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Article

Bridging the Gap: Exploring the Impact of Human Capital Management on Employee Performance through Work Engagement

by
Njanjobea Isah Leontes
* and
Crystal Hoole
Department of Industrial Psychology and People Management, College of Business and Economics, University of Johannesburg, Johannesburg 2006, South Africa
*
Author to whom correspondence should be addressed.
Adm. Sci. 2024, 14(6), 129; https://doi.org/10.3390/admsci14060129
Submission received: 17 March 2024 / Revised: 12 May 2024 / Accepted: 14 May 2024 / Published: 19 June 2024
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Abstract

:
The aim of this study was to suggest and test a research model that examines whether work engagement mediates the effect of human capital management practices (HCMPs) on employee performance. These relationships were assessed through the Smart PLS 4 (Partial Least Square) software using Partial Least Square-based Structural Equation Modelling (PLS-SEM). Data were elicited from full-time hotel employees and their managers in South Africa. Based on the PLS-SEM results, work engagement was identified as a mediator of the effects of HCMPs on employee performance. Specifically, HCMPs, manifested by training, career opportunities, and rewards, improve work engagement, triggering employee tasks, and adaptive and creative performance. Additionally, work engagement had a positive influence on task performance, adaptive performance, and contextual performance. This research contributes by empirically testing work engagement as a full mediator in the relationship between HCMPs and employee performance, offering insights into the crucial role of HCMPs in fostering employee engagement and performance within the hospitality industry. Implications of the results are discussed, and directions for future research are presented.

1. Introduction

Employee performance in the hospitality industry remains a major challenge as the industry is faced with high turnover rates and worker disengagement as a result of unfavorable working conditions (Baum 2012; Hon and Chan 2013; Zwane et al. 2014). To ensure employee performance in the hospitality industry, managers will need to first develop ways of retaining existing employees, a critical problem in the industry, by implementing HCMPs (Shamim et al. 2017; Zeb et al. 2018). The presence of HCMPs sends a message regarding management’s willingness to improve employee’s capability to perform their duties (Jamal and Saif 2011; Zeb et al. 2018). Among the HCM practices, career opportunities, training, and rewards are useful indicators that can improve employee performance in the hospitality industry (Tang and Tang 2012). HCMPs, in the form of training, career opportunities, and rewards in an organization, signify not only management’s commitment to improving employees’ capabilities but also its dedication to employee wellbeing (Armstrong and Taylor 2014; Hecklau et al. 2016). Employees tend to believe that their efforts and values are recognized when HCMPs are implemented. As a result, employees will give their best by displaying more energy, dedication, and involvement in their work.
Work engagement, as a motivational construct, is defined as a state of mind made up of vigor, dedication, and absorption (Schaufeli et al. 2002). Vigor denotes that employees are robust, energetic, and mentally resilient in the workplace. Dedication implies employees embrace their jobs with enthusiasm and are deeply engrossed in their jobs. Absorption indicates that employees focus on their jobs joyfully and wholeheartedly, thus making it impossible for them to detach from work (Schaufeli et al. 2002; Bakker et al. 2012). Work engagement has a positive impact on employee job performance (Schaufeli 2015) as well as client satisfaction and loyalty (Taris and Schaufeli 2016). It is vital to understand how HCMPs motivate employees to perform exceptionally in the organization. Human capital theory and the Motivational Process of the Job Demands-Resource (JD-R) Model perspectives present useful information for expatiating the aforementioned relationships. Employees will choose whether to be engaged or not in their jobs based on the treatment they receive through HCMPs from management (Saks 2019).
Using human capital theory and the motivational process of the JD-R perspectives as the theoretical framework, this study proposes and tests a conceptual model that investigates whether work engagement mediates the relationship between HCMPs and employee performance. Training, career opportunities, and rewards are the indicators of HCMPs assessed in the present study. The study objectives are to investigate whether the selected HCMPs have a positive relation with work engagement, assess whether work engagement influences employee performance, and examine the full mediating effect of work engagement between HCMPs and employee performance. Data obtained from the hospitality industry in South Africa were used to examine these relationships. Several contributions are made to existing knowledge by this study in the following ways.
First, though there are several indicators of HCMPs, training, career opportunities, and rewards are regarded as the indicators of HCMPs predicting work engagement. That is, the presence of HCMPs manifested in training, career opportunities, and rewards communicates a strong signal of management’s willingness to improve employee wellbeing. The presence of training communicates management’s commitment to improving employees’ capabilities and performance in the workplace (Ployhart et al. 2017).
Training is a key human capital management (HCM) indicator that facilitates employee skill enhancement (Lasi et al. 2014). Rewards and career opportunities practices communicate a strong signal of management’s commitment to employees and their wellbeing (Shakeel and But 2015).
Reward has been observed to improve work ethics, motivate employees to achieve high performance, impact organizational performance, and reduce intention to resign (Karatepe et al. 2014).
The presence of career opportunities motivates employees to stay and improve their performance in the organization (Karatepe and Vatankhah 2014). A thorough literature search indicates that training, career opportunities, and rewards are among the top six human resource management practices, also known as high-performance work practices (Karatepe and Vatankhah 2014; Karatepe and Olugbade 2016; Kibatta 2019; Isah Leontes 2022). Such human resource management practices have been shown to be very important indicators of management commitment to service quality. Further, training, career opportunities, and rewards are regarded as valuable managerial implications for business practice in the hospitality industry (Karatepe 2013a; Karatepe and Olugbade 2016). Hence, this study examines the effect of HCMPs, as manifested by training, career opportunities, and rewards, on work engagement.
Secondly, in a recent study, Hendrik et al. (2021) noted that little is known about work engagement as a determinant of employee work performance. This creates a research gap that needs to be addressed. With this realization, this study tests employee performance outcomes as a consequence of work engagement to address the void in the existing knowledge base.
Thirdly, there is a paucity of research pertaining to the antecedents and consequences of work engagement in the hospitality industry (Karatepe 2013b; Karatepe and Olugbade 2016; Isah Leontes 2022). As such, in this study work, engagement is considered and examined as a mediator of HCMPs and employee performance in a bid to address the existing knowledge gap in the literature. The findings of this study will contribute to the management and retention of engaged employees with heightened performance. The next section of this article comprises the description of human capital theory and the motivational process of the JD-R Model as theoretical underpinnings of this article. This is followed by a hypothesis developed based on the theoretical framework and the conceptual model of the study. Then, discussions of the method and results are presented. The article concludes with managerial implications and direction for future study.

2. Literature Review and Hypothesis

2.1. Human Capital Theory

HC theory informs the theoretical foundation for this study and underscores the vital role of HCM in the relationship of the study variables. Hence, a strong theoretical argument can be made for the investigation of the impact of HCMPs manifested in training career opportunities and rewards. The HC theory upholds that employees with special skills perform exceptionally well in their job roles and that the greater the investment in employees, the better their productivity levels are (Marginson 2019). In accordance with HC theory, effective implementation of HCMPs in the workplace will lead to improved employee performance because people make decisions in the organization on the basis of perceived benefits (Harsasi and Muzammil 2017). Thus, if employees are managed well, their performance level in the organization improves (Armstrong and Taylor 2014). The effectiveness of management activities, such as training career opportunities and rewards, is evaluated by their influence on employee performance (Yildiz et al. 2020). Training is a key HC variable that can help to mitigate the skills crisis in organizations (Lasi et al. 2014). A highly skilled employee will deliver quality services that will have a positive impact on the industry. Since learning, training, and innovative capability are critical to employee performance and success (Lasi et al. 2014; Shamim et al. 2017), attention should be placed on developmental programs that will enhance the relevant skills (Lubis et al. 2019). Thus, HC theory validates the perception that employees display higher levels of engagement and performance when motivated through the implementation of effective HCMPs (Andrew and Sofian 2012; Saks 2019).

2.2. The Motivational Process of the Job Demands-Resources (JD-R) Model

The concept of work engagement was developed in association with the JD-R model (Bakker and Demerouti 2017). The present study draws from the motivational pathway in the JD-R model to elucidate employees’ engagement and performance. In line with the tenets of the motivational pathway of the JD-R model, job resources improve employees’ wellbeing and control or reduce job demands (Bakker and Demerouti 2018). Job resources refer to those psychological, social, physical, and organizational characteristics of the job that are useful in achieving work goals, reducing job demands and related psychological and physiological costs and stimulating personal development and growth (Lesener et al. 2019). The resources may contain both intrinsic motivational value by promoting self-development as well as extrinsic value by supplying specific information for achieving goals (Schaufeli and Bakker 2013). Hence, they stimulate employees to achieve their goals. Such resources can be identified at an organizational level (e.g., rewards, career opportunities training, etc.), interpersonal level (e.g., team climate supervisor coworker support, etc.), task level (variety of skills, task identities, task significance, performance feedback, autonomy, etc.) and the job-specific position (participation in decision making and role clarity). Employees are committed when they derive fulfillment from job resources (Kehoe and Wright 2013; Park et al. 2014). Previous studies (Hakanen et al. 2006; Saks 2006; Xanthopoulou et al. 2007; Allen and Shanock 2013; Kotzé and Nel 2020) indicate that job resources, such as autonomy, supervisory, coaching, social support feedback on performance, and professional development opportunities, correlate positively with work engagement. Bakker and Demerouti (2018), Saks (2019), and Lesener et al. (2019) share a similar opinion that job resources are positively correlated to work engagement, especially under the condition of high job demands (Schaufeli and Bakker 2013). Thus, it can be inferred that the more resources an employee has, the more engaged they become in their job (Saks and Gruman 2014).
In addition, engaged employees have a low tendency to leave the organization since they are provided with job resources that induce learning development and growth (Schaufeli and Bakker 2013). Bakker and Demerouti (2008) noted that from previous studies, there is substantial evidence to support the argument that work engagement and job resources, such as rewards and learning opportunities, correlate.
Employees’ perceptions of job resources influence their behaviors in the workplace (Allen and Shanock 2013; Kotzé and Nel 2020). By showing an interest in employees’ wellbeing, career enhancement, and work input, HC managers create a supportive work environment (Paterson et al. 2014). Formal job resources like training rewards and career opportunities can influence employees’ work engagement levels in the organization (Alzyoud et al. 2015). Hence, as per the motivational pathway of the JD-R model, it is theorized in the present study that the construct of work engagement mediates the effect of HCMPs, manifested by training career opportunities and rewards as potential job resources, on employee task adaptive and contextual performance.

2.3. Human Capital Management Practices and Work Engagement

Training career opportunities and rewards are the indicators of HCMPs employed in this study. Such indicators are important and are crucial in in-service jobs and organizational success in the hospitality industry (Karatepe 2013a). As mentioned earlier, training, career opportunities, and rewards are among the most important indicators of HCMPs (Karatepe and Vatankhah 2014; Karatepe and Olugbade 2016; Kibatta 2019; Isah Leontes 2022). Such HCMPs have been shown to be very relevant indicators of management commitment to service quality (Isah Leontes 2022). HCMPs have a positive effect on employee engagement (Harsasi and Muzammil 2017).
Training enhances commitment as employees will be delighted in doing what they are skilled at and capable of (Bakker and Demerouti 2018). Organizations can engage their employees by providing them with training and developmental opportunities, as most employees want to upgrade their skills rather than become redundant (Andrew and Sofian 2012; Ahmetoglu et al. 2015; Osborne and Hammoud 2017). Similarly, Aladwan et al. (2015) opined that training enhances employee’s dedication and absorption in their job. Further, Singh and Rana (2013) suggest that training has a positive impact on employee service quality performance and engagement. Training programs should be designed to enhance employees’ capability, which will result in exceptional performance (Parham and Tamminga 2018).
Reward and career opportunities have been identified as vital retention indicators because they are important elements of work engagement (Anitha 2014; Osborne and Hammoud 2017; Wood et al. 2020). Reward motivates employees’ commitment to meet performance targets in the organization. Hence, it is an indispensable quality of employee engagement (Andrew and Sofian 2012; Anitha 2014; Barrick et al. 2015). Barrick et al. (2015) and Karanges et al. (2015) asserted that recognition and rewards have been proven to be important factors in encouraging work engagement. According to Kahn (1990), the level of employee engagement is proportionate to the benefits received. Accordingly, for higher engagement levels, management must present acceptable standards of compensation and career opportunities (Anitha 2014; Ahmetoglu et al. 2015; Albrecht et al. 2015; Osborne and Hammoud 2017). An appropriate reward system is a booster for work engagement (Raina and Kalse 2018). Thus, from the theoretical evidence presented in the foregoing discussion, work engagement is a product of effective HCMPs in the organization. As a result of such practices, employees demonstrate loyalty and commitment to handle customers’ challenges.
Accordingly, the following hypothesis is proposed:
Hypothesis 1 (H1).
Human capital management practices (training, career opportunities, and rewards) are positively related to work engagement.

2.4. The Relationship between Work Engagement and Employee Performance

Studies have suggested that there is a positive relationship between engagement and employee role performance (Bakker and Albrecht 2018) and an organization’s financial returns (Taris and Schaufeli 2016; Ghlichlee and Bayat 2020). This assertion proposes that engaged employees can perform extremely well in the organization (Schaufeli 2015). Similarly, Andrew and Sofian (2012) noted that work engagement is considered a strategy to enhance staff commitment in organizations. Thus, it can be presumed that employee performance is a valuable outcome of work engagement (Karatepe 2013a). Engaged employees do their jobs with passion, leading to high performance and extra role performance (Karatepe 2013a; Pradhan and Jena 2017). The service quality and communication between the employees and the customer yield a competitive advantage to a hospitality enterprise (Mmutle and Shonhe 2017), and engaged employees who cannot be easily imitated can provide such quality services (Eldor 2020). Employees in the hospitality industry are expected to deal with customer problems with a lot of care and professionalism because customer satisfaction and perception regarding service quality are highly dependent on how they are treated by employees (Karatepe 2013b). When employees are vigilant and focused on their tasks, they handle customers’ problems successfully with high-quality job performance (Karatepe 2013a). This fosters work engagement among hotel employees, potentially motivating their task adaptive and contextual performance (Karatepe 2013a; Pradhan and Jena 2017). Having presented theoretical findings on the relation between work engagement and employee performance, it is prudent to empirically test the effect of work engagement on employee performance.
On the basis of the foregoing discussion, the following hypotheses are proposed:
Hypothesis 2 (H2a).
Work engagement is positively related to employee performance (task performance).
Hypothesis 2 (H2b).
Work engagement is positively related to employee performance (adaptive performance).
Hypothesis 2 (H2c).
Work engagement is positively related to employee performance (contextual performance).

2.5. Work Engagement as a Mediator between Human Capital Management Practices and Employee Performance

Based on the human capital theory and the motivational process of the JD-R perspectives, work engagement is proposed as a mediator between HCMPs and employee performance. The human capital theory insinuates that employees with special skills perform exceptionally in their job roles and that the greater the investment in employees, the better their productivity levels are (Marginson 2019). HCMPs, if implemented effectively in the workplace, can lead to improved employee performance because people make decisions in the organization based on the perceived benefits that they envisage (Harsasi and Muzammil 2017). Accordingly, if employees are managed well, their performance level in the organization increases (Armstrong and Taylor 2020). Employees will choose to be engaged or not in their jobs based on the treatment they get from management through HCMPs (Saks 2019). This shows a reciprocal relationship between the support of management to the employees and their employees’ willingness to perform effectively (Andrew and Sofian 2012; Saks 2019). The JD-R model affirms the mediating role of work engagement as the concept was developed in association with this model (Bakker and Demerouti 2017). The motivational pathway of the JD-R model assumes HCMPs (job resources) influence employee work engagement positively (Demerouti et al. 2001; Bakker and Demerouti 2017), which, in turn, results in improved employee performance and extra role behavior. It is suggested that HCMPs lead to employee performance through increased work engagement (Karatepe et al. 2014; Raina and Kalse 2018). Thus, managers in the hospitality industry should invest in employees as the presence of HCMPs communicates a strong signal to employees of management’s commitment to their wellbeing. This will, in turn, improve employee work engagement levels and consequent performance (Zwane et al. 2014).
On the basis of the foregoing discussion, the following hypotheses are proposed:
Hypothesis 3 (H3a).
Work engagement mediates the effects of human capital management practices on task performance.
Hypothesis 3 (H3b).
Work engagement mediates the effects of human capital management practices on adaptive performance.
Hypothesis 3 (H3c).
Work engagement mediates the effects of human capital management practices on contextual performance.

2.6. Research Model

The research model displaying the hypothesized relationships between the constructs of the study is presented in Figure 1 The model presents training career opportunities and rewards as indicators of HCMPs. The model proposes that employees’ perception of HCMPs in the form of training, career opportunities, and rewards are positively correlated to their engagement at work. The model further suggests that work engagement is a mediator between HCMPs and employee performance.
Training programs in the hospitality industry are scarce despite the training needs (Makumbirofa and Saayman 2018), and the few that are implemented most often do not empower employees to adapt to the dynamic work context associated with the industry (Zijm and Klumpp 2017). Moreover, there has been a growing concern about the expertise of training providers, the effectiveness of the training programs they offer, and the quality and relevance of the training programs offered in the industry (Shamim et al. 2017). The most prevalent type of training in the hospitality industry is the online training approach, where staff are given dissimilar electronic training manuals for monotonous jobs (Shamim et al. 2017). Such training is non-participative and non-collaborative. Further, despite the importance of training in industry management, the literature on the sub-Saharan Africa situation is still silent on this practice (Sultana et al. 2012; Ongalo and Tari 2015; Mzimela and Chikandiwa 2017).
Additionally, opportunities for career growth in the hospitality industry are very rare (Kaur and Singhal 2019), not to mention the need for a clear career growth path (Kaur and Singhal 2019). There is no evidence of a structured or planned system displaying growth in managerial positions in hotels (Kaur and Singhal 2019).
Additionally, the hospitality industry has been recognized for poor remuneration of employees (Adler and Rigg 2012; Baum 2019; Heimerl et al. 2020). Reward practices need to be implemented in the hospitality industry to induce excellent performance, considering the demanding nature of jobs in this industry (Bustamam et al. 2014).
The limitation of the existing HCMPs in the form of training career opportunities and rewards in the hospitality industry has resulted in employee disengagement and substandard performance levels (Zwane et al. 2014; Shava and Hofisi 2017).
Carter et al. (2018) asserted that more than 70% of hotel employees are disengaged and that there is a steady decline in employee engagement in the hospitality industry, especially in developing countries. The low engagement level has significantly affected employee performance levels in the industry as work engagement determines employee performance level (Karatepe 2013a; Cain et al. 2018). Due to the need for a productive workforce, the demand for HCMPs to promote work engagement and the consequent employee performance in the industry has increased (Chiwawa and Wissink 2021). Such circumstances indicate a need for improved HCMPs in the form of training career opportunities and rewards that will facilitate engagement and employee performance.

3. Methods

3.1. Samples

Data were collected from frontline employees, back door personnel (line managers), customer relation personnel, consultants, financial advisers, and senior managers working full-time in rated hotels in South Africa.
One thousand eight hundred and ninety (1890) respondents were invited to participate in the study online. Questionnaires were administered to respondents after the ethical clearance permission from the University of Johannesburg. Four hundred and twenty-eight (428) questionnaires were filled in by respondents. The total number of valid responses retained for the study was 273. The respondents comprised senior management, middle-level management, and non-management employees at rated hotels in nine provinces of South Africa. The demographic distribution of respondents is presented in Table 1.

3.2. Measures

This study adopted measures from prior research that had previously been used to evaluate the constructs under investigation. The assessment was conducted using a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The measurement of training employed a 5-item scale adapted from Rod et al. (2006) with a Cronbach’s alpha score of 0.87. An example item was “I receive continued training that is relevant to my task performance”.
The assessment of reward utilized a 5-item scale developed by Boshoff and Allen (2000) with a Cronbach alpha score of 0.83. An example item was “I am rewarded when I improve my service level to customers”. Career opportunity was measured using a 6-item scale developed by Landau and Hammer (1986) with a Cronbach’s alpha score of 0.79. An example item was “I am involved in activities that promote my personal professional development”.
To measure work engagement, the study adapted the 9-item Utrecht Work Engagement (WE) scale from Schaufeli et al. (2006). This multidimensional scale included vigor, dedication, and absorption. The vigor scale comprised 3 items with a Cronbach’s alpha score of 0.77. The dedication scale consisted of 3 items with a Cronbach’s alpha score of 0.85, while the absorption scale comprised 3 items with a reported Cronbach’s alpha score of 0.78 (Schaufeli et al. 2006). Examples of the scale items were “At my job I feel strong and vigorous” “I am enthusiastic about my job “ and “When I get up in the morning I feel like going to work”.
For the measurement of employee performance, pre-authenticated items from scales used in studies by Koopmans et al. (2014) and Pradhan and Jena (2017) were adopted. The scale was categorized into three dimensions: task performance, adaptive performance, and contextual performance. Task performance reported a Cronbach’s alpha score of 0.82, adaptive performance (0.88), and contextual performance (0.84). Examples of the scale items included “I always work to finish my task”, “I am able to manage change in my job very well whenever the situation demands”, and “I work hard to update my skills.

3.3. Procedure

The partial least squares structural equation modeling (PLS-SEM) was employed to integrate unobservable variables measured using relative indicators and to evaluate path models and latent variables (Hair et al. 2014). The measurement model underwent assessment for internal consistency reliability, convergent validity, and discriminant validity through confirmatory factor analysis (CFA). A PLS-SEM algorithm was executed with a path-weighting outline that examined latent variables through regression and correlation analysis to ascertain the constructs’ parameters in the measurement model. The measurement items loading on each latent variable were delineated in the model with their correlation values (Hair et al. 2017). The CFA results were utilized to scrutinize the elements to ensure convergent and discriminant validity before hypothesis assessment. The structural path model mediation analysis was performed to establish the hypothesized mediated relationship following Nitzl et al.’s (2016) approach.

3.4. Common Method Variance

The self-administered survey method was employed for the present study, necessitating the need to test for a common-method variance (CMV) to ascertain the absence of systematic bias (Spector and Brannick 2010; Malhotra et al. 2017). The common method variance was tested using Harman’s (1976) single-factor test approach. A total of 41 variables for human capital management practices (HCMPs), work engagement (WE), and employee performance constructs were tested using the principal component analysis method. The results (Table 2) indicate that eight factors with eigenvalues exceeding 1.000 accounted for a combined variance of 68.249%, with the first factor accounting for 32.257% (less than 50%). Therefore, based on the results, no CMV was found in the dataset.

4. Results

4.1. Characteristics of the Model

In evaluating the outer model, the reliability was first examined using Nunnally’s (1978) Cronbach’s Alpha threshold of 0.7 and convergent validity using individual indicator reliability. Results in Table 3 show that Cronbach’s alpha and the composite reliability coefficients exceeded the 0.7 threshold, thus confirming reliability. Additionally, all outer loadings of indicator items scored above the 0.70 threshold (Vinzi et al. 2010), except for CO6, TP3, AP4, and AP7, which reported outer loadings ranging from 0.50 to 0.70, and ADP5 and CO4, whose loadings were -0.232 and 0.199, respectively (Table 3). Outer loading values from 0.5 are acceptable, as demonstrated by previous studies (Chen and Tsai 2007; Ertz et al. 2016); hence, items CO4, TP3, AP4, and AP7 were retained. However, items AP5 and CO4 were eliminated from the study because of their extremely low factor loadings (Hashemiparast et al. 2017; Zakria et al. 2019).
Discriminant validity was tested, and the AVEs of the constructs with the inter-construct correlations were compared to determine whether each latent variable shared greater variance with its own measurement variables or with other constructs (Fornell and Larcker 1 981). Further, the square root of the AVE for each construct was compared with the correlations of other constructs in the model (Table 4). As presented in Table 4, all the correlation coefficients of the constructs were lower than their respective AVEs, thus ensuring discriminant validity.

4.2. Model Estimations and Hypothesis Testing

The inner model was evaluated to estimate the structural path model. A non-parametric bootstrap procedure was used to ascertain whether the path coefficients were significant (Hair et al. 2017). The t and p values were used to evaluate the significance of the path coefficients in the structural model (Hair et al. 2017). The results (Table 5) indicated that HCMPs (training, career opportunities, and rewards) significantly and positively influenced WE (path coefficient = 0.543, t = 11.074 p < 0.01). Thus, Hypothesis 1 was supported.
Further, the result also indicated that WE had a significant and positive influence on task performance (path coefficient = 0.312, t = 4.116, p < 0.01). Hypothesis 2a was therefore supported. WE had a significant and positive effect on adaptive performance (path coefficient = 0.418, t = 6.035, p < 0.01) to support Hypothesis 2b. WE significantly and positively influenced contextual performance (path coefficient = 0.392, t = 5.008, p < 0.01). Consequently, Hypothesis 2c was supported.

4.3. Predictive Power

The value of R2 (coefficient of determination) was employed to ascertain the predictive accuracy of the evaluated structural path model (Cohen 1988). The R2 value for WE task performance, adaptive performance, and contextual performance are shown in Table 6.
The evaluated structural path model showed a significant predictive accuracy with R2 values ranging from 0.314 to 0.176 (Cohen 1988). Values with limits of 0.260, 0.130, and 0.020 for criteria variables reflected substantial, moderate, and weak effects, respectively (Cohen 1988).

4.4. Mediation Analysis

Mediation analysis was conducted to assess the mediating effect of the WE construct on the relationship between HCMPs and employee performance, as manifested in task performance, adaptive performance, and contextual performance.
We applied Nitzl et al.’s (2016) two-step approach to test for mediation through the bootstrapping procedure in smart PLS-SEM. The Bca approaches to PLS-SEM were used to obtain the bootstrap confidence intervals (Henseler et al. 2009). The t values were used to determine the level of significance—i.e., 1.65 for a significance level of 10% (α = 0.1, two-tailed test), 1.96 for a 5% significance level (α = 0.5, two-tailed test), and 2.57 for a 1% significance level (α = 0.01, two-tailed test) (Hair et al. 2017).

4.4.1. Mediation Path 1: Human Capital Management Practices and Task Performance via Work Engagement

As depicted in Table 7, HCMPs had a significant positive indirect effect on task performance through WE (path coefficient = 0.169, t = 4.004, p < 0.01). Simultaneously, the direct effect of HCMPs on task performance was insignificant (path coefficient = 0.113, t = 2.715, p > 0.05). The path coefficient values of the indirect effect were significant, and the direct effect insignificant, with WE exerting full mediation. It is, therefore, inferred that WE fully mediated the effect of HCMPs on task performance. Thus, Hypothesis 3a was supported.

4.4.2. Mediation Path 2: Human Capital Management Practices and Adaptive PERFORMANCE via Work Engagement

The indirect effect of HCMPs on adaptive performance through WE was significant and positive (path coefficient = 0.227, t = 5.769, p < 0.01 (Table 7)). However, the direct effect of HCMPs on adaptive performance was insignificant (path coefficient = 0.145, t value = 1.984 p > 0.05). Based on these results, WE fully mediated the effect of HCMPs on task performance. Since the path coefficient values of the direct were insignificant and that of the indirect effects significant, it can be concluded that WE had a full mediation in the relationship between HCMPs and adaptive performance. Thus, Hypothesis 3b was supported.

4.4.3. Mediation Path 3: Human Capital Management Practices and Contextual Performance via Work Engagement

Findings indicate an indirect positive and significant effect of HCMPs on contextual performance through WE (Path coefficient = 0.213, t value = 0.665, p < 0.01 (Table 7)). However, the direct effect of HCMPs on contextual performance was insignificant (Path coefficient = 0.285, t value = 1.793, p value > 0.01). It is concluded that WE mediated the effect of HCMPs on contextual performance, and since the direct effect was insignificant and the indirect effect significant, WE had a full mediation in the relationship between HCMPs and contextual performance. Hypothesis 3c was thus supported.

5. Discussion and Conclusions

5.1. Overall Outcomes

The findings of this study indicate the reliability of human capital management practices (HCMPs) indicators, with rewards emerging as the most reliable, followed by training and career opportunities. Empirical evidence establishes a positive relationship between HCMPs and work engagement, aligning with the human capital perspectives (Marginson 2019; Huang et al. 2021).
The presence of a rewards mechanism in the workplace sends potent signals to employees, conveying appreciation, recognition, and acknowledgment of their efforts within the organization. Additionally, training programs focusing on enhancing task-related and behavioral skills along with career opportunities serve as crucial signals of management’s commitment to HCMPs. Such practices foster synergy in the workplace, leading employees to reciprocate through heightened work engagement. The results also suggest that work engagement concurrently influences employee task adaptive and contextual performance in line with the motivational pathway of the JD-R model. Engaged employees are likely to cultivate trusting, high-quality relationships with their employer (Sendawula et al. 2018), subsequently enhancing their task effectiveness and adaptability within the organization and manifestation of organizational citizenship behavior.
The existing literature suggests that HCMPs should motivate employees to exhibit positive behavioral outcomes (Huselid 1995; Sourchi and Liao 2015; Hermans and Ulrich 2021), a proposition supported by the results of this study. As a motivational construct, work engagement assumes a full mediator role between HCMPs and employee performance. Specifically, the availability of HCMPs, as evidenced by training rewards and career opportunities, leads to the complete immersion of employees in their work. Consequently, these engaged employees demonstrate high-quality job performance and willingly go above and beyond to assist clients.

5.2. The Implications for Theoretical Development

The primary objective of this study was to propose and empirically test a research model that examines work engagement as a mediator in the relationship between human capital management practices (HCMPs) and employee performance. The significance of the study is expounded upon below.
Firstly, the investigation focused on examining the relationship between HCMPs and work engagement. HCMPs were conceptualized and assessed as a second-order variable manifesting through training career opportunities and rewards. These components were identified as effective human resource practices (Pfeffer 1994; Karatepe 2013a; Kibatta 2019; Isah Leontes 2022), ranked among the top six human resources practices (Boselie et al. 2005; Karatepe 2013a), and were deemed crucial indicators of management commitment to service quality (Kim et al. 2009; Karatepe 2013b). Furthermore, these HCMPs have been recognized as having significant implications for hospitality managers in business practices (Kusluvan et al. 2010; Karatepe 2013a).
Secondly, this study contributes to the existing knowledge by empirically testing work engagement as a full mediator between HCMPs and employee performance utilizing data collected from employees within the hospitality industry.
Lastly, the outcomes derived from the partial least squares structural equation modeling (PLS-SEM) analysis indicate robust support for all hypotheses, establishing the viability of the research model. While the cross-sectional nature of the study precludes the determination of causality, the identified HCMPs have been shown to influence work engagement, ultimately leading to elevated levels of job performance.

5.3. The Implications for Management Practice

The findings of this study underscore several valuable implications for the effective management of HCMPs and the preservation of engaged employees in the workplace. Recognizing work engagement as a prolonged and continuous process, managers must grasp the pivotal motivational role of job resources in fostering work engagement and subsequently influencing performance outcomes (Saks 2019). It is imperative for managers to proactively furnish employees with training, career opportunities, and rewards, fostering a sense of obligation that prompts elevated levels of work engagement and performance. Specifically, hotel management should ensure the implementation of appropriate reward policies.
Additionally, continuous training programs are crucial for enhancing employees’ technical and behavioral skills, thereby facilitating effective task performance. A strategic focus on continuous and effective training and career opportunities coupled with employees’ perceptions of fairness in rewards empowers managers to retain a cadre of engaged employees proficient in their roles and adaptable within the organization, exhibiting organizational citizenship behavior effortlessly. As highlighted by Bakker and Demerouti (2008), retaining engaged employees is crucial due to their tendency to harbor positive emotions and maintain superior psychological and physical health compared to their disengaged counterparts.
Furthermore, job resources such as rewards and training are posited to play both intrinsic and extrinsic motivational roles. These resources not only fulfill employee needs but also foster commitment to the job within resourceful work environments, ensuring successful task completion and the attainment of work goals.
In conclusion, these implications are not only pertinent globally but also hold significance for hospitality managers in South Africa, given the prevailing scarcity of qualified workforce in organizations lacking contemporary human capital management practices.

5.4. Limitations and Directions for Future Research

This study employed a cross-sectional design, capturing data at a specific point in time, thereby limiting the observation of the long-term impact of HCMPs on employee performance through work engagement. To address this, a longitudinal study is recommended to discern changes in respondent behavior over time and establish causal relationships between variables.
Moreover, while this study focused on training career opportunities and rewards as indicators of HCMPs, other crucial indicators such as teamwork, employment security, staffing, selectivity, and work–family balance were not included. Future research should consider incorporating these additional indicators to provide a comprehensive understanding of their relationships with work engagement and employee performance.
Self-reported responses pose a potential bias, though Harman’s single-factor test was employed to control for common method variance. For future studies, it is recommended to collect data through multiple methods to mitigate methodological risks.

5.5. Concluding Remarks

This study proposed and assessed a research model that examined work engagement as a mediator between HCMPs and employee performance. The results demonstrated that the presence of HCMPs, exemplified by training, career opportunities, and rewards, led to work engagement, consequently enhancing employees’ job performance. Work engagement was identified as a full mediator of the effects of HCMPs on employee performance. In light of these findings, hotel managers are encouraged to invest in these HCMPs to maintain a pool of engaged employees capable of delivering high-quality performance. In the dynamic global competitive market, an ongoing exploration of various HCMPs linked to work engagement and employee performance remains crucial. This study aims to inspire further research on the mediating role of work engagement in the relationship between HCMPs and various performance outcomes, drawing from diverse data sources (Figure 2).

Author Contributions

Conceptualization, N.I.L.; methodology, N.I.L.; software, N.I.L.; validation, C.H. and N.I.L.; formal analysis, N.I.L.; investigation, N.I.L.; resources, C.H.; data curation, N.I.L.; writing—original draft preparation, N.I.L.; writing—review and editing, N.I.L.; visualization, N.I.L.; supervision, Professor C.H.; project administration, N.I.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by IPPM Research and Ethical Committee at the University of Johannesburg (protocol code IPPM-2023-832).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy and ethical reasons.

Conflicts of Interest

The authors declare no conflicts of interest.

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Admsci 14 00129 g001
Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. Structural path model estimation results.
Figure 2. Structural path model estimation results.
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Table 1. Respondent demographic profile (n = 273).
Table 1. Respondent demographic profile (n = 273).
Demographic VariablesFrequencyPercent (%)
Education
Matric11943.6
Bachelor’s degree5921.6
Honours degree51.8
Master’s degree103.7
Diploma8029.3
Total273100
Department
Front desk work9233.7
Back door personnel248.8
Senior management7728.2
Customer relation officer4215.4
Consultant3412.5
Financial adviser41.5
Total273100
Length of employment in the organization
0–5 years16058.6
6–10 years7828.6
11–15 years155.5
16–20 years134.8
21 years>72.6
Total273100
Table 2. Harman single factor test.
Table 2. Harman single factor test.
FactorInitial EigenvaluesExtraction Sums of Squared Loadings
Total% of VarianceCumulative %Total% of VarianceCumulative %
113.22532.25732.25713.22532.25732.257
24.79711.70143.9584.79711.70143.958
33.1347.64451.6023.1347.64451.602
41.7864.35655.9581.7864.35655.958
51.493.63559.5921.493.63559.592
61.4263.47963.0711.4263.47963.071
71.0892.65565.7271.0892.65565.727
81.0342.52268.2491.0342.52268.249
Table 3. Convergent validity.
Table 3. Convergent validity.
ConstructsItemsFactor
Loadings		Cronbach’s
Alpha		Composite
Reliability		Average Variance
Extracted (AVE)		Conclusion
TrainingTR10.8480.9130.9140.742***
TR20.829
TR30.886
TR40.881
TR50.861
RewardRE10.8780.9380.9390.802***
RE20.891
RE30.906
RE40.920
RE50.884
Career opportunitiesCO10.7010.8030.8060.560***
CO20.802
CO30.789
CO50.751
CO60.695
VigorV10.8940.8870.8880.816***
VI20.922
VI30.894
DedicationDE10.9130.9030.9070.837***
DE20.935
DE30.897
AbsorptionABS10.9300.8820.8820.895***
ABS20.917
ABS30.912
Task performanceTP10.8250.7760.7940.598***
TP20.813
TP30.675
TP40.773
Adaptive performanceADP10.7460.8110.8220.512***
ADP20.755
ADP30.805
ADP40.559
ADP60.720
ADP70.681
Contextual performanceCP10.7090.8520.8580.630***
CP20.821
CP30.843
CP40.812
CP50.778
ns = not significant * p < 0.10 (t > 1.65),** p < 0.05 (t > 1.96), *** p < 0.01 (t > 2.57).
Table 4. Discriminant validity test.
Table 4. Discriminant validity test.
ABAPCOCPDERETPTRVI
AB0.946
AP0.4890.715
CO0.5000.3600.749
CP0.4030.6820.3310.794
DE0.7670.4280.5200.4520.915
RE0.4480.2190.6430.1420.4100.896
TP0.3390.5710.3140.5490.4460.1870.774
TR0.3530.3170.5720.2500.3030.5140.3250.861
VI0.8570.4260.4870.3350.6960.4530.3060.3490.903
Diagonal values (in bold) denote the square root of the AVE of each construct. AB = absorption, AP = adaptive performance, CP = contextual performance, DE = dedication, RE = reward, CO = career opportunities, TP = task performance, TR = training, VI = vigor. Source: Fornell and Larcker (1981).
Table 5. Estimation of structural path model: hypothesis testing.
Table 5. Estimation of structural path model: hypothesis testing.
PathPath Coefficientt Valuesp ValuesSignificance95% Confidence Interval
Human capital management -> work engagement0.54311.0740.000***0.440, 0.632
Work engagement -> contextual performance0.3925.0080.000***0.223, 0.531
Work engagement -> task performance0.3124.1160.000***0.160, 0.452
Work engagement -> Adaptive performance0.4186.0350.000***0.262, 0.537
ns = not significant. * p < 0.10 (t > 1.65), ** p < 0.05 (t > 1.96), *** p < 0.01 (t > 2.57).
Table 6. R2 Values of dependent variables.
Table 6. R2 Values of dependent variables.
Dependent (Criterion) VariableR2 Values
Work engagement0.314
Task performance0.176
Adaptive performance0.245
Contextual performance0.185
Table 7. Mediation analysis.
Table 7. Mediation analysis.
IEPCStd Errort Valuesp ValuesSigCIDEPCStd Errort Valuesp ValuesSigCI
HCM -> WE -> TP0.1690.0424.0040.000***0.093–0.258HCM -> AP0.1130.0631.7930.073ns−0.009–0.235
HCM -> WE -> CP0.2130.0464.6650.000***0.123–0.302HCM -> CP0.0600.0740.8160.415ns−0.082–0.203
HCM -> WE -> AP0.2270.0395.7690.000***0.149–0.305HCM -> TP0.1450.0731.9840.047ns−0.004–0.284
ns = not significant. * p < 0.10 (t > 1.65), ** p < 0.05 (t > 1.96), *** p < 0.01 (t > 2.57). HCM = human capital management, WE = work engagement, TP = task performance, CP = contextual performance, AP = adaptive performance, PC = path coefficient, Std error = standard error, CI = confidence interval, Sig = significance, DE = direct effect, IE = indirect effect.
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Isah Leontes, N.; Hoole, C. Bridging the Gap: Exploring the Impact of Human Capital Management on Employee Performance through Work Engagement. Adm. Sci. 2024, 14, 129. https://doi.org/10.3390/admsci14060129

AMA Style

Isah Leontes N, Hoole C. Bridging the Gap: Exploring the Impact of Human Capital Management on Employee Performance through Work Engagement. Administrative Sciences. 2024; 14(6):129. https://doi.org/10.3390/admsci14060129

Chicago/Turabian Style

Isah Leontes, Njanjobea, and Crystal Hoole. 2024. "Bridging the Gap: Exploring the Impact of Human Capital Management on Employee Performance through Work Engagement" Administrative Sciences 14, no. 6: 129. https://doi.org/10.3390/admsci14060129

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