Innovation Impact of Star Employees
A Contingency Approach to the Innovation Impact of Star Employees
High-achieving employees, the innovative “stars” of an organization, are widely credited with supplying the overwhelming preponderance of value-enhancing contributions. Strategic human resource management initiatives have increasingly made “star systems” a centerpiece of corporate efforts to generate innovative products and services. Meanwhile, management literature has largely propounded the positive effects of stars on firms’ performance. Recently, however, scholars have begun to ask if there are conditions in which star systems may actually exert a negative impact on firm performance. In seeking to address the need for useful boundary conditions pertaining to the impact of star employees, we have developed a contingency-based framework for the assessment of stars’ impact on firm-level innovation. Specifically, we inject the element of time and a fuller consideration of the employee-firm relationship by examining three employee life-cycle phases — hiring, embeddedness and turnover – across two innovation activities, exploration and exploitation. Our contingency framework introduces a new theoretical perspective that frames star employees as neither all good nor all bad, but rather a spectrum of distinct impacts depending upon the main processes of innovation. The approach reconciles the contradictory views on stars, opening new pathways for both scholars and practitioners to deploy star performers more effectively in the knowledge economy.
Keywords: Star performers, Contingent framework, Innovation, Exploration, Exploitation
“There is no shortage of fault to be found amid our stars”
Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â – John Green, The Fault in Our Stars
Who wouldn’t want a star on their team? It seems obvious that virtually any organization would be better off with top achievers than without them. Stars are rare, high-achieving individuals who attract prodigious quantities of organizational resources and who are perceived to deliver an unmatched level of innovative value. Scholarship examining the impact of stars and non-stars has deep roots in management research, extending back to Whyte’s comparison of average and exceptional achievers (1956). The prevailing view has been that average performers are preferable in static environments while star performers are vital under dynamic, fast-changing conditions. O’Boyle and Aguinis (2012) argue that market-changing talent is so rare that the impact of individuals on a firm’s output is best conceived as a power law distribution, rather than a normal distribution, suggesting that the performance of a few alpha-tail employees routinely outweighs the other 99%. Likewise, Bhattacharya, Sen and Korschun (2008) show that most entrepreneurial opportunities cannot be grasped but by stars who constitute a small number of individuals within each industry. Over time, then, the presumption that the development of a “star system” is indispensable to successful corporate entrepreneurship (CE) initiatives has passed largely unchallenged (Boynton & Fischer, 2005; Wright, Coff, & Molierno, 2014), especially in knowledge-intensive environments and in the context of CE initiatives, where stars are believed to have a significant effect on firms’ innovative output (Aguinis & O’Boyle, 2014).
However, new studies have begun to reveal counterproductive facets of star systems (e.g. Hunt & Asgari, 2015; Kehoe & Tzabbar 2015), suggesting that a much more nuanced approach is warranted. In particular, extant literature has not fully addressed the matter of time and context; namely, the differential impact stars have upon corporate entrepreneurship based on both the life-cycle stage of the presence of a star and characteristics/goal of a firm. Realizing that star systems are neither all good nor all bad, what profile of conditions favor or disfavor a star’s ability to enhance a firm’s performance?
This paper is the first we are aware of that offers useful, comprehensive boundary conditions for the impact of “star” employees in the context of corporate entrepreneurship. In taking a contingency approach to stars and a firm’s innovative outcomes, we argue that star employees’ impacts on CE is contingent upon the specific approach a firm is taking towards organizational renewal as well as the specific phase of an employee’s tenure with a given firm. CE is defined as “activities that enhance a company’s ability to innovate, take risk, and seize opportunities in its markets” (Zahra, 1991:259). We propose that varied contingencies – i.e. different strategies to be entrepreneurial like exploitation or exploration, and stages of hiring, embeddedness and turnover of stars- promote or inhibit stars’ effectiveness on successful achievement of firms’ goals.
While extending existing research on the importance of innovative stars, our thorough review states new theory on the uniformity of stars’ impact on CE initiatives. Our framework presents an important and much-needed riposte to the presumption that star systems are invariably value-enhancing. On the contrary, when firms pursue the path of CE as a process of “organizational renewal” (Sathe, 1989), they may unwittingly imperil those efforts by relying on a star system under the wrong conditions. In this study, we argue that the quest to attract and retain stars is not essential to all CE approaches and outcomes. In addressing the gap on a comprehensive review on the effect of stars under different contingencies, we build a new framework. We classify stars’ impact into 3 categories based on the time they are present in the firm and after they leave the firm. The first category refers to hiring stage when stars are either hired to shift and move technologies beyond existing lines of business (Tzabbar, 2009), or are hired solely to improve existing lines of business. The second stage is when stars are hired and become embedded in the organization. The third stage refers to stars’ impact post-turnover when they leave the firm. In the same vein, we explore the effect of stars during these three stages when firms aim to explore or attempt to exploit innovative initiatives. Our contingency approach has the potential to provide much-needed boundary conditions and a more veridical, more nuanced framework for the development, implementation and evaluation of CE initiatives, offering scholars and practitioners important new insights.
THEORY DEVELOPMENT AND PROPOSITIONS
Challenge of Innovation and Knowledge Workers
The impact of stars on innovation has been addressed extensively in multiple streams of management literature: entrepreneurship, innovation technological change, and strategic management. No field holds a greater stake in the pros and cons of star systems than strategic human resources management (SHRM).
Stars are considered as main drivers of innovation. Although recently some studies have addressed counterproductive impacts of stars, illustrating the need for a more dynamic approach towards star system in firms. Innovation, although difficult to achieve, is the lifeblood of sustainable competitive advantage (Eisenhardt & Tabrizi, 1995; McGrath, Tsai, Venkatarman, & MacMillan, 1996; Porter, 2011). Innovative products and services have long been known to be the key driver of firm performance differentiation and economic growth (Schumpeter, 1942). To achieve sustainable competitive advantages firms need to recombine existing knowledge in new ways to generate innovation (e.g. Kogut, & Zander, 1992). Knowledge as a source of innovation (Nonaka, 1994), draws attention to human capital as microfoundation of firm capabilities (Felin & Foss, 2005). Individuals’ abilities and interactions have been the focus of knowledge generation (Felin & Hesterly, 2007). Not surprisingly, stars have largely been addressed in the literature as pivotal contributors to firms’ innovative output (Aguinis & O’Boyle, 2014). Star knowledge workers are considered main drivers of innovation, in their various roles as transmitters, carriers and generators of superior knowledge and ideas (Groysberg, Lee, & Nanda, 2008; Lacetera, Cockburn, & Henderson, 2004; Rothaermel, & Hess, 2007; Zucker, Darby, & Brewer, 1994). According to Groysberg et al. (2008) stars are highly exceptional performers who are accorded high visibility in the market (Groysberg et al., 2008; Trevor, & Nyberg, 2008). Stars are considered to be exceptionally productive (Hess & Rothaermel, 2011), and difficult to replace (Zucker et al., 1994).
Specifically, when organizations’ value creation is largely dependent on individual abilities, stars and non-stars are differentiated largely. As Groysberg noted:
“One study found that the top 1 percent of employees in highly complex jobs outperform average performers by 127 percent. Another reported an eight-to one productivity difference between star computer programmers and average programmers. The top 1 percent of inventors was found to be five to ten times as productive as average inventors” (Groysberg 2010: 616).
The importance of stars is a pressing matter even among prominent chief executive officers (CEOs) of top firms. For example, according to Mark Zuckerberg, CEO of Facebook”[s]omeone who is exceptional in their role is not just a little better than someone who is pretty goodâ€¦they are 100 times better” (Helfat, 2011: A1). Oldroyd and Morris (2012) noted that consistent with resource-based view of the firm (Barney, 1991), stars provide a rare and valuable opportunity for firms in order to create competitive advantage. Value creation and capture is a central preoccupation of all firms, the outs-sized impact driven by a talented group of elite performers makes it tempting for firms to develop SHRM programs for which the primary consideration involves hiring and retaining star performers (Kelley & Caplan, 1993).
The near-universal acclaim for stars and star systems is not without controversy, though scholarly research offering alternative perspectives is rare. A few select studies investigating the potential short-comings of star systems have concluded that stars sometimes adversely impact organizations. For example, Oldroyd and Morris (2012) found that although stars benefit from large social capital, they suffer dramatically from information overload, which decreases their performance and stifles organizational performance. Results from an empirical analysis of National Basketball Association (NBA) teams revealed that the temporary absence of stars positively impacts firm performance as non-stars develop new routines and capabilities (Chen & Garg, 2015). Kehoe and Tzabbar (2015), argue that stars’ presence often stifles the emergence of new leaders, while Hunt and Asgari (2015) showed that star systems crowd out the value-adding contributions of non-star performers.
How can we explain inconsistencies in empirical findings and differences in theoretical perspectives in the literature? Is there any time when stars have different impacts on firms’ innovation outcomes? Would considering different innovation outcomes explain stars heterogeneous impacts? In the following section, we discuss further in detail how stars would impact innovation in firms considering different stages of their presence (i.e. early after hiring and when they are embedded), followed by their impact on firm innovation post-turnover. We argue that time is an important contingent factor explaining the impact of star performers on firms’ innovation outcomes. Taking a contingency approach (Lawrence & Lorsch, 1967), we aim to develop a finer grained framework towards stars’ impact.
Our use of contingency framework follows extensive scholarship in management wherein phenomena cannot be explained in a static manner (Aragon-Correa & Sharma, 2003; Mone, McKinley, & Barker, 1998; Sillince, 2005). Contingency frameworks are vitally important when phenomena are subject to boundary conditions that involve dynamic shifts and differentiated outcomes as a consequence of those shifts. Given the conflicting streams concerning high-achieving employees, it is our expectation that time-driven shifts in an employee’s relationship to the firm across the lifecycle of the employee’s tenure with the firm will moderate a star’s impact on firm-level outcomes. Moderated impacts of this nature are best treated through the development of a contingency-based framework (Mone, McKinley, & Barker, 1998). The use of contingency-based frameworks (Galbraith, 1977; Hellriegel & Slocum, 1978; Kast & Rosenzweig, 1974; Thompson, 1967; Tosi & Carroll, 1976) has witnessed strong empirical support and widespread use. Its central premise maintains that there are contingencies and constraints inside and outside organizational boundaries that determine the contexts and consequences of multi-level phenomena. The theory states that there are few, if any, absolutes in organizational phenomena. As such, there is seldom a single, indisputably best way to attain the fit among organizational factors in the pursuit of generating superior organizational performance. In this regard, both environmental and organizational characteristics must be comprehended and incorporated. We argue that in order to understand stars’ impact on firms’ innovation outcomes, we need to study how stars’ fit vis a vis organizational structures and aims fluctuates as a function of the employees’ lifecycle phase in their respective firms. Our framework enhances and extends existing studies that have used this contingency theory to explain phenomena that require more in-depth analysis of highly variant conditions. Galbraith (1974) showed that different information processing capabilities require different information processing requirements. Matusik and Hill (1998), focusing on dynamic environments for labor in knowledge-intensive contexts, show that contingent work in technological domains can be a means to create valuable knowledge. Supported by these, and numerous other, successful applications of the contingency approach, we consider different conditions that foster or hinder the fit between stars and firms. To date, we have not found clear attempts to use a contingency framework in explaining stars impact on firm innovation outcomes. By taking the shifting dynamics of stars’ presence into account, we offer a more thorough and more useful basis upon which to explain heretofore conflicting views in the literature and extend current theories in a more nuanced fashion.
A Contingency Framework for Innovation and Knowledge Workers
Although we have not seen prior attempts to take contingent factors into account when studying stars’ effectiveness on innovation outcomes, there has been a growing awareness of the need to think about the role of star performers in a more critical fashion. The purpose of these efforts have sought to examine stars’ productivity in its social context, including network effects that engage other stars and non-stars. The result is an increasingly nuanced approach that is better attuned to the shifting impact of stars. For example, Grigoriou and Rothaermel (2014) developed the concept of “relational stars’ rather than “traditional productivity stars”, thereby shifting the focus from the exceptionally high-functioning abilities of stars to generate ideas and knowledge to stars who not only generate knowledge but also display the capacity to manage social relationships and knowledge flows in firms.Â According to Grigoriou and Rothaermel (2014: 587), “[stars] can be individuals who have large, dense, or far-reaching networks of collaborators. They can be individuals whose collaborative behavior allows them to operate as the linking pins among internally distant and otherwise unconnected clusters of knowledge.” Another stream of research attempting to address the impact of star performers on firm outcomes has attempted to focus on organizational routines (Chen & Garg, 2015; Tzabbar & Kehoe, 2013). Routines are viewed as patterns of activities that are repeatedly used to ensure the reliability of firms’ actions (Nelson & Winter, 1982). Superior routines have been shown to be an indispensable foundation of innovative organizations (Nelson & Winter, 1982). In order for firms to be both productive and innovative they benefit both from the routines associated with exploitation and for exploration (March, 1991). Taking stars as indispensable parts of creators and implementers of routines, scholars have built a more nuanced theoretical understanding on their impacts on firms (Chen & Garg, 2015).
Although recently scholars have attempted to develop more robust theoretical foundations concerning why and how stars impact innovation outcomes, these have not evolved into a framework that contemplates a full range of explanatory and predictive contingencies. A more refined distillation taking contingency factors into account is needed. Answering this call, our proposed framework apprehends the most relevant contingent factors: time and the nature of a star’s relationship with his or her respective firm. Our central claim is that the phase of stars’ tenure lifecycle functions as a contingent factor explaining how stars’ presence and turnover impact firms’ innovation. Figure 1 shows how we distinguish among different phases of stars’ relationship with the firm, pre, present and post-turnover. Further, we posit that stars have distinct, well-differentiated impacts on exploration and exploitation endeavors of firms.
Distinctions between explorative and exploitative innovative activities have been demarcated in existing literature, most significantly in entrepreneurship and strategy. Literature in entrepreneurship addresses the exploration and exploitation differentiation in the context of opportunities (Eckhardt, & Shane, 2003). In order to be innovative, firms need to explore entrepreneurial opportunities; further, in order to make financial gains of the opportunities, firms need to focus more on exploitation. On the other hand, strategy research refers to exploration and exploitation considering knowledge domains of the firm (March, 1991). Exploration, involving the search for new knowledge beyond current technological domains, and exploitation, involving the search for new knowledge within existing technological boundaries (Levinthal & March, 1993; March, 1991), are main activities firms undertake in order to generate new technologies and also to innovate in the main domain of existing firm activities. One of the most important challenges firms face in order to simultaneously achieve efficient returns to scale and to develop new products and services is to ensure that the firm benefits from both types of innovation. In developing this framework, we focus on large established firms and mostly address exploration and exploitation considering how they are addressed in the strategic management field (i.e. knowledge domains of firms). The reason for doing so stems from the reality that large, established firms are far more likely to encounter the challenges of producing efficient returns and innovation.
Insert Figure 1 about here
Contingent Phase 1 – Hiring
As Figure 1 suggests, the hiring phase sets into motion the depth and breadth of relationship that the employee and firm will develop. Hiring is one of the strategic actions firms take in order to gain unique knowledge and skills difficult to develop internally, but necessary to innovate (Almeida & Kogut, 1999; Tzabbar, Silverman, & Aharonson, 2007). Hiring knowledge workers is one of the most important mechanisms firms take in order to gain knowledge of other geographical locations or knowledge of prior employers of newly hired employees, or the knowledge stars have gained themselves throughout their education and working experience (Rosenkopf & Almeida, 2003). On the other hand, successful knowledge assimilation of newly hired employees is controversial in the literature. Thus, we argue that hiring is an important stage to be studied in investigating stars impact on innovation in firms.
Contingent Effects of Hiring on Exploration
Firms commonly attempt to go beyond existing lines of business and develop technologies distant from established ones. Scholars have referred to this type of innovation as radical innovation, opposed to incremental innovation (Dewar & Dutton, 1986). Although radical innovation might greatly challenge profitable current lines of business and involve disruptive technologies (Christensen, 1997), according to knowledge-based view, they are inevitable for sustainable competitive advantage (Kogut & Zander, 1992; Teece, Pisano, & Shuen, 1997). When firms attempt to transform their capabilities to more distant ones, they need to recombine their technological knowledge and explore new knowledge domains beyond existing, more established technologies (Kogut & Zander, 1992). Constraints on current routines (Nelson & Winter, 1982) and search behavior of individuals (March, 1991) might make achieving the goal difficult. Thus, firms may attempt to hire knowledge carriers (i.e. star scientists) in order to learn and acquire knowledge (Almeida & Kogut, 1999) required to search for new technologies. “Learning by hiring” research has shown that hiring stars can be a strategic step in order to gain the knowledge that firm requires for exploring new areas. However, it may not be sufficient and easy to accomplish (Almeida, Dokko, & Rosenkopf, 2003; Tzabbar 2009).
Tzabbar et al. (2007) showed that when firms hire star scientists, citations of patents are made by newly hired scientists themselves, and not by non-star scientists in the firm, showing the difficulty of new knowledge assimilation by incumbent scientists when star scientists are newly hired. They highlighted constraints such as firm’s size, innovation history and patterns of co-innovation. This shows hiring star scientists does not necessarily help to explore new knowledge domains by recombination of distant knowledge components. Studying how hiring stars would impact exploring new technologies, Tzabbar (2009) found two contingencies that would impact motivation and ability to benefit from newly hired star scientists as a mechanism to develop radical innovation. The two contingencies he noted are power asymmetries emerging from unbalanced distribution of innovation outcomes and existing breadth of organization’s knowledge. As he argued: “The more a firm’s existing technological knowledge is driven by one or a few “star” scientists, the less likely the firm is to significantly shift its technological position. Furthermore, recruiting technologically distant scientists may have greater effects at moderate levels of technological breadth than at very high or very low technological breadth”. He further argues: “the social and technological structures within which innovation efforts take place will influence scientists’ ability and willingness to recombine their knowledge and hence will affect the likelihood of successful transformation of a firm’s technological capabilities” (Tzabbar, 2009: 874). In a same vein, Groysberg et al. (2008), emphasizing on general and firm specific knowledge, showed that stars’ performance drops after they are hired to a new firm. This also shows challenges that firms face when hiring a star scientist. Taken together, we argue stars’ presence does not make a pronounced contribution to exploration endeavors shortly after the star scientist is hired.
Proposition 1: Hiring a star scientist will not increase a firms’ exploration capabilities.
Contingent Effects of Hiring on Exploitation
As mentioned above, hiring star scientists is one of the strategic actions firms take in order to generate innovation (Almeida & Kogut, 1999; Tzabbar et al., 2007). However, hiring a star scientist may not be sufficient to exploit knowledge. Tzabbar et al. (2007) showed that making a difference on transformation, assimilation and use of knowledge by existing incumbent scientists through hiring new star scientists, is difficult to achieve. Exploitation is dependent on many firm characteristics, most importantly search behavior of the firm (March, 1991). Acquisition of knowledge -i.e. hiring star scientists- does not necessarily equal to successful exploitation of knowledge (Zahra & George, 2002). Although current employees (stars or non-stars) may have the potential absorptive capacity to recognize and value knowledge, they may not have the realized absorptive capacity in order to assimilate and transform knowledge for exploitation (Cohen & Levinthal, 1990). Sirmon, Gove and Hitt (2008) highlighted realized absorptive capacity as a means to exploit knowledge through local search, emphasizing on configuration of human capital with existing internal resources.
In order to transform potential absorptive capacity to realized absorptive capacity (i.e. to recombine knowledge in completely new ways), star performers need to share knowledge with incumbent non-stars (Zahra & George, 2002). On the other hand, incumbent stars and non-stars need to familiarize newly hired stars with existing routines in order to be able to go beyond narrow recombination of existing knowledge through local search. Routines which are tied to organizations play a pivotal role in successful exploitation and knowledge search. This is particularly important when stars utilization of knowledge is accomplished by teamwork. Teamwork is usually tied to existing routines in organizations. This highlights the importance of the fit between organization and the star (Call, Nyberg, & Thatcher, 2015). As it is noted by Pfeffer (1998) “It is tempting to hire on the basis of ability or intelligence rather than fit with the organization” (Pfeffer, 1998: 72). Assuming high interdependence (Thompson 1967), hiring star scientists may hinder successful deep search within knowledge domains of the firm. This is mainly because hiring a star scientist can disrupt existing social structures (Bendersky & Hays, 2012; Groysberg, Polzer, & Elfenbein, 2011). Benefiting from individual capabilities might not come true without a cost in high interdependent task environments. Taken all together, we argue that:
Proposition 2: Hiring a star scientist will not increase a firms’ exploitation capabilities.
The summary of our arguments both for exploration and exploitation in the hiring stage is shown in Figure 2. In the next section, we investigate the impact of stars when they are embedded in firms.
Insert Figure 2 about here