Contemporary Management: Issues and Challenges
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r Academy of Management Journal 2018, Vol. 61, No. 5, 2000–2019. https://doi.org/10.5465/amj.2015.1101
FROM EMPLOYEE-EXPERIENCED HIGH-INVOLVEMENT WORK SYSTEM TO INNOVATION: AN EMERGENCE-BASED
HUMAN RESOURCE MANAGEMENT FRAMEWORK
YIXUAN LI Purdue University
MO WANG University of Florida
DANIELLE D. VAN JAARSVELD University of British Columbia
GWENDOLYN K. LEE University of Florida
DENNIS G. MA University of British Columbia
The influence of human resource management on innovation has attracted considerable research attention over the last decade. However, existing studies have primarily fo- cused on the macro-level human resource management architecture, limiting our un- derstanding about the cross-level origin of innovation. Developing an emergence-based human resource management framework, we propose that an employee-experienced high-involvement work system (HIWS) promotes innovation by eliciting collective in- teractions for knowledge exchange and aggregation. Further, we investigate the emergence-enabling process that facilitates an employee-experienced HIWS to give rise to organization-level innovation. Specifically, we probe three distinct emergence en- ablers that amplify the positive influence of HIWS on innovation by shaping the con- certedness, direction, and adaptability of collective interactions: (1) the homogeneity of HIWS experiences as the internal mechanism, (2) the strategic importance of innovation as the external mechanism, and (3) the churn in human resources as the temporal mechanism. We tested our theoretical model using data from a nationally representative sample of workplaces in Canada (n 5 2,639). Our results suggest that an employee- experienced HIWS was positively related to innovation. In addition, this positive effect was amplified by all three emergence enablers (i.e., the homogeneity of HIWS experi- ences, the strategic importance of innovation, and the churn in human resources).
Considering the dynamic market environment and short product life cycles, “innovation”—defined as the intentional introduction and application of new ideas, processes, products, or procedures (West & Farr, 1990)—is crucially important in helping firms discover new market opportunities, adapt to envi- ronmental changes, and sustain competitive ad- vantage. Yet, the management of innovation is challenging, because the knowledge creation pro- cess is discontinuous. In particular, although knowledge creation arises from the coalescence of human resources, this macro phenomenon cannot be reduced to its constituent elements (Kozlowski &
We would like to thank our action editor Dr. Riki Take- uchi and the three anonymous reviewers for their con- structive and insightful comments. We are also grateful to Dr. Cheri Ostroff for her helpful feedback on an earlier version of this article. Mo Wang’s work on this research was supported in part by the Lanzillotti-McKethan Emi- nent Scholar Endowment. Research funding from the So- cial Sciences and Humanities Research Council of Canada supported Danielle D. van Jaarsveld and Dennis G. Ma.
Correspondence regarding this article should be addressed to Yixuan Li, Organizational Behavior and Human Resource Management, Krannert School of Man- agement, Purdue University, West Lafayette, IN 47907. Email: [email protected].
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Klein, 2000). As such, articulating the process that enables the discontinuous emergence of knowledge creation contributes much to our understanding about the origin of innovation in organizations (Felin & Hesterly, 2007).
Despite the considerable research attention that strategic human resource management (HRM) scholars have devoted to examining the connection between HRM and innovation, existing research has primarily focused on the macro-level HRM archi- tecture, investigating the influence of HRM systems on innovation-related activities, capability, and performance (e.g., Chang, Jia, Takeuchi, & Cai, 2014; Collins & Smith, 2006; Patel, Messersmith, & Lepak, 2013). While this macro focus informs organizations how to design HRM architecture to promote inno- vation, our understanding of the emergence pro- cess whereby human resources aggregate to generate innovation is still limited. As noted by Kozlowski and Klein (2000), there might be a danger of super- ficiality and triviality inherent in adopting a single- level perspective to account for organizational phenomena. Investigating the emergence process (i.e., the amplifying process whereby lower-level elements are aggregated to form higher-level phe- nomena; Kozlowski & Klein, 2000) linking the micro level to the macro level can engender a more in- tegrated science of organizations. Thus, it is impor- tant for strategic HRM research to move beyond HRM architecture to uncover how organizations advance innovation by managing the emergence of human resources.
According to Ployhart and Moliterno (2011), organization-level human capital resources are cre- ated from the emergence of individuals’ knowledge, skills, abilities, and other characteristics (KSAOs) via collective interactions (i.e., interpersonal exchange of information, affect, and resources; Kozlowski & Klein, 2000). Through this process, the KSAOs em- bedded in individual employees represent elemental raw materials, the collective interactions denote the amplifying process whereby raw materials are com- binedand aggregated,andthehumancapital resources embody the emergent macro reservoirs (Kozlowski & Klein, 2000). However, directly examining collective interactions, which is the key driver in the emer- gence process, is nearly impossible due to the sheer complexity of capturing dynamic social interactions (Colbert, 2004). Recognizing the missing ingredient connecting the micro and macro organizational re- search, Ployhart and Moliterno (2011) proposed a new theoretical account—the emergence-enabling process (i.e., the mechanisms through which individual-level
KSAOs are amplified to become organization-level human capital resources)—to explicate the features of collective interactions.
Building on the human capital resource emer- gence perspective (Ployhart & Moliterno, 2011), we propose an emergence-based HRM framework to investigate how employees’ HRM experiences give rise to organization-level innovation. Specifically, we propose that employees’ experiences with a high- involvement work system—that is, an employee- experienced high-involvement work system (HIWS)— can promote innovation, as HIWS-based collective interactions serve as the primary source for knowledge exchange and aggregation (Argote & Ingram, 2000). Further, drawing on the complex adaptive system (CAS) theory from complexity science (Colbert, 2004; Dooley, 2004), we propose three distinct emergence- enabling mechanisms that may amplify the positive influence of a HIWS on innovation by shaping the features of collective interactions (i.e., concerted- ness, direction, and adaptability). First, the internal mechanism reflects the internal implementation of HRM systems. It centers on the “concertedness” of collective interactions, which captures the extent to which HRM systems manage employees properly to facilitate implicit and explicit coordination in accomplishing work tasks. In this study, we con- sider the homogeneity of employees’ HIWS expe- riences (i.e., employees’ consensus regarding their general experiences of HIWSs) as an important en- abler for a HIWS to induce concerted interactions. Second, the external mechanism examines an or- ganization’s strategic needs based on the business environment it encounters. It represents the “di- rection” of collective interactions, manifested as the strategic goals and values that organizations communicate to their employees. In this study, we use the strategic value that an organization attaches to innovation (i.e., the strategic importance of in- novation) to probe the extent to which collective efforts are channeled toward innovation. Third, the temporal mechanism concerns the dynamics of hu- man resources, including both its inflow (i.e., em- ployees joining the organization; joiners) and outflow (i.e., employees leaving the organization; leavers). It evaluates the “adaptability” associated with collec- tive interactions, manifested as the extent to which human resource flow reduces stagnation and in- creases responsiveness. We probe this temporal mechanism using the churn in human resources (i.e., the total quantitative flow of human resources into and out of the focal organization), which is a fundamental means for organizations to reshape
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collective interactions for innovative changes in adapting to the dynamic environment.
Our study contributes to the existing literature in several ways. First, by closely examining three dis- tinct types of emergence-enabling mechanisms that facilitate an employee-experienced HIWS to give rise to innovation, we contribute to the multilevel per- spective of strategic HRM. As Jiang, Takeuchi, and Lepak (2013) suggested, the influence of HRM on organizational performance is fundamentally a mul- tilevel phenomenon, because organization-level HRM systems impact organization-level outcomes by influencing individual-level employees’ HRM experiences and reactions. Embracing this multi- level perspective, one recent trend in strategic HRM research is to adopt a top-down approach to in- vestigate the cascading effects of HRM systems on individual attitudes and behaviors (e.g., Chang et al., 2014; Liao, Toya, Lepak, & Hong, 2009; Takeuchi, Chen, & Lepak, 2009). Far less research, however, has examined the bottom-up process whereby in- dividuals’ HRM experiences and reactions lead to organization-level outcomes. One exception is Nishii, Lepak, and Schneider (2008), who showed that employees’ attributions regarding HRM prac- tices had important consequences for their col- lective attitudes and behaviors, and, ultimately, unit performance. Another exception is Liu, Gong, Zhou, and Huang (2017), who demonstrated that employee-experienced HRM systems could influ- ence employee creativity, which in turn promoted firm innovation. Building upon these ideas, we propose an emergence-based HRM framework to examine the emergence-enabling process that fa- cilitates employees’ HIWS experiences to give rise to innovation, complementing previous multilevel HRM research by providing a novel lens for eval- uating the bottom-up HRM process.
Further, by establishing three enablers that corre- spond to the emergence-enabling mechanisms, we contribute to the contingency perspective of strategic HRM. To date, studies that investigated the connec- tion between HRM and innovation mainly focused on universalistic predictions, implying that imple- menting “high road” HRM enhances innovation in an isomorphic manner across organizations (Colbert, 2004). Recognizing this research oversight, HRM researchers increasingly call for the investigation of contingent factors for the HRM–innovation re- lation to understand the boundary conditions un- der which organizations may benefit more from adopting “high road” HRM systems (e.g., Chang, Gong, Way, & Jia, 2013; Collins & Smith, 2006).
Answering this research call, our study investigates three emergence enablers that allow organizations to extract additional value from HRM in facilitating innovation, providing an effective integration of the multilevel HRM perspective with the contingency HRM perspective.
In addition, to explicate the emergence-enabling process linking HRM to innovation, we draw on the CAS theory in developing the taxonomy for the emergence-enabling mechanisms. Although the CAS theory was introduced to the organizational literature in theorizing organizational change (Dooley, 2004) and HRM architecture (Colbert, 2004), the features of the emergence process in the organizational system have received little research attention, limiting our understanding about the core premise of the CAS theory. By examining the internal, external, and temporal emergence- enabling mechanisms that facilitate employees’ HRM experiences to give rise to innovation, the current study delineates patterns of collective in- teractions (i.e., concertedness, direction, and adapt- ability) that enrich the emergence process in the CAS theory.
THEORETICAL BACKGROUND
The emergence perspective focuses on the bottom- up process wherein the aggregate influence of lower-level elements leads to higher-level holistic phenomena (Kozlowski & Klein, 2000). The theo- retical foundation of this perspective is rooted in complexity science, which combines general system theory with basic principles and characteristics of living systems (Dooley, 2004). The work organiza- tion can be categorized as one type of complex living system, in which individuals’ affect, cognition, and behaviors unfold over time to yield collective phe- nomena (Kozlowski & Klein, 2000). Adopting this complexity lens, Colbert (2004) introduced the CAS theory from complexity science to the strategic HRM field to explain how and why HRM created sustain- able competitive advantage by generating causal am- biguity and social complexity in human resources.
According to Colbert (2004), a CAS is character- ized by two features: (1) a large number of interacting agents and (2) the presence of stable emergent pat- terns and properties. As basic elements of a CAS, “agents” are defined as semi-autonomous units (e.g., cells in a biological system or sellers in an economic system) that seek to optimize their fitness level by evolving over time (Dooley, 2004). In the organizational system, “agents” refer to individual
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employees who interact with one another for the exchange of information and resources. The in- teractions of agents, in turn, give rise to emergent macro-level phenomena. The interactive dyna- mism is complex and unpredictable, because it arises from many linear and nonlinear interre- lations among agents. Due to causal ambiguity and social complexity, inimitable competitive advan- tage is afforded by the emergence process (Colbert, 2004).
To uncover the inner workings of interactive dy- namism in organizations, Ployhart and Moliterno (2011) proposed a new theoretical account—the emergence-enabling process—to describe the mech- anisms through which lower-level KSAOs are aggregatedandtransformedintovaluableorganization- level human capital resources. Importantly, through repeated interactions among organizational members, emergence-enabling states (i.e., emergent cognitive states, emergent behavioral processes, and emer- gent affective states) arise and gradually regularize, crystallize, and stabilize to guide subsequent col- lective interactions (Ployhart & Moliterno, 2011). Despite its valuable insight, the human capital resource emergence perspective offers us little guidance about how organizations can effectively manage the emergence process of human resources. Thus, Ployhart and Moliterno (2011) underscored the importance of future studies to investigate the role of HRM in facilitating human capital resource emergence.
Answering the research call sounded by Ployhart and Moliterno (2011), we propose an emergence- based HRM framework to study the linkage between the employee-experienced HIWS and organization- level innovation. According to strategic HRM re- searchers, a HIWS includes a set of management practices implemented to create opportunities for collective interactions through increasing em- ployees’ discretion, coordination, and collaboration (Batt & Colvin, 2011; Lawler, 1986, 1992). Thus, employees’ experiences with the HIWS play a cen- tral role in eliciting collective interactions in the organizational system. Further, applying an agent- centered approach specified by the CAS theory, we derive three emergence-enabling mechanisms that may amplify the effect of a HIWS on innovation by shaping the features of collective interactions (i.e., concertedness, direction, and adaptability) among employees (i.e., agents in the organizational system).
Specifically, the internal mechanism focuses on agent concertedness and examines how the internal
implementation of HRM systems in an organization facilitates interconnected employees to exhibit con- certed actions in pursuing collective goals. We consider the homogeneity of employees’ HIWS ex- periences to be an important enabler for a HIWS to induce concerted interactions. According to Nishii et al. (2008), meaningful variability exists within organizations in terms of employees’ experiences with and reactions to HRM systems. Nishii and Wright (2008) further pointed out that variability in employees’ HRM experiences should be captured when examining the relation between HRM and or- ganizational performance, because such variability might operate as an important boundary condition for the HRM-performance linkage. Consistent with previous studies, we consider the homogeneity of employees’ HIWS experiences as a contingent factor that shapes the relation between HIWS and in- novation. In particular, we propose that homoge- neous experiences amplify the positive effect of HIWS on innovation by facilitating the concertedness of collective interactions via developing implicit coordination, explicit coordination, and emotional “bonds” for the collective.
The external mechanism focuses on agent direction, which depends on the business environment-based strategic needs that organizations communicate to employees. As organizations vary in terms of strategic values and goals, their HRM systems are deployed to elicit different employee behaviors in support of corresponding business strategies (Delery & Doty, 1996; Wright & Snell, 1998). For organizations seeking to achieve innovation, employees’ syner- gistic efforts need to be directed toward knowledge creation. To elicit relevant employee behaviors, organizations ought to clearly express to employees their strategic need and priority regarding innova- tion. In this study, we use the strategic importance that an organization attaches to innovation to probe the extent to which collective efforts are channeled toward innovation. This conceptualization is ap- propriate, because, when an organization places higher importance on innovation, employees are more likely to engage in innovation-related activi- ties (e.g., knowledge generation, knowledge ex- change, and knowledge aggregation), as these activities are more likely to be recognized and rewarded by the organization (Tsui, Pearce, Porter, & Tripoli, 1997).
The temporal mechanism focuses on agent adapt- ability. We consider the churn in human resources as a fundamental organizational dynamism that re- shapes collective interaction patterns in adapting to
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the unstable and turbulent competitive environment. This construct focuses on the quantity of human resource flow regardless of the quality, which is aligned with the labor economics literature viewing employee mobility as reallocation or churning of labor in the labor market (e.g., Burgess, Lane, & Stevens, 2000; Franco & Filson, 2006). Indeed, the CAS theory suggests that the churn in human resources is associated with changes in schema (i.e., mental templates that define how reality is interpreted and what appropriate responses are for a given stimulus; Holland, 1995), information and resource flow, and agent connectivity in the orga- nizational social networks (Dooley, 2004). As such, the churn in human resources serves as the basic means for organizations to shift the patterns of collective interactions. In this study, we submit that the churn in human resources can facilitate HIWS to give rise to innovation by shaping the nature of human capital resources that HIWS leverages. We base our logic on the fact that functional human resource flow can refresh the human resource pool with updated KSAOs and reduce the stagnation of collective interactions (Hannan & Freeman, 1984). Consequently, innovation is more likely to happen when HIWS leverages churning human resources. We present our theoretical model in Figure 1.
HYPOTHESES DEVELOPMENT
An Employee-Experienced HIWS and Innovation
In contrast to Taylorized work design emphasiz- ing narrow job specifications and constricted work autonomy, the high-involvement approach to work design combines employee discretion with group problem-solving, assuming that employees need discretion to solve problems with problem-solving best achieved through collaboration (Batt & Colvin, 2011). Correspondingly, a HIWS refers to a set of HRM practices that are designed to promote em- ployee empowerment, collective collaboration, and relational coordination (Lawler, 1986, 1992). Typical high-involvement work practices include team-based design (e.g., problem-solving teams and self-directed teams), information sharing (e.g., employee suggestion program), aggregate compen- sation strategy (e.g., gainsharing), flexible job de- sign (e.g., job rotation), and employee training (von Bonsdorff, Zhou, Wang, Vanhala, von Bonsdorff, & Rantanen, 2016; Zatzick & Iverson, 2006). Impor- tantly, in this study, we focus on employees’ actual HIWS experiences rather than managerial reports of a HIWS. Stated by Jiang et al. (2013), HRM systems designed at the organizational level needed to be experienced by individual employees to exert an
FIGURE 1 An Emergence-Based HRM Framework for Innovation
Workplace-Level
Employee-Level
Innovation
Innovation)(Employee-Experienced HIWS
Employee-Experienced High-Involvement Work System
B o
tt o
m -u
p H
R M
P ro
ce ss
Emergence Enabling Mechanisms
Internal Mechanism: The Homogeneity of HIWS Experiences
External Mechanism: The Strategic Importance of Innovation
Temporal Mechanism: The Churn in Human Resources
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actual impact on the organization. Similarly, Wright and Nishii (2013) argued that HRM practices must be perceived and interpreted subjectively by in- dividuals to elicit their affective, cognitive, and behavioral reactions. As we investigate how a HIWS and its associated emergence-enabling mecha- nisms facilitate the aggregation of human resources for innovation, focusing on an employee-experienced HIWS aligns more closely with the current re- search goal.
We propose that an employee-experienced HIWS promotes innovation by eliciting collective in- teractions. First, team-based high-involvement ac- tivities provide employees with the opportunity to collaborate with other team members in problem- istic search (i.e., the type of search that is stimulated by a problem and aimed at finding a solution for that problem), a fundamental mechanism that gen- erates innovation (Greve, 2003). Through frequent collaboration in problem-solving, team members are able to develop relational coordination for group learning and creativity (Gittell, Seidner, & Wimbush, 2010). Second, employees’ participation in information sharing, training, and job rotation can help them accumulate a sufficient level of knowledge overlap for effective communication and increase knowledge exchange between in- dividuals with diverse repertoires of local knowl- edge (Collins & Smith, 2006). As such, efficient knowledge aggregation is more likely to take place. Third, employee discretion and gainsharing con- tribute to innovation by reducing the stickiness in knowledge transfer (i.e., the difficulty experienced in the knowledge transfer process; Szulanski, 2000). Specifically, increasing employee discre- tion can release employees from the confines of narrow job specifications and enable them to capi- talize on their tacit knowledge for knowledge generation and knowledge exchange, in turn facil- itating the knowledge transfer process. Further, adopting gainsharing practices can align individual and organizational objectives to cope with agency problems (Gomez-Mejia & Balkin, 1989). In pursu- ing common goals that move beyond individual goal optimization, employees are more motivated to exchange their knowledge to improve the overall work process and are more likely to develop mutual trust and psychological safety through the knowl- edge transfer process (Gong, Cheung, Wang, & Huang, 2012).
Hypothesis 1. An employee-experienced HIWS is positively related to innovation.
The Internal Mechanism: The Homogeneity of HIWS Experiences
According to Ployhart and Moliterno (2011), three emergent enabling states are the determinants of concerted collective actions: (1) emergent cognitive states, (2) emergent behavioral processes, and (3) emergent affective states. Accordingly, we expect the homogeneity of employees’ HIWS experiences to amplify the positive influence of an employee- experienced HIWS on innovation by influencing these three emergent states, respectively. First, when employees’ HIWS experiences are homogeneously high, they cultivate concerted emergent cognitive states for knowledge aggregation. Specifically, when employees are uniformly exposed to a HIWS, they are more likely to hold overlapping cognitive repre- sentations of work tasks and develop shared un- derstandings regarding organizations’ expectations (Bowen & Ostroff, 2004). Such shared perceptions, in turn, facilitate the formation of shared mental models and promote the synchronization, pacing, and quality of organizational processes, thus im- proving the effectiveness of knowledge exchange and aggregation. Further, homogeneous HIWS ex- periences benefit organizational learning by de- veloping a high-quality transactive memory system for the organization (i.e., a shared organizational system for encoding, storing, and retrieving information; Wegner, 1986). In particular, they pro- mote or generate broad and intensive intercon- nections among employees, and in turn facilitate the development of organization-level knowledge ar- chitecture with a specialized division of labor from different knowledge expertise, reducing individuals’ cognitive load and providing organizations with full access to a large reservoir of knowledge (Argote & Ingram, 2000).
Second, when employees’ HIWS experiences are homogeneously high, they cultivate concerted emergent behavioral processes for knowledge ex- change. The collective behavioral processes of an organization are manifested as employees’ overt communication and explicit coordination in the work process (Ployhart & Moliterno, 2011). When employees are uniformly exposed to a HIWS, they are more likely to be involved in the communication and coordination process in accomplishing inter- dependent work tasks. Through the communication process, involved employees can exchange task- relevant information and knowledge, which are important sources of innovation (Argote & Ingram, 2000). In addition, through explicit coordinating
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activities, involved employees are able to aggregate their discretionary behaviors toward the formation of a unified workforce, removing the barriers for the creation and implementation of creative ideas.
Third, when employees’ HIWS experiences are homogeneously high, they cultivate concerted emergent affective states for knowledge transfer. The collective affective states of an organization largely depend on the strength of emotional “bonds” that tie its members together (Ployhart & Moliterno, 2011). Homogeneous HIWS experiences can strengthen the emotional “bonds” by cultivating social capital in the workforce (Tsai & Ghoshal, 1998). In particular, through HIWS-based interactions, employees can develop mutual understanding regarding one an- other’s expectations, needs, and goals, and adjust their communication and behavioral patterns ac- cordingly to fit the social situation. Over time, such unfolding social interactions can help the workforce develop collective trust and cohesion, facilitating knowledge exchange and aggregation (Jones & George, 1998).
Hypothesis 2. The homogeneity of HIWS experi- ences moderates the relation between an employee- experienced HIWS and innovation, such that this positive relation is more (vs. less) pronounced when HIWS experiences are more (vs. less) homogeneous.
The External Mechanism: The Strategic Importance of Innovation
The contribution of HRM practices to organiza- tional performance is contingent on the extent to which they align with the business strategy (Becker & Gerhart, 1996; Wright & Snell, 1998). To achieve in- novation, organizations ought to clearly communi- cate to the employees their strategic need for innovation to direct collective efforts toward in- novation. As such, we expect the strategic impor- tance that organizations attach to innovation to amplify the positive influence of a HIWS on in- novation. In particular, we argue that the amplifying effect of the strategic importance of innovation can be explained by the complementarity between em- ployees’ HRM experiences and organizations’ stra- tegic needs. On the one hand, if organizations value innovation, an employee-experienced HIWS can better elicit organization-desired collective behav- iors that satisfy the strategic need for innovation (Delery & Doty, 1996). This line of logic is based on the idea that, when innovation is important to orga- nizations, organizations are more likely to recognize
and reward employee behaviors that benefit in- novation through HRM (Tsui et al., 1997). As such, emergent human capital resources garnered by a HIWS are more likely to be channeled toward knowledge creation. On the other hand, organiza- tions emphasizing the strategic importance of in- novation may facilitate collective efforts directed toward innovation to materialize as new products or processes by providing a beneficial context for knowledge creation. Specifically, organizations that value innovation tend to be learning oriented, en- couraging divergent thinking and knowledge ex- change. Thus, employees are more motivated to criticize existing routines, challenge one another’s opinions, and express their novel insights. As such, high-involvement work practices are more likely to result in innovative ideas when the strategic impor- tance of innovation is higher. Further, organizations that emphasize the strategic value of innovation are more tolerant of errors and may even encourage employees to learn through a trial-and-error process (Levitt & March, 1988). Therefore, when employees are involved in problem-solving, they are less guarded about new ideas and are more willing to develop potential solutions progressively through multiple trial-and-error learning processes.
Hypothesis 3. The strategic importance of innova- tion moderates the relation between an employee- experienced HIWS and innovation, such that this posi- tive relation is more (vs. less) pronounced when the strategic importance of innovation is high (vs. low).
The Temporal Mechanism: The Churn in Human Resources
As mentioned above, a HIWS benefits innovation by eliciting collective interactions for knowledge exchange and knowledge aggregation. As such, the extent to which a HIWS contributes to innovation largely depends on the nature of human capital re- sources that a HIWS leverages. In particular, to create new knowledge, a HIWS needs to leverage a human resource pool that contains updated knowledge and presents low rigidity to changes. Following this logic, we expect the churn in human resources to amplify the positive effect of a HIWS on innovation for three reasons. First, the churn inhuman resources facilitates a HIWS to generate innovation by contin- uously updating organizations’ knowledge reser- voir, with employee mobility serving as a powerful mechanism for tacit and explicit knowledge transfer across organizations. According to prior studies,
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labor mobility can facilitate innovation through the learning-by-hiring effect (i.e., knowledge accumula- tion through new employees due to knowledge dif- fusion; Song, Almeida, & Wu, 2003) and the social network effect (i.e., departing employees remain in contact with former colleagues for knowledge ex- change; Shipilov, Godart, & Clement, 2017). Conse- quently, a churning labor force can update the knowledge reservoir by infusing new knowledge, reducing information redundancy, and expanding the knowledge network. Owing to the increased va- riety of perspectives associated with the quickly updated knowledge reservoir, the effect of a HIWS on innovation may be strengthened.
Second, the churn in human resources also re- duces organizational stagnation, an obstacle that impedes a HIWS from generating innovation. Spe- cifically, stable and long-term employment tends to develop a shared schema that locks organizational members into the existing interest, culture, and re- lations embedded in the organization (Hannan & Freeman, 1984). Consequently, organizations with stagnant human resources tend to rely heavily on established routines and are less open to novel so- lutions (Sørensen & Stuart, 2000). As such, the churn in human resources can be viewed as a revitalizing process that increases the flexibility and adaptability of collective interactions (Shaw, 2011). Through the inflow and outflow of human resources, the work- force may reshape its collective interaction patterns and discover creative ways for resource deployment, facilitating a HIWS to promote innovation.
Third, the churn in human resources can improve organizations’ receptivity to creative solutions that are distant from or incompatible with the current routines. This is because the human resource flow can mitigate the tendency of local search, reduce the escalation of commitment in ongoing routines, and improve orga- nizational openness to divergent solutions (Rietzschel, Zacher, & Stroebe, 2016; Staw, 1981). Furthermore, the churn in human resources can reduce the formation of “groupthink” (Schneider, Goldstein, & Smith, 1995). As such, churning organizations are less narrow- minded or constricted in evaluating, processing, and absorbing knowledge and information. Therefore, in- novative changes are more likely to happen when a HIWS leverages churning human resources.
Hypothesis 4. The churn in human resources mod- erates the relation between an employee-experienced HIWS and innovation, such that this positive relation is more (vs. less) pronounced when the churn in hu- man resources is high (vs. low).
METHOD
Sample
We tested our hypotheses with the Workplace and Employee Survey (WES) data collected by Statistics Canada (Statistics Canada, 2009). The workplaces are akin to “establishments,” which refer to stand-alone entities with a business address (Takeuchi et al., 2009). The advantage of surveying establishments over firms is that multiple establishments of the same firm may pursue distinct business strategies and adopt different HRM systems. In addition, respondents in an estab- lishmentaremorelikelytoaccuratelyassessthespecific situations within the unit (Batt & Colvin, 2011). The WES consists of two parts: the Workplace Survey and the Employee Survey. This unique dataset links em- ployees to workplaces, enabling us to study the effect of employee-experienced HRM on workplace outcomes. Different sampling frames were used in the two parts of the WES. For the Workplace Survey, Statistics Canada used stratified random sampling by industry, region, and size to build a representative sample of workplaces in Canada. The sampled locations were followed over time, with new locations added every two years to re- place those that ceased to participate due to attrition. The primary respondent for the Workplace Survey was a workplace HR manager, except in small locations, where a general manager or business owner completed the survey. For the Employee Survey, Statistics Canada sampled participants from the surveyed workplaces and followed them for two years. Thus, fresh samples of employeesweredrawneveryotheryear.Amaximumof 12 employees were sampled at each workplace using a probability mechanism. Previous HRM researchers have published several studies using the WES data set (e.g., Shin & Konrad, 2017; Yanadori & van Jaarsveld, 2014; Zatzick & Iverson, 2006).
From the multi-wave WES data set (1999–2006), we selected the 2005–2006 waves to test our hypotheses, because the 2005–2006 waves included the most recent and complete set of variables used in the study and had the largest sample size across all the surveyed years. Specifically, the independent variables and control variables were measured in 2005 and the dependent variable (i.e., innovation) was measured in 2006. In or- ganizing the data for the current analyses, we excluded employees who did not provide answers to questions about HRM practices (16.28%). Further, guided by Yanadori and van Jaarsveld (2014), we excluded Em- ployee Survey responses from managerial employees (10.48%), because our research focuses on leveraging human resources of frontline employees. In addi- tion, we excluded workplaces with fewer than three
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employee reports (5.37%), to obtain a more robust assessment of the within-workplace homogeneity in employees’ HIWS experiences. Finally, we excluded non-profit workplaces (14.01%), because our study focuses on for-profit organizations. Applying the above exclusion criteria, our research sample included 2,753 workplaces and 13,032 employees. After deleting ob- servations with missing values in variables of interest, our final sample consisted of 2,639 workplaces and 12,519 employees. The mean workplace size was ap- proximately 49 employees (SD 5 100.09) and the av- erage length that workplaces had been located at their current address was 19.32 years (SD 5 17.56).
Measures
Innovation (Workplace Survey in 2006). To mea- sure innovation, previous studies have adopted various approaches, including measuring innova- tion activities (e.g., patents; Sørensen & Stuart, 2000), innovation capability (e.g., Patel et al., 2013), and in- novation launches (e.g., Greve, 2003). We used two items from the Workplace Survey to measure in- novation launches: (1) product innovation—the work- place has introduced new products or services that differ significantly from previously produced goods or services; and (2) process innovation—the workplace has introduced new processes, including the adoption of new methods of goods production or service de- livery. Specifically, we coded workplaces that reported no product or process innovation as 0, workplaces that reported either product or process innovation as 1, and workplaces that reported both product and process innovation as 2.1,2
An employee-experienced HIWS (Employee Survey in 2005). Following Zatzick and Iverson (2006), we measured a HIWS with six HRM practices that are commonly used by organizations to involve employees: flexible job design, information sharing, problem-solving teams, self-directed teams, gain- sharing, and training (1 5 yes, 0 5 no). According to Zatzick and Iverson (2006), the six HRM practices were observed indicators that reflected the high- involvement approach to work design. However, in contrast to Zatzick and Iverson (2006), who used the Workplace Survey reported by managers to measure HIWSs, we used the Employee Survey reported by employees to evaluate employees’ experiences with a HIWS. To assess the construct validity of our measure, we performed a categorical indicator-based confirmatory factor analysis. The one-factor model resulted in a good fit to the data, x2 5 210.38, df 5 9, p , .01, comparative fit index 5 .97, Tucker–Lewis index 5 .95, and root mean square error of approxi- mation 5 .04. Cronbach’s alpha for the scale was .71, indicating that this is an acceptable measure. We calculated the employee-experienced HIWS for each workplace ( �X) by aggregating employees’ ratings of their individual HIWS experiences to the workplace level, using the formula listed below. In support of aggregation, rWG(J) was calculated for each work- place. The mean rWG(J) across workplaces was .57, indicating that the majority of workplaces displayed moderate to high within-workplace agreement in HIWS ratings (LeBreton & Senter, 2008). Further support for aggregating employee responses to a HIWS to the workplace level was provided by the interrater correlation coefficients: ICC(1) 5 .24, ICC (2) 5 .61. The one-way random-effect analysis of variance showed that there were significant vari- ances in the workplace-level means of HIWS ratings, F(2,752, 10,279) 5 2.53, p , .01.
�X 5 +
n i51Xi N
, Xi 5 +
6 j51Xij
6 (1)
where i is the ith employee, j is the jth item, and N is the number of employee reports.
The homogeneity of HIWS experiences (Em- ployee Survey in 2005). The homogeneity of em- ployees’ HIWS experiences captures the extent to which employees present low variability in their HIWS experiences in the focal workplace. This measure was derived from employee responses to a HIWS using a dispersion model, a type of compo- sition model that specifies the functional relation- ships among constructs at different levels of analysis
1 Alternatively, innovation could be coded as a dichoto- mous measure (0 5 no product or process innovation, 1 5 had product or/and process innovation). We conducted a robustness check with this dichotomous innovation mea- sure using probit regression. The result pattern was virtually the same as the one reported here.
2 Our emergence-based HRM framework cannot suffi- ciently differentiate the influence of HRM on product in- novation and process innovation, as the mechanisms we propose are not specific to different types of knowledge creation (i.e., product vs. process). Therefore, in this study, we adopted a general innovation measure combining product innovation and process innovation to capture or- ganizations’ knowledge creation. We conducted a robust- ness check by treating product innovation and process innovation as two separate outcomes using probit re- gressions. The result patterns of both product and process innovation were virtually the same as the one reported here.
2008 OctoberAcademy of Management Journal
in multilevel research (Chan, 1998). In accordance with previous researchers, homogeneity can be assessed by either standard deviation or rWG(J) sta- tistics (Harrison & Klein, 2007; James, Demaree, & Wolf, 1993). Accordingly, we used both standard deviation and rWG(J) to measure the homogeneity of HIWS experiences. When standard deviation was used, to represent homogeneity and facilitate in- terpretation of our findings, we followed Koopmann, Lanaj, Wang, Zhou, and Shi (2016) by multiplying the standard deviation by 2 1 so that higher values represent higher levels of homogeneity. Our results were virtually the same for the two measures. Due to space constraints, we only present our findings using the first measure. It is noteworthy that, as our ho- mogeneity measure was derived from employee re- sponses to a HIWS, the range of homogeneity was restricted by that of an employee-experienced HIWS. As explained by Harrison and Klein (2007: 1214):
. . . because the SD of a within-unit distribution is of- ten lower when the mean is near the lower or upper bound . . . there may be an artifactual overlap of means and SDs across units. In short, mean and SD can be confounded.
Therefore, guided by Harrison and Klein (2007), we modeled the employee-experienced HIWS and the homogeneity of HIWS experiences simultaneously to address this range restriction issue.
The strategic importance of innovation (Work- place Survey in 2005). We measured the strategic importance of innovation with three items that re- flect the importance of innovation to the workplace business strategy. Specifically, in the Workplace Survey, managers rated the importance (from 1, of no importance, to 6, crucial) of the following innovative activities to the general business strategy of their workplaces: (1) undertaking research and develop- ment, (2) developing new products/services, and (3) developing new production/operating techniques. Cronbach’s alpha was .81 for this scale.
The churn in human resources (Workplace Survey in 2005). According to the labor economics literature (e.g., Burgess et al., 2000; Burgess, Lane, & Stevens, 2001), two groups of employees constitute the churn- ing labor force for a focal workplace (w) at a specific year (t): joiners (i.e., workers who were not employed at the workplace w at time t 2 1 and joined at time t) and leavers (i.e., workers who were employed at the workplace w at time t 2 1 and left at time t). Therefore, we measured the churn in human re- sources using the total number of joiners and leavers scaled by the total number of employees in
the workplace. Our measure corresponds closely with thedefinition of the churn in human resources, which captures the aggregate level of human re- sources that flow into and out of an organization. It is important to note that the churn in human re- sourcesis anemergentcollective construct describing the total quantitative flow of human resources. As such, this measure captures periodic changes in hu- man resources, a type of resource stock that orga- nizations need to manage across times (Barney & Wright, 1998).
Control variables (Workplace Survey in 2005). Based on previous HRM studies (e.g., Guthrie, 2001; Zatzick & Iverson 2006), we controlled for numerous workplace-level variables. Specifically, we con- trolled for workplace age (i.e., the length of years for which the workplace had been located at the current location), because workplaces that are able to endure may have a higher level of management quality (Guthrie, 2001). We controlled for workplace size (i.e., the total number of employees in the workplace) and profitability (i.e., workplace profit scaled by the total number of employees), because organizations with a larger operating scale and higher profitability are more likely to possess slack resources and man- agerial capability to implement a HIWS and launch innovation (Shin & Konrad, 2017). In addition, we controlled for union density (i.e., the proportion of employees covered by a collective agreement), be- cause workplaces with a higher degree of unioniza- tion may provide employees with more opportunities for involvement in the management process (Guthrie, 2001). Finally, following previous research (e.g., van Dalen, Henkens, & Wang, 2015; Zatzick & Iverson, 2006), we controlled for dichotomous industry sec- tors, to tease out establishment-level effects from potential industry-level effects.3
Analytical Strategy
Because the dependent variable—innovation—is ordinal, we conducted ordered probit regressions.
3 There were 14 dichotomous industry sectors: forestry, mining, oil and gas extraction; labor intensive tertiary manufacturing; primary product manufacturing; second- ary product manufacturing; capital intensive tertiary manufacturing; construction; transportation, warehousing and wholesale; communication and other utilities; retail trade and consumer services; finance and insurance; real estate, rental and leasing operations; business services; education and health services; and information and cul- tural industries.
2018 2009Li, Wang, van Jaarsveld, Lee, and Ma
To test the moderating effects (Hypotheses 2–4), three-step hierarchical regressions were used. In Step 1, only the control variables were entered into the regression model. In Step 2, we entered the control variables, the employee-experienced HIWS, and the three emergence enablers to gauge the main effects of independent variables. In Step 3, we en- tered the variables in Step 2 and the interaction terms to test the hypothesized moderating effects. For ease of interpretation, the control variables and inde- pendent variables were grand mean-centered. The interaction terms were the products of grand mean- centered independent variables. In estimating these models, we used the workplace survey weights pro- vided by Statistics Canada for the representativeness of sample estimates. We used Mplus 7.11 to conduct analyses (Muthén & Muthén, 1998-2012).4
RESULTS
Table 1 presents the descriptive statistics and correlations of our studied variables. Table 2 pres- ents the results of ordered probit regression models for innovation. Together, employee-experienced HIWS, the homogeneity of HIWS experiences, the strategic importance of innovation, and the churn in human resources accounted for 10.8% variance in predicting innovation above and beyond the control variables.
As shown in Model 2 (Table 2), HIWS was posi- tively and significantly related to innovation, b 5 1.55, p , .01. For employee-experienced HIWS, a one standard deviation increase from its mean resulted in a 2.2-percentage-point increase in the probability of having either product or process in- novation and a 7.5-percentage-point increase in the probability of having both product and process in- novation, supporting Hypothesis 1.
Further, the strategic importance of innovation was positively and significantly related to innova- tion, b 5 .20, p , .01. For the strategic importance of innovation, a one standard deviation increase from its mean resulted in a 2.4-percentage-point increase in the probability of having either product or process innovation and an 8.5-percentage-point increase in the probability of having both product and process innovation, demonstrating that workplaces were more likely to innovate when their strategic impor- tance of innovation was higher. In addition, the
churn in human resources was also positively and significantly related to innovation, b 5 .06, p , .01. A one standard deviation increase from its mean resulted in a 0.8-percentage-point increase in the probability of having either product or process in- novation and a 2.1-percentage-point increase in the probability of having both product and process innovation.
Model 3 presents the results for the hypothesized moderating effects. In Model 3.1 (Table 2), the three hypothesized interaction terms were entered into the regression models. In Model 3.2 (Table 2), we con- trolled for the three interaction terms among the moderators so that all six interaction terms were entered into the regression model simultaneously. Our main findings were virtually the same for the two models. For the purpose of brevity, the results we discuss focus on Model 3.2.
In Hypothesis 2, we expected that, when the ho- mogeneity of HIWS experiences was higher, the pos- itive relationship between employee-experienced HIWS and innovation would be stronger. As shown in Model 3.2, the interaction between employee- experienced HIWS and the homogeneity of HIWS experience was positive and significant, b 5 7.95, p , .01. To further examine this moderating effect, we plotted the interaction pattern in Figure 2. As shown in this figure, when the homogeneity of HIWS experience was higher, the positive effect of HIWS on the probability of innovation was stronger, providing support for Hypothesis 2.5
In Hypothesis 3, we hypothesized that the positive relationship between employee-experienced HIWS and innovation would be stronger when workplaces placed more emphasis on the strategic importance of innovation. According to Model 3.2, the interaction term between HIWS and the strategic importance of innovation was positive and significant, b 5 .45, p , .01. Further, as shown in Figure 3, when the strategic importance of innovation was higher, the positive effect of HIWS on the probability of having both product and process innovation was stronger. There- fore, we found general support for Hypothesis 3.
In Hypothesis 4, we expected the churn in human resources to strengthen the positive relation be- tween employee-experienced HIWS and innovation. According to Model 3.2, the interaction term between
4 We conducted a sensitivity analysis by controlling for the innovation baseline (2005) during the analysis. The results were similar to the ones reported here.
5 Following Hoetker (2007), we also examined Hypoth- eses 2–4 by plotting the marginal effects of the interactions. The results were consistent with the ones reported here. For the purpose of brevity, we only present cumulative probabilities in the figures.
2010 OctoberAcademy of Management Journal
T A B L E 1
M ea
n s, S ta n d a rd
D ev
ia ti o n s, a n d C o rr el a ti o n s fo r S tu d ie d V a ri a b le s
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a 3 .4 3
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3 M u lt i- p la n t fi rm
0 .2 7
0 .4 4
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4 P ro fi ta b il it y a
7 .0 8
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.0 9
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5 U n io n d en
si ty
0 .1 3
0 .2 9
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6 P er ce
n ta ge
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p lo y ee
s 0 .5 4
0 .2 9
2 .0 3
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.0 0
.1 4
7 P er ce
n ta ge
o f fu ll -t im
e em
p lo y ee
s 0 .7 6
0 .2 6
2 .0 3
.0 2
2 .0 2
.0 2
2 .0 2
.5 3
8 P er ce
n ta ge
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p lo y ee
s 0 .9 0
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9 A v er ag e em
p lo y ee
w ag ea
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1 0
E m p lo y ee
-e x p er ie n ce
d H IW
S 0 .4 5
0 .1 6
2 .0 7
.0 8
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.0 3
2 .1 0
.0 2
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1 1
T h e h o m o ge n ei ty
o f H IW
S ex
p er ie n ce
s 2 0 .2 0
0 .1 0
.0 1
2 .0 8
2 .0 4
2 .0 7
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2 .1 6
2 .0 5
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1 2
T h e st ra te gi c im
p o rt an
ce o f in n o v at io n
2 .9 3
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.0 0
1 3
T h e ch
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re so u rc es
0 .6 0
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2 .0 5
2 .0 2
2 .0 4
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2 .0 5
2 .0 1
2 .0 4
2 .0 1
2 .1 1
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1 4
In n o v at io n
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0 .8 3
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2 .0 4
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te d d es cr ip ti v e st at is ti cs .D
ic h o to m o u s in d u st ry
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v ar ia b le .
2018 2011Li, Wang, van Jaarsveld, Lee, and Ma
HIWS and the churn in human resources was positive and significant,b 5 .57, p , .01. The interactionplot is presented in Figure 4. As shown, when the churn in human resources was higher, the positive effect of HIWS on the probability of innovation was stronger. Thus, Hypothesis 4 was also supported.
DISCUSSION
Moving beyond HRM architecture, our study at- tempts to uncover the origin of innovation by in- vestigating the emergence-enabling process for an employee-experienced HIWS to give rise to in- novation. Developing an emergence-based HRM framework, our study reveals three distinct emer- gence enablers (i.e., the homogeneity of HIWS ex- periences, the strategic importance of innovation, and the churn in human resources) that facilitate an
employee-experienced HIWS to yield innovation. Below, we discuss the theoretical and practical im- plications of our research.
Theoretical and Practical Implications
Our emergence-based HRM framework furthers our understanding about the resource-based view in the HRM field (Barney & Wright, 1998). According to Colbert (2004), HRM can serve as a source of sus- tainable competitive advantage by cultivating het- erogeneous human capital resources through developing and nurturing interpersonal relations with causal ambiguity and social complexity. Nev- ertheless, we have limited understanding about the process through which HRM yields knowledge- based value creation, partly because it is nearly im- possible to directly explicate the configuration of
TABLE 2 Ordered Probit Regression Models for Innovation (2006)
Predictors (2005)
Model 1 Model 2 Model 3.1 Model 3.2
Estimate SE Estimate SE Estimate SE Estimate SE
Control variables Workplace age 20.01** .00 20.01** .00 20.01** .00 20.01** .00 Workplace size 0.26** .10 0.20* .10 0.20* .10 0.20* .10 Multi-plant firm 0.29 .18 0.28 .18 0.28 .17 0.28 .16 Profitability 20.004** .00 20.01** .00 20.01** .00 20.01** .00 Union density 20.28 .33 20.20 .32 20.20 .32 20.20 .32 Percentage of male employees 20.63 .39 20.61 .34 20.59 .32 20.58 .30 Percentage of full-time employees 0.32* .13 0.11 .24 0.08 .20 0.06 .20 Percentage of on-site employees 0.59** .16 0.57** .12 0.58** .11 0.57** .11 Average employee wage 20.10** .04 20.19** .04 20.22** .03 20.22** .03
Main effects Employee-experienced HIWS (HIWS) 1.55** .07 1.45** .07 1.45** .07 The homogeneity of HIWS experiences (Internal) 20.17 .14 20.36* .16 20.35* .14 The strategic importance of innovation (External) 0.20** .04 0.18** .04 0.18** .04 The churn in human resources (Temporal) 0.06** .01 0.01 .01 0.02 .01
Interaction terms Internal 3 External 20.30 .17 Internal 3 Temporal 20.17* .08 External 3 Temporal 20.02 .03 HIWS 3 Internal 7.68** .73 7.95** .69 HIWS 3 External 0.47** .06 0.45** .07 HIWS 3 Temporal 0.55** .12 0.57** .19
Intercepts Intercept 1 20.26** .01 20.27** .01 20.27** .01 20.27** .01 Intercept 2 20.81** .04 20.86** .04 20.87** .04 20.87** .04 R2 15.7% 26.5% 27.8% 28.0%
Notes: n 5 2,639. Unstandardized regression coefficients are reported. The workplace survey weights were used during the analyses. R2 was calculated using the continuous latent response variable approach (Snijders & Bosker, 2012), which is better than model fit-based pseudo R2
(e.g., Cox–Snell R2 andMcFaddenR2) in terms of indicating the effect sizes of the predictionin orderedprobit regression. Dichotomous industry sectors were controlled, but are not listed in the table (for brevity).
*p , .05 **p , .01
2012 OctoberAcademy of Management Journal
complex collective interactions. As Colbert and Kurucz (2011: 401) noted, the core of the resource- based view includes a paradox:
. . . those features of resources which create and pro- tect the essence of a sustained resource-based ad- vantage (i.e., characterized by causal ambiguity, based upon socially embedded, complex knowledge and capabilities), also make them inscrutable and unpredictable, and therefore difficult if not impossi- ble to engineer and manage.
However, our findings suggest that, although the “re- verse engineering” of collective interactions is almost impossible, organizations can manage the emergence of human resources by shaping the features of col- lective interactions(i.e., concertedness,direction, and adaptability), furthering our understanding about the microfoundations of the resource-based view.
For the internal mechanism, we investigated how the homogeneity of employees’ HIWS experiences shaped the HIWS–innovation relation. Our result demonstrated that the positive relation between a HIWS and innovation was stronger when em- ployees’ HIWS experiences were more homogeneous.
As Wright and Nishii (2013) stated, strategic HRM researchers primarily focus on examining the vari- ances across organizations, with far less attention be- ing paid to the variances within organizations (e.g., variability in employees’ HRM experiences), limiting our understanding about the cross-level na- ture of organizational phenomena. As one of the first empirical studies examining the moderating role of homogeneity in employees’ HRM experiences, we demonstrated the importance of introducing within- organization variances in understanding the process for HRM to impact organizational outcomes. In addi- tion, it is important to note that our focus on the ho- mogeneity of employees’ HRM experiences diverges from Bowen and Ostroff’s (2004) focus on HRM sys- tem strength. Specifically, “HRM system strength” refers to the extent to which an HRM system sends unambiguous messages about the responses and be- haviors that are expected, rewarded, and valued by the organization (Ostroff & Bowen, 2016). In other words, it is based on whether employees share an understanding about organizations’ desired work be- haviors. By contrast, our study focuses on the vari- ability of employees’ HRM experiences, rather than
FIGURE 2 The Interaction between Employee-Experienced HIWS and the Homogeneity of HIWS
Experiences on Innovation
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0% Low High
Probability (Innovation = 2) at Low Homogeneity of HIWS Experiences (M – 1SD)
Probability (Innovation = 1) at Low Homogeneity of HIWS Experiences (M – 1SD)
Probability (Innovation = 2) at High Homogeneity of HIWS Experiences (M + 1SD)
Probability (Innovation = 1) at High Homogeneity of HIWS Experiences (M + 1SD)
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their shared understanding of desired work behaviors signaled by the HRM system.
For the external mechanism, our finding demon- strated that high strategic importance of innova- tion amplified the positive relation between an employee-experienced HIWS and innovation. Prior studies on the strategic contingency of HRM have mainly focused on the alignment of HRM systems with competitive strategies (e.g., differentiation vs. cost leadership; Huselid, 1995). As summarized by Jackson, Schuler, and Jiang (2014), empirical find- ings on the moderating roles of competitive strate- gies have been inconsistent, which might reflect the fact that firms achieve their strategy through various ways (e.g., firms can pursue a differentiation strategy by focusing on innovation or quality management) and the measures of business strategies often ignore different strategic priorities. Our study distinguishes from previous research in that we push beyond competitive strategies to directly capture organiza- tions’ strategic need and priority regarding innova- tion. By directly examining the strategic importance of innovation, our study contributes to the strategic contingency of HRM.
For the temporal mechanism, we introduced the concept of churn to the HRM literature to investigate how human resource flow shaped the relation be- tween HIWSs and innovation. It is important to note that the churn in human resources is an emergent construct that describes the total quantitative human resource flow, regardless of the qualitative changes associated with employee departures and re- placements (Nyberg & Ployhart, 2013). Our finding demonstrated that the churn in human resources amplified the positive effect of an employee- experienced HIWS on innovation by improving the adaptability associated with collective interactions. Our findings suggest that the departures of existing employees and the addition of new employees may benefit innovation by reducing organizational stag- nation and increasing the variety of perspectives in the organization. However, it is also possible that employee mobility has a negative influence on op- erating efficiency, due to the disruption of existing routines and the loss of accumulated experience and expertise (Burmeister & Deller, 2016; Shaw, 2011; Shaw, Park, & Kim, 2013). Therefore, when it comes to organizational performance, the churn in human
FIGURE 3 The Interaction between Employee-Experienced HIWS and the Strategic Importance of
Innovation on Innovation
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0% Low High
Probability (Innovation = 2) at Low Strategic Importance of Innovation (M – 1SD)
Probability (Innovation = 1) at Low Strategic Importance of Innovation (M – 1SD)
Probability (Innovation = 2) at High Strategic Importance of Innovation (M + 1SD)
Probability (Innovation = 1) at High Strategic Importance of Innovation (M + 1SD)
Employee-Experienced HIWS
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resources may function as a double-edged sword. One relevant study that helps explain the tension between efficiency and innovation was conducted by Argote, Insko, Yovetich, and Romero (1995), who found that the negative effect of human resource churn on work group performance was less pro- nounced for complex (vs. simple) tasks. They attrib- uted this difference in the human resource churn’s effect to the fact that performing complex tasks re- quires a greater level of innovation.
For managerial practice, this study informs the central role that HRM plays in promoting innovation. For organizations that are actively competing by in- novating, we demonstrated the importance of apply- ing a HIWS to elicit collective interactions for knowledge exchange and aggregation. Further, in fa- cilitating a HIWS to generate innovation, we suggest that organizations need to systematically manage the emergence-enabling process for the aggregation of human resources. Specifically, according to our re- searchfindings,thehomogeneityof employees’ HIWS experiences amplifiedthe positive effect of a HIWSon innovation, indicating that organizations could ex- tract additional value from HRM by implementing a HIWS uniformly across employees. Yet, if an
organization is trying to compete on the basis of in- novation, simply focusing on HRM systems is far from enough. In particular, based on our research findings, a HIWS could better promote innovation when orga- nizations attached more strategic value to innovation. As such, organizations should have clear strategic goals and recognize the potential value of innovation in support of their goals. In addition, organizations ought to clearly communicate their strategic needs to the workforce to ensure that employees’ collective efforts are channeled toward the right direction. Fi- nally, this study demonstrated the importance of maintaining a dynamic workforce to refresh the knowl- edge reservoir and improve organizational adapt- ability. Therefore, firms should take a dynamic perspective and actively manage the inflow and out- flow of employees over time.
Limitations and Future Directions
Our study has several limitations. First, we ana- lyzed archival data collected by Statistics Canada, so our measures were constrained by the survey ques- tions in the WES. In particular, with a dichotomous measure, we only evaluated the existence of
FIGURE 4 The Interaction between Employee-Experienced HIWS and the Churn in Human Resources on Innovation
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0% Low High
Probability (Innovation = 2) at Low Churn in Human Resources (M – 1SD)
Probability (Innovation = 1) at Low Churn in Human Resources (M – 1SD)
Probability (Innovation = 2) at High Churn in Human Resources (M + 1SD)
Probability (Innovation = 1) at High Churn in Human Resources (M + 1SD)
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employees’ experiences with a HIWS and were un- able to adequately capture the extent to which each HRM practice was implemented across employees. Nevertheless, by aggregating employee responses to the workplace level, our measure reflected the cov- erage of a HIWS among employees. Further, when examining human resource flow, we only focused on the quantity of employee mobility and were unable to evaluate the quality of employee mobility, limiting our understanding about the inner workings of hu- man resource flow (Nyberg & Ployhart, 2013). In ad- dition, our measure for innovation was also restricted by the available survey questions. Because the two items used to measure innovation were dichotomous, we were unable to adequately capture the level of product and process innovation.
Second, the scope of the WES measures restricted our ability to directly test the mediating processes for proposed emergence enablers to amplify the benefi- cial influence of a HIWS on innovation. Therefore, to gain in-depth understanding regarding the emer- gence process of human resources, we suggest that researchers conduct field studies to empirically test the inner workings of the three emergence enablers. For example, researchers could build a moderated mediation model to examine the potential mediators (e.g., transactive memory system, interpersonal com- munication, and collective cohesion) through which the emergence enablers shape the HIWS–innovation relation.
Third, due to the limitation of the WES data set, we were unable to distinguish between exploitative in- novation and explorative innovation, both of which are critical for organizational adaptation. Whereas “exploitative innovation” seeks to exploit existing products or services by leveraging current knowl- edge and competencies, “explorative innovation” develops new products or services by pursuing fun- damentally new knowledge and capabilities (Benner & Tushman, 2003). Therefore, we raise an important research question about whether the two types of innovation differ in terms of their human capital re- source emergence processes.
In this study, we used the homogeneity of HIWS experiences, the strategic importance of innovation, and the churn in human resources to probe and test the emergence-enabling mechanisms for innovation. We acknowledge that other organizational factors might also shape the patterns of collective in- teractions. Thus, an important area for future re- search will be to move beyond the three studied variables to identify other factors that can either fa- cilitate or inhibit the emergence of human resources
cultivated by HRM systems. For example, future re- search might consider the developmental stages of organizations and investigate how organizational life cycles (e.g., organizational initiation, functional growth, controlled growth, and strategic integration; Milliman, Von Glinow, & Nathan, 1991) shape the HIWS–innovation relation by influencing the adaptability associated with collective interactions. For example, compared with those at later develop- mental stages, organizations at earlier developmen- tal stages may be more sensitive to environmental changes and open to knowledge transfer. Thus, these organizations are more likely to innovate when tak- ing advantage of a HIWS. We hope our emergence- based HRM framework can stimulate more in-depth work adopting the lens of emergence to study organization-level phenomena.
REFERENCES
Argote, L., & Ingram, P. 2000. Knowledge transfer: A basis for competitive advantage in firms. Organizational BehaviorandHumanDecisionProcesses, 82:150–169.
Argote, L., Insko, C. A., Yovetich, N., & Romero, A. A. 1995. Group learning curves: The effects of turnover and task complexity on group performance. Journal of Applied Social Psychology, 25: 512–529.
Barney, J. B., & Wright, P. M. 1998. On becoming a strategic partner: The role of human resources in gaining competitive advantage. Human Resource Manage- ment, 37: 31–46.
Batt, R., & Colvin, A. J. S. 2011. An employment systems approach to turnover: Human resources practices, quits, dismissals, and performance. Academy of Management Journal, 54: 695–717.
Becker, B. E., & Gerhart, B. 1996. The impact of human resource management on organizational performance: Progress and prospects. Academy of Management Journal, 39: 779–801.
Benner, M. J., & Tushman, M. L. 2003. Exploitation, ex- ploration, and process management: The productivity dilemma revisited. Academy of Management Re- view, 28: 238–256.
Bowen, D. E., & Ostroff, C. 2004. Understanding HRM– firm performance linkages: The role of the “strength” of the HRM system. Academy of Management Review, 29: 203–221.
Burgess, S., Lane, J., & Stevens, D. 2000. Job flows, worker flows, and churning. Journal of Labor Economics, 18: 473–502.
Burgess, S., Lane, J., & Stevens, D. 2001. Churning dy- namics: An analysis of hires and separations at the employer level. Labour Economics, 8: 1–14.
2016 OctoberAcademy of Management Journal
Burmeister, A., & Deller, J. 2016. Knowledge retention from older and retiring workers: What do we know, and where do we go from here? Work, Aging and Retirement, 2: 87–104.
Chan, D. 1998. Functional relations among constructs in thesame contentdomain at different levels of analysis: A typology of composition models. The Journal of Applied Psychology, 83: 234–246.
Chang, S., Gong, Y., Way, S. A., & Jia, L. 2013. Flexibility- oriented HRM systems, absorptive capacity, and mar- ket responsiveness and firm innovativeness. Journal of Management, 39: 1924–1951.
Chang, S., Jia, L., Takeuchi, R., & Cai, Y. 2014. Do high- commitment work systems affect creativity? A multi- level combinational approach to employee creativity. The Journal of Applied Psychology, 99: 665–680.
Colbert, B. A. 2004. The complex resource-based view: Implications for theory and practice in strategic hu- man resource management. Academy of Manage- ment Review, 29: 341–358.
Colbert, B. A., & Kurucz, E. C. 2011. A complexity per- spective on strategic human resource management. In P. Allen, S. Maguire & B. McKelvey (Eds.), The SAGE handbook of complexity and management: 400–417. London, England: SAGE.
Collins, C. J., & Smith, K. G. 2006. Knowledge exchange and combination: The role of human resource prac- tices in the performance of high-technology firms. Academy of Management Journal, 49: 544–560.
Delery, J. E., & Doty, D. H. 1996. Modes of theorizing in strategic human resource management: Tests of universalistic, contingency, and configurational performance predic- tions. Academy of Management Journal, 39: 802–835.
Dooley, K. J. 2004. Complexity science models of organi- zational change and innovation. In M. S. Poole & A. H. Van de Ven (Eds.), Handbook of organizational change and innovation: 354–373. New York, NY: Oxford University Press.
Felin, T., & Hesterly, W. S. 2007. The knowledge-based view, nested heterogeneity, and new value creation: Philosophical considerations on the locus of knowl- edge. Academy of Management Review, 32: 195–218.
Franco, A. M., & Filson, D. 2006. Spin-outs: Knowledge diffusion through employee mobility. The Rand Jour- nal of Economics, 37: 841–860.
Gittell, J. H., Seidner, R., & Wimbush, J. 2010. A relational model of how high-performance work systems work. Organization Science, 21: 490–506.
Gomez-Mejia, L. R., & Balkin, D. B. 1989. Effectiveness of individual and aggregate compensation strategies. Industrial Relations, 28: 431–445.
Gong, Y., Cheung, S.-Y., Wang, M., & Huang, J.-C. 2012. Unfolding the proactive process for creativity:
Integration of the employee proactivity, information exchange, and psychological safety perspectives. Journal of Management, 38: 1611–1633.
Greve, H. R. 2003. A behavioral theory of R&D expendi- tures and innovations: Evidence from shipbuilding. Academy of Management Journal, 46: 685–702.
Guthrie, J. P. 2001. High-involvement work practices, turnover, and productivity: Evidence from New Zea- land. Academy of Management Journal, 44: 180–190.
Hannan, M. T., & Freeman, J. 1984. Structural inertia and organizational change. American Sociological Re- view, 49: 149–164.
Harrison, D. A., & Klein, K. J. 2007. What’s the difference? Diversity constructs as separation, variety, or dispar- ity in organizations. Academy of Management Re- view, 32: 1199–1228.
Hoetker, G. 2007. The use of logit and probit models in strategic management research: Critical issues. Stra- tegic Management Journal, 28: 331–343.
Holland, J. H. 1995. Hidden order: How adaptation builds complexity. Reading, MA: Addison-Wesley.
Huselid, M. A. 1995. The impact of human resource man- agement practices on turnover, productivity, and corporate financial performance. Academy of Man- agement Journal, 38: 635–672.
Jackson,S.E.,Schuler,R.S.,&Jiang,K.2014.Anaspirational framework for strategic human resource management. The Academy of Management Annals, 8: 1–56.
James, L. R., Demaree, R. G., & Wolf, G. 1993. rWG: An as- sessment of within-group interrater agreement. The Journal of Applied Psychology, 78: 306–309.
Jiang, K., Takeuchi, R., & Lepak, D. P. 2013. Where do we go from here? New perspectives on the black box in strategic human resource management research. Jour- nal of Management Studies, 50: 1448–1480.
Jones, G. R., & George, J. M. 1998. The experience and evo- lution of trust: Implications for cooperation and team- work. Academy of Management Review, 23: 531–546.
Koopmann, J., Lanaj, K., Wang, M., Zhou, L., & Shi, J. 2016. Nonlinear effects of team tenure on team psychologi- cal safety climate and climate strength: Implications for average team member performance. The Journal of Applied Psychology, 101: 940–957.
Kozlowski, S. W. J., & Klein, K. J. 2000. A multilevel approach to theory and research in organizations: Contextual, tem- poral, and emergent processes. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and methods in organizations: Foundations, extensions, and new directions: 3–90. San Francisco, CA: Jossey-Bass.
Lawler, E. E. III. 1986. High-involvement management: Participative strategies for improving organiza- tional performance. San Francisco, CA: Jossey-Bass.
2018 2017Li, Wang, van Jaarsveld, Lee, and Ma
Lawler, E. E. III. 1992. The ultimate advantage: Creating the high-involvement work organization. San Fran- cisco, CA: Jossey-Bass.
LeBreton, J. M., & Senter, J. L. 2008. Answers to 20 ques- tions about interrater reliability and interrater agree- ment. Organizational Research Methods, 11: 815–852.
Levitt, B., & March, J. G. 1988. Organizational learning. Annual Review of Sociology, 14: 319–340.
Liao, H., Toya, K., Lepak, D. P., &Hong, Y. 2009. Dothey see eyeto eye?Management and employee perspectives of high-performance work systems and influence pro- cesses on service quality. The Journal of Applied Psychology, 94: 371–391.
Liu, D., Gong, Y., Zhou, J., & Huang, J.-C. 2017. Human resource systems, employee creativity, and firm in- novation: The moderating role of firm ownership. Academy of Management Journal, 60: 1164–1188.
Milliman, J., Von Glinow, M. A., & Nathan, M. 1991. Or- ganizational life cycles and strategic international human resource management in multinational com- panies: Implications for congruence theory. Academy of Management Review, 16: 318–339.
Muthén, L. K., & Muthén, B. O. 1998-2012. Mplus user’s guide (7th ed.). Los Angeles, CA: Muthén & Muthén.
Nishii, L. H., Lepak, D. P., & Schneider, B. 2008. Employee attributions of the “why” of HR practices: Their effects on employee attitudes and behaviors, and customer satisfaction. Personnel Psychology, 61: 503–545.
Nishii, L. H., & Wright, P. M. 2008. Variability within or- ganizations: Implications for strategic human re- sources management. In D. B. Smith (Ed.), The people make the place: Dynamic linkages between in- dividuals and organizations: 225–248. New York, NY: Lawrence Erlbaum.
Nyberg, A. J., & Ployhart, R. E. 2013. Context-emergent turnover (CET) theory: A theory of collective turnover. Academy of Management Review, 38: 109–131.
Ostroff, C., & Bowen, D. E. 2016. Reflections on the 2014 decade award: Is there strength in the construct of HR system strength? Academy of Management Review, 41: 196–214.
Patel, P. C., Messersmith, J. G., &Lepak, D. P. 2013. Walking the tightrope: An assessment of the relationship be- tween high-performance work systems and organiza- tional ambidexterity. Academy of Management Journal, 56: 1420–1442.
Ployhart, R. E., & Moliterno, T. P. 2011. Emergence of the human capital resource: A multilevel model. Acad- emy of Management Review, 36: 127–150.
Rietzschel, E. F., Zacher, H., & Stroebe, W. 2016. A lifespan perspective on creativity and innovation at work. Work, Aging and Retirement, 2: 105–129.
Schneider, B., Goldstein, H. W., & Smith, D. B. 1995. The ASA framework: An update. Personnel Psychology, 48: 747–773.
Shaw, J. D. 2011. Turnover rates and organizational per- formance: Review, critique, and research agenda. Orga- nizational Psychology Review, 1: 187–213.
Shaw, J. D., Park, T.-Y., & Kim, E. 2013. A resource-based perspective on human capital losses, HRM invest- ments, and organizational performance. Strategic Management Journal, 34: 572–589.
Shin, D., & Konrad, A. M. 2017. Causality between high- performance work systems and organizational per- formance. Journal of Management, 43: 973–997.
Shipilov, A., Godart, F. C., & Clement, J. 2017. Which boundaries? How mobility networks across countries and status groups affect the creative performance of organizations. Strategic Management Journal, 38: 1232–1252.
Snijders, T. A. B., & Bosker, R. J. 2012. Multilevel analysis: An introduction to basic and advanced multilevel modeling. Thousand Oaks, CA: SAGE.
Song,J.,Almeida,P.,&Wu,G.2003.Learning-by-hiring:When is mobility more likely to facilitate interfirm knowledge transfer? Management Science, 49: 351–365.
Sørensen, J. B., & Stuart, T. E. 2000. Aging, obsolescence, and organizational innovation. Administrative Sci- ence Quarterly, 45: 81–112.
Statistics Canada. 2009. Workplace and Employee Survey (WES). Retrieved from http://www23.statcan.gc.ca/imdb/ p2SV.pl?Function5getSurvey&SDDS52615&lang
Staw, B. M. 1981. The escalation of commitment to a course of action. Academy of Management Review, 6: 577–587.
Szulanski, G. 2000. The process of knowledge transfer: A diachronic analysis of stickiness. Organizational Behavior and Human Decision Processes, 82: 9–27.
Takeuchi, R., Chen, G., & Lepak, D. P. 2009. Through the looking glass of a social system: Cross-level effects of high-performance work systems on employees’ atti- tudes. Personnel Psychology, 62: 1–29.
Tsai, W., & Ghoshal, S. 1998. Social capital and value creation: The role of intrafirm networks. Academy of Management Journal, 41: 464–476.
Tsui, A. S., Pearce, J. L., Porter, L. W., & Tripoli, A. M. 1997. Alternative approaches to the employee–organization relationship: Does investment in employees pay off? Academy of Management Journal, 40: 1089–1121.
van Dalen, H. P., Henkens, K., & Wang, M. 2015. Recharging or retiring older workers? Uncovering the age-based strategies of European employers. The Gerontologist, 55: 814–824.
von Bonsdorff, M. E., Zhou, L., Wang, M., Vanhala, S., von Bonsdorff,M.B.,&Rantanen,T.2016.Employeeageand
2018 OctoberAcademy of Management Journal
company performance: An integrated model of aging and human resource management practices. Journal of Management. Published online ahead of print. doi: 10.1177/0149206316662314.
Wegner, D. M. 1986. Transactive memory: A contemporary analysis of the group mind. In B. Mullen & G. R. Goethals (Eds.), Theories of group behavior: 185–205. New York, NY: Springer-Verlag.
West, M. A., & Farr, J. L. 1990. Innovation at work. In M. A. West & J. L. Farr (Eds.), Innovation and creativity at work: Psychological and organizational strategies: 3–13. Chichester, NH: Wiley.
Wright, P. M., & Nishii, L. H. 2013. Strategic HRM and organizational behavior: Integrating multiple levels of analysis. In D. E. Guest, J. Paauwe & P. M. Wright (Eds.), HRM and performance: Achievements and challenges: 97–110. Oxford, England: Wiley.
Wright, P. M., & Snell, S. A. 1998. Toward a unifying framework for exploring fit and flexibility in strategic human resource management. Academy of Manage- ment Review, 23: 756–772.
Yanadori, Y., & van Jaarsveld, D. D. 2014. The relationships of informal high performance work practices to job satisfaction and workplace profitability. Industrial Relations, 53: 501–534.
Zatzick, C. D., & Iverson, R. D. 2006. High-involvement management and workforce reduction: Competitive advantage or disadvantage? Academy of Manage- ment Journal, 49: 999–1015.
Yixuan Li ([email protected]) is an assistant pro- fessor in the Krannert School of Management at Purdue University. She received her PhD in Management from Warrington College of Business at the University of
Florida. Her research interests include strategic human resource management, learning and innovation, work groups and teams, and workplace diversity.
Mo Wang ([email protected]) is the Lanzillotti- McKethan Eminent Scholar chair at the University of Florida. He received his PhD in industrial–organizational psychology and developmental psychology from Bowling Green State University. His research interests include older worker employment and retirement, newcomer and expatriate adjustment, occupational health psychol- ogy, teams and leadership, and advanced quantitative methods.
Danielle D. van Jaarsveld ([email protected]) is the E.D. MacPhee chair in Management at the University of British Columbia’s Sauder School of Business. She re- ceived her PhD from Cornell’s ILR School. Her research interests include organizational and employee outcomes in services, nonstandard work arrangements, and strategic human resource management.
Gwendolyn K. Lee ([email protected]) is the Chester C. Holloway professor at the University of Florida. She holds a PhD in business administration from the University of Cal- ifornia at Berkeley, and MS and BS degrees in chemical en- gineering from the Massachusetts Institute of Technology. Focusing on entry and exit dynamics, her research examines strategies for competition, cooperation, innovation, and entrepreneurship.
Dennis G. Ma ([email protected]) is a doctoral student in the Organizational Behavior and Human Re- sources division at the Sauder School of Business at the University of British Columbia. His research interests in- clude technology, innovation, entrepreneurship, labor mar- ket dynamics, and resource distributions.
2018 2019Li, Wang, van Jaarsveld, Lee, and Ma
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