Environmental human resource management and competitive advantage

HIGH-INVOLVEMENT WORK

PRACTICES AND ENVIRONMENTAL

CAPABILITIES: HOW HIWPS

CREATE ENVIRONMENTALLY

BASED SUSTAINABLE

COMPETITIVE ADVANTAGES

J A V I E R M A R T Í N E Z - D E L - R Í O , J O S É C É S P E D E S -L O R E N T E , A N D E V A C A R M O N A - M O R E N O

We study how high-involvement work practices (HIWPs) may enhance a fi rm’s economic performance through the development of a proactive environmental strategy (PES). HIWP systems facilitate the implementation of a PES by promoting employees’ ability, motivation, and opportunities to behave in ways consistent with environmental management goals. In turn, PESs contribute to the development of strategic competitive advantages and drive superior performance. The results of our empirical analysis of 233 fi rms broadly confi rm our hypotheses and suggest that although the direct effect of HIWPs on performance was not signifi cant in our sample, HIWPs do have a signifi cant indirect effect through a PES as a mediator.

Keywords: environmental proactivity, high-involvement work practices, fi rm performance, resource-based view

Introduction

In a recent overview of the state of the art in human resource management studies, Janssens and Steyaert (2009) suggested that studying the effects of HRM practices on sustainability would help to expand

the dominant focus on firm-level economic performance by placing the emphasis on a “broader concept of outcome,” including the social and ecological dimensions. With this perspective, research on HRM was able to move

from its sometimes criticized emphasis on purely economic outcomes (e.g., Janssens & Steyaert, 2009; Keegan & Boselie, 2006) toward a focus that takes into account the effects of HRM on society and the natural environment.

The extant literature has stressed the influence of the human factor on compa-nies’ environmental policies. For exam-ple, Ramus and Steger (2000) assessed the relationships among environmental policy, supervisory support behaviors, and employee

Correspondence to: Javier Martínez-del-Rio, Carretera de Sacramento s/n, 04120 La Cañada, Almeria, Spain, Phone: (+34) 950214033, E-mail: [email protected].

Human Resource Management,Human Resource Management, November–December 2012, Vol. 51, No. 6. Pp. 827–850

© 2012 Wiley Periodicals, Inc.

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

DOI:10.1002/hrm.21505

828 HUMAN RESOURCE MANAGEMENT, NOVEMBER–DECEMBER 2012

Human Resource Management DOI: 10.1002/hrm

Although proactive

environmental

strategies are

human resource–

based, there is

relatively little

empirical evidence

describing the

relationship

between HRM

practices and

environmental

management.

environmental initiatives. Russo and Harrison (2005) and Berrone and Gomez-Mejia (2009) studied the link between com-pensation systems and environmental performance. Although proactive environ-mental strategies are human resource–based (e.g., Hart, 1995), there is relatively little empirical evidence describing the relation-ship between HRM practices and environ-mental management (Egri & Hornal, 2002; Jabbour, Santos, & Nagano, 2010). Moreover, existing quantitative studies have an atomis-tic focus in that most of them are based on only one or two HRM practices, whereas the HRM literature suggests that “coherent sys-tems of mutually reinforcing HR practices

are likely to better support sus-tainable performance outcomes than are any individual practices” (Kehoe & Wright, 2010, p. 423). Our article attempts to build on these studies by adopting a more comprehensive and systemic focus.

A substantial body of lit-erature suggests that there is a direct relationship between high- involvement work practices (HIWPs) and financial perfor-mance (e.g., Apospori, Nikandrou, Brewster, & Papalexandris, 2008; Delery & Doty, 1996; Guthrie, Flood, Liu, & MacCurtain, 2009; Huselid, 1995). Recent reviews of the research (Boselie, Dietz, & Boon, 2005; Combs, Liu, Hall, & Ketchen, 2006; Guest, 2011) have emphasized that although this topic constitutes a central issue in the HRM field and a large majority

of the studies have found a positive relation-ship between HIWPs and financial perfor-mance, there were still some inconsistencies that did not allow an explanation of why this association exists.

The resource-based view (RBV) suggests that a resource (e.g., HIWPs) needs to fulfill various necessary conditions to be a source of competitive advantage (e.g., Barney, 2001). Consequently, a given resource should not be a universal source of competitive advantage

per se. Some scholars (Becker & Huselid, 2006; Paauwe, 2004, 2009) suggest that the relationship between HIWPs and financial performance is mediated by other strategic capabilities. Therefore, in those cases in which HIWPs cannot be considered to provide a com-petitive advantage, HIWPs may still influence financial performance by acting as enablers of other mediating strategic capabilities. In this context, we conceptualize HIWPs as a firm capability (Delaney & Huselid, 1996; Huselid, 1995) to empirically study their relationship with a proactive environmental strategy (PES) and the indirect relationship, through PESs, of HIWPs to financial performance.

Therefore, this study aims to provide two main contributions to the literature on HRM and the natural environment. First, this study analyzes the role of HIWPs as a capability related to the development of environmentally sustainable strategies. Specifically, we build on previous studies linking isolated HIWPs with PESs to provide a more comprehensive explanation for how and why those two capabilities are related. Second, this study explores the direct effects of HIWPs on performance and their indirect effects through PESs. In doing so, we address the requests of previous stud-ies for a richer understanding of the link-ages between HRM and its outcomes (Evans & Davis, 2005; Snape & Redman, 2010), including environmental and social out-comes (Janssens & Steyaert, 2009). We also address the suggestions of giving high pri-ority to examining the mediating link-ages in HRM-performance research (Ferris, Hochwater, Buckley, Harrell-Cook, & Frink, 1999; Kehoe & Wright, 2010; Snape & Redman, 2010) and to examining the driv-ers of the acquisition of green competencies (Marcus & Fremeth, 2009).

Theoretical Framework

HIWPs as Internal Drivers for Imple-menting Environmental Strategies

The environmental literature has described corporate environmental strategies and

HIGH-INVOLVEMENT WORK PRACTICES AND ENVIRONMENTAL CAPABILITIES 829


Firms with strong

HIWP approaches

already in place

may benefit from

the creation and

dissemination

of a shared

environmental

vision when they

implement or

improve their PES.

proposed different classification schemes (Kolk & Mauser, 2002). For example, corpo-rate choices with regard to environmental strategies can be placed along a continuum ranging from conformity with regulations and standard industry practices to voluntary initiatives intended to protect the environ-ment (Sharma, 2000). The typical classifica-tion distinguishes between reactive and proactive environmental strategies (i.e., Aragón-Correa, 1998; Hunt & Auster, 1990). Reactive strategies are typically defensive. These strategies might include pollution mea-surement, end-of-pipe solutions, following the environmental initiatives of competing firms, and compliance with environmental regulations and stakeholder requirements. Conversely, proactive strategies involve con-tinuous improvement and the implementa-tion of environmental practices that extend beyond competition and industry standards; anticipating and preparing for future changes in regulations and social trends; and design-ing or altering operations, processes, and products to prevent negative environmental impacts (Aragón-Correa & Sharma, 2003).

Environmentally proactive firms must invest in the development of their human resources because a PES is “people inten-sive and depends upon tacit skill develop-ment through employee involvement” (Hart, 1995, p. 993). In particular, firms with strong HIWP approaches already in place may benefit from the creation and dissemination of a shared environmental vision when they implement or improve their PES. HIWPs may create shared men-tal models characterized by overlapping knowledge. These shared mental models enhance coordination and create a sense of common purpose among employees (Evans & Davis, 2005; Rentsch & Klimoski, 2001). As Bowen and Ostroff (2004, p. 203) indi-cate, “a strong HRM system process can enhance organizational performance owing to shared meanings in promotion of collec-tive responses that are consistent with orga-nizational strategic goals.” Therefore, when a company is committed to a PES, strong HIWPs help it to transmit a clear message to its employees that the environmental

strategy is important. HIWPs also promote collective responses consistent with the envi-ronmental strategy and help to align the individual goals of the employees with the organization’s environmental goals. Russo and Harrison (2005) found empirical evi-dence that companies tend to adapt some HIWPs—for instance, improving formal communication and linking the compen-sation of CEOs and environmental manag-ers to the achievement of environmental goals—when trying to improve their environmental results.

HIWPs are conceptualized in the literature as mutually rein-forcing and synergistic HR prac-tices that are intended to acquire, train, retain, involve, and moti-vate employees and to improve communication (e.g., Apospori et al., 2008; Guthrie, Spell, & Nyamori, 2002; MacDuffie, 1995). Although the specific HR practices included in HIWP sys-tems have varied across stud-ies, one commonality among these different approaches is a focus on ability-enhancing prac-tices, such as hiring selectiv-ity and training opportunities; motivation-enhancing practices, such as regular formal evalua-tion mechanisms and incentives dependent on formal evaluations; and opportunity-enhancing practices, such as the use of internal communication systems and infor-mation sharing (Kehoe & Wright, 2010).

Ability-enhancing practices, such as selective hiring and further employee training, may produce superior technical knowledge that ultimately facilitates the implementation and development of a PES. Numerous practices that are often linked to environmental proactivity, such as product life-cycle analysis, the implementation of emission prevention systems, and process redesign, are typically complex and require a high command of various skills that can be obtained through intensive training or by hiring employees with superior technical competences. Employees should have deep



Ability-enhancing

practices, such as

selective hiring and

further employee

training, may

produce superior

technical knowledge

that ultimately

facilitates the

implementation and

development of a

PES.

knowledge of the production processes if they are to contribute to the improvement of these processes in a way that generates environmental competitive advantages (e.g., Alberti, Caini, Calabrese, & Rossi, 2000). Thus, HIWPs intended to improve employee skills will enhance the effectiveness of envi-ronmental proactivity. Conversely, losing experienced and qualified staff might dam-age organizational knowledge and, conse-quently, make it more difficult to develop a PES. HIWPs may help to reduce these situations because they strengthen employ-ees’ motivation to remain with the organi-zation and because they reduce absenteeism (Kehoe & Wright, 2010).

Previous literature shows that motivation-enhancing practices, such as formal evaluation sys-tems or incentives linked to goal attainment, also play a role in the introduction and develop-ment of superior environmental practices. Ramus (2001) notes that the motivation for intro-ducing environmental practices may be inherent to individual employees but can also be facili-tated by the company through setting environmental objec-tives, offering rewards for achiev-ing those objectives (Fernandez, Junquera, & Ordiz, 2003), pro-viding supervisor support for positive behaviors, or monitor-ing employee activities related to environmental issues. Moreover, some pollution-preventing envi-ronmental practices may require

costly investments, and managers may perceive high degrees of uncertainty when it comes to whether such investments will yield a profit (Aragón-Correa, 1998; Berrone & Gomez-Mejía, 2009). However, perfor-mance-related incentives may erode such barriers by rewarding managers for assum-ing those risks and, as a result, may dissuade managers from avoiding costly environmen-tal investments (Berrone & Gomez-Mejia, 2009). Bottom-level employee motivation may also facilitate the implementation of

a PES. When a company introduces new environmental practices, highly moti-vated employees will show lower levels of resistance to change and may even sug-gest further improvements. Therefore, both supervisor and employee motivation will positively influence the development and maintenance of environmental strategies.

Opportunity-enhancing practices, such as internal communication and informa-tion-sharing systems that foster top man-agement and employee participation, also play a relevant role in introducing and fur-ther developing a PES (Fernandez et al., 2003; Hart, 1995; Ramus & Steger, 2000). Enhanced internal communication facilitates the identification of sources of pollution and encourages the suggestion of new ideas by employees (Boiral, 2002). Many waste-reduction programs emphasize the role of ideas from bottom-line employees (e.g., Hanna, Newman, & Johnson, 2000). In addi-tion, with effective organizational commu-nication and information-sharing systems in place, it is easier to transmit environmen-tal goals and programs. Employees must be aware of an organization’s environmental strategy to feel involved and to be able to contribute.

Thus, firms with strong HIWP systems promote a shared environmental vision and the workforce’s ability, motivation, and opportunity to perform in ways consistent with organizational environmental goals.

Hypothesis 1: The implementation of HIWPs will have a positive effect on the development of a PES.

PES and Its Effect on Performance

There is extensive research that addresses the relationship between the PES and firm per-formance in different industries. Although early studies suggested that environmental strategies might lead to unproductive invest-ments, higher costs and the deterioration of competitive advantages (e.g., N. Walley & Whitehead, 1994), and some of them did not find any significant relationship among the constructs (e.g., Christmann, 2000), later studies revised this view (Marcus &Fremeth,



2009). In particular, a few meta-analyses on the topic (Horváthová, 2010; Margolis, Elf-enbein, & Walsh, 2009; Margolis & Walsh, 2003; Orlitzky, Schmidt, & Rynes, 2003) found a significant positive relationship be-tween PES and firm financial performance. According to the natural-resource-based view of the firm (Hart, 1995; Hart & Dowell, 2011), proactive environmental strategies are associated with the development of complex resources and capabilities, which may ex-plain the positive relationship between PESs and financial performance (e.g., Russo & Fouts, 1997; Sharma & Vredenburg, 1998). In addition, the most recent studies demon-strate other ways in which environmental management may influence firm perfor-mance. For example, PESs are regarded as driving innovation and corporate entrepre-neurship (Cohen & Winn, 2007; Dean & McMullen, 2007).

The traditional distinction between cost and differentiation advantages may be useful in analyzing the impact of a PES on finan-cial performance, as suggested by Shrivastava (1995). As part of their PES, firms can increase their efficiency and reduce input and waste disposal costs in their operational processes. This efficiency can be achieved, for example, by “redesigning production processes to be less polluting, substituting less-polluting inputs, recycling byproducts of processes, and innovating less-polluting processes” (Christmann, 2000, p. 664). For example, agricultural producers may achieve a cost advantage by saving costly inputs whose consumption has environmental implications, such as water, pesticides, and chemical fertilizers. In addition, firms may also obtain cost advantages by anticipating regulatory changes; when the changes even-tually occur, proactive companies will avoid having to make costly investments to rapidly adapt to new standards, limits, and technol-ogies (e.g., Porter & van der Linde, 1995).

Differentiation advantages are typically obtained when the customer perceives that one company is providing a better value than its competition in some sense. Therefore, dif-ferentiation advantage depends on the fit between the customer’s needs and the features

of the product (Delmas, Russo, & Montes-Sancho, 2007). Organic foods are a typical example of a product whose value is greater because of environmental practices. These products are perceived as healthier and as fulfilling higher-quality standards (K. Walley, Custance, & Parsons, 2000) and enjoy better access to markets (Ecker & Coote, 2005).

In summary, a PES as a strategic resource in specific contexts may provide a firm with the opportunity to reduce costs and improve product differentiation, which, in turn, can yield superior financial performance.

Hypothesis 2: The implementation of a PES will exert a positive effect on organizational perform-ance.

A Resource-Based Approach to the Relationship Between HIWPs and PESs

The previous arguments, taken together, imply a path from HIWPs to organizational performance via PES. Specifically, if HIWPs favor—or enable—the development of envi-ronmental strategies (Hypothesis 1), and if using a PES exerts a positive effect on firm performance (Hypothesis 2), it follows that firm performance will be indirectly influ-enced by HIWPs through the PES.

Several studies have used the RBV frame-work to analyze the role of HRM in the development of competitive advantage (e.g., Barney & Wright, 1998; Lado & Wilson, 1994; Mueller, 1996; Wright, McMahan, & McWilliams, 1994). In particular, several the-oretical and empirical studies (e.g., Apospori et al., 2008; Delery & Doty, 1996; Guthrie et al., 2002, 2009; Koch & McGrath, 1996; MacDuffie, 1995; Youndt, Snell, Dean, & Lepak, 1996) have suggested that HIWPs can enhance financial performance, addressing a central issue in the HRM field (Janssens & Steyaert, 2009; Paauwe, 2009). Some stud-ies have employed a universal approach (Delaney & Huselid, 1996; Huselid, 1995), suggesting a direct relationship between HIWPs and performance. Three reviews of the literature (Boselie et al., 2005; Combs et al., 2006; Guest, 2011) confirmed that



Even in industries

in which HIWPs

do not fulfill the

prerequisites to

be considered

competitive

advantages per

se, they can still

make an impact

on organizational

performance

through other

capabilities of the

firm such as PESs.

the large majority of published studies have found a positive relationship between HRM and performance. Nevertheless, in spite of all the attention received, the three reviews also emphasized that there was insufficient evi-dence of why there is an association.

To explain these inconsistencies, Becker and Huselid (2006) suggested that the imple-mentation of strategic business processes is the mediating variable in the relation-ship between HRM strategy and organiza-tional performance. Similarly, Paauwe (2004, 2009) studied the role of HRM as an enabler for a wide range of strategic options, which implies “that the HR architecture’s main goal is the development of a workforce with

a sufficient degree of flexibility/ adaptability to implement a range of strategic options” (2009, p. 138). This means that HIWPs also influence firm performance indirectly by enabling other stra-tegic options that ultimately affect performance. In this con-text, assuming that a proactive environmental strategy is one strategic option, HRM should affect performance through PESs.

Becker and Huselid’s (2006) and Paauwe’s (2004, 2009) ideas are consistent with the RBV framework. Under the RBV of the firm, if HIWPs have the poten-tial to add value and are diffi-cult to imitate and to substitute, they can help to develop supe-rior organizational capabilities and competitive advantages that ultimately enhance firm perfor-mance. When a firm has success-fully implemented HIWPs, it will be easier to implement a PES and

increase the probability of doing so success-fully. Consequently, even in industries in which HIWPs do not fulfill the prerequisites to be considered competitive advantages per se, they can still make an impact on organi-zational performance through other capabili-ties of the firm such as PESs. In other words, a PES can mediate the relationship between HRM and organizational performance.

Hypothesis 3: The implementation of HIWPs will exert an indirect effect on organizational perform-ance (mediated by PESs).

Methods

Sample Selection and Data CollectionTo test the hypotheses, we identified a popu-lation of agri-food firms in three geographi-cal clusters. These firms differed in the de-gree to which they implemented HIWPs and PESs. An advantage of this field setting is that the companies that we studied are simi-lar in size, rely on similar production tech-nologies, and compete in similar markets. Moreover, we can control for geographical differences, which is relevant because agri-cultural production systems and, conse-quently, their inputs and outputs tend to vary by region. In addition, environmental strategies have been found to be relevant in the food industry (e.g., Cordano, Marshall, & Silverman, 2009).

We used a combination of archival data and a mail survey. We obtained the archi-val data from the Sistemas de Análisis de Balances Ibéricos (SABI) database for 2005, 2006, and 2007. Companies located in Spain are obliged to disclose their yearly accounts in an official and public register overseen by Spanish economic authorities. The SABI database includes financial information, information on locations and industrial sec-tors, and other miscellaneous information.

We obtained the survey data from a sample of companies with more than 10 employees operating in three Spanish agri-cultural regions. There was no established threshold for organizational size in terms of the number of employees. We sampled orga-nizations with 10 or more employees to gen-erate a sufficient sample size. Pretests with industry experts did not reveal any problem with this threshold. We ran the same model using a sample consisting of firms with 20 or more employees because previous studies had used this threshold (e.g., Kotey & Slade, 2005), and we obtained similar results.1 However, the number of firms diminished



significantly, as did the representativeness of the sample. Thus, (a) this smaller sample of firms would not yield different results, and (b) our ability to generalize the results to other contexts would be reduced.

The companies included belong to agri-cultural sectors and include fruit and veg-etable producers, grape and wine producers, agricultural cooperatives, seed producers, agricultural derivatives, pesticide manufac-turers, and equipment manufacturers. One of the regions is in the north of the country and is responsible for producing a sparkling white wine similar to champagne, called cava. The other two clusters produce fresh fruit and vegetables and are located in the south of the country.

To pretest the questionnaire and obtain qualitative information about the industry and the relationships tested, we conducted interviews with seven managers. Feedback from these executives was incorporated into a revised version of the survey instrument along with comments and suggestions from industry experts and several colleagues expe-rienced in survey design.

Given the focus of this study, the appro-priate respondents were managers with adequate knowledge of their companies’ competitive strategies, resources, and capabil-ities and the HRM policies of their firms. In addition, the individuals were required to be familiar with the environmental issues affect-ing their firms and the effects of the firm’s environmental strategy on its competitive advantage. We addressed the survey to CEOs/presidents because our primary field inter-views indicated that these individuals were best able to respond to those questions about their respective firms. This approach is con-sistent with the selection of key informants who are knowledgeable about organizational matters by virtue of their position (Huber & Power, 1985; Venkatraman & Grant, 1986) and the research practice of relying on individual senior-level informants in stud-ies of small organizational units (Zaheer & Venkatraman, 1995).

The survey data came from a question-naire sent to a total of 895 CEOs or presi-dents in three mailings between February

and November 2005. We obtained 266 (29.72 percent) responses. Four responses were incomplete or invalid, and we were not able to find secondary data for 2007 regard-ing the dependent variable for this study for another four firms. We excluded 25 firms pertaining to sectors with business mod-els that were fundamentally different from those of the firms included in the sample. Therefore, the sample includes complete, valid data from 233 firms.

We addressed the potential for non-response bias by comparing certain key attributes of the respondents (number of employees, annual sales, and return on assets [ROA]) with those of the full sampling frame. We obtained size, sales, and ROA data from the SABI database. t-tests revealed no sig-nificant differences between the mean size (t = 0.87), mean sales (t = 1.36), and mean ROA (t = 1.06) of the respondents and the full sample. To further confirm the repre-sentativeness of our sample, we conducted Kolmogorov-Smirnov two-sample tests. For size, sales, and ROA, we found no significant differences between the respondents and the full sampling frame; the p-values were, respectively, .201, .256, and .221, suggesting that the two samples were drawn from the same population. Of the respondents, 59.9 percent were located in the fruit and vegeta-ble regions, and the remaining 42.1 percent were located in the wine region. In our sam-ple, 75 percent of the firms had 67 or fewer employees, and 37.8 percent were coopera-tives. On average, the CEOs had 13 years of experience in their current position, and 63.8 percent of them were male. The firms in our sample had average sales of $2.7 mil-lion. Of the studied firms, 32.62 percent pro-duced or commercialized fruits or vegetables, 27.04 percent produced or commercialized grapes or wine, 7.3 percent were seed pro-ducers, 6.87 percent were pesticide produc-ers, 9.44 percent were agricultural machinery producers, and the remaining 13.73 percent belonged to other sectors such as irrigation systems and fertilizer manufacturing. We are confident that our sample adequately repre-sented the majority of businesses operating in the three regions.



To measure the

dependent latent

variable, we used a

four-item scale that

included returns on

total assets, profit

margins, profits

per employee, and

economic profits

based on secondary

data.

Measures

The existing measurement instruments were identified in a review of the prior research. We selected scales previously validated in multi-industry samples. However, the results made it difficult to interpret similar scores in different sectors (Dess, Newport, & Rasheed, 1993; Hambrick, 1983). Consistent with Aragón-Correa (1998), we standardized the HIWP, PES, and performance scales using the average and SD of the sector of activity in

the cluster to overcome these dif-ficulties and to avoid bias caused by a different average situation in each sector. This procedure made the standardized scores more comparable among sectors and provided an indicator relative to the HIWPs, PESs, and performance of the competitors. Table I pres-ents the details of the measure-ment instruments used to opera-tionalize our theoretical constructs.

Firm Performance

To measure the dependent latent variable, we used a four-item scale that included returns on total as-sets, profit margins, profits per employee, and economic profits based on secondary data obtained

from the SABI database. These data were originally collected from the firms’ annual accounts for 2006 and 2007. The items repre-sent the average for those two years and were calculated from operational measures. There-fore, the figures do not account for the result of financial operations or taxes. The Cron-bach’s alpha of the construct was 0.82, indi-cating the reliability of the measurements. After a principal components analysis, there was only one factor with an eigenvalue higher than 1.0, and that factor explained 81.74 percent of the variance. We also ran a confirmatory factor analysis to assess the convergent validity of the measure. The re-sults of the CFA indicated a good fit (CFI = 0.99; RMSEA = 0.056; Hu & Bentler, 1999), and the loads of all the indicators were

significant (p < .001), confirming the exis-tence of an underlying unidimensionality (Hair, Anderson, Tatham, & Black, 1998).

Proactive Environmental Strategy

To measure the extent to which firms em-ployed a proactive environmental strategy, we used the scale of environmental proactivity developed by Aragón-Correa (1998). Each CEO was asked to rate his or her firm’s envi-ronmental performance compared with that of the competition in 14 different environ-mental practices on a Likert scale ranging from 1 (“we have not addressed this issue at all and have no plans to do so in the future”) to 7 (“we are the leaders on this in our sec-tor”). A detailed description of the items is included in Table I. The coefficient alpha of the 14-item construct was 0.92, indicating good reliability of the measurement. We conducted a CFA to check for underlying uni-dimensionality. Four items did not show an estimated parameter of more than 0.70. As is recommended for formative scales, we re-moved those items to improve the parsimony of the model (Bollen & Lennox, 1991; Hair et al., 1998). The 10-item CFA appeared to ap-propriately fit the data; the goodness-of-fit indexes were good in general terms (CFI = 0.98; RMSEA = 0.058), and the loads of all of the indicators were significant (p < .001). These results confirmed the existence of un-derlying unidimensionality. The Cronbach’s alpha of the final, more parsimonious 10-item construct was 0.94, indicating a marginal improvement in the reliability of the construct.

High-Involvement Work Practices

Our review of the HIWPs examined in pre-vious research within strategic HRM and environmental management studies led us to construct a list of 13 HR practices that reflect a high-performance HR approach based on Huselid’s (1995) scale. We consid-ered ability-enhancing practices, such as hiring selectivity and training opportuni-ties; motivation- enhancing practices, such as regular formal evaluation and

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We considered

ability-enhancing

practices, such as

hiring selectivity

and training

opportunities;

motivation-

enhancing practices,

such as regular

formal evaluation

and incentives

based on formal

evaluations; and

opportunity-

enhancing practices,

such as internal

communication and

information sharing.

incentives based on formal evaluations; and opportunity- enhancing practices, such as internal communication and informa-tion sharing. To confirm underlying unidi-mensionality, we conducted a CFA. Six indicators did not show an estimated pa-rameter greater than 0.70. Consequently, we removed those items and included only those indicators that contributed unique variance to the total score (Bollen & Lennox, 1991; Hair et al., 1998). We kept at least two indicators each for ability-enhanc-ing, motivation- enhancing, and opportunity-enhancing practices.2 The model appeared to be a good fit to the data, the goodness-of-fit indexes were good in general terms (CFI = 0.99; RMSEA = 0.056) and the loads of all the indicators were significant (p < .001). These results confirmed the existence of underlying unidimensionality. The Cronbach’s alpha of the final, more parsimonious seven-item con-struct was 0.90, indicating the high reliability of the measurements.

Control Variables

To assess the unique contribution of our pre-dictions, we control for alternative explana-tions for the sources of capabilities, including the following variables in the model: cluster type, firm size, CEO experience, interaction with regional associations, and perceived competitive intensity.

Cluster Type

Depending on their industry, companies were classified as (0) located in a wine cluster or (1) located in a fresh fruit and vegetable cluster. We included this control because there is evi-dence that regional configurations may influ-ence the environmental strategy of firms (e.g., McEvily & Marcus, 2005; McEvily & Zaheer, 1999; Russo, 2003) and that agricultural pro-duction processes and technologies tend to vary across geographical areas.

Firm Size

This variable was introduced as a control be-cause it may be related to firm performance

(e.g., through economies of scale). Moreover, larger firms have greater slack resources, mak-ing experimentation with new practices and techniques more feasible (Kelley & Brooks, 1991). Consequently, larger firms may find it easier to develop competitive capabilities than smaller firms. Firm size was measured as the log number of employees on December 31, 2005, as reported in the SABI database.

CEO Experience

CEOs were asked to report the number of years in their current position because a more experienced CEO may be more knowledge-able about the practices that con-figure competitive capabilities and eventually make firms more profitable.

Regional Associations

McEvily and Marcus (2005) and McEvily and Zaheer (1999) found significant evidence of a connec-tion between regional institutions and environmental practices and other firm capabilities in instances in which the firms in question were geographically clustered like the firms in our sample. These or-ganizations provide access to tech-nical knowledge and may act as facilitators or provide information and training regarding environ-mental or HRM best practices. Therefore, they may favor the de-velopment of those capabilities in firms and, in turn, may enhance the performance of the firms in-teracting with them. There are four regional industry associations with which some of the firms in the sample are affiliated: Fre-shuelva, Tecnova, Coexphal, and UVIPE. These associations regu-larly organize innovation projects, discussion groups, seminars, meet-ings, and training courses and disseminate information among their members using association magazines, e-mail lists, and



intranets. To operationalize the interaction with regional associations, we included the fol-lowing variables: “being a formal member of one or more of these associations” (measured as a dummy variable with 0/1 values) and “fre-quency of contacts with the association staff” (measured using a seven-point Likert scale). Subsequently, the values of both variables were multiplied to reflect their interaction.

Perceived Local Rivalry

This variable indicates the extent to which the CEO thinks competitive intensity will af-fect his or her company in the future. The potential values range from “very positively” to “very negatively” on a seven-point Likert scale. Local rivals compete to hire the best engineers, to attract the same capital inves-tors, and to establish the best cooperative re-lationships with customers and suppliers (Pouder & St. John, 1996). This rivalry exerts continuous pressure on firms to generate best practices and stay ahead of local competitors (Porter, 1990). Therefore, perceived local ri-valry might positively influence the develop-ment of HIWPs and a PES.

Construct Validity of the Measures

We verified that the three composite mea-sures had sufficient validity by assessing their convergent and discriminant validity using nested model comparisons. For this purpose, we conducted the sequential chi-square dif-ference tests suggested by Anderson and Gerbing (1988). The discriminant validity of a scale implies that this scale measures a sin-gle fundamental construct rather than mul-tiple constructs. We compared the fit of the unconstrained model with that of a nested model in which the correlation between one

pair of factors was constrained to unity. A significantly lower chi-square value for the unconstrained model indicates that the traits are not correlated and that discriminant va-lidity is achieved (Anderson & Gerbing, 1988; Bagozzi & Phillips, 1982). As reported in Table II, in each case, the fit of the nested model was significantly poorer than that of the unconstrained model. The results showed no serious issues with regard to discriminant validity.

Controlling for Common Method VarianceCommon method bias can imply problems for survey research that relies on self-reported data, specifically when the dependent and the independent variables are reported by the same person at the same time. To control for common method bias, we obtained measures for the dependent variable in this study (firm performance) from a different data source. In addition, our dependent variable measures are based on objective archival data, which excludes the presence of variance related to the respondent’s social desirability, lenience, or acquiescence biases. In addition, we fol-lowed Podsakoff, MacKenzie, Lee, and Podsa-koff’s (2003) recommendations for the survey design: (1) in our cover letter, we guaranteed respondent anonymity and asked for sincer-ity to reduce evaluation apprehension; and (2) we used previously well-known, tested, and validated scales to reduce the chances that a deficient wording of the questions would lead the respondents to provide so-cially desirable answers.

However, the potential for common method variance may exist in our model because a common data source was used to measure the HIWPs and PESs. To control

T A B L E I I Discriminant Validity

ModelsConstrained Model

Chi-Squ.Unconstrained Model Chi-Squ. Difference p-Value

PES and performance 194.54 134.03 60.51 .00

PES and HIWPs 241.90 181.78 60.12 .00

HIWPs and performance 148.83 63.94 84.89 .00



for common method bias in this relation-ship, we tried to control for the effect of a single unmeasured latent method factor, as recommended by Podsakoff et al. (2003). Toward this end, we specified a model that included the constructs from our concep-tual model and one additional method fac-tor. However, we encountered problems with identification of the model, and we were unable to make it converge. This is a relatively common pitfall of this procedure (Podsakoff et al., 2003). Therefore, we used Harman’s single-factor test to estimate the extent of the common method bias based on these two scales. If common method variance existed, a single factor would emerge from a factor analysis of all of the measure-ment items, or there would be a factor that accounted for most of the variance. The factor analysis revealed four factors with eigenvalues greater than 1.0 that accounted for 71.54 percent of the variance. The first factor accounted for 28.93 percent of the variance.

In short, taking into account the use of archival data for the dependent variable and the results of Harman’s single-factor test, we felt confident that common method bias was not a serious problem in this study.

Analysis and Results

The hypotheses developed earlier were esti-mated specifying a series of alternative struc-tural equation models (SEMs) using EQS 6.1.3 The sample showed the presence of multi-variate non-normality (the Mardia’s normal-ized estimate was 16.64); therefore, the esti-mation was conducted using the robust maximum likelihood procedure proposed by Satorra and Bentler (1988). This system recti-fies the effect of multivariate non-normality over goodness-of-fit estimators.

Table III shows descriptive statistics and correlations among aggregated constructs. The general low level of correlations pro-vides initial evidence of discriminant valid-ity of the constructs and suggests that multicollinearity is not an issue in this study.

We followed standard reporting guide-lines suggested by Schumacker and Lomax

(2004) and Hair et al. (1998). Strategic structural equation modeling (SEM) allows different models to fit the data; thus, we specified alternative nested models to deter-mine which of the possible models best fit the data. First, we tested the full model, measuring all of the theoretically justified relationships with the addition of the five control variables included in this study. We tested the relationship between each of the controls—size, cluster type, CEO experi-ence, perceived competitive intensity, and the intensity of the interaction with regional associations—and HIWPs, PESs, and per-formance for a total of 15 links between controls and endogenous variables. The Appendix describes the results of the ini-tial model. This model fit the data moder-ately well (p = .0; CFI = 0.88; NNFI = 0.85; RMSEA = 0.054). However, this model included several paths that linked the con-trol variables with the endogenous variables that were not significant. As recommended (e.g., Hair et al., 1998), to find a more par-simonious model that fit the data well, we sequentially erased the least significant rela-tionships among the nonsignificant paths step by step to ensure that we did not erase any nonsignificant paths that became sig-nificant in a subsequent model. Table IV describes the final, simplified model, includ-ing only the significant relationships.

The model appeared to be a good fit to the data. The fit indices (NNFI, CFI, and IFI) yielded values of approximately 0.95, and the RMSEA exhibited a value of 0.037. The Satorra-Bentler scaled chi-square value was nonsignificant (χ2 = 324.22, d.f. = 246, p = .00). However, the chi-square test may be misleading, with sample sizes larger than 200; the chi-square values will be inflated and thus might erroneously imply a poor data-to-model fit (Hair et al., 1998; Schumacker & Lomax, 2004). The normal chi-square value was good at 1.31. Taking all of the indices into account, the model seems to fit the data well (Hair et al., 1999).

Hypothesis 1, which predicts that the implementation of the HIWPs will have a positive effect on the development of a PES, was supported; the path parameter

TA

BL

E

II

I

Corr

elat

ions

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ong

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rega

ted

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truc

ts

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nst

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23

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8

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ES

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0

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610.

157*

−0.

019

0.01

70.

000

1.00

0

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90.

121

0.00

60.

000

0.00

01.

000

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g s

ize

3.19

1.36

0.12

50.

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104

0.00

00.

000

0.00

01.

000

8C

EO

exp

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13.1

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93−

0.07

40.

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estimate was significant (t = 3.36, p < .001). Hypothesis 2, which posits a positive rela-tionship between the PES and performance, was confirmed by the significant parameter estimate (t = 3.58, p < .001).

To evaluate Hypothesis 3 properly, we conducted a test of the universal approach that posits a direct positive relationship between the HIWPs and performance (Baron & Kenny, 1986). In an alternative model, we included a direct link between these two latent variables. Both mod-els showed a very similar fit to the data. However, the relationship between the HIWPs and performance was not significant (t = .14, ns), whereas the links between the

PESs and performance remained significant (t = 3.35, p < .001).

Hypothesis 3, which states that HIWPs exert an indirect effect on organizational per-formance (mediated by the PES), was also sup-ported. The direct link between HIWPs and performance—the universalistic approach—was not significant. Therefore, we can con-clude that there is not a significant direct relationship between the HIWPs and per-formance in our sample. However, EQS 6.1 decomposes the total indirect effect that works through a mediating variable. The estimated indirect effect through the PES is significant (t = 2.74; p < .01). Thus, our data support the indirect effect of HIWPs on performance

T A B L E I V Results of Structural Equation Modeling, Final Model

Parameter Std Estimate (t) Conclusion

Structural coeffi cients

H1: HIWPs PES 0.23 (3.36)*** H1: Supported

H2: PES Performance 0.23 (3.58)*** H2: Supported

HIWPs Performance 0.04 (.45) Not signifi cant

Indirect effect estimation

H3: HIWPs Performance 0.05 (2.47)** H3: Supported

Controls

Regional associations PES 0.22 (3.18)*** Signifi cant

Regional associations HIWPs 0.16 (2.14)** Signifi cant

Local rivalry PES 0.17 (2.66)** Signifi cant

Size Performance −0.13 (−1.63) Not signifi cant

Size PES 0.11 (1.89)* Signifi cant

Goodness-of-fi t statistics

Chi-square 324.22

d.f. 246

p 0.00

NNFI 0.94

CFI 0.95

IFI 0.95

RMSEA 0.037

Normal Chi-square 1.31

*p < .05; **p < 01; ***p < .001.



We found a positive

relationship

between HIWPs

and PESs and a

positive relationship

between PESs

and performance.

Further supporting

our prediction,

we found that

the indirect

effect of HIWPs

on performance

was positive and

significant, revealing

the mediating role of

the PES.

through the PES, even when the direct link is not significant. Overall, our results support the hypothesis that the PES partially mediates the link between HIWPs and performance (Kenny, Kashy, & Bolger, 1998).

To provide a more rigorous test of whether the mediated effect tested in the model was statistically significant, we conducted boot-strap analyses as described by Hayes (2009). We created 2,000 bootstrap samples and thus 2,000 estimations of the mediated effect. To test the significance of the mediated effect, we created a percentile-based confidence interval. If zero is not between the upper and the lower bound of the interval, then we can conclude

that the indirect effect is not zero. Across the bootstrap samples, the values of the indirect effect ranged from −.09 to .46, and the mean value was .09. Only 92 of the 2,000 samples were less than zero, indicating that the indirect effect was significant (p < .05).

CEO experience and cluster type did not have a significant effect on performance, PESs, or HIWPs, whereas size had a nega-tive marginal effect on the devel-opment of PESs. We expected CEO experience to have a posi-tive effect on the development of HIWPs, PESs, and performance. This nonsignificance may be a result of ossification and a lack of openness to new practices on the part of long-tenured CEOs. Regional associations were found to be a significant predictor of the development of both capabili-ties tested, confirming the results obtained by McEvily and Marcus (2005) and McEvily and Zaheer (1999). Finally, local rivalry was found to have a positive, sig-nificant effect on environmental proactivity.

Discussion

The main aim of the study was to examine the role of the HIWP system as an enabler for

PESs by exploring how and why HIWPs im-prove environmental strategies and to test the direct and indirect effects of these com-petitive capabilities on financial perfor-mance. We argue that a proper management of HR-related knowledge and skills facilitates the introduction and development of environmental proactivity. In turn, a PES is considered a strategic capability and a source of competitive advantage; therefore, HIWPs should have an indirect effect on organiza-tional performance through the develop-ment of PESs. In a sample of firms that be-long to different sectors related to the agri-food and wine industries, we found a positive relationship between HIWPs and PESs and a positive relationship between PESs and performance. Further supporting our pre-diction, we found that the indirect effect of HIWPs on performance was positive and sig-nificant, revealing the mediating role of the PES.

A large majority of the published studies have found a positive relationship between HRM and financial performance (Boselie et al., 2005; Combs et al., 2006; Guest, 2011). However, the RBV logic suggests that the value of resources and capabilities depends on the context (Barney, 2001), and, there-fore, the effect of HIWPs on performance should be context-specific. Our study con-tributes to the existing literature by sug-gesting and finding empirical support for the idea that HIWPs, even when they are not directly related with financial perfor-mance (as in our sample), may contribute indirectly to firm profitability by acting as enablers (Paauwe, 2004, 2009) for other stra-tegic options or strategic business processes (Becker & Huselid, 2006). Consequently, the relationship of HIWPs to firm perfor-mance should be mediated by other strate-gic options, which is the role played by the PES in our study. These results are consistent with the research stream in the area of HRM that postulates that this capability’s main goal is the enhancement of the workforce flexibility to implement profitable strategic options (Paauwe, 2004, 2009).

An HRM strategy that is intended to increase HR involvement and performance



This research

builds on the

limited empirical

literature related to

HRM and PESs by

analyzing, in a more

comprehensive

way than previous

studies, how and

why HIWPs and PES

are related.

(HIWP) facilitates a PES because it increases its effectiveness and facilitates its imple-mentation. Although previous studies vary slightly in the specific HIWPs considered, they all include practices aimed to enhance HR ability, motivation, and opportunity to contribute to improving the company’s internal processes (Kehoe & Wright, 2010; Paauwe, 2009). We link this literature with previous studies that have emphasized the relevant role of HR technical ability (e.g., Alberti et al., 2000), motivation (e.g., Berrone & Gomez-Mejia, 2009; Fernandez et al., 2003; Ramus, 2001), and opportunity (Boiral, 2002; Fernandez et al., 2003; Hanna et al., 2000; Hart, 1995; Ramus & Steger, 2000) for the efficiency of a PES. Therefore, this research builds on the limited empirical literature related to HRM and PESs by ana-lyzing, in a more comprehensive way than previous studies, how and why HIWPs and PES are related.

Our findings contribute to the existing HRM literature by broadening the concept of outcome (Janssens & Steyaert, 2009), tak-ing into account the environmental conse-quences of the firm’s activity. Therefore, the outcomes of HIWPs can be extended from firm performance to other firm capabilities that are eventually the source of competitive advantage. HIWPs might also have positive implications for society in terms of environ-mental sustainability.

Finally, this study may extend the natu-ral environmental literature. The proposed relationship between proactive environmen-tal capabilities and the achievement of sus-tainable competitive advantages proved to be significant in our sample. These results are consistent with those obtained by previ-ous studies in other industrial and geograph-ical contexts (e.g., Horváthová, 2010). In addition to the traditional question of “Does it pay to be green?”, our study also addresses the internal contextual antecedents that favor the efficiency of a PES.

Limitations

However, there are limitations associated with the study. There are some unobserved

variables that might influence the tested model. In particular, both HIWPs and PESs may have positive effects on “soft” perfor-mance dimensions such as employee turn-over, recruitment, retention, satisfaction, and motivation and, as a result, may affect “hard” dimensions of performance. For example, it can be argued that a firm’s envi-ronmental strategy strongly affects how attractive an organization is to recruits (Aiman-Smith, Bauer, & Cable, 2001; Bauer & Aiman-Smith, 1996). Therefore, the relation-ships of HIWPs and PESs to financial perfor-mance might be mediated by the “soft” dimensions of performance (e.g., improved employee selection) that have not been in-cluded in our study.

Coff and Kryscynski (2011) argued that the critical path to human-capital-based competi-tive advantage requires attract-ing, retaining, and motivating employees with valuable human capital more efficiently than competitors. However, generic HR practices that reflect best practices “cannot fully explain human capital-based advantages” (p. 4). By implementing envi-ronmental practices, firms can develop unique or idiosyncratic HR systems or a specific set of organizational best practices that may mitigate “specific dilem-mas arising from the idiosyn-cratic attributes of a given firm’s pool of human capital” (Coff & Kryscynski, 2011, p. 11).

Another limitation is the potential gener-alizability of the results. Our research was con-ducted in the context of Spanish agricultural geographical clusters; therefore, the conclu-sions obtained should be taken bearing this in mind, and it should not be automatically assumed that they hold true for other indus-tries or geographical areas. In particular, in the agri-food industry, there are frequent opportunities for differentiation related to environmental practices because final con-sumers perceive organic products as health-ier, safer, and of better quality. This might



Managers should

understand the

strategic importance

of HIWPs, even in

contexts in which

the latter are not

direct sources

of competitive

advantages, in

sharpening and

enabling other

strategic options.

not be the case in other industries. However, we have included several different sectors in the study, which may lead to reasonable expectations with regard to generalization.

The cross-sectional nature of the data used to measure the relationship between HIWPs and PESs does not allow us to statis-tically exclude a reversed direction of this particular link. However, we have provided theoretical arguments indicating that the direction of the causal relationship extends from HIWPs to PESs.

Finally, our empirical study considered single-respondent measures of HRM prac-tices and PESs. We used archival data for the dependent variable and controlled

for common method variance in our model. Although it does not seem to be a severe prob-lem in this study, we cannot exclude the presence of com-mon method bias in this par-ticular relationship.

Implications for Future ResearchDespite these limitations, some managerial implications arise from this study. Managers might find it helpful to know that HIWPs make the develop-ment of other competitive capa-bilities or strategic outcomes—in this case, PESs—easier and more profitable. Therefore, managers should understand the strategic importance of HIWPs, even in contexts in

which the latter are not direct sources of competitive advantages, in sharpening and enabling other strategic options. This might also imply that HR managers have a relevant role in implementing PESs through the de-ployment of specific HR best practices.

Several avenues for future research seem promising and encourage further work. As

previously explained, it would be useful to evaluate the overall effect of HIWPs and their links with PESs across several perfor-mance dimensions, including both “soft” and “hard” dimensions (Wagner, 2011), to fully understand the path model of mediation from HIWPs to financial performance.

It would also be interesting to analyze the relationship between HIWPs and PESs in different empirical settings to assess the generalizability of the results and to identify contextual variables (e.g., sector conditions and resources and capabilities related to both HIWPs and PESs, such as innovation) that may affect this relationship.

Conclusion

Most firms that deploy PESs continue to focus on incremental strategies such as eco-efficiency and pollution prevention. Such strategies will be insufficient to respond to the challenges of environmental degrada-tion, climate change, and poverty (Hart & Dowell, 2011). HRM practices have played an important role in the implementation of these strategies and must also play an impor-tant role in the development of the bold in-novation strategies necessary to achieve sus-tainability and resolve social and environmental problems (Wagner, 2011). The analysis of this role constitutes a chal-lenge to research within strategic human re-source management.

Acknowledgments

The authors would like to thank the guest editor managing our review, Sully Taylor, and the anonymous reviewers for their con-structive, insightful, and useful suggestions that have greatly improved this article. We also gratefully acknowledge the financial sup-port from the Spanish Ministry of Economy and Science and Feder (Project ECO2011-24921).



Notes

1. Results are available upon request.

2. To ensure the validity of the results, we ran an al-

ternative model, including all items deleted from

the HIWP and PES scales. Although the model is

less parsimonious and does not fit the data so well,

the significance of the paths among the latent con-

structs remains very similar or even greater. The

results are available upon request.

3. We believe that SEM was the appropriate analyti-

cal technique for several reasons. First, the previ-

ous literature has consistently used both HIWPs

and PESs with multiple indicator scales (e.g.,

Aragón-Correa, 1998; Huselid, 1995). Using previ-

ously tested scales increases the comparability of

the results and guarantees that the different stud-

ies employ the same concepts, thereby enhanc-

ing the theory development potential of the re-

sults. Second, SEM makes it possible to measure

unobserved latent variables based on multiple

indicators and to test the relationships among

them when all of the relevant paths are directly

tested and none are omitted, as in ANOVA (Baron

& Kenny, 1986). Third, this procedure simultane-

ously assesses the fit of a complete model in

which the dependent latent variables in one re-

gression equation—in this case, the PES—

become independent variables in a subsequent

regression equation. Fourth, SEM incorporates

explicit estimations of the measurement errors

and the correlated measurement errors instead of

assuming that the constructs are measured with-

out errors.

JAVIER MARTÍNEZ-DEL-RÍO is an assistant professor and the vice-dean for international relations in the Business School (FCCEE) of the University of Almería (Spain). His main research interests are environmental management, HRM, innovation, and geographical clusters.

JOSÉ CÉSPEDES-LORENTE is a full professor and the dean of the Business School (FC-CEE) of the University of Almería (Spain). His main research interests are environmental management, HRM, and innovation.

EVA CARMONA-MORENO is an associate professor in the Business School (FCCEE) of the University of Almería (Spain). Her main research interests are environmental man-agement, HRM, and resource complementarities.

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A P P E N D I X Results of Structural Equation Modeling, Initial Model

Parameter Std Estimate (t) Conclusion

Structural coeffi cients

H1: HIWPs PES 0.23 (3.35)*** H1: Supported

H2: PES Performance 0.23 (3.46)*** H2: Supported

HIWPs Performance 0.04 (.49) Not signifi cant

Indirect effect estimation

H3: HIWPs Performance 0.05 (2.44)** H3: Supported

Controls

Regional associations PES 0.22 (3.35)*** Signifi cant

Regional associations HIWPs 0.16 (2.16)** Signifi cant

Regional associations Performance 0.12 (1.41) Not signifi cant

Local rivalry PES 0.15 (2.43)** Signifi cant

Local rivalry HIWPs −0.03 (−0.26) Not signifi cant

Local rivalry Performance −0.02 (−0.13) Not signifi cant

CEO experience PES −0.04 (−0.44)** Signifi cant

CEO experience HIWPs 0.03 (0.37)** Signifi cant

CEO experience Performance 0.02 (0.19)** Signifi cant

Size PES 0.12 (2.04)** Signifi cant

Size HIWPs −0.08 (−1.02) Not signifi cant

Size Performance −0.15 (2.30)** Signifi cant

Cluster type PES 0.04 (0.58) Not signifi cant

Cluster type HIWPs 0.12 (1.69) * Signifi cant

Cluster type Performance −0.02 (0.38) Not signifi cant

Goodness-of-fi t statistics

Chi-square 446.58

d.f. 266

p 0.00

NNFI 0.85

CFI 0.88

IFI 0.83

RMSEA 0.054

Normal Chi-square 1.67

*p < .05; **p < 01; ***p < .001.

Documents

Environmental human resource management and competitive advantage