Master Thesis
The agency perspective: Influence of executive compensation structures on digital innovation
“The agency perspective: What is the influence of executive compensation structures on
digital innovation?”
Lisanne Bosma S3651940
L.J.Bosma.2@student.rug.nl
University of Groningen
Faculty of Economics and Business
June 2020
Supervisor: Dr. Sebastian Firk
Title
The agency perspective: Influence of executive compensation structures on digital innovation.
Abstract
In this study the relationship between executive compensation and digital innovation performance is examined. For this I used the agency theory as starting point. I also look at chief executive officer (CEO) tenure as a moderating influence in this relationship. Digital innovation is becoming more important every day and the rapid growth of new technologies changes complete industries. If an organization does not invest time and money in digital innovation, they will likely fall behind the competition. This study extends the existing literature with empirical evidence of how executive compensation relates to the level of digital innovation of a company. To test this relationship, a fixed effects regression analysis is done with a sample of 162 U.S. listed organizations. The data is collected for the years 2010 until 2017. I found evidence of a positive relationship between long-term compensation and digital innovation. The reason for this positive relationship is that innovation is a long-term process. This means that most innovations pay off after a few years. When a CEO is paid based on more short-term performance, he is less likely to innovate because the results of the company are most likely lower in the first years after the innovation. This means that the compensation is likely to be lower in the first years (Xue, 2007). When a CEO is paid based on long-term performance, he or she is more tempted to innovate. This means that he or she is more likely to invest time and money in digital innovation. This is in line with the agency theory. The agency theory states that compensation can be used as in incentive to align the goals of managers and shareholders (Pfeiffer & Shields, 2015). I did not find evidence that CEO tenure has a moderating influence on the relationship between long-term compensation and digital innovation. A possible reason for the behavior of a CEO is based on many different things and not only the tenure (Barker & Mueller, 2002).
Keywords: Executive compensation, digital innovation, CEO tenure, agency theory, long-term compensation
Introduction
digital innovation. In the last decades an easy to use worldwide digital infrastructure of computers, smartphones and application platforms is developed. This easy to use worldwide digital infrastructure leads to many opportunities for digital innovation. A few examples of these digital innovations of the last years are 3D printing, cloud computing and even social media (Fichman, Dos Santos & Zheng, 2014). Because of the created opportunities for digital innovation, organizations from all over the world have been exploring this.
Digital innovation can be described as products, processes or business models that are changed, combined or improved by using digital technologies (Ciriello, Richter & Schwabe, 2018). These ongoing digital innovations allow companies to take advantage of entrepreneurial opportunities and implement new technologies. These new technologies make it possible to implement new ways of doing business (Fitzgerald et al., 2014). The rapid growth of technology changes the face of complete industries. In the economic market of this time, an organization has to digitally innovate. It is not a choice anymore, it is something organizations need to cope with. Digitalization can improve productivity, efficiency and even sales over time (Kraus, Roig-Tierno & Bouncken, 2019). Digitalization can also increase the value that an organization delivers to the customer in terms of quality or speed (Boston Consulting Group, 2017).
Digital innovation can improve the operations of almost every organization that exists in present times. However, only a few organizations are making the necessary changes to achieve digital innovation (Kane, 2017). Why are most organizations still spending more time on the traditional activities than on digital opportunities? Organizations experience a lot of pressure to innovate. There is also pressure to develop and apply new digital technologies. Many organizations are not ready to respond to those digital opportunities (Kohli & Melville, 2019). A reason for this is that there are limited business cases and not many practices to draw from. Digital innovation is a recent topic and something organization did not have to engage in previously. Most organizations do not know where to start (McKinsey, 2018). Another reason for this lack of respond on digital opportunities is the rapidly changing and competitive business environment. Many finance organizations do not have digital basics that they need to respond to those digital opportunities. Without these basics it is almost impossible to respond to real-time changes. Due to this lack of digital basics an organization is (or will) likely fall behind on the competition (Boston Consulting Group, 2017).
line (Shapiro, 2005). The agency theory gives an empirically valid perspective on information systems, outcome uncertainty, risk and incentives (Eisenhardt, 1989). CEOs have discretion inside their organization, which they can use to influence corporate decisions and to benefit their own goals even though these goals might not be the same as the goals of the shareholders (Bertrand & Schoar, 2003). To make sure that the goals of the CEO and the shareholders are in line with each other, the shareholders need to provide the CEO with the right incentives (Adithipyangkul & Leung, 2018). An example of an incentive is compensation. For this study we look especially at the effects of the executive compensation structure and how this influences the behavior of the CEO.
Executive compensation structures are not the only factor that influence the behavior of the CEO. The tenure of a CEO also influences his or her behavior. The behavior of an CEO changes over time (Hambrick and Mason, 1984). Simsek (2017) argues that longer tenured CEOs are less entrepreneurial than shorter tenured CEOs. Longer tenured CEOs are known to focus more on stability and efficiency instead of innovation (Barker & Mueller, 2002). A longer tenured CEO most likely acts different than a shorter tenured CEO. This means CEO tenure influences the behavior of a CEO and according to the agency theory, compensation also influences the behavior of a CEO. These insights lead to the following question: Does the effect of executive compensation on digital innovation change when the CEO is longer-tenured?
Executive compensation is an complex and contentious phenomenon. There have been a lot of debates about the nature of executive compensation contracts and the outcomes these contracts produce (Frydman & Jenter, 2010). Previous studies about the effects of executive compensation gave different results of the effect of the executive compensation structures (Hou, Priem & Goranova, 2017). But how can executive compensation be used to drive innovation and is this influenced by the tenure of the CEO? In this study, I am going to try to answer this question. The goal of this study is understanding the effects that executive compensation has on digital innovation.
Based on the identified literature gap, I have formulated a research question. The literature gap is based on the missing knowledge on why organizations are still spending more time on traditional financial activities instead of digital innovation. This in combination with the existing knowledge about the agency theory and the fact that compensation can be used as an incentive to make sure that managers are acting in the best interest of the shareholders, leads to the following research question:
There are limited studies available about the effects of the executive compensation structure and the risk-taking behavior of a manager (Jouber, 2013). The impact of the executive compensation structure on digital innovation performance is yet to be empirically tested. With this study, I contribute to the finance literature by empirically testing the relationship between the executive compensation and the digital innovation performance. Even though journals are calling for papers and this topic is discussed intensively in corporate practices, empirical studies on this subject are very limited (Möller, Schäfer & Verbeeten, 2018) (Van Veen-Dirks & Wouters, 2019). My theoretical contribution is to try to fill this research gap with empirical evidence and to give insight on the extend of the relationship between executive compensation and digital innovation performance. Managerial interests in this study are the insight of how organizations could use compensation structures to increase their digital innovation.
Literature review
Digital innovation
Digital innovation can be defined as a change in market offerings that is the result of the use of digital technologies (Hanelt et al., 2020). Digital innovation has changed the nature and structure of products and services (Nambisan et al., 2017). Digital innovation includes four activities. These four activities are: initiating, developing, implementing and exploiting existing products for new purposes (Cooper & Zmud, 1990). Those four activities all need to be present, only not in any sequential order (Kohli & Melville, 2019). It takes time and money to develop new digital technologies. The outcomes of digital innovation are characterized by convergence and generativity. First of all, convergence consists of different components that were separate before and combined now. An example of this convergence is a smartphone. A smartphone is not only a phone but also a camera, a music player and a GPS and even more things (Yoo, Henfridsson & Lyytinen, 2010). Secondly, generativity means that they are dynamic, extensible and malleable (Ciriello, Richter & Schwabe, 2018).
& Feldman, 2008). However, new technologies can also change the organizational environment by enabling new strategies or business models (Fichman, Dos Santos & Zheng, 2014). Digital innovation changes the way products are designed, manufactured, sold and delivered. Digital innovation also forces the CEO to think about the organizational processes and how they are executed (Siebel, 2017).
Agency theory
To better understand the possible influence of executive compensation structures on digital innovation performance I take a look at the agency theory. The agency theory explains the relationship between shareholders and managers. A problem is called an agency problem when the goals of the managers and shareholder are not aligned (Shapiro, 2005). An agency problem could emerge when the manager and shareholder have different risk preferences (Datta, Musteen & Herrmann, 2009). The relationship between shareholder and managers is an important aspect in corporate finance. The agency theory might not be the only theory explaining the relationship between managers and shareholders. However, the agency theory is the most accepted and influential theory. The agency theory explains the way shareholders and managers interact and how they can align their goals (Carausu, 2015).
Managers do not always automatically seek to maximize shareholder value. It is important to provide managers with the right incentives to do so (Bebchuk & Fried, 2003). Managers often have their own “style” when making a decision (Bertrand & Schoar, 2003). A way to reduce agency problems is complete observation. This is an situation were the shareholders overserve every decision a manager makes. Complete observation of the manager is almost impossible and takes a lot of time. For this reason, it is necessary to find another way to make sure that the managers act in the best way for the organization. Shareholder can use compensation contracts linked to performance as an incentive for the managers to act in the best interest of the organization (Bol, 2008). Executive compensation can be used as an incentive to make sure that the managers act in the best interest of the organization and not in their own best interest. Executive compensation can also be used to align the risk preferences of managers and shareholders and to reward desired behavior, which reduces the agency problem (Adithipyangkul & Leung, 2018). The executive compensation can be laid out in an incentive contract. When the incentive contracts are properly structured, they provide the incentives to align the interests of managers and shareholders (Pfeiffer & Shields, 2015).
Executive compensation structures
Executive compensation is the compensation paid to the management team and people in executive positions. CEO is an executive position in an organization. According to the agency theory, the main purpose is that executive compensation is used as an incentive to achieve short-term and long-term management goals (Demirer & Yuan, 2013).
The structure of the executive compensation contracts is an essential factor in guiding the behavior of a CEO. The article of Farris et al. (2014) state that in many U.S. organizations the CEOs act like a bureaucrat instead of acting like the value-maximizing entrepreneur he or she should be. A reason for this behavior is that the compensation contracts are structured in a way that stimulates this behavior (Farris et al., 2014). The way the executive compensation is structured has more influence on the behavior of the CEO than the height of the compensation (Mehran, 2016). The executive compensation structure is such an important factor in CEO behavior. The structure of an executive compensation contract is something that an organization really needs to think about. There are different ways of structuring the executive compensation. Within executive compensation, the smallest component most of the time is a fixed salary. The biggest part of the executive compensation is based on performance measures. Performance based executive compensation is seen as the most powerful way to make sure that the goals of the management and shareholders are in line. Performance-based executive compensation has a big influence on the behavior of the manager. How much a manager gets paid depends on if they reach the desired performance goals (Jouber, 2013).
There are many different types of performance-based executive compensation, but for this research I divide compensation in two groups. I focus on short-term executive compensation and long-term executive compensation. Short-term executive compensation is mostly based on the accounting-based performance measures. Short-term executive compensation is most of the time paid in cash (Xue, 2007) and is often based on the profit on an organization. Long-term executive compensation measures are often accounting-based as well. They focus more on other elements such as the profit of an organization over a period of three years. An example of long-term executive compensation is stock-options. Stock-based compensation is becoming increasingly important in the topic of executive compensation, in fact it is the fastest growing component of executive compensation (Li & Yu, 2011).
Long-term compensation
(Jouber, 2013). There is a significant relationship between stock option-based executive compensation and developing new technology internally (a make strategy). When the executive compensation is based on accounting measurements it is more likely that a manager will buy new technology instead of making it internally. It is expensive to make a new technology. Innovations pay themselves back on the long-term and not on the short-term. Another reason why long-term executive compensation is more suitable if the goal is making new technologies, is that making new technology costs time. Developing new technologies can cost years and after it is developed it can take years before it is useable. Digital innovation is a long-term process and will most likely not lead to higher results on a short-long-term period. The reason for this is that a “make” strategy has a greater negative effect on accounting earnings than a “buy” strategy (Xue, 2007).
The agency theory states that executive compensation is used to make sure that the goal of the shareholders and managers are in line. Past research shows that long-term executive compensation is positively related with innovation performance. Making your own innovation internally has a greater effect on short term accounting earnings than buying new technology. For this reason, managers with a compensation contract based on short-term results are less likely to innovate, because their compensation will be lower if they do innovate. The literature review above is about innovation in general, because digitalization is a rather new subject in the accounting literature, but it is expected that those relationships also apply for digital innovation in specific. For those reasons I expect a positive relationship between executive compensation contracts based on long-term compensation and digital innovation performance. Thus, I hypothesize:
H1: A higher amount of long-term compensation leads to higher digital innovation.
CEO tenure
CEOs over time choose to surround themselves with individuals that have the same views on the strategies of the company. These individuals are less likely to disagree with them. This will increase the power of the CEO (Mintzberg, 1983)(Kitchell, 1997). The longer the CEOs tenure is, the less individuals around the CEO challenge his or her decisions. Longer tenured CEOs can lose their interest in implementing changes. Due to this, CEOs might lose their touch with the environment of the organization (Miller 1991). Longer tenured CEOs have less interest in pursuing risky strategies through research and development (R&D) expenses, they prefer to focus on stability and efficiency instead (Barker & Mueller, 2002). New CEOs are better in hiring new people and by doing this, replacing employees that have worked for the organization for a long time (Keck & Tushman, 1993). Also, new CEOs tend to delegate more and tend to be more democratic, because they do not have all the institutional knowledge yet (Kitchell, 1997).
But long-term compensation such as stock-options give longer tenured CEOs enough incentives to boost the R&D spending’s (Zona, 2016). A longer tenured CEO is less intrinsically interested in innovating than a short-tenured CEO (Hambrik and Fukitomi, 1991). Longer tenured CEO can be motivated to innovate by focusing more on long-term compensation. Without long-term compensation such as stock-options, longer tenured CEOs are more likely to devote resources to for example, buying big cars or other things that will benefit only them (Devers et al., 2008).
Past research shows that CEO tenure is negatively related with entrepreneurial strategies and risk-taking behavior. Longer tenured CEOs are more likely to focus on stability and efficiency and not choose to invest in new strategies. Lower entrepreneurial strategies and risk-taking behavior together are likely to result in less innovation. Also, longer tenured CEOs like to surround themselves with individuals that share their ideas and because of this the CEO is not challenged anymore. However, long-term compensation gives them the incentives to boost R&D spending’s, which can lead to innovation. For those reasons I expect that a longer CEO tenure positively influences the relationship between long-term based compensation and digital innovation performance. Long-term compensation has a bigger influence on the digital innovation performance when there is a longer tenured CEO. Thus, I hypothesize:
Conceptual model
The conceptual model below is based on the previously stated hypothesis. This conceptual model is a visual representation of my theory.
Methodology
A theory testing approach is used to try and give an answer on the research question. The theory testing approach is used when there is still a literature gap in the theoretical explanations of a theory. The agency theory explains how compensation can be used to make sure that the goals are in line, but they fail to explain how executive compensation structures can be used to trigger innovative behavior. The theory testing approach consists of four steps: “(1) identify untested or competing theoretical explanations; (2) formulate hypotheses and operationalize variables; (3) collect data and test hypotheses; (4) draw conclusions and derive implications” (Van Aken & Berends, 2019). The hypothesis is tested with a quantitative approach.
Data collection
performance and the executive compensation structures of U.S listed organizations. U.S. listed organizations are used because there is more sufficient data about compensation structures and digital innovation available in the U.S. Another reason is that the biggest organizations of the world are based in the U.S. The sample consists of 162 U.S. listed organizations which are picked at random. The data is collected for the years 2010 until 2017. The data after 2017 was not available yet. The reason why this data was not available yet, is that the statements of 2019 contain the information about the year 2018, but are filed around April of the year 2020. When I started this study in February 2020 the statements of 2018 were the last available statements. These statements contained the information about the compensation structure of 2017.
Measurements
Independent variable
The independent variable of this study is the ratio of long-term compensation of the CEO (lt_compensation_ratio). The data about the long-term compensation is hand collected. The long-term compensation will be measured using the information in the SEC Edgar statements. The data is collected by me and two fellow students. We each did different organizations. We searched the Edgar company fillings section and looked at the DEF14A file of the different organizations. In those DEF14A files we looked at the executive compensation structures. We divided the compensation in five different categories. These different categories are: base salary, cash short grant, cash long grant, equity grant related to performance and equity grand not related to performance. The categories are cash long grant, equity grant related to performance and equity grand not related to performance are all types of long-term compensation. For all the categories we looked at the metrics that are used and the weight of those metrics. If the weight of the metric was not disclosed in the statements, we divided them in equal parts. We also looked specifically at how much the target pay-out was of every category. All of this information was added in a Excel sheet and later transformed into variables in Stata.
Moderator
As moderator of this study I use CEO tenure (CEO_tir). CEO tenure is measured by looking at the years that an individual is CEO of a company. To find this data I used the DEF14A files that can be found in the SEC Edgar statements. The average tenure of a CEO in this sample is 4.7 years. In other studies, the average tenure of a CEO is around the four till seven years (McClelland, Barker & Oh, 2012)(He, & Fang, 2016)(Conyon, 1994). A possible explanation for the fact that the mean of this study is on the lower side of the average mean is that the CEO tenure is declining the last decades (Weisman, 2008). Because of the fact that the sample average is similar to the average in other studies I expect that my results of the influence of CEO tenure are reliable.
Dependent variable
As dependent variable in this study I use digital product innovation. It is hard to measure digital innovation, because that it is subjective concept. For this reason, I use the number of digital product innovations to measure the digital innovation of an organization for the next three years. This is measured by comparing the number of digital product releases in comparison to the total product releases. To identify the digital product releases, the headlines of large papers are used and after analyzing the headlines the product releases are divided into two categories: Non-digital and digital product releases. At last the ratio of digital product releases of the current and next year is calculated. For this I use the variable logarithm of digital product innovations for the next three years (log_dig_prodinn1_f3y). A logarithm is a way of showing how big a number is.
Control variables
In addition to these organizational characteristics I include some CEO characteristics as control variable. CEO characteristics can influence the digital innovation performance. I include the four CEO characteristics. The CEO’s gender (gender) and the age of the CEO (ageyrs). The gender of the CEO is used as a control variable, because studies show that women are more likely to specialize in sectors where innovation is less common and they are less likely to be involved in highly innovative firms (Carrasco, 2014). In addition to this, studies show that women are less entrepreneurial and have higher risk-aversion tendencies (Dawson & Henley, 2015)(Arano, Parker, Terry, 2010). These arguments also apply to the age of the CEO. It is expected that older CEOs are more risk averse than younger CEOs most of the time (Croci, Del Giudice & Jankensgård, 2017). The reason why older CEOs are more risk averse than younger CEOs is that older CEOs are more focused on the potential losses of a decision (Albert & Duffy, 2012). Next to that, I also include the digital expertise of the CEO (digital_expertise_ceo). When a CEO knows more about technologies he or she is more likely to use this knowledge to benefit the company. At last I add CEO duality (duality) as a control variable. CEO duality increases the likelihood that an innovative business model is adopted, because it is expected that CEO duality influences the digital innovation of an organization (Abebe & Myint, 2018).
Analysis
The first step in this research is to look at the descriptive statistics and a correlation analysis. The descriptive statistics will give me information about the mean and the deviations of the sample. With the correlation analysis I can determine the correlations between my variables and see whether they are positive or negative and if they are significant. After that I checked for multi-correlation and did a Hausman Test to determine if a random- or fixed effects model is more fitted to use in this study. (Kaiser, 2015). The result of the Hausman Test is a chi2 score of 37.96 (0.0009) ***. This means that a fixed effects regression analysis is best fitted for this research. Fixed effects regression analysis is used to remove variable bias. This is done by measuring the changes in groups across time (Glen, 2018).
Digital product innovation f3i,t
=0 + 1 (long-term)i,t + 2 (tenure)i,t
+ 1,2 (long-term * tenure)i,t + 3 (controls)i,t + i + ui,t
In this equation the i indexes the organizations and the t indexes the time in years. The items other than the dependent variable digital product innovation f3i,t ,are the independent variables (long-term), (tenure) and
the interaction between those two (long-term*tenure) and the control variables (controls) control for the likelihood that an organization engages in digital innovations. The i stands for the firm specific effect, this
is relevant for a fixed effects analysis. The fixed effect is constant over time but differs for each organization. The last part in the equation is the ui,t , this is the error term.
Results
Descriptive statistics
Table 1. Descriptive Statistics, means and Correlations Variables Mean S.D. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (1) log_dig_prodi~3y 3.928 3.581 1.000 (2) lt_compensatio~o .776 .119 -0.135 -0.135 (3) win_op_pro_marg 15.056 9.59 0.024 0.024 0.024 (4) firm_size 16.621 1.222 0.496 0.496 0.496 0.496 (5) rd_intensity .02 .027 0.190 0.190 0.190 0.190 0.190 (6) leverage1 .302 .146 0.341 0.341 0.341 0.341 0.341 0.341 (7) female .065 .247 -0.123 -0.123 -0.123 -0.123 -0.123 -0.123 -0.123 (8) ageyrs 57.551 5.26 0.056 0.056 0.056 0.056 0.056 0.056 0.056 0.056 (9) duality .653 .476 -0.019 -0.019 -0.019 -0.019 -0.019 -0.019 -0.019 -0.019 -0.019 (10) win_net_sales 2.38e+07 3.80e+07 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 (11) digital_exper~o .033 .179 -0.033 -0.033 -0.033 -0.033 -0.033 -0.033 -0.033 -0.033 -0.033 -0.033 -0.033 (12) ceo_tir 4.683 4.243 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105 0.105
Long-term compensation and digital product innovation (H1)
Table 2 shows the results of testing the impact of long-term compensation on digital product innovation. As stated in Hypothesis 1, I expected a positive relationship between long-term compensation and digital innovation. To test this relationship, I ran a fixed effects regression analysis with long-term compensation ratio as the independent variable and digital product innovation as the dependent variable. As result of this regression I found a significant (P < 0.10), positive coefficient of my independent variable long-term compensation. This result supports H1 and indicates that there is evidence found that there is a positive significant relationship between long-term compensation and digital innovation.
Effects of CEO tenure (H2)
Table 2. Regression results (H1 and H2)
Fixed-effects analysis of the effect of long-term compensation on digital product innovation.
Model 1
Dependent variable digital product innovations
logarithm 3 years Long-term (lt_compensation_ratio) 0.956* (0.081) Lt_compensation_ratio_cent * ln_ceo_tir_cent 0.686 (0.224) Ceo tenure 0.027 (0.248) Controls Firm_size 1.118*** (0.001)
CEO gender (female) 0.106 (0.658)
CEO age (ageyrs) -0.336 (0.552)
Operating profit margin (win_op_pro_marg) -0.018 (0.312)
Net sales (net_sales) 0.000** (0.028)
CEO duality (duality)
-0.366 (0.117)
Digital_expertise_ceo -0.211 (0.726)
Rd_intensity 13.417** (0.041)
Firm leverage (leverage1) -2.310** (0.013)
Constant -12.965
R-squared 0.170
N = 603 *p < 0.10, **p < 0.05,***p < 0.01.
Robustness of results
Table 4. Robustness independent variable
Robustness Fixed-effects analysis of the effect of long-term compensation on digital product innovation.
Model 1
Dependent variable digital product innovations
logarithm 3 yrs
Long-term (lt_compensation_ratio) 0.956* (0.081)
Lt_compensation_ratio_cent * ln_ceo_tir_cent 0.686 (0.224)
Long-term (ln_lt_compensation_ratio) 0.025 (0.358)
Long-term cash (lt_cash_metric_ratio) -0.589 (0.402)
Equity 0.000 (0.829)
CEO tenure (ceo_tir) 0.005 (0.561)
Controls
Firm_size 0.208* (0.071)
CEO gender (female) 0,108 (0.530)
CEO age (ageyrs) 0.004 (0.554)
Operating profit margin (win_op_pro_marg) -0.005 (0.391)
Net sales (win_net_sales) 0.000 (0.227)
CEO duality (duality) -0.178*** (0.005)
Digital_expertise_ceo -0.016 (0.806)
Rd_intensity -0.651 (0.717)
Firm leverage (leverage1) -0.707** (0.039)
Constant -2.716
R-squared 0.066
N = 603 *p < 0.10, **p < 0.05, ***p < 0.01.
Table 3
Robustness Fixed-effects analysis of the effect of long-term compensation on digital product innovation.
Model 1 Model 2 Model 3
Dependent variable digital product innovations
logorithm 3 years relative 2 yrs relative 3 yrs
Long-term (lt_compensation_ratio) 0.956* (0.081) 0.178 (0.144) 0.234*(0.069)
Lt_compensation_ratio_cent * ln_ceo_tir_cent 0.837 (0.224) 0.172 (0.305) 0.231(0.105)
CEO tenure (ceo_tir) 0.027 (0.248) -0.006 (0.353) 0.005 (0.561)
Controls
Firm_size 1.118*** (0.001) 0.126 (0.184) 0.208* (0.071)
CEO gender (female) 0.106 (0.658) 0.120 (0.258) 0,108 (0.530)
CEO age (ageyrs) -0.336 (0.552) 0.003 (0.575) 0.004 (0.554)
Operating profit margin (win_op_pro_marg) -0.018 (0.312) -0.005 (0.287) -0.005 (0.391)
Net sales (win_net_sales) 0.000** (0.028) 0.000 (0.766) 0.000 (0.227)
CEO duality (duality) -0.366 (0.117) -0.095* (0.075) -0.178*** (0.005)
Digital_expertise_ceo -0.211 (0.726) 0.292** (0.012) -0.016 (0.806)
Rd_intensity 13.417** (0.041) -1.291 (0.443) -0.651 (0.717)
Firm leverage (leverage1) -2.310** (0.013) -0.536* (0.053) -0.707** (0.039)
Constant -12.965 -1.581 -2.716
R-squared 0.170 0.062 0.066
Discussion and conclusion
Findings
The expected relationship between long-term compensation and digital innovation is proven to be significantly positive. This means that companies can use the executive compensation structure to stimulate innovative behavior of the CEO. This result is consistent with the agency theory that states that compensation can be used as an incentive to align goals between the managers and shareholders (Pfeiffer & Shields, 2015). Another reason for this positive significant relationship is that innovating is a long-term process and will not give positive results on the term. So, when a CEO is paid mostly based on short-term results, he is less likely to innovate then when he is paid for long-short-term results (Xue, 2007).
The expected moderating influence of CEO tenure on the relationship between long-term compensation and digital innovation was positive but not significant. There is no evidence found to support this moderating relationship. This means that the effect of long-term performance on digital innovation is not higher when a CEO is longer tenured. A possible explanation for this insignificant relationship is that the relationship between CEO tenure and a lack of entrepreneurial behavior of the CEO is not a linear relationship but an inverted U-shape relationship. Short-term CEOs often need to learn more about the company and the environment to identify and exploit entrepreneurial opportunities (Boling, Pieper & Covin, 2016). This means that it is possible that not only long-term CEOs show a lack of entrepreneurial behavior, but short-term CEOs show the same behavior. This has influence on the moderating relationship of CEO tenure, because that expected relationship is based on the even more positive effects of long-term compensation on digital innovation when the CEO has a long tenure. This possible inverted U-shape shows that long-term compensation has also a more positive effect on digital innovation when the CEO is short tenured. Another reason for this insignificant result can be that the tenure of the CEO is not the only factor that influences the behavior of the CEO. There are more factors that influence the behavior of a CEO (Zor, Linder & Endenich, 2019). For example, CEO stock ownership, experience, age or education (Barker & Mueller, 2002).
Theoretical implications
The relationship between long-term compensation and digital innovation has proven to be significantly positive. This study extends the existing literature of digital innovation with the empirical evidence of a positive relationship between those two variables. Despite an expected positive relationship based on the existing literature, this study shows that the moderating influence of CEO tenure on the relationship between long-term performance and digital innovation is not significant. This gives new insights and shows that it is necessary to do more research about this topic.
Managerial implications
The results of the first hypothesis show that a higher amount of compensation based on long-term performance results in more digital innovation. Organizations can use these results for when they need to decide about how they should structure their executive compensation. This insight helps organization to see how they can influence the behavior of the CEO the way they want to regarding digital innovation. The results of the second hypothesis show that CEO tenure cannot explain the behavior of a CEO regarding digital innovation. This means that companies have to look at other explanatory factors that can influence the behavior of a CEO when he or she is not showing the desired behavior.
Limitations and further research
My study has some limitations that need to be discussed. Firstly, this study only consists of U.S. listed firms. This means that the results are not generalizable for firms worldwide. It is possible that the results will be different when the sample is taken in a different country. A possible reason for this could be the fact that different countries have a different culture, but also different rules and regulations. It is recommended that this study is done in different countries, so the results can be compared and be more generalizable.
Another limitation is that the research sample consists data of the years 2010 until 2017 only. In the introduction I explain that digital innovation is a fast-growing topic and because of this it is possible that the results will be different or even more strongly related in the years after 2017. For this reason, it is recommended to do this research again when the data of later years is available. That way it is possible to see if there is a difference.
recommend to look into those different types of measures of digital innovation and test if these will give different results.
The moderating influence of CEO tenure on the relationship between long-term performance and digital innovation was not significant. It can be possible that the moderating influence of CEO tenure is not a linear relationship but an inverted u-shaped relationship. This means that it is possible that CEO tenure has a moderating influence on the relationship between long-term performance and digital innovation but not a linear one. I advise to test the moderating relationship of CEO tenure again to see if CEO tenure does moderate the relationship. But this time look at if the effect is positive when a CEO just started, then negative for a few years and is positive again when the CEO is longer tenured.
There was limited robustness found when trying out different independent variables. Only the long-term compensation ratio was significant. This means that the conclusions probably do not hold under different assumptions. The other results were mostly positive but just not significant. There is no evidence that these other really similar independent variables influence the digital innovation of an organization. This lowers the validity of this study. I recommend to do more research about the reason why the relationship with the long-term compensation ratio is significant and the other relationships are not significant. This could help find the reason behind these different results. These insights might help organizations even more when trying to find the best executive compensation structure that stimulates innovative behavior.
At last, in this study I looked only at one factor that can influence the relationship between long-term performance and digital innovation, which is CEO tenure. It is possible that there are many more factors that influences CEO behavior and they have to be considered. Tenure is not the only factor that can influence behavior. There are other personal, environmental and professional variables to consider when you look at moderations of this relationship. For this reason, I recommend that the variables that influences a CEOs innovative behavior are studied to give a clearer picture of how to use long-term executive compensation the way that there will be more digital innovativeness in the company.
References
Adithipyangkul, P., & Leung, T. Y. (2018). Incentive pay for non-executive directors: The direct and interaction effects on firm performance. Asia Pacific Journal of Management, 1-22.
Ahuja, G., & Katila, R. (2001). Technological acquisition and the innovation performance of acquiring firms: A longitudinal study. Strategic Management Journal, 22 (3), 197–220.
Albert, S. M., & Duffy, J. (2012). Differences in risk aversion between young and older adults. Neuroscience and neuroeconomics, 2012(1).
Arano, K., Parker, C., & Terry, R. (2010). Gender‐based risk aversion and retirement asset allocation. Economic Inquiry, 48(1), 147-155.
Archibugi, D., & Planta, M. (1996). Measuring technological change through patents and innovation surveys. Technovation, 16(9), 451-519.
Barker III, V. L., & Mueller, G. C. (2002). CEO characteristics and firm R&D spending. Management Science, 48(6), 782-801.
Bebchuk, L. A., & Fried, J. M. (2003). Executive compensation as an agency problem. Journal of economic perspectives, 17(3), 71-92.
Bertrand, M., & Schoar, A. (2003). Managing with style: The effect of managers on firm policies. The Quarterly journal of economics, 118(4), 1169-1208.
Bol, J. C. (2008). Subjectivity in compensation contracting. In AAA Management Accounting Section (MAS) 2006 Meeting Paper.
Boling, J. R., Pieper, T. M., & Covin, J. G. (2016). CEO tenure and entrepreneurial orientation within family and nonfamily firms. Entrepreneurship Theory and Practice, 40(4), 891-913.
Boston Consulting Group. (2017). How Digital CFOs Are Transforming Finance, available at:
Carausu, N. (2015). Monitor and control in companies: An Agency theory approach. Journal of Public Administration, Finance and Law, (special), 46-60.
Carrasco, I. (2014). Gender gap in innovation: an institutionalist explanation. Management Decision.
Ciriello, R. F., Richter, A., & Schwabe, G. (2018). Digital innovation. Business & Information Systems Engineering, 60(6), 563-569.
Conyon, M. J. (1994). Tenure and contracts: the experience of UK CEOs. Personnel Review.
Cooper, R. B., & Zmud, R. W. (1990). Information technology implementation research: A technological diffusion approach. Management Science, 36(2), 123–139.
Croci, E., Del Giudice, A., & Jankensgård, H. (2017). CEO age, risk incentives, and hedging strategy. Financial Management, 46(3), 687-716.
Datta, D., Musteen, M., & Herrmann, P. (2009). Board Characteristics, Managerial Incentives, and the Choice Between Foreign Acquisitions and International Joint Ventures. Journal of Management.
Dawson, C., & Henley, A. (2015). Gender, risk, and venture creation intentions. Journal of Small Business Management, 53(2), 501-515.
Demirer, I., & Yuan, J. J. (2013). Executive compensation structure and firm performance in the US restaurant industry: An agency theory approach. Journal of foodservice business research, 16(5), 421-438.
Devers, C.E., McNamara, G., Wiseman, R.M., Arrfelt, M., 2008. Moving closer to the action: examining compensation design effects on firm risk. Org. Sci. 19 (4), 548–566.
Farris, P. W., Haskins, M. E., Lynch, L. J., Pfeifer, P. E., & Reynolds, R. S. (2014). Executive Compensation: Do Economic Profits Matter?. Compensation & Benefits Review, 46(5-6), 276-286.
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), 329–353.
Fitzgerald, M., Kruschwitz, N., Bonnet, D., & Welch, M. (2014). Embracing digital technology: A new strategic imperative. MIT sloan management review, 55(2), 1.
Francis, B. B., Hasan, I., Sharma, Z., & Waisman, M. (2019). Motivating high‐impact innovation: Evidence from managerial compensation contracts. Financial Markets, Institutions & Instruments, 28(3), 291-318.
Frydman, C., & Jenter, D. (2010). CEO compensation. Annu. Rev. Financ. Econ., 2(1), 75-102.
Glen, S. (2018, 24 mei). Fixed Effects / Random Effects / Mixed Models and Omitted Variable Bias. Statistics How To. https://www.statisticshowto.com/experimental-design/fixed-effects-random-mixed-omitted-variable-bias/
Hambrick, D.C. & Fukutomi, G.D.S. (1991). The seasons of a CEO’s tenure. Academy of Management Review, 16(4), 719–742.
Hanelt, A., Firk, S., Hildebrandt, B., & Kolbe, L. M. (2020). Digital M&A, digital innovation, and firm performance: an empirical investigation. European Journal of Information Systems, 1-24.
Hambrick, D.C. & Mason, P.A. (1984). Upper echelons: The organization as a reflection of its top managers. The Academy of Management Review, 9(2), 193–206.
He, L., & Fang, J. (2016). CEO overpayment and dismissal: The role of attribution and attention. Corporate Governance: An International Review, 24(1), 24-41.
Hilbe, J. M. (2011). Negative binomial regression. Cambridge University Press.
Hylving, L., Henfridsson, O., & Selander, L. (2012). The role of dominant design in a product developing firm’s digital innovation. Journal of International Technology Theory and Application, 13(2), 7–21.
Jouber, H. (2013). Are over-paid Chief Executive Officers better innovators?. Journal of Economics Finance and Administrative Science, 18(35), 63-71.
Kaiser, B. (2015). RHAUSMAN: Stata module to perform robust Hausman specification test.
Kane, G. C., Palmer, D., Nguyen-Phillips, A., Kiron, D., & Buckley, N. (2017). Achieving digital maturity. MIT Sloan Management Review, 59(1).
Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., & Buckley, N. (2015). Strategy, not technology, drives digital transformation. MIT Sloan Management Review and Deloitte University Press, 14(1-25).
Keck, S.L.,& Tushman, M.L. (1993). Environmental and organizational context and executive team structure. Academy of Management Journal, 36, 1314-1344.
Kitchell, S. (1997). CEO characteristics and technological innovativeness: A Canadian perspective. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l'Administration, 14(2), 111-121.
Kohli, R., & Melville, N. P. (2019). Digital innovation: A review and synthesis. Information Systems Journal, 29(1), 200-223.
Kraus, S., Roig-Tierno, N., & Bouncken, R. B. (2019). Digital innovation and venturing: an introduction into the digitalization of entrepreneurship.
Li, M. Y. L., & Yu, S. E. S. (2011). Do large firms overly use stock-based incentive compensation?. Journal of Applied Statistics, 38(8), 1591-1606.
Lu, X., & White, H. (2014). Robustness checks and robustness tests in applied economics. Journal of econometrics, 178, 194-206.
Makri, M., Lane, P. J., & Gomez‐Mejia, L. R. (2006). CEO incentives, innovation, and performance in technology‐intensive firms: a reconciliation of outcome and behavior‐based incentive schemes. Strategic Management Journal, 27(11), 1057-1080.
McClelland, P. L., Barker III, V. L., & Oh, W. Y. (2012). CEO career horizon and tenure: Future performance implications under different contingencies. Journal of Business Research, 65(9), 1387-1393.
McKinsey. (2018). Memo to the CFO: Get in front of digital finance or get left back, available at: https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/memo-to-the-cfo-get-in-front-of-digital-finance-or-get-left-back#
Mehran, H. (1995). Executive compensation structure, ownership, and firm performance. Journal of financial economics, 38(2), 163-184.
Miller, D. (1991). Stale in the saddle: CEO tenure and the match between organization and environment. Management Science, 37(1), 34–52.
Mintzberg, H. 1983. Power in and around organizations. Engle-wood Cliffs, NJ: Prentice-Hall.
Möller; K., U. Schäffer and F. M. Verbeeten. 2018. Digitalization of Management Accounting and control (Journal of Management and Control – Special Issue Call for Papers), available at: https://www.springer.com/cda/content/document/cda_downloaddocument/CfP+187_JoMaC_Special+Issu e+Digitalization+of+MA+and+MC.PDF?SGWID=0-0-45-1617024-p1099685
Nambisan, S., Lyytinen, K., Majchrzak, A., & Song, M. (2017). Digital innovation management: Reinventing innovation management research in a digital world. MIS Quarterly, 41(1), 223–238.
Pfeiffer, G. M., & Shields, T. W. (2015). Performance-Based Compensation and Firm Value— Experimental Evidence. Accounting Horizons, 29(4), 777-798.
Shapiro, S. (2005). Agency theory. Annual Review of Sociology, 31(1), 263-284.
Siebel, T. M. (2017). Why digital transformation is now on the CEO’s shoulders. McKinsey Quarterly, 4(3), 1-7.
Simsek, Z. (2007). CEO tenure and organizational performance: An intervening model. Strategic Management Journal, 28(6), 653–662.
Van Aken, J. E., & Berends, H. (2018). Problem solving in organizations. Cambridge University Press.
Van Veen-Dirks and M. Wouters. 2019. Performance Measurement in Dynamic Environments (Management Accounting Research – Special Issue Call for Papers), available at:
https://www.journals.elsevier.com/management-accounting-research/call-for-papers/performance-measurement-in-dynamic-environments
Weisman, R. (2008). Being a CEO has its perks, but tenure isn't one of them. The Boston Globe, 11.
Yoo Y, Henfridsson O, Lyytinen K (2010) Research commentary – the new organizing logic of digital innovation: an agenda for information systems research. Inf Syst Res, 21(4), 724–735.
Xue, Y. (2007). Make or buy new technology: The role of CEO compensation contract in a firm’s route to innovation. Review of Accounting Studies, 12(4), 659-690.
Zona, F. (2016). Agency models in different stages of CEO tenure: The effects of stock options and board independence on R&D investment. 45(2), 560-575.
