Download - Internal Commitment or External Collaboration? The Impact of Human Resource Management Systems on Firm Innovation and Performance

INTERNAL COMMITMENT OR

EXTERNAL COLLABORATION? THE

IMPACT OF HUMAN RESOURCE

MANAGEMENT SYSTEMS ON FIRM

INNOVATION AND PERFORMANCE

Y U Z H O U , Y I N G H O N G , A N D J U N L I U

Complementing previous research that showed a positive effect of general human resource management (HRM) systems on general fi rm performance, this article undertakes an integrative approach to compare the main effects and examine the interaction effects of two particular HRM systems on in-fl uencing fi rm innovation and performance. Using data from 179 organiza-tions in China, we found that both the commitment-oriented system, which emphasized internal cohesiveness, and the collaboration-oriented system, which was intended to build external connections, contributed to fi rm innova-tion and, subsequently, bottom-line performance. We also found an attenuated interaction between the two HRM systems in predicting fi rm innovation. We employed a mediated-moderation path model to extricate the relationships. Results suggested that organizations that implemented both HRM systems to promote innovation might face ambidexterity challenges. Ideas for future research and practical implications are discussed.

Keywords: human resource management, innovation, performance, china

Correspondence to: Yu Zhou, Department of Organization & Human Resources, School of Business, Renmin University of China, Zhongguancun Avenue 59, Haidian District, Beijing, 100872, China, Phone: +86 10 62513473, Fax: +86 10 82509169, E-mail: [email protected]. Yu Zhou and Ying Hong contributed equally to this paper.

Human Resource Management, March−April 2013, Vol. 52, No. 2. Pp. 263−288

© 2013 Wiley Periodicals, Inc.

Published online in Wiley Online Library (wileyonlinelibrary.com).

DOI:10.1002/hrm.21527

T raditional research in strategic human resource management (HRM) has taken a systematic approach to understand the bundle of high- performance HRM practices in im-

pacting bottom-line business performance (e.g., Boselie, Dietz, & Boon, 2005; Huselid,

1995). To unravel the “black box” of HRM−firm performance linkage, recently Becker and Huselid (2006) recommended introduc-ing differential HRM systems to target various organizational capabilities. This consider-ation extends a direction to focus HRM research on specific intermediate outcomes,

264 HUMAN RESOURCE MANAGEMENT, MARCH–APRIL 2013

Human Resource Management DOI: 10.1002/hrm

Much is still

unknown with

regard to which and

how HRM systems

will contribute

to organizational

innovation in

particular. The

current study

attempts to address

this issue.

rather than general financial performance. For example, the strategic capabilities of knowledge exchange (Collins & Smith, 2006; Smith, Collins, & Clark, 2005), production quality (Gibson, Benson, Porath, & Lawler, 2007), and customer service (Batt, 2002; Liao, Toya, Lepak, & Hong, 2009) have all been considered as intermediate outcomes of HRM. The impact of HRM on innovative ca-pabilities of organizations, however, has not been well understood.

In an era characterized by rapid techno-logical change, innovation has increasingly been considered as a critical competitive advantage of organizations (Spender & Grant, 1996). In his early conceptualization of what constituted firm competitive advantage,

Porter (1985) suggested that inno-vation in technology could con-tribute to both cost reduction as well as product differentia-tion. Interest in innovation has also been reflected in the recent emerging research in dynamic capabilities (Rothaermel & Hess, 2007), absorptive capacity (Zahra & George, 2002), and innova-tive capabilities (Subramaniam & Youndt, 2005) of organizations. Although researchers have gen-erally agreed on the importance of human resources for innova-tion for more than two decades (Schuler, 1986; Schuler & Jackson, 1987; Schuler & MacMillan, 1984; Van de Ven, 1986), the synergy between HRM practices and orga-nizations’ innovation has received

little attention until recently (Shane & Ulrich, 2004) when the empirical research focusing on innovative HRM systems just began (Agarwala, 2003; Beugelsdijk, 2008; Jiménez-Jiménez & Sanz-Valle, 2008; Laursen & Foss, 2003; Shipton, West, Dawson, Birdi, & Patterson, 2006). However, these initial efforts to under-stand the HRM-innovation relationship tended to borrow general high-performance work practices as predictors. Little research has been done to understand the effects of specific HRM systems in predicting innova-tion. Thus, to date, much is still unknown

with regard to which and how HRM systems will contribute to organizational innovation in particular. The current study attempts to address this issue.

One central argument for HRM’s contribu-tion to performance is that high-performance HRM develops employee abilities, motivation, and opportunities to perform (AMO model; Delery & Shaw, 2001), which can in turn be translated into financial performance. There are three assumptions underlying this line of research. First, high-performance HRM sys-tems, or variants such as high-investment/commitment systems, often take a generic orientation to improve firm performance. However, there is more than one way to achieve high performance. There may be multiple HRM systems in an organization, and each has a specific anchor to effectively create the important capabilities (Becker & Huselid, 2006; Lepak, Liao, Chung, & Harden, 2006). Second, human resources are often narrowly defined as the traditional employees who are internal to the organiza-tion. Although this focus is justified by the reasoning that internal employees are most valuable and unique to the organization, the increasing flexibility and connectedness of the business arena also demands that HRM be more broadly defined to include all exter-nal entities that contribute business perfor-mance, such as subcontractors and alliances (Lepak & Snell, 1999). Third, prior studies largely assume employees are independent individuals. Yet, individuals are embed-ded in social relationships with supervisors, coworkers, subordinates, clients, and part-ners (Brass, 1995). Social relationship is also an important mechanism through which HRM systems exert influences on innovation and learning (Gittell, Seidner, & Wimbush, 2010; Kang, Morris, & Snell, 2007). How HRM systems can be configured to impact social relationships among individuals for innovation, beyond managing individuals as isolated actors, is less understood.

The social network literature has not reached a conclusion regarding two proto-types of social dynamics in breeding innova-tion. One perspective, the “bonding view,” which emphasizes internal social capital,

IMPACT OF HRM ON FIRM INNOVATION AND PERFORMANCE 265


Although

researchers often

compare one

network prototype to

another to determine

its superiority, few

have taken an

integrative approach

to examine the

complementarity

between HRM

systems that

promote both types

of relationships

in contributing to

innovation.

originates from the network closure theo-ries (Coleman, 1988). This perspective is echoed by advocates of trust (Leana & Van Buren, 1999) and strong ties (Nelson, 1989) within organizations. It emphasizes build-ing a cohesive work environment in which individuals share knowledge and resources that are critical input for innovation. The other perspective, the outreaching view, is upheld by proponents of structural holes bridging unconnected network (Burt, 1997), diverse network range (Collins & Clark, 2003), organizational embeddedness (Uzzi, 1997), and weak ties (Granovetter, 1973; Hansen, 1999). These researchers value the external connectedness that can bring in nonredundant information and knowledge, which also stimulate innovation. Although researchers often compare one network pro-totype to another to determine its superior-ity (Perry-Smith & Shalley, 2003), few have taken an integrative approach to examine the complementarity between HRM systems that promote both types of relationships in con-tributing to innovation.

The goals of this article are threefold. First, in response to the call for understand-ing the effects of HRM on influencing spe-cific strategic capabilities (Becker & Huselid, 2006), we introduce innovative capabilities as the mediator in the HRM-performance relationship. Second, in contrast to prior research that often focused on the unitary high-performance work system, we syn-thesize theories of social relationships and employee creativity to examine a dual model of commitment-oriented and collaboration-oriented HRM systems, emphasizing internal cohesiveness and external connectedness, respectively. Third, to extricate the complex moderating relationship between the two HR systems, we use the path decomposition analytical approach to construct a mediated-moderation model.

Theoretical Background

Innovation research has gained its momen-tum over the past two decades due to the increasingly critical role that innovation plays in the competitive market landscape

(Damanpour, 1991). Research using resource-based theory (Barney, 1991), capability-based theory (Teece, Pisan, & Shuen, 1997), and knowledge-based theory (Grant, 1996) all suggest that the successful management of knowledge innovation has become a strategic imperative for organizations to gain competi-tive advantages.

From an HRM standpoint, both macro research and organizational behavior research have devoted substantial effort to understand human resources as a form of critical capital that supports innovation (Lado & Wilson, 1994; Wright, McMahan, & McWilliams, 1994). Traditional innovation research has focused on the roles of indi-vidual traits, expertise, skills, and motivation to innovate (Amabile, 1998; Taggar, 2002), as well as the contextual factors such as teamwork, rewards, and leadership in influencing creativ-ity (Shalley, Zhou, & Oldham, 2004). Individual creativity then aggregates to group- and orga-nizational-level creativity in an interactive and complex process, subject to contextual enhanc-ers and constraints of creativities (Woodman, Sawyer, & Griffin, 1993). Based on these theorizings, HRM systems can be configured to promote these desirable indi-vidual attributes/behaviors and work context that is conducive to innovation (Delery & Doty, 1996).

Another thread among HRM-innovation studies is an emphasis on social relation-ships in contributing to inno-vation (Collins & Smith, 2006; Kogut & Zander, 1996). As Burt (1997) pointed out, the value of human capital is meaningless without social capital. Drawing from both perspectives, we differentiate between two innovation-enhancing HRM architectures: commitment-oriented and collaboration-oriented HRM systems. Both systems exert influences on innovation outcomes through



Empirical research

has demonstrated

the important

impact of a high-

commitment

HRM system on

performance

outcomes such as

higher productivity,

higher quality, lower

scrap rate, and

lower employee

turnover.

managing employee AMO (Delery &Shaw, 2001; Lepak et al., 2006), as well as cultivating desirable social relationships (Evans & Davis, 2005; Gittell et al., 2010) that are conducive to innovation.

Commitment-Oriented HRM System and InnovationCommitment-oriented HRM system refers to the configuration of HRM practices that value employees and build a relational environ-ment in which employees are committed to the organization. This philosophy originates from traditional strategic HRM research, which emphasizes a high-investment/

performance philosophy to de-velop internal idiosyncratic knowl-edge and skills, cultivate employee motivation, and empower employ-ees to utilize discretion to perform (Lawler, 1992; Walton, 1985). This philosophy is realized by practices such as selective hiring, extensive training, performance appraisal, performance-based pay, job en-richment, teamwork, internal ca-reer opportunities, information sharing, employment security, and job rotation (Delery & Doty, 1996; Pfeffer, 1995). Empirical research has demonstrated the important impact of a high-commitment HRM system on per-formance outcomes such as higher productivity, higher quality, lower scrap rate, and lower employee turnover (e.g., Arthur, 1994; MacDuffie, 1995).

A high-commitment philoso-phy is also beneficial for achieving inno-vation outcomes. According to the AMO model, practices such as employment secu-rity not only establish employees’ psycho-logical commitment to organizations, but also develop their motivation to take risks (Jackson, Schuler, & Rivero, 1989). Selective hiring and extensive training for creativity not only create a valuable talent pool, but also convey a value for innovation (Koch &McGrath, 1996); employee involvement,

teamwork, and flexible job assignment pro-grams motivate employees as well as ensure employee discretion and opportunities to innovate (Batt, 2002; Ichniowski, Shaw, & Prennushi, 1997). This evidence is aligned with innovation research that shows that a healthy environment for innovation is one in which employees have encouragement and autonomy, and are free of impediments to innovate (Amabile, Conti, Coon, Lazenby, & Herron, 1996).

Aside from developing the creativity of employees as individuals, social relation-ship is another critical source of innovation that could be developed through commit-ment-oriented HRM practices. As noted by Subramaniam and Youndt (2005, p. 459), “unless individual knowledge is networked, shared, and channeled through relation-ships, it provides little benefit to organiza-tions in terms of innovative capabilities.” A commitment-oriented HRM system may cul-tivate innovation through creating a cohe-sive internal environment in which employees trust and share knowledge with each other (Kang et al., 2007). First, a work environment that values teamwork and job security is more likely to cultivate a sense of “public good” or collectivity (Ibarra, Kilduff, & Tsai, 2005), which invites individuals to trust the organi-zation, share knowledge, and innovate with-out private concerns (Moran, 2005). Second, the internal career ladder, cross-functional teamwork, and information sharing may develop internal knowledge and commu-nication protocols that serve as a common cognitive basis for effective communication and knowledge sharing (Gittell et al., 2010; Nahapiet & Ghoshal, 1998). Third, practices such as teamwork, job rotation, and internal career ladder create structural connectedness among employees (i.e., strong ties or norms). These enable the exploitation of more fine-grained, complex knowledge to facilitate innovation (Reagans & Zuckerman, 2001; Reagans, Zuckerman, & McEvily, 2004). As such, commitment-oriented HRM practices also create a cohesive environment for inno-vation, which is characterized by relational, cognitive, and structural connectivity among employees (Evans & Davis, 2005; Nahapiet



If a commitment-

oriented

environment is

helpful for the

management

of internal fine-

grained knowledge

to innovate, a

collaboration-

oriented

environment is

conducive to

seeking external

knowledge input for

innovation.

& Ghoshal, 1998). Integrating the effects of commitment-oriented HRM on both employee creativity and social dynamics, we propose:

Hypothesis 1: Commitment-oriented HRM sys-tems will be positively associated with organiza-tional innovation.

Collaboration-Oriented HRM System and Innovation

Collaboration-oriented HRM systems take an outreaching approach and emphasize devel-oping connections and quality relationships with external stakeholders and partners (Collins & Clark, 2003; Powell, Koput, & Smith-Doerr, 1996). For example, AT&T and IBM relied on external institutions for input on basic research (Lepak & Snell, 1999). This reflects a broader definition of HR to include not only internal human capital, but also external human capital that could be of value to the organization. Our definition of a collaboration-oriented HRM system is based on the original theorizing of “alliance employment mode,” “partnership employ-ment relationship,” and “collaborative HR configuration” (Lepak & Snell, 1999, pp. 40−42) to distinguish from the manage-ment of individuals who have a formal em-ployment relationship with the organiza-tion. We exclusively refer to various external entities that may share a common interest with the organization, such as alliances, learning partnerships with other profession-als, businesses, and academic institutions. It should be noted that this definition differs from some other studies, which operated collaborative HR systems as the management of internal human capital to develop team-work and cross-functional teamwork skills (Lopez-Cabrales, Pérez-Luño, & Cabrera, 2009). We distinguish collaboration-oriented HR systems from commitment-oriented HR systems to focus on the utilization of exter-nal human capital for internal innovation.

If a commitment-oriented environ-ment is helpful for the management of internal fine-grained knowledge to inno-vate, a collaboration-oriented environment

is conducive to seeking external knowl-edge input for innovation (Anand, Glick, & Manz, 2002). From an HRM standpoint, developing committed talents from within is not the only way to innovate; organiza-tions can also build collaborative relation-ships with various external entities such as stakeholders, consultants, partners, expatri-ates, and other institutions, treating them as valuable intellectual capital. The decision on which HRM system to adopt depends on the consideration of human capital’s value and exclusiveness to the organization (Lepak & Snell, 1999)—when the knowledge of inter-est is not idiosyncratic within each organi-zation, external HRM partnerships can be desirable.

Outreaching connections con-sist of the structural, adminis-trative, institutional, resource, relational, and cognitive ties with external entities, which assist in the collection of use-ful knowledge and information for innovation (Goes & Park, 1997). These external partners include strategic alliances, joint ventures, joint technical com-mittees, industrial districts, and collaborations. Through the part-nership, organizations can create inter-organizational structures that support knowledge sharing (Brass, Galaskiewicz, Greve, &Tsai, 2004; Inkpen & Tsang, 2005; Rosenkopf, Metiu, &George, 2001). These partners are characterized by the unique-ness of human capital (Lepak &Snell, 1999)—that is, not eas-ily transferable nor readily available on the factor market. Organizations that are exposed to such broad and complemen-tary knowledge sources have a higher propensity to explore new knowledge (Zahra & George, 2002). An outreaching network can also encompass relationships with consultants, customers, suppliers, investors, and govern-ment institutions (Matusik & Hill, 1998;



Yli-Renko, Autio, & Sapienza, 2001). These are also useful sources for diverse, non-redundant information and knowledge. Besides the partnership structure, the trans-mission of tacit knowledge across organi-zational boundaries is most effective when the connections are relational/noncom-petitive (Maurer & Ebers, 2006). Uzzi (1997) found that inter-organizational sharing of fine-grained knowledge was most effective among organizations that trusted each other than among organizations with arm’s-length connections. Organizations should therefore focus not only on developing a connection, but also maintaining quality relationships with these external partners. In addition, organizations with common goals can share communication channels and culture that form the basis of effective knowledge sharing (Inkpen & Tsang, 2005). It is thus important that organizations also establish a common interest with these partners to facilitate the collaboration. In sum, collaboration-oriented HRM systems play an important role in cre-ating the structural, relational, and cognitive relations for exploring new, nonredundant information and knowledge, which are criti-cal resources for innovation.

Hypothesis 2: Collaboration-oriented HRM sys-tems will be positively associated with organiza-tional innovation.

Interaction Between Commitment-Oriented and Collaboration-Oriented SystemsSo far we have discussed commitment-oriented and collaboration-oriented HRM systems as two independent systems, target-ing two different aspects of innovative capa-bilities. In fact, the value and inimitability of capabilities often reside in the integration of multiple processes into a seamless system (Teece et al., 1997). The integration of both systems is based on an HRM architecture per-spective (Lepak & Snell, 1999) and is rooted in the internal/configurational fit argument (Delery, 1998; Delery & Doty, 1996). Although both systems target different prototypes of

social relationships—strong ties within organi-zations and weak ties between organizations—the two systems may coexist and comple-ment each other in facilitating innovation.

From a human capital standpoint, the two HRM systems target different types of human capital (Lepak & Snell, 1999). Commitment-oriented HRM systems value internal firm-specific skills and knowledge as critical input for innovation; such firm specificity can take years to develop and is more amenable to be developed internally. Collaboration-oriented HRM systems target external information and knowledge that are equally valuable to the organization; these external resources may not need to be specific to each organization and can be obtained by utilizing external human capital (Lepak & Snell, 1999). As both HRM systems promote human capital of different value and inclusiveness, the effective integration of the two is critical for customizing exter-nally acquired resources for firm-specific exploitation (Zahra & Nielsen, 2002). The effective assimilation of external resources for internal use, realized by the integration of internal- and external-oriented HRM sys-tems, is also aligned with the central argu-ment of absorptive capacity (Cohen & Levinthal, 1990; Zahra & George, 2002), which suggests that externally acquired knowledge does not automatically translate into new knowledge creation except when internal connectedness enable effective knowledge transfer and integration.

We also draw on theories of strong ties and weak ties to illustrate the rationales for commitment- versus collaboration-oriented HRM systems. Network theories have not generated a conclusion with regard to the superiority of one prototype against the other; instead, research often suggests that both network types can complement each other (Evans & Davis, 2005; Leana & van Buren, 1999). Although strong, cohesive ties within organizations are beneficial for shar-ing tacit, “sticky” knowledge, the knowl-edge that is shared may represent redundant information, because individuals tend to know what coworkers know (Burt, 1997; Reagans & McEvily, 2003). Diverse weak ties



with external entities, on the contrary, are more likely to assimilate rich and nonredun-dant information externally (Brass, 1995; Granovetter, 1973; Hansen, 1999). As such, the power of internal cohesion may be lim-ited without the complementary external outreaching ties. If both internal strong ties and external nonredundant ties exist, orga-nizations are more likely to absorb diverse information and knowledge externally and then effectively digest them internally. In fact, at the team/unit level, it was shown that groups that had appropriate internal cohesiveness, as well as bridging network ties that spanned both vertical and horizontal boundaries, were the most effective (Hansen, 1999; Oh, Chung, & Labianca, 2004; Tsai & Ghoshal, 1998). In this vein, we propose that commitment-oriented and collaboration-oriented HRM systems interact to predict innovation outcomes:

Hypothesis 3: Commitment-oriented HRM and collaboration-oriented HRM systems interact to infl uence organizational innovation such that the more organizations implement collaboration-oriented HRM, the stronger the positive effect of commitment-oriented HRM on organizational in-novation.

Impact on Bottom-Line Performance

The existence of and interplay between commitment-oriented HRM and collaboration-oriented HRM systems also have an impact on firm performance. As discussed earlier, human resources have been considered a critical and inimitable source of competitive advantage (Becker & Gerhart, 1996; Huselid, Jackson, & Schuler, 1997). Empirical research on strategic HRM has shown a direct impact of commitment-oriented HRM systems and collaboration-oriented HRM systems on firm performance (Arthur, 1994; Lepak, Takeuchi, &Snell, 2003).

In addition, innovation is a critical source of competitive advantage and directly related to organizational performance (Danneels, 2002; McGrath & Tsai, 1996). We include three aspects of innovation: technol-ogy innovation refers to the regeneration of

new technology know-how and machinery; product innovation involves the introduction of new products and services that meet cus-tomers’ needs; and administrative innovation is the renewal of managerial philosophy, structure, and procedures. All three aspects of innovation have been shown to contrib-ute to firm performance (Damanpour, 1991; Damanpour & Evan, 1984). As such, along with the reasoning that commitment- and collaboration-oriented HRM systems culti-vate employee creativity and social dynamics that facilitate innovation, innovation carries the effects of HRM systems in contributing to firm bottom-line performance.

Hypothesis 4: Commitment-oriented HRM and collaboration-oriented HRM systems interact to infl uence a fi rm’s bottom-line performance, such that the more organizations implement collabora-tion-oriented HRM, the stronger the positive effect of commitment-oriented HRM on organizational performance.

Hypothesis 5: Organizational innovation mediates the effects of commitment-oriented HRM and col-laboration-oriented HRM systems and their interac-tions on organizational bottom-line performance.

Method

Sample and Procedure

Applying the methodologies used by macro HRM studies (e.g., Wright & Boswell, 2002), we collected data at the firm level. Surveys were sent to 300 firms of different owner-ships and industries in Beijing and Shanghai, two of the most developed and knowledge-intensive areas in China. For each firm, fol-lowing the multisource procedures suggested by Huselid and Becker (2000) and to reduce common method bias, two respondents from each firm reported different information. The senior HR manager with responsibilities across HR functions was asked to complete an HR measure, and the company’s CEO or general manager was invited to provide infor-mation on the firm’s innovation and bottom-line performance.



The surveys were administered to the sampled organizations from 2008 to 2009. Access to the organizations was obtained and survey instruments were distributed by trained investigators, who were postgradu-ate students majoring in business fields at local universities. A cover letter was attached to each questionnaire explaining the objec-tive of the survey and ensuring confidential-ity. Identifying numbers were also assigned to each top executive−senior HR dyad to enable the matching of responses. Due to the confidentiality policy of some com-panies and some incomplete instruments, observations with missing values were excluded. The final sample consisted of 179 matching dyads representing an overall response rate of 59.67 percent. This exceeds the average response rate of 55.6 percent as reported by Baruch (1999), and is substan-tially higher than the comparative firm-level studies reviewed by Becker and Huselid (1998), which had response rates ranging from 6 percent to 28 percent.

The 179 responding firms varied in own-ership: 34.6 percent were state-owned, 25.8 percent were domestic private, 12.4 percent were joint venture, and 27.2 percent were foreign-owned. The proportion of firms in the manufacturing industry was 25.5 per-cent, compared to 74.5 percent in the service industry. Public and nonpublic firms repre-sented 27.3 percent and 72.7 percent, respec-tively. Firms with high-tech certification and with labor unions took up 37.3 percent and 55.0 percent. The effective samples reported an average firm age (the longevity of operat-ing business in China) of 14.78 years (SD = 17.29) and an average firm size by employee scale of 5.90 (with Ln function transforma-tion; SD = 1.86). Statistical comparisons (t-tests) between the initial sample and final sample yielded no significant differences in firm characteristics (ownership, industry, public status, high-tech status, and union-ized status).

Measures

Our survey instrument was constructed based on those used in prior studies. As the theories

and variables were largely grounded in the western literature while data were collected in China, we used the back-translation proce-dures (Brislin, 1980) to resolve any possible discrepancies in the questionnaire items. Subsequently, we employed a pilot test using 20 firms in the Beijing area, which were not included in the ultimate sample mentioned earlier. In the pilot study, we employed a statement-checked questionnaire, as well as a semistructured interview approach, through which we collected some qualitative data of firms’ actual HRM practices. Based on both quantitative and qualitative feedback, we carefully reworded a few items to enhance clarity, and to refine the instrument to ap-propriately adapt to the context in China.

Although some earlier empirical stud-ies measured HRM practices by percentage scales (see Becker & Huselid, 1998; Huselid &Becker, 2000), several prominent studies using Likert scales also yielded robust results (Delaney & Huselid, 1996; Liao et al., 2009; Youndt, Snell, Dean, & Lepak, 1996). Except where otherwise indicated, the senior HR managers were asked to indicate the extent to which HRM practices had been adopted in their firms, by using a seven-point Likert scale, which ranged from 1 (“not at all”) to 7 (“to a strong extent”). The top general man-agers rated on a six-point scale to assess firm innovation and bottom-line performance growth over the past three years, which ranged from 1 (“not at all”) to 6 (“to a strong extent”). Researchers have shown that sin-gular and plural scales did not exemplify a significant difference in reliability or valid-ity (Dawes, 2008). In fact, using plural scales helps reduce the central tendency of respon-dents (Meyers, Anthony, & Glenn, 2005). Our approach is consistent with previous studies that examined the HRM-performance relationship (Delaney & Huselid, 1996). The specific measures and their reliability and validity are described next.

Commitment-Oriented HRM System

Although research on commitment-oriented workforce management has grown in the past two decades, terms such as “high



The HRM

configuration with

a collaborative

orientation was

expected to

facilitate a mutual

partnership-based

employment

relationship

between a firm and

external talents.

commitment” (Arthur, 1994; J. N. Baron & Kreps, 1999; Walton, 1985), “high involve-ment” (Guthrie, 2001), “high performance” (Appelbaum, Bailey, Berg, & Kalleberg, 2000; Huselid, 1995), and “knowledge-based” (Lepak et al., 2003) work practices/system were used divergently, and the relevant scale items were inconsistent (Becker & Huselid, 2006; Combs, Liu, Hall, & Ketchen, 2006). Xiao and Björkman (2006) developed a pre-liminary measure of high-commitment work practices in Chinese organizations. We con-structed the measure of commitment-ori-ented HRM by synthesizing these prominent studies, instead of relying on one single tem-plate. In addition, we carefully involved some creativity-related management prac-tices regarding diversity, empowerment, and sharing orientation (Shalley et al., 2004) into the commitment-oriented HRM system.

As a result, a bundle of 15 items was adopted to measure the commitment-oriented HRM system: diversity-oriented selective recruitment, job enrichment, self-managed teamwork, egalitarian participation, extensive training, job rotation, information sharing, result-based appraisal, development-oriented feedback, skill-based pay, high remu-neration, promotion from within, employment security, employee proposal mechanism, and overarching goal setting. The Cronbach’s alpha of the seven-point scale was .90.

Collaboration-Oriented HRM System

The HRM configuration with a collaborative orientation was expected to facilitate a mu-tual partnership-based employment relation-ship between a firm and external talents (Lepak & Snell, 1999). Practices such as an ex-tensive learning program with external busi-ness partners (i.e., suppliers and distributors), autonomous consultants for new insights and professional solutions, and collaborative projects with external knowledge communi-ties (i.e., universities, research institutions, and professional associations) were consid-ered beneficial for cultivating a firm’s exter-nal knowledge exchange and social capital, thus strengthening innovations (Lepak & Snell, 1999). We adapted the description of

the alliance-based HRM model from Lepak et al.’s (2003) empirical work and measured firms’ adoption of collaboration-oriented HRM using six items: formal external learning program with business partners, consulting ser-vice buy-in, flexible partnership with autono-mous external professionals, long-term personnel alliance with external academic institutions, building extensive social networks, and profes-sional HR outsourcing. The Cronbach’s alpha of this scale was .76.

Firm Innovation

Organizational innovation was traditionally conceptualized as technological inventions, new product or service develop-ment, and administration renewal (Damanpour, 1991). The innova-tion scales recently used in HRM-innovation empirical studies were largely based on these categories (Jiménez-Jiménez & Sanz-Valle, 2008). Accordingly, we measured firm innovation using three items, which assessed the extent to which the firm exploited new tech-nologies, developed new products or services, and adopted new adminis-trative approaches in the last three years. A six-point scale was used, and the Cronbach’s alpha was .78.

Firm Performance

Previous studies showed that scales of perceived organizational performance correlated positively (with mod-erate to strong associations) with objective measures of firm performance (Wall et al., 2004). In addition, perceptual assessment has been used by many studies as a proxy for mea-suring HRM effectiveness in China (e.g., Wei, Liu, Zhang, & Chiu, 2008). In fact, perceptual measures are even considered as a preferred approach in China because of the low reli-ability of objective financial performance disclosed by Chinese firms (Peng & Luo, 2000). Thus, we adopted three items from a scale used by Delaney and Huselid (1996), which contained the current status of the firm’s



competitive ranking in industry, the growth of productivity, and the growth of profitability over the past three years. The first item was mea-sured with five levels from 1 (“strongly disad-vantaged”) to 5 (“strongly advantaged”), while the latter two were measured by six-point scales. The reliability of the three items was .65.

Control Variables

We introduced several firm characteristics as control variables, which may be associated with both firm innovation and performance. These included firms’ industry, age (years since the firm opened its business in main-land China), size (measured by the total number of its employees), and whether they were publicly listed, unionized, and high-technology. Damanpour (1996) found in a meta-analysis that organizational size was positively related to both radical and incre-mental innovation. In addition, firm size was significantly related to innovation only in manufacturing industries, but not in service industries. Unionization may also potentially have influence on employee pro-ductivity and production cost. A recent meta-analysis found that unions were nega-tively associated with organizational profits (Doucouliagos & Laroche, 2009). High-technology firms were shown to be posi-tively associated with the number of patents, while firm age was negatively related to in-vestment in R&D (Heeley, Matusik, & Jain, 2007). We also included a series of dummy variables to indicate the ownership types: state-owned, domestic-private-owned, joint venture, and foreign-owned. Firm ownership has been found to have significant impacts on both HRM practice application and inno-vative outcomes in Chinese firms (Wang & Zang, 2005).

Measure Validation and Analyses StrategyThe Cronbach’s alpha values for the con-structs of two HRM configurations and firm innovation ranged from .76 to .90, all above the suggested cutoff value (Bagozzi & Yi,

1988). While the alpha value of the firm per-formance scale was relatively lower at .65, we considered it occurred, as scholars argued (Xiao & Björkman, 2006), largely due to a contextual difficulty of data collection at the company level. However, the indicator was still greater than the suggested concessive criterion of .60 (Nunnally, 1978).

Using LISREL 8.50 (Jöreskog & Sörbom, 1993), we conducted confirmatory fac-tor analyses (CFAs) to assess the discrimi-nant validity of multi-indicator constructs, including all independent, mediating, and dependent variables. We simpli-fied the structural model by reducing the number of items for HRM systems to pre-vent the fit problem led by including too many indicators (Bentler & Chou, 1987). Specifically, we combined the two items with the highest and lowest factor loadings into one aggregate score, then the second-highest and second-lowest factor loading, and so on. The method is widely accepted and commonly used by management researchers when employing SEM (Aryee, Budhwar, & Chen, 2002; N. Li, Liang, &Crant, 2010; Mathieu & Farr, 1991). Since the commitment-oriented HRM system was measured by 15 items, we conducted two stages of item aggregation. In the first stage, we reduced the item number from 15 to 8 via the way described earlier; in the second stage, we reduced the eight items to four composite items. For collaboration-oriented HRM system measure, we reduced the six items to three composite items. We finally modeled 13 items to four latent factors (four for commitment-oriented HRM and three items each for collaboration-oriented HRM, innovation performance, and bottom-line performance). We also compared the mea-surement model with two alternative mod-els. For instance, a three-factor model was created by specifying the seven HRM items to a “grand” HRM factor. The second alterna-tive model was obtained via modeling all the 13 items to a common factor. Results showed that the four-factor model fit the data well, χ2(df) = 157.65(59), p < .05, RMR = .067, IFI = .91, and CFI = .91. In contrast, the three-factor and the one-factor alternative



The main effect

of commitment-

oriented HRM on

firm performance

was significant and

positive

(β = .41, p < .05),

while the effect

of collaboration-

oriented HRM on

firm performance

was not found.

models did not generate satisfactory fit to our data (three-factor model: χ2(df) = 250.13(62), p < .05, RMR = .081, IFI = .84, and CFI = .83; one-factor model: χ2(df) = 391.16(65), p < .05, RMR = .110, IFI = .72, and CFI = .71). In addition, we used Akaike’s information criterion (Akaike, 1987) to eval-uate the relative fit of non-nested models (Jöreskog & Sörbom, 1993) and found that both alternative models showed a worse fit than the four-factor model. These results indicated the discriminant validity of com-mitment-oriented HRM, collaboration-ori-ented HRM, innovation, and bottom-line performance.

Results

Table I reports the means, standard devia-tions, and zero-order correlation coefficients among the study variables. As shown in the table, the commitment-oriented and collabo-ration-oriented HRM configurations were both related to firm innovation (r = .48 and .44, p < .01), and firm performance (r = .52 and .40, p < .01). Furthermore, firm innova-tion was related to the bottom-line perfor-mance (r = .68, p < .01).

Hierarchical regression was used to exam-ine our hypotheses. To avoid multicollinear-ity between the predictors and the interaction items and to enhance the interpretation of the main effects, we centered all variables involved in the interactions (Aiken & West, 1991). The control variables were entered into model 1, followed by commitment-oriented and collaboration-oriented HRM systems into model 2 (see Table II for details). Consistent with the results of zero-order correlations, after controlling for the effects of firm own-ership type, industry, age, size, public status, high-tech status and unionized status, com-mitment-oriented HRM (β = .30, p < .01), and collaboration-oriented HRM (β = .26, p < .01) were both positively associated with firm innovation and accounted for a significant amount of the explained variance in innova-tive outcomes (∆R2 = .23, p < .01). These find-ings provide support for Hypotheses 1 and 2.

Moderated hierarchical regression was used to test Hypothesis 3. As Model 3 (in

Table II) indicated, the interaction between the two HRM bundles was negatively asso-ciated with firm innovation (β = −.13, p < .05), and explained a significant amount of variance in innovation (∆R2 = .01, p < .05). Although the interaction was significant, the interactional pattern was opposite to what we predicted in Hypothesis 3.

Based on the same procedure, Model 4, Model 5, and Model 6 presented results for testing Hypothesis 4. The main effect of commitment-oriented HRM on firm perfor-mance was significant and positive (β = .41, p < .05), while the effect of collaboration-oriented HRM on firm performance was not found. The two-way interaction between the two HRM configurations entered in Model 6 was negatively related to firm performance (β = −.14, p < .05), which was also inconsis-tent with Hypothesis 4.

To specifically illustrate the interaction mechanisms, we fol-lowed the procedures suggested by Aiken and West (1991). The interactive relationships in Model 3 and Model 6 were plot-ted according to high (one stan-dard deviation above the mean) and low (one standard devia-tion below the mean) levels of collaboration-oriented HRM sys-tems. As shown in Figure 1 andFigure 2, for both high and low collaboration-oriented HRM, commitment-oriented HRM was positively associated with innova-tion and performance. However, the commitment-oriented HRM-performance relationship in firms with low collaboration-oriented HRM is stronger than what manifests in high collaboration-oriented HRM firms. This indicated that when collaboration-oriented HRM was high (+ 1 SD), the positive effects of commitment-oriented HRM on firm innovation and performance were attenu-ated. These results were not consistent with Hypotheses 3 and 4.

To test mediation effects, we followed the standard procedure recommended by



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R. M. Baron and Kenny (1986). Based on the presence of Model 3 and Model 6, which reported significant associations between the independent variables and the mediator (firm innovation), and between the indepen-dent variables and the dependent variable (firm performance), we included firm inno-vation in Model 7 and observed a signifi-cant decrease in the regression coefficient of commitment-oriented HRM systems (from .41 to .24, p < .01), and the interaction item became insignificant. These results partially supported Hypothesis 5.

Additionally, since firm innovation medi-ated the interaction effect of two HRM sys-tems on firm performance, a path model

with mediated moderation was created. Following previous suggestions by Muller, Judd, and Yzerbyt (2005), we segregated the mediated interaction effect, by which firm innovation subtracted the association between two-way HRM interaction and firm perfor-mance from the unmediated int eraction effect on bottom-line performance. These results are presented in Figure 3 and Figure 4. In the mediated moderation, the solid line repre-sented the relationship between commit-ment-oriented HRM and firm performance with a high level of collaboration-oriented HRM, which was consistently above the dashed line representing a low level of col-laboration-oriented HRM. In the unmediated

T A B L E I I Regression Analyses of HRM Systems and Interactions in Predicting OutcomesDependent Variables

Innovation Bottom-Line Performance

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7

Control Variables

Ownership (State-owned)

.05 .10 .10 −.07 −.03 −.03 −.09

Ownership (Private) .18* .22** .21* −.05 −.02 −.02 −.15*

Ownership (Joint venture)

−.08 −.04 −.03 −.17* −.14* −.13* −.11*

Industry (Service) −.12 −.15* −.15* −.09 −.11 −.11 −.03

Firm age −.03 .00 .02 −.07 −.02 .00 −.01

Firm size .09 .05 .05 .19* .16* .16* .13*

Publicly listed .13 .08 .09 .04 −.00 .01 −.04

High-tech .17* .08 .09 .07 −.00 .01 −.05

Union .01 −.08 −.08 .10 .01 .01 .06

Predictors

Commitment HRM .30** .29** .41* .41** .24**

Collaboration HRM .26** .26** .13 .13 −.02

Interactions Commitment HRM* −.13* −.14* −.06

Collaboration HRM

Mediator

Firm innovation .58**

R 2 .13** .36** .37** .10* .33** .35** .56**

F 2.62 7.75 7.49 1.84 6.82 6.66 14.57

∆R 2 – .23** .01* – .23** .02* .21**

∆F – 26.88 3.38 – 26.48 3.68 72.09*p < .05; **p < .01.



FIGURE 1. The Interaction Between HRM Systems in Predicting Innovation

Low High

High-collaboration HRM

Low-collaboration HRM

Commitment-oriented HRM

Firm

inno

vatio

n

FIGURE 2. The Interaction Between HRM Systems in Predicting Performance

Low High




Firm

per

form

ance

FIGURE 3. Mediated Moderation Between HRM Systems in Predicting Performance

Low High




Firm

per

form

ance

FIGURE 4. Unmediated Moderation Between HRM Systems in Predicting Performance

Low High




Firm

per

form

ance

moderation, the interaction became disor-dinal. Both mediated and unmediated mod-erations pointed to negative interactions between the two HRM systems on firm per-formance. Specifically, considering both fig-ures together, we found the slope difference of the solid and dashed lines in the mediated moderation (Figure 3) was bigger than that in the unmediated moderation (Figure 4), which implied a greater degree of negative interac-tion was due to innovation as the mediator, whereas the negative interaction was attenu-ated when it was directly loaded on organi-zational performance.

Edwards and Lambert (2007) criticized Muller et al.’s (2005) approach and argued that it does not reveal how a mediating vari-able specifically influences the moderation/interaction. They suggested using a General Path Analytic Framework (GPAF) to com-bine moderation and mediation. This frame-work decomposes a moderated-mediation or a mediated-moderation model into two specific moderated stage effects and a mod-erated direct effect. The moderated first-stage effect evaluates how the relationship between the independent variable (IV) and the mediating variable (Me) varies across levels of the moderating variable (Mo). The moderated second-stage effect evaluates how the Me-dependent variable (DV) rela-tionship varies across levels of Mo. Finally, the moderated direct effect evaluates the IV-DV relationship (in the presence of Me) varying as a function of Mo. Both moder-ated first-stage and second-stage effects con-stitute a moderated indirect effect. Edwards and Lambert (2007) also recommended using bootstrapping to simulate the coeffi-cients because the interaction term is a mul-tiplication of two related IVs, which creates an asymmetric distribution. Following this analytic procedure and using 1,000 boot-strap samples, we treated commitment HRM as the “IV” and collaboration HRM as the “Mo” and calculated the first-stage, second-stage, direct, indirect, and total effects in the low- and high-collaboration HRM firms. Group differences of those effects were also examined in the procedure. Results are pre-sented in Table III.



As shown in Table III, in firms with low and high levels of collaboration HRM, all the first-stage, second-stage, direct, indirect, and total effects were significant; however, all the effects appeared to be more or less “weaker” in those firms with a high level of collabo-ration HRM. Specifically, the first-stage, indi-rect, and total effect manifested significant group difference, while the second-stage and direct effects didn’t show significant group difference. These findings, which were consistent with those from the analy-ses of Muller et al.’s (2005) approach, indi-cated that (1) the effects of the HRM system interaction on organizational innovation and performance were both negative and (2) the negative interaction of HRM systems on firm performance was mediated by innova-tion. Furthermore, the simulation specified differential strengths of the direct and indi-rect effects and their group difference. The group difference of the indirect effect (−.13, p < .01) was significantly greater than that of the direct effect (−.08, p > .05). This result showed that the negative interaction embed-ded in the indirect effect was stronger than that in the direct effect, which reinforced the fact that innovation as a mediator might

account for the negative interaction of two HRM systems on firm performance.

Discussion

Researchers in strategic HRM have long been intrigued by the “black box” of why and how HRM can influence firm performance. For example, early work by Schuler and Jackson (1987) and Jackson et al. (1989) suggest that HRM systems influence performance through cultivating desirable behaviors of employees that will support firm strategy, such as inno-vative behaviors to support an innovation strategy. This paves the foundation of the contingency perspective of HRM. Subsequent empirical studies have also considered inno-vation strategy as a moderator on the effect of HRM systems on performance (Arthur, 1994; Delery & Doty, 1996). Our study de-parts from these previous approaches by viewing innovation as a mediating capability between HRM systems and performance. In addition, we contribute to the literature by synthesizing the innovation and the network perspective of strategic HRM to examine the influence mechanisms of two HRM systems on organizational innovation and bottom-line

T A B L E I I I Results of the Simple Effect Analysisa

Firms With Low-Collaboration HRM

Firms With High-Collaboration HRM

Group Difference

1. First stage(Commitment HRM’s effect on fi rm innovation)

.51** .25** −.26**

2. Second stage(Firm innovation’s effect on performance)

.46** .44** −.02

3. Direct effect(Commitment HRM’s direct effect on performance)

.28** .20* −.08

4. Indirect effect(Commitment HRM’s effect on performance via fi rm innovation)

.24** .11** −.13**

5. Total effect(Commitment HRM’s total effect on performance)

.52** .31** −.21*

*p < .05; **p < .01.a1,000 bootstrap samples were used to generate estimates; unstandardized path coeffi cients are reported in the table.



Incorporating

multidisciplinary

perspectives, our

framework extends

the previous

theorizing of

the relationship

between HRM and

firm performance.

Our findings

also shed light

on research in

managing HR for

innovation.

performance. First, we demonstrated the pos-itive effects of both commitment-oriented and collaboration-oriented HRM systems on firm innovation. Theoretically, these two work systems serve distinct purposes. On the one hand, commitment-oriented HRM sys-tems reinforce firms’ internal innovative ca-pability by enhancing employee creativity and cohesiveness to exploit knowledge. On the other hand, collaboration-oriented HRM systems stimulate innovation by building social networks with external sources that help explore new knowledge and inspira-tions. Second, we found significant, negative interactions between the two HRM systems on both firm innovation and performance.

These are opposite to our hypothe-ses (H3 and H4) that predicted a synergy between the two systems. This finding points to a new direc-tion to examine the “ambidexterity” of HRM systems within organiza-tions, which we will elaborate on later. Third, we presented a medi-ated-moderation model that showed that innovation carried the negative interaction of HRM systems on firm performance.

Theoretical Implications

Our work was among the first to integrate research in macro HRM with multiple theoretical perspec-tives, including individual creativity in organizations, orga-nizational innovative capability, and the role of social networks. Incorporating multidisciplinary perspectives, our framework ex-tends the previous theorizing of the relationship between HRM and firm performance. Our find-

ings also shed light on research in managing HR for innovation.

First, whereas traditional strategic HRM research employed a generic approach (e.g., Arthur, 1994) to examine the effect of one HRM system on general firm performance, our study examined two distinct systems’ effects on innovation in particular. This

approach echoes the call for more research to align multiple differentiated HRM systems with strategic capabilities of organizations (Becker & Huselid, 2006). It allows a more accurate conceptualization of HRM systems targeting specific strategic outcomes of the firm. Recent research emphasized innova-tion as a strategic capability and linked HRM with various innovative outcomes including organizational intellectual capital (Youndt, Subramaniam, & Snell, 2004), knowledge creation (Collins & Smith, 2006), and prod-uct and technology innovation (Beugelsdijk, 2008; Jiménez-Jiménez & Sanz-Valle, 2008; Shipton et al., 2006). Some studies also focused exclusively on the Chinese con-text (Y. Li, Zhao, & Yi, 2006; Wang & Zang, 2005). However, one common approach among these studies was that the configura-tions of HRM practices tended to be generic in nature.

Keeping this in mind, in this study we specifically targeted commitment-oriented HRM practices that may contribute directly to individual traits, skills, specialties, and work contexts that were identified as condu-cive to individual creativity (Amabile et al., 1996; Oldham & Cummings, 1996; Shalley et al., 2004). These practices also strengthen the internal ties and develop trust (Nelson, 1989), which encourage individuals to uphold the collective good and share knowl-edge without concerns (Reagans & McEvily, 2003). In addition, we drew from social network theories advocating inter-organi-zational collaboration and embeddedness (Powell et al., 1996; Uzzi, 1997) to target collaboration-oriented HRM practices that bring in external human resources for inno-vation. These two systems are concerned with investment on internal and external human capital, respectively, and are shown to have discriminant validity against one another. Our results supported both perspec-tives of cultivating internal commitment and developing external collaboration in breeding innovation, which enabled us to propose a construct of a “high-innovation work system” by combining both internal-commitment and external-connection HRM practices for future empirical studies.



If an organization

ambidextrously

invests in both

commitment- and

collaboration-

oriented HRM

systems, each

system may divert

the resources

devoted to the other

system. Thus, in

such a context, the

best equilibrium may

be an “alternative

balancing

mechanism.”

Second, one of the most notewor-thy findings was the interaction between commitment-oriented and collaboration-oriented HRM systems. We followed the theoretical arguments regarding the syn-thesis of HRM practices (e.g., Delery, 1998; Lepak et al., 2006) and extended horizontal fit theory among HRM practices to configu-rational complementarities between HRM systems (Y. Zhou & Hong, 2008). Zahra and Nielsen (2002) showed positive effects of both “internal” and “external” human resources on organizational technology com-mercialization, but they did not test the interplay between the two systems. Lepak et al. (2003) examined the interaction between knowledge-based and partner-ship-based HRM configurations on return on equity and market-to-book but did not find significant results. Our results for the first time showed significant interactions between commitment- and collaboration-oriented HRM systems. Interestingly, the pattern of interaction was opposite to our hypotheses. Strategic capabilities theory sug-gests that the capability to integrate multiple processes (internal creativity and external connectivity) can be inimitable and criti-cal for organizational competitive advan-tages (Teece et al., 1997). Empirically, human capital was found to positively moderate the relationship between social capital and radi-cal innovation (Subramaniam & Youndt, 2005). We therefore hypothesized that there would be a synergy between commitment- and collaboration-oriented HRM systems.

We draw on ambidexterity theory to explain the unexpected negative interactions between commitment- and collaboration-oriented HRM systems. Ambidexterity the-ory argues for a dynamic equilibrium of two interrelated innovation—exploration and exploitation (Benner & Tushman, 2003; Gibson & Birkinshaw, 2004; O’Reilly III & Tushman, 2004). These two managerial pro-cesses, however, may compete for the same piece of the pie of organizational resources (March, 1991). In a similar vein, if an orga-nization ambidextrously invests in both commitment- and collaboration-oriented HRM systems, each system may divert the

resources devoted to the other system. Thus, in such a context, the best equilibrium may be an “alternative balancing mechanism” (Gupta, Smith, & Shalley, 2006; p. 698). As our results showed, although the interaction between two systems was negative (a higher collaboration system attenuated the posi-tive effect of a commitment-oriented sys-tem on innovation and performance), the main effects of both systems were positive. This implies that there may be a punctuated equilibrium: before reaching this equilib-rium, the usage of both systems positively contributes to innovation and performance; after this tipping point, the two systems will exhaust the available resources and start to attenuate the effect of each other.

In addition, the interaction graph in Figure 1 shows that when the commitment-oriented HRM sys-tem is low, the high-collaboration HRM system may play as a sub-stitute. Indeed, the innovation level of low-commitment- coupled with high-collaboration-oriented HRM systems is similar to that of low-collaboration- coupled with high-commitment-oriented HRM systems. However, this does not mean that only one system could exist in the organization. It means that one system may be able to substitute for another if resources are limited. In decid-ing which system to invest more in, organizations can consider the resources that are available to them (Lepak & Snell, 1999). If an organization has resource slack and the capability to develop the internal human capital for innovation, it can invest in the commitment-oriented HRM sys-tem as well as the collaboration-oriented HRM system. A good example is Microsoft, which hires its own scientists to conduct basic research, while at the same time collaborat-ing with academic and research institutions. On the contrary, when an organization lacks the internal specialty or is concerned with



We call for future

research to examine

the role of HRM

configurations in

developing other

strategic capabilities

that are critical

for sustaining

competitive

advantages.

short-term return on investment on internal human capital, it can consider collaborating with external institutions for innovation, such as IBM and AT&T, which relied on external institutions for basic research that did not directly relate to consumer products (Lepak & Snell, 1999).

Another interesting finding is that when we tested the mediated-moderation model, we found that innovation as a mediator car-ried the substitute relationship between two HRM systems, thereby reducing the nega-tive interaction effect of the two HRM sys-tems on firm bottom-line performance. This means that when it comes to innovation as the strategic goal, perhaps external human

capital and internal human capi-tal can play the same role and substitute for one another, and investing on both may not sub-stantially improve overall inno-vation after all. Both sources of human capital may provide similar input for innovation, although the costs associated with investing on this human capital may differ. Although we did not test other outcomes as the mediator, we may surmise that if the goal is something other than innovation, such as to improve product quality or ser-vice experience, external human capital may not be able to substi-tute for internal human capital. That may be the reason why the negative interaction between the

two systems was less salient on the direct path to bottom-line performance.

In addition, organizations may also take into account their innovation strat-egy in deciding how to use their resources. To shed light on this consideration, future research should directly measure different types of innovation (e.g., radical vs. incre-mental innovation [Dewar & Dutton, 1986] and exploratory vs. exploitative innovation [Benner & Tushman, 2003; March, 1991]) to further explicate the effects of commit-ment-oriented and collaboration-oriented HRM systems and their interaction on these

innovation types. If, for example, collabora-tion-oriented HRM systems are found to be more amenable for radical/exploratory inno-vation, while commitment-oriented HRM systems are more conducive to incremental/exploitative innovation, then organizations can take their innovation strategy into con-sideration when determining how to deploy their limited resources. If an organization uses both strategies for different projects and has resource slack to invest on both systems, then it may consider using different systems for different innovation projects.

Third, our study was among the first to examine innovation as a mediator between HRM configurations and firm bottom-line performance. We found that inno-vation was an important intermediate mechanism that carried the effects of com-mitment- and collaboration-oriented HRM systems and their interaction. By employing the mediated-moderation approaches sug-gested by Muller et al. (2005) and Edwards and Lambert (2007), our results indicated that innovation as a mediating capability absorbed the negative interaction between two HRM systems on firm performance. Together these results suggest that when it comes to innovation as the targeted capa-bility, both commitment-oriented and collaboration-oriented HRM systems are useful for promoting innovation. However, implementing both types of HRM systems may also lead to competition for the same resources for innovation, thereby reducing the synergy of the HRM systems on per-formance. Whether commitment-oriented and collaboration-oriented HRM systems will have a positive synergy when it comes to other strategic capabilities is an interest-ing question for future research. This line of research is part of the endeavors to unravel the “black box” between HRM and firm performance, supporting the “HRM−inter-mediate capabilities−firm performance” paradigm emphasized recently (Becker & Huselid, 2006). We call for future research to examine the role of HRM configurations in developing other strategic capabilities that are critical for sustaining competitive advantages.



Practical Implications

The results of the current study also shed light on the managerial implications of HRM in businesses. We suggest that firms consider differentiating HRM systems for specific organizational capabilities (i.e., innovation), rather than implementing the so-called HRM best practices. First, focusing on innovation in particular, our results indicate that organi-zations that adopt a commitment-oriented HRM system have better innovation perfor-mance and ultimate performance. In fact, many prominent organizations have adopted high-empowerment or high-involvement management practices. For instance, 3M Company’s “15% Rule” stipulates that em-ployees can spend up to 15 percent of their time on any research activities that they per-sonally deem interesting. This empowers employees to fully utilize their discretion and creativity, and thereby contribute to or-ganizational innovation. General Electric creates a “Work-Out” procedure and adheres to a “boundary-less” culture, where employ-ees are encouraged to transcend the organi-zational structure and come together to solve problems using their discretion and exper-tise. Another example is the “Kaizen Teian” system developed in Japan, which realizes continuous improvement through employee suggestions. All these practices are examples of the successful application of commitment-oriented HRM systems.

Second, our results also showed that building collaborative relationships with external institutions and utilizing exter-nal human capital contributed to innova-tion performance. Organizations can use practices such as participating in strate-gic alliances, joint ventures, joint techni-cal committees, or industrial districts to exchange and obtain valuable knowledge and resources for innovation. Besides com-petitors, organizations can also benefit from the knowledge and resources offered by customers, suppliers, investors, and gov-ernment institutions. Likewise, organiza-tions can obtain useful information and resources from external human capital such as consultants.

Third, we also found that organizations should balance the usage of both commit-ment and collaboration HRM systems. Due to the limited resources that organizations can offer, organizations need to consider vari-ous factors and evaluate the relative benefits of having committed HRM and collaborative HRM in contributing to innovation and per-formance. In addition, since there has been little effort to examine an HRM effectiveness model with the integration of mediation and moderation, our mediated-moderation path model also provided implications for reinforc-ing the specialized systems. This stretches that with a focus on innovative and other strategic capabilities, organizations can better manage the synergy among multiple HRM systems.

Limitations and Future Research DirectionsThe study results should be interpreted in light of several limitations. First, the diffi-culty to collect objective data on firm-level performance in China constrained our per-formance measures. The company perfor-mance was rated by managers and focused on growth in competitiveness in time and in re-lation to competitors. However, previous studies have shown that the correlations be-tween perceived and objective measures of firm performance were moderate to high (Wall et al., 2004). Due to the low reliability of objective financial performance disclosed by Chinese firms (Peng & Luo, 2000), perceptual measures could be a preferred approach. None-theless, in order to reduce common method bias of using perceived measures, we em-ployed a multisource approach—we had HR managers and general managers report HR measures and performance, respectively.

Second, the cross-sectional data we col-lected do not allow a causal interpretation. Thus, a longitudinal approach is encour-aged in future efforts. Third, although it is theoretically feasible to extend this study to other contexts, the specific contextual dif-ferences between China and Western coun-tries may restrict the generalizability of the findings. Thus, a cross-cultural comparative study is called upon.



Our results indicated

that there was

an ambidexterity

between the two

prototypical HRM

systems. This will

allow researchers

to further align

commitment-

oriented and

collaboration-

oriented HRM

systems with

ambidextrous

organizations,

and understand

the effect of

HRM systems in

contributing to

different types of

innovation.

commitment and collaborative HRM systems differs. According to Gibson and Birkinshaw (2004), organizational ambidexterity also consists of structural and behavioral ambi-dexterity. Future research can examine the integration of the structures related to com-mitment and collaboration systems, as well as the underlying relational/behavioral mechanisms that support both systems, in influencing the effective integration of both systems.

Finally, we draw from theories of cre-ativity and social networks in developing our research hypotheses. We encourage future research to further open the black box by including these variables as inter-mediate variables. In addition, there has been an increasing pool of empirical studies that examined individual creativity in work contexts (Shalley et al, 2004), as well as cre-ativity in a multilevel context (Ford, 1996; Woodman et al., 1993; J. Zhou & Shalley, 2008). We call for studies to examine the effect of HRM systems on employee creativ-ity, team creativity, and firm innovation by conceptualizing multilevel models.

Conclusion

To further tackle the “black box” between HRM and firm performance, we departed from the previous approach of focusing on one universal HRM system and examined two distinct HRM systems: commitment- and collaboration-oriented HRM systems. These systems are configurations of “high-innovation work systems” but are distin-guished as two prototypical HRM patterns serving different foci. In addition, we con-sidered innovation as an intermediate ca-pability instead of moderating strategy. We found positive effects of both HRM systems on firm innovation and performance. We also found a negative interaction between the two HRM systems on both firm innova-tion and bottom-line performance—one system could attenuate the positive impact of another. In addition, innovation played a mediating role among HRM systems, their interaction, and firm performance. Based on our findings, we suggest that

In terms of future research, we offer three suggestions. First, we examined innovation as consisting of product/service, technology, and administration innovation. Our results indicated that there was an ambidexter-

ity between the two prototypi-cal HRM systems. Therefore, we recommend that future research examine innovation such as radical vs. incremental innova-tion (Dewar & Dutton, 1986) or exploitative vs. exploratory innovation (Benner & Tushman, 2003; March, 1991). This will allow researchers to further align commitment-oriented and col-laboration-oriented HRM systems with ambidextrous organizations, and understand the effect of HRM systems in contributing to different types of innovation.

Second, building on our find-ing on the interaction effect of commitment- and collaboration-oriented HRM systems, we also encourage more research to be done to examine the particular interactions among cost-oriented, quality-oriented, innovation-oriented, service-oriented, and safety-oriented HRM systems and so on, to understand the trade-off between differential HRM configurations.

Third, our study also stimu-lates additional research ques-tions regarding the contexts of balancing the two HRM systems. According to ambidexterity the-ories developed by Gupta et al. (2006), organizational contexts may influence the balance of dual managerial capabilities. One approach is to include relevant organizational attributes as mod-

erators of the two HRM configurations. By examining three-way interactions, we can gain insight as to under different organiza-tional complexity/dynamism levels and with different levels of organizational resources, whether the synergistic effect between



firms can be innovative by employing ei-ther high-commitment or high-collabora-tion HRM systems, or a balanced combina-tion of the two, taking into consideration the resource scarcity in organizations. This is analogous to the strategy commonly used in boxing. To produce the most powerful hit, a boxer would not use both fists simul-taneously. Instead, he/she would advance one fist with the balance of the other at the back.

Acknowledgments

This research was supported by the National Natural Science Foundation of China (Proj-ect no. 71002096). Thanks to the Research Center for Corporate Innovation and Com-petitiveness, Renmin University of China, for providing the data (Grant no. 11XNS001). Any and all remaining errors are ours and do not necessarily represent the position of the institution.

YU ZHOU, PhD, is an assistant professor in the Department of Organization and Human Resources at the School of Business of Renmin University of China. He received his doc-toral degree in human resource management from Renmin University, and his doctoral research was carried out in a joint-PhD program with visiting study at Rutgers University. He specializes in research on strategic HRM, HRM effectiveness within the Chinese con-text, HRM-innovation/creativity linkage, and interaction of HRM and leadership.

YING HONG, PhD, is an assistant professor in the DeGoote School of Business at McMaster University and received her doctorate in industrial relations/human resources from Rut-gers University. She specializes in research on the strategic role of human resource man-agement and the linkage between human resource management and service excellence.

JUN LIU, PhD, is an associate professor in the Department of Organization and Human Resources at the School of Business of Renmin University of China. He received his doc-toral degree in management from the Chinese University of Hong Kong. His research inter-ests include leadership, HR practices, psychological contracts, and organizational politics.

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