Linking human resources practices with performance

The International Journal of Human Resource Management

ISSN: 0958-5192 (Print) 1466-4399 (Online) Journal homepage: http://www.tandfonline.com/loi/rijh20

Linking human resources practices with performance: the simultaneous mediation of collective affective commitment and human capital Andrés Raineri To cite this article: Andrés Raineri (2016): Linking human resources practices with performance: the simultaneous mediation of collective affective commitment and human capital, The International Journal of Human Resource Management, DOI: 10.1080/09585192.2016.1155163 To link to this article: http://dx.doi.org/10.1080/09585192.2016.1155163

Published online: 16 Mar 2016.

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Date: 16 March 2016, At: 11:31

The International Journal of Human Resource Management, 2016 http://dx.doi.org/10.1080/09585192.2016.1155163

Linking human resources practices with performance: the simultaneous mediation of collective affective commitment and human capital

Downloaded by [Pontificia Universidad Catolica de Chile] at 11:31 16 March 2016

Andrés Raineri School of Management, Pontificia Universidad Católica de Chile, Santiago, Chile

KEYWORDS

ABSTRACT

Independent streams of research propose that human capital and employee motivation serve as mediation mechanisms that explain the relationship between high-performance work systems (HPWS) and performance. A multiple mediation analysis is used to test employee perceptions of subsets of HPWS practices, which emphasize the enhancement of employees’ abilities, motivation and opportunities, as predictors of business units’ performance, when mediated by human capital and motivational paths, in a sample of business units in Chilean organizations. Collective affective commitment is used as proxy for the motivational path, and differentiated from other measures used to test such path. Results indicate that personnel selection, performance evaluation and training, job descriptions, and empowerment practices make simultaneous contributions to the human capital and affective commitment paths. The partial mediation effects observed suggests that additional mechanisms contributing to the HPWS–performance relationship should be explored. However, compensation and incentives practices only contribute to the motivational path, while the human capital path and direct effects are non-significant. Results support the complementarity of both mediation mechanisms, the differential effects of subsets of HR practices on the mediation paths, and the significance of these effects at a business unit aggregate level of analysis. Theoretical contributions, practical implications and limitations are discussed.

High-performance work systems; collective affective commitment; human capital; performance

Introduction Extensive empirical evidence indicates that investment in high-performance work systems (HPWSs) is positively related to business unit and organizational outcomes (Combs, Liu, Hall, & Ketchen, 2006). HPWSs are described in the literature

CONTACT Andrés Raineri © 2016 Taylor & Francis

[email protected]


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as a set of practices that enhance employees’ skills, motivation and participation, inducing their engagement in efforts that result in better performance. HPWSs are considered a group of separate but interconnected human resource practices that act as a system of internally coherent practices (Appelbaum, Bailey, Berg, & Kalleberg, 2000; Boxall & Macky, 2009). Although there is no consensus among researchers about which specific practices constitute HPWSs (Boxall & Macky, 2009; Sun, Aryee, & Law, 2007; Wright, Gardner, & Moynihan, 2003), some of the most frequently cited practices include selective hiring, extensive development and training, performance feedback, performance-linked compensation, and employees’ participatory mechanisms. The authors in this area refer interchangeably to these practices as high-performance work systems, high-involvement work systems and high-commitment management (Boxall & Macky, 2009). In this study, the term ‘high-performance work system’ is preferred because these practices have been suggested to work in an interrelated manner when affecting employees’ performance and because HPWSs have been suggested not only to impact employees’ motivational states but also to enhance their knowledge and skills. Despite the extensive literature that indicates that HPWSs have an impact on performance, several issues still remain unclear. Several authors indicate that different practices included in HPWSs generate diverse effects on employees (Appelbaum et al., 2000; Siemsen, Roth, & Balasubramanian, 2008). For example, some practices enhance employees’ motivation; others have an impact on their knowledge and skills; and some other practices empower and induce participative behaviors in employees. Which human resources (HR) practices induce each of these potential effects remains an open debate in the literature. A second area of discussion that has been raised in the HPWS–performance literature refers to the mechanisms through which HPWSs induce employees’ performances. At least two different theoretical approaches have proposed links to explain the HPWS–performance relationship. The motivational approach proposes that HPWSs enhance employees’ attitudes, such as organizational affective commitment, which encourages them to exert effort to perform (Gardner, Moynihan, & Wright, 2007). The human capital approach states that HPWSs allow organizations to accumulate and develop human capital, defined as the knowledge and abilities that employees possess, which, when used by employees, allows them to achieve higher levels of performance (Wright, Dunford, & Snell, 2001). Some authors have proposed the need to study the simultaneous contributions of both the motivational and human capital paths to the HPWS–performance relationship (i.e. Jiang, Lepak, Hu, & Baer, 2012; Messersmith, Patel, Lepak, & Gould-Williams, 2011). A few authors have attempted to test for the simultaneous mediation of these two paths. For example, Takeuchi, Lepak, Wang, and Takeuchi (2007) found a significant joint mediation of human capital and social exchange mechanisms in the HPWS–performance relationship. Takeuchi et al. (2007) used social exchange as a proxy for the motivational path. However, social exchange has been considered one of several possible antecedents of motivation



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(Meyer, Becker, & Vandenberghe, 2004). On the other hand, affective commitment, another proxy more frequently used in the HPWS–performance literature to measure the motivational path, is considered a component of motivation itself at a stage of the motivational path that is more proximal to performance (Meyer et al., 2004). Such differences in the conceptualization and measurement of the motivational path need to be further clarified to advance the understanding of the HPWS–performance relationship. An additional issue recently raised in the HPWSs and affective commitment literature, is that members within a work group influence each other’s perceptions of the HR practices with which they are managed (i.e. Kehoe & Wright, 2013), as well as influence each other levels of affective commitment (i.e. Gardner, Wright, & Moynihan, 2011). It has been proposed that employees within a work group or business unit, share their opinions and emotions about these issues and develop what has been termed as unit-level collective perceptions of HPWSs and collective affective commitment (i.e. Gardner et al., 2011; Kehoe & Wright, 2013). Morgeson and Hofmann (1999) argue that collective level perceptual and attitudinal constructs can capture the effect of social influence processes on the relations among the research variables that are not captured when research is conducted with individual-level constructs. It is therefore relevant to further explore the relation between within-group shared perceptions of HPWSs and collective affective commitment. For this purpose, the analyses of these variables is conducted in this study at an aggregate group level. This study contributes to the understanding of the relationship between HPWSs and business unit results in several ways. First, it highlights several theoretical issues that need more clarification to disentangle the understanding of the mediation mechanisms in the HPWS–performance relationship. Second, it tests for the significance of the motivational and human capital path as simultaneous mediators in the HPWS–performance relationship. Differing from Takeuchi et al. (2007), this study uses affective commitment as a proxy for the motivational path, which is a more direct measure of the attitudinal states that reflect employees’ motivation to perform (Meyer et al., 2004). Third, it tests for the contribution of different subsets of HR practices to the motivation and human capital paths by estimating the effect size of each of these two mediation paths when activated by different sets of HR practices. Fourth, this study further explores the relevance and validity of analyzing the relationship between HPWSs perceptions and collective affective commitment at the business unit level. Finally, this paper allows for the generalizability of the HPWS–performance relationship within a Latin-American context. In the following sections, the literature is reviewed in relation to two central issues. First, the contribution of three dimensions of HPWSs, those inducing skill enhancement, motivation enhancement and empowerment enhancement practices, as related to work performance. Second, the existence of human capital and motivation as simultaneous mediation mechanisms explaining the HPWS–performance relationship is discussed, with particular attention to the


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characteristics of the motivational path. The relevance of measuring these relations among the study variables at a collective, business unit level is also discussed. Then, using a sample of business units, from firms representing a wide variety of industries and job types in Chile, the simultaneous mediation of the motivation and human capital paths in the HPWS–performance relationship is tested, using collective affective commitment as a measure of the motivational path. A comparative analysis is performed on the impact of subsets of HPWS practices, associated with skills enhancement, motivation enhancement and empowerment enhancement, on the two mediation paths. In the last sections of the paper, the results are presented and practical and theoretical implications are discussed. Literature review and theoretical background Early research shows a positive relationship between investing in HPWSs and organizations’ financial and operational outcomes (Huselid, 1995). More recently, authors have noted that employees have a more direct impact on performance at a business unit level. Furthermore, differences exist across units in terms of the nature of work performed, the profiles of the workers most suitable to perform such work, and the outcomes produced by those units in order to contribute to the overall organizational performance. These issues have emphasized that a more appropriate place to test for the HPWS–performance relationship is at the business unit level (Wright et al., 2003). However, there is less agreement regarding the impact of different HR practices on performance, and the causal mechanisms through which HPWSs influence performance. The study of the HPWS–performance relationship can be traced back to the study of work performance. Early propositions on antecedents of work performance have identified performance as a function of motivation and ability (Vroom, 1964), as well as the situational factors surrounding workers, which enhance or inhibit the opportunities for employees to perform (Peters & O’connor, 1980). In an attempt to unify the determinants of work performance into a single model, Blumberg and Pringle (1982) proposed that performance is dependent on the workers’ capacities, willingness and opportunities to perform. Based on such work, some authors have proposed that HPWSs are composed of different sets of HR practices that enhance employees abilities (A), motivation (M) and opportunities (O) to exert their best performances, in what is known as the AMO model (Appelbaum et al., 2000). Empirical research suggests that the AMO dimensions of HPWSs usually appear as highly correlated but distinct constructs (Siemsen et al., 2008). The AMO model has become a frequently accepted framework that is used to understand how different HR practices have an impact on performance; it is also considered a useful model in deciding the most appropriate HR practices to develop and implement in different work settings. For example, recruitment, selection and training can be used to acquire and develop the abilities that employees need to improve their performance. Additionally, the use of incentive systems



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or career development programs can serve as levers that motivate employees’ to perform. Finally, participatory management practices, such as teamwork and empowerment, allow employees to expand their opportunities to perform in a wider range of situations and forms. Some authors have suggested that several of these HR practices can simultaneously serve more than one purpose (Boxall & Macky, 2009; Jiang et al., 2012). For example, performance evaluation systems can be a motivational tool when linked to salary increases, but they can also serve to enhance employees’ skills when used to detect their training needs. The complementary contributions of the AMO dimensions have led to the consideration of HPWSs as systems of interdependent and distinctive practices (Appelbaum et al., 2000; Boxall & Macky, 2009). Recent literature also attempts to understand the mechanisms through which HPWSs impact performance. Some authors have labeled this issue as the ‘black box’ problem in the HPWS–performance relationship (Messersmith et al., 2011; Sun et al., 2007). The two most discussed mediation mechanisms of the HPWS– performance relationship have been separately proposed from different theoretical perspectives. The human capital path, supported in resource-based theory, proposes that HPWSs impact performance by attracting, retaining, developing and managing human capital, that is, employees’ knowledge, skills and abilities (Wright et al., 2001). This approach focuses on how HPWSs allow organizations to acquire and develop human capital, in order to differentiate themselves from their competition and to achieve competitive advantages (Wright et al., 2001). Subramaniam and Youndt (2005) argue that human capital may stay or leave an organization depending on the HR practices utilized by the firm. Therefore, by investing in the appropriate HPWS, firms can secure the human capital needed to attain their goals. Empirical research supports the mediatory role of human capital (Huselid, 1995; Takeuchi et al., 2007). The behavior motivation approach attempts to explain the HPWS–performance relationship using a psychology framework (Jackson, Schuler, & Rivero, 1989). According to this approach, HPWSs motivate employees to exert efforts to perform. Among authors, a lesser degree of consensus exists when explaining how the motivational path works. Some authors have proposed individual-level attitudes, such as affective commitment, job satisfaction and psychological empowerment, to explain how the motivational path works (i.e. Messersmith et al., 2011). Affective organizational commitment is the most studied of such attitudes (Jiang et al., 2012). Affective commitment is a widely researched construct that refers to an emotional attachment to, identification with, and involvement in an organization (Allen & Meyer, 1990, p. 2). In particular, Meyer et al. (2004) argue that employees with a strong affective commitment to an organization will generally share its values and goals and pursue them autonomously. As such, affective commitment is considered an energizing force that is part of the motivation process (Meyer et al., 2004, p. 1002). Consequently, the motivational path perspective states that HPWSs affect employees’ behaviors by promoting attitudes, such as affective commitment,


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which enhance their willingness to engage in productive behaviors (Jackson et al., 1989; Wright et al., 2003). Other authors who attempt to explain the motivational path emphasize the antecedent processes that contribute to the development of the employee attitudes that mirror motivation, such as social exchange theory, social identity theory and high-involvement processes. Social exchange theory (Blau, 1964) proposes that when firms take good care of their employees, for example, by treating them fairly, the employees, in turn, will develop positive attitudes towards their firms, which will increase the employees’ efforts to reciprocate with effective work behaviors (Cropanzano & Mitchell, 2005). As such, a mutually accepted relationship of reciprocity has been understood as an antecedent to the attitudinal states that motivate employees to perform (Cropanzano & Mitchell, 2005). In a study of the HPWS–performance relationship, Takeuchi et al. (2007) found that when a firm provides its employees with an HPWS, a social exchange process of reciprocity is initiated, which motivates employees to reciprocate by exerting efforts to perform. However, other researchers find empirical evidence that employees’ motivation-laden attitudes can only be partially attributed to social exchange. For example, Buch (2015) finds that a portion of affective commitment variance can be explained through social exchange, while the intrinsic motivation inherent to work itself explains additional variance of affective commitment. Alternative processes that help explain how affective commitment develops among employees have been proposed. For example Marique, Stinglhamber, Desmette, Caesens, and De Zanet (2012) and van Knippenberg (2000), propose that HR practices that lead employees to believe that they are valued and considered valid organizational members, promote the fulfillment of employees’ needs for esteem, approval and affiliation, allowing for the enrichment of their perceptions of their status and social identity within the organization. According to Marique et al. (2012) and van Knippenberg (2000), the increased perception of role status and organization-related social identity augments employees’ affective commitment and consequently their willingness to perform. Furthermore, Vandenberg, Richardson, and Eastman (1999) consider that high-involvement work practices facilitate employees’ participation, decision-making and increased work autonomy; and that these practices facilitate employees’ attitudes such as affective commitment and work satisfaction, which in turn generate a willingness to perform. A common issue underlying these process models (social exchange, social identity, involvement processes) is that they all suggest that these processes help to develop motivation-related attitudes in employees, such as affective commitment, which serve as a final link that induces their efforts to perform. From research mentioned above, it is possible to state that, when explaining the motivational path mediating the HPWS–performance relationship, some authors emphasize the processes aspects of the mediation path, such as social exchange, social identity and empowerment processes (i.e. Marique et al., 2012; Takeuchi et al., 2007;



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Vandenberg et al., 1999), while other authors emphasize the role of attitudes (i.e. affective commitment) that are considered a proximal motivational trigger to employee performance (Gardner et al., 2011; Messersmith et al., 2011). Therefore, a more complete sequence of events can be proposed, where the motivational path starts at the implementation of HPWSs, which facilitate processes, such as reciprocal social exchanges and/or the enhancement of employees’ social identity. Over time, these processes develop employees’ motivation-laden attitudes, such as affective commitment, which finally induce them to performance. Research that attends to the psychosocial processes explains the ‘how’ of the motivational path (i.e. social exchange or social identity processes); while research that attends to the attitudinal states that induce efforts to perform attempt to explain the ‘what’ or content of the motivational path (i.e. affective commitment or other motivation-related attitudes). Measuring the motivational mediation path through affective commitment has several similarities as well differences when compared with measures of social exchange. First, affective commitment is a more proximal measure to employee performance, allowing for a more direct link between motivational states and performance (Cropanzano & Mitchell, 2005). Second, affective commitment might be induced in employees through different psychosocial processes, such as social exchange and social identity processes, as well as other sources related to demographic characteristics or the nature of work (Meyer et al., 2004); therefore, it can embody a more direct assessment of the overall motivational state of employees. Finally, a more direct measure of motivation, as is the case of affective commitment (Meyer et al., 2004), facilitates linking HPWSs to the AMO model of HR practices, which proposes that some HR practices contribute by enhancing employees’ motivation to exert effort to perform. On the other hand, social exchange and social identity theories contribute in understanding earlier stages in the motivational path by explaining how employees develop motivation-laden attitudes. Testing for a significant contribution of affective commitment as a mediator in the motivational path helps provide support for the argument that the motivational path is a multi-stage path, which can be tested in the early stages of psychosocial processes (i.e. Takeuchi et al., 2007), and in the latter stages of attitudes that reflect motivational states more proximal to performance. Additionally, measuring the motivational path during the affective commitment stage allows for the assessment of the degree of the path’s contribution at a stage when the diverse sources that contribute to develop affective commitment have probably intervened to enhance commitment (i.e. social exchange, social identity, empowerment processes and work characteristics), accounting for motivational attitudes that considers all these potential sources of contribution. For the above reasons, the present study assesses the contribution of the motivational path at the latter affective commitment stage. An additional objective of this study is to expand the literature on the HPWSs – commitment relation at a group level. Early literature has shown a positive


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association between a firm’s use of HPWSs and individual commitment (i.e. Appelbaum et al., 2000). Recently, authors have extended the analyses of HPWSs and employee commitment from an individual to a group level analysis (i.e. Gardner et al., 2011; Kehoe & Wright, 2013). In particular Kehoe and Wright (2013) find a significant mediation of a group level aggregated measure of affective commitment in the relation between group level aggregated HR practices perceptions and organizational citizenship behavior. In a similar vein, Gardner et al. (2011) measured the relation between managers’ perceptions of HR practices and their business unit members’ voluntary turnover as mediated by a collective measure of affective commitment at the business unit level. The aggregation of both HR practices perceptions and affective commitment at a work group or business unit level can be supported both by social information processing and social emotional contagion models. Salancik and Pfeffer (1978) social information processing approach proposes that employees make use of information provided by other individuals surrounding then, in order to make sense of their work experiences, and influence each other’s attitudes and cognitions, leading to the development of shared perceptions about their work experiences. Similarly, Barsade (2002) shows that group members can develop shared emotions about their work environment through an emotional contagion process, and that a positive emotional contagion among group members can improve cooperation and performance. The present study builds on Gardner et al. (2011) and (Kehoe and Wright (2013) contributions by aggregating in a single study both employees’ HPWSs perceptions and affective commitment at a business unit level in order to predict business unit level performance. The relevance of aggregating HPWSs perceptions and affective commitment at a group level is highlighted by Morgeson and Hofmann (1999) whom argue that collective level perceptual and attitudinal constructs can capture the effect of social influence processes on the relations among the research variables that are not captured when research is conducted with individual-level constructs. From this perspective, the collective assessment of HPWSs perceptions and affective commitment, by capturing the assessment of the group’s social influence processes, can be considered a more accurate approach to evaluate the contribution of these antecedents on business unit level performance (i.e. Gardner et al., 2011; Kehoe & Wright, 2013). Therefore, the present study uses aggregate employee perceptions of HPWSs and affective commitment at a collective business unit level to predict business unit level results. The first objective of this paper is to test for the simultaneous mediation of the motivation and human capital path in the HPWS–performance relationship at a business unit level, using a measure of collective affective commitment to test the motivational path. This test supports the relevance of affective commitment as a proximal link between the motivational path and performance and also gauges the degree of its contribution simultaneously with that of the human capital path, while letting other research attend to the psychosocial processes that induce motivation-laden attitudes. The multiple mediation model depicted in Figure 1 is used



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to test simultaneous mediation of both the human capital and affective commitment pathways in this study. To help clarify the HPWS–performance relationship, some authors have suggested the need to relate different sets of HR practices with different mediation paths (i.e. Boxall & Macky, 2009). By differentiating subsets of practices within HPWSs, the AMO model helps promote an understanding of how HR dimensions help explain these mediation paths. For example, HPWS practices that promote abilities enhancement, such as recruitment, selection and training, could be stronger antecedents of the human capital path, while motivation enhancement practices, such as compensation and incentives, could have a stronger relationship with the affective commitment path. Additionally, empowerment enhancement practices, such as team work or participatory processes, could also be more related to the affective commitment path (Vandenberg et al., 1999). It has also been proposed that particular HR practices can affect both employee abilities and motivation (Boxall & Macky, 2009; Jiang et al., 2012). For example, a rigorous selection process allows a firm to hire more qualified employees, but it can also serve as a signal to employees of the value of working at such a firm, indirectly enhancing employees’ organizational commitment. Similarly, a performance evaluation system can motivate employees to give their best performance, especially if consequences are attached to the evaluation outcomes. However, the same performance evaluation system can also give employees feedback about their lack of abilities and lead them into skills development. Likewise, participatory mechanisms have a strong motivational effect on employees (Vandenberg et al., 1999), but such practices can also serve as an opportunity for employees to interact and learn from each other, and enhance their knowledge and skills. In a meta-analysis of previous research relating the AMO model dimensions to the human capital and motivational paths, Jiang et al. (2012) find that skill enhancement HR practices make a stronger contribution to the human capital path, while the motivation and empowerment enhancement HR practices make a stronger contribution to the motivational path, despite the fact that their results indicate that all AMO dimensions significantly contribute to both mediation paths. However, Jiang et al. (2012) discuss several limitations to their study. First, most of the studies considered in their meta-analysis include a partial set of the variables here presented in Figure 1. Many of the studies in the meta-analysis include only a few or none of the AMO HR components and one or none of the mediation mechanisms. Such omissions can generate potential biases in the estimation of relationships between variables, due to the incomplete subset of variables entered with each source studied in their meta-analysis. For example, in a multiple mediation model, an effect size bias might occur for one mediator when omitting other mediators (Preacher & Hayes, 2008). Additionally, several of the original studies included in the Jiang et al. (2012) meta-analysis use a single source to report dependent, mediation, and outcome measures, which may lead to common method bias. Moreover, the studies in the Jiang et al. (2012) meta-analysis

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use a wide variety of proxies to measure human capital (i.e. average education, Collective Affective HPWS Indices

Business Unit Performance Human Capital


Figure 1. Model predicting direct effects and human capital and affective commitment mediation effects of HPWS indices on business unit performance.

customer knowledge and tacit knowledge) and a mix of psychosocial processes, employees’ attitudinal states, and other indirect proxies for the motivational path (i.e. social exchange, organizational climate, helping behavior and quit rate). This diversity of measures for both paths might introduce additional noise when estimating the effect sizes of these paths. Finally, the Jiang et al. (2012) meta-analysis did not include control variables (e.g. industry, firm size and organizational age) because the source studies did not provide such information. Because of these limitations, Jiang et al. (2012) call for further research with more accuracy in the simultaneous mediation of both the human capital and motivational paths and the extent of their contribution to the HPWS–performance relationship. The second objective of this paper is to test for the existence of differences in the contributions of the AMO model dimensions to the simultaneous mediation of the motivational and human capital paths. The present study takes in account several limitations identified by Jiang et al. (2012) in their meta-analysis. First, it measures the three AMO model dimensions, both human capital and motivational paths, and business unit results, in every business unit included in the study, which decreases the potential biases generated by aggregating partial results from different studies or by using different proxies to measure the same latent variables. Second, this study uses several sources of data gathering to diminish the risks of same source variance bias. Finally, this study includes measures for several control variables to account for their influence in the mediation model. By considering the limitations described by Jiang et al. (2012), it is expected in this study to help gage a more accurate estimation of the size of the contribution of the AMO model dimensions to the human capital and motivational paths. Based on the discussion above, the following is proposed: Hypothesis 1: Skill enhancement practices should make a stronger contribution to the human capital mediation path when compared to their contribution to the affective commitment path. Hypothesis 2: Motivation enhancement practices should make a stronger contribution to the affective commitment mediation path when compared to their contribution to the human capital path.


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Hypothesis 3: Empowerment enhancement practices should make a stronger contribution to the affective commitment mediation path when compared to their contribution to the human capital path.

To assess the impact of the AMO model dimensions on the mediation paths in the HPWS–performance relation, the impact of the AMO model dimensions will be tested one at a time using the model in Figure 1. By including both mediators in the model, whether the mediators make simultaneous significant contributions, and the extent of each mediator’s contribution, can be determined (Preacher & Hayes, 2008). The latter helps identify to what extent each path helps explain the HPWS–performance relationship, conditional on the presence of the other mediator in the model. For example, if both mediators were highly correlated, the effect of one mediator could overshadow that of the other. Additionally, when both mediators are kept in a single multiple-mediation model, the probability of effect size bias due to the omission of the other mediator is reduced (Preacher & Hayes, 2008). Figure 1 also presents a direct effect of HPWSs on performance. Mediation mechanisms can explain the total variance in the relationship between the dependent and outcome variables or only a part of that variance. The significance of any unaccounted portion of the variance between the predictor and the outcome in a multiple-mediation model is revealed statistically as a direct effect (Hayes, 2013). The significance of a direct effect can be subject to different interpretations. For example, a significant direct effect might imply that other additional mediation mechanisms, not considered in the study, might also intervene in the predictor– outcome relationship, such as the intrinsic motivation of work (Buch, 2015) or self-efficacy perceptions (Meyer et al., 2004). Hypothesis 4: There is a positive direct relationship between subsets of HPWS practices and business unit performance that accounts for the variance in the HPWS–performance relationship that is not explained by the affective commitment and human capital paths.

A last objective of this study is to extend research on the HPWS–performance relationship to a Latin-American context. By using a sample from Chile, this paper attempts to expand the generalizability of the HPWS–performance relationship and its human capital and affective commitment mediation paths to a Latin-American context. Since the early 1970s, Chile has adopted major social and economic transformations characterized by a free and open market economy and more flexible labor laws, creating a highly competitive business environment. Such changes press organizations to search for competitive advantages, including the professionalization of HR practices (Koljatic & Rosene, 1993). Additionally, the propagation of university-level programs in the practice of HRM at Chilean universities has facilitated the dissemination and professionalization of HR management (Perez-Arrau, Eades, & Wilson, 2012). On the other hand, the work culture in Chile, as in most of Latin America, has been characterized by paternalism, high power distance, and uncertainty avoidance. The literature suggests that

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commitment is positively related to paternalism and uncertainty avoidance and negatively related to high power distance (Cohen, 2007). As such, some Chilean cultural values foster organizational commitment, while other values inhibit commitment. Therefore, it is impossible to predict a priori the impact of Chilean work culture in the affective commitment mediation path. Additionally, single-country studies present serious limitations when attempting to account for cultural differences in the study of behavioral phenomena (Spector, Liu, & Sanchez, 2015).


Sample and procedures

Data are obtained from a convenience sample of 216 business units at 134 firms operating in Chile. The concept of business unit is here used to refer to a part of a firm that operates as a separate unit within the whole firm, based for example on functional, operational, project allocation or accounting basis (i.e. profit center). It is from these business units, embedded within the firms, from where data are collected (for example, a firms’ strategic planning department, a particular production line within a factory, or a sales team within a larger store). Sometimes, more than one unit per firm is obtained because independent contacts are made in different areas of the firm (i.e. sales and operations). The firm size, measured by the total amount of employees working at each firm, varied widely between 20 and 22,000 employees, with an average of 2431 employees. The company age ranged between 8 and 178 years, with an average of 51.2 years. When classified by US Standard Industrial Classification codes, 10% of the firms in the sample corresponded with transportation, communication, and utilities, 13% with finance, insurance and real estate, 9% with agriculture, 3% with mining, 12% with manufacturing, 10% with wholesale trade, 17% with retail trade, 22% with services and 4% with public administration. Finally, the nature of work in the main occupations in each business unit was coded using the US Standard Occupational Classification System’s major groups (Bureau of Labor Statistics, 2010). Twelve percent of the business units corresponded with management occupations, 13% with business and finance operations, 7% with life, physical, and social science occupations, 3% with education, 7% with health and personal care, 20% with sales, 14% with office and administrative support, 20% with production occupations and 5% with transportation and material moving occupations. The initial contact with business units is secured through managers in executive education programs at a well-known business school in Chile. Even though convenience sampling is limited by issues of representativeness, personal contacts help ensure higher response rates. This study also uses snowball sampling with managers in these companies. Participation is voluntary, and subjects receive no monetary compensation for their participation. These forms of sampling are frequently used in HPWSs–performance research (e.g. Gittell, Seidner, & Wimbush, 2010; Qiao, Khilji, & Wang, 2009; Sun et al., 2007; Takeuchi et al., 2007). Once consent from management is secured, surveys are distributed through the contact



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manager and/or site visits. Survey retrieval is secured by email, sometimes requiring site visits, additional emails or phone calls. Respondents and firms are assured of the anonymity of data treatment. Several authors contend that a better understanding of the HPWS–performance relationship requires distinguishing between the HR practices that the organizations declare in use and those that employees experience as implemented (Kehoe & Wright, 2013; Nishii, Lepak, & Schneider, 2008). Empirical research indicates that managers’ descriptions of the HR practices in use do not necessarily agree with employees’ reports of the HR practices that they experience at work (Khilji & Wang, 2006; Nishii et al., 2008). Nishii et al. (2008) contend that it is employees’ experiences of HR practices that have an effect on their skills and affective states. Therefore, a group of subordinates at each business unit responded to a questionnaire that measured the HPWS in use. To avoid single-source biases, a second group of subordinates within the same business unit, responded to a questionnaire that measured organizational affective commitment (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Within each business unit, subordinates were paired in dyads. In each dyad, one unit member responded the HPWS survey, and the other member responded the affective commitment survey. The business unit size, from where data is obtained, ranged between 5 and 49 employees, with an average of 15.23 employees. Response rates across business units ranged from 36 to 100% of unit members, where half of the participants responded the HPWS survey and the other half, the commitment survey. The average number of business unit members responding the HPWS survey was 4.12 (29.1%) while an additional number of members 4.12 (29.1%) responded the affective commitment survey. Therefore, an average of 8.24 (58.2%) members per business unit participated in the study. Finally, unit managers reported business unit outcomes and rated the quality of their units’ human capital. Sometimes, in larger business units, which had several managers, more than one manager responded the surveys. In these last cases, managers’ responses for a single unit were averaged. An average of 1.19 managers per unit responded the survey. Measures

To measure HPWSs, a 24-item scale was adapted from similar scales developed by Sun et al. (2007) and Wright et al. (2003). The items cover the three AMO model dimensions of HR practices: skill enhancement, motivation enhancement and empowerment enhancement practices, and are presented in Appendix 1. All HPWS items are phrased avoiding references to any specific industry or occupation categories, allowing for its application at different industry sectors and occupation types. The following instructions to respondents precede HPWS items: Below are descriptions of the practices that organizations may use in the management of their employees. For each practice, indicate the extent of your agreement or disagreement about whether the practice has been used by your organization


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to manage employees in your unit over the last three years. A five-point Likert scale, ranging from 1 (totally disagree) to 5 (totally agree), is used by subjects in their responses. A different subset of employees within the same business unit responded to four items from the Allen and Meyer (1990) organizational affective commitment subscale, using a seven-point Likert scale, ranging from 1 (totally disagree) to 7 (totally agree). The literature suggests the use of subjective measures of performance when objective measures are unreliable or unavailable, for example, when privately held and nonprofit firms are studied, when the comparability of different types of firms is difficult to achieve, when managers are unwilling to provide data because of competitive concerns, and when units of analysis vary in their objectives and procedures (Robinson & Pearce, 1988). Many of the aforementioned factors precluded the possibility of collecting objective financial or operational data in this study. Although perceptual data introduce limitations through increased measurement errors, research has found that measures of perceived organizational and business unit performance correlate positively (with moderate to strong associations) with objective measures of performance (Powell, 1992). Additionally, Wall et al. (2004) empirically show that subjective measures of company performance have construct validity (equivalent relationships) with a range of independent variables. Therefore, a perceptual measure of business unit performance is adapted from Delaney and Huselid (1996), which includes five items that assess respondents’ perceptions of their unit performance over the last year. These items, phrased in terms of business unit performance, measure product/service quality, work unit productivity, customer satisfaction, new product/service development and manager–employee relations. Managers are asked to assess their units’ performances relative to the performances of similar units of comparable organizations in their industry for the last year, using a six-point Likert scale ranging from 1 (worse) to 6 (much better). Unit managers also assessed their business units’ human capital with two items adapted from Subramaniam and Youndt (2005). Human capital items asked unit managers to compare their employees’ skills and knowledge to perform their work with those of similar units in other organizations; these items were answered on a six-point Likert scale, ranging from 1 (worse) to 6 (much better). In all surveys, item translation is conducted through separate translator, reviewer and receptor processes (Harkness, 2003). The researcher first translated the items into Spanish, and two reviewers, who specialize in HR management and are proficient in English, improved the translation through an iterative process in which discrepancies between the English and Spanish versions were addressed. To validate the translation, a pilot study with a small sample of respondents, from different occupations and educational levels, judged the readability and comprehension of the translated version. Pilot study subjects responded all items and took notes on a sheet of paper whenever encountering an item that was ambiguous or that had phrasing or words that they did not understand. The researcher and



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reviewers later discussed pilot subjects concerns and made appropriate changes in the final version of the surveys. Several control variables are included in this study. It has been suggested that larger companies invest more in HPWSs (Sun et al., 2007). To capture firm size effects, the natural logarithm of the number of employees in the whole firm is included as a control variable (Delaney & Huselid, 1996). To control for business unit size effects, the number of employees working in each business unit is included as a control variable. Business unit size is included because it can be argued that the more employees working in a particular unit, the more the firm will be willing to implement HR practices for that unit, for example, because the investment in HR practices pays off for larger groups of employees. The number of years that the organization has been operating serves as a control for organizational age, as it is possible to argue that organizations that have been in operation for a longer period of time have the advantage of developing and testing HPWSs. Statistical analyses

Principal component analysis on all data extracted eight factors with an eigenvalue of greater than 1.0. Rotated item factor loadings, the percentage of variance accounted for, and eigenvalues for each factor are presented in Appendix 1. Average item loadings for all scales range from 0.52 to 0.91. The first factor includes all the affective commitment items; the second factor groups the business unit items; and the third factor includes the human capital items, confirming the treatment of affective commitment, human capital and business unit results separately. Additionally, the factor analysis supports the distinctiveness of three subsets of practices associated with the skill enhancement practices of the AMO model – that is, the fourth, fifth and sixth factors include personnel selection items, job description items, and performance evaluation and training items, respectively. The seventh factor includes motivation enhancement practices, and the eighth factor includes empowerment enhancement items. An index for each factor is created by averaging all factor items. Because the factor analysis suggests five different subsets of practices related to the AMO model, testing for the differential effects of subsets of HR practices on the affective commitment and human capital mediation mechanisms, as proposed in hypotheses 1 through 3, is performed using the five HPWS indices identified in the factor analysis. To decrease the potential effects of common method variance, actions were taken at the research design stage, and post hoc analyses were performed on collected data. Different sources of information were used to collect data from key measures (Podsakoff et al., 2003). A subset of employees in each business unit respond to the HPWS survey, while a second subset of employees in these units provides information for affective commitment; human capital and business unit results are collected from business unit managers. Another ex-ante measure to control for common method variance is the use of different Likert scales for most


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measures, which allows for the reduction of anchor effects (Podsakoff et al., 2003). The results from the principal component analysis, indicating eight factors that account for significant proportions of variance, also suggest low risks of serious common method variance. To further test for common method variance, the marker variable technique of Lindell and Whitney (2001) is used. Lindell and Whitney (2001) suggest using variables that have very low or no correlations with the study measures to test for common method variance. These variables can be designed a priori as a part of the research design, or researchers can use uncorrelated measures from the study itself in a post hoc analysis (Lindell & Whitney, 2001, p. 115). In this study, managers’ surveys and subordinates’ HPWS surveys included two items that asked them to judge, on a six-point Likert scale ranging from ‘never’ to ‘every day’, how often they received company information about the ‘financial performance of their company’ and the ‘competitive performance of their company (i.e. market share and rival firms’ strategies)’. Following the technique of Lindell and Whitney (2001), these two items were used as post hoc marker variable items. The relationships between these items and the study variables are all non-significant, several with correlation coefficients smaller than 0.001, which suggests that CMV is not a serious problem. In this study, the clustering of some business units within firms might generate a dependency of unit performance on firm variance. To assess this potential effect, the design effect statistic (DEFF) is estimated (Muthen & Satorra, 1995). The DEFF value obtained is 1.00, well below the suggested cut-off value of 2.0. When DEFF exceeds 2.0, a multilevel analysis is recommended (Muthen & Santorra, 1995). Finally, dummy variables are constructed for every firm with 3 or 4 business units in the sample (9 and 2 firms, respectively, out of a total of 134) to test for nested effects. Multiple regression preliminary analyses results, not presented here, show no significance for firm-specific nested effects. All the aforementioned arguments and data analyses do not imply lack of firm effects on business unit results; they instead indicate that it is impossible to differentially test between business unit effects and firm effects in this study. This study follows previous research (i.e. Gardner et al., 2011; Kehoe & Wright, 2013) that argues that perceptions about HPWSs and affective commitment are likely to be influenced by other unit members’ opinions and responses and should be analyzed at a unit level. Table 1 presents statistics to justify the aggregation of data at the business unit level for both measures. Rwg(j) calculates the within-group agreement for multiple item measures. The Rwg(j) for all variables is greater than the commonly accepted value of .70, indicating that aggregation is justified (Biemann, Cole, & Voelpel, 2012). The intra-class correlations ICC(1) reflects the proportion of total variance in a variable that can be explained by group membership. Although there is no standard for the ICC(1) value, the values presented in Table 1 exceed the values presented in similar research (Messersmith et al., 2011; Takeuchi et al., 2007). ICC(2) provides an estimate of the reliability of the group means (Bliese, 2000), indicating how reliably the aggregate means


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Table 1. Unit-level aggregation, reliability and internal consistency statistics for all measures.

Variable Personnel selection


Performance evaluation and training Job descriptions Compensation and incentives Empowerment enhancement HR practices Commitment Human capital Business unit results

Raykov’s composite reliability rho 0.80

Average inter-item correlations 0.63

Rwg(j) 0.73 (F = 4.33***) 0.78 (F = 5.04***) 0.70 (F = 3.34***) 0.71 (F = 4.15***) 0.80 (F = 5.51***)

ICC(1) 0.45

ICC(2) 0.77

Cronbach’s alpha 0.86

0.50

0.80

0.91

0.84

0.61

0.36

0.70

0.84

0.68

0.73

0.44

0.76

0.85

0.82

0.55

0.46

0.78

0.90

0.86

0.61

0.74 (F = 3.22***) 0.89 (F = 3.00***) 0.96 (F = 2.96***)

0.35

0.69

0.94

0.92

0.79

0.47

0.67

0.89

0.90

0.83

0.47

0.66

0.79

0.83

0.43

***p