Master Thesis MSc. BA Strategic Innovation Management
Digital expertise on the boards and the success of
firms' strategic transformation
June, 24th 2019 Lucas Wähler
University of Groningen Faculty of Economics and Business
S3567648
Supervisor: Prof. Dr. J.D.R. (Jana) Oehmichen Co-assessor: Dr. Florian Noseleit
Abstract:
Management literature based on the Upper Echelon theory has acknowledged the beneficial impact of directors’ psychological characteristics and managerial backgrounds on firm performance as well as on other strategic variables related to firm performance. Given the rising relevance of digitalization, this thesis aims to ascertain the impact of digital expertise on the board of directors on firms’ financial performance, strategic change and R&D intensity. Using a multi-country data sample of 2,074 firms within the time period from 2008 until 2019, this study does not provide empirical evidence for conclusive relationships between digital expertise on the board on firms’ financial performance, strategic change or R&D intensity. These findings, in turn, provide several implications of theoretical and practical relevance. Theoretically, the findings suggest that the consideration of individual characteristics of board members on firm performance or related variables should incorporate contextual aspects in order to draw comprehensive conclusions. Practically, the findings imply that the sole employment of directors with digital expertise might be not as beneficial as expected. Hence, this thesis contributes to the appointment and education of directors on the board.
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Table of Contents
1 INTRODUCTION ... 1
2 THEORETICAL BACKGROUND AND HYPOTHESES DEVELOPMENT ... 3
2.1DIGITAL TRANSFORMATION AND FIRM PERFORMANCE ... 3
2.2UPPER ECHELON THEORY AND THE BOARD OF DIRECTORS ... 5
2.3DIGITAL EXPERTISE ... 6
2.3.1DIGITAL EXPERTISE AND FIRMS’FINANCIAL PERFORMANCE ... 7
2.3.2DIGITAL EXPERTISE AND STRATEGIC CHANGE ... 9
2.3.3DIGITAL EXPERTISE AND R&D INTENSITY ... 11
2.3.4CONCEPTUAL MODEL ... 13
3 METHODOLOGY ... 13
3.1DATA COLLECTION AND SAMPLE ... 13
3.2MEASUREMENTS ... 15 3.2.1DEPENDENT VARIABLES ... 15 3.2.2INDEPENDENT VARIABLE ... 16 3.2.3MODERATOR VARIABLE ... 17 3.2.4CONTROL VARIABLES ... 17 3.3ANALYTICAL METHOD ... 17 4 RESULTS ... 18
4.1DESCRIPTIVE STATISTICS AND CORRELATIONS ... 18
4.2REGRESSION RESULTS AND HYPOTHESIS TESTING ... 20
4.3ROBUSTNESS CHECKS ... 24
5 DISCUSSION ... 25
5.1THEORETICAL IMPLICATIONS ... 25
5.2MANAGERIAL IMPLICATIONS ... 30
6 LIMITATIONS AND FUTURE RESEARCH ... 31
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1 Introduction
During the last decade, digital technologies have enormously altered the environment and infrastructure of business activities. This is an opportunity as well as a challenge for companies and scholars argue that firms' digital transformation (DT) is a pivotal process with critical impact on firms' success (e.g. Bharadwaj et al., 2013; Fitzgerald et al., 2014; Matt et al., 2015; Pagani, 2013; Singh and Hess, 2017). According to Fitzgerald et al. (2014: p.2), digital transformation is defined as "the use of new digital technologies … to enable major business improvements (such as enhancing customer experience, streamlining operations or creating new business models)". To embrace digitalization, it requires the adoption of digital technologies like big data, analytics, cloud computing, social media or mobile platforms that result in shorter product life cycles, faster product and service innovation and interconnected products, processes and services (e.g. Bharadwaj et al., 2013; Hanelt et al., 2015; Kroll et al., 2018). Although firms were previously successful by offering mainly physical products, examples like Uber, Airbnb or Netflix, show the relevance of digitalized products and services that replace or alter physical offerings (Haffke et al., 2016). These examples of fast-growing start-ups rapidly obtaining huge market shares with their disruptive innovations and represent a threat for established players and their business models even within rather stable industries (Haffke et al., 2016). Industries like telecommunication, publishing or banking witnessed major shifts towards digital applications and scholars predict that no industry will stay untouched by digital technologies (Bohnsack et al., 2018; Hanelt et al., 2015). As a consequence, firms need to find strategies to digitally transform in order to implement and assimilate digital technologies to foster innovation and to remain competitive in the new digital era (Pagani, 2013; Westerman and Bonnet, 2015).
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members enables them to interpret the business environment in order to detect potential threats (Cho and Hambrick, 2006) as well as to match resources to embrace business opportunities (Kor, 2003). Besides that, the boards' involvement in resource allocation also matters for the R&D activities within the firm (e.g. Alexiev et al., 2010). Although scholars (e.g. Pagani, 2013; Westerman and Bonnet, 2015) and practitioners (e.g. Ringel et al., 2018) acknowledged the relevance of digitalization, the phenomenon still lacks a clear understanding about how firms can manage the transition towards a digitally capable company. Past research did not yet focus on digital expertise on the board of directors and its impact on firms' financial performance, strategic change or R&D intensity which is yet to be empirically tested. The Academy of Management Discoveries (AMD) emphasizes the need for exploratory empirical research that contributes to the understanding of the effects of DT as well as how DT is affected by strategy and innovation (Lorenz et al., 2018). This study addresses this literature gap by examining the impact of digital expertise of board members on firms' financial performance as well as on the two strategic variables of strategic change and R&D intensity. Accordingly, I focus on the following research question:
How does digital expertise on the board of directors impact firms’ financial performance, strategic change and R&D intensity?
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The remainder of this paper is structured as follows: The second section will provide an overview of the relevant literature which will be used to theoretically ground the hypotheses. Third, the research methodology will be described including data collection and sample, the explanation of the used measurements and the analytical method. The fourth section will present the empirical results including robustness checks. Next, the fifth section will interpret and discuss the findings incorporating theoretical and managerial implications as well as limitations and prospective directions for future research. Finally, the conclusions drawn from this research will be presented as the last part of this research paper.
2 Theoretical Background and Hypotheses Development
The following section will introduce at first the relevant literature on digital transformation (DT) in the context of firm performance. Afterward, the research stream of the Upper Echelon Theory will be outlined as a theoretical foundation to illustrate the impact of the board of directors on firm performance. Next, digital expertise on the board of directors will be defined, based on board literature and linked to DT and firm performance in order to provide an understanding of the contemporary debate amongst academics. Afterward, the relationship of digital expertise and strategic change will be provided. Finally, digital expertise on the board of directors will be linked to R&D intensity.
2.1 Digital Transformation and Firm Performance
Recently, the phenomenon of DT has been discussed within strategy (Matt et al., 2015) and innovation management literature (Lichtenthaler, 2017). The DT of companies is facilitated by information technologies which are according to Dehning et al. (2003) defined as "all modes of information collection, processing, storage, and dissemination". The rising diffusion of IT-based technologies generates opportunities for combinations among those technologies and accessibility of digital applications in real time (Hanelt et al., 2015). Consequences of the emergence of digital technologies are fast-paced product and service innovation, shorter product life-cycles and interconnected products, processes and services (Bharadwaj et al., 2013; Hanelt et al., 2015). These digital technologies are embodied by applications based on cloud computing, social media, mobile technologies or big data (Bharadwaj et al., 2013; Lucas et al., 2013; Setia et al., 2013).
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technical and social aspects. By technical, the authors describe SMACIT technologies (social, mobile, analytics, cloud, and Internet of things), while the internet is described as the most influential technology for DT of firms (Sebastian et al., 2017). Previously, studies have shown that the firms' productivity can be enhanced by access to faster internet which, in turn, increases the likelihood of commercial transactions to enter new markets by sales over the internet (Grimes et al., 2012). Big data, as another technical component of DT, enables companies to optimize operations or to improve innovation activities in order to achieve a competitive advantage (Chen et al., 2017). Hence, it is crucial for companies to radically transform their decision-making processes as well to acquire the necessary analytic capabilities in order to profit from big data (Dremel et al., 2017). Another building block discussed within the multi-dimensional framework is the process of DT that incorporates the transformational process, the digital strategy of companies and digital innovation. The process of DT illustrates "how" digitalization can be successfully implemented by considering strategic elements like firms' use of digital technologies and its profitable exploitation as well as structural changes towards a digitally capable company (Bohnsack et al., 2018). In line with that, Bharadwaj et al. (2013) focus specifically on the digital business strategy for DT. The authors claim that by aligning IT with business strategy towards a digital business strategy, firms can create differential value (Bharadwaj et al., 2013). Regarding digital innovations as part of the process of DT, scholars argue that this kind of innovation affects not only products, services or processes but even the overarching business models of companies (Fichman et al., 2014). The last building block discussed by Bohnsack et al. (2018) is the outcome of DT as the digital change and economic return. Scholars discuss the shift of paradigms, business models, inter- as well as intra organizational changes and DT is expected to alter not only firms but also whole industries (e.g. Bohnsack et al., 2018; Hanelt et al., 2015).
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In sum, the literature has elaborated on the phenomenon of firms DT as well as its subsequent impact on firm performance. Accordingly, DT has become strategic imperative which is highly prioritized on leadership agendas and companies need to embrace digitalization for better firm performance (e.g. Bharadwaj et al., 2013; Hess et al., 2016).
2.2 Upper Echelon Theory and the Board of Directors
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Figure 1. Hambrick and Mason’s (1984) upper echelons perspective.
According to Hambrick (2007), the UE perspective is based on two interconnected parts. Firstly, the assumption that executive cognition and the objective situations are the foundations of directors' actions. Secondly, those actions are also shaped by executives' experiences and personalities (Hambrick, 2007). The central premise of the UE theory is based on bounded rationality which is according to Mischel (1977) defined as "the idea that informationally complex, uncertain situation are not objectively „knowable" but… interpretable". In line with that, Carpenter et al. (2004) state that, in many situations, top managers face circumstances in which decision-making is complicated due to an overload of information or competing objectives. In those situations, TMT members perceive and interpret the situations based on their individual cognition. Therefore, the UE theory is also described as an information processing theory that explains how managers act based on their filtered perceptions within the situations they face (Cho and Hambrick, 2006). According to Hambrick and Mason (1984), UE observable characteristics like age, functional background or education are used as indicators for the psychological constructs of executives values and cognitions which shape the perception of the top management team regarding internal and external situations to facilitate strategic choices (Carpenter et al., 2004).
2.3 Digital Expertise
Digital expertise, in the context of board research, is a rather unexplored topic that has not been observed as an independent characteristic of boards yet. Thus, there is no universal definition of
Upper Echelon Characteristics
Psychological Cognitive base Values Observable Age Functional tracks Other career experiences Education, Socioeconomic roots Financial position,
Group characteristics The objective situation
(external and internal)
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digital expertise or expertise in general (Gul and Leung, 2004). Scholars suggest that expertise can be derived from well-developed knowledge structures in grouping problems (Day and Lord, 1992) which directors on the board acquire over time within their tenure in different positions inside and outside the firm (e.g. Carpenter and Westphal, 2001; Fama and Jensen, 1983). Prior board research suggests that directors who are members on several boards are perceived as experts due to the reputation they built within their appointments (Fama and Jensen, 1983; Gul and Leung, 2004).
Due to the definitional ambiguity of digital expertise on boards of directors, it will be defined within this research as directors' knowledge acquired through (functional) experience in the context of digital issues. Considering the UE theory as a theoretical foundation, digital expertise unites individual knowledge about DT as well as managerial backgrounds which are required to digitally transform a company.
2.3.1 Digital Expertise and Firms’ Financial Performance
Even though studies did not examine digital expertise specifically, recent and past research suggests the beneficial impact of expertise and functional background on strategic choices and consequently firm performance (e.g. Cannella et al., 2008; Carpenter, 2002; Finkelstein and Hambrick, 1990). The following part aims to provide a better understanding of the potential impact of digital expertise on the boards of directors on firms' financial performance. Since digital expertise has not been examined by prior literature yet, the following part will incorporate arguments from previous studies about the expertise of board members in general as well as including arguments from recent CIO literature.
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to different perceptions of board members resulting in better decision making (Bantel and Jackson, 1989, Lant et al., 1992, Wiersema and Bantel, 1992). Lant et al. (1992) stress the relevance of diversity among board members in the context of the "group think" phenomenon. Diversity leads to a reduced likelihood of "group think" and fosters consequently discussions about strategic issues as well as learning opportunities within the whole board. In case there is disagreement about strategic decisions, the board is rather willing to spend additional resources on examination of the certain issue to move forward. Besides the potential positive effects of disagreement, Cho and Hambrick (2006) argue that executives with various knowledge bases scan and interpret the business environment differently according to their experiences. Hence, their attention and access to external information are highly dependent on their expertise. Besides the general board literature, related prior research examining CIO appointments also provides evidence for a beneficial impact of expertise on firms' financial performance. Ranganathan and Jha (2008) for instance, found that boards with a CIO achieve better financial performances than boards without CIOs. According to the findings of the authors, the CIOs provide IT management capabilities to the firm which in turn enhances the business value of IT that contributes to the overall performance of firms. Kor (2003) stresses the relevance of tacit and historical knowledge of top managers in order to match firms' resources with opportunities within the business environment.
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Hypothesis 1: Greater digital expertise on the board of directors is associated with better firm financial performance.
2.3.2 Digital Expertise and Strategic Change
After the consideration of the impact of digital expertise on firms' financial performance, the subsequent part will discuss the potential effect of digital expertise on the board on strategic change. Compared to the effect of directors' characteristics on firm performance, the strategic change allows a closer examination of the impact of board members based on their roles and responsibilities (Oehmichen et al., 2017).
Facilitated by mounting digital disruptions within business environments, companies need to strategically change in order to become digitally capable firms, also described as "digital imperative" by Fitzgerald et al. (2014). According to Zhang and Rajagopalan (2010), strategic change can be defined as "the variation over time in a firms' pattern of resource allocation in key strategic dimension …". Another definition by Gioia and Chittipeddi (1991) incorporates the firms' external position and defines "... strategic change involves an attempt to change current modes of cognition and action to enable the organization to take advantage of important opportunities or to cope with consequential environmental threats". These two definitions together enable a better understanding of strategic change in the context of DT. As discussed above, digitalization leads to disruptions within and across industries and one way to adapt to these changes within the environment is the active alteration of resource patterns (Carpenter, 2000). Firms' strategic change enables a shift of organizational focus which in turn could foster differentiation from competitors (Porter, 1996; van de Ven and Poole, 1995). Applying these theoretical foundations on the phenomenon of digitalization, firms may focus on successfully transforming towards a digitally capable company in order to achieve strategic differentiation by means of digital technologies. Prior board research examined specifically the impact of directors characteristics', such as expertise, on strategic change (e.g. Oehmichen et al., 2017; Zhang and Rajagopalan, 2010).
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can act to reduce environmental uncertainty and dependence. However, the consideration of RDT is described as insufficient on its own while scholars add the agency perspective as a complementary theoretical lens (Hillman and Dalziel, 2003). Hillman et al. (2009) complement the resource provision function of boards by also stressing the agency perspectives and describing monitoring management on behalf of shareholders as a key activity of boards.
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competitors strategies or as boards driven change towards differentiation. Digitalization poses threats and opportunities and thus experiences concerning digital issues on the board may be pivotal in order to successfully manage the digital transformation. Besides that, digital experts are also expected to better monitor managerial actions in the context of firms' DT. By fulfilling their function of providing counsel and allocating resources to key strategic dimensions as well as monitoring managers, digital experts on the board could foster strategic change within the firm. The above-mentioned arguments regarding the impact of digital expertise on strategic change lead to the following second hypothesis: Hypothesis 2: Greater digital expertise on the board of directors is associated with higher strategic
change.
2.3.3 Digital Expertise and R&D intensity
The subsequent part will further elaborate on the influence of digital expertise on the board of directors on resource allocation which has been extensively discussed within the previous literature regarding strategic change. This section will focus specifically on the impact of digital expertise on the board of directors on the resource R&D intensity.
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innovation across the entire company. I expect that, if there is digital expertise on the board, the firm promotes R&D activities, since the directors should be able to detect emerging digital trends and to allocate resources to R&D activities in order to foster digital innovation. In sum, these arguments support the following third hypothesis:
Hypothesis 3: Greater digital expertise on the board of directors on firm performance is positively associated with higher R&D intensity.
Additionally, scholars have acknowledged differences between dependent and independent boards of directors in the context of R&D intensity (e.g. Chen and Hsu, 2009; Kor, 2006). Particularly, Kor (2006) argues that independent boards are more likely to invest in R&D in order to improve firms' innovation capabilities while more dependent boards rather limit R&D investments. Previous research, based on the Agency theory, pointed out a conflict of interests between owners and executives (Fama and Jensen, 1983; Jensen and Meckling, 1976). Particularly, in the context of R&D intensity executives may be less willing to encourage R&D activities since investment decisions that do not pay off can risk their employment and the financial health of the company (Kor, 2006). The owners, however, are more open to high-risk investments in innovation activities because they expect large returns. Regarding this conflict of interests, independent boards play a pivotal role since they monitor the managerial activities to alleviate the agency problem and remind managers to prioritize innovation capability (Eisenhardt, 1989; Mahoney, 1992; Mahoney and Mahoney, 1993). As discussed above, boards of directors provide important resources to the firm such as board capital (Hillman and Dalziel, 2003). The human capital on boards enables the directors to advise and counsel (Hillman et al., 2000) executives decisions regarding R&D. Besides that, relevant expertise and experience also help directors to identify opportunities and risks concerning R&D processes (Chen and Hsu, 2009). Furthermore, independent directors can increase the required human capital by spotting and employing qualified executives (Miller and Le Breton-Miller, 2006). Concerning the second constitute of board capital, the relational capital, independent directors are embedded in networks which in turn helps the company to access financial resources which can be allocated to expensive R&D projects (Chen and Hsu, 2009). Concluding the aforementioned arguments I expect board independence to positively impact the underlying relationship between digital expertise on the boards of directors and R&D intensity resulting in the following fourth hypothesis:
Hypothesis 4: Board independence moderates the relationship between digital expertise on the board of directors and R&D intensity, in that the underlying relationship (H3) is significantly
13 2.3.4 Conceptual Model
3 Methodology
As previously outlined, research concerning digital expertise on the board of directors and its impact on firm performance, strategic change, and R&D intensity is yet to be empirically tested. Therefore, this thesis provides a multivariate statistical analysis in order to test the impact of digital expertise. Panel data was used to capture the longitudinal effects of digital expertise on firm performance and strategic change. The following section will describe the data utilized within this study as well as a description of the data sample. Next, the measurements of the variables will be outlined. In the last part, an overview of the analysis method will be provided.
3.1 Data Collection and Sample
This study builds on data from two archival sources utilized by Oehmichen et al. (2017) while board data were obtained from BoardEx database and financial firm data from Thompson Financial DataStream. In order to test the hypotheses, I included all firms listed on the MSCI All Country World Equity index (MSCI ACWI) resulting in a multi-country data set. The MSCI ACWI includes mid-cap and large representations across 23 developed markets and 24 emerging markets with 2,757 constituents and covers approximately 85% of the global investable equity opportunity set (MSCI
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ACWI, 2019). This index is well-known and has been used due to the expectation of higher information value regarding individual information of board members. Consequently, I was able to find most of the relevant secondary data, including board and financial information. I decided for a multi-country sample in order to derive results that also enable a closer look at differences across the regions. In order to address the proposed research objectives, individual information about board members from companies listed in the MSCI ACWI between January 1, 2008, and March 1, 2019, were retrieved from the database "BoardEx" which contains corporate governance data, such as company details, director profiles, compensation, and board composition. BoardEx further provides information about the regions of North America, Europe, the UK and the rest of the world.
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not much firm information from the rest of the world which could be explained by cultural differences (e.g. publicly listing of companies, etc.) on these markets which may consequently decrease the amount of data available for the BoardEx database. By considering the different industries, the least companies are from the real estate sector (54 firms), while the most represented industry is financials with 379 firms. The most represented country is the USA (869 firms) and the least represented are Korea and Argentina (each one firm). A detailed overview of the data distributed across the regions, countries, and industries is revealed in appendix A.
3.2 Measurements
The following subsection will provide a detailed description of the variables and their measurements which were used within this research and which have been derived from measurements used in previous comparable studies.
3.2.1 Dependent Variables
Firms' financial performance. Firms financial performance is measured by Tobin's Q, as the ratio of firms' market value to the costs of replacements of its assets. Tobin's Q is calculated by market capitalization plus preferred stock plus the long-term debt divided by the total assets minus short term debt. The Tobin's Q measurement has been used because of its future-oriented measurement of firm performance compared to return on assets and return on equity which include rather current results (e.g. Wang and Li, 2008).
Strategic change.Firms' strategic change is measured based on prior research from Zhang and Rajagopalan (2010) and Oehmichen et al. (2017). Accordingly, strategic change is calculated as the change in the financial resource allocation of companies. Therefore, a composite variable was built based on (1) plant and equipment newness (net P&E/gross P&E); (2) nonproductive overhead (selling, general, and administrative expenses/sales); (3) inventory levels (inventories/sales); and (4) financial leverage (total debt/equity). First, the absolute values from the ratios were obtained from the difference between two consecutive years. Afterward, the resulting values were standardized by year across the firms to further calculate the average across the four prior standardized values in order to finally obtain the composite variable of strategic change.
16 3.2.2 Independent Variable
17 3.2.3 Moderator Variable
Board independence. The moderator variable board independence has been measured as the ratio of outside directors on the board. This measurement has been adopted from Kor (2006) however with the limitation that I did not assess whether the outside director was appointed before the CEO or not.
3.2.4 Control Variables
Return on equity. As a performance control for firms' financial performance, Rothaermel and Alexandre (2009) propose a return on equity as a control for prior firm performance. The control variable measures how effectively the company assets are used to generate profit and it is calculated by dividing firms' net income by shareholders' equity as a percentage. The control variable is transformed by taking the natural logarithm to avoid outliers and to normalize the data. Compared to the Firms' financial performance variable, Return on equity is rather a performance measure for productivity which enables a different perspective compared to rather market value oriented measurement like Tobin's Q.
Company size. The control for company size is measured by the number of employees of a
firm. The control variable has been transformed by using its natural logarithm due to the same reason like return on equity.
Year. In order to control for year effects year dummies are included (Yanadori and Marler, 2006).
Industry. Industry dummies have been included following Oehmichen et al. (2017). This control is important since it checks for industry specific effects.
The board controls were derived from previous research by Oehmichen et al. (2017). As board controls I used board size as the number of directors on the board of a firm for each year, board age as the age average of all board members, board tenure as the average of time in positions of the directors on the board. These controls are necessary in order to check for board specific effects.
3.3 Analytical Method
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potential effects. Therefore panel data will be used. In order to avoid unobserved heterogeneity, Mundlak (1978) suggests two strategies namely, fixed or random effect models that are applicable for that concern. While random effect models assume no correlation between individual effects and the independent variable, the fixed effect assumes such a correlation. In order to test for the appropriateness for each model, a Hausman test was conducted to decide between random and fixed effect models to yield appropriate results. Additionally, the variance inflation factors were checked for potential multicollinearity. Moreover, the control variables company size and return on equity were altered by taking the natural logarithm to enable normality as well as to avoid skewness. These two firm-level controls were additionally winsorized at the 1st and 99th percentile levels by taking the stata command winsor2 to exclude outliers (Haas et al., 2018).
4 Results
The following section will discuss the statistical results derived from this study. First, the descriptive statistics and the correlation of the variables will be presented. Second, the two regression models will be detailed in order to test the hypotheses. Finally, I will provide additional robustness checks to test the findings.
4.1 Descriptive Statistics and Correlations
Table 1 reports the summary statistics and the correlations between the study variables, including mean values, standard deviations and significance levels of the correlations.
19 Table 1: Descriptive statistics and correlations
Variable Mean Standard Deviation (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (1) Firm performance 1.597 1.519 1.00 (2) Digital expertise 0.008 0.026 0.08** 1.00 (3) Strategic change 0.016 0.701 0.15** -0.01 1.00 (4) R&D intensity 0.116 7.051 0.03** 0.03** 0.16** 1.00 (5) Board size 12.797 3.958 -0.14** 0.09** -0.10** -0.01 1.00 (6) Board tenure 7.155 5.610 0.05** 0.02** -0.02** 0.01 0.04** 1.00 (7) Board independence 0.550 0.222 0.08** 0.09** 0.03** 0.01 -0.09** 0.08** 1.00 (8) Board age 68.548 5.040 -0.05** -0.03** -0.04** -0.01 0.06** 0.17** 0.10** 1.00 (9) Company size 9.437 1.631 -0.20** 0.11** -0.21** -0.03** 0.34** -0.04** -0.12** -0.01 1.00 (10) Return on equity 2.745 0.951 0.37** 0.05** 0.05** 0.06** -0.05** 0.01 0.01 -0.05** 0.02** 1.00 Note: Number of Observations: 10,308.
Additionally, table 2 reports the Variance Inflation Factors (VIFs) as another test for multicollinearity within the sample. The conducted test reports a mean VIF of 1.08 including all variables which is below the suggested maximum threshold of 10. The highest Variance Inflation Factor is reported for company size at 1.19. This test supports the assumption previously drawn based on the summary statistics. Hence, the consideration of the summary statistics as well as the Variance Inflation Factors serve as evidence for no issues concerning multicollinearity.
4.2 Regression Results and Hypothesis Testing
Table 3, table 4 and table 5 present the results based on the conducted panel data regression using fixed effect estimators. As outlined previously, Hausman tests were used in order to decide for the most appropriate model. Generally, random effect estimates should be used unless the Hausman test rejects that. Practically speaking, not rejecting the random effect estimates either means that both estimates (random effect and fixed effect) are so close that it does not really matter which model is used or that the variation within the sample in the FE estimates are so large that there are no significant differences (Wooldridge, 2012). In this study, all Hausman tests rejected the beforehand assumed null hypothesis with p-values below 0.05 which suggests fixed effect estimators as the most appropriate.
Table 3 reports the results of the sequential regression for firm performance. Model 1 contains the control variables and has a within R-square of 0.096 and is significant (p<0,05). In model 2 additionally to the control variables the independent variable digital expertise is added which reveals a within R-square of 0.096 and is significant (p<0.05).
Variable VIF 1/VIF
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Table 3: Regression results of board digital expertise on firm financial performance (Tobin's Q)
Model 1 Model 2 Constant 3.879*** 3.891*** (0.479) (0.479) Control Variables Board size 0.00270 0.00206 (0.00556) (0.00557) Board tenure -0.00253 -0.00250 (0.00165) (0.00165) Board independence 0.129 0.127 (0.207) (0.207) Board age -0.00767* -0.00773* (0.00422) (0.00422) Strategic change 0.0315* 0.0312* (0.0170) (0.0169) Company size -0.295*** -0.295*** (0.0189) (0.0189) R&D intensity 0.126 0.130 (0.404) (0.404) Return on equity 0.269*** 0.269*** (0.0113) (0.0114)
Year dummies Yes Yes
Industry dummies Yes Yes
Independent Variable Digital expertise -1.348 (0.838) R-squared within 0.096 0.096 R-squared between 0.164 0.159 R-squared overall 0.126 0.123 Observations 10,308 10,308 Number of firms 1,756 1,756
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
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size, return on equity and strategic change stay consistently significant while strategic change and board age are significant at the 0.05 level (p<0.05) and company size and return on equity are highly significant at the 0.01 level (p<0.01) level. In hypothesis 2, I proposed a positive relationship between digital expertise and strategic change. I performed another sequential regression including the used variables of the first relationship in order to be consistent with the previous analysis. The results are presented in table 4 and in order to check hypothesis 2, model 2 needs to be considered. The examination of the impact of digital expertise on boards of directors on strategic change revealed insignificant results. Therefore, hypothesis 2 needs to be rejected.
Table 4: Regression results of board digital expertise on firms' strategic change
Model 1 Model 2 Constant 0.271 0.277 (0.307) (0.307) Control Variables Board size -0.000248 -0.000464 (0.00355) (0.00356) Board tenure 0.00187* 0.00187* (0.00105) (0.00105) Board independence -0.0329 -0.0338 (0.132) (0.132) Board age 0.00227 0.00225 (0.00269) (0.00269) Company size -0.0348*** -0.0346*** (0.0122) (0.0122) Return on equity 0.00337 0.00344 (0.00750) (0.00750) Firm performance 0.0129* 0.0127* (0.00692) (0.00692) R&D intensity 0.327 0.329 (0.258) (0.258)
Year dummies Yes Yes
Industry dummies Yes Yes
Independent Variable Digital expertise -1.348 (0.838) R-squared within 0.006 0.006 R-squared between 0.003 0.002 R-squared overall 0.003 0.003 Observations 10,308 10,308 Number of firms 1,756 1,756
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Table 5: Regression results of board digital expertise on firms’ R&D intensity
Model 1 Model 2 Model 3
Constant 0.0330*** 0.0335*** 0.0338***
(0.0125) (0.0129) (0.0129)
Control Variables
Board size 8.34e-05 8.94e-05 8.81e-05
(0.000149) (0.000149) (0.000149)
Board tenure 2.10e-05 2.09e-05 2.03e-05
(4.41e-05) (4.41e-05) (4.41e-05)
Board age -0.000117 -0.000116 -0.000117
(0.000113) (0.000113) (0.000113)
Strategic change 0.000575 0.000578 0.000578
(0.000454) (0.000454) (0.000454)
Company size 2.04e-05 1.73e-05 1.87e-05
(0.000512) (0.000513) (0.000513)
Return on equity -0.00154*** -0.00154*** -0.00154***
(0.000314) (0.000314) (0.000314)
Year dummies Yes Yes Yes
Industry dummies Yes Yes Yes
Moderator Variable Board independence -0.00112 -0.00163 (0.00554) (0.00559) Independent Variable Digital expertise 0.0132 -0.0265 (0.0225) (0.0636) Interaction Effect
Digital expertise X Board independence 0.0661
(0.0989) R-squared within 0.005 0.005 0.005 R-squared between 0.084 0.076 0.068 R-squared overall 0.068 0.061 0.055 Observations 10,308 10,308 10,308 Number of firms 1,756 1,756 1,756
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
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relationship, model 1 includes just the control variables and their impact on R&D intensity. The within R-square value of the first model of strategic change is 0.005 and by adding the independent variable digital expertise into the first model, model 2 shows no increase of the within R-square value at 0.005, which means that the weak explanatory power of the relationship is not improved by adding the independent variable. In model 3 the interaction effect was added however, the within R-square remained the same compared to model 2. In hypothesis 3, I predicted a positive impact of digital expertise on R&D intensity in the sense that greater digital expertise is associated with higher R&D intensity. I could not find empirical support for the proposed relationship between digital expertise and R&D intensity. Therefore, hypothesis 3 needs to be rejected. Also, the interaction effect between digital expertise and board independence remains insignificant. Consequently, I reject hypothesis 4. Considering the other variables, the models show that only the variable return on equity stays consistently highly significant at the 0.01 level (p<0.01).
Finally, I will shortly describe the development of digital expertise across years, regions and industries which is revealed in the appendix A. This description aims to add to a better understanding of digital expertise in general. Considering the development of digital expertise on the board across years (Appendix A; Table A.2) the descriptive statistics show that digital expertise is, starting from the year 2008 (m=.0058; sd= .0209), steadily increasing and doubled until the year 2018 (m=.0124; sd=.0307). Regarding the distribution of digital expertise across regions (Appendix A; Table A.2), firms from North America have on average the highest digital expertise on the board (m=.0110; sd=.0288), while firms belonging to the region rest of the world have the lowest percentage of digital experts on the board (m=.0028; sd=.0168). The distribution across industries is revealed in (Appendix A; Table A.4) and shows that the healthcare sector includes firms with the highest digital expertise on the board (m=.0174; sd=.0362), followed by the information technology sector (m=.0163; sd=.0376).
4.3 Robustness Checks
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Using another measurement for Tobin's Q revealed insignificant results and supports, therefore, the results of the initially used measurement. Additional measurements for firm performance were used that are focused on other aspects than the market value which is incorporated by Tobin's Q. Therefore, I used return on investment (ROI) and return on assets (ROA). The examination of digital expertise on ROI and ROA revealed insignificant results for the relationships. The third robustness check includes the impact of digital expertise on different industries. Therefore, I considered the five industries that are the most represented in the dataset which are Financials (343 firms), industrials (295 firms), consumer discretionary (286 firms), information technology (198 firms) and materials (191 firms). For all five industries, no significant effects could be found. Fourth, digital expertise was examined by just considering the companies which have digital expertise and excluding companies which do not have any digitally experienced board members. The results indicated the usage of a fixed effect model as well, however, all results remained insignificant after using the fixed effect estimators. Next, I examined the impact of digital expertise on the boards of directors among the four most represented countries within the data sample, namely USA (869 firms), United Kingdom (141 firms), Canada (109 firms) and France (92 firms) but all revealed insignificant results. Finally, I used board independence as a moderator for the links between digital expertise on the board on firms' financial performance as well as on strategic change. However, no significant results could be derived. In appendix B additional statistics and a short discussion considering board independence is provided. Those similar results to the main estimate support the robustness of my findings.
5 Discussion
The following section will discuss and interpret the empirical findings derived from this study. Therefore, the results will be interpreted in the context of the hypotheses and the stated research question to bring the findings in the theoretical context. Afterward, theoretical as well as managerial implications will be provided. Finally, the section will discuss the limitations of this study and will outline suggestions for future research.
5.1 Theoretical Implications
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intensity. Based on recent and previous literature it was assumed that digital expertise on the board of directors will positively affect firms' financial performance, strategic change, and R&D intensity. Additionally, it was proposed that board independence may positively moderate the underlying relationship between digital expertise and R&D intensity. Surprisingly, the results derived from the used regression models deviate from the beforehand proposed conceptual model. The following part will outline potential reasons in order to explain the findings of this study.
Generally, it is not possible to say that the study findings reliably demonstrate the non-existence of the relationships or whether the data or the methodological approach are inadequately robust in pointing out the presence of the relationship. However, the findings can be related to several studies which found that these relationships can be influenced by contextual variables. Therefore, figure 2 shows the theoretical framework provided by Carpenter et al. (2004) which represents a "second-generation" model (Carpenter et al., 2004) that incorporates recent research in order to update the original UE model provided by Hambrick and Mason (1984; Figure 1). The theoretical framework is complemented by the study variables of digital expertise (theoretical construct proxied by TMT demographics), R&D strategies (strategic – organizational outcome), strategic change and financial performance, which are bold in order to build a better comprehension of the mechanisms used in this study.
Figure 2. Carpenter et al. (2004) stylized model of the upper echelons perspective.
• Skills and orientationsor social • Cognitions • Behavioral propensities • Access to information • Access to resources • Human capital • Social Capital
• Relative status within TMT or across firms • Heir apparent • (Digital expertise) External Environment • External Stakeholders • External managerial labor markets • Environmental characteristics Organizational • Firm characteristics • Board characteristics • Internal labor market Organizational Outcomes • Power • Discretion • Incentives • Integration
• Team processes Strategic
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The above presented theoretical framework provided by Carpenter et al. (2004) will be used to discuss the findings of this study. In comparison to the original UE model of Hambrick and Mason (1984), the theoretical framework of Carpenter et al. (2004) includes additional factors that affect the impact of the board of directors on organizational outcomes. Prior research argues that it is necessary to consider the contextual factors like antecedents represented by environmental and organizational aspects as well as moderators and mediators of the TMT demographic effects due to the dependence of managerial effects on these contingencies (e.g. Carpenter et al., 2004; Nielsen, 2010; Yamak et al., 2014).
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Since the board is concerned with other tasks, digital experts might not be able to effectively provide counsel as well as to monitor managerial actions. In other words, if there are members on the board who pose digital expertise, but the whole team is facing complexity within the increasingly digital environment, digital expertise alone may not be sufficient to make an impact on firms' financial performance or strategic change decisions. To partially conclude, the effect of digital expertise on firms' financial performance and strategic change may have revealed insignificant results due to the missing consideration of the contextual variables of environmental complexity, represented by digitalization, that influences these relationships.
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have required process research of TMT decision making like the study of Denis et al. (2001). Conducting such process research, however, is crucial as it helps to understand the impact of the individual characteristics of a board member. This understanding is, in turn, necessary to draw conclusions about how much individual digital expertise values for the team level and, how this results in an organizational outcome. Concluding the discussed aspects, individual or even accumulated digital expertise on the board might also be influenced by team processes like decision making and other social interactions and therefore it is not sufficient to consider individual characteristics outside of the team level context. The expectation of a positive impact on firm performance as well as on strategic change decision by the sole presence or the appointment of a director with digital expertise remains consequently disputable.
The question still remains why digital expertise on the board also does not significantly impact R&D intensity under the moderation of board independence. R&D intensity is influenced by the resource allocation function of board members and consequently, it can be assumed that the missing effect can be explained by the same reasoning applied for the link between digital expertise on the board and strategic change. Considering the impact of board independence on this relationship, a potential reason can be derived from the study from Kor (2006). She found that there is a substitution effect of various governance mechanisms. In her study, she found also insignificant results regarding the interaction effect of board independence and team experience on R&D investments. She found correlations between governance mechanisms and concluded that these different mechanisms serve as substitutes which is in line with previous research (e.g. Beatty and Zajac, 1994; Zajac and Westphal). She further claims that this substitution effect makes it difficult to conclude from individual effects on strategic choices which supports the assumption that team processes need to be considered in drawing conclusions about individual effects of characteristics like digital expertise.
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digital experts (1,1%) which leads to the assumption that the phenomenon is rather present in this region compared to the others. The robustness analysis of USA and Canada however, not provides empirical evidence that digital expertise on the boards of directors has an impact on the chosen study variables. Hence, the effect, considered within this sample, does not seems to be different at a higher level of digital expertise. This assumption also finds support by considering the findings of analyzing only firms with digital expertise, embodied by the fourth robustness check, that revealed also no significant results. Finally, it is surprising that the health care sector, within this sample, has on average the highest amount of digital expertise on the board, while information technology follows at the second place. The information technology sector was expected to have the highest amount of digital experts on the board since digitalization is mainly IT-enabled. However, these results cannot be explained by this analysis.
Concluding the above-mentioned theoretical arguments, the insignificant results can be explained by the missing consideration of contextual variables like environmental complexity (represented by digitalization) and the mediation and moderation effects of team processes. Furthermore, the investigation of the impact of digital expertise on R&D intensity supports the assumption that individual characteristic effects are difficult to observe due to the substitution effect of other governance mechanisms. Methodologically considered, the number of digital experts on the board is still very low (around 1%) which leads to the assumption that digital expertise on the board might be currently not emphasized enough given the relevance of DT for firms' success.
5.2 Managerial Implications
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in turn, may also impact firms' performance outcomes. As concluded from the methodological interpretation of the results, a general assumption can be drawn from the increasing mean values of digital expertise among the years. There is a tendency that boards appointing more digital experts, however with around 1% of digital experts on the board the emphasis on digitalization seems to be still low. Therefore, boards should find ways to drive digitalization by other means than solely appointing digital experts on the board.
6 Limitations and Future Research
This research is subject to certain limitations, which, in turn, allow for suggestions regarding fruitful prospective research in the field of board research.
First, the digital transformation of firms is a pivotal process in order to become a digitally capable firm that is equipped for digital developments within every industry. Due to the limited scope of this research, it was not possible to assess particularly the levels of DT of individual firms. However, the examination of digital expertise on the level of the digital transformation of a firm individually could be subject to future research and could reveal fruitful insights. Future research could investigate the actual impact of digital expertise on the board on firms' DT which could reveal crucial insights since previous literature describes firms' DT as a pivotal process for firm survival (e.g. Bharadwaj et al., 2013; Fitzgerald et al., 2014; Matt et al., 2015; Pagani, 2013; Singh and Hess, 2017).
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information and data exchange by vertical and horizontal networks which lead to flexibility and adaptability in managing and controlling production systems (Kagermann et al. 2013). Consequently, the digital transformation towards „Industry 4.0" allows companies to produce faster and cheaper at a high-quality level (Kroll et al., 2018). In Appendix C.2, I revealed a potential measurement for assessing firms' DT. I suggest that digital expertise in the context of innovation regarding "Industry 4.0" could deliver insights about the impact of digital expertise on the board on the "Industry 4.0" implementation resulting in an impact on firm performance. Further information is revealed in appendix C.
Third, since the keyword list was developed by the author and only partially derived from past studies (which did not aim to assess digital expertise) it remains disputable if the results of this research hold also in the future. A keyword list that captures digital expertise or general aspects related to firms' DT could be examined as part of future research.
Fourth, due to the limited time for this research, a simple panel data regression was used instead of a more sophisticated (and possibly more suitable) Generalized Method of Moment (GMM) (Arellano and Bond, 1991) model introduced by Hansen (1982). Comparable studies like the one from Oehmichen et al. (2017) used the GMM estimators to observe similar relationships. The GMM model as a dynamic panel data estimator allows controlling for the endogeneity of the lagged dependent variable in dynamic panel models. Hence, it controls for possible correlation between the explanatory variable and the error term in a model and to control for unobserved firm-specific heterogeneity. Thus, the GMM could be more suitable for this kind of research and could provide further insights which could not have been derived from this study.
7 Conclusion
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change decisions (including R&D investments), the board of directors positively affects firms' strategic change by providing resources to the company as counsel and by monitoring strategy implementation. This study aimed to relate these findings on digital expertise on the board of directors and, therefore, investigates the following research question: How does digital expertise on the board of directors impact firms’ performance, strategic change and R&D intensity?
Alexiev, A. S., Jansen, J. J., Van den Bosch, F. A., & Volberda, H. W. (2010). Top management team advice seeking and exploratory innovation: The moderating role of TMT heterogeneity. Journal of Management Studies, 47(7), 1343-1364.
Amason, A. C., & Sapienza, H. J. (1997). The effects of top management team size and interaction norms on cognitive and affective conflict. Journal of management, 23(4), 495-516.
Arellano M, Bond S. 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58(2): 277–297.
Bantel, K. A., & Jackson, S. E. (1989). Top management and innovations in banking: does the composition of the top team make a difference?. Strategic management journal, 10(S1), 107-124. Becker, G. (1964). Human capital. New York: Columbia University Press.
Beatty, R. P., & Zajac, E. J. (1994). Managerial incentives, monitoring, and risk bearing: A study of executive compensation, ownership, and board structure in initial public offerings. Administrative Science Quarterly, 313-335.
Berman, S. J., & Bell, R. (2011). Digital transformation: Creating new business models where digital meets physical. IBM Institute for Business Value, 1-17.
Bharadwaj, A., El Sawy, O., Pavlou, P., & Venkatraman, N. (2013). Digital business strategy: toward a next generation of insights.
Bohnsack, R., Hanelt, A., Marz, D., & Marante, C. (2018, July). Same, same, but different!? A systematic review of the literature on digital transformation. In Academy of Management Proceedings (Vol. 2018, No. 1, p. 16262). Briarcliff Manor, NY 10510: Academy of Management.
Bonnet, D., Ferraris, P., Westerman, G., & McAfee, A. (2012). Talking ‘bout a revolution. Digital Transformation Review, 2(1), 17-33.
35
Bunderson, J. S. (2003). Team member functional background and involvement in management teams: Direct effects and the moderating role of power centralization. Academy of Management journal, 46(4), 458-474.
Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial marketing management, 31(6), 515-524.
Cannella Jr, A. A., Park, J. H., & Lee, H. U. (2008). Top management team functional background diversity and firm performance: Examining the roles of team member colocation and environmental uncertainty. Academy of management Journal, 51(4), 768-784.
Cannella, A. A., & Holcomb, T. R. (2005). A multi-level analysis of the upper-echelons model. In Multi-level issues in strategy and methods (pp. 195-237). Emerald Group Publishing Limited. Carpenter, M. A. (2000). The price of change: The role of CEO compensation in strategic variation and deviation from industry strategy norms. Journal of Management, 26(6), 1179-1198.
Carpenter, M. A. (2002). The implications of strategy and social context for the relationship between top management team heterogeneity and firm performance. Strategic Management Journal, 23(3), 275-284.
Carpenter, M. A., Geletkanycz, M. A., & Sanders, W. G. (2004). Upper echelons research revisited: Antecedents, elements, and consequences of top management team composition. Journal of management, 30(6), 749-778.
Carpenter, M. A., & Westphal, J. D. (2001). The strategic context of external network ties: Examining the impact of director appointments on board involvement in strategic decision making. Academy of Management journal, 44(4), 639-660.
Chandy, R. K., & Tellis, G. J. (2000). The incumbent's curse? Incumbency, size, and radical product innovation. Journal of marketing, 64(3), 1-17.
Chen, H. L., & Hsu, W. T. (2009). Family ownership, board independence, and R&D investment. Family business review, 22(4), 347-362.
36
Cho, T. S., & Hambrick, D. C. (2006). Attention as the mediator between top management team characteristics and strategic change: The case of airline deregulation. Organization Science, 17(4), 453-469.
Cooper, R. G., & Kleinschmidt, E. J. (2000). 2 New Product Performance: What Distinguishes the Star Products. Australian Journal of Management, 25(1), 17-46.
Day, D. V., & Lord, R. G. (1992). Expertise and problem categorization: the role of expert processing in organizational sense‐making. Journal of Management Studies, 29(1), 35-47.
Dehning, B., Richardson, V. J., & Zmud, R. W. (2003). The value relevance of announcements of transformational information technology investments. Mis Quarterly, 637-656.)
Denis, J. L., Lamothe, L., & Langley, A. (2001). The dynamics of collective leadership and strategic change in pluralistic organizations. Academy of Management journal, 44(4), 809-837.
Diestre, L., Rajagopalan, N., & Dutta, S. (2015). Constraints in acquiring and utilizing directors' experience: An empirical study of new‐market entry in the pharmaceutical industry. Strategic Management Journal, 36(3), 339-359.
Doz, Y. L., & Kosonen, M. (2007). The new deal at the top. Harvard Business Review, 85(6), 98-104.
Dremel, C., Wulf, J., Herterich, M. M., Waizmann, J. C., & Brenner, W. (2017). How AUDI AG Established Big Data Analytics in Its Digital Transformation. MIS Quarterly Executive, 16(2). Eisenhardt, K. M. (1989). Agency theory: An assessment and review. Academy of management review, 14(1), 57-74.
Fama, E. F., & Jensen, M. C. (1983). Separation of ownership and control. The journal of law and Economics, 26(2), 301-325.
Fichman, R. G., Dos Santos, B. L., & Zheng, Z. E. (2014). Digital innovation as a fundamental and powerful concept in the information systems curriculum. MIS quarterly, 38(2).
37
Finkelstein, S., Hambrick, D., & Cannella, A. (2009). Strategic leadership: Theory and research on executives, top management teams, and boards. New York, NY: Oxford University Press.
Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology: A new strategic imperative. MIT sloan management review, 55(2), 1.
Geletkanycz, M. A., & Black, S. S. (2001). Bound by the past? Experience-based effects on commitment to the strategic status quo. Journal of Management, 27(1), 3-21.
Gioia, D. A., & Chittipeddi, K. (1991). Sensemaking and sensegiving in strategic change initiation. Strategic management journal, 12(6), 433-448.
Golden, B. R., & Zajac, E. J. (2001). When will boards influence strategy? Inclination× power= strategic change. Strategic management journal, 22(12), 1087-1111.
Grimes, A., Ren, C., & Stevens, P. (2012). The need for speed: impacts of internet connectivity on firm productivity. Journal of Productivity Analysis, 37(2), 187-201.
Gul, F. A., & Leung, S. (2004). Board leadership, outside directors’ expertise and voluntary corporate disclosures. Journal of Accounting and public Policy, 23(5), 351-379.
Haas, V. A., & Oehmichen, J. (2018). Do Green Boards Make a Difference? Board Environmental Expertise and Stakeholder Orientation. In Academy of Management Proceedings (Vol. 2018, No. 1, p. 15878). Briarcliff Manor, NY 10510: Academy of Management.
Haffke, I., Kalgovas, B. J., & Benlian, A. (2016). The Role of the CIO and the CDO in an Organization’s Digital Transformation.
Hambrick, D. C. (2007). Upper echelons theory: An update.
Hambrick, D. C., & Mason, P. A. (1984). Upper echelons: The organization as a reflection of its top managers. Academy of management review, 9(2), 193-206.
Hanelt, A., Piccinini, E., Gregory, R. W., Hildebrandt, B., & Kolbe, L. M. (2015, March). Digital Transformation of Primarily Physical Industries-Exploring the Impact of Digital Trends on Business Models of Automobile Manufacturers. In Wirtschaftsinformatik (pp. 1313-1327).
38
Haynes, K. T., & Hillman, A. (2010). The effect of board capital and CEO power on strategic change. Strategic Management Journal, 31(11), 1145-1163.
Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2).
Hillman, A. J., Cannella, A. A., & Paetzold, R. L. (2000). The resource dependence role of corporate directors: Strategic adaptation of board composition in response to environmental change. Journal of Management studies, 37(2), 235-256.
Hillman, A. J., & Dalziel, T. (2003). Boards of directors and firm performance: Integrating agency and resource dependence perspectives. Academy of Management review, 28(3), 383-396.
Hillman, A. J., Withers, M. C., & Collins, B. J. (2009). Resource dependence theory: A review. Journal of management, 35(6), 1404-1427.
Jansen, J. J., Van Den Bosch, F. A., & Volberda, H. W. (2006). Exploratory innovation, exploitative innovation, and performance: Effects of organizational antecedents and environmental moderators. Management science, 52(11), 1661-1674.
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of financial economics, 3(4), 305-360.
Jensen, M., & Zajac, E. J. (2004). Corporate elites and corporate strategy: How demographic preferences and structural position shape the scope of the firm. Strategic Management Journal, 25(6), 507-524.
Kagermann, H., Helbig, J., Hellinger, A., & Wahlster, W. (2013). Recommendations for implementing the strategic initiative INDUSTRIE 4.0: Securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion.
Kallinikos, J., Aaltonen, A., & Marton, A. (2013). The ambivalent ontology of digital artifacts. Mis Quarterly, 357-370.
39
Kor, Y. Y. (2003). Experience-based top management team competence and sustained growth. Organization Science, 14(6), 707-719.
Kor, Y. Y. (2006). Direct and interaction effects of top management team and board compositions on R&D investment strategy. Strategic Management Journal, 27(11), 1081-1099.
Kor YY, Misangyi VF. 2008. Outside directors’ industry-specific experience and firms’ liability of newness. Strategic Management Journal 29(12): 1345 – 1355.
Kroll, H., Horvat, D., & Jäger, A. (2018). Effects of automatisation and digitalisation on manufacturing companies' production efficiency and innovation performance (No. 58). Fraunhofer ISI Discussion Papers Innovation Systems and Policy Analysis.
Lant, T. K., Milliken, F. J., & Batra, B. (1992). The role of managerial learning and interpretation in strategic persistence and reorientation: An empirical exploration. Strategic Management Journal, 13(8), 585-608.
Lerch, C., Jäger, A., & Meyer, N. I4. 0-Readiness–Baden-Württemberg auf dem Weg zur Industrie 4.0?.
Lichtenthaler, U. (2017). Shared value innovation: Linking competitiveness and societal goals in the context of digital transformation. International Journal of Innovation and Technology Management, 14(04), 1750018.
Lorenz, A., Miron-Spektor, E., Schilling, M., Solinas, G., & Tucci, C. (2018, September). Digital Transformation: What Is New If Anything? (G. Lanzolla, Ed.). Academy of Management Discoveries (AMD. Retrieved May 28, 2019, from https://journals.aom.org/doi/10.5465/amd.2018.0103
Lucas Jr, H., Agarwal, R., Clemons, E. K., El Sawy, O. A., & Weber, B. (2013). Impactful research on transformational information technology: an opportunity to inform new audiences. Mis Quarterly, 371-382.
Mahoney JT. 1992. Organizational economics within the conversation of strategic management. In Advances in Strategic Management, Vol. 8, Shrivastava P, Huff AS, Dutton J (eds). JAI Press: Greenwich, CT; 103-155.
40
Matt, C., Hess, T., & Benlian, A. (2015). Digital transformation strategies. Business & Information Systems Engineering, 57(5), 339-343.
Miller, C. C., Burke, L. M., & Glick, W. H. (1998). Cognitive diversity among upper‐echelon executives: implications for strategic decision processes. Strategic management journal, 19(1), 39-58.
Miller, D., & Le Breton-Miller, I. (2006). Family governance and firm performance: Agency, stewardship and capabilities. Family Business Review, 19, 73-87.
Mischel, W. (1977). The interaction of person and situation. Personality at the crossroads: Current issues in interactional psychology, 333, 352.
Mone, M. A., McKinley, W., & Barker III, V. L. (1998). Organizational decline and innovation: A contingency framework. Academy of management review, 23(1), 115-132.
MSCI ACWI Index. (n.d.). Retrieved June 4, 2019, from https://www.msci.com/acwi
Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica: journal of the Econometric Society, 69-85.
Nielsen, S. (2010). Top management team diversity: A review of theories and methodologies. International Journal of Management Reviews, 12(3), 301-316.
Oehmichen, J., Schrapp, S., & Wolff, M. (2017). Who needs experts most? Board industry expertise and strategic change—a contingency perspective. Strategic Management Journal, 38(3), 645-656. Pagani, M. (2013). Digital business strategy and value creation: Framing the dynamic cycle of control points. Mis Quarterly, 37(2).
Pfeffer, J., & Salancik, G. (1978). The external control of organizations: A resource dependence perspective. Harper & Row.
Porter ME. 1996. What is strategy? Harvard Business Review 74(6): 61–78.
Puthiyamadam, T. (2017). How The Meaning of Digital Tranformation Has Evolved. Harvard Business Review, 29.
41
Ranganathan, C., & Jha, S. (2008). Do CIOs matter? Assessing the value of CIO presence in top management teams. ICIS 2008 Proceedings, 56.
Ringel, M., Zablit, H., Grassl, F., Mainly, J., & Möller, C. (2018). „The Most Innovative Companies 2018.“
Rothaermel, F. T., & Alexandre, M. T. (2009). Ambidexterity in technology sourcing: The moderating role of absorptive capacity. Organization science, 20(4), 759-780.
Sebastian, I., Ross, J., Beath, C., Mocker, M., Moloney, K., & Fonstad, N. (2017). How big old companies navigate digital transformation.
Setia, P., Venkatesh, V., & Joglekar, S. (2013). Leveraging digital technologies: How information quality leads to localized capabilities and customer service performance. Mis Quarterly, 565-590. Simons, T., Pelled, L. H., & Smith, K. A. (1999). Making use of difference: Diversity, debate, and decision comprehensiveness in top management teams. Academy of management journal, 42(6), 662-673.
Singh, A., & Hess, T. (2017). How Chief Digital Officers Promote the Digital Transformation of their Companies. MIS Quarterly Executive, 16(1).
Smith, K. G., Grimm, C. M., Gannon, M. J., & Chen, M. J. (1991). Organizational information processing, competitive responses, and performance in the US domestic airline industry. Academy of Management journal, 34(1), 60-85.
Song, M., Im, S., Bij, H. V. D., & Song, L. Z. (2011). Does strategic planning enhance or impede innovation and firm performance?. Journal of Product Innovation Management, 28(4), 503-520. Tabachnick, B. G., & Fidell, L. S. (2014). Using multivariate statistics. Harlow: Pearson.
Thomas, J. B., Clark, S. M., & Gioia, D. A. (1993). Strategic sensemaking and organizational performance: Linkages among scanning, interpretation, action, and outcomes. Academy of Management journal, 36(2), 239-270.
