hambrick, 2007), future research might utilize cross-country samples.
Finally, our study has also practical implications. Due to investigating the impact of the CDO’s characteristics on firm’s digital transformation activities and accounting for the current industry’s digitalization status, we aim to draw practitioners’ attention to the characteristics of digital executives, which
might influence the digital transformation outcomes such as the amount of digital transformation activities, and the potential role of the industry in this context. Thus, we aim to provide recommendations to practitioners on whether they should appoint a CDO with a certain educational, functional, and
career background based on their industry and the intended digital transformation intensity.
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