Empirical Analysis of the Impact of Board Digital Expertise on Organizational Outcomes

Empirical Analysis of the Impact of Board Digital Expertise on Organizational Outcomes

Master Thesis MSc Business Administration Change Management

Petra Bezemer S3352528 Noorderhavenkade 96a

3038 XP Rotterdam p.a.bezemer@student.rug.nl

June 2019

University of Groningen Faculty of Economics and Business

MSc Business Administration: Change Management

Supervisor: Prof. dr. Oehmichen Co-assessor: Prof. dr. ir. Langley

Word count: 10661

ABSTRACT

In this study, I examined the impact of board digital expertise on organizational outcomes and the moderating role of board independence on the relationship between board digital expertise and organizational outcomes. Evidence was found for the effect of board digital expertise on market value, strategic change and R&D intensity. However, the results did not indicate a significant relationship between board digital expertise and efficiency. Furthermore, evidence was found for a negative moderating effect of board independence on the relationship between board digital expertise and market value. To test the hypotheses, a systematic method was introduced to measure board digital expertise.

The findings underline the importance of board digital expertise in the digital era.

Keywords: Board digital expertise, corporate governance, agency theory, resource dependence theory,

firm performance, firm strategy, board independence.

INTRODUCTION

The questionable contribution of board members in the global financial crisis and its corporate scandals have intensified the critical view towards the position of the board of directors (Christopher, 2010). Moreover, the faulty actions of managers under the supervision of boards, led to many doubting the functioning of boards. In contrast, corporate governance research emphasizes the important role of boards for the success of a firm (Daily, Dalton, & Cannella, 2003). More specifically, recent research highlights the importance of board member expertise (Diestre, Rajagopalan, & Shantanu, 2014; Haynes

& Hillman, 2010; Oehmichen, Schrapp, & Wolff, 2016). The theoretical foundation for these findings stems from the agency and resource dependence theory.

Another development is the rise of digital technology. Digital innovations have a major impact on organizations. The current era is, therefore, known as the age of digital transformation (Rogers, 2016). Due to this, organizations must cope with an increasingly changing environment making digital transformation key to organizational success. Companies seizing digital transformation are 26% more profitable than their competitors (Westerman, Tannou, Bonnet, Ferraris, & McAfee, 2012). In 2019, digital transformation remains central to almost every corporate strategy (Little, 2018). However, almost half of the executives believe their companies lack the necessary knowledge to conduct a digital transformation strategy (PriceWaterhouseCoopers, 2015). Due to the vital role of digital transformation and strategies for firm success, it is important to integrate digital technology within corporate governance research.

This study aims to contribute to corporate governance research with a focus on board member digital expertise. Digital technology is shaping the way we consume and work (Hanelt, Piccinini, Gregory, Hildebrandt, & Kolbe, 2015; McDonald & Rowsell-Jones, 2012). In a world where digitization dominates the course of organizations, it is even more interesting to determine whether the digital expertise of board members matters and can be used to benefit the firm.

Despite the importance of digital expertise, there is a lack of empirical research regarding the

effect of board digital expertise on organizational outcomes. Both the agency and resource dependence

theory, explain that the board has an important role in improving organizational outcomes (Pfeffer, 1972;

Zahra & Pearce, 1989). However, the effect of board digital expertise has not been studied yet. By addressing this gap, I aim to contribute to the field of corporate governance by empirically testing the effect of board digital expertise on organizational outcomes. This way I want to increase the understanding of board tasks in the digital era. Furthermore, I want to make an empirical contribution by introducing a systematic method that can be applied worldwide to measure board digital expertise.

The expectation is that boards with a higher level of digital expertise are more suitable to deal with challenges in the digital era, therefore improving organizational outcomes. This and future research about board digital expertise will help firms to improve the composition of their boards to benefit firm success in the digital era.

Research question

Based on the identified literature gap, I propose the following research question: How does board digital expertise affect organizational outcomes?

THEORETICAL BACKGROUND AND HYPOTHESES

In this chapter, the theoretical background of this research is presented. This research aims to contribute to the topic of corporate governance. Corporate governance refers to ‘‘the set of mechanisms that influence the decisions made by managers when there is a separation of ownership and control’’

(Larcker, Richardson, & Tuna, 2007). The board is one of these monitoring mechanisms. The agency and resource dependence theory are commonly used to investigate the link between the board and firm performance. In this research, I draw on the view of a rapidly growing research stream that integrates both the agency and resource dependence perspectives (Hillman & Dalziel, 2003). This perspective acknowledges that experienced boards may be better able to fulfil both monitoring and resource providing functions. The following topics are elaborated on in subsequent order; agency theory, resource dependence theory, board digital expertise, organizational outcomes and board independence.

Agency Theory

Agency theory is the primary theory used in the research on boards (Dalton, Hitt, Certo, &

Dalton, 2007; J. Johnson, Daily, & Ellstrand, 1996; Zahra & Pearce, 1989). From 1960 onwards, risk

sharing among individuals and groups was explored (e.g., Arrow, 1971; Wilson, 1968). This risk-sharing literature was expanded on in the agency theory by including the agency problem (Jensen & Meckling, 1976; Ross, 1973). The agency problem occurs within an agency relationship, which describes a relationship in which one party (the principal) delegates the execution of work to another (the agent) (Jensen & Meckling, 1976). Agency relationships are present in most modern corporations, where control and ownership are separated. This separation can cause managers (agents) to pursue their own interests at the expense of the interests of shareholders (principals) (Berle & Means, 1991). These costs are called agency costs (Hillman & Dalziel, 2003).

Agency theory attempts to resolve two kinds of agency problems (Eisenhardt, 1989). The first problem is the inability of a principal to verify if the agent behaves appropriately. This problem can arise when it is difficult for the principal to monitor the behavior of the agent. Which can become problematic when goals or ambitions of the principal and agent conflict. The second problem is the problem of risk sharing. This problem can arise when the principal and agent may prefer different actions because of different risk preferences.

Monitoring by boards can reduce agency costs and improve firm performance by addressing these problems (Zahra & Pearce, 1989). Consequently, agency theorists see monitoring as the primary function of boards (Eisenhardt, 1989; Jensen & Meckling, 1976). Because digital products, services and processes become increasingly important for every organization, it is expected that board members must understand the digital part of the organization in order to monitor its activities.

However, in practice boards do not only monitor but also provide resources (Korn/Ferry, 1999).

Consequently, it is recommended to use agency theory with complementary theories (Eisenhardt, 1989).

Therefore, we draw on both the agency theory and the resource dependence theory as set forth by Hillman and Dalziel (2003).

Resource dependence theory

Although the agency theory is most commonly used to study boards, empirical evidence shows

that the resource dependence theory is considered to be a more successful perspective for understanding

boards (J. Johnson et al., 1996; Zahra & Pearce, 1989). Therefore, resource dependence theory is used

as a complementary theory.

Pfeffer and Salancik (1978) combined findings from both management literature and social exchange theory to develop the resource dependence theory. Within the resource dependence perspective, organizations are viewed as open systems which depend on contingencies in the external environment. “to understand the behavior of an organization you must understand the context of that behavior—that is, the ecology of the organization’’ (Pfeffer & Salancik, 1978: 1). The resource dependence theory primarily investigates how organizations manage or adapt to external constraints:

organizations need to acquire resources from the environment in order to survive, leading to interdependence and uncertainty. In order to survive, organizations need to effectively cope with this uncertainty (Pfeffer & Salancik, 2003; Thompson, 2003). Although, constraint by the environment, management is seen as an important link between environmental contingencies and organizational response. Resource dependence theory acknowledges that managers can attempt to reduce environmental uncertainty and dependence (Pfeffer & Salancik, 2003).

In this perspective, the goal of management is to increase their power by controlling vital resources (Ulrich & Barney, 1984). The board is important for enabling firms to minimize dependence by providing or securing resources through linkages to the external environment (Boyd, 1990; J. Johnson et al., 1996; Pfeffer, 1972; Pfeffer & Salancik, 2003; Zahra & Pearce, 1989). Firms attempt to increase power over others and reduce the power that other organizations have over them. The board can provide these benefits to firms in the form of the following resources; (a) information (advice and counsel), (b) access to sources of information in the external environment, (c) favored access to resources, and (d) legitimacy (Pfeffer & Salancik, 2003). These resources need to match the needs of the firm.

Consequently, the type and experience of board members matters for the ability to respond to the environment (Boyd, 1990; R. A. Johnson & Greening, 1999; Pfeffer & Salancik, 2003; Stearns &

Mizruchi, 1993).

Digital disruption changes the way in which firms compete (Porter & Heppelmann, 2014) and affects firm interdependencies and power (Vendrell-Herrero, Bustinza, Parry, & Georgantzis, 2017).

When environments change, different types of board members may become valuable (Hillman, Canella,

& Paetzold, 2000). In order to deal with digital disruption, it is interesting to see whether board digital

expertise provides beneficial resources for firms in managing interdependencies and uncertainty. Within this research, I want to investigate this by testing the effect of board digital expertise on the effect of organizational outcomes in terms of performance and strategy.

Board digital expertise

As elaborated on above, board members are likely to perform tasks related to both monitoring and providing resources (Korn/Ferry, 1999). To be more specific, Garrat (2010) defined the function of the board as a collective responsibility to; (a) decide on the firm’s purpose and ethics, (b) determine the direction (strategy) (c) plan, (d) control and monitor management, (e) make recommendations and report to shareholders.

The impact of digital technology also affects these board tasks. Studies have shown the impact of digital technology on organizational performance (e.g., Bharadwaj, 2000; Wade & Hulland, 2004).

Top management commitment, organizational structure and corporate culture are important constructs affecting the ability to use digital technology to benefit firm performance (Barley, 1990; Neo, 1988;

Sambamurthy & Zmud, 1999). To be able to govern decisions related to digital technology, it is expected that related knowledge and experience is needed. A board member is expected to have an increased ability to provide resources (such as information) when he or she has knowledge, experience and access to helpful resources which are specific to digital technology. Additionally, board members with digital expertise will have an increased understanding of what actions are needed in the digital era.

Consequently, these board members will have an increased ability to asses and monitor managerial actions. Therefore, in this research, board digital expertise is defined as the accumulation of digital expertise that a board has gained via the individual career paths of its board members. Digital technology is defined as ‘‘combinations of information, computing, communication, and connectivity technologies’’ (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013). Due to the importance of digital technology, the expectation is that board digital expertise enhances the ability of board members to perform their duties and contribute to organizational outcomes.

Organizational outcomes

In this research, I tested the effect of board digital expertise on organizational outcomes, namely

firm performance and firm strategy. To increase the contribution of this study to corporate governance research, dependent variables are selected in line with prior research on boards. Most corporate governance studies focus on financial outcomes, such as firm performance. In this study, firm performance is measured in terms of efficiency and market value. Studies have found evidence for the impact of board composition on firm performance, both in terms of efficiency (Palaniappan, 2017;

Pfeffer, 1972; Pfeffer & Salancik, 2003) and market value (Palaniappan, 2017; Pfeffer, 1972; Pfeffer &

Salancik, 2003). In this study, the value of board digital expertise is investigated by testing its effect on firm performance.

In addition to firm performance, the ability of a firm to adapt to its environment via its strategy is another key outcome variable of corporate governance research (Brunninge, Nordqvist, & Wiklund, 2007; Goodstein & Boeker, 1991; Hillman & Dalziel, 2003; Pettigrew, 1992). Consequently, in this research, attention is paid to the effect of board digital expertise on firm strategy. The effect on firm strategy is measured in terms of strategic change and research and development (R&D) intensity. Prior research has shown that board expertise affects firms on a strategic level (Oehmichen et al., 2016). Board members are aware of their role in the strategic direction of a firm and share their wide range of experience, knowledge and judgement to advise management on significant issues facing the firm (Business Roundtable, 2016; Carpenter & Westphal, 2001; Finkelstein, Hambrick, & Cannella, 2008;

Finkelstein & Mooney, 2003). Domain expertise is a necessary trait of a board member to have the capability and credibility to exert influence on firm performance as well as firm strategy (Lungeanu &

Zajac, 2019). In this research, strategic change is included as an independent variable and defined as

‘‘[change in the] fundamental pattern of present and planned resource deployments’’ (Hofer & Schendel, 1978). Prior research also included R&D intensity when investigating the effect of boards on firm strategy (Haynes & Hillman, 2010; Kor, 2006). Boards with members owning a better educational and professional background are expected to have a greater understanding of digital technology which positively affects R&D investments (Chen, 2014).

Digital transformations have great consequences and many things can go wrong. A CEO takes

a risk when responding to digital developments in the environment, therefore digitization is not always

in his or her best interest. So, digital transformation can create conflicts of interest. It is important for a

board to be able to assess the decisions of management and recognize when management makes decisions to avoid harm to their own careers. In addition, board digital expertise also increases the ability to provide resources. Experience, knowledge and connections in the digital world will improve resources provided by a board, which increases the ability to support management in making the best choices for the firm. Together, improved monitoring and providing resources abilities will improve firm performance. Because digitization is vital to the success of an organization, the expectation is that boards with digital expertise will impact the organization on a strategic level as well. Therefore, the following hypotheses are taken.

𝑯

: Board digital expertise is positively related to firm performance.

𝑯

: Board digital expertise affects firms on a strategic level.

Board independence

Board independence is among one of the most debated governance issues that modern corporations face (Kang, Cheng, & Gray, 2007). In line with agency theory, an independent board member is expected to be better able to protect shareholder interests (Hermalin & Weisbach, 1988).

When addressing unwanted behavior, a board member with close ties to management will have to take relational consequences into consideration. Consequently, dependencies affect monitoring abilities (Adams, Hermalin, & Weisbach, 2010). Moreover, external members do not suffer from ‘group think’

and are not subject to the same potential conflicts that are likely to affect dependent board members (Jensen & Meckling, 1976; Rhoades, Rechner, & Sundaramurthy, 2000). Additionally, management literature suggests that independent board members are desirable because of their breadth of knowledge and experience (Kesner, 1988; Lungeanu & Zajac, 2019). This is the result of interaction with different firms, industries and management teams. However, evidence for the beneficial effect of board independence on firm performance is mixed (e.g., Adams et al., 2010; Bhagat & Black, 1998; Zahra &

Pearce, 1989). An explanation can be the complexity to measure board independence and the use of different methodological definitions throughout the literature (Van Den Berghe & Baelden, 2005; Zahra

& Pearce, 1989). Moreover, it is difficult to find reliable empirical evidence that board independence

matters at all for performance (Duchin, Matsusaka, & Ozbas, 2010). However, in this study, we follow the idea that independent board members will have an increased ability to monitor and provide resources in line with agency and resource dependence theory. Previous studies generally acknowledge a board member to be independent when he or she is independent of management of the firm (Dulewicz &

Herbert, 2004; Hermalin & Weisbach, 1998; Hooghiemstra & Van Manen, 2004; Huson, Parrino, &

Starks, 2001). Consequently, in this research, board independence is defined as the ratio of independent board members within a board. Independent board members are considered to be non-executive and therefore independent from management (Ajinkya, Bhojraj, & Sengupta, 2005; Hermalin & Weisbach, 1998). In this research, I investigate if board independence moderates the relationship between board digital expertise and organizational outcomes.

Independent board members will be less subject to the conflicts of interest or group think of which dependent board members suffer from. Additionally, they often have a larger network and connections with multiple boards. Therefore, independent board members are expected to have better monitoring and resource providing abilities. This results in the following hypothesis.

𝑯

: Board independence will positively moderate the relationship between board digital expertise and organizational outcomes.

Prior experiences increase the knowledge of board members about markets and opportunities

and have a significant impact on decision making (Patzelt, Knyphausen-aufse, & Nikol, 2008). Also,

the external network contributes to the experience of a board member. A good network enables board

members to have quicker access to relevant information and contacts (Hoang & Antoncic, 2003). One

could reason that if prior experiences and networks of board members are related to digital technology,

it will affect their decision-making regarding the many digital issues.

Conceptual model

Figure 1: Conceptual model

METHODOLOGY

The available literature regarding the agency and resource dependence theory provides a convenient basis to address the identified literature gap. Therefore, I will use a theory testing approach which appears to be most appropriate when literature streams are already developed (Van Aken, Berends, & van der Bij, 2012). To test the hypotheses, panel data or longitudinal data that entails multi- dimensional data measurements over multiple time periods for the same firms in the sample is used (Hsiao, 2014). The advantage of using panel data compared to strictly cross-sectional data is that it offers the opportunity to better evaluate causal propositions (Fitzmaurice, Laird, & Ware, 2010).

Sample

All firms listed on MSCI All Country World Index (ACWI) for the period of December 2008 to January 2019 are included. The MSCI ACWI includes 23 developed and 24 emerging markets.

Financial data is obtained from Thomson Financial DataStream and board data from the BoardEx database. The final sample includes 6702 firm-year observations from 62 countries. It is recommended to check data ranges and outliers to avoid significant findings mainly driven by a few outliers (Zhang &

Shaw, 2012). Therefore, to improve the analyses, observations were controlled on errors and extreme outliers based on wrongfully entered data were replaced (appendix A). Outliers were determined by using boxplots and spikeplots. Due to these results, 20 observations were replaced.

Board digital expertise

Organizational outcomes - Firm performance - Firm strategy

Board independence

Dependent variables

Firms performance is measured in terms of efficiency and market value. The return on assets ratio (net income divided by total assets) is used to calculate efficiency (Subramanian & Nilakanta, 1996). Tobin’s q (total market value divided by total asset value) is used to measure market value (Walls

& Hoffman, 2013).

Firm strategy is measured in terms of strategic change and R&D intensity. The change in a firm’s financial resource allocation profile is used to calculate strategic change (Oehmichen et al., 2016;

Quigley & Hambrick, 2011; Zhang & Rajagopalan, 2010). For this measurement, the following items are taken into account; (1) financial leverage (total debt/equity), (2) plant and equipment newness (net P&E/gross P&E), (3) inventory levels (inventories/sales), (4) nonproductive overhead (selling, general, and administrative expenses/sales). When ratios change over time it suggests strategic change, because it indicates a deviation from the prior profile of a firm (Oehmichen et al., 2016). For these ratios, the absolute value of the difference between two consecutive years is computed. Afterwards, the average of the four standardized values is taken to create the strategic change variable. For R&D intensity, the proportion of expenditure divided by total sales is calculated (Haynes & Hillman, 2010; Osma, 2008).

Independent variable

For the development of the construct board digital expertise, an archival measure was used based

on the work of Walls and Hoffman (2013) and Walls and Berrone (2017). This method allows the use

of comprehensive information of each board member to build an aggregated score reflecting prior

experience with digital technology. The following categories were included in the measurement of board

member digital expertise; previous employment, directorships, other corporate positions and any honors

or awards that board members received (Carpenter & Westphal, 2001). Data on the involvement of

board members in digital positions, activities, committees and awards were retrieved from BoardEx. To

identify board member digital expertise, I used keywords to search for titles and descriptions. Keywords

included ‘digital technology’, ‘cloud computing’, ‘mobile technology’, ‘software’, ‘e-business’, ‘data

systems’, ‘IT infrastructure’ and similar (Bharadwaj et al., 2013; Hanelt et al., 2015; Weill & Ross,

2005). Afterwards, coding was checked for misinterpretations and eliminated when needed. To indicate

whether a board member had experience in on or more categories, a dummy variable was used. These scores were combined to obtain an overall score resembling board member digital expertise. To calculate board digital expertise, for each year the score of all board members on the focal board in the data set were combined. Lastly, the scores for digital expertise are not fixed but evolve over time. For example, a ‘digital’ nomination received in 2015 is not included in the calculation of board digital expertise in 2009.

Moderator variable

To calculate board independence, the proportion of independent members on the board was calculated. Board members are considered independent when they were non-executive and less than two years on the board (Boivie, Lange, McDonald, & Westphal, 2011; McDonald, Westphal, & Graebner, 2008). After a considerable amount of time, it becomes more likely that ties between the board members and management will develop. Therefore, a time period of two years is taken.

Controls

Based on previous literature, I identified the following control variables. Firm Size, calculated by taking the logarithm of the full-time and part-time equivalent of employees (Heyden, Oehmichen, Nichting, & Volberda, 2015; Pashigian, 1968). Financial leverage was controlled for with the debt-to- total asset ratio and the debt to equity ratio (Barnhart & Rosenstein, 1998). R&D expenses, added relative to total assets (Barnhart & Rosenstein, 1998). Board-level controls include board size, (average) board age, and board tenure (Hambrick & Mason, 1984; Heyden et al., 2015; Oehmichen et al., 2016).

The models also include industry-, year-, and region dummies (Heyden et al., 2015; Oehmichen et al., 2016).

Analytical model

In order to deal with firm effects, I used statistical tests to select the most appropriate model for the data analyses (Park, 2011). The Hausman test was used to check whether the data was appropriate for a fixed effects model or a random effects model (Zhang & Shaw, 2012). The null hypothesis that X

and α

are uncorrelated was tested using the Hausman test. A fixed effects model was used when the null

hypothesis of a Hausman test is rejected (Verbeek, 2008). When the null hypothesis of a Hausman test was not rejected, the Breusch-Pagan test was used to determine whether a random effects model or a pooled OLS model was most appropriate. A random effects model was used when the null hypothesis of a Breusch-Pagan test was rejected (Breusch & Pagan, 1979). Stata/SE 15 for windows was used to conduct the analyses.

RESULTS

The central summary statistics and pairwise correlations of all the variables with exception to

year, industry and region dummies are presented in Table 1. The variable R&D expenses was highly

correlated to R&D intensity and therefore excluded as a control variable in model 4. The results of the

regressions for testing hypothesis 1 and 2 are presented in Table 2. All regression analyses were

estimated using a fixed effects model (appendix B). The same control and dummy variables were used

for the different models, with the exception of model 4 where R&D expenses was excluded as a control

variable. Model 1 in Table 2 provides the results for the regression analysis of board digital expertise on

efficiency. This model is insignificant, inconsistent to hypothesis 1 no evidence for the effect of board

digital expertise on efficiency was found. Model 2 provides the results for the regression analysis of

board digital expertise on the market value of a firm. Consistent with hypothesis 1, model 2 reveals a

positive and significant relationship (β = 0.351*, P < 0.1). Model 3 provides the results for the regression

analysis of board digital expertise on strategic change. Consistent with hypothesis 2, model 3 reveals a

positive and significant relationship (β = 0.307*, P < 0.1). Model 4 provides the results for the regression

analysis of board digital expertise on R&D intensity. Consistent with hypothesis 2, model 4 reveals a

positive and significant relationship (β = 0.028***, P < 0.01). The results of the regression analyses

including board independence as a moderator to test hypothesis 3 are presented in Table 3. Hypothesis

3 suggested that board independence would have a positive moderating effect on the relationship

between board digital expertise and organizational outcomes. However, evidence for a moderating effect

of board independence was only found for model 5. Contrary to hypothesis 3, this model shows a

significant but negative moderating effect of board independence on the relationship between board

digital expertise and market value (β = -0.915*, P < 0.1).

Table 1. Descriptive statistics

Variables Mean S.D. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

(1) Efficiency 0.06 0.00

(2) Market value 2.10 0.01 0.43

(3) Strategic change 0.02 0.01 0.01 0.15

(4) R&D intensity 0.05 0.00 0.05 0.41 0.17

(5) R&D expenses 0.03 0.00 0.04 0.47 0.15 0.88

(6) Firm size 10.00 0.01 0.06 0.33 0.21 0.31 0.26

(7) Debt to equity 2.31 0.17 0.01 0.02 0.00 0.02 0.02 0.02

(8) Debt to total assets 0.25 0.00 0.19 0.16 0.03 0.17 0.20 0.08 0.02

(9) Board age 68.17 0.02 0.05 0.05 0.01 0.08 0.07 0.04 0.01 0.04

(10) Board size 11.42 0.02 0.05 0.13 0.09 0.06 0.05 0.24 0.00 0.00 0.02

(11) Board tenure 7.36 0.03 0.10 0.09 0.01 0.05 0.06 0.05 0.01 0.04 0.17 0.00

(12) Board independence 0.20 0.00 0.03 0.02 0.01 0.04 0.02 0.02 0.01 0.02 0. 0.10 0.16

(13) Board digital expertise 2.92 0.05 0.02 0.08 0.02 0.16 0.18 0.06 0.00 -0.01 -0.04 0.06 0.02 0.03

Table 2. Estimation results of board digital expertise on organizational outcomes

Robustness

To further understand the findings as well as to check for their robustness, additional regression analyses were performed including the same control variables. The aim of these robustness checks was to see if the evidence found in this study could be reproduced with alternative measurements. First, an alternative measurement was used for the independent variable board digital expertise. Instead of a binary score, a ratio was used to reflect the board members with digital expertise in proportion to the full board. This alternative measurement was used to show if evidence for the effect of board digital expertise on market value, strategic change and R&D intensity could be reproduced. However, robustness results for the main effect of board digital expertise on market value (β = 2.276*, P < 0.5), strategic change (β = 2.765*, P < 0.3), R&D intensity (β = -0.063*, P < 0.6) do not confirm the findings.

Method Model 1

Firm performance

Model 2 Firm performance

Model 3 Firm strategy

Model 4 Firm strategy Sample

Dependent variable

Full sample Efficiency

Full sample Market value

Full sample Strategic change

Full sample R&D intensity

Constant 15.678** 203.790* 105.372 7.912*

Controls

R&D expenses -68.265*** 2.290*** 33.527

Firm size -0.090 -6.041 2.491 0.243*

Debt-to-equity -0.006*** -0.026 -0.009 0.000

Debt-to-total asset -18.217*** -45.411*** -10.863 0.340

Board age 0.020 0.249 0.750 0.031

Board size -0.015 -0.916 -0.389 0.022

Board Independence -971.220 -68.454** 27.53 387.86

Board tenure 0.049** -0.017 0.176 -0.004

Predictor

Board digital expertise -0.013 0.351* 0.307* 0.028***

n = 6702 firm-years; significance levels; ***p<0.01, **p<0.05, *p<0.1; dummies for industry, region, and

time effects are included, but not reported; coefficients are multiplied by 100 to improve readability.

This reduces the certainty of the results and could indicate that the binary measurement of board digital expertise requires improvement. Another explanation can be that these results indicate that the depth of knowledge residing in a board is more important than the number of board members with digital expertise.

Second, the results remained robust when alternative measurements for the dependent variables were used. The log value of market capitalization (cost per share multiplied by the number of shares) was used as an alternative measurement for market value. The results provide evidence for a positive relationship between board digital expertise and market capitalization (β = 0.057 *, P < 0.1). R&D intensity (R&D expenses divided by the number of employees) was used as an alternative measurement for R&D intensity (R&D expenses divided by total sales). The results provide evidence for a positive relationship between board digital expertise and the alternative R&D intensity variable (β = 5.228*, P <

0.1). No analysis was performed to check the robustness for the results on the dependent variable strategic change. The data used in this study did not provide for a convenient alternative measurement for strategic change.

Third, alternative measurements for board independence were used to check the robustness for the moderating effect of board independence on the relationship between board digital expertise and market value. For both alternative measurements, the proportion of non-executive members on the board was taken. However, the requirement for an independent board member to be two years or less on the board was adjusted. For the first alternative measurement of board independence, a limit of five years as a member of the board was used. The results show a positive moderating effect of board independence (5-year constraint) on the relationship between board digital expertise and market value (β = 0.932*, P

< 0.01). The second alternative measurement for board independence only considers the proportion of non-executive board members, without regard to their time on the board. The results show a positive moderating effect of board independence (no time constraint) on the relationship between board digital expertise and market value (β = 1.227*, P < 0.01). These results contradict the results of the moderating effect of board independence found in this study. This indicates that the results for board independence in this study cannot be assured with certainty. However, the results can also be explained conceptually.

It could be that the benefits that are found for board independence in prior research, may not be present

in the first two years of membership. Having limited knowledge about the firm and its environment may cause the independent board member to have a disadvantage early on. However, the results of the alternative measurements show that this disadvantage may resolve over time. It suggests that it is when the independent board member has more knowledge and information about the organization, that the benefits of independence surface.

Table 3. Estimation results of board digital expertise on organizational outcomes including the the . . moderator board independence

Method Model 5

Firm performance

Model 6 Firm strategy

Model 7 Firm strategy Sample

Dependent variable

Full sample Market value

Full sample Strategic change

Full sample R&D intensity

Constant 201.683* 113.972 8.113*

Controls

R&D expenses 227.051*** 31.068

Firm size -5.966 2.124 0.236*

Debt-to-equity -0.027 0.009 0.000

Debt-to-total asset -45.479*** -11.332 0.336

Board age 0.273 0.676 0.029

Board size -0.952 -0.335 0.024

Board independence -871.394 -74.998 27.53

Board tenure -0.005 0.160 -0.005

Moderator

Board independence x Board Digital Expertise

-0.915* -0.003 0.018

Predictor

Board digital expertise 0.548* 0.371* 0.024***

n = 6702 firm-years; significance levels; ***p<0.01, **p<0.05, *p<0.1; dummies for industry, region, and time effects are included, but not reported; coefficients are multiplied by 100 to improve readability.

DISCUSSION AND CONCLUSION

Recent research in the field of corporate governance emphasizes the benefits of board member expertise (Diestre et al., 2014; Haynes & Hillman, 2010; Oehmichen et al., 2016). In the age of digital transformation, it is particularly interesting to examine the value of digital expertise among board members. Therefore, in this study, I investigated the effect of board digital expertise on organizational outcomes. I introduced a systematic method to measure board digital expertise. The average board digital expertise has increased over the years (Appendix C). The highest average of board digital expertise was found in the region ‘North America’ and the lowest average of board digital expertise in

‘The rest of the world’ (Appendix D). The empirical examinations in this research partly confirm the value of board digital expertise. However, it should be noted that the results indicate that the magnitude of board digital expertise is limited. In line with prior empirical research, a significant effect is found between board digital expertise and firm strategy (Haynes & Hillman, 2010; Oehmichen et al., 2016).

The relationship between board digital expertise and firm performance was only confirmed for market value. No evidence was found for a positive moderating effect of board dependence on the relationship between board digital expertise and organizational outcomes. In this chapter, the results of this study are discussed. Additional literature about the effects of digital technology on firms was used to interpret the results. First, the results for board digital expertise on firm performance are discussed. Then, the relationship between board digital expertise and firm strategy is elaborated on. Next, the moderating effect of board independence is discussed. Afterwards, the theoretical and practical implications are discussed. Lastly, an overview of the limitations of this study and suggestions for future research is presented.

In this research, firm performance was investigated in terms of efficiency and market value. The results partly confirm hypothesis 1. Prior empirical research found that the composition of the board affects the efficiency of firms (Pfeffer, 1972; Pfeffer & Salancik, 2003). However, prior empirical research was often limited to board size and composition without regard to the expertise of board members. In this study, no evidence was found for the effect of board digital expertise on efficiency.

Several reasons may explain why. First, it could be that the measurement of board digital expertise used

in this study was not sophisticated enough to investigate the effect on efficiency. This is further

elaborated on in the limitations. Second, it could indicate that digital activities do not create efficiency

gains in the short term. Research shows that the effects of digitization are considerably larger when measured over a longer time period (Brynjolfsson & Hitt, 2000). Therefore, it could be that the influence of board members with digital expertise is not visible in terms of short-term efficiency. Third, implementing digital technology leads to an increased demand for skilled labor (Brynjolfsson & Hitt, 2000; Brynjolfsson & Renshaw, 1997). So, before fully benefiting from digital technology investments, large investments in organizational learning are required. In conclusion, it takes time, money and changes to benefit from digital technology (Brynjolfsson & Hitt, 2000; Brynjolfsson & Yang, 1997;

Hitt, 1995). Financial gains are suppressed by the need for digital technology to be accompanied by complementary investments and time in order to produce benefits. These costs may outweigh short term efficiency gains. Consequently, it will be interesting to study the effect of board digital expertise on organizational outcomes in the long term.

Findings in this study do suggest a positive relationship between board digital expertise and market value. This implicates that board digital expertise improves the market value of a firm. A possible reason is that boards with digital expertise pay more attention to the digitization of processes and products which gets noticed by customers and investors. Their digital knowledge and experience may make them more effective in doing this. The findings may also be explained by the characteristics that are inherent to digital technology itself. Hall (2001) suggests that Tobin’s q can be used to measure the value of produced elements within a firm, including the value of technology. Prior findings show that digital technology capital is more costly but generates a substantial higher additional stock market value than ordinary capital (Brynjolfsson & Yang, 1997). Additionally, it is said that the contribution of digital technology is, rather than increased productivity, more important for intangible aspects of existing products and services like convenience, timeliness, quality and variety (Brynjolfsson & Hitt, 2000).

Consequently, resource providing and monitoring by boards with digital expertise effects market value rather than efficiency.

In this research, firm strategy was investigated in terms of strategic change and R&D intensity.

The results confirm hypothesis 2. First, evidence was found for a positive relationship between board

digital expertise and strategic change. This indicates that boards with digital expertise are more likely

to adjust the strategy of a firm than boards without digital expertise. This is in line with findings

suggesting that the implementation of digital technology should be accompanied by a larger collection of reciprocally organizational changes (Milgrom & Roberts, 1990).

Second, evidence was found for a positive relationship between board digital expertise and R&D intensity. This suggests that boards with digital expertise are more likely to direct an organization towards the development of new products and services. Tobin’s q can be used to measure the relative value of observable assets such as R&D (Brynjolfsson & Hitt, 2000). The combined findings of market value and R&D intensity could implicate that the effect of board digital expertise on R&D benefits market value. However, more research on this possible link is needed.

In this research, attention was paid to the moderating effect of board independence on the relationship between board digital expertise and organizational outcomes. Prior findings on the effect of board independence on organizational outcomes are mixed (e.g., Adams et al., 2010; Bhagat & Black, 1998; Zahra & Pearce, 1989). This might be explained by the complexity to measure board independence and the use of different methodological definitions throughout the literature (Van Den Berghe & Baelden, 2005; Zahra & Pearce, 1989). In this study, evidence was found for a moderating effect of board independence on the relationship between board digital expertise and market value.

However, in contrast to hypothesis 3, a negative moderating effect was found. This suggests that a higher proportion of independent board members reduces the positive impact of board digital expertise on market value. These results are inconsistent with prior research showing that board independence benefits organizational outcomes (Adams et al., 2010; Hermalin & Weisbach, 1988; Jensen & Meckling, 1976; Kesner, 1988; Rhoades et al., 2000). However, the results are in line with prior findings that emphasize the drawbacks of independent board members (Berle & Means, 1991; Bhagat & Black, 2002). I will discuss several explanations for my results. First, the main reason given for a reduced influence of independent board members is the ‘‘information gap’’ which they might experience (Duchin et al., 2010). The effectiveness of independent board members depends on the costs of acquiring information about the firm. Additionally, prior findings question if the information conditions for effective monitoring and decision making by independent board members even exist (Nowak &

McCabe, 2003). Having less knowledge and information about the firm can reduce board members’

monitoring and resource providing activities. Second, board independence can reduce the effectiveness

of a board. A higher level of board independence is likely to result in a more heterogeneous board in terms of the background, skills and experiences of their members (Castro, De La Concha, Gravel, &

Periñan, 2009). This heterogeneity can hinder effective communication among the board members (Xie

& Neill, 2013). Combining these explanations, both dealing with higher information acquiring and communication costs could lead to the negative moderating effect of board independence. The robustness checks confirm that independent board members might need time to get to know the firm and its environment before displaying the benefits related to board independence. However, further research is needed to determine the nature of this relationship and underlying mechanisms.

In this study, no evidence was found for a moderating effect of board independence on the relationship between board digital expertise and the remaining organizational outcomes (strategic change and R&D intensity). These results are consistent with a collection of empirical studies that have failed to support the agency theory predisposition that board independence affects the outcomes of corporate governance (Bhagat & Black, 2002; Daily et al., 2003). Therefore, I agree with the request from prominent researchers in the area of board research to pay attention to other board attributes (Daily et al., 2003; Hillman & Dalziel, 2003). This study underlines the potential importance of other attributes, such as board digital expertise, to be essential for organizational outcomes.

In conclusion, the results of this study contribute to the agency and resource dependence theory.

This study shows that board digital expertise enables firms to increase their market value. Digital expertise provides boards with an increased ability to use their knowledge and experience to support an organization in the digital era. Moreover, with their digital expertise, boards affect firms on a strategic level. It can also be reasoned that management is more willing to accept and agree with boards with a higher level of digital expertise when it comes to strategic change and R&D. The results do not show that board digital expertise contributes to efficiency gains. However, this can be explained by the nature of digital technology to contribute to intangible assets and long-term effects.

This study contributes to the literature in several ways. First, it adds to the understanding of the

novel concept of board digital expertise and its importance for different organizational outcomes. The

results show that board digital expertise can help to further understand the drivers of market value,

strategic change and R&D intensity. Additional evidence is provided for the simultaneous importance

of resource providing and monitoring functions of the board, supporting the integration of the agency and resource dependence theory. Additionally, another theoretical lens, the upper echelon theory, might be affected by the findings. Second, our results also add to the academic discussion about the potential moderating effect of board independence. Evidence was found for a negative moderating effect of board independence on the relationship between board digital expertise and market value. This demonstrates that board independence can weaken the effect of board digital expertise on market value. However, the results do not indicate a moderating effect of board independence on the relationship between board digital expertise and firm strategy. The results underline the request of researchers to pay attention to additional board attributes and context while studying boards (Lungeanu & Zajac, 2019). Third, this study adds to the use of archival data in research by introducing a standardized measurement for board digital expertise that can be applied worldwide.

This study also has implications for practice. Foremost, I hope to raise awareness for the benefits embodied in board digital expertise for increasing market value. Because the level of board digital expertise can affect organizational outcomes, nominating board members with digital expertise is vital for firms in the digital era. However, results point out that higher board independence can weaken the benefits of board digital expertise for market value. Therefore, to maximize the benefits of board digital expertise for market value, attention should be paid to reducing potential drawbacks of board independence. Suggestions would be to do this by improving board members’ access to information and communication between board members.

The study has limitation which should be addressed in future studies. Moreover, the results

provide interesting directions for future studies. First, this study introduced a binary measurement for

board digital expertise and the results did not remain robust when using a ratio for board digital expertise

as an alternative measurement. Further research is needed to investigate whether this is due to an

imperfect measurement of board digital expertise. It could also indicate that the depth of the digital

expertise of the board together is more important than the proportion of board members with digital

expertise. The data used in this study might be subject to non-random sampling. Future studies can use

the Heckman selection model to correct possible selection biases. Improving and testing the

measurement of board digital expertise will improve the reliability and validity of results.

Second, although often used in prior research, the measurement for board independence used in this study has limitations. When a single indicator (proportion of independent board members) is used for a complex construct such as board independence, it can result in a measurement error (Larcker et al., 2007). This potentially leads to inconsistent regressions coefficients. Additionally, contextual factors that are related to board independence are not considered. Other variables may be useful to investigate similar effects, like board tenure (Lungeanu & Zajac, 2019). The outcomes of this research contradict the predictions of board independence to improve board effectiveness. Therefore, I agree with the perspective that independent board members are not necessarily better than dependent board members (Coles, Daniel, & Naveen, 2008). Instead, I would recommend acknowledging the strengths and weaknesses of both independent and dependent board members. Depending on the context, different types of board members may be needed. When including contextual factors, like the information cost environment, this will help to understand when which type of board member is most beneficial.

Third, the measurement of board digital expertise and its results highlight potential opportunities for new fruitful avenues of research. I want to elaborate on three topics in specific; combining corporate governance with findings on digital technology, the effect of board digital expertise on external partnerships and, subcommittees as a unit of analysis. The expectation is that board digital expertise provides a contribution to the digitization processes of a firm. It will be interesting to align research with findings in the area of digital technology. This brings us to the first topic of interest namely, combining corporate governance research with findings on digital technology. According to literature, it takes time before a firm can fully benefit from digital technology (Brynjolfsson & Hitt, 2000). Prior research, using a focus on productivity growth instead of productivity levels, found substantially larger effects of digital technology (Brynjolfsson & Hitt, 2000). Therefore, it will be interesting to see if the benefits of board digital expertise increase over time. Another characteristic of digital technology is its large impact on the intangible assets of a firm (Brynjolfsson & Hitt, 2000). Therefore, future research on the effect of board digital expertise on intangible assets of a firm provides opportunities. Lastly, for successful implementation, digital technology requires additional investments (Brynjolfsson & Hitt, 2000;

Brynjolfsson & Yang, 1997; Hitt, 1995; Milgrom & Roberts, 1990). Therefore, it will be interesting to

see whether those additional investments have a moderating effect on the benefits created by board

digital expertise. Examples that are described in this study are human capital and organizational learning.

The effects of digital technology differ from conventional firm assets. Therefore, it is important that corporate governance research in the area of board digital expertise cooperates with the area of digital technology. The second topic of interest is external partnerships. This study remains limited to investigating the effect of board digital expertise to firm-specific outcomes. However, digital technology breaks down conventional organizational boundaries and is an important driver of globalization (Bharadwaj et al., 2013). Digital technology provides new opportunities for inter-organizational collaborations. Therefore, it would be interesting to study the effects of board digital expertise on inter- organizational interactions like pooling resource and forming alliances. The third topic of interest is to investigate the effect of digital expertise residing in subcommittees. When only the full board is included in the analysis, the effect of subcommittees might be overlooked (Daily, 1994). Many critical processes and decisions of board members take place in subcommittees like audit, compensation and nomination (Bilimoria & Piderit, 1994; Kesner, 1988; Lorsch & MacIver, 1989). Therefore, in addition to the full board, subcommittees might be a promising avenue for future research (Dalton, Daily, Ellstrand, &

Johnson, 1998).

Overall, the findings of this study provide evidence for the importance of board digital expertise.

I hope that researchers will further investigate the concept of board digital expertise and how it can benefit firms.

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