Linking Human Resource Management Practices and Employee Performance through Networking Behavior: A Perspective from Malaysian Private University

This paper aims to explore the relationship between human resource management practices and employee performance in Malaysian private university. This paper proposed networking behavior as mediator between HRM-employee performance linkages. To develop this conceptual model, the paper reviews previous literature on HRM, networking behavior and employee performance. This paper proposes a theoretical model of HRM-employee performance linkage with networking behavior as mediator that will better explain the relationships. It argues that HRM practices namely as recruitment and selection practices, and compensation and benefits practices may influence employee’s networking behavior and their performance. At the same time, networking behavior would also affect employee performance. Each of these variables is explored and looking for possibility on the role of networking behavior as mediator. Two hundred and forty-two usable questionnaires are collected to empirically test the hypothesis using IBM SPSS Statistics 23 software and Smart Partial Least Square (SmartPLS) version 3. This paper concludes that recruitment and selection practices, and networking behaviour have significant relationship towards employee performance. Furthermore, networking behavior did not mediate the relationships between recruitment and selection practices, and compensation and benefits practices on employee performance. Therefore, these results should assist organization in providing insight towards managing employees of Malaysian private universities.


Introduction
In today's dynamic and continuous changing world, employees are an important intangible asset for any organization as they are the greatest determinant to the organization success. In developing countries such as Malaysia for instance, employees are experiencing poor performance in private universities (Basu, Jeyasingam, Habib, Letchmana, & Ravindran, 2017). A study by Ghasemy et al. their employees to build their own network as networking also vital for employees in private higher education for them to improve their university performance for instance inter-university collaboration and partnership with industries.

Research Question
Based on the literature reviewed above, the main issues can be addressed in this paper are: R1: What is the relationship between human resource management practices and employee performance? R2: What is the relationship between networking behavior and employee performance? R3: Can networking behavior mediate the relationship between human resource management practices and employee performance?

Research Objectives
From the problem that has been discussed above, the objectives of this paper are: • To examine the relationship between human resource management practices and employee performance. • To examine the relationship between networking behavior and employee performance.
• To examine whether networking behavior mediate the relationship between human resource management practices and employee performance.

Literature Review and Hypothesis Development
The term human resource management (HRM) is used by (Jackson, Schuler, & Jiang, 2014) to refer HRM as an organizational management activity group of practices that used to manage their employees. For Armstrong (2006), HRM means strategic and integrated management approach towards their employees which collectively supporting their organization objective. In 2002, Guest in his study reported that the performance of organization solely depending on their employees. In case their employees are not performing well, it will affect their organization performance too. In recent studies by Amin et al, there have been an identified HRM into several practices such as career planning, job definition, employee participation, selection and recruitment, compensation and benefits, performance appraisals, employee participation, and training and development. These practices have been identified to improve employees and organization performance. On other hand, Jiang et al., (2012) in his analysis identifies HRM into selection system, training, job definition, performance appraisal system, compensation system, career planning system and employee participation. The following HRM practices are therefore considered for the purpose of this study: selection and recruitment, training and development, performance appraisal and, compensation and benefits. The previous research on the function of human resource management practices has been rooted on organizational performance while employees should be the focus in term of development and performance. For instance, recruitment and selection practices is the foremost function in human resource management practices. The quality of employee recruited by the university is depends on how efficient the recruitment and selection are conducted (Nasir, 2017). Jolaosho et al., (2018) examines the effect of recruitment and selection practices towards employee performance in Nigeria. By using simple random sampling technique, 50 members of customer service center were selected to take part in this study. The result showed that recruitment and selection practices have positive impact towards the organisation through their employee's performance. The researcher also concluded that recruitment and selection practices should be prioritized by the organisation and should be included in their key performance index (KPI).
Meanwhile, study by Baba & David (2020) on the relationship between HR practices and employee performance found that compensation and benefits practices have significant effect on the work performance of the employees. This study was conducted in multi-national companies (MNCs) at Ghana. Another study by Rashid, Hamza, & Said, (2018) in investigating the impacts of rewards on employees has also produced the positive outcome. The study which was conducted among academic staff in Malaysian universities found that rewards has significant relationship on their work performance. Previous studies on the relationship between HRM and employee's performance have used human capital theory as underlining the relationship between the variables (Chang & Chen, 2011;(Lin et al., 2017)). However, there is lack of literature on the usage of ability-motivation-opportunity (AMO) theory even Juan A. Marin-Garcia & Juan Martinez Tomas (2016) has considered the theory as excellent model to improve employee importance. This study employed the theory of AMO as underlining justification on the relationship between HRM practices and employee performance.This review formulated the hypothesis that human resource management practices, namely recruitment and selection, training and development, rewards and benefits, and performance appraisals will have positive relationship toward employee performance. Therefore, the study hypothesized that: H1: Recruitment and selection practices have positive relationship on employee performance. H2: Compensation and benefits practices have positive relationship on employee performance. It is necessary here to clarify exactly what is meant by networking. The definition of networking behavior by Orpen (1996) is process of building up and maintaining a set of informal cooperative relationships with individuals other than the manager's direct reports. This is because the immediate manager in the expectation that such relationships will enable the manager to develop and perform more effectively or to achieve more objective and subjective career success. Another definition by Forret & Dougherty (2001) is an individual who attempts to develop and maintain relationships with others who have the potential to assist them in their work or career. While a variety of definitions of the term networking behavior have been suggested, this paper will use the definition suggested by Y. McCallum, L. Forret, & Wolff (2014) who saw it as developing, building and maintaining relationship with internal and external contacts in aiding assistance towards knowledge sharing on work related matters.
There is a large volume of published studies describing the role of networking behavior on career developments and job promotion (Porter, Woo, & Campion, 2016), enhancing internal visibility (Forret & Dougherty, 2004), establishment of business (Sharafizad, 2012). However, research on networking as a strategy to improve employee performance is scarce. Research conducted by Y. McCallum et al., (2014) on networking behavior found that networking positively related to employee performance (Ravi, 2019;Wu, Liu, & Shang, 2018;Marqués-Sánchez et al., 2018). This review formulated the hypothesis that networking will have positive relationship toward employee performance. H3: Networking behavior have positive relationship on employee performance This paper attempts to combine AMO theory with social capital theory to maximize the performance outcomes of the employees as recommended by Nor & Abdullah (2020) andAlbrecht, Bakker, Gruman, Macey, & Saks (2015. Besides that, Chang & Chen (2011) and Liu, Hui, Lee, & Chen, (2013) points out that the linkages of HRM-employee performance have been investigated in centuries but there is possibility to have intervene variables to better explain the relationship. This view is supported by Garg & Sharma (2015) who investigates the role of employee engagement as mediator between HRM practices and employee performance. The outcome of the study shown significant role of employee engagement as mediator towards HRM-employee linkages.
Elsewhere, Gibson et al., (2014) and Spurk, Kauffeld, Barthauer, & Heinemann, (2015) highlighted that networking is influenced by a variety of individual, employee and organizational level. Recent study by Amin et al 2014 suggested that the most initiative taken in Malaysian universities to empower their employees are professional development training and continuous improvement, staff affairs management and encouragement on communication among staff through discussion and dialogue.
The theoretical argument is this study states that employees are driven by human resource management to their networking behavior, and thus will improve their performance. This study attempts to use ability-motivation-opportunity (AMO) theory, and social capital theory to explain the relationship between HRM, networking behavior and employee performance in Malaysian private university. Wright, Dunford, & Snell (2001) argued that human resource practices lead to higher performance through their effects on employee-based firm capabilities and resources. We have argued that strategic human resources management can be used to manage managerial networks of top management team, and managerial networking derived organization performance. Therefore, we expect that one-way HRM affect employee performance is through their effect on managerial networking among top management team. Furthermore, Hernandez-Espallardo, Osorio-Tinoco, & Rodriguez-Orejuela (2018) in his empirical research on organizational performance also found that inter-organizational collaboration can mediate the relationship of human resource management and firm's performance. By using structural equation model, the hypothesis was tested and analyzed from data collected through surveys among sample of Colombian manufactures. Another research conducted by Subramony, Segers, Chadwick, & Shyamsunder (2018) also found evidence that social capital mediates the human resource management and their performance. By utilizing a sample of 223 organization in a growing economy in India, they found that social capital fully mediates the relationship of the variables measured.
The researcher believe that networking activity can interfere with the practice of human resource management and employee performance by incorporating social capital theory and AMO theory. Therefore, the study hypothesized that: H4: Networking behavior can mediate the relationship between recruitment and selection practices and employee performance. H5: Networking behavior can mediate the relationship between compensation and benefits practices, and employee performance. Based on the literature discussed above, this study proposed human resource management are strongly affecting employee's performance through their networking behavior. In addition, networking behavior has proven to influence performance of employees. Figure 1: Conceptual Framework of networking behavior as mediator between recruitment and selection practices, compensation and benefits practices, performance appraisal practices and employee job performance Research Methodology This study will use quantitative methods to examine the relationship between variables (human resource management, networking behavior and job performance). Quantitative approach provides valid and reliable statistical evidence of relationship strength and guidance (Amaratunga, Baldry, Sarshar, & Newton, 2002). As stressed by (Punch, 2003) that the choice of conducting research should align with the research problem and objectives therefore quantitative approach is used to answer the research questions and test the hypothesis formulated in previous section.
The researcher has developed a set of questionnaires as research instrument to collect the data among management staff at the private university in Malaysia. The management staff participated are Executive, Assistant Managers, Managers, Deputy Dean, Head of Section and Dean. There are three section for the questionnaires. The indicators for human resource management practices which comprises of recruitment and selection practices, and compensation and benefits practices are adopted from originally Loo & Beh (2015). The indicators for networking behaviors are adopted from (Wolff, Schneider-Rahm, & Forret (2011) which comprises of internal contact and external contact. The last indicators are employee job performance which adopted from Williams & Anderson (1991). The measured indicators for every construct were based on Five-point Likert scale. Pre-test and pilot test were conducted to ensure the validity and reliability of the instrument. From a total of 300 sets of questionnaires distributed, 250 sets were safely received and only 242 are usable to be used. The data then been recorded in IBM SPSS version 23 and analyzed using SmartPLS version 3. The statistical calculation and model measurement were performed, and the results of findings are interpreted in the next section.

Data Analysis and Results
From the total of 242 questionnaire distributed, female respondents contributed to 53.7% and it is slightly more than male respondents (46.3%). Majority of the respondents are degree holder (40.1%), and age between range 31-40 years old (45.5%). Most of them also have working experience between 5-10 years in the university, and 52.1% respondents are among executive level.
In the first stage of analysis, reliability and validity of the model will be assessed based on internal consistency reliability, convergent validity and discriminant validity. The results as in Table 1 below. Internal consistency reliability measures the extent to which items are measures of a construct (Hair Jr, Sarstedt, Ringle, & Gudergan, 2017). Internal consistency reliability holds true when Composite Reliability (CR) of each construct is greater that threshold value of 0.7 (Hair Jr, Hult, Ringle, & Sarstedt, 2016). Composite reliability is a more precise measure as compared to Cronbach alpha for assessing internal consistency reliability (Bagozzi & Yi, 1988). Table 2.0 shows that CR value for each construct in the model ranges from 0.873 to 0.938 and this is above the recommended cut off value of 0.7. Hence, results showed that indicators measuring a construct have satisfactory internal consistency reliability.
Convergent validity measures that indicators of a construct highly correlate with each other than with indicators of any other construct (Hair Jr et al., 2017). It is assessed by examining outer loadings, Average Variance Extracted (AVE) and Composite Reliability (CR) value. As a rule of thumb for acceptable convergent validity, Chin (1997) recommended outer loadings should be greater than 0.6 and AVE should be above threshold of 0.5 (Hair Jr et al., 2016;Nunnally, 1978). Furthermore , Hair Jr et al., 2016) suggested to retain weak factor loadings (such as 0.4 to 0.7) if other indicators loadings are high and can explain 50% variance. However, the indicator with outer loadings below 0.4 must be dropped. Table 4.6 exhibits that all indicators loadings are above the threshold value of 0.6 and AVE values for all indicators are above 0.5. Thus, indicators of the constructs have satisfactory convergent validity. Discriminant validity measures the degree to which a construct is distinct from another construct in the model (Hair Jr et al., 2017). This articles will use HTMT to assess discriminant validity (Fornell & Larcker, 1981). The HTMT is defined as the mean value of the item correlations across constructs relative to the mean of the average correlations for the items measuring the same construct. The HTMT approach is an estimate of what the true correlation between two constructs would be, if they were perfectly measured. Discriminant validity problems are present when HTMT values are high (Hair Jr et al., 2017). According to Kline (2015), HTMT value must less than 0.85. However, the latest research by Franke & Sarstedt (2019) proposed that the value must between 0.85 or 0.90. In the second stage of analysis, the latent variable score for the first order construct is obtained from smartPLS analysis report and a new model is formed using latent variable score as formative indicators for second order constructs in the model. The assessment of formative constructs will include Variance Inflation Factor (VIF) for multicollinearity evaluation, outer weights for significance checking, and outer loadings for absolute contribution.  Table 3 showed that VIF value are all less than 5. So, there is no issue of multicollinearity among second order formative constructs in this model. Outer weights of formative indicators are assessed for significance of relative contribution. The recommended value for outer weight is greater than 0.1 (Lohmöller, 2013) . If any indicator has significant outer weight, it is further checked for absolute contribution based on outer loadings of indicators (Hair Jr et al., 2016). The bootstrapping procedure using 5000 resamples was applied to identify significance of weights of formative indicators. The results for the outer weights in Table 3 showed that all formative indicators are significant. Outer loadings are assessed for significance of absolute contribution. Indicators with outer loadings above 0.50 are considered acceptable for formative measurement model. As shown in Table 3, all formative indicators have outer loadings above 0.5 and its t-value is also significant, as t-value is above 1.96 at 5% significance level.
The evaluation of structural model provides assessment for predictive capability of model and relationship among constructs. Evaluation criteria for structural model includes collinearity test, significance of path coefficients, level of determination (R 2 ) and effect size (f 2 ). Like the assessment of formative constructs, all the constructs in structural model are first checked for multicollinearity by assessing VIF value . Hair Jr et al., (2016) recommended that the acceptable value for VIF should be less than 5. The results in the Table 4 showed that VIF value for all predictor constructs is below 5 and possibility of multicollinearity among constructs is ruled out for the structural model in this study. Significance of path coefficients is determined through application of bootstrapping procedure (Hair Jr et al., 2016). Using bootstrapping for 5000 resamples with 5% significance level, Table 5 shows the results for t value, p value and 95% confidence interval for the hypothesized relationship among constructs in the structural model. At 5% significance level, significant t value should be above 1.96 and significant p value should be below 0.05. According to Hair Jr et al., 2016) reporting confidence interval (CI) along with t values and p values provides additional information about significance testing. The confidence interval for specified path coefficient is said to be significant if it does not include value 0 within lower and upper limits of interval (Hair Jr et al., 2016) With regards to the relation between human resource management practices and employee performance, recruitment and selection, and training and developments have significant relationship on employee performance. Compensation and benefits, and performance appraisals have no significant influence on employee performance. The relation between human resource management practices and networking behavior also produce only two significant relationships which are training and development, and performance appraisals towards networking behavior. Networking behavior shows significant relationship towards employee performance. Yes R 2 determines model predictive accuracy by examining the variance explained in endogenous variable by exogenous variable (Hair Jr et al., 2016). According to Cohen (1988), R 2 value of 0.02 are considered weak, 0.13 to 0.26 are considered moderate and above 0.26 are taken as substantial. SmartPLS algorithm function is used in this analysis to calculate R 2 values, as given in the table below. The findings in the table below suggest that HRM practices including recruitment and selection practices, compensation and benefit practices, training and development practices and performance appraisals practices explain 25.3% of total variation in Networking Behavior construct. Moreover, Networking Behavior also contributed 33.6% variation in Job Performance. Furthermore, results indicate that R 2 values are satisfactory based on cut off values given by Cohen (1988) as predictive accuracy level for both Job Performance and Networking Behavior are substantial in this model.  The bootstrapping analysis showed that the indirect effect β = 0.00 was significant with a t-value of 3.000. also, the 95% Bootstrapping Confidence Interval (CI) (Preacher & Hayes, 2008) does not straddle a 0 in between [LL = 0.00, UL = 0.0] indicates that there is a mediation. Thus, we conclude that the mediation effect is statistically significant.
The mediation test will be conducted using bootstrapping technique to calculate the t-value and confidence interval for determining the significance of indirect effect. For acceptance or rejection of mediation hypothesis t-value should be significance (t > 1.96, two-tailed, p < 0.05) and confidence interval should not contain value 'zero' between lower and upper limit (Hair Jr et al., 2016;(Latan & Noonan, 2017). By using bootstrapping procedure with 500 resamples at 5% significance level and 95% confidence interval, the results for mediation relationship shown as below table.
From the results it shown that networking behavior only mediates the relationship between training and development and employee performance (t=2.009, LL=0.016, UL=0.080). Networking behavior did not mediate the relationship between recruitment and selection, and employee performance due to t-value is below than 1.96 and 95% CI include zero between UL and LL (t=0.383, LL=-0.014, UL=0.032). The same reason happens to networking behavior which did not mediate the relationship between compensation and benefits and employee performance (t=0.025, LL=-0.015, UL=0.018). Lastly, networking behavior did not mediate the relationship between performance appraisals and employee performance due to t-value is below than 1.96 (t=1.701, LL=0.006, UL=0.076).

Discussion
The first research objective of this study has produced mixture outcome. Recruitment and selection practices have significant relationship towards employee performance. The findings can expand further the idea in relation to the present study as proposed by Lin et al., (2020) and Sendawula et al., (2018). We can also conclude that the ability of HRM practices to improve their employee's performance is depending on how the organization conduct their recruitment and selection process as well as how effective the university recruited the best talent for their performance. However, compensation and benefits practices are not significant towards employee performance. In private sector, compensation and employee benefits are one of challenges given to employees especially in education sector (Quinlan, 2004). Furthermore, the findings indicated that the compensation and benefits package offered by the university are not attractive enough to motivate their employee to boost their performance in workplace. The second research objective of this study which to examine the relationship between networking behaviour and employee performance is successfully achieved. Networking behaviour found to be significant towards employee performance. The culture of networking among employees of the university is proven to give them benefits rather than negative impact on their work performance. Furthermore, the university which has twelve campuses around peninsular Malaysia fostering the networking behaviour among them to discuss on work related issues which enhance their performance.
The last research objective also produced mixture outcome whereby most of the hypothesis are rejected. The mediation impact of networking behaviour on recruitment and selection practices, compensation and benefits practices, and performance appraisals towards employee performance are not significant. However, networking behaviour can only mediate the relationship between training and development practices, and employee performance. The employees who are given skills and training will have better performance when they perceived networking behaviour in their working place. For instance, the top management required to deal with external stakeholders such government agencies, private industry as well as non-government organization as part of knowledge sharing and information seeking (Avellaneda, 2016) Nonetheless, there are several limitations in this paper. First, this study was conducted among top management and non-academic staff without considering academic staff as sample. Academic staff certainly have their role and contribution towards university performance. Future research must include academic staff in investigating their networking behavior towards individual performance. Second, this study was adopted individual self-rating on employee performance. This approach was adopted due to difficulty to get objective measures on employee performance from organization. Future research should consider using objective measure of employee performance from organization database. Lastly, this study was conducted in Asia countries, specifically in Malaysia. It is unclear whether the findings can be generalized for any other countries such in another Asian countries or any other countries in the world. In fact, the findings may be varied from this study. Future research must be conducted at other countries to generalize the findings.

Conclusion
This study intended to present empirical evidence of the relationship between human resource management practices and employee performance through networking behavior of the employees. The researchers have further examined whether networking behavior of employees were able to act as mediator between human resource management practices and performance of the employees. This study has formed new conceptual model of HRM-employee performance, and new instrument which is valid and reliable. Future empirical research can be conducted to investigate the HRMemployee linkages in different scope like academic staff, or other industries involving employees at low level management hierarchy. This study has also furthered modify the HRM-employee performance model with new variables, networking behavior which is overlooked in the mainstream discussion. The findings will contribute on the best practices in private university on producing the best performance among management employees. This study has contributed to literature on theoretical, managerial and contextual contribution. From theoretical perspective, this study has contributed to the literature by advancing the knowledge in HRM and employees networking behaviour that emerged as contemporary global concern, especially toward Industrial Revolution 4.0. From managerial perspective, this study offers significant insight to employee' policy makers in higher education institutions. They should encourage networking activities among employees and employed the best practices of HRM to their respective employees to gain their performance objective. At the same time, strategic training and development practices could be implied in the university in order to develop their employees networking order which later enhancing their work outcome. From contextual perspective, this study offers new perspective in the management of private university in Malaysia. Employers may focus on the recruitment and selection practices as good application of such practices may affect employee performance for instance individual commitment and work success (Pahos & Galanaki, 2019). At the same time, networking behavior among management staff in private universities should be encouraged and formalized as it will boost their employee performance at work.