Examining the antecedents of employee retention among Jordanian private Universities: The moderating role of knowledge sharing

Abstract

This study aims to examine the effect of employee engagement, job satisfaction, task complexity and talent management on employee retention in private universities based in Jordan. The study also examines the moderation effect of knowledge sharing on the relationship between employee engagement and employee retention. The data collected from 183 academics and analyzed through Statistical Package for the Social Sciences (SPSS) and partial least squares-structural equation modeling (PLS-SEM) indicated that employee engagement, talent management and job satisfaction significantly impact employee retention. However, task complexity was found to have a non-significant relationship with employee retention. Further, it was found that knowledge sharing positively moderates the relationship between employee engagement and employee retention. This study is a signpost for future research regarding the antecedents of employee retention and provides useful insights to the policy makers of higher education institutions in Jordan. The study also highlights several avenues for future research.

Keywords:

• Employee engagement
• job satisfaction
• task complexity
• talent management
• knowledge sharing
• employee retention

1. Introduction

Employee retention has become a critical issue for organizations in today’s highly competitive and ever-changing corporate climate. Private universities are also no exception. They need highly skilled and devoted workers to ensure the continuation and success of educational programs and maintain productivity. An understanding of the elements that might drive employee retention is therefore crucial for these institutions (Chandra, 2019; Lengnick-Hall & Lengnick-Hall, 2002).

Research has shown that employees’ intention to stay with or leave the educational institutions is affected by several personal and contextual factors. For example, highly committed and contented employees are more likely to stay with the educational institutions (George, 2015; R. Biason, 2020). Similarly, efficient talent management practices are likely to result in increased loyalty and retention (e.g., Tamunomiebi & Worgu, 2020; Wiradendi Wolor, 2020). Talent management is a continuous activity in the life cycle of corporate operations that results in increased productivity, performance, and retention (e.g., Baharin & Hanafi, 2018; Martin, 2015; Oladapo, 2014; Van Zyl et al., 2017). Besides, talent management can lower employee turnover, which has become a major concern for the educational institutions (Narayanan, 2016; Rana & Abbasi, 2013).

Employee engagement is another important factor that can positively affect academicians’ intentions to stay with educational institutions (Kamil, 2015; Macey & Schneider, 2008). This is mainly because engagement includes an element of happiness that may enhance retention (Alias et al., 2016). That is perhaps why employee engagement has been regarded as a “means” to keep employees involved in the goals and values of educational institutions (e.g., Bhatnagar, 2007; Lartey, 2021). Furthermore, information sharing, which is an important element of a knowledge management (e.g., King & Marks, 2008; Small & Sage, 2005), can play key role in bolstering employee retention. Generally, organizations employ knowledge sharing to formalize and manage tacit information. However, this study contends that knowledge sharing in the universities (i.e., seminars, workshops, written reports, conferences, in-person interactions, social events, informal gatherings, formal training, periodic meetings, mentoring programs, and other forms of internal communication. Hammouri & Altaher, 2020) can foster academicians’ retention and amplify the effects of employee engagement on employee retention.

Apart from the factors noted above, academicians’ retention may also be affected by certain elements of their job (e.g., task complexity) and important job-related attitudes (e.g., job-satisfaction). However, studies examining the association between said factors and academicians’ retention in Jordanian context do not exist. Therefore, this research attempts to address this research gap.

2. Literatures review and hypotheses

2.1. Employee engagement and retention

Employees leave universities for a variety of reasons, which can primarily be explained through the lens of employee engagement. It is a well-established fact that employee engagement is a major driving force of productivity. Employees who are not sufficiently motivated to work in a university can depart owing to factors such as, job discontent, their role as a line manager or supervisor, insufficient training and development and inadequate compensation incentives (Chandra, 2019). However, employee engagement can play key role in this regard (e.g., Andrew & Sofian, 2012; Bin, 2015; Jha & Kumar, 2016). As a construct, employee engagement has been introduced in late 1990s and regarded as a strong intellectual and emotional link between employees and their employers. It has been argued that engaged employees are enthusiastic about their jobs and committed to their companies, and are willing to put forth extra and/or more effort (Attridge, 2009; Bhuvanaiah & Raya, 2014). Considering these arguments, it may be asserted that employee engagement will result in increased retention. A plausible reason for this postulate may be that engaged employees identify more with their organizations, due to which they are less inclined to quitting their jobs. Therefore, it is proposed that;

H1:

Employee engagement will positively affect employee retention.

2.2. Job satisfaction and employee retention

Employee job satisfaction refers to the happiness that an employee feels because of his or her work. It is an unshakeable emotion about one’s employment that arises from the assessment of its attributes (Robbins et al., 2009). Rather than being a behaviour, job satisfaction is an attitude. Job satisfaction (or lack thereof) is inextricably tied to an individual’s behaviour at work (Davis et al., 1985; George & Jones, 2002). For example, employees who are dissatisfied with their jobs are more likely to miss work (Bigley et al., 1996) while satisfied employees are less likely to be absent and more likely to be productive and loyal to their organizations (Irabor & Okolie, 2019). Further, research has shown that satisfied employees tend to have high levels of motivation and morale, and exhibit greater performance (Irabor & Okolie, 2019). Using these insights, it may be asserted that job satisfaction can be key to employee retention (N. F. AlQudah et al., 2022). Therefore, the following hypothesis is proposed;

H2:

Job satisfaction will positively affect employee retention.

2.3. Talent management and employee retention

The term “talent management” is a well-known term in the organizational literature. It gained prominence after the introduction of the phrase “battle for talent” (Michaels et al., 2001). Since then, there has been a massive boom of literature in the topic of talent management, which is still emerging. An increasing number of papers and books on “talent management” lead one to believe that it is a well-defined field of practise with significant research and a core set of principles (Narayanan, 2016). When defining talent management, it is necessary to evaluate the two major methods: exclusive and inclusive approaches. The exclusive approach is founded on the concept of “workforce differentiation,” which sees talent as a select set of people who can make a difference in the university’s success (CIPD, 2007). Because everyone in the organisation has potential “talent,” the inclusive approach is based on “humanistic” concepts, and suggests that all organisational resources be allocated equally among employees (Iles et al., 2010).

Talent management has been referred to as a comprehensive collection of HR processes and activities aimed at attracting, developing, motivating, and retaining high-performing individuals who are needed now and, in the future, (Al Ariss et al., 2014; Wuim-Pam, 2014). Talent management includes identifying, combining, and supporting talent, as well as developing, deploying, engaging, and rewarding talent. Talents with high potential ensure that the university maintains a sufficient talent route to fulfil its goals (e.g., Hughes & Rog, 2008; Mohammed et al., 2018).

Succession planning, human resource planning, employee performance management, and other activities fall under the umbrella of talent management. Talent management demands a systematic strategy involving the dynamic interaction of multiple activities and processes. Since talent management entails attracting, developing, motivating, and maintaining qualified and highly skilled employees who can lead are all part of it (Mugambwa, 2018), it may be therefore be anticipated that it will positively affect employee retention. Simply put, when employees perceives that their talent is valued and properly managed in the organization, they are likely to stay with it. Hence, the following hypothesis is proposed;

H3:

Talent management will positively affect employee retention.

2.4. Task complexity and employee retention

The information cues and/or information regarding a task is a significant input that defines the knowledge, skills, and resources that individuals need to complete a task or job successfully (Wood, 1986). Task complexity, which specifies the interactions between task inputs, is a key predictor of human performance (H. M. Alqudah et al., 2019b). There are three dimensions of task complexity: component complexity, coordinative complexity, and dynamic complexity. Component complexity is the extent to which a task involves distinct acts while coordinative complexity indicates to the relationship between task inputs and task products. The dynamic complexity highlights the extent to which the relationship of task inputs and task products is stable (Wood, 1986). In summary, task complexity is a dynamic construct that entails the quantity, interaction, and variability of task components. In academia, the tasks of academics are highly complex, i.e. they have to perform a number of distinct tasks (e.g. lecture delivery, academic advising and supervision, administrative tasks, and scientific research) and juggle between the demands of such tasks, which can be very cumbersome and invoke several negative attitudes such as intentions to leave. Therefore, the following hypothesis is proposed:

H4:

Task complexity will negatively affect employee retention.

2.5. Knowledge sharing and employee retention

Knowledge sharing is vital to knowledge production, corporate learning, and performance achievement (Bartol & Srivastava, 2002). Individuals in businesses have always created and shared knowledge; therefore, knowledge sharing has been considered as a natural function of the workplace, something that happens on its own (Chakravarthy et al., 1999). According to Bartol and Srivastava (2002), knowledge sharing entails the exchange of information, ideas, suggestions, and relevant knowledge. Individuals’ knowledge can be both explicit and implicit. Organizations that are committed to sharing knowledge are less likely to experience negative employee outcomes (e.g. turnover) because they provide a ready access of relevant information, tools, and resources to employees, enabling them to perform their duties/job well and enhancing their commitment to and engagement with the employer. Knowledge sharing also promotes communication and invokes a sense of support and harmony in employees. Consequently, employees are less likely to quit their jobs. Similarly, knowledge sharing allows individuals to freely collaborate with their peers/other organizational members and maximize the value of their expertise. As such, they are more likely to stay with the organization. Based on these arguments, we propose that

H5:

Knowledge sharing will positively affect employee retention.

2.6. Knowledge sharing as a moderator

This study postulates that knowledge sharing can positively moderate the relationship between employee engagement and employee retention. Knowledge sharing is seen to be a natural function of the workplace, something that happened on its own (Chakravarthy et al., 1999). Nevertheless, in the context of the universities, it could enhance the retention of the academic staff. Whereby, sharing the knowledge among the academic staff will prepare a good and comfortable environment within universities, which in turn, can foster retention. When academics feel that the environment of the universities is comfortable, they are likely to stay in their places without looking for another job vacancy. Apart from bolstering retention, knowledge sharing is expected to strengthen the association between employee engagement and employee retention. To be more specific, employee engagement is more likely to result in high retention when the level of knowledge sharing is high. Therefore, it is proposed that:

H6:

Knowledge sharing will positively moderate the relationship between employee engagement and employee retention.

3. The research model

This study has five independent variables (i.e., employee engagement, job satisfaction, talent management, task complexity, and knowledge sharing), and a dependent variable, employee retention. Further, this study also examines the moderation effects of knowledge sharing on the association between employee engagement and employee retention as shown in figure 1.

Figure 1. Research model.

To test the relationships between employee engagement, job satisfaction, task complexity, talent management, knowledge sharing, and employee retention is a contribution because all of these factors are interconnected and can have a significant impact on the overall performance and success of a university. For example, employee engagement and job satisfaction are inextricably intertwined, because engaged individuals are more likely to be content with their positions and devoted to the business. Second, task complexity can influence employee engagement and job happiness, since employees are more likely to feel involved and happy if their work challenges and motivates them. Third, talent management is critical because it assists institutions in attracting and retaining top people, which is critical for driving performance and development. Fourth, knowledge sharing is essential for organizational learning and performance because it allows employees to learn from one another while also improving their skills and capacities. Fifth, because excessive turnover may be costly and disruptive, employee retention is also critical for organizational effectiveness

4. Methodology

The current study adopted a quantitative approach (Wilkinson & Birmingham, 2003). The population of this study comprised academic staff of Jordanian private universities. According to some careful estimates, the total population of academic staff in 18 Jordanian private universities is 2380 (Alsharari, 2010).

According to Krejcie and Morgan’s (1970) sample size criteria, a sample of 248 is required for a population of approximately 2400 individuals. Since the sample frame was known, systematic random sampling was employed to draw a sample from the population. We used this sampling method because it is a fair and unbiased method for selecting a representative group of participants from the larger population. The method also allows researchers to make generalizable conclusions.

To begin, the questionnaire survey (details of scales used can be seen in Table 1) was sent to 250 academic staff. The data collection comprised approximately four weeks (from 10 August 2021 to 7 September 2021). At the end of the data-gathering process, 183 valid questionnaires were received for analysis. To mitigate the impact of common method bias, several initiatives were taken. For example, the data were gathered in two steps and the anonymity of respondents was also assured. Further, the items in the survey were randomized so that the respondents would not guess the antecedents and outcome variables. The result of the Harman single-factor test was also satisfactory, suggesting that a single factor explained only 39.14% variance.

Table 1. Scales used

Variables	Source
Employee Engagement (6 items) Job Satisfaction (3 items) Talent Management (5 items) Knowledge Sharing (8 items) Task Complexity (8 items) Employee Retention (3 items)	Banhwa et al. (2014) Moqbel et al. (2020) Addy Koranteng (2014) Hooff & Huysman (2009) Sanajou et al. (2017) Kyndt, Dochy, Michielsen, & Moeyaert (2009)

5. Data analysis and findings

SPSS was used for preliminary analysis (coding the data and descriptive statistics) while SmartPLS was used to test hypotheses (Alqudah, 2020; Hair et al., 2016; Gefen et al., 2011). According to Hair et al. (2014), the PLS-SEM technique is particularly suitable in situations where the “study is exploratory” in nature. It is also relevant when the study objectives emphasize predicting and explaining variance in the main indigenous variable using different exogenous variables (Hair et al., 2016).

Table 2 illustrates the demographic information of respondents.

Table 2. Demographic information of respondents (N = 183)

Variable	Category	Frequency	Percent (100%)
Gender	Male	111	60.7
	Female	72	39.3
Age	less than 35	18	9.8
	35–44	33	18.0
	45–54	49	26.8
	55 and above	83	45.4
Experience	less than 5	8	4.4
	5–9	24	13.1
	10–14	29	15.8
	15–19	87	47.5
	20 and above	35	19.1

As indicated in Table 2, the majority of the respondents were men (60.7%). Regarding age, the majority of respondents were aged“55 and above” (45.4%), following the age groups of “45–54 years”(26.8%), “35–44 years” (18%), and “less than 35 years” (9.8%). In terms of experience, 47.5% had a work experience of “15–19 years, 19.1% had a work experience of more than 20 years, 15.8% had a work experience of“10–14 years”, and 4.4% a work experience of “less than 5 years”.

It is important to see which variable has the highest mean in order to highlight how respondents replied to the questionnaire (H. Alqudah et al., 2021). Hence, as shown in Table 3, this study sorted the scales by their mean scores (highest to lowest). Employee retention had the highest mean (3.70) and a standard deviation of (0.597) while the mean and standard deviations of the other scales/variables were as follows: employee engagement (Mean = 3.42; SD = 0.518), job satisfaction (Mean = 3.31, SD = 0.631), talent management (Mean = 3.19, SD = 0.787), knowledge sharing (Mean = 3.26, SD = 0.678), and task complexity (Mean = 2.26 and SD = 0.677).

Table 3. Descriptive statistics of the study variables

	Mean	Std. Deviation	Rank
Employee Engagement	3.42	.518	2
Job Satisfaction	3.31	.631	3
Talent Management	3.19	.787	4
knowledge Sharing	3.26	.678	6
Task Complexity	2.29	.677	5
Employee retention	3.70	.597	1
Valid N (listwise)

For exploratory purpose, the item-loadings were computed (see Table 2). 2 items from the Task Complexity scale (TC4 and TC7), 2 items from the knowledge sharing scale (KS2 and KS3) had low loadings and were therefore omitted from the analysis (Hair et al., 2014). The findings were in agreement with the test of the “discriminant validity” where all items loaded greater on their own variable than on other variables of the model (Hair et al., 2016). Then, the analysis of both the measurement model and structural model was conducted (Hair et al., 2016).

Table 4 displays the convergent validity of the variables’ items. The Cronbach’s alpha, CR and loadings of all items were greater than 70%, while the average variance extracted (AVE) values for all variables were greater than the threshold of 0.50, providing evidence for the validity of convergent in the current study (Hair et al., 2014). The discriminant validity was tested to determine whether each variable is truly separate from another variable in the model. Table 5 displays the results of the Fornell—Larcker criterion associations between variables. The AVE square roots for all variables’ pairs were higher than the associations between the variables, hence meeting the criteria for discriminant validity (Hair et al., 2014; Fornell and Larcker, 1981).

Table 4. Mean, indicators reliability, VIF, CR, Cronbach’s α, CV (after deletion)

Constructs	Items name	Items loading	VIF	CR	Cronbach’s α	AVE
Employee Retention (ER)	ER1	.736	2.029	.918	.906	.53
	ER2	.667	1.329
	ER3	.713	2.186
Employee Engagement (EE)	EE1	.737	1.301	.83	.746	.57
	EE2	.751	1.267
	EE3	.731	1.252
	EE4	.756	1.917
	EE5	.732	1.548
Task Complexity (TC)	TC1	.665	2.054	.86	.82	53
	TC2	.711	1.367
	TC3	.722	1.452
	TC5	.753	1.317
	TC6	.749	1.648
	TC8	.727	1.567
Job Satisfaction (JS)	JS1	.626	1.244	.85	.777	.53
	JS2	.713	1.423
	JS3	.75	1.702
Knowledge Sharing (KS)	KS 1	.675	1.416	.87	.82	.53
	TMS 2	.721	1.563
	TMS 3	.732	1.49
	TMS 4	.763	1.712
	TMS 5	.739	1.664
	TMS 6	.717	1.538
Talent Management (TM)	TM1	.695	1.502	.79	.88	.51
	TM2	.741	1.611
	TM3	.772	1.703
	TM4	.733	1.422
	TM5	.779	1.542

Table 5. Discriminant validity

Constructs	TM	KS	JS	ER	TC	EE
TM	.73
KS	.51	.71
JS	.06	.02	1
ER	.59	.62	.08	.71
TC	.46	.55	−.07	.59	.71
EE	.41	.36	.05	.58	.32	.73

Results for the analysis of structural model are shown in Table 6. The results revealed that all the independent variables (employee engagement [H1: ß = 0.321, p < 0.01], job satisfaction [H2: ß = 0.321, p < 0.01], talent management [H3: ß = 0.242, p < 0.01] and knowledge sharing [H5: ß = 0.254, p < 0.01]) have a significant positive effect on employee retention, except for task complexity (H4) (ß = 0.071, p > 0.01) (see Figure 3).

Table 6. Result of hypotheses testing

Hypothesis testing	Path coefficient	T statistics	p-values	Result
H1: EE -> ER	.321	3.86	.000**	Supported
H2: JS -> ER	.220	3.37	.002**	Supported
H3: TM -> ER	.242	3.93	.001**	Supported
H4: TC -> ER	.071	.973	.168	Not supported
H5: KS -> ER	.254	4.37	.000**	Supported

Notes: $R^2=.52$ percent, adjusted $R^2=.503$ percent

In regard to the moderating effect, the interaction effects between knowledge sharing and employee engagement on employee retention were computed. We found that the moderating effect of employee engagement * knowledge sharing (H6) was significant (t-value = 2.286, p < 0.5) (see Figure 3). This implies that the positive effect of employee engagement on employee retention was undoubtedly stronger for a high level of knowledge sharing than for a high level of knowledge sharing (Figure 2).

Figure 2. Interaction effect between knowledge sharing and employee engagement.

Figure 3. Research model with significant findings.

Table 7 presents the variance that is explained by the variables in Model 1, which was moderate in forecasting employee retention (R2 = %52). As stated by Hair et al. (2011), the values of R2 (0.25, 0.5 and 0.75) can be expressed respectively as (weak, moderate and substantial).

Table 7. R2 Values for direct and indirect effect model

Model	R^2
Model 1. direct effect	.52
Model 1. direct effect	.536

The R² was appropriate compared with other research in the management field using the PLS-SEM. Further, the addition of interaction effect raised the R² from 0.52 to 0.536. To realize the advantage of the interaction effect being added to the model, the f² was computed by applying Cohen’s (1988) “effect size formula: f² = [(R² interaction model—R² direct model)/(1 - R²)].” Thereafter, the f² of the interaction effect was 0.033 [i.e. (0.536–0.52)/(1–0.52) = 0.033]. The effect size for the interaction effect (f²⁾ in this study was small.

6. Discussion and implications

The majority of employee retention research has been conducted in developed nations. Despite its significance, the research on the employee retention in developing nations is low, even among previous studies conducted in developing countries the private universities sector has been neglected. Among the prior studies investigating the factors affecting employee retention, the current study is unique in that it highlights task complexity as a unique factor that might affect academics retention. Further, the study is unique in that it highlights a factor that can bolster the positive effects of engagement on employee retention. In summary this study highlights factors that can have a positive impact on academics’ retention in Jordanian private universities.

As the results indicate, four of the identified factors (i.e. employee engagement, job satisfaction, talent management, and knowledge sharing) were found to be significant antecedents of employee retention in the Jordanian private universities. With respect to employee engagement, our findings reveal that private universities with a higher level of employee engagement tend to get high employee retention. This finding aligns with that reported by Chandra (2019). In particular, Chandra (2019) found that employee engagement can have significant effects on employee retention. Further, this study highlights that “job satisfaction” also plays an important role in shaping employee retention in the Jordanian private universities, attesting to the notion that employee satisfaction tends to be a crucial factor in organizational context (R. Biason, 2019). Moving forward, this study also unpacked the association between talent management and employee retention. The presence of talents with high potential ensure that the university maintains a sufficient talent route to fulfil its goals (Mohammed et al., 2018). This study’s results support the arguments of Li Qi and Jia Qi (2021) that talent management is a method for managing a university’s talent pool for a certain role. This research also reveals that “knowledge sharing” has a significant and positive path to the employee retention in the Jordanian private universities. Knowledge sharing entails sharing of information, ideas, suggestions, and knowledge that is relevant to the organisation, which is the main antecedents of the employee retention (Bartol and Srivastava (2002). This result is congruent with past research (Hooff & Huysman, 2009; Bartol & Srivastava, 2002). However, we did not find support for one of the hypotheses, i.e. the association of task complexity and employee retention was found to be non-significant. This finding is different from the studies suggesting that complexity of tasks significantly affect employees’ behaviors (Huang et al., 2008; Siew et al., 2020, H. M. Alqudah et al., 2019a). Nevertheless, future researchers can look into the association between task complexity and employee retention in academic context.

This study contributes to the literature on HRM by presenting evidence regarding the antecedents (i.e., employee engagement, job satisfaction, talent management, and knowledge sharing) of academics retention in the Jordanian private universities. This study also offers a unique contribution by introducing knowledge sharing as a moderator of the employee engagement-employee retention relationship. Furthermore, this study may also serve as a preliminary platform for future research on the antecedents of employee retention in academia. This study also sheds light on the variables most affecting employee retention. The current study strengthened and refined the existing theoretical perspective of predicting the relationship between the adopted variables.

Our research adds to the body of knowledge by focusing on the specific aspects of geographical location and Middle Eastern/Muslim culture on general descriptions of well-researched variables such employee engagement, talent management, work complexity, job satisfaction, and employee retention. It also uses information sharing as a moderator. The study’s practical consequences include assisting Jordanian universities in determining the amounts of such characteristics among their employees, as well as determining the factors that cause these employees to leave their positions or improve their performance. Future studies can usefully address the effect of talent management aiming to find further information in this regard.

Correction

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Nidal Fawwaz AlQudah

Nidal Fawwaz AlQudah (Corresponding author) an Assistant Professor of Business Administration, Ajloun National University, Business Administration Department, His areas of interests include Human Resources Management, Innovation, Organizational Behaviour, Conflict Management and Entrepreneurship

Muhammad Adeel Anjum

Muhammad Adeel Anjum Department of Management Sciences Balochistan University of Information Technology, Engineering & Management Sciences (BUITEMS), Quetta Pakistan.

Kamran Naeem

Kamran Naeem Assistant Professor In-charge chairperson Department of management sciences Mir Chakar Khan Rind university (Luni road, Sibi) address and City (Balochistan) province Pakistan

Mamoun M Alqudah

Mamoun M Alqudah Ajloun National University School of Business Accounting Department

Ammarah Ahmed

Ammarah Ahmed Department of Management Sciences Balochistan University of Information Technology, Engineering & Management Sciences (BUITEMS), Quetta Pakistan

Hisham Shtnaoui

Hisham Shtnaoui Business Administration, Ajloun National University, Business Administration Department, His areas of interests include Human Resources Management, Innovation, Organizational Behaviour.