Quality of Work Life (QWL) and Its Impact on the Performance of the Banking Industry in Saudi Arabia

Abstract

The quality of work life (QWL), job satisfaction, and individual work performance are the lynchpins of organizational performance and sustained business growth (SBG). Numerous researchers have recognized an association between QWL and SBG. Positive QWL dimensions ensure a workforce’s commitment to SBG. Like SERVQUAL, the QWL has several dimensions, and the most common are: (1) job satisfaction, (2) autonomy, (3) physical working environment, (4) remuneration, (5) career growth, (6) collegial relationships, and (7) relationship with management. A career in the banking industry has always been considered a symbol of prestige, prosperity, job security, and job satisfaction. To understand this, we present the WRKLFQUAL model to measure QWL and its impact on job security and satisfaction (JSS) and individual work performance (IWP). The dimensions and subdimensions of WRKLFQUAL are different from the dimensions and subdimensions of SERVQUAL; however, mechanisms measuring service quality and QWL have similar approaches. Accordingly, this study applied gap analysis to find what workforces expected from their work environments, as well as what they have actually experienced. Many researchers have argued that gaps in service quality significantly influence business performance. In this regard, our research found that almost all dimensions of WRKLFQUAL have negative gaps, meaning poor QWL causes job dissatisfaction and hampers IWP. Regression analysis also shows that average gaps have a significant relationship with job satisfaction. Finally, research proves that job security and satisfaction plays a mediating role in average gap scores and individual work performance. This study was carried out with reference to the banking sector’s performance in the Kingdom of Saudi Arabia, as follows. Cronbach’s α score suggests that 95% of the sample is free of error. To apply WRKLFQUAL on the same lines those of SERVQUAL, we developed seven dimensions and 28 subdimensions. Based on these dimensions, seven factors were extracted, all with factor loading between 0.745 and 0.835, confirming that all components had quite a high level of common variance. Accordingly, gaps in QWL, ranging from −0.997 to −1.149, also show that almost all the dimensions and subdimensions need improvements. Carrying this analysis further, we also compared QWL between Saudi and non-Saudi multinational banks and found that the QWL of the Saudi banking system has a slight edge over non-Saudi multinational banks. A correlation among seven predictors, ranging from 0.625 to 0.812, suggests that all seven predictors are highly correlated. Similarly, regression analysis with R² 0.704 shows that we have a good-fitting model. Hence, we argue that JSS depends on QWL and conclude that negative QWL causes job dissatisfaction and insecurity. We also examined the mediating impact of JSS on QWL and IWP and conclude that the Sobel test, in most cases, provided results higher than 1.98, which is the minimum criterion of having Sobel be significant and effective. Hence, we prove that JSS has a mediating role in QWL and IWP. Finally, we conclude that poor QWL causes job dissatisfaction and eventually reduces organizational efficiency.

1. Introduction

Custodian of the Two Holy Mosques King Sulman bin Abdulaziz, in his speech to Shura on 18 November 2018, precisely emphasized that Crown Prince Muhammad bin Sulman should focus on creating jobs for Saudi youths to achieve the targets of his vision for 2030. While preparing the Saudi workforce for employment in the banking industry, there is a need for the workforce to be ready to contribute toward sustained business growth (SBG). Accordingly, the workforce must understand their quality of work life (QWL) issues for a healthier use of their skills. Between the 1980s and 2006, many studies have shown that QWL has a positive impact on job satisfaction and that eventually improves individual work performance (Mirkamali and Narenji 2008; Armstrong 2006). Before that, Walton (1973) explained that the QWL, with its several dimensions, fundamentally leads workers toward job satisfaction and, consequently, inspires individual work performance (IWP). There is further evidence from the literature that strengthens our stance that QWL motivates individuals to deliver their best (Royuela et al. 2009).

Our study is based on the principles explained above, that is, that QWL factors significantly influence job satisfaction, which eventually motivates individuals to deliver their bests effort in their jobs. We therefore present the following model, that job security and satisfaction act as mediators between QWL and IWP (Figure 1).

Based on the above theoretical model, we present, in the forthcoming Figure 2, comprehensive dimensions and subdimensions of QWL suitable for the Saudi banking system. Section 2 explains the research objectives; Section 2.1 explains the research methodology. Section 3 explains the research design, including: a literature review under Section 3.2; questionnaire development and data collection in Section 3.3; data tabulation and reliability testing in Section 3.4; application of factor analysis in Section 3.4.1; factor loading under Section 3.4.2; gap analysis in Section 3.5; a GQWL comparison between Saudi and non-Saudi banks in Section 3.6; the relationship between QWL and JSS in Section 3.7; and the relationship between GQWL and IWP, with JSS playing a mediating role, in Section 3.8. The discussion and conclusion are presented in Section 4 and Section 4.1, as well as the conflicts of interest statement. Finally, a questionnaire and a list of references are presented at the end of this article.

2. Research Objectives

We applied three different approaches in this study, all aimed at individual work performance. The first approach measures QWL by applying WRKLFQUAL (a tool for measuring the gap between the expected quality of work life and perceived quality of work life) and its impact on job security and satisfaction (Kandasamy and Sreekumar 2009). The second approach presents a correlation between all components of the average gaps in QWL. We further plan to present a regression analysis to determine the impact of GQWL on JSS. The third approach measures the mediating impact of job security and satisfaction on individual work performance. The main objectives of this study are as follows:

• Examine whether or not gaps exist in QWL (GQWL) by applying the WRKLFQUAL model.
• Analyze and identify the correlation between GQWL (gaps in QWL) and job security and satisfaction (JSS)
• Analyze and identify whether JSS has a mediating impact on individual work performance (IWP) in the banking sector of the KSA.

The model presented in Figure 1 was further extended in light of the literature (Section 3.1) to reach our research objectives.

2.1. Research Methodology

To achieve our research objectives, the following methodology was adopted in this study.

• Research design, a literature review, and the development of dimensions and subdimensions for the components of QWL for analysis through the WRKLFQUAL model.
• The development of a questionnaire and data collection under the supervision of a senior academic team member.
• A data profile to explain the characteristics of the sample population.
• Data quality determination through Cronbach’s α analysis.
• Correlation, regression analysis, and extracting factors to study the common variance among components to determine gaps in QWL.
• Determining relations between GQWL (gaps in quality of work life) and JSS.
• Finally, examining the relationship between QWL and IWP, with JSS playing a mediating role, to draw conclusions.

3. Research Design

This research passed through the following stages.

3.1. Stage One: Literature Review

An extensive literature review helped us to understand and put together the dimensions and subdimensions of QWL and to examine their influence on individual work performance. Literature reviews also led us to understand the relevance of QWL factors in the Saudi banking industry. The core issue of this research is to elaborate on the impact of the relationship between an organization and its employees. This concept is not new and has been highlighted by Lawler (2005). However, the application of WRKLFQUAL in the case of Saudi banking provides a new dimension to establish a relationship between Saudi banking management and their employees. In this respect, employee satisfaction requires employees to have certain expectations fulfilled so they will remain loyal to their organizations (Woods 1993; Conlon 2003; McDonald and Hite 2005). In this perspective, Kotzé (2005) provided a very strong notion: “employees who commit themselves fully to achieving the organization’s objectives should also experience a High Quality of Work Life”.

Quality is essential for sustained organizational growth—whether it is service delivery or work life (Spreng and Mackoy 1996; Ishfaq et al. 2020). Accordingly, Saraji and Dargahi (2006) argued that QWL has the same impact on sustained organizational growth as the quality of product or service has. They further argued that employers have to recognize that workforces have lives before and after work that must be taken into consideration. Hence, QWL, with its variety of dimensions, is a focal point of our study. Most researches with reference to QWL concentrate on healthcare services (Cole et al. 2005; Saraji and Dargahi 2006; Nadler and Lawler 1985). Accordingly, studies have mainly focused on QWL factors and their impact on job satisfaction or organizational performance. Among various models, we found Walton’s QWL model (1975). Presented by Timossi et al. (2008), to be quite relevant to our study in measuring the quality of work life. Walton’s (1973) study offers eight evaluation criteria with 27 QWL sub-criteria, which we consider to be very close to our seven WRKLFQUAL dimensions and 28 subdimensions. There are some studies with reference to the banking system that present relations between job satisfaction and QWL dimensions (Buvaneswari 2011; Daljeet 2010). With reference to the service industry, Lau (2000) explored the links between performance, profitability, and quality of work life. We believe that job satisfaction relies on QWL, and many researchers have a similar belief—for example Huselid (1995); Chan et al. (2000); Koys (2001); Ellinger et al. (2002); Mafini and Pooe (2013); and Latif et al. (2015). The same is also argued by Mamedu (2016) with reference to the “perception of Quality of Work-Life and University Goal Attainment”. We also learned from the literature that organizational success greatly relies on employee efforts to improve service quality, and this is consequently achievable with QWL according to employee expectations (Narang and Singh 2016). Therefore, we conclude from the above discussion that QWL, job performance, and profitability are significantly linked. Therefore, profitability can by no means be achieved without the required IWP.

With reference to bank performance, some studies from different angles are worth mentioning here because they support our paradigm that QWL helps justify bank executives being motivated toward risk-taking. Positive QWL can increase bank efficiency and reduce the need for hedging (Belkhir and Boubaker 2013; Boubaker et al. 2022; Samet et al. 2018). Although these papers are in different contexts, nevertheless, they have significant potential in explaining that QWL has a deep impact on bank performance. This is what we are focusing on in the case of Saudi banking performance through QWL, as well as with respect to combining its outcomes with the mediating process.

Now the question arises of how to determine QWL. The solution was presented by Kandasamy and Sreekumar (2009) in their study using WRKLFQUAL as a tool for measuring QWL. Based on gap analysis methodology, Kandasamy and Sreekumar (2009) determined the difference between expected QWL (EQWL) and perceived QWL (PQWL). Their study basically applied the model of Parasuraman et al. (1988), as provided below.

$$\mathrm{SERVQUAL}=\mathrm{CP}-\mathrm{CE}$$

(1)

We adopted the same model of QWL (Equation (1)) in the form of WRKLFQUAL, as follows:

$$\mathrm{WRKLFQUAL}=\mathrm{PQWL}-\mathrm{EQWL}$$

(2)

Here, EQWL is the expected quality of work life, and PQWL is the perceived quality of work life.

Accordingly, the following statistical model is presented (Ishfaq et al. 2020; Kandasamy and Sreekumar 2009) to determine gaps in QWL.

$${\mathrm{G}}_{\mathrm{i}}=\frac{1}{\mathrm{N}}{\displaystyle \sum }_{\mathrm{i}=1}^{\mathrm{N}}\mathrm{PQWL}-\mathrm{EQWL}$$

(3)

where N is the number of items in each dimension.

Having discussed the model and the necessary dimensions of QWL in the literature review, in Figure 2, we present the seven dimensions of QWL and the sub-dimensions (number of items) to be used in the WRKLFQUAL model.

Note that JSS and IWP (Figure 1) have not been included in the WRKLFQUAL analysis, as we plan to determine the impact of gaps in JSS and IWP separately.

Based on the above model of determining QWL, in line with those of SERVQUAL, we plan to apply its outcome to examine the relationship between gaps in QWL and JSS and finally study the mediating role of JSS in IWP. This will provide a new dimension in the theoretical framework of gap analysis and its further implications for organizational performance. This is the major contribution we are making to the literature, that is, that the outcome of gap analysis can by combined to study the mediating role between the quality of service or quality of work life and organizational profitability.

3.2. Stage Two: Questionnaire Development and Data Collection

Based on the literature review, we expanded the dimensions into the subdimensions of QWL (Figure 2), and, accordingly, a questionnaire was developed to collect primary data from employees of the Saudi banking sector. Our focus was on national banks and multinational banks operating in the KSA in order to determine GQWL (gaps in QWL) and their impact on employee work performance. The questionnaire is provided in Supplementary Materials. We ensured that the questionnaire was well understood by adding an Arabic translation so that we could obtain correct answers. The questionnaire was administered by research assistants under the supervision of a senior academic. The data were collected from a sample of 300 out of the 350 targeted banking employees in Jeddah. The response rate was around 86%, which we consider quite satisfactory.

The validity of the questionnaire’s content was confirmed by distributing it to around 10 experts to offer their opinion on whether the respondents would be able to understand and complete the questionnaire correctly. The participants rated the questionnaire items on a Likert scale of 1 to 5, where 5 stands for the highest rating and 1 indicates the lowest rating for the quality of work life (Gržinić 2007).

The collected data were input into SPSS software for both analysis and interpretation. The statistical tests were then carried out to assess the reliability and quality of the data. Finally, a factor analysis was carried out, and gaps in QWL were determined. A profile analysis of the Saudi banking workforce is provided in

Table 1. Demographic characteristics of the sample.

Demographic Variables	Demographic Characteristics	Frequencies in %	Cumulative %
Age	Up to 25 years	11.3	11.3
	26–35 years	34.8	46
	36–50 years	34.4	80.5
	50+	19.5	100
Gender	Male	55.3	55.3
	Female	44.7	100
Nationality	Saudi	76.8	76.8
	Non-Saudi	23.2	100
Marital Status	Single	29.8	29.8
	Married	47.4	77.2
	Widow	7.6	84.8
	Divorced	15.2	100
Education	Grade 12 and below	11.6	11.6
	Graduation	42.7	54.3
	Master	28.8	83.1
	Ph.D.	16.9	100
Job Position	Receptionist	7.3	7.3
	Cashier	7.3	14.6
	Customer service	10.3	24.8
	Online banking	9.3	34.1
	Phone operator	6.6	40.7
	Business account manager	14.6	55.3
	Branch account manager	2	57.3
	Regional office nonexecutive	8.9	66.2
	Junior executive	6.3	72.5
	Executive	7	79.5
	Senior executive	7	86.4
	Middle management position	7	93.4
	Board members/CEOs	6.6	100
Income	<SAR5000	8.3	8.3
	5000–10,000	23.2	31.5
	10,001–15,000	28.5	59.9
	15,001–20,000	23.5	83.4
Bank	Saudi national bank	37.4	37.4
	Multinational in the KSA	19.5	57
	Saudi/foreign JV	16.9	73.8
	Saudi Islamic bank	26.2	100

.

The Saudi workforce is quite sophisticated and expects the best quality from its work environment. Table 1 depicts a fairly balanced demographic structure of the sample. Around 69.2% of the population is between the ages of 26 to 50 years, which means the large sample size is young, vibrant, physically active, and willing to perform well, provided they perceive QWL to be up to their expectations. Male and female employees in the sample almost equally seek positive QWL. A large percentage of the sample comprises Saudi nationals with a good level of education. Similarly, the sample’s position on job levels and income levels, and its presence in Saudi and non-Saudi multinational banking sectors, is fairly normal. This suggests that expectations and perceptions of QWL across the banking sector are highly uniform.

3.3. Stage Three: Data Tabulation and Reliability Testing

The data collected were compiled by making use of SPSS both for analysis and interpretation. Statistical tests were carried out to determine the reliability and the quality of the data. We applied Cronbach’s alpha test in this respect, and the results are shown in

Table 2. Cronbach’s α: Data reliability.

Reliability Statistics
	Cronbach’s Alpha	N of Items
AW	0.802	4
CR	0.798	4
FAC	0.781	4
OC	0.795	4
PWE	0.808	4
RC	0.807	4
TR	0.775	4
All together	0.958	28

.

Table 2 provides a Cronbach’s α score of 0.958, which implies that 95.8% of the sample is free of error (Ishfaq et al. 2020; Parasuraman et al. 1988). The Cronbach’s α is of individual items not being below 0.775 means that each dimension’s data are highly reliable. Further tests removing items from the analysis demonstrate that Cronbach’s α drops if an element is deleted, as shown in

Table 3. QWL dimensions and subdimensions and their reliability tests.

Dimensions	Cronbach’s Alpha for Each Dimension	Cronbach’s Alpha if Item Deleted	Item
AW	0.802	0.779	AW1
		0.736	AW2
		0.74	AW3
		0.752	AW4
CR	0.798	0.755	CR1
		0.758	CR2
		0.734	CR3
		0.744	CR4
FAC	0.781	0.749	FAC1
		0.719	FAC2
		0.711	FAC3
		0.734	FAC4
OC	0.795	0.717	OC1
		0.747	OC2
		0.753	OC3
		0.76	OC4
PWE	0.808	0.772	PWE1
		0.733	PWE2
		0.765	PWE3
		0.765	PWE4
RC	0.807	0.768	RC1
		0.74	RC2
		0.764	RC3
		0.762	RC4
TR	0.775	0.711	TR1
		0.728	TR2
		0.728	TR3
		0.717	TR4

. This means each dimension’s elements are significantly reliable and must not be removed from the analysis.

3.4. Stage Four: Analysis and Interpretation of Results

Having tabulated and interpreted the reliability of the data, further analysis was carried out in the light of objectives of the research. The results are presented in the following sections.

3.4.1. Application of Factor Analysis

In many SERVQUAL applications, we observed that factor analysis was used. Main researchers argue that data collected on a Likert scale do not provide statistical inferences as averages or standard deviations. Applications of factor analysis, by way of factor loading, are used to obtain weighted scores in a smaller format (Siddique et al. 2013).

3.4.2. Factor Loading

Normally, the number of factors extracted is the same as the total number of variables (Hair et al. 1998); nonetheless, only those factors whose eigenvalues ≥1 were extracted. Following

Table 4. Factors extracted (principal components).

	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Factor 6	Factor 7	% of Variance	Cumulative %
PWE1	0.776							46.96	46.96
PWE2	0.835							4.665	51.625
PWE3	0.788							3.77	55.396
PWE4	0.788							3.659	59.055
OC1		0.83						3.143	62.198
OC2		0.782						2.848	65.046
OC3		0.777						2.742	67.788
OC4		0.763						2.592	70.381
RC1			0.781					2.242	72.622
RC2			0.823					2.193	74.815
RC3			0.789					2.113	76.928
RC4			0.791					1.982	78.91
TR1				0.787				1.91	80.82
TR2				0.762				1.747	82.567
TR3				0.763				1.705	84.271
TR4				0.779				1.662	85.933
CR1					0.778			1.579	87.512
CR2					0.773			1.463	88.976
CR3					0.81			1.435	90.411
CR4					0.796			1.378	91.789
FAC1						0.745		1.353	93.142
FAC2						0.792		1.267	94.409
FAC3						0.804		1.131	95.539
FAC4						0.769		0.998	96.537
AAW1							0.747	0.959	97.496
AAW2							0.816	0.924	98.42
AAW3							0.811	0.809	99.229
AAW4							0.793	0.771	100

presents the factors extracted.

3.5. Stage Five: Gap Analysis

Lassar et al. (2000) argued in their study of “service quality perspectives and satisfaction” that consumer satisfaction relies on the quality of service or product. Here, we put forward the same paradigm with a small modification, that is, that employee job satisfaction is conditional on the quality of work life. Accordingly, based on Equation (3), we present gaps in the quality of work life (GQWL) in

Table 5. Gaps in the quality of work life (GQWL) in all dimensions and subdimensions of QWL (all banks).

S.N.	Dimensions and Subdimensions	Employees’ Perceptions (EP)	Employees’ Expectations (EE)	GAP
Physical Work Environment (PWE)
PWE1	Good and highly motivating	4.003311258	5	−0.996688742
PWE2	Time off for personal matters	3.907284768	5	−1.092715232
PWE3	Opportunities to enhance personal abilities	3.913907285	5	−1.086092715
PWE4	Enough information to discharge responsibilities	3.90397351	5	−1.09602649
	Total	15.72847682	20	−4.271523179
	Average	−4.271523179	5	−1.067880795
Organizational Culture (OC)
OC1	Cooperation between all departments	3.993377483	5	−1.006622517
OC2	Proud of my bank	3.92384106	5	−1.07615894
OC3	Good wage policies	3.933774834	5	−1.066225166
OC4	Effective communication on new changes	3.894039735	5	−1.105960265
	Total	15.74503311	20	−4.254966887
	Average	−4.254966887	5	−1.063741722
Relations and Cooperation (RC)
RC1	Harmonious relationship with colleagues	3.966887417	5	−1.033112583
RC2	Strong sense of belonging in my bank	3.867549669	5	−1.132450331
RC3	Good relationship between managers and employees	3.88410596	5	−1.11589404
RC4	Good support from team member	3.927152318	5	−1.072847682
	Total	15.64569536	20	−4.354304636
	Average	−4.354304636	5	−1.088576159
Training and Development (TR)
TR1	Harmonious relationship with colleagues	3.88410596	5	−1.11589404
TR1	Strong sense of belonging in my bank	3.834437086	5	−1.165562914
TR3	Good relationship between managers and employees	3.917218543	5	−1.082781457
TR4	Good support from team members	3.894039735	5	−1.105960265
	Total	15.52980132	20	−4.470198675
	Average	−4.470198675	5	−1.117549669
Compensation and Rewards (CR)
CR1	Adequate and fair	3.930463576	5	−1.069536424
CR2	Salary according to responsibilities	3.874172185	5	−1.125827815
CR3	Rewards according to performance	3.940397351	5	−1.059602649
CR4	Promotions handled fairly	3.90397351	5	−1.09602649
	Total	15.64900662	20	−4.350993377
	Average	−4.350993377	5	−1.087748344
Facilities (FAC)
FAC1	Fringe benefits are good	3.850993377	5	−1.149006623
FAC2	Good social security benefits	3.907284768	5	−1.092715232
FAC3	Good transporting facilities	3.956953642	5	−1.043046358
FAC4	Safety measures are good	3.894039735	5	−1.105960265
	Total	15.60927152	20	−4.390728477
	Average	−4.390728477	5	−1.097682119
Autonomy at Work (AAW)
AAW1	I am given tasks where I do my best	3.966887417	5	−1.033112583
AAW1	I have freedom to use my skills and abilities	3.927152318	5	−1.072847682
AAW1	I can work partly from home	3.986754967	5	−1.013245033
AAW1	I am always ready to take additional responsibilities	3.913907285	5	−1.086092715
	Total	15.79470199	20	−4.205298013
	Average	−4.205298013	5	−1.051324503

.

It is clear from Table 5 that the dimensions and subdimensions have negative gaps that can cause employee job dissatisfaction and eventually negatively influence individual work performance (IWP). Although we can say that gaps are quite trivial. they indicate that management should focus on issues and make efforts to improve their employees’ quality of work life.

Table 6. Unweighted gap scores.

Dimension	Score
PWE	−1.067880795
OC	−1.063741722
RC	−1.088576159
TR	−1.117549669
CR	−1.087748344
FAC	−1.097682119
AAW	−1.051324503
Mean	−1.082071902

presents unweighted average scores showing almost all areas of work life need some attention.

This analysis can be further refined by calculating weighted gap scores, as provided in

Table 7. Weighted average gap score for each dimension.

Weighted Score
Dimension	Unweighted Score	Importance Weight	Weighted Score
PWE	−1.067880795	14.3374585	−15.31069657
OC	−1.063741722	14.35255056	−15.26740684
RC	−1.088576159	14.26199819	−15.52527121
TR	−1.117549669	14.15635376	−15.82042845
CR	−1.087748344	14.2650166	−15.51674819
FAC	−1.097682119	14.22879565	−15.61869457
AAW	−1.051324503	14.39782674	−15.13678805
Mean	−1.082071902	100	−15.45657627

.

The weighted average scores in Table 7 suggest that employees give almost equal weight to all dimensions of QWL; therefore, gaps in any dimension could cause employee dissatisfaction, eventually having a negative impact on IWP. Keeping a close eye on weighted scores, we see that TR, with a weighted score of 15.8%; FAC, with a weighted score of 15.6%; CR, with a weighted score of 15.5%; and RC (relation and co-operation) are considered more important by employees. Hence, we accept that there are gaps between expected QWL and perceived QWL—as mentioned in the first objective of this research.

3.6. GQWL Comparison between Saudi and Multinational Banks Operating in the KSA

One important aspect of this research was to compare GQWL between Saudi and non-Saudi banks operating in the Kingdom.

Table 8. Comparison of GQWL between Saudi and non-Saudi banks.

Saudi Banks	PWE	OC	RC	TR	CR	FAC	AAW	AV.
Mean	−0.948	−0.918	−0.984	−0.971	−0.935	−1.004	−0.918	−0.954
Standard Deviation	0.812	0.733	0.770	0.732	0.758	0.805	0.746	0.765
Variance	0.660	0.537	0.594	0.536	0.575	0.648	0.556	0.587
Skewness	−1.387	−1.164	−0.994	−0.999	−1.172	−1.078	−1.216	−1.144
Kurtosis	2.287	1.946	1.280	1.294	1.679	1.107	1.623	1.602
One-Sample t-test	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Non-Saudi Banks
Mean	−1.277	−1.318	−1.270	−1.373	−1.355	−1.261	−1.284	−1.306
Standard Deviation	0.904	0.878	0.884	0.847	0.875	0.811	0.867	0.867
Variance	0.817	0.770	0.781	0.718	0.765	0.658	0.752	0.752
Skewness	−0.914	−1.043	−1.027	−0.814	−0.902	−1.162	−1.116	−0.997
Kurtosis	0.745	1.249	0.942	0.864	0.499	1.666	1.096	1.009
One-Sample t-test	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

presents such a comparison.

Table 8 presents one-sample t-tests for both Saudi and non-Saudi banks to determine whether any differences in the mean gaps of all dimensions in both systems exist. From Table 8 we find that the mean gap of each dimension in the Saudi banking system is significantly smaller than that of the multinational banks. Standard deviation, variance, and skewness are also significantly different. Therefore, we conclude that QWL in Saudi banks is relatively better compared to non-Saudi multinational banks.

3.7. Relationship between QWL and JSS

The second objective of this research was to analyze and identify a correlation between GQWL (gaps in QWL) and job security and satisfaction (JSS). In this context, Kermansaravi et al. (2015), citing Mirkamali and Narenjisani 2008 and Armstrong 2006, argued that, from 1980 to 2006, most research studies found a positive relationship between QWL and job satisfaction. Royuela et al. (2009) argued that “QWL is one of the most important factors for human motivating and improving job satisfaction”. There are many studies around the globe about job satisfaction and QWL; however, studies determining the relationship between QWL and job satisfaction in Saudi Arabia are quite limited. Accordingly, we conducted a regression analysis between QWL and JSS and present a correlation between the seven dimensions by considering them as predictors in

Table 9. Correlation between the seven predictors (dimensions of QWL).

	AveGapPWE	AveGapOC	AveGapRC	AveGapTR	AveGapCR	AveGapFAC	AveGapAAW
AveGapPWE	1	0.757887739	0.742597	0.693476	0.711255	0.656863	0.625494
AveGapOC		1	0.790814	0.776557	0.758778	0.675258	0.671372
AveGapRC			1	0.706163	0.765989	0.684879	0.65447
AveGapTR				1	0.772786	0.734119	0.696396
AveGapCR					1	0.811742	0.716853
AveGapFAC						1	0.73529
AveGapAAW							1

.

Table 9 shows a correlation between the average gaps of all dimensions and that they are significantly and positively correlated, ranging from 0.625 to 0.812. Further regression analysis results are presented in

Table 10. Regression analysis.

Regression Statistics
Multiple R	0.838833
R Square	0.703641
Adjusted R Square	0.696585
Standard Error	0.453444
Observations	302
ANOVA
	df	SS	MS	F	Significance F
Regression	7	143.5249	20.50356	99.72013	7.73 × 10−74
Residual	294	60.44964	0.205611
Total	301	203.9745
	Coefficients	Standard Error	t Stat	p-Value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	0.337	0.111	3.041	0.003	0.119	0.555	0.119	0.555
AveGapPWE	0.154	0.064	2.389	0.018	0.027	0.280	0.027	0.280
AveGapOC	−0.141	0.064	−2.194	0.029	−0.268	−0.015	−0.268	−0.015
AveGapRC	0.080	0.059	1.351	0.178	−0.037	0.196	−0.037	0.196
AveGapTR	0.164	0.061	2.707	0.007	0.045	0.284	0.045	0.284
AveGapCR	0.226	0.069	3.264	0.001	0.090	0.362	0.090	0.362
AveGapFAC	0.255	0.076	3.379	0.001	0.107	0.404	0.107	0.404
AveGapAAW	0.295	0.052	5.659	0.000	0.192	0.398	0.192	0.398

.

The above results provide robust evidence to support applying WRKLFQUAL, a gap analysis tool for measuring quality of work life (Kandasamy and Sreekumar 2009). The multiple regression equation applied in our case is:

$$Y_t=b_0+b_1{X}_{1t}+b_2{X}_{2t}+\dots +b_k{X}_{kt}+e_t$$

where Y_t is JSS (job security and satisfaction), b₀ is Y intercept, b is the coefficient of regression, and e_t is an error of the estimate. An R² of 0.704 suggests that we have a good-fitting model. Thus, we can conclude that JSS is an inverse function of the average gaps in all dimensions, as follows:

JSS = ∫(AveGapPWE, AveGapOC, AveGapRC, AveGapTR, AveGapCR, AveGapFAC, AveGapAAW)

Since the gap scores are negative, we thus infer that, as gaps increase (dimensions move away from zero towards negativity), the JSS decreases. If all gap scores approach zero, then the quality of work life will result in job satisfaction. Hence, we accept that gaps have a strong impact on job security and satisfaction—the second objective. In fact, JSS increases as gaps decrease.

3.8. Relationship between GQWL and IWP, JSS Playing Mediating Role

The final objective of this research was to look at the relationship between GQWL and IWP, with JSS playing a mediating role. Many studies have provided evidence that the relationship between individual job satisfaction and individual work performance does exist (Petty et al. 1984; Kermansaravi et al. 2015; Bakotič 2016; Shmailan 2016). Our research is unique in the sense that many behavioral scientists use mediating studies in their behavioral science research, for example, the mediating role of job satisfaction between personality and citizen behavior (Ilies et al. 2009) and the mediating role of job satisfaction toward the organizational commitment of employees in the public sector (Ingsih et al. 2020).

Our study uses a different approach by combining gap results to mediate the process. Since the quality of work life has an impact on IWP to a great extent, we will therefore briefly explain how our research found that JSS plays a mediating role between GQWL and IWP. The Baron and Kenny (1986) protocol explains the testing of mediating effects, and the same was applied in this research. The theoretical model in this respect is presented in Figure 1.

The first step in this respect was to carry out regression analysis, as presented in

Table 11. Model Summary.

Model		R	R Square	Adjusted R Square	Std. Error of the Estimate
1		0.871 a	0.759	0.752	0.31612
ANOVA b
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	92.169	8	11.521	115.291	0.000 a
	Residual	29.280	293	0.100
	Total	121.449	301

^a Predictors: (Constant), AveGapAAW, AveGapPWE, AveGapTR, AveGapRC, AveGapFAC, AveGapJS, AveGapCR, AveGapOC; ^b dependent variable: IWPall.

.

From Table 11, it can be seen that R² is 0.759, slightly higher than R² 0.704 in Table 10. This suggests that we have a good-fitting model. F value 115.291 is also substantially high, with a significance level of 0.000. Here it is worth discussing that we used eight predictors, with IWP being dependent on eight of those predictors, including JSS.

Table 12. Coefficients.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	−0.141	0.033		−4.219	0.000
	AveGapPWE	0.006	0.036	0.009	0.179	0.858
	AveGapOC	0.024	0.045	0.030	0.521	0.603
	AveGapRC	0.083	0.041	0.108	20.020	0.044
	AveGapTR	0.097	0.043	0.122	20.270	0.024
	AveGapCR	0.101	0.043	0.131	20.344	0.020
	AveGapFAC	0.081	0.042	0.104	10.926	0.055
	AveGapJS	0.125	0.041	0.162	30.079	0.002
	AveGapAAW	0.255	0.038	0.326	60.654	0.000

suggests that all predictors are significant except AveGapPWE, AveGapOC, and AveGapFAC, their significance values being 0.858, 0.603, and 0.055.

Having completed this process, we now turn toward the mediating impact of JSS between GQWL and individual work performance (IWP). As mentioned earlier, there are a number of studies proving the relationship between individual job satisfaction and individual work performance; here, our purpose is to make use of WRKLFQUAL through gap analysis and statistically prove that quality of work life influences IWP, with job satisfaction playing a mediating role.

Baron and Kenny (1986) proposed a three-step procedure to determine the role of the mediating element. In our case, we have eight predictors, i.e., the average gaps (GQWL) presented in Table 5. It would be difficult to perform a mediating procedure on all eight predictors and dependent variables, i.e., IWP (individual work performance). However, we took the first three predictors to explain how the mediating role can be identified. Accordingly, we found that JSS (job security and satisfaction) significantly mediates the gap in quality of work life and individual work performance. Some of our analyses are presented as follows.

The first analysis was to find the mediating role of JSS in AveGapPWE and IWP via that Sobel test, making use of SPSS. The result of Sobel test is presented in Figure 3.

There is a clear indication from the above statistics that the Sobel test (2.991) result was higher than 1.98, which is the minimum criterion for Sobel to be significant and effective. Furthermore, this probability value was also less than 0.05. Thus, the null hypothesis was rejected and a significant indirect effect was found. This indicates that AveGapJS has a mediating role in AveGapPWE and IWP.

Similarly, the second analysis was to find the mediating role of JSS in AveGapOC and IWPall. The result of Sobel test is presented in Figure 4.

The above statistics indicated that the Sobel test result (2.983) was higher than 1.98, which is the minimum criterion for Sobel to be significant and effective. Furthermore, this probability value was also less than 0.05. Thus, the null hypothesis was rejected and a significant indirect effect was found. The above result explains that AveGapJS has a mediating role in AveGapOC and IWPall.

The third analysis was to find the mediating role of JSS in AveGapFAC and IWP. The result of Sobel test is presented in Figure 5.

The study applied the Sobel test to measure the mediating role of AveGapJS in AveGapFAC and IWPall. The above statistics of the Sobel test show that the result, 3.014, was higher than 1.98, which is a clear indication of mediation. Thus, we rejected the null hypothesis and accepted the alternative hypothesis.

We have presented a Sobel test for three types of analysis, and we could have continued Sobel testing for all eight predictors; however, we inferred that JSS would show clear indications of mediation for the remaining predictors and felt no need to carry out further analysis.

4. Discussion and Conclusions

A study of the literature brings us to the conclusion that the work of the proponents of the QWL provides a theoretical framework for this study. Accordingly, we presented the results of our analyses regarding the quality of work life and its impact on individual work performance from three different approaches. In the first approach, we applied WRKLFQUAL in line with SERVQUAL in order to determine GQWL, and we found that all dimensions of the quality of work life proved to be negative (see Table 5). This demonstrated that gaps in the expected and perceived quality of work life results in job dissatisfaction. Kotzé (2005) strongly supports our approach, in that GQWL causes dissatisfaction that demotivates employees from committing fully to achieving organizational goals. This is proven in the literature. We further carried out a comparison of WRKLFQUAL between Saudi banks and the multinational banks operating in Saudi Arabia. This comparison shows that the quality of work life in Saudi banks is slightly better than that of multinational banks.

The second objective was to analyze and identify the correlation between GQWL (gaps in QWL) and job security and satisfaction (JSS). Table 9 presents a significant correlation between the seven dimensions of QWL, ranging from 0.625494 to 0.811742. This implies that all dimensions are highly correlated, further suggesting that the movement of variables is strongly associated. Taking this analysis further, we carried out a regression analysis between JSS (dependent variable) and GQWL (predictors) with an R² of 0.704. JSS decreases as GQWL increases. This supports our finding for the second objective, in that increases in GQWL decrease employee satisfaction.

The third objective of this research was to identify whether GQWL has any impact on IWP (individual work performance), with JSS playing a mediating role. There are many studies that prove a relationship between individual job satisfaction and individual work performance (Petty et al. 1984; Kermansaravi et al. 2015; Bakotič 2016; Shmailan 2016). In our study, we presented the results of statistical procedures to determine a relationship between the average gaps of QWL and IWP, with JSS playing a mediating role (Ali and Zia-ur-Rehman 2014; Baron and Kenny 1986).

This study is unique in its scientific approach and provides robust results indicating that the quality of work life is a vital element of employees’ satisfaction and, ultimately, ensures a high level of individual work performance. The application of WRKLFQUAL with reference to the banking sector of the KSA is a new dimension that provides methods of determining gaps between perceived and expected quality of work life. This is a new addition to studies carried out in line with those of gap analysis—traditionally used to evaluate service or product quality—and its impact on consumer satisfaction. The practical implications of this study provide guidelines for Saudi banking management, academics, and management practitioners to identify areas of weaknesses causing poor work performance. The correlation between all dimensions of GQWL and the regression between JSS and the average gaps in QWL are rarely applied in research to determine a significant association among variables. The third objective of this study was a unique attempt to determine the relationship between average gaps in QWL and individual work performance, with JSS playing a mediating role. This part of the study suggests that JSS mediates the relationship between GWQL and IWP, meaning that a higher JSS results in higher IWP. The unique matter here is combining the results of WRKLFQUAL with the mediating process under the theoretical model presented in Figure 1.

4.1. Conclusions

From our analysis, we have proved that the quality of work life (QWL), job satisfaction, and individual work performance are lynchpins for organizational performance and sustained business growth (SBG). Data analysis through Cronbach’s α suggests that 95% of the sample is free of error. The factors extracted with factor loading between 0.745 and 0.835 show that all components have quite a high common variance. The WRKLFQUAL model applied in lines with SERVQUAL yielded the result that all dimensions and subdimensions have negative gaps ranging from −0.997 to −1.149, hence confirming that almost all dimensions and subdimensions are negatively away from zero. This proves that there is poor quality of work life in the Saudi banking system. A comparison of QWL between Saudi and non-Saudi multinational banks explains that the Saudi banking system has a slight edge over the QWL of non-Saudi multinational banks.

Further analysis of the correlation shows that all seven predictors are highly correlated, ranging from 0.625 and 0.812. Similarly, the regression of the dependent variable, JSS, over seven predictors is R² 0.704, meaning that we have a good-fitting model. Hence, we have proved that JSS depends on QWL; therefore, we conclude that negative QWL causes job dissatisfaction and insecurity.

Finally, we studied the mediating impact of JSS on QWL and IWP and conclude that the Sobel test, in most cases, yielded a result higher than 1.98, which is the minimum criterion for Sobel to be significant and effective. In this respect, we claim that poor QWL causes job dissatisfaction and eventually reduces IWP with a negative impact on organizational efficiency.

The scope of this study is quite deep and wide, and it can be utilized by the banking industry, health services, the artifact industrial sector, the traveling and tourism industry, and the educational sector to address issues of poor work performance. This study presents a new theoretical framework for behavioral scientists to include WRKLFQUAL as part of their research, as we believe that if QWL is poor, then it will have a negative impact on profitability, organizational performance, and efficiency. Likewise, if organizational performance analysts ignore the mediating role of employee satisfaction in various components of sustained business growth, their studies will have serious limitations.

In the case of our study, there were certain limitations with respect to resources and time shortages that restricted us to collecting data on a larger scale and generalizing their results. However, we are opening up doors for many researchers to utilize our approach in future studies.