Traditional and Discretionary HRM Practices and ...

HRM, Networks, and Team Learning

Traditional and Discretionary HRM Practices and Social Networks and their Associations with Team Learning Behaviors

Paper to be presented at the Workshop Social Network Perspectives in HRM Copenhagen Business School, Center for Strategic Management and Globalisation, March 2010 IJ. Hetty van Emmerik Maastricht University School of Business and Economics Department of Organization and Strategy Tongersestraat 53, 6211 LM Maastricht, The Netherlands Ph. +31 43 3883812, Fax +31 43 3884893 E-mail: [email protected] Bert Schreurs European University College Brussels Centre for Corporate Sustainability Stormstraat 2, 1000 Brussels, Belgium Ph. +32 2 609 8276, Fax +32 E-mail: [email protected] Nele de Cuyper Dept of Organizational Psychology University of Leuven, Belgium Ph. +32.16.326014 [email protected] I.M. Jawahar Illinois State University Department of Management & Quantitative Methods 250 College of Business, Normal, Illinois 61790, USA PH: 309 438 5703, FAX: 309 438 8201 E-Mail: [email protected]

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Abstract To cater to the growing importance of teamwork, organizations have started to target their Human Resource Management (HRM) practices toward teams. The present study distinguished traditional from discretionary HRM practices and used the Resource Based View and Exchange Theory to propose that both types of HRM practices and social networks will be important to explain team learning behaviors. The study sample consisted of 233 teachers in 34 teams and 14 school management representatives from 14 secondary schools in The Netherlands. The results showed that discretionary HRM practices and breadth of the networks had a positive direct effect on team learning behaviors. Further, two moderating effects of HRM practices on the associations between advice networks and the breadth of social networks with team learning behaviors were found. We discuss potential limitations of the study and offer directions for future research.

Keywords: HRM Practices, Social Networks, Team Learning Behaviors


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INTRODUCTION As teams represent the basic work units in many organizations today, effective functioning of organizations depends heavily on the success of teams (Cummings & Worley, 2005; Jex, 2002; Mathieu, Maynard, Rapp, & Gilson, 2008). Human Resource Management (HRM) is the pattern of planned human resource activities intended to enable the organization to achieve its goals (Wright & McMahan, 1992). Given the growing significance of teamwork over the past decades, an important meso-level HRM goal is enhancing team learning behaviors. Consequently, an increasing emphasis is placed on HRM practices geared toward enhancing team learning and team effectiveness (Lepak & Shaw, 2008). Broadly speaking, HRM practices come in two flavors: Traditional HRM and discretionary HRM (Hayton, 2003). Traditional HRM, basically an efficiency-oriented model, is based upon the premise of matching the skills and abilities of the individual or team with the needs of the organization. Examples of such practices include selection, placement, and training. In contrast, discretionary HRM aims to promote discretionary (citizenship behavior/extra-role) job performance, and for example, takes the form of incentive compensation, continuous employee development, empowerment, and employee engagement programs. While both traditional and discretionary HRM practices may relate to team effectiveness, the underlying mechanisms through which they influence team effectiveness may be different. We propose team learning behaviors as a key construct that may develop from HRM practices and social networks. Our assertion is consistent with Edmonson‟s (1999) model of team learning in which team learning is regarded as a process through which outcomes, such


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as adaptation to change, greater understanding, or improved performance can be achieved. In this model of Edmonson, team learning is conceptualized as behaviors such as seeking feedback, sharing information, experimenting, asking for help, and talking about errors; all activities that are part of the interaction processes between team members (Van Der Vegt & Bunderson, 2005). In other words, we consider the direct effects of HRM practices and social networks on team learning behaviors as well as the potential moderating role of HRM on the relationship between social networks and team learning behaviors. Each component of our research model is discussed in more detail in the next section. A particular strength in this study is that we gathered data from different sources: Whereas information about the advice networks and the breadth of the social networks were evaluated by team members themselves, i.e. teachers (primary study participants), traditional and discretionary HRM practices and team learning behaviors were evaluated by management representatives from the schools in which the participating teams resided.

Theoretical Background and Hypotheses Development HRM and Team Learning Behaviors The Resource Based View (Barney, Wright, & Ketchen, 2001; Wernerfelt, 1984) has made important contributions in the area of strategic HRM. With an emphasis on people as strategically important to an organization’s performance, RBV can be used to explain associations between HRM, social networks, and team learning. The RBV emphasizes the necessity of investing in human resources as an avenue to create and sustain a competitive advantage. Investing in human resources, such as through HRM practices is expected to enhance work attitudes and positive work behaviors (Huselid, 1995) that are necessary to maintaining a competitive advantage. In the present study, we will focus on two types of HRM practices and elaborate on the expected effects below. In addition to the role of HRM in


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the RBV, employee behaviors also form an independent component that affects the outcomes of the organization and in the present study; we will focus for this component on social networks, to be elaborated in the next section. Our assertions about the associations between HRM practices and team learning behaviors are also consistent with the basic premise of social exchange theory (Blau, 1964). According to social exchange theory when one party provides benefits to another party (i.e., in this case, providing HRM practices to employees may well be conceived as a benefit), it leads to a sense of obligation that impels the receiving party to reciprocate (Gouldner, 1960) (i.e., putting in more effort resulting in more team learning behaviors). Traditional HRM practices aim to accomplish a match between the individual and the job and job environment based on an efficiency-oriented approach. A key underlying principle guiding these practices is person-environment fit ((P-E fit, Huselid, 1995; Wright & McMahan, 1992). The concept of P-E fit has enjoyed a long tradition in organizational research and practice (Huselid, 1995; Kristof, 1996). Accordingly, traditional HRM practices are aimed at matching team member characteristics to the team as a whole. For instance, applicants are selected and allocated to jobs based on how well they fit the team-working style (e.g., Burch & Anderson, 2004). Hence, traditional HRM practices, including selection, placement, and training, are expected to contribute to enhanced fit between workers and their teams, and have the potential to positively impact team effectiveness. The basic premise is that people for instance will be more satisfied with their job and will perform better if they work in a working environment that matches their interests. Specifically, for the present study, we expect that when the fit as underlying mechanism of traditional HRM practices explains why more traditional HRM is associated with more team learning of employees. Hypothesis 1: Traditional HRM practices relate positively to team learning behaviors.


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Discretionary HRM practices seek to promote discretionary behavior of employees through, for example, employee participation, and involvement. Such practices generally lead to more productive and engaged workers (Mathieu et al., 2008). In addition, the construct of discretionary HRM has (some) overlap with high involvement work practices (HIW) which aim to enhance organizational performance (e.g., Arthur & Rousseau, 1996; Huselid, 1995). These HIW practices have been defined in various ways but generally consist of three dimensions: high skill requirements, jobs designed to provide the opportunity to use those skills in teams or in collaboration with other workers and an incentive structure to induce discretionary effort (Appelbaum, Delage, Labib, & Gault, 1997). For the present study it is important to realize that, in contrast to HIW practices, discretionary HRM practices are more focused attempts to improve discretionary behaviors -- sometimes referred to as extra-role behavior -- and this type of practices can be expected to enhance team learning by stimulating these behaviors. We expect that these discretionary HRM practices by improving participation and involvement of employees will enhance team learning behaviors. Thus, we hypothesize that: Hypothesis 2: Discretionary HRM practices relate positively to team learning behaviors. Although we hypothesize that traditional and discretionary HRM practices improve learning behaviors within the organization (Khatri, Wells, McKune, & Brewer, 2006) it may well be that this relationship is stronger for discretionary HRM than for traditional HRM practices. Traditional HRM practices tend to focus upon identifying the tasks, duties, and responsibilities that are needed to successfully perform jobs. Typically, these aspects are derived through formal job descriptions, and recruitment and selection decisions rely on the technical competencies of individuals relative to job descriptions in order to result in a quick person-job fit. Learning may occur to facilitate this person-job fit, but it is perhaps not the


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main aim. In contrast, discretionary HRM does not specify all of the requirements with formal job descriptions and does not try to monitor the contributions of employees extensively. Quite the reverse, discretionary HRM practices foster the transfer of knowledge and thereby facilitate the organizational learning processes. Therefore, discretionary HRM practices may be better able to promote a learning orientation, referring to the desire of a team to gain new skills, improve overall competence, and master new situations (see Wilkens & London, 2006) within teams and thus may improve ultimate team learning behaviors.

Social Networks and Team Learning Behaviors Central focus of the social networks perspective is on the structure of social interactions and how this structure enhances or constrains access to valued resources (Lin, Ensel, & Vaughn, 1981; Sandefur & Laumann, 1998; Seibert, Kraimer, & Liden, 2001). The social network of each person can be described in terms of his or her ties with other people in their network at work, i.e., the set of job-related contacts, that ego relies on to provide access to task-related, career, or emotional coping resources in the work place (Ibarra, 1995). The social networks perspective relates to a number of different levels of analysis that can be used to determine the interaction between individuals and their environment (Hatala & Fleming, 2007). Since we are studying the associations of HRM and social networks with team learning behaviors, especially the meso-level is important. For this purpose, (group) social capital theory (Leana & Van Buren, 1999; Nahapiet & Ghoshal, 1998; Oh, Chung, & Labianca, 2004; Oh, Labianca, & Myung-Ho, 2006) refines our predictions concerning the associations of social networks and team learning behaviors. Social capital refers to the generation of actual and potential resources embedded within, available through, and derived from the network of social relationships of an employee (Granovetter, 1973; Nahapiet & Ghoshal, 1998). The concept of group social capital is a way of examining in


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greater depth how social relationships within teams are related to group outcomes, i.e., team learning behaviors. Group social capital is defined as „the configuration of a group‟s members‟ social relationships within the social structure of the group itself, as well as in the broader social structure of the organization to which the group belongs, through which necessary resources for the group can be accessed‟ (Oh et al., 2004, p 861). In the present research, social networks refer to the sets of relationships employees within teams have with each other (advice networks) and with other people in their own organization (breadth of the social networks). First, advice networks are forms of work-related networks that refer to relationships between employees through which they share resources such as information, assistance, and guidance that are related to the completion of their work (Sparrowe, Liden, Wayne, & Kraimer, 2001). The contents of these relationships between employees contain information exchange. For these relationships, Adler and Kwon (2002) use the term social relational dimension of social capital. Advice relationships are likely to emerge over the course of workrelated interactions as individuals mobilize resources to obtain desired outcomes (Dabos & Rousseau, 2004). The more employees are involved in the exchange of personal information advice, the more information is likely to be shared, and this should make each employee more aware of other group employees‟ roles in the unit, team, or department. Second, breadth of the networks within the organization is used as a measure of social networks, referring to „knowing whom‟. Breadth of the networks is not tied to the relationships within the team but instead to the relationships within the organization. Building this type of continuously developing social relationships not only within the team but in the whole organization allows individuals to stay connected and opens doors to new opportunities (De Janasz, Sullivan, & Whiting, 2003; Eby, Butts, & Lockwood, 2003).


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Studies generally supported associations of social networks and team functioning. For instance, in their study among 35 groups of MBA students, Shah, Dirks, and Chervany (2006) found that groups achieve superior team functioning when they use internal networks. Bowler and Brass (2006), in their study in a manufacturing firm, provided evidence that social networks are related to team functioning. By exchanging and by advising one another, and by knowing whom, employees learn about the responsibilities of their colleagues. Such knowledge of the roles of colleagues makes task behavior more visible and at the same time clarifies expectations and accountability (Sparrowe et al., 2001) and facilitates the development of a „shared mental model‟, which can be expected to act positively on team learning behaviors. Shared mental models are knowledge structures that employees can use to organize new information, to describe and explain the work to be done, as well as to guide their interaction with other team members. A shared mental model reflects the team objectives, team mechanisms, individual roles, individual responsibilities, and relationships among employees. It is this shared mental model that can be thought of as one of the most important underlying mechanisms of team learning: The mutual understanding of each other by the exchange via social networks (compare Paris & Salas, 2000). Similarly, the underlying mechanism of team learning can also be found in the idea of a “collective mind”, a grouplevel phenomenon that can be characterized as a property of the team (Choi, Price, & Vinokur, 2003) and it can be expected that this process will enhance by team learning. Hypothesis 3: Social networks are associated with team learning behaviors. Moderating Role of HRM Practices In addition to a direct association between HRM practices and social networks on the one hand and team learning behaviors on the other hand, it is also possible that HRM practices moderate the association between social networks and team learning behaviors. That is, employees may benefit most from their social networks in an environment where they are

HRM, Networks, and Team Learning 10 supported by HRM instruments. More effective use of social networks then will pave the way for better team learning. Support for this moderating role of HRM practices was demonstrated by Tregaskis (1997). HRM practices may lead to higher firm performance through developing and reinforcing employee-based resources (Wright, Dunford, & Snell, 2001). Collins and Clark (2003) in their study using 73 high-technology found that network-building HRM practices led to higher firm performance through the HRM practices' effect on the external and internal social networks within the organization (in this case top management teams). Thereby lending support for the argument that HRM facilitates the use of social networks to benefit team learning. Hypothesis 4: HRM practices moderate the relationship between social networks and team learning behaviors. More specifically, the relationship between social networks and team learning behaviors will be stronger when more HRM practices are deployed than when less HRM practices are deployed.

METHOD Procedure and Respondents Data were collected from teachers working in fourteen Dutch secondary schools. The multi-wave data collection was a part of a research project investigating the adaptation after organizational restructuring within secondary schools. For the present study we used data from the 2007 wave of data collection. Fourteen schools (out of 16 approached schools) participated in the 2007 wave. School management announced the study to the team leaders and the teachers, explained the purpose of the study and solicited their participation in the study. A total of 1049 written questionnaires were sent to the teachers, and 442 were returned, resulting in a response rate of 42%. From the total pool of 52 participating teams, 34 teams

HRM, Networks, and Team Learning 11 that were rated on Team Learning Behaviors by a representative of the school management met the criteria for inclusion in the present study. The final sample consisted of 237 teachers within 34 teams, 49% were male and 51% female teachers. Their mean age was 41.4 years (SD = 11.6). Mean number of members per team was 9.7 (SD = 5.2).

Measures Traditional and discretionary HRM practices were measured with five items for traditional and four items for discretionary HRM practices from Hayton (2003), who adapted items from previous studies by Huselid (1995) and Chandler and colleagues (Chandler, Keller, & Lyon, 2000; Chandler & McEvoy, 2000). The items were rated on a five-point Likert scale (1 completely disagree to 5 completely agree). Factor analysis PCA showed the expected two dimensions of traditional and discretionary HRM. The first factor reflected the traditional HR practices (Eigenvalue 2.8, 35% explained variance). A sample item is “We have formal job descriptions” (Alpha = .94). The second factor (Eigenvalue 2.6, explaining an additional 32% variance) reflected discretionary HRM practices. A sample item is “We have programs in place to encourage employee participation” (Alpha = .76). Team Learning Behaviors. Team learning behaviors were rated on a 10-point response scale (1 representing poor and 10 representing excellent) using nine items from Beehr, Ivaniskaya, et al (2001). Sample items were “This team solves problems on its own when appropriate” and “This team learns from mistakes” (Alpha = .90). To rule out common method bias as an explanation for relationships with team learning behaviors as perceived by the respondents, we relied on ratings of the team learning behaviors by a representative of the school‟s management team. Thus, these team learning behaviors scores captured a group-level phenomenon that is not based on aggregation of individual team members‟ scores/responses.

HRM, Networks, and Team Learning 12 Social Networks. The items to measure advice networks (Van Emmerik & Euwema, Klein, Lim, Saltz, & Mayer, 2004; 2008) were preceded by the following sentences: "The following question concerns your relationships with other team members. On how many of your colleagues in your present team can you count on?". The advice Network was computed from counting the number of team members for (1) whom they went to for advice in their team and (2) whom asked them for work-related advice within their team. The measurements of advice networks will be dependent on the number of members within the group, thus we controlled for number of members by dividing this measure by number of team members. Breadth of networks within the organization (and not within the team as was the case for the advice networks) was assessed using the three items from Eby, Butts, and Lockwood (2003). Sample items are „I am well connected within the organization,‟ „I have a lot of contacts within the organization‟ (Alpha .82). The scale used a seven-point Likert scale response format (1 – strongly disagree, to 7 – strongly agree). Finally, as control variable, team size was included in the analyses because of its potential implications for job performance through phenomena such as social loafing, diffusion of responsibility, and nonparticipation (Bowers, 2000; Carletta & Garrod, 1998) . Tenure (in years) was also included, since tenure in the organization can have potential implications for the development of both advice networks as for the breadth of networks.

RESULTS Descriptive results Table 1 presents the means, standard deviations, and bivariate correlations for all research variables. ------------------Insert Table 1 about here

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In contrast with the results of Hayton (2003), traditional and discretionary HRM practices are not correlated. Larger team are associated with less team learning behaviors (r = -.26, p