Discussion: Work Engagement

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A process model of employee engagement: The learning climate and its relationship with extra-role performance behaviors

LIAT ELDOR1* AND ITZHAK HARPAZ2 1The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A., 2Department of Business Administration, Faculty of Management, University of Haifa, Haifa, Israel,

Summary Employee engagement has recently been introduced as a concept advantageous to organizations. However, little is known about the value of employee engagement in explaining work performance behaviors compared with similar concepts. The learning climate, defined as the organization’s beneficial activities in helping employees create, acquire, and transfer knowledge, has also been proposed as an antecedent of employee engagement. Using data from a sample of 625 employees and their supervisors in various occupations and organizations throughout Israel, we investigated employee engagement as a key mechanism for explaining the relationship between perceptions of the organization’s learning climate and employees’ proactivity, knowledge sharing, creativity, and adaptivity. We also tested whether employee engagement explained the relationship more thoroughly than similar concepts such as job satisfaction and job involvement. Multilevel regression analyses supported our hypotheses that employee engagement mediates the relationship between the perceived learning climate and these extra-role behaviors. Moreover, engagement provides a more thorough explanation than job satisfaction or job involvement for these relationships. The implications for organizational theory, research, and practice are discussed. Copyright © 2015 John Wiley & Sons, Ltd.

Keywords: employee engagement; the employee–organization relationship; discriminant validity; organizational climate; proactivity

Introduction

The changing nature of work and the dynamic reality of organizations have challenged the traditional view of employees’ performance. Given that competitiveness, rapid innovation, and continuous change have come to dominate the current market, the focus has shifted from employees’ proficiency to their ability to adapt to new organizational challenges (Griffin, Neal & Parker, 2007). The constantly changing environment and fast-paced nature of modern work are also challenging the classical view of the employee–organization relationship, particularly regarding the level of activity expected from employees (Frese, 2008) and the need to achieve more with less (Masson, Royal, Agnew, & Fine, 2008). As a result, the concept of employee engagement, characterized by high energy and deep dedication, has been introduced into the literature as a potentially optimal means of redefining the employee–organization relationship (e.g., Bakker, Albrecht & Leiter, 2011; Vigoda-Gadot, Eldor & Schohat, 2013). Clearly, the apparent desirability of engaged employees in their work should lead scholars to try to determine the

mutually beneficial resources that reinforce employees’ engagement (Macey & Schneider, 2008). Accordingly, this study focuses on the learning climate in the workplace as a resource that enhances employees’ engagement. We focus on this organizational resource because it has been associated with two emerging organizational phenomena:

*Correspondence to: Liat Eldor, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A. E-mail: leldor@wharton. upenn.edu

Revised 24 May 2015, Accepted 29 May 2015

Journal of Organizational Behavior, J. Organiz. Behav. 37, 213–235 (2016) Published online 18 September 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/job.2037

R esearch

A rticle

the growing importance for employees of having ongoing learning opportunities in their workplace and the growing trend of self-molded careers (Baruch, 2006; Ng, Eby, Sorensen & Feldman, 2005). Moreover, the literature increasingly views the learning climate as a key element in providing organizations with an advantage and as a currency for employees’ effectiveness (e.g., Ellinger & Cseh, 2007; Marsick, 2009). Still, to the best of our knowledge, insufficient attention has been paid to the link between the perceived learning climate and employee engagement. This omission is quite surprising considering that personal fulfillment is the essence of the concept of engagement (Schaufeli, Salanova, Gonzalez-Roma & Bakker, 2002). Furthermore, although scholars have argued that engagement as a motivational concept leads to a high level of

employee effectiveness, very little is known about how employee engagement is associated with desirable and extra-role behaviors such as proactivity, knowledge sharing, creativity, and adaptivity (Bakker & Xanthopoulou, 2013; Demerouti & Cropanzano, 2010; Macey & Schneider, 2008; Rothbard & Patil, 2010). Determining the relationship between these factors is essential in providing organizations with a competitive edge and helping them retain good employees (Griffin et al., 2007). Accordingly, this study investigates whether employee engagement mediates the relationships between the

perceived learning climate and behaviors such as proactivity, knowledge sharing, creativity, and adaptivity. Moreover, given the somewhat limited conclusions about the added value of the employee engagement concept compared with similar concepts about the employee–organization relationship in predicting performance (e.g., Newman, Joseph & Hulin, 2010), this study also investigates the mediating role of engagement compared with similar concepts such as job satisfaction and job involvement in these relationships.

Theoretical Conceptualization and Hypotheses

Employee engagement

Kahn (1990) originally defined employee engagement as “the simultaneous employment and expression of a person’s preferred self in task behaviors that promote connections to work, personal presence (physical, cognitive, and emotional) and active full performances” (p. 700). Drawing from Kahn’s engagement conceptual framework, Macey and Schneider (2008) offer a theoretical taxonomy of employee engagement, proposing engagement as an aggregate, multidimensional construct embracing three different types of engagement: trait, state, and behavioral. Each of these types of engagement builds on the preceding one, eventually leading to complete engagement. In this study, employee engagement is treated as a motivational concept in line with the approach of Schaufeli et al. (2002). Accordingly, employee engagement is defined as “…a positive, fulfilling work-related state of mind that is characterized by vigor, dedication, and absorption” (p. 74). Vigor refers to high levels of energy and willingness to invest effort in one’s work. Dedication means being deeply involved in one’s work and experiencing a sense of significance, enthusiasm, inspiration, pride, and challenge. The third component, absorption, involves full concentration on one’s work, to the point of experiencing time as passing quickly and difficulty in detaching oneself from work (Schaufeli et al., 2002). In sum, employee engagement is an active, motivational, fulfilling concept that reflects the simultaneous expression of multiple investments of physical, affective, and cognitive resources in work. We argue that focusing on employee engagement may be advantageous for organizations and also beneficial to employees in terms of personal flourishing and growth. Figure 1 illustrates one approach to the study of employee engagement. At the heart of the model lies the assumption that employee engagement has an immediate effect on extra-role

performance such as proactivity, knowledge sharing, creativity, and adaptivity. The model also examines employee engagement as a mediator in the relationship between the perceived learning climate and these extra-role behaviors. The rationale for this process model rests on several resource theories such as the job demands and resources model (Demerouti, Bakker, Nachreiner & Schaufeli, 2001), the broaden-and-build theory (Fredrickson, 2001, 2003), and

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the conservation-of-resources theory (Hobfoll, 2002) about employee gains and the upward spirals between learning climate, engagement, and extra-role performance. Thus, the access that engaged employees have to learning opportunities in their organizations has a positive effect on their ability and desire to go beyond the confines of their job description and be proactive, creative, and adaptive and share their knowledge with other employees.

Perceived learning climate as a resource antecedent to employee engagement

Perceived learning climate refers to perceptions of the employees about the organization’s beneficial activities in helping them create, acquire, and transfer knowledge (e.g., opportunities for continuous learning, inquiry and dialogue, empowerment toward a collective vision, and a learning leadership; Marsick & Watkins, 2003). Providing such an environment aids employees in meeting the organization’s strategic goals and helps the organization in modifying its behavior to tackle new challenges (Pedler, Burgoyne & Boydell, 1997; Schein, 1993; Watkins & Marsick, 1997). Furthermore, organizations need to find ways to retain good employees. Providing them with opportunities to expand their skills and share them with others is an excellent means of accomplishing this goal. It is an approach that benefits both the employees and their organizations (Ellinger & Cseh, 2007; Marsick, 2009; Yang, Watkins & Marsick, 2004). However, although in recent decades scholars have made great strides in developing the concept of the learning

climate, scientific studies on its implications for job attitudes and performance are relatively lacking. Its advocates argue that a learning climate should enhance employees’ attitudes and performance (Ellinger, Ellinger, Yang, & Howton, 2002; Jashapara, 2003; Joo & Lim, 2009; Yang et al., 2004). While these arguments are largely descriptive and grounded in practice, they still provide growing evidence of the potential relationship between perceived learning climate and employee engagement. Note that in the current study the concept of the learning climate reflects employees’ perceptions of the degree to which the atmosphere in the organization encourages learning. We have chosen to focus on their subjective perspective because our goal was to understand how employees’ interpretation of their contextual environment influences their job attitudes and work performance. Building on Demerouti et al.’s (2001) job demands and resources (JD-R) model, we argue that the empowering

environment of the learning climate will most likely be associated with the experience of engagement at work. According to the JD-R model (Demerouti et al., 2001), job resources are those aspects of work that are functional in achieving work goals as well as in stimulating personal growth. Therefore, they play a key role in facilitating engagement because they can have both extrinsic motivational potential by helping employees achieve their work goals and intrinsic motivational potential by facilitating their personal development (for a meta-analysis, see Crawford, LePine & Rich, 2010; Halbesleben, 2010). We contend that the learning climate reflects both of these

Figure 1. The research model

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motivational components, increasing the willingness to be fully engaged at work. The learning climate provides employees with opportunities for dialogue, feedback, and connection to the organization’s vision, helping them develop a deeper connection between their jobs and the organizational goals (Burke, Holman, & Birdi, 2006). Therefore, when employees work in such an environment, they are more likely to find their roles valuable and believe that goals can be accomplished. In addition, the learning climate provides greater opportunities for challenge, responsibility, and control, nurturing the employees’ sense of fulfillment. Hence, seeing their organization as one that offers them the opportunities for both accomplishment of their work goals and self-development strengthens their willingness to engage themselves fully at work (Bakker & Demerouti, 2007). This argument is also consistent with previous studies that utilized Demerouti et al.’s (2001) JD-R theory and

demonstrated the positive relationships between job resources such as autonomy, job control, role fit, skills variety, task identity, task significance, autonomy, supervisor support and performance feedback, and employee engagement (e.g., Bakker et al., 2011; Bakker & Demerouti, 2007; Bakker, Demerouti, & Verbeke, 2004; Bakker, Hakanen, Demerouti, & Xanthopoulou, 2007; Schaufeli & Bakker, 2004; Schaufeli, Bakker & Van Rhenen, 2009; for a meta-analysis, see Crawford et al., 2010; Halbesleben, 2010). This focus in previous studies on job resources provides further support for our approach of examining the role of employees’ perceptions of organizational resources, such as the perceived learning climate, as an antecedent to employee engagement. Moreover, despite the active academic discussion of these two concepts—perceived learning climate and employee engagement— separately from one another, to the best of our knowledge, no empirical study has yet examined the relationship between them. Hence, we posit the following:

H1: Perceived learning climate will be positively related to employee engagement.

The consequences of employee engagement for extra-role performance

The changing nature of work has challenged the traditional views of fixed, in-role performance, which no longer accounts for the full range of behaviors needed today (Ilgen & Pulakos, 1999). Competitiveness, rapid innovation, and continuous change dominate the current market. Organizations are therefore looking for specific competencies and extra-role behaviors in employees that facilitate adaptation to new organizational requirements and contribute to the need for effectiveness in the dynamic contemporary organizational reality (Griffin et al., 2007). Accordingly, our focus here is on these essential behaviors—proactivity, knowledge sharing, creativity, and adaptivity—with the goal of determining whether employee engagement promotes them. Proactivity represents self-initiated and future-oriented performance that seeks to change either the situation or oneself (Grant & Ashford, 2008; Griffin et al., 2007). Knowledge sharing reflects a process whereby individuals exchange their tacit and explicit knowledge to create new organizational knowledge (Inkpen & Tsang, 2005; Van den Hooff & De Ridder, 2004). Creativity means the production of new and useful ideas and fuels innovation in products, services, processes, and procedures in organizations (Amabile, 1996; Zhou & Shalley, 2008). Finally, adaptivity refers to employees’ ability to respond constructively to new and unexpected circumstances (Griffin et al., 2007; Pulakos, Arad, Donovan & Plamondon, 2000). There are several reasons for positing that the relationship between employee engagement and extra-role perfor-

mance behavior will be positive. We will discuss two reasons here (for an overview, see Demerouti & Cropanzano, 2010). First, those who score high in employee engagement also score high in arousal or activation (Langelaan, Bakker, Schaufeli, & Van Doornen, 2006). Therefore, the strong and persistent energy embedded in employee engagement may fuel behaviors such as proactivity and knowledge sharing in which the employees take the initiative to achieve the organization’s goals (Frese & Fay, 2001; Grant & Ashford, 2008; Shirom, 2010). Second, engaged employees experience positive emotions (Bindl & Parker, 2010). According to the broaden-and-build theory (Fredrickson, 2001), positive emotions such as joy, interest, contentment, enthusiasm, and inspiration enlarge people’s thought–action repertoires and build their resources by expanding their thoughts and actions. For instance, joy expands resources by promoting the urge to be creative, outgoing and helpful to others, and more sensitive

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toopportunities at work (Cropanzano & Wright, 2001). Interest fosters the desire to explore, assimilate new information and experiences, and be more outgoing (e.g., Fredrickson & Losada, 2005). Indeed, studies have shown that positive emotions increase people’s openness to new experiences and lead to more outgoing behaviors at work (Cropanzano & Wright, 2001; Fredrickson & Branigan, 2005; Schaufeli, Taris, Le Blanc, Peeters, Bakker & De Jonge, 2001). Thus, engaged employees who experience positive emotions such as enthusiasm, challenge, and inspiration in their work may be more likely to think outside the box and break their habitual modes of thought, becoming more creative and adaptive in their work. Previous studies that have demonstrated the relationship between employee engagement and in-role performance also support our argument (see Albrecht, 2010; Bakker & Bal, 2010; Bakker, Demerouti & Brummelhuis, 2012; Demerouti & Bakker, 2006; Demerouti & Cropanzano, 2010; Halbesleben & Wheeler, 2008; Rich, LePine & Crawford, 2010; Salanova, Agut & Peiró, 2005; Shuck, 2013; Xanthopoulou, Bakker, Heuven, Demerouti & Schaufeli, 2008). Moreover, the previous focus on in-role performance provides further support for our model, which focuses on extra-role behaviors that offer an organization a contemporary competitive advantage. Hence, we posit the following:

H2: Engagement will be positively related to extra-role performance as reflected in proactivity, knowledge shar- ing, creativity, and adaptivity.

The mediating role of employee engagement

Earlier, we argued that the learning climate promotes employee engagement in a work role and that this simultaneous and holistic investment of the self (physical, emotional, and cognitive) in turn translates into extra-role performance. In other words, a perceived learning climate affects employees’ motivation to redefine their tasks in a broader way and their sense of responsibility for meeting their organizational challenges. Thus, we have implicitly described a model in which employee engagement mediates the relationship with the organization’s learning climate as an antecedent to extra-role performance. According to Hobfoll’s (1989) conservation-of-resources theory, resources in the workplace lead to the acquisition of new resources. This theory is based on the premise that employees seek to protect, retain, and accumulate the resources that are desirable and instrumental in realizing higher-order goals and improving their well-being (Hobfoll, 2001). In addition, the conservation-of-resources theory argues that employees who already have resources have a better chance of obtaining more resources (Hobfoll, 2002). This phenomenon, which Hobfoll (1989) called “gain spirals,” is plausible, because when initial gains are made, even greater resources become available, providing employees with a surplus that they can invest (Hobfoll, Johnson, Ennis & Jackson, 2003). Moreover, when crucial job resources are available, the employees’ level of engagement may rise, improving the likelihood of them taking advantage of their current job resources and being able to create new ones (Gorgievski & Hobfoll, 2008). Doing so leads to positive organizational outcomes in the form of better performance (Hobfoll, 2001, 2002). Applying this idea to our proposed mediation model, we maintain that a challenging and enriching learning climate in which employees can draw upon many resources generates high levels of engage- ment, which in turn spurs proactive, creative, and adaptive performance behaviors and encourages the sharing of knowledge. Therefore, consistent with the conservation-of-resources theory (Hobfoll, 2001), we expect the perceived learning climate to affect employees’ extra-role performance through simultaneous investments of the self as reflected in the motivational mechanism of employee engagement. Moreover, research has thus far devoted limited attention to the gain spirals phenomenon, presumably because the conservation-of-resources theory emphasizes the notion of loss (Hobfoll, 2001). Thus, our study also contributes to broadening the focus of the research on gain spirals to encompass the role it plays in the relationship between the perceived learning climate and extra-role performance behaviors. Hence, we posit the following:

H3: Employee engagement will mediate the relationship between perceived learning climate and extra-role performance as reflected in proactivity, knowledge sharing, creativity, and adaptivity.

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As noted, employee engagement is defined as the experience of physical, emotional, and cognitive investment at work in a holistic and simultaneous manner (Kahn, 1990; Schaufeli et al., 2002). Thus, engagement provides a more comprehensive explanation for employees’ performance than do parallel concepts such as job satisfaction and job involvement, which emphasize narrower aspects of the employee’s resource investments. According to this perspective, job satisfaction refers to “a pleasurable or positive emotional state resulting from the appraisal of one’s job” (Locke, 1976, p. 1300). These positive feelings result from positive perceptions about organizational resources (in this study, the learning climate) that broaden habitual modes of thinking and cause employees to perform in a manner that contributes to their organization. Job involvement, defined as a “cognitive or belief state of psychological identification” (Kanungo, 1982 p. 342), is another parallel concept in the employee–organization relationship based on a relatively narrow aspect of resource investment in terms of cognition. This deeply cognitive investment, which is influenced by organizational characteristics, helps employees focus their efforts on work and utilize situations as opportunities in their work activities (Brown, 1996). Drawing on Gorgievski and Hobfoll’s (2008) notion of resource investment can help us understand the contribution of engagement above and beyond job satisfaction and job involvement. By conceptualized engagement as a state in which employees have more physical, emotional, and cognitive resources at hand, we can view engaged employees as those who are in a better position to invest their resources in a manner that leads to better performance (Gorgievski & Hobfoll, 2008). Job satisfaction or job involvement may also explain the relationship between the learning climate and extra-role performance. However, given that both are based on a single aspect of the resource investment of the self (affective or cognitive, respectively), they do not account for employees’ ability to choose to invest their physical, affective, and cognitive resources in the simultaneous and holistic manner that characterizes engagement. Thus, based on the conservation-of-resources theory (Gorgievski & Hobfoll, 2008; Hobfoll et al., 2003), we argue that the mediating role of employee engagement provides a more thorough explanation of the indirect relationship between perceived learning climate and extra-role performance than other parallel concepts such as job satisfaction and job involvement. Hence, we posit the following:

H4: Employee engagement will be a stronger mediator in the relationship between the perceived learning climate and extra-role performance behaviors than either job satisfaction or job involvement.

Method

Sample and procedure

Between July 2012 and March 2013, we collected 625 questionnaires from employees and their supervisors in organizations in Israel. These participants came from 12 heterogeneous organizations that differed in number of employees, geographical location, and occupational field. We included two industrial organizations, five communi- cations and technology organizations, three service industry organizations, one finance organization, and one organization from the municipal sector. Of these, 66 percent were small to medium in size (50–150 employees), and 34 percent were large (200–500 employees). The profile of the employee participants in the study was heterogeneous in all demographic and occupational aspects, with a wide range of ages, educational levels, seniority, jobs, and occupations. They had various jobs such as engineers, industrial workers, chemists, economists, sales and marketing representatives, and administrators. With regard to gender, 64 percent of the sample was male, and the average age was 38 years (standard deviation 10.47). Most participants were married (63 percent). On average, they had 15 years of education (standard deviation 2.73). Average time in the current organization was 6.41years (standard deviation 8.81), and the overall number of years spent in the workforce was 14.73 years on average (standard deviation 10.57years). The average number of workplaces per employee was 3.95 (standard deviation

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4.02). As for employee distribution in different organizational sectors, 19.8 percent worked in the public sector, 25.6 percent in industry, 23.2 percent in communications and technology, 20 percent in services, and 11.4 percent in finance. Participants completed two different questionnaires, one about their perceptions of the organization’s learning

climate and the other about their own attitudes about work (i.e., employee engagement, job satisfaction, and job involvement). In addition, the employees’ supervisors completed an extra-role performance evaluation for each of their employees. To increase the participation rate, the survey was advertised in the workplace in multiple ways (e.g., e-mails from the executive management and team meetings) along with an explanation of its contribution to the organization and the employees and the promise of full confidentiality. In addition, several days prior to the data collection stage, we sent out a personal email to all employees emphasizing our position as academic researchers and the purpose of the study and its importance for the organization and its employees and giving a further guarantee of complete secrecy. All employees were assured that the data would be used only for the purpose of the study and that the management would receive only a summary of the results at the organizational level. To maximize the return rate and increase employees’ trust, we used a direct method. We alone distributed the

questionnaires and collected the data in the workplace without any involvement of the organizational managers on any level. We also informed the employees that participation was voluntary, and no member of the management would know who participated and who did not. Moreover, we also informed the employees that the management would receive only a summary of the results at the organizational level and identification by the management at the personal level would not be possible. These issues were of great importance, because we needed the employees to supply the last four digits of their IDs so we could cross-reference their data with their managers’ evaluations of their extra-role performance. We allowed the employees to complete the questionnaire in complete privacy, which took about 10 minutes, without permitting their colleagues to view their responses. We collected all of the questionnaires immediately upon completion, which promoted maximum return rates. In all 12 participating organizations, the measures described above were identical. Ultimately, we achieved a turnaround rate of 83.7 percent from the employees and a 90 percent response rate from their supervisors. We attribute this high response rate to the fact that the entire data distribution and collection process was conducted solely by us, with no involve- ment of any of the organization’s managers. Furthermore, all of the materials remained with us alone during the entire course of the study, completely inaccessible to the organization. We believe that such meticulous actions drastically reduced response bias and sampling and measurement errors in our study.

Measures

We adopted previously well-validated and most widely used measures in the literature to assess the variables of the study. These measures are described below.

Perceived learning climate We used Marsick and Watkins’ (2003) 20-item self-report version of the Dimensions of Learning Organization Questionnaire (DLOQ). It contained items reflecting employees’ perceptions of the degree to which the atmosphere in the organization encouraged learning and provided opportunities for continuous learning, inquiry and dialogue, team learning, empowerment toward a collective vision, and a learning leadership (Marsick & Watkins, 2003). In their validation study, Marsick and Watkins (2003) suggested that researchers utilize the measure as a single factor, because all of its dimensions have strong correlations with one another (0.63≤ r ≤ 0.75). In this study, the intercor- relations of the learning climate dimensions were not unusual (0.58≤ r ≤ 0.75). Previous studies have indeed examined the DLOQ as a single structure, and the overall Cronbach α reliability was between 0.86 and 0.94 (Egan, Yang & Bartlett, 2004; Ellinger et al., 2002; Joo, 2010; Song, Joo & Chermack, 2009; Wang, Yang & McLean, 2007; Yang, 2003; Zhang, Zhang & Yang, 2004). Nevertheless, we chose to assess the construct validity of the learning climate measure to obtain the optimal explanation of our findings. Accordingly, we used AMOS software

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to conduct a confirmatory factor analysis of the learning climate construct. We evaluated the fit of the model with the data using an established set of goodness-of-fit indices (i.e., χ2, root mean square residual (RMR), SRMR, NFI, and CFI). The findings indicated that a unifactor construct provided an unsatisfactory explanation for our data in all goodness-of-fit aspects (χ2(170) = 859.53, RMR = 0.06, SRMR =0.05, NFI = 0.86, CFI= 0.88). However, the alternative second-order model of the correlated dimensions provided the optimal explanation for our data, and all goodness-of-fit aspects were above the accepted criteria (χ2(160) = 424.723, RMR = 0.07, SRMR = 0.05, NFI= 0.96, CFI = 0.94). Moreover, we considered whether aggregating the learning climate variable was the correct approach. If employees regard the learning climate as a unit-level or organizational-level variable, we should aggregate it to that level by calculating the mean. This procedure is recommended if within-group agreement (i.e., Rwg) of the learning climate measure is above 0.7 (Bliese & Halverson, 1998; Castro, 2002). Moreover, if intraclass correlation coefficient (ICC)2 is above 0.7, the recommendation is also to aggregate the measure (Bliese & Halverson, 1998; Schneider, Hanges, Smith & Savaggio, 2003). Our examinations of both ICC2 and Rwg for the learning climate both at the unit level (ICC2 = 0.48; 0.42< Rwg < 0.64) and the organizational level (ICC2 = 0.53; 0.38<Rwg<0.67) determined that they were below 0.7. Therefore, we followed the recommendation of investigating the employees’ perceptions of the learning climate at the individual level. The items were ranked on a 5-point scale from 1 (almost never) to 5 (almost always). Sample items include the following: “In my organization, people are given time to support learning,” “In my organization, people are encouraged to ask ‘why’ regardless of rank,” “My organization invites people to contribute to the organization’s vision,” and “In my organization, leaders mentor and coach those they lead.” Reliability was 0.94.

Employee engagement We used the short version of the Utrecht Work Engagement Scale (UWES), validated by Schaufeli, Bakker & Salanova (2006), which relies on the definition of Schaufeli et al. (2002). The scale includes nine items pertaining to three dimensions: vigor, dedication, and absorption (three items for each dimension). Studies have established the validity of the UWES-9, and its Cronbach’s α reliability is typically around 0.90 (e.g., Bakker et al., 2011; Schaufeli et al., 2006). Recent confirmatory factor analysis studies have supported the one-factor structure (e.g., Bakker et al., 2011, 2012; Hallberg & Schaufeli, 2006; Schaufeli et al., 2006), because the three dimensions of employee engagement are very closely related (with correlations between 0.65 and 0.90). We therefore decided to create one overall score of employee engagement (see the recommendations of Schaufeli & Bakker, 2010; Schaufeli et al., 2006). Respondents were asked to rank their answers on a 5-point scale from 1 (never) to 5 (always). Sample items include the following: “At my work, I feel I am bursting with energy” and “When I get up in the morning, I feel like going to work.” Reliability was 0.90.

Job satisfaction We measured job satisfaction using four items from Schriesheim and Tsui’s (1980) job satisfaction scale whose Cronbach’s α reliability was 0.80. Respondents were asked to rank their answers on a 5-point scale from 1 (do not agree at all) to 5 (strongly agree) regarding satisfaction with their current job, salary, opportunities for promotion, and work in general. Sample items include the following: “I am satisfied with my current job” and “In general, I am satisfied with my job.” Reliability was 0.81.

Job involvement We measured job involvement using responses to three items from Kanungo’s (1982) job involvement scale, ranked on a scale ranging from 1 (do not agree at all) to 5 (strongly agree). Cronbach’s α reliability in previous studies was around 0.85 (e.g., Kanungo, 1982). Sample items include the following: “Most of my interests are centered on my job” and “The most important things that happen to me involve my present job.” Reliability was 0.83.

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Extra-role performance To provide more objective, less biased data, the participants’ supervisors were asked to indicate how often in the preceding month each employee behaved in the described manner. They provided their answers on a 5-point Likert-type scale ranging from 1 (very little) to 5 (a great deal).

Proactivity. Griffin et al.’s (2007) three-item proactivity scale examines the employee’s degree of actively initiating changes. Sample items include the following: “Made changes to the way his/her core tasks are done” and “Initiate better ways of doing his/her core tasks.” Reliability was 0.81.

Knowledge sharing. We took three items from Van den Hooff and Hendrix’s (2004) recently developed validated scale, whose Cronbach’s α reliability was 0.84. Sample items include the following: “Regularly informs colleagues of what s/he is working on” and “When he/she gains new knowledge, makes sure that his/her colleagues will know it too.” Reliability was 0.83.

Creativity. We used Zhou and George’s (2001) 12-item scale, whose Cronbach’s α reliability was 0.95, to measure creativity. Sample items include the following: “Comes up with creative solutions for problems” and “Being an inspiring source for creative ideas.” Reliability was 0.95.

Adaptivity. Griffin et al.’s (2007) three-item adaptivity scale gauges how well the employee adapts to change. Sample items include the following: “Coped with changes in the way s/he was asked to do his/her core tasks” and “Successfully adapted changes in his/her core tasks.” Reliability was 0.76.

Control variables Gender, age, education, and seniority in the current organization were examined as control variables, following previous studies indicating that these variables were related to employees’ engagement and extra-role performance (e.g., Kerr & Lloyd, 2008; Liu, Chen & Yao, 2011; Maurer, Weiss & Barbeitte, 2003; Oldham & Cummings, 1996; Schaufeli & Bakker, 2003; Schaufeli et al., 2006; Shalley, Zhou & Oldham, 2004; Veldhoven & Dorenbosch, 2008; Warr & Fay, 2001).

Data analysis

We used multilevel modeling in our analysis because the nesting of employees within organizations raised the possibility of dependencies in the data (Raudenbush & Bryk, 2002). The advantage of multilevel modeling is that by modeling residuals at the organization level as well as at the individual level, such models acknowledge that employees within the same organization may be more similar to one another than to employees from different organizations (Raudenbush, Bryk, Cheong, Congdon & Toit, 2011). In our study, the 625 sampled employees came from 12 organizations in five different fields. Therefore, the assumption of the independence of observations did not exist. Moreover, we determined the existence of this dependence by estimating a null model of each of the study’s dependent variables, namely, the ICC1 (Castro, 2002; Grawitch & Munz, 2004; Klein & Kozlowski, 2000). The ICC1s of the mediating and dependent study variables were as follows: ICC1 employee engagement = 0.05, ICC1 job satisfaction = 0.11, ICC1 job involvement= 0.10, ICC1 proactivity =0.03, ICC1 knowledge sharing = 0.02, ICC1 creativity =0.07, ICC1 adaptivity = 0.02. Of the seven variables, the ICC1 values of four of them were above the minimal criterion of 0.05, and the random variance effect was significant (p< 0.05). Given that dependence between the sample observations exists, a multilevel modeling analysis, using HLM7, has to be performed (Raudenbush et al., 2011). Accordingly, employees were nested within units under the same manager, and these units were in turn nested within the same organizational type.

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Results

Descriptive statistics

Table 1 presents the means, standard deviations, and correlations (Pearson’s r) among the study’s main variables. As the table shows, the relationship between perceived learning climate and employee engagement was strong, positive, and significant (r= 0.52, p≤ 0.001), as were the relationships between employee engagement and all four performance variables: proactivity (r =0.35, p ≤ 0.001), knowledge sharing (r= 0.38, p ≤ 0.001), creativity (r= 0.49, p ≤ 0.001), and adaptivity (r= 0.44. p ≤ 0.001). Moreover, the relationship of each the two parallel variables (job satisfaction and job involvement) and the

extra-role performance variables was weaker than the relationship between employee engagement and these performance variables. The relationships between job satisfaction and the performance variables were as follows: proactivity (r= 0.13, p ≤ 0.001), knowledge sharing (r =0.25, p ≤ 0.001), creativity (r= 0.28, p ≤ 0.001), and adaptivity (r = 0.24, p≤ 0.001). The relationship between job involvement and the performance variables followed a similar pattern: proactivity (r = 0.16, p ≤ 0.001), knowledge sharing (r= 0.26, p≤ 0.001), creativity (r= 0.28, p≤ 0.001), and adaptivity (r= 0.21, p≤ 0.001). These findings provide preliminary support for the hypotheses about the direct relationships in the study’s model. The results also indicated moderate to strong relationships between employee engagement and the similar concepts of job satisfaction (r= 0.55, p ≤ 0.001) and job involvement (r= 0.37, p ≤ 0.001), pointing to a lack of multicollinearity (see recommendations by Field, 2005).

Test of the substantive relationship

To test Hypothesis 1, we ran two multilevel regressions for the core variable in the study, employee engagement. Table 2 presents the results. In these regressions, employee engagement was considered a dependent variable. The first regression included

only the control variables (see Model 1), while the other included both the control variables and the perceived learning climate variable (see Model 2). Hypothesis 1 assumed a positive relationship between perceived learning climate and employee engagement. As Model 2 (Table 2) shows, perceived learning climate had a significant, positive impact on employee engagement at the employee level (estimate = 0.532, p ≤ 0.001). This model was significantly different from Model 1, which contained only the control variables (Δ-2log-likelihood = 161.1, p≤ 0.001; R2 = 0.25, p≤ 0.001), indicating that the perceived learning climate was related to employee engagement above and beyond the control variables (Model 1). Thus, Hypothesis 1 was supported. To test Hypothesis 2, we conducted two multilevel model regressions for each dependent extra-role performance

variable (proactivity, knowledge sharing, creativity, and adaptivity). Employee engagement was the independent variable. The first regression model contained only the control variables, whereas the second included the employee engagement variable as well. Table 3 presents the multilevel model regressions that examined the relationship between employee engagement and each of the extra-role performance variables. As Model 4 indicates, employee engagement also had a significant, positive impact on proactivity at the employee

level (estimate = 0.270, p ≤ 0.001). This model was significantly different from Model 3, which included only the control variables (Δ-2log-likelihood = 52.6, p ≤ 0.001; ΔR2 = 0.11, p ≤ 0.001). As Model 6 (Table 3) shows, employee engagement had a significant and positive impact on knowledge sharing at the employee level (estimate = 0.422, p≤ 0.001). This model was significantly different from the one including only the control variables (Δ-2log-likelihood =78.5, p≤ 0.001; ΔR2 = 0.13, p ≤ 0.001), indicating that employee engagement was significantly related to knowledge sharing above and beyond the control variables (Model 5). As Model 8 demonstrates, employee engagement had a significant, positive impact on creativity at the employee level (estimate= 0.472, p≤ 0.001). This model was significantly different from the one including only the control variables (Δ-2log-

222 L. ELDOR AND I. HARPAZ

T ab le

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A PROCESS MODEL OF EMPLOYEE ENGAGEMENT 223

likelihood = 125.2, p ≤ 0.001; ΔR2 = 0.20, p ≤ 0.001), indicating that employee engagement was significantly related to creativity above and beyond the control variables (Model 7). Finally, as Model 10 shows, employee engagement had a significant, positive impact on adaptivity at the employee level (estimate =0.348, p≤ 0.001). This model was significantly different from Model 9, which included only the control variables (Δ-2log-likelihood = 100.2, p≤ 0.001; ΔR2 = 0.17, p≤ 0.001). Thus, Hypothesis 2 was supported.

The mediating role of employee engagement

Following Bauer, Preacher and Gil’s (2006) guidelines for probing mediation effects in multilevel models, we examined the two mediation hypotheses illustrated in Figure 2. As the figure indicates, in a mediation process where the variable M serves as a mediator, the paths labeled a and b

represent the indirect effect of X (the independent variable) on Y (the dependent variable), while the path labeled c′ represents the direct effect of X on Y, and c = a* b + c′ is the total effect of X on Y. Hence, we analyzed the mediation effect by assessing the significance of the indirect effect a *b. An a *b test is considered the best all-around available method for mediation testing (MacKinnon, Lockwood, Hoffman, West & Sheets, 2002; MacKinnon, Lockwood & Williams, 2004), because it tests the statistical significance of the difference between the total effect (c path) and the direct effect (c′ path), which is the impact of the independent variable on the dependent variable, adjusted for the effect of the mediator. The Sobel (1982) test is the most familiar one for testing the significance of the mediation effect. However, it assumes a normal distribution of the indirect effect a* b, which is generally incorrect, even when the variables constituting the product a* b are normally distributed (Edwards & Lambert, 2007). Recognizing this problem, we used the bootstrapping method with a bias-corrected confidence estimate to test the significance of our mediation hypotheses (Preacher & Hayes, 2004). Moreover, researchers have suggested that bootstrapping and the empirical mediation effect (a * b) test are preferable for testing multilevel mediation effects, especially when the variables are not normally distributed (e.g., Pituch & Stapleton, 2008; Zhang, Zyphur, & Preacher, 2009). The

Table 2. Multilevel models analyses of the effect of the learning climate on employee engagement

Effect

Dependent variable

Employee engagement Employee engagement

Model 1 Model 2

Estimate SE Estimate SE

Intercept 3.53*** 0.25 1.60*** 0.24 Gender �0.009 0.70 0.027 0.06 Age 0.005 0.00 0.008* 0.00 Education �0.004 0.01 �0.002 0.01 Seniority 0.0001 0.00 0.005 0.00 Learning climatea 0.532*** 0.03 Random effect variance 0.042* 0.02 0.006 0.00 �2log-likelihood 1303.5 1142.4 Δ-2log-likelihoodb 161.1*** Residual variance 0.56*** 0.42*** ΔR2 0.25***

Note. N = 554 (due to missing values in the control variables). Random effect variance = unit level within organization level. aPerceived. bWith the same n. *p ≤ 0.05; ***p ≤ 0.001.

224 L. ELDOR AND I. HARPAZ

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A PROCESS MODEL OF EMPLOYEE ENGAGEMENT 225

bootstrapping method takes k samples of the original size from the obtained data (with replacements), and the mediation effects (a * b) are calculated in each sample. In the present set of analyses, parameter estimates were based on 5000 bootstrapped samples (Preacher & Hayes, 2004). The point estimate of the mediation effect is the mean of these 5000 samples, which ensures the stability of the analyses. The bias is corrected and accelerated using a confidence interval calculation of 95 percent performed on the samples, a calculation that is equal to the 2.5 and 97.5 percentile scores of the obtained distribution over the samples, but with z-score-based corrections for bias due to the underlying distribution. If the confidence intervals do not contain zero, the point estimate is significant at the level indicated (Preacher & Hayes, 2004). Table 4 presents the results of the analyses for the examination of Hypothesis 3, which suggested that employee

engagement would mediate the relationship between perceived learning climate and each extra-role performance variable: knowledge sharing, adaptivity, proactivity, and creativity. The results revealed that employee engagement mediated the relationship between perceived learning climate and

the four suggested extra-role performance variables: proactivity (mediated effect = 0.168, SE= 0.04, 95% CI [0.08, 0.24]), knowledge sharing (mediated effect = 0.163, SE =0.03, 95% CI [0.09, 0.24]), creativity (mediated ef- fect= 0.246, SE= 0.03, 95% CI [0.18, 0.31]), and adaptivity (mediated effect = 0.178, SE= 0.02, 95% CI [0.12, 0.23]). Given that the confidence interval did not contain zero, we can conclude that in each case employee engage- ment had a significant effect on the relationship between perceived learning climate and the four suggested perfor- mance variables: proactivity, knowledge sharing, creativity, and adaptivity. Thus, Hypothesis 3 was supported.

Table 4. Bootstrapped point estimate and confidence intervals of the mediating effect of employee engagement on the relation of learning climatea to extra-role performance variables: proactivity, knowledge sharing, creativity, and adaptivitya.

Mediation path via employee engagement

X–M M(X)–Y X–Y X(M)–Y Mediation effect SE

Bootstrapping (95%) CI

a path b path c path c′ path Lower limit Upper limit

Learning climate → proactivity 0.537* 0.292* 0.199* 0.031, n.s. 0.168 0.04 0.08 0.24 Learning climate → knowledge sharing 0.525* 0.310* 0.400* 0.239* 0.163 0.03 0.09 0.24 Learning climate → creativity 0.534* 0.461* 0.300* 0.054, n.s. 0.246 0.03 0.18 0.31 Learning climate → adaptivity 0.520* 0.362* 0.251* 0.033, n.s. 0.178 0.02 0.12 0.23

Note. N = 554 (due to missing values in the control variables). a Perceived bootstrap sample size = 5000. CI = confidence interval; n.s. = not significant. *p ≤ 0.001.

Figure 2. A mediation process

226 L. ELDOR AND I. HARPAZ

Finally, Hypothesis 4 suggested that employee engagement would be a stronger mediator of the relationship between perceived learning climate and extra-role performance than job satisfaction and job involvement. As Table 5 illustrates, job satisfaction did not mediate the relationship between perceived learning climate and knowledge sharing (point estimate = 0.032, 95% CI [�0.02, 0.09]), while job involvement partially mediated this indirect relationship with a relatively weak level of size effect (point estimate = 0.028, 95% CI [0.009, 0.05]). In addition, neither job satisfaction nor job involvement significantly mediated the relationship between perceived

learning climate and proactivity (point estimate =0.039, 95% CI [�0.6, 0.14]; point estimate =0.041, 95% CI [�0.004, 0.07], respectively), nor the relationship between perceived learning climate and adaptivity (point estimate = 0.083, 95% CI [�0.01, 0.18]; point estimate= 0.031, 95% CI [�0.003, 0.05], respectively). Job satisfaction and job involvement partially mediated the relationship between perceived learning climate and creativity, with a relatively weak level of size effect (point estimate = 0.065, 95% CI [0.008, 0.12]; point estimate = 0.030, 95%CI [0.01, 0.06], respectively). These results support Hypothesis 4. Moreover, we took another step to corroborate these findings. Using a quasi-Bayesian Monte Carlo approach

(Imai, Keele & Yamamoto, 2010), we reanalyzed the mediation hypothesis (Hypothesis 4), controlling for job satisfaction and job involvement. Doing so allowed us to assess the mediating effect of employee engagement on the relationship between perceived learning climate and the extra-role performance, above and beyond job satisfaction and job involvement. The results clearly indicate that employee engagement mediated the relation- ship between perceived learning climate and the four extra-role performance variables: proactivity (total effect =0.16; mediated effect = 0.11, SE= 0.03, 95% CI [0.06, 0.17]), knowledge sharing (total effect = 0.34; mediated effect = 0.10, SE= 0.02, 95% CI [0.03, 0.14]), creativity (total effect = 0.25; mediated effect =0.16, SE= 0.03, 95% CI [0.09, 0.24]), and adaptivity (total effect =0.17; mediated effect = 0.10, SE= 0.03, 95% CI [0.05, 0.16]), even when controlling for job satisfaction and job involvement. Given that the confidence interval did not contain zero, we can conclude that in each case employee engagement had a significant effect on the relationship between perceived learning climate and the four extra-role behaviors above and beyond job satisfaction and job involvement.

Discussion

This study focuses on employee engagement and its contribution to the extra-role performance behaviors essential for organizations. The significance of this focus lies in our belief that employee engagement has the potential to

Table 5. Bootstrapped point estimate and confidence intervals of the mediating effect of the competing concepts of job satisfaction and job involvement on the relation of learning climatea to extra-role performance variables: proactivity, knowledge sharing, creativity, and adaptivitya.

Direct relationship path

Mediation effect

Job satisfaction

Bootstrapping (95%) CI Job

involvement

Bootstrapping (95%) CI

Lower Upper Lower Upper

Learning climate → proactivity 0.039, n.s. �0.060 0.14 0.041, n.s. �0.004 0.07 Learning climate → knowledge sharing 0.032, n.s. �0.020 0.09 0.028 0.009 0.05 Learning climate → creativity 0.065 0.008 0.12 0.030 0.010 0.06 Learning climate → adaptivity 0.083, n.s. �0.010 0.18 0.031, n.s. �0.003 0.05 Note. N = 554 (due to missing values in the control variables). a Perceived bootstrap sample size = 5000. CI = confidence interval; n.s. = not significant.

A PROCESS MODEL OF EMPLOYEE ENGAGEMENT 227

broaden our view of the meaning of the employee–organization relationship, provide organizations with a competitive advantage, and offer employees opportunities for growth. The results of our study support this thesis.

Theoretical contributions

The most important contribution is the finding that employee engagement fully mediates the relationship between perceived learning climate and extra-role performance behaviors such as proactivity, knowledge sharing, creativity, and adaptivity and is a mechanism that provides a more thorough explanation for such behaviors than similar concepts such as job satisfaction and job involvement. An effective learning climate promotes a sense of challenge, meaningfulness, and purpose in employees, which prompts them to invest their full physical, emotional, and cognitive resources in a broad range of extra-role performance behaviors. These findings are consistent with Hobfoll’s (2002) conservation-of- resources theory and Fredrickson’s (2003) broaden-and-build theory on employee gains and the upward spirals between organizational resources, engagement, and extra-role performance. Furthermore, this full-mediation effect did not emerge in models that contained job satisfaction or job involvement as mediators. Accordingly, our findings reveal that employee engagement is a different and more effective factor than more traditional explanations of job attitudes such as job satisfaction and job involvement. Moreover, the literature is inconclusive about the added value of employee engagement as a predictor of work performance compared with similar concepts about the employee–organization relationship (e.g., Dalal, Brummel, Wee & Thomas, 2008; Newman et al., 2010). However, according to our findings, employee engagement offers more benefits and advantages for organizations than job satisfaction or job involvement. By indicating the potential of employee engagement to redefine the optimal employee–organization relationship,

our study also contributes to the body of knowledge about this relationship. First, most research about this relation- ship has adopted the view of the organization as the starting point, with the employee’s perspective as an incidental benefit (Coyle-Shapiro & Shore, 2007; Shore, Porter & Zahra, 2004). In contrast, the emerging field of positive organizational scholarship regards the employee as an essential party in a relationship beneficial to both sides (Cameron, 2005; Luthans & Youssef, 2007). Second, the fast-paced nature of modern work is generally less supervised, a circumstance that challenges the classical view of the relationship, particularly regarding the level of activity required of employees (Frese, 2008; Ilgen & Pulakos, 1999; Masson et al., 2008). Our findings on employee engagement corroborate this important dynamic, demonstrating that engagement provides benefits to both the employee and the organization. By reorienting our approach to one that regards both employees and organizations as the primary focus, we can determine the necessary components for their relationship. Furthermore, our results strengthen the growing consensus that employee engagement is not just a repackaging of the old employee–organization relationship concept (Bakker et al., 2011; Vigoda-Gadot et al., 2013). Another major contribution of our study is that it demonstrates that a relatively understudied organizational

resource, namely, the learning climate, exerts a positive effect on employee engagement. The addition of this resource extends the previous scholarly view, which focused mostly on the positive effect of job resources on engagement. We argue that employees become engaged in their work when they believe that their employers offer them opportunities for learning. This result acknowledges the critical role of the learning climate as a motivator for employees and highlights the need to develop it to increase employee engagement. We believe that this strong contribution results from the tight bond between organizational learning activities and the physical, emotional, and cognitive resources embedded in engagement. A learning environment creates the confidence that goals can be accomplished and fulfills the employees’ need to belong. Thus, promoting team learning, encouraging learning feedback, and associating them with the organization’s vision connect employees to the organizational goals, fostering their sense of meaning and fulfillment, and enhance positive feelings such as enthusiasm and the desire to meet challenges. In other words, a positive learning climate promotes employee engagement. Moreover, this positive relationship between the learning climate and employee engagement is particularly

important given that employees today tend to manage their careers themselves. In other words, they place increasing

228 L. ELDOR AND I. HARPAZ

importance on versatile learning, knowledge opportunities, and personal growth through their workplace (Eby, Butts & Lockwood, 2003; King, 2004; Lankau & Scandura, 2002; Lee & Bruvold, 2003; Ng et al., 2005). Thus, our study empirically validates the theoretical argument that allowing employees to grow via career development is a key factor in encouraging employee engagement (Schaufeli & Salanova, 2007). This study’s portrayal of how engaged employees behave is another contribution to the literature. Such employees

showed a desire to share their knowledge with their colleagues, were more open to change, were more likely to take the initiative, and were more involved in creative thinking and problem solving. In addition, this is the first study to link employee engagement to extra-role performance behaviors such as proactivity, knowledge sharing, creativity, and adaptivity. Our findings demonstrate that employee engagement increases the breadth of activities that employees consider part of their roles. In fact, these are the precise factors that provide an organization with a competitive advantage, one that their competitors find very difficult to emulate. Our results also support the notion of job crafting (Wrzesniewski & Dutton, 2001), which reflects the idea that

employees can be partial architects of their jobs. As Wrzesniewski and Dutton (2001) argued, employees who view their work as enjoyable and fulfilling are more likely to engage in job crafting by “changing the number, scope, or type of job tasks done at work. By choosing to do fewer, more, or different tasks than prescribed in the formal job, employees create a different job” (p. 185). Scholars have indeed theorized that engaged employees are more likely to find many alternative means of solving problems and to take non-traditional approaches (Macey & Schneider, 2008) and tend to exhibit discretionary effort (May, Gilson & Harter, 2004). The present study provides empirical support for these theories about engaged employees.

Limitations and suggestions for future research

As a new addition to the employee engagement literature, our research design had limitations that could be addressed in future studies. First, our study was cross-sectional, so any inferences about causality are limited. Although our study was well grounded in the theories of employee engagement and its associated variables, which provide a solid basis for assuming a causal relationship, we caution that alternative causal models are possible. For example, to fully understand how employee engagement and extra-role performance are associated, we could consider the impact of behaviors such as adaptivity on employee engagement. Therefore, other research designs, for example, experimental and longitudinal, are desirable. Second, the antecedents and mediators in our model were all from self-report measures, raising the possibility that social desirability and common source bias might have possibly inflated the relationships among these variables. However, our primary focus was on the substantive relationships with the extra-role performance variables that were not from the same source (the employees’ supervisors). Perhaps more importantly, the tests of mediation rested on a fairly complex pattern of relations among the variables that would be very difficult to explain by method variance alone. Nevertheless, future studies taking a qualitative approach and using indicators of learning objectives are recommended. Third, future studies can improve our approach by including other elements such as ethical climates, accountability, transparency, and organizational politics instead of focusing on just the learning climate. Fourth, we tested our model only in Israel, raising the possibility that the results might be relevant only to that country. However, we note that Israel could be considered a microcosm for studies representing Western society (Harel & Tzafrir, 1999). Like most industrialized Western nations, Israel is a pluralistic and individualistic society (Harpaz & Meshoulam, 2010). The Israeli labor market has adopted the norms of free market competition that include assumptions about the short-term and restricted mutual commitment between the employee and the employer (Sagie & Weisberg, 2001). These values are not unique to Israel and are representative of the situation in many individualistic societies (Hofstede, 2013). Thus, this limitation is likely not a major issue. Finally, in terms of our data analysis, one might argue that our model should be tested using other algorithms for mediation such as structural equation modeling (SEM) (e.g., AMOS and MPLUS). However, the relatively small sample size at the unit and organizational levels (<100, see the recommendations of Preacher, Zyphur & Zhang, 2010, and Preacher, 2011) prevented us from adopting this approach. We

A PROCESS MODEL OF EMPLOYEE ENGAGEMENT 229

acknowledge the need for future studies to test our model using larger samples at the unit and organizational levels with a multilevel SEM framework.

Practical implications

Although our study was primarily intended to test theoretically derived hypotheses, our findings also have some practical implications. First, a clear message emerges for management and human resources professionals: a learning climate should be encouraged in organizations. Managers should encourage employees to learn, collaborate in knowledge exchanges, and be involved in creating an organizational vision. Employees could thereby be empowered and motivated to achieve high levels of engagement and work performance. Second, our results empha- size that employee engagement matters for organizations. The positive relationship between employee engagement and extra-role performance demonstrates the significance of employee engagement to the organization’s life and ef- fectiveness, and the ways in which an organization can benefit from engaged employees. Their strong and persistent energy fuels creative, adaptive, and proactive behaviors and prompts them to exert extra effort to meet organiza- tional goals. In an era when organizations are becoming leaner, it is essential that they have engaged employees who are willing to take on greater responsibilities in order to achieve the organization’s goals. Moreover, the trend toward global organizations and decentralization in workplaces makes it harder for supervisors to oversee their em- ployees’ performance (Buchner, 2007), particularly in desirable but hard-to-achieve areas such as proactivity and creativity. Thus, promoting employee engagement may be a more effective approach for managers, one that focuses less on performance management and more on performance facilitation. Third, given this ever-changing organiza- tional environment, organizations must develop strategies to help employees deal with and adjust to organizational changes. Engaged employees are not passive. On the contrary, when they perceive a lack of fit between their current job description and the reality around them, they craft and reshape their jobs to meet the reality. Therefore, organi- zations that want to make sure that they have employees who can deal with organizational changes should consider enhancing their learning climate, which should lead to more engaged employees.

Conclusion

Our results strongly support the advantage of employee engagement for both organizations and employees. Briskin (1998) argued, “To explore the challenge to the human soul in organizations is to build a bridge between the world of personal and subjective individual experience and the world of organizations that demands efficiency…we must be willing to shift our viewpoint back and forth between what organizations want of people and what constitutes human complexity: the contradictory nature of human needs, desires, and experience” (p. xii). From our perspective, employee engagement seems to bridge the gap between these objectives of organizations and employees because it represents the combination of well-being and motivation in employees. By providing a key to fulfilling these two needs, employee engagement broadens our view of the meaning of the employee–organization relationship.

Author biographies

Liat Eldor is a Post-doctorate Researcher at The Wharton School, University of Pennsylvania. Her current research of interests are employee engagement, learning climate, job crafting and dynamics work performance.

230 L. ELDOR AND I. HARPAZ

Itzhak Harpaz is a Professor of Management, Dean of Graduate Studies, Director of the Center for the Study of Organizations & HRM, Department of Business Administration Faculty of Management, University of Haifa, Israel.

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