The effects of CEO incentives on
Organizational Ambidexterity
C.L. Bakker S2759268
Master Thesis
MSc B.A. Strategic Innovation Management University of Groningen
Faculty of Economics and Business
Supervisor: prof. dr. J. Surroca Co-assessor: dr. Q. Dong
Groningen, February 27th 2017
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ABSTRACT
This thesis investigates the influence of CEO incentives on organizational ambidexterity. Following existing literature, four hypotheses were developed regarding CEO incentives, distinguishing between annual bonus, stocks, stock options, and restricted stocks, to see what effect they have on a firm’s ambidexterity. A sample of 168 U.S. firms in the manufacturing industry were used to test these hypotheses using a Probit regression analysis. Data on CEO compensation was obtained from the ExecuComp database and patent data for measuring organizational ambidexterity was gathered using the Orbis database. Results show that stock options are significant in influencing organizational ambidexterity, whereas annual bonus and stocks are only related to exploitation. Interestingly, restricted stock are not significant in influencing a firm’s ambidexterity. The main contribution of this research is the increase in understanding which incentives influence organizational ambidexterity and which incentives do not.
Keywords: Innovation; organizational ambidexterity; managerial incentives; CEO incentives; annual
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INTRODUCTION
To deal with complex challenges, technological change, and to stay competitive, firms need to exhibit organizational ambidexterity (OA): the ability to simultaneously pursue explorative and exploitative innovation (Hiebl, 2015; Hitt et al., 2001; Shane and Venkataraman, 2000). Exploration refers to the search for new possibilities whereas exploitation involves reinforcement of existing advantages, technologies, and markets (March, 1991). OA is important for firm survival and has been shown to increase shareholder value as it helps the firm to meet customer’s needs. However, executives may not necessarily be inclined to engage in OA, because they don’t have appropriate incentives or avoid risk (Kraft, 2013; Eisenhardt, 1989). The lack of ambidexterity may be against the interests of shareholders who are interested in a strategy aiming at value creation for which new ideas, or the adoption of new and higher value strategies are needed in order to improve greater long-term firm value (Kilroy, 1999). However, this is a more riskier approach with uncertain outcomes (Eisenhardt, 1989). Therefore, shareholders may introduce incentives that seek to align their interests with the interests of the executives in order to achieve OA, and thus conducting exploration and exploitation.
Ambidextrous firms are able to use current capabilities for short-term success (exploitation), and at the same time look after their long-term future competitiveness (exploration) (Raisch et al., 2009). These are in nature contradictory processes since exploration and exploitation emerge from different knowledge and information outputs. Exploration often uses new knowledge and information outputs while exploitation deepens the existing knowledge and information outputs. Additionally, they are often self-reinforcing to each other’s exclusion (Cao et al., 2010; Floyd & Lane, 2000; March, 1991). For example, exploitation reinforces exploitation since successful outcomes motivate to repeat the existing processes because they are efficient, enhance current capabilities and are more likely to provide short-term returns because the firm can exploit its current capabilities. However, this also means that firms are less likely to conduct exploration because it entails higher risks and uncertainty. And, since executives tend to be risk-averse and therefore prefer to strengthen established well-working processes, exploitation will reinforce exploitation (Lavie & Rosenkopf, 2006; March, 1991). This implies the following difficulty and predicament of OA. Since executives are in nature risk-averse, and thus will generally prefer exploitation over exploration, they tend to unequally divide their attention between exploration and exploitation. This creates an imbalance due to managerial myopia because of, for example, personal interests (Cao et al, 2010; Lubatkin et al., 2006; Smith and Tushman, 2005). A focus of research has therefore been on avoiding managerial myopia to enable OA (Lubatkin et al., 2006; Smith and Tushman, 2005; Tushman and O’Reilly, 1997).
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& Birkinshaw, 2008). Moreover, executives are important antecedents of OA through their decisions and actions. This is because executives determine the organization’s systems, e.g. (de)centralization of decision-making, that have to make an organization’s context effective for OA (Gibson & Birkenshaw, 2004; Smith & Tushman, 2005; Tushman & O’Reilly, 1996). Executives are important but they have their own interests that may be incompatible with shareholders’ interests. Thus, corporate governance a very important aspect if a firm wishes to be ambidextrous since executives have to make effective decisions, which can only be done if they evaluate and pursue an appropriate set of opportunities (Li, 1998; Slater et al., 2006). This is especially true when the firm operates in a dynamic environment with competitive pressures because OA allows the firm to quickly adapt to its environment. Quick adaptation could lead to outperforming competitors and increasing revenue, thereby meeting the needs of the shareholders (Bellow et al., 2016; Kilroy, 1999). Thus, corporate governance is crucial for OA because the determining role of executives in organizational success or failure through their role in in strategy formation (Hambrick, 2007; Hambrick & Mason, 1984). In other words, if executives focus too long on either exploration or exploitation, it could be detrimental to firm success. It is therefore important for shareholders that executives pursue OA and they should thus promote it. Shareholders can foster OA by means of corporate governance mechanisms that will align the interests of shareholder and management.
There are several corporate governance mechanisms that may serve this end, such as incentives and monitoring mechanisms (Ward et al., 2009). Monitoring mechanisms include the role of independent boards, which have the authority to replace top management team members in case of insufficient results, to confirm major decision, and implement policies that constrain decision-making (Hart, 1995; Michael & Pearce, 2004; Strebel, 2004; Weir & Laing, 2003). Incentive alignment, on the other hand, aligns the executive’s and shareholder’s interests through executive compensation packages (Eisenhardt, 1989; Walsh & Seward, 1990). Compensation linked to long-term performance can solve the alignment problem and motivate to invest in innovations (Datta et al., 2009; Sheikh, 2012). This is, for example, achieved by linking managerial compensation to stock performance as this represents the market’s expectation of future cash flows (Fama and Jensen, 1983; Smith & Stulz, 1985; Gibbons & Murphy, 1992).
While corporate governance mechanisms and OA1 have been studied in the literature, the influence
of incentives has not attracted the same interests from research scholars. Existing research has explored the relationship between CEO incentives and innovations, but it has neglected OA. For example, Lin et al. (2011) looked at managerial incentives, CEO characteristics and corporate innovation in China’s private sector and found that CEO incentive schemes increased innovation performance and effort.
1 For example, OA and behavioural integration of the board and top management teams (Cao et al., 2010;
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Manso (2011) researched motivation of innovation and discovered that the optimal incentive scheme is a combination of stock options with long vesting periods, option repricing, golden parachutes, and managerial entrenchment. Sheikh’s study (2012) shows the interdependence between firm compensation and innovation measures. His results indicate that CEO incentives indeed increase innovation input (R&D expenditures) and output (patents and citations). However, each individual incentive has a different effect on the firm’s tendency to innovate. For example, stock options are more effective than stocks in increasing innovation expenditures, and patents and citations. He finds that stock options are overall more effective than stocks and additionally, restricted stocks prove to be more efficient than unrestricted stocks in promoting innovation. It would therefore be interesting to see if the incentives have the same or a different effect on OA, and subsequently exploration or exploitation.
Following this line of research, this research examines the impact of CEO incentives, in particular annual bonuses, stocks, stock options, and restricted stocks, on OA. Present studies, to the best of my knowledge, have only focused on the effect of managerial incentives on innovation and lack a focus on OA. Since corporate governance can significantly impact the innovation activities of a firm (Zahra, 1996), the lack of studies on the effect of CEO incentives on OA is limiting our understanding of firm survival and success.
My contribution will be twofold. Firstly, I aim to fill the research gap by empirically investigating what the effect of CEO incentives on OA is. Secondly, this research aims to contribute to the OA literature by introducing the principal–agent aspect of the agency theory. This contribution can be beneficial to shareholders as they can use this to more effectively use incentives management according to what goals they have concerning innovation. Based on the research gap, elaborated on above, I have formulated a research question:
“What is the effect of CEO incentives on organizational ambidexterity?”
The structure of this thesis is as followed. The next part presents the used theories and four hypothesis. Then, the methodology section describes the methods used along with a justification. Next, the results of the Probit regression analysis are presented after which a discussion of theoretical and practical implications is provided as well as limitations and possible future research directions. Finally, the conclusion of this research is presented.
THEORY AND HYPTHESES
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is important when looking at firm survival and success when it is facing constant shifting market conditions. The firm’s ability to adapt to these conditions through balancing exploration and exploitation will determine the level of success (Birkinshaw & Gibson, 2004). These theories complement each other in the sense that CEOs determine a firm’s strategy and thus if it will reach OA. Moreover, shareholders can influence CEOs and their decisions by providing certain incentives (Wang et al., 2015). Given my interest in studying the relationship between managerial incentives and OA, I will start revise the literature on OA in the next paragraph, followed by an elaboration on CEO incentives which can be classified as long- or short-term. Additionally, I will provide hypotheses for each type of incentive.
Organizational ambidexterity
Research has long been interested in understanding why some firms manage to survive in changing environments while others do not. The ability of a firm to exploit current resources for revenue and profit, while at the same time exploring new skills, markets and technology, is crucial for adaptation to changing environments (Helfat & Peteraf, 2003; Holmqvist, 2004; March, 1991). The research on OA started with the paper of Duncan (1976), who labelled this term. OA is defined as ability to conduct explorative and explorative innovation at the same time (March, 1991). Tushman and O’Reilly (1996), building upon the work of Duncan (1976), were the first to present a theory of OA by describing structural mechanisms that enable ambidexterity and suggesting that superior performance is a result of OA. Multiple definitions has been given to OA2, however, in this research the following definition will
be used: “Organization’s ability to pursue and balance both explorative and exploitative activities” (Hieble, 2015, p. 1062). For exploitation, a firm aligns its activities so it can improve short-term performance, for example shortening a production process. For exploration, a firm’s activities are focused on adaptability, for example quickly responding to a change in customer needs, and are geared towards improving long-term performance. If a firm focuses on one over the other, problems and tensions will arise. March (1991, p. 71) state that firms conducting exploration to the exclusion of exploitation “are likely to find that they suffer the costs of experimentation without gaining many of the benefits”, and firms conducting exploitation to the exclusion of exploration “are likely to find themselves trapped in suboptimal stable equilibria”. A reason for these tensions and problem stems from ambiguous messages that cause the firm to fractionate, resulting in employees trying to reduce these frustrations and tensions. While these defensive attitudes of the employees have at first a positive effect, in the end it will produce opposite, unintended consequences that intensify the underlying tensions (Argyris, 1993; Lewis, 2000). To overcome these tensions, a vision has to be created of these tensions being positive, interwoven, and complementary (Denison et al., 1995; Lewis, 2000; Schneider, 1990). Thus, to be
2 In various literature streams different definitions have been given to OA. In the technical innovation literature,
for example, OA is defined as “ability to simultaneously pursue both incremental and discontinuous innovation” (Tushman & O’Reilly, 1996, p. 24). To give another example, in the organizational design literature, OA is defined as “a firm’s ability to operate complex organizational designs that provide for short-term efficiency and long-term
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ambidextrous “a firm has to reconcile internal tensions and conflicting demands in their task environments” (Raisch & Birkinshaw, 2008, p. 375). Simultaneous development of exploration and exploitation, thus OA, therefore is a primary factor in survival and performance.
Following the arguments above, it is clear that OA is important for performance improvement, adaptation, and survival. However, this also raises the question: “How does a firm become ambidextrous?” 3 and “How does OA create value?”. The outcomes of OA are explained by several
authors. Levinthal and March (1993), for example, elaborated on OA’s possibility to enhance performance by enabling a firm to be innovative, effective, and flexible while at the same time having the benefits of routinization, efficiency, and stability. This is important because it allows a firm to adapt quickly to its environment which means that it can meet the needs of its stakeholders better and therefore can increase its revenue and outperform competitors (Kilroy, 1999). This is, of course, in the interests of a firm’s shareholders, who seek a significant return on their investment in form of dividends paid or increases in stock price (Ellig, 2014; Kilroy, 1999). Investors have been pressuring firm’s senior management to focus more on shareholder wealth creation and maximizing it (Kilroy, 1999; Martikainen, 1992). However, this is not possible without new ideas, or the adoption of a new and higher value strategy that brings greater long-term cash flow than the current one (Kilroy, 1999). To give an example, Hamel (1997) stated that between 1986 and 1996 only a few organizations managed to grew total shareholder return by 35 percent or higher per year due to radically changing the basis of competition in their industries. Moreover, that if established firms do not reinvent their industries and themselves, new firms will be the creators of new wealth. Essential to wealth creation is innovation and hybrid thinking – “The process by which an idea accessed through an intuitive insight, is first given form as a potential value proposition, then tested in terms of customer value creation potential, and finally developed into an alternative strategy and evaluated in terms of shareholder wealth creation potential” (Killroy, 1999, p. 367) – which allows for unlocking the creative potential of organizations by using both creative analytical modes of thought. Thus, shareholder wealth is “Created in the product market by using innovative thinking to develop products or devise services that provide greater value for customers” (Kilroy, 1999, p. 364) and is done through creating value for the organization’s customers. If done efficiently, revenue will increase, which results in higher profits and therefore
3 Organizational learning literature suggests that exploitation and exploration is associated with learning activities
(e.g. Mom et al., 2007). Technological innovation literature focuses on radical (exploration) and incremental (exploration) innovation with a focus on capabilities needed for OA (e.g. Colbert, 2004; Tushman & O’Reilly, 1996). Organizational adaptation literature focuses on the balance between continuity and change, leading to long-term success, characterized by long periods of convergence (exploitation) and short periods of discontinuous change (exploration) (e.g. Floyd & Woolridge, 1996; Shrivastava, 1986; Tushman & Romanelli, 1985). Strategic
management literature makes a distinction between induced strategic processes (exploitation) and autonomous
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increases in shareholder value. The problem here is that today’s environment has become very dynamic and customers’ needs change rapidly. Therefore, firms need adaptability (exploration) which allows for quick action on new opportunities, adjustment to volatile markets, and avoidance of complacency, and alignment (exploitation) which gives the organization a clear view of how value is created in the short-term, and how activities should be streamlined and coordinated to deliver that value (Birkinshaw & Gibson, 2004). Too much focus on one over the other will result in either being blindsided for industry changes or building the firm on tomorrow’s business at expense of today’s. In other words, a firm needs to balance both in order to achieve long-term success. Given the connection of OA to shareholder’s wealth, shareholders should stimulate the promotion of a firm’s OA. A way of doing this is through managerial incentives. It is clear that managing OA is a difficult task and that value creation is not always achieved easily (e.g. Junni et al., 2013; March, 1991; Simsek et al., 2009; Tushman & O’Reilly, 1997), and for this reason, instead of a balance between exploration and exploitation, in idiosyncratic cases executives prefer exploration over exploitation or vice versa.
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new external knowledge, thus a reinforcing effect (Lavie & Rosenkopf, 2006). Following these arguments, it is clear that a shortage or abundance of exploration or exploitation could have great consequences (O’Reilly & Tushman, 2013). Due to limited available resources, firms tend to favour exploration over exploitation or the other way around, even though they are both crucial for survival and prosperity (Lavie et al, 2010). An imbalance between exploration and exploitation, however, can damage a firm’s long-term performance (Levinthal & March, 1993). Hence, it is in the shareholder’s best interest to promote OA as this helps to avoid the traps, and to increase shareholder wealth. A way of fostering OA is by means of incentive management.
CEO incentives
Incentives are performance-based rewards that are designed to extrinsically motivate employees to pursue the goals of the organization (Gibbs et al., 2004). In the OA perspective this means that shareholders have to motivate executives to conduct exploration and exploitation at the same time, and in a balanced way. The difficulty for shareholders lies in motivating executives to invest in risky innovations, for several reasons. First, executives tend to be risk-averse and insufficiently diversified since the majority of financial and human capital is tied to the firm’s performance (Fama, 1980). Second, risky innovation investments lowers short-term earnings since R&D expenditures cannot be capitalized early on as it delays generating revenue due to uncertainty and high (monetary) investments, which take time to create profit. This could result in reduced managerial bonuses that are tied to performance. Therefore, executives prefer not to invest in risky innovations (Jensen & Meckling, 1976; Amihud & Lev, 1981; Smith & Stulz, 1985). Third, innovation is a high risk investment and characterized by long-term payoffs since cash flows could stretch beyond the tenure of executives (Dechow & Sloan, 1991; Gibbons & Murphy, 1992). Stakeholders could motivate the CEO to act in their interests by providing appropriate incentives. The types of optimal contracts driving exploration or exploitation are essentially different from each other, each incorporating different types of incentives (e.g. annual bonuses and stock options). In essence, optimal contracts for exploitation are similar to pay-for-performance contracts that motivate repeated effort. Optimal contracts for exploration, on the other hand, include a tolerance (or even reward) for early failure and rewards long-term success (Manso, 2011; Holstrom, 1989). This is due to their different nature as elaborated on earlier in this paper. A difference can be made in long-term and short-term incentives and has different implications for each type of innovation (exploration or exploitation). Below, both will be discussed and hypothesis will be given.
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encouraging exploitation of the same skills. Hence, long-term incentives should be more relevant for exploration due to the lack of pay-offs in the short run (Holmstrom, 1989; Manso, 2011). The different forms of long-term incentives analysed in this research are described below.
Stock options. This type of incentive is defined as: “The right, but not the obligation [of a CEO] to purchase shares in the future at some pre-specified exercise price” (Conyon, 2006, p. 26). The goal of this incentive is to induce more risk taking (Jensen & Meckling, 1976) which is required for explorative innovation. Sheikh (2012) states that stock options align the interests of management and shareholders which, in turn, stimulates innovation investments. Stock options cannot be exercised before they vest and executives have to abandon unexercised portfolio of options if they decide to leave the firm. Option values have convexity in their payoffs since their value cannot become negative which insures managerial wealth even if the project fails. However, if the innovation turns out to be successful, option values have no upper limits, and can, due to success, motivate to continue innovating (Manso, 2011). Therefore, options appear to be more effective in promoting explorative innovation compared to stock since the convexity in payoffs can be interpreted as tolerance for failure and reward for long-term success (Sheikh, 2012; Manso, 2011). Additionally, options help maximizing firm value through tying firm performance and managerial wealth via stock options (Haugen & Senbet, 1991). Moreover, options mitigate risk-aversion since the option values increase both with stock return volatility and stock price (Ryan & Wiggins, 2001). Additionally, stock options vest over time and becomes exercisable in different percentages. For example, 25 percent of an option might become exercisable in each of the four years following grant. This means that executives can exercise (part of) the option already after one year (Hall & Murphy, 2003). Further, ‘accelerated vesting provisions’ are often available for executives which provide the possibility of early vesting of options if the performance conditions are satisfied (Bettis et al., 2010), especially when the executive is being replaced or leaves the firm (Hall & Murphy, 2003). Thus, it could be said that the vesting time of stock options may not be enough to obtain payoffs from explorative innovations. Therefore, one could argue that options appear to be more effective in promoting exploitative innovation. Since stock options, on the one hand, help in maximizing firm value (Haugen & Senbat, 1991), and mitigates the long-horizon and managerial risk aversion problems (Ryan & Wiggins, 2001). And on the other hand have the possibility of being vested in a short time period, I expect that stock option incentives can lead to both explorative and exploitative innovation, leading to the following hypothesis:
H1: Stock options as incentives for CEOs are positively related to both exploitation and exploration, and will therefore lead to organizational ambidexterity
Restricted stocks. This form of compensation are stocks of a firm that are not fully transferable
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1997). Since restricted stocks have longer vesting schedules, the terms of incentive compensation contracts lengthens due to restricting payoff from exercising stock after a specific date. This creates a retention effect leading to a CEO staying with the firm for a longer period of time (Kole, 1997) which is beneficial for explorative innovation. Moreover, restricted stocks are relatively stable incentives regardless of stock price compared to stock options, where the incentive value depends on the market price (Hall & Murphy, 2003). However, restricted stocks also make it more costly for CEOs to take risks which will, in turn, help implement appropriate investment decisions. This also implies that restricted stocks are less effective in motivating discovery efforts or exploration since shares also reward CEOs for continuing doing business as usual (Laux, 2015), and are more effective in motivating exploitation activities. This is due to the linear payoff mechanisms that restricted stocks entail and can intensify CEO’s aversion to risk-taking (Bryan et al., 2000; Latham & Braun, 2009). Also, restricted stocks reveal the downside risk to managers, hence providing incentives for CEOs to evade investments that include uncertain pay offs (Ryan & Wiggins, 2002). Especially when the firm is facing organizational decline, CEOs face greater residual risk than with stock options. Due to risk of losing a substantial part of their accumulated wealth, CEOs may opt to invest in less risky investments because the outcomes are less uncertain, and in that way they can safeguard personal investments. Thus, CEOs may redirect investments to lower risk investments that are aimed at maintaining or improving firm performance in the short-term, which is aimed at exploitative innovation (Latham & Braun, 2009). As restricted stocks on the one hand increases the CEO’s incentive to search for new opportunities and on the other hand reward for doing business as usual (Laux, 2015), I expect that restricted stock incentives lead to both exploitative and explorative innovation, leading to the following hypothesis:
H2: Restricted stocks as incentives for CEOs are positively related to both exploitation and exploration, and will therefore lead to organizational ambidexterity
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Stocks. Stock ownership should incentivise executives to take on a longer-term mind-set which
is necessary for innovation (Gaver & Gaver, 1995). However, according to Wright et al. (2002), this depends on the level of ownership. If executives have low to moderate levels of stock ownership, making large investments does not pose too much of a threat to their personal wealth. Executives may therefore be more likely to make risky investments that involve long-term, uncertain payoffs that will increase the stock value. However, when executives own high levels of stock, there is risk aversion due to managerial risk-bearing that could lead to decreased managerial wealth (Wright et al., 1996; Wright et al., 2002). Thus, investments may be limited to less risky ones aimed at reducing performance variability in order to protect the executives’ own interest (e.g. Ryan & Wiggins, 2002; Sanders, 2001; Wright et al., 2002). This is due to linear payoffs and could incline a proportional decrease or increase in managerial firm-specific wealth as it depends on the success or failure of the investment in innovation causing a rise or fall in stock price. Since executives are generally risk-averse and poorly diversified, stock compensation can worsen the risk-aversion affect due to the linear payoffs (Sheikh, 2012). Moreover, executives can sell their stocks easily and are encouraged to do so because of the advantage of short-term price fluctuations at the expense of long-term advantages (Bolton et al., 2006; Gopalan et al.,2014). Gopalan et al. (2014) also showed that there is a focus on short-term performance when the vesting period of CEO’s stocks is short. However, when CEOs become shareholders of the firm, they should also have an interest in OA and therefore be motivated to invest in exploitation as well as exploration. Because, even if a CEO wants to sell his or her shares and increase its wealth, it would be more beneficial if this is done when the share prices are high since this will mean more profit. Therefore, the CEO’s focus has to be on a profitable strategy increasing firm value which could be done through OA (Conyon, 2006). Yet, Sheikh (2012) states that if stocks are awarded as incentives, stock options are more effective in promoting innovation because of the stock’s linear pay offs. Therefore, stocks are only effective in promoting innovation when the long-horizon effect overrules the managerial risk-aversion effect. Thus, since stocks are easily sold resulting in immediate rewards and owning a high amount of stocks can intensify risk-aversion, it is more likely that stock incentives will lead to exploitation rather than exploration. Executives will therefore most likely focus on short-term performance (Levinthal & March, 1993), leading to the following hypothesis:
H3: Stocks as incentives for CEOs are positively related to exploitation, and negatively related to exploration, and will therefore not lead to organizational ambidexterity
Annual bonus. Bonuses are a short-term incentive that is aimed at short-term productivity and
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executives have a fair degree of control regarding profitability within a short time horizon and the factors that impact this. Annual bonuses that are linked to profitability, therefore, reinforce a focus on a stream of innovations (Makri et al., 2006). Since bonuses are short-term and result-based, it will discourage CEOs from long-term innovations (exploration) due to the long-term involved and the lack of compensation predictability. Moreover, due to the specific performance criteria that is often tied to a bonus, CEOs are more likely to only do what is necessary to achieve the bonus and stop there (Wheatley & Doty, 2010). It also provides the top management team with less ambiguity and better forecasting techniques, however, this is, of course, easier for short-term implications of decisions (Wheatley & Doty, 2010). Since annual bonuses are a short-term incentive and result based, I expect that it will lead to exploitation, leading to the following hypothesis:
H4: Annual bonus as incentives for CEOs are positively related to exploitation, and negatively related to exploration, and will therefore not lead to organizational ambidexterity
METHODOLOGY
The methodology part will describe and justify the methodological choices made in this research. To analyse the effects of CEO incentives on OA, a Probit regression was used, given the binary nature of the dependent variable (OA). The dependent variable, however, has a skewed distribution. To address this, several robustness checks were conducted using a negative binominal regression, which is suited to modelling rare events (Phene et al., 2006) with different dependent variables defining OA. Moreover, I incorporated two integrated models which test the effect of all independent variables on the dependent variable. A summary statistics of the variables and correlations is presented in table 1.
Research setting and data collection
I tested the hypothesis using three manufacturing subindustries in the United States (U.S.); chemicals, pharmaceuticals, and computer, electronic and optical products. These industries were chosen because of their dynamic and competitive nature for which conducting both exploitation and exploration is essential (Kristal et al., 2010; Rothaermel & Alexandre, 2009). Moreover, firms in the resource-based manufacturing industries face the fiercest competition (Tang, 2006; Walsh, 1984) and thus would benefit from being ambidextrous. Also, patenting within this industry is seen as a good indicator of a firm’s innovation activities (Podolny & Stuart, 1995; Sorenson & Stuart, 2000).
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narrowed the search. This provided 352 firms and their identification numbers (BvD and ticker). Second, with these ID numbers, I used the ExecuComp database to collect data on executive compensation: annual bonus, restricted stock, stock options, and stocks over the year 2009. Execucomp is a database that contains data on over 80 different compensation, executive, director, and firm related items. Further, I obtained information on fixed salary ($), executive age (years), and CEO tenure (years). The ExecuComp sample yielded 341 firm observations in the year of 2009. Third, with the BvD ID numbers I collected data from the Orbis database, which contains data on over 200 million firms including patent data. I used this database to collect data on basic firm information (e.g. firm size, year of incorporation, and firm performance) and to select firms with one or more patents published between 01-01-2010 and 31-12-2010. This resulted in an excel file of 318 companies and 23.686 patents. As I then had two separate databases, they had to be merged into one which I did using the TICKER symbols to match the firms from each database in excel. All firms that did not match or did not have complete information on all the variables were excluded from the dataset. The merge of the dataset provided a total of 168 firms. Since only granted patents were used in the dataset, non-granted patents were excluded from the database, resulting in a total of 11.063 granted patents. Additionally, for each patent, the cited patents (patents that the focal patent builds upon) and citing patents (future patents that cite the focal patent) were identified. Finally, the dataset consisted of 11.063 patents. Given that the unit of analysis is the firm, I organized the data to include one firm per row, with all the information items included in columns.
Sample
The final sample this research used, consisted of 168 U.S. companies operating in the manufacturing industry. The U.S. was chosen since CEO compensation is a lot higher for U.S. executives than, for example, executives in Asia or Europa (Boyd, 1994). Also, there is more reliable information on CEO compensation in the U.S. This is important since access to data on executive compensation is difficult to obtain since boards are often unwilling to provide information, and boards are only made accountable for justification of their compensation policies for a short period of time (Laksmana, 2008).
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Measurement of variables
This part describes the used measures for the dependent variable (organizational ambidexterity), independent variables (annual bonus, stocks, stock options, and restricted stocks), and control variables (firm age, executive age, return on assets (ROA), firm size, CEO tenure, and backward citations) in this research. I selected the measurements according to previous literature.
Dependent variable: Organizational Ambidexterity. OA has been measured using the patent
data and the forward citations, and is a critical aspect in this research. Previous literature has suggested several ways to measure innovation4. Considering that the aim of this research is to measure OA, the
measurement is somewhat more complex. A way of measuring OA is through patents since this is a tool for firms to protect their innovations, and offers exclusive rights in exchange for detailed disclosure of innovation information for a limited time period (Fagerberg et al., 2006). Patents are a commonly used method to analyse innovation due to their standardized application process which makes them a robust measurement over time and because the number of patents a firm has resembles an indicator of innovativeness (Satta et al., 2016).
OA is coded as a binary variable, where 1 indicates a firm being ambidextrous. To define a firm as ambidextrous, the information from patents that cited the focal patent (forward citations) were used. Solely granted patent were used since these have passed the examiner’s evaluation of patentability and therefore better reflect the patent requirements of usefulness of invention, novelty, and non-obviousness (Belderbos et al., 2014). Every patent had a six year time window, which allows for enough time for the focal patent to receive forward citations since this is important for the technological impact of the innovation (Schoenmakers & Duysters, 2010; Trajtenberg, 1990). Moreover, important elements of a patent’s market valuation are the number of citations a patent receives (Sandner & Block, 2011). Therefore, exploration will be captured by forward citations, thus the number a patent is cited by other, later patents (Dahlin & Behrens, 2005). In conclusion, the more forward citations a company has, the more focus it has on exploration. Exploitation, on the other hand, will also be measured according to patent citations. This measure is based on backward citations which provide information on the knowledge used to create the focal patent. Patents with backward citations rely on internal technologies and tend to create less impact and, therefore, reflect exploitation better (Rosenkopf & Nerkar, 2001). Since OA is defined in this research as ‘Organization’s ability to pursue and balance both explorative and exploitative activities’ (Hieble, 2015, p. 1062), a firm receives a 1 if it has both forward and backward citations, and can therefore be defined as being ambidextrous, and a 0 otherwise.
4 For example R&D spending (e.g. Baysinger et al., 1991; Graves, 1988; Hansen & Hill, 1991). However, there
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Independent variables. The main independent variables are the CEO incentive mechanisms.
The variables used in this research are annual bonus (Makri et al., 2006), stocks (Alessandri & Pattit, 2014), stock options (Balkin et al., 2000), and restricted stocks (Sanders, 2001). All the data was retrieved from the ExecuComp database over 2009 and in dollar values (thousands). Following Lewellen et al. (1987), all compensation variables are measured as the proportion of the CEO’s fixed salary. To obtain the final measure, each independent variable was divided by the fixed salary of the CEO in 2009.
Control variables. To exclude alternative explanations, I used an ample list of control variables:
Firm age. Firm age is measured as the number of years since the founding of the firm (year of incorporation in Orbis). This control is incorporated since older firms are often focused on exploitation due to organizational inertia (Gilbert, 2005; Phene et al., 2006).
CEO age. The study of Bantel and Jackson (1989) indicates that younger CEOs are more likely to focus on exploration due to less commitment to the status quo. Therefore indicating that older CEOs are more likely to steer strategies in the exploitative direction. Executive age is measured in number of years (Barros & Lazzarini, 2012).
Blockholder presence. A blockholder is defined as a shareholder with a minimum of 5 percent of the firm’s shares (Zajac & Westphal, 1994). Blockholders are important since they are often able to influence the firm with the voting rights they obtain with their holding. This variable is calculated as a binary variable in which 1 is coded as the presence of minimal one blockholder and 0 if the firm does not have any blockholders.
ROA. To proxy for firm performance, return on assets (ROA) is used as a control variable. To construct this control variable, data on total assets and net income was collected. To compute the variable, net income was divided by total assets (Chen et al., 2014). ROA indicates how profitable a firm is according to the firm’s total assets. A higher ROA means that a firm is performing better since it uses its assets in a more efficient way (Barber & Lyon, 1996).
Firm size. This control variable is measured as the natural log of total assets (Anderson & Reeb, 2003). It is argued that larger firms have more resources, however, lack flexibility to pursue exploration and exploitation simultaneously (Ahuja & Lampert, 2001). Thus, it could be argued that larger firms are less likely to be ambidextrous.
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Backward citations. These are citations to prior patents that the focal patent uses, and measures if the firm has used prior technological knowledge in order to come to the focal patent (Duguet & Macgarvie, 2005). According to Ahuja and Lampert (2001), patents with few backward citations generate a higher impact and, therefore, real novel innovations have no or few antecedents. To control for this, a binary variable was created in which a firm receives a 1 if it used backward citations and a 0 otherwise.
Industries. Since this research uses data from three subindustries of the manufacturing industry (chemicals, pharmaceuticals, and computer, electronic and optical products), a dummy variable for each industry was created to control for this effect.
Analysis
To test the four hypotheses, the empirical analysis was done using the Probit regression model due to the dependent variable (OA) taking binary values (Rao-Nicholson et al., 2016). The dependent variable shows a positively skewed distribution of 0.65 and a significant Kurtosis of 1.42, thus presenting a light tailed distribution. This is due to 35 percent of the sample valued at 1 and 65 percent at 0. To address this issue, a negative binominal regression analysis (nbreg in STATA) was run, which is suited for modelling rare events, and commonly used when analysing patent data and forward citations (Phene et al., 2006; Yayavaram & Ahuja, 2008). In order to run a negative binominal regression, the dependent variable (OA) became a count variable resembling the total patents with forward citations per firm. Here, all the firms that were marked a 0 in the binary dependent variable, still remained a 0.
RESULTS
Descriptive statistics and correlations
The sample size of this research is 168 firms with an average of 66 granted patents per firm in 2010. The firm with the highest amount of patents has 2236, and the firm with the lowest amount of patents has 1. The maximum number of forward (backward) citations for one firm is 77 (1503), whereas the lowest is 0. Within the sample, a total of 58 companies were marked as ambidextrous. The annual bonus has a mean of 0.17, stocks have a mean of 1.19, stock options have a mean of 1.73, and restricted stocks have a mean of 3.17. Meaning that the bonus has the lowest proportion compared to the CEO’s fixed salary, and the restricted stocks the highest average proportion. For the control variables, the average firm is 47.8 years old, with the oldest firm existing for 211 years and the youngest firm for 15 years. The average age of the CEOs is 54.9 years, with the highest age of 66 years and the lowest of 39 years old. The CEOs have an average tenure of 14.3 years with the longest tenure of 23 years and the shortest tenure of 3 years. As for the blockholders, 44 firms (26 percent) have shareholders with a minimum of 5 percent in shares. Firm performance was measured using ROA, with an average -0.91, minimum of -13.67, and a maximum of 0.56. The average firm size, measured by the natural log of a firm’s total assets, is 7.2.
Regression results
The four hypothesis were tested using a Probit regression analysis to see what effect each incentive has on the OA. The results are presented in table 2, with the binary dependent variable of organizational ambidexterity. Model 1 shows the base model only including the control variables, and is significant with p < 0.001 and a Pseudo R2 of 0.242. The control variables firm size (p < 0.05) and backward
citations (p < 0.001) both influence OA significantly. Model 2 includes the independent variable of annual bonus and tests for hypothesis 2. Here, firm size and backward citations both influence OA at a 0.01 and 0.001 significance level. Pseudo R2 and the overall significance of the model increased,
however, the results shows that the incentive annual bonus does not significantly influence OA. Thus, hypothesis 1 is supported. Model 3 incorporates the independent variable stocks and tests for hypothesis 2. The overall significance of the model and the Pseudo R2 have increased compared to model 1, and
firm size and backward citations influence OA at a 0.01 and 0.001 significance level. However, stocks are not significant and therefore provide support for hypothesis 2. In model 4, the independent variable stock options is included and tests for hypothesis 3. This model is the most significant compared to model 1, with a likelihood ratio test for the Pseudo R2 (χ2) of 7.33 (df = 1, p <0.01). However, it is
interesting to notice that firm size is not at a significant level. Backward citations, on the other hand, is influencing OA at a 0.001 significance level. Additionally, stock options show to have a significant effect on OA (B = 0.138, p < 0.05), and therefore provide support for hypothesis 3. Finally, model 5 includes the independent variable restricted stocks of hypothesis 4. The overall significance of this model is only slightly higher than model 1, as well as the Pseudo R2. Firm size and backward citations
Table 2. Probit regression analysis results and integrated model
Organizational ambidexterity Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Firm age 0.0036 (0.97) 0.0033 (0.90) 0.0035 (0.96) 0.0054 (1.40) 0.0037 (0.99) 0.0055 (1.41) CEO age -0.017 (-0.90) -0.017 (-0.94) -0.016 (-0.86) -0.010 (-0.54) -0.017 (-0.93) -0.0101 (-0.52) Blockholder presence 0.041 (0.15) 0.021 (0.08) -0.017 (-0.06) -0.044 (-0.16) 0.037 (0.14) -0.107 (-0.37) ROA 0.272 (0.35) 0.167 (0.22) 0.211 (0.28) 0.271 (0.32) 0.271 (0.34) 0.143 (0.18) Firm size 0.210* (2.57) 0.229** (2.71) 0.220** (2.61) 0.144 (1.66) 0.220* (2.56) 0.149 (1.56) CEO tenure -0.037 (-1.61) -0.033 (-1.40) -0.028 (-1.14) -0.044 (-1.79) -0.038 (-1.62) -0.0338 (-1.29) Backward citations 1.120*** (4.33) 1.097*** (4.18) 1.069*** (4.05) 1.205*** (4.42) 1.130*** (4.34) 1.155*** (4.11) Industry 1 -0.355 (-1.02) -0.350 (-1.01) -0.392 (-1.12) -0.342 (-0.96) -0.372 (-1.06) -0.368 (-1.02) Industry 2 0.291 (1.02) 0.310 (1.07) 0.253 (0.87) 0.182 (0.61) 0.278 (0.96) 0.175 (0.57)
Industry 3 Omitted Omitted Omitted Omitted Omitted Omitted
Annual bonus -0.359 (-1.21) -0.222 (-0.61) Stocks -0.125 (-1.27) -0.120 (-1.00) Stock options 0.138* (2.49) 0.144* (2.47) Restricted stocks -0.010 (-0.38) 0.0016 (0.06) Constant -1.466 (-1.34) -1.530 (-1.39) -1.503 (-1.35) -1.588 (-1.43) -1.478 (-1.35) -1.589 (-1.41) Observations 168 168 168 168 168 168 Pseudo R2 0.242 0.251 0.253 0.276 0.243 0.290
Likelihood ratio test for Pseudo R2 (χ2) 1.90 2.18 7.33** 0.15 10.26*
Overall significance 52.61*** 54.51*** 54.79*** 59.93*** 52.75*** 62.86***
Robustness
To check for the robustness of the results of table 2, additional analyses have been conducted which can be found in table 2 (model 6), table 3, appendix A, appendix B, and appendix C. First of all, table 1 model 6 shows an integrated (or complete) model which includes the dependent variable (organizational ambidexterity), all independent variables, and all the control variables. This test is done because table 2 (model 1 to 5) lacks results on the effect of all the independent variables combined on OA. Conducting the Probit regression method, model 6 shows the highest overall significance (p < 0.001), and the highest Pseudo R2 with 0.290 of table 2. However, it is worth noting that firm size here is not significant which
is probably due to the significant stock options variable (B = 0.144, p < 0.05). Backward citations, on the other hand, influences OA with a significance level of 0.001. The results of the integrated model (model 6) provide the same results as model 1 – 5 in table 2, supporting hypothesis 1, 2, and 3, but not hypothesis 4. Hence, the integrated model supports the findings.
The second robustness check I conducted was a negative binominal regression analysis with the dependent variable, organizational ambidexterity, changing from a binary variable to a count variable. Now, the dependent variable provides the total number of patents that have forward citations for each firm (ranging from 0 – 44) which could provide different results. The firms that have 0 forward citations are thus not ambidextrous. The results are presented in table 3 and includes the same variables as in table 2. The Pseudo R2 has dropped significantly compared to table 2 which is probably because of the
dependent variable changing to a count variable. Model 4 shows that firm size became significant (p < 0.001) contrary to the findings table 2. Additionally, industry 2 became significant (p < 0.05), meaning that firms are less likely to be ambidextrous when operating in the pharmaceutical industry. Interestingly, the independent variable stock options significantly influences a firm’s ambidexterity. Moreover, stock options became even more significant than in table 2 (B = 0.158, p < 0.001). additionally, an integrated model including all independent variables is presented in model 6, which has the highest overall significance (64.80, p < 0.001). Moreover, firm size (p < 0.01), and industry 2 (p < 0.05) became significant in influencing OA compared to table 2. Thus, even when controlling for the effects of all independent variables with a different regression analysis, and changing the dependent variable to a count variable, hypothesis 3 is still significant. Therefore, table 3 supports the findings.
Table 3. Negative binominal regression analysis results and integrated model
Organizational ambidexterity Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Firm age 0.0047 (0.84) 0.0045 (0.80) 0.0046 (0.83) 0.0091 (1.59) 0.0050 (0.88) 0.0090 (1.55) CEO age -0.0155 (-.055) -0.0134 (-0.47) -0.0131 (-0.46) 0.0052 (0.18) -0.0147 (-0.52) 0.0090 (0.30) Blockholder presence -0.585 (-1.52) -0.631 (-1.61) -0.606 (-1.57) -0.601 (-1.56) -0.590 (-1.53) -0.663 ((-1.69) ROA -0.0041 (-0.00) 0.0034 (0.00) 0.0028 (0.00) 0.160 (0.13) -0.0002 (-0.00) 0.179 (0.15) Firm size 0.429*** (3.96) 0.444*** (4.00) 0.436*** (4.01) 0.318*** (2.79) 0.446*** (3.84) 0.323** (2.62) CEO tenure -0.0261 (-0.93) -0.0255 (-0.91) -0.0138 (-0.41) -0.0394 (-1.37) -0.0279 (-0.98) -0.0293 (-0.88) Backward citations 1.504*** (3.44) 1.484*** (3.40) 1.455*** (3.30) 1.806*** (4.04) 1.511*** (3.45) 1.750*** (3.86) Industry 1 -0.835 (-1.52) -0.791 (-1.43) -0.856 (-1.55) -1.015 (-1.84) -0.845 (-1.54) -0.982 (-1.76) Industry 2 -0.6161 (-1.41) -0.613 (-1.39) -0.675 (-1.51) -0.980* (-2.21) -0.629 (-1.49) -1.021* (-2.23)
Industry 3 Omitted Omitted Omitted Omitted Omitted Omitted
Annual bonus -0.222 (-0.72) -0.267 (-0.67) Stocks -0.0713 (-0.67) -0.0476 (-0.46) Stock options 0.158*** (2.69) 0.161** (2.71) Restricted stocks -0.0139 (-0.43) 0.0084 (0.23) Constant -2.728 (-1.79) -2.879 (-1.87) -2.956 (-1.88) -3.508* (-2.26) -2.836 (-1.83) -3.758* (-2.36) Observations 168 168 168 168 168 168 Pseudo R2 0.118 0.119 0.119 0.135 0.119 0.138
Likelihood ratio test for Pseudo R2 (χ2) 0.50 0.49 8.07** 0.18 9.09*
Overall significance 55.71*** 56.21*** 56.20*** 63.78*** 55.89*** 64.80***
sample size. Then, two Probit regression analyses were performed using the binary variables for exploration and exploitation. Next, a negative binominal regression was conducted using the count variables for exploration and exploitation, thus a total of four tests were done. To avoid econometric problems, I removed the control variable backward citations from the estimations of exploitation, as both use backward citations as a measure. The effect was confirmed for exploration, as is presented in appendix A (Probit regression) and appendix B (negative binominal regression). Both appendices include highly significant models (p < 0.001), and hypothesis 3 (stock options) is supported in both appendices, thus supporting the exploration part of OA. The effect of exploitation was confirmed in the negative binominal regression, however, not in the Probit regression analysis. The effect is, therefore, (partially) confirmed for both exploration and exploration, and thus OA. The results obtained conducting the negative binominal regression for exploitation, are presented in appendix C.
DISCUSSION
The interest in innovation and managerial incentives has been increasing in both academia and practice (e.g. Sheikh, 2012). However, the effects of CEO incentives on OA still raises questions due to lack of empirical research. Subsequently, the literature on OA lacks understanding of the effect of CEO incentives on a firm conducting both explorative and exploitative activities at the same time. Thus, the aim of this research was to study the effect of CEO incentives, specifically annual bonus, stocks, stock options, and restricted stocks, on firm ambidexterity. To test the hypotheses, multiple Probit and negative binominal regression analyses were conducted, using a sample of 168 firms that had over 11.000 granted patents over 2010. The results show that each type of CEO incentive influences OA in a different way, and provide important implications for practice and theory which will be elaborated on below. Additionally, the main limitations of this research will be discussed and suggestions will be provided for possible directions of future research.
Theoretical implications
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within one year, raising the question if this fosters a long-term focus. However, given the results of this research, stock options contribute to a firm’s innovation level in both an explorative and exploitative manner. This because stock options incentivize to induce more risk taking on the one hand, and help increase firm value on the other hand. Moreover, stock options help CEOs to focus on the long-term strategies since this is required for OA, which is probably due to the longer vesting period that stock options incline.
Psychology literature argues that performance-based incentives, like stock options, even inhibit creativity and innovation (Ederer & Manso, 2013). It is argued that it would encourage repetition of previous successful experience and would therefore cause a focus on exploitation over exploration. Further, that these incentives would only work for tasks that require physical effort, however, not for creative thinking which is needed for exploration. Stock options are ‘extrinsic motivators’ meaning that they can only motivate and incentivize a CEO up to a certain point. As the CEO of Deutsche Bank, John Cryan, once said in an interview with Sean Farrell in The Guardian (2015): “I have no idea why I was offered a contract with a bonus in it because I promise you I will not work any harder or any less hard in any year, in any day because someone is going to pay me more or less”. This could then also be said for stock options. It may imply that stock options, therefore, will not motivate CEOs to make decisions that will lead a firm to ambidexterity and that this is better driven by “intrinsic motivation”. Meaning that a CEO has to be motivated by personal believes that OA is better for the firm and will make those decisions regardless of the incentives provided by the firm. However, if mainly intrinsic motivation would lead to OA, why are stock options such a big aspect of compensation? Stock options support the view that OA can be achieved by managerial incentives and that it helps to focus on the short- and long-term since it inclines a vesting period. This motivates CEOs to stay with the firm longer and to invest in long-term projects that are riskier but, at the same time, can incline higher profits.
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this could explain why restricted stocks don’t influence OA. The inconclusive findings regarding restricted stocks may suggest that more research is needed in order to explore their relationship to ambidexterity.
Interestingly, backward citations are an important influence on a firm’s ambidexterity for all types of incentives. Backward citations are, as earlier stated, sources of knowledge on which the focal patent is build. They are important for exploitation since it will allow for an improvement and refinement of current processes and products, therefore making the innovation derivative and less valuable. Moreover, it is stated that true explorative patents do not involve backward citations at all (Lanjouw & Schankerman, 1999). However, prior knowledge is needed for exploring new opportunities since this provides a knowledge base needed to identify unmet needs. Thus, in order for a firm to achieve OA, there has to be a dynamic between forward and backward citations. Moreover, it could be argued that backward citations provide a basis for a firm’s ability to explore new ideas and reflect the technological antecedents of innovations. As OA requires both exploration and exploitation, it is therefore not that surprising that backward citations are important for a firm being ambidextrous.
Finally, this research contributes to the OA literature in several ways. Firstly, I show that different CEO incentives have different effects on a firm’s ambidexterity. Annual bonus and stocks do not provide enough incentives for a CEO to have a focus on OA, however, stock options do. Additionally, I show that restricted stocks are not beneficial to OA, something that was suggested in prior literature. Moreover, backward citations prove to be influential for a firm’s ambidexterity and that a knowledge base thus proves to be useful for exploration. These findings are important for scholars because if certain incentives prove to be more beneficial than others for achieving OA, the full chain of incentive variables that can influence CEO’s strategic decisions need be identified by researchers. While this research only covers a few incentive variables, it does stress the importance of their influence on OA.
Practical implications
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incentives do not incentivize CEOs, however, they are not enough when shareholders aim for a firm to conduct both exploration and exploitation. Thus, being aware of the effects of different CEO incentives on OA allows for a more carefully chosen incentive package.
Limitations and future research
This research, of course, has some limitations. However, the limitations also provide scholars with directions for future research. First of all, this research is conducted using a cross-sectional analysis. A limitation of this method is that it can limit the ability to distinguish causality (Hillman, 2005). Although I reduced this possibility by including a one year lag between the dependent variable and CEO incentives, an appropriate analysis would require cross-section time-series data. Therefore, it could be interesting to look at the effects of CEO incentives on OA over time. Since the exploration aspect of OA takes multiple years to develop, studying the CEO incentives over several years could show different effects on OA. Also, stock options are argued to only be high incentives if the markets are strong (Chang, 2001). Hence, the strength of the market could influence the effect of incentives on OA, and could provide an interesting research opportunity. Further, I only analysed the effects of four types of CEO incentives. Future research could look at the effect of all types of incentives or different (optimal) combinations of incentives on OA. Moreover, each type of incentive often has multiple ways of implementation, for example, restricted stocks providing the opportunity of early vesting when performance criteria is met (Ryan & Wiggins, 2002). Future research could examine whether this has different effects on OA. Further, the sample of this study consisted of only U.S. based firms, therefore the results can only be interpreted for that geographical region. Other countries, for example the Netherlands, have stricter rules when it comes to CEO incentives. It would be interesting to study if this has different implications for a firm’s ambidexterity. Also, this research only looked at three subindustries of the manufacturing industry. Thus, there is a possibility that there will be different results in other industries.
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choice and therefore not all innovations may be patented. Firms often rely on various protection mechanisms, e.g. secrecy, to prevent knowledge spill overs (Cohen et al., 2000). Third, patents differ in technological or economic significance and thus can only capture innovation imperfectly. Moreover, patents do not include marketable products that are not technology based, e.g. services (Fagerberg et al., 2006). Still, patents are considered a reliable source assuming that the majority of innovation is being protected by it (Balkin et al., 2000). Finally, strategic management research has increasingly been using patent data to capture organizational innovations and external knowledge utilizations due to the availability and uniformity that characterizes patent data (Ahuja, 2000; Rosenkopf & Nerkar, 2001; Sorenson & Stuart, 2000; DeCarolis & Deeds, 1999).
CONCLUSION
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