Keywords

Communication Satisfaction, Accounting, Finance, Engagement, Quality of Work-Life, Job Performance, Hospital.

Introduction

Improving the health care system of a country can result in to better economic and social development. This is true because improving the health population can be directly related to positive economic growth as more healthy people will be engaged in efficient activities. At the same time, higher healthcare affects quality of life more than any other service sector (Abdallah et al., 2021; Audi et al., 2017; Elsinga et al., 2017; Sosnowski et al., 2017).

The rapidly growing middle class, with its growing purchasing power, has resulted in a well-documented increase in demand for healthcare services in emerging markets, particularly in Middle Eastern countries, where medical tourists' focus has shifted recently. Jordan's healthcare outlook has been significantly altered by changes in the demographic and sociocultural environment, increased health awareness, and information technology. With more informed and educated patients, the quality of healthcare has become a critical component of the Jordanian healthcare industry in recent years. While it is critical to continuously improve and manage service quality, cost containment remains a significant challenge for the majority of Jordan's healthcare providers (Jalghoum & Khasawneh, 2016; Nazer & Tuffaha, 2017; Yousef et al., 2021).

Hospitals have long been a place of care for the general public, providing both curative and preventive care. Patient outcomes and patient satisfaction are used to assess the quality of care in hospitals. A person's sentiments of joy or disappointment as a result of comparing a product's or service's perceived performance (or outcome) in relation to his or her expectations are referred to as communication satisfaction (Ghaith, 2020; Kotler, 2000). In Jordan, several studies have tried to find factors that affect employees’ performance in hospital. Most of these studies were concerned about employees performance when they interact with patients (Kieft et al., 2014; Shantz et al., 2016). This study assessed factors affecting staff performance about a healthcare facility in the public hospitals in Jordan.

Hypotheses Development

The primary objective of this thesis is to provide insight into the employee’s initiative of using organizational communication satisfaction, employee engagement, and quality of work-life in developing relationship with employees, and their effects on the adoption of job performance in Jordanian public hospitals.

Organizational Communication Satisfaction and Employee Engagement

Organizations and supervisors who engage with their employees on a regular basis encourage social gratitude, and trust (Blau, 2017; Emerson, 1976; Ruck & Welch, 2012). Employee engagement and corporate communication are thought to be linked through social exchange, in which employees feel obligated to repay the favorable benefits they get (Ghaith & Mutia, 2019; Saks, 2006). The act of reciprocity characterizes favorable exchanges based on duty (Cropanzano & Mitchell, 2005). Duck (1994), agrees with this assessment and adds that organizational communication is a critical component of all trade interactions. Furthermore Rich et al. (2010) argue that honest, courteous, polite, and dignified communication inside a business is likely to play an essential role in generating optimal employee engagement. The following hypotheses have been formed based on these considerations.

H1a: Communication climate has significant positive relationship with intellectual engagement.

H1b: Communication climate has significant positive relationship with social engagement.

H1c: Communication climate has significant positive relationship with affective engagement.

H2a: Superior subordinate communication has significant positive relationship with intellectual engagement in Jordanian public hospitals.

H2b: Superior subordinate communication has significant positive relationship with social engagement in Jordanian public hospitals.

H2c: Superior subordinate communication has significant positive relationship with affective engagement in Jordanian public hospitals.

H3a: Organizational integration has significant positive relationship with intellectual engagement.

H3b: Organizational integration has significant positive relationship with social engagement.

H3c: Organizational integration has significant positive relationship with affective engagement.

H4a: Media quality has significant positive relationship with intellectual engagement.

H4b: Media quality has significant positive relationship with social engagement.

H4c: Media quality has significant positive relationship with affective engagement.

H5a: Horizontal communication has significant positive relationship with intellectual engagement.

H5b: Horizontal communication has significant positive relationship with social engagement.

H5c: Horizontal communication has significant positive relationship with affective engagement.

H6a: General organizational perspective has significant positive relationship with intellectual engagement.

H6b: General organizational perspective has significant positive relationship with social engagement.

H6c: General organizational perspective has significant positive relationship with affective engagement.

H7a: Feedback has significant positive relationship with intellectual engagement.

H7b: Feedback has significant positive relationship with social engagement.

H7c: Feedback has significant positive relationship with affective engagement.

Employee Engagement and Job Performance

According to the study, there are both direct and indirect links between engagement and performance. In terms of direct relationships, individual and group engagement has been connected to both organizational and employee success (Buckingham & Coffman, 2014; Macey et al., 2011).

Employee engagement has been shown to be a strong predictor of positive organizational outcomes like customer satisfaction, retention, productivity, and profitability (Buckingham & Coffman, 2014). Macey et al. (2011), found that the top 25% of an engagement index had a greater return on assets, profitability, and more than double the shareholder value than the bottom 25% in a study of 65 firms in various industries. According to these findings, there is a direct link between involvement and performance. Many businesses fail owing to a lack of commitment, as a dedicated team is essential for effective job performance (Osei et al., 2017; Yousef et al., 2021). Furthermore, it is obvious that high employee performance helps organizational success regardless of the industry of work (Harter et al., 2002; Vosburgh, 2005). It's worth noting, though, that motivation is difficult to measure and must be inferred from behavior and body language, as well as responses (Ghaith et al., 2018; Sonsale, 2017). This makes evaluating performance difficult.

H8a: Intellectual engagement has significant positive relationship with job performance.

H8b: Social engagement has significant positive relationship with job performance.

H8c: Affective engagement has significant positive relationship with job performance.

Employee Engagement as a Mediator between Organizational Communication Satisfaction and Job Performance

According to the study, there are both direct and indirect links between engagement and performance. In terms of direct relationships, employee and organizational success have both been related to individual and group engagement (Aktar & Pangil, 2017; Anitha, 2014; Sendawula et al., 2018; Yousef et al., 2021).

The findings of a study by Xanthopoulou et al. (2008), supported the premise that involvement as a mediator explained links between antecedents and performance outcomes better than other factors (e.g., self-efficacy). They looked at whether self-efficacy and work engagement were factors in predicting job performance. Self-efficacy did not influence the association between colleague support and performance, but engagement did, according to their findings. Engagement mediated not just the association between colleague support and performance, but it also the relationship between self-efficacy and job performance. As a result, Xanthopoulou et al. study supported Rich et al. (2010) argument that participation has a higher predictive value as a mediator and can help businesses acquire a competitive advantage.

Despite the fact that many studies have found a link between employee engagement and organizational performance (Harter et al., 2002), There is a scarcity of theory and empirical evidence on the role of involvement in generating competitive advantages for businesses (Rich et al., 2010), According to academics, engagement has higher value as a mediator (e.g., Rich et al., 2010; Xanthopoulou et al., 2008).

H9a: Intellectual engagement has a mediating influence on the relationship between communication climate and job performance.

H9b: Social engagement has a mediating influence on the relationship between communication climate and job performance.

H9c: Affective engagement has a mediating influence on the relationship between communication climate and job performance.

H10a: Intellectual engagement has a mediating influence on the relationship between superior subordinate communication and job performance.

H10b: Social engagement has a mediating influence on the relationship between superior subordinate communication and job performance.

H10c: Affective engagement has a mediating influence on the relationship between superior subordinate communication and job performance.

H11a: Intellectual engagement has a mediating influence on the relationship between organizational integration and job performance.

H11b: Social engagement has a mediating influence on the relationship between organizational integration and job performance.

H11c: Affective engagement has a mediating influence on the relationship between organizational integration and job performance.

H12a: Intellectual engagement has a mediating influence on the relationship between media quality and job performance.

H12b: Social engagement has a mediating influence on the relationship between media quality and job performance.

H12c: Affective engagement has a mediating influence on the relationship between media quality and job performance.

H13a: Intellectual engagement has a mediating influence on the relationship between horizontal communication and job performance.

H13b: Social engagement has a mediating influence on the relationship between horizontal communication and job performance.

H13c: Affective engagement has a mediating influence on the relationship between horizontal communication and job performance.

H14a: Intellectual engagement has a mediating influence on the relationship between general organizational perspective and job performance.

H14b: Social engagement has a mediating influence on the relationship between general organizational perspective and job performance.

H14c: Affective engagement has a mediating influence on the relationship between general organizational perspective and job performance.

H15a: Intellectual engagement has a mediating influence on the relationship between feedback and job performance.

H15b: Social engagement has a mediating influence on the relationship between feedback and job performance.

H15c: Affective engagement has a mediating influence on the relationship between feedback and job performance.

Quality of Work-Life as a Moderator between Organizational Communication Satisfaction and Employee Engagement

Work has always played a significant role in the lives of people. It is widely acknowledged that work has an impact on people's emotional states and well-being. Today, quality of work life (QWL) is regarded as an important aspect of overall life satisfaction. Furthermore, a high QWL is necessary for businesses to recruit and retain employees (Wallapa Boonrod, 2009). When a company provides a high-quality work environment for its employees, it improves the company's image in terms of attracting and retaining people. This is significant because it implies that businesses can provide employees with a suitable working environment (Noor & Abdullah, 2012). Many elements influence QWL, including adequate and fair remuneration, safe and healthy working conditions, and social integration in the workplace, which allows an individual to grow and use all of his or her abilities (Gupta & Sharma, 2011).

Quality of Work Life (QWL) is a philosophy and collection of principles that claims that people are an organization's most valuable resource because they are dependable, accountable, and capable of making a substantial contribution, and that they should be treated with dignity and respect (Rose et al., 2006; Tabassum et al., 2011). Quality of working life is a multifaceted concept that is a way of thinking about people, work, and how it is organized (Hsu & Kernohan, 2006). Quality of work life has a significant impact on organizational performance and employee motivation (Gupta & Sharma, 2011; Yousef et al., 2021). Employees' mental perceptions of physical and psychological desirability in the workplace are referred to as Quality of work life. Quality of work life is concerned with the well-being of employees at work and is distinct from the topic of job satisfaction. Domain of quality of work life has an impact on employees' job happiness as well as their lives outside of work, such as their family, leisure, and social demands. When employees' requirements at work are not met, they are likely to feel a lot of work stress, which has negative effects for employee well-being and job performance (Emadzadeh et al., 2012).

The majority of studies look at the link between QWL and variables like job satisfaction, organizational commitment, job performance, turnover intention, labor relations, and so on. Which are vital in defining any industrial organization's overall well-being. Despite this, there is a scarcity of empirical evidence linking QWL to employee job satisfaction. Work engagement is a motivational concept that describes the active deployment of personal resources to tasks linked with a job function (Christian et al., 2011). Pleasant organizational behavior principles and positive emotions have been more prominent in recent efforts to improve organizational performance. Work engagement has risen to prominence as the most popular positive organizational notion, particularly among consultants (Burke et al., 2009). As a result, the purpose of this study is to learn more about these issues by examining the link between QWL and work engagement in the hospital context.

H16a: Quality of work-life has a moderating influence on the relationship between communication climate and intellectual engagement.

H16b: Quality of work-life has a moderating influence on the relationship between communication climate and social engagement.

H16c: Quality of work-life has a moderating influence on the relationship between communication climate and affective engagement.

H17a: Quality of work-life has a moderating influence on the relationship between superior subordinate communication and intellectual engagement.

H17b: Quality of work-life has a moderating influence on the relationship between superior communication and social engagement.

H17c: Quality of work-life has a moderating influence on the relationship between superior subordinate communication and affective engagement.

H18a: Quality of work-life has a moderating influence on the relationship between organizational integration and intellectual engagement.

H18b: Quality of work-life has a moderating influence on the relationship between organizational integration and social engagement.

H18c: Quality of work-life has a moderating influence on the relationship between organizational integration and affective engagement.

H19a: Quality of work-life has a moderating influence on the relationship between media quality and intellectual engagement.

H19b: Quality of work-life has a moderating influence on the relationship between media quality and social engagement.

H19c: Quality of work-life has a moderating influence on the relationship between media quality and affective engagement.

H20a: Quality of work-life has a moderating influence on the relationship between horizontal communication and intellectual engagement.

H20b: Quality of work-life has a moderating influence on the relationship between horizontal communication and social engagement.

H20c: Quality of work-life has a moderating influence on the relationship between horizontal communication and affective engagement.

H21a: Quality of work-life has a moderating influence on the relationship between general organizational perspective and intellectual engagement.

H21b: Quality of work-life has a moderating influence on the relationship between general organizational perspective and social engagement.

H21c: Quality of work-life has a moderating influence on the relationship between general organizational perspective and affective engagement.

H22a: Quality of work-life has a moderating influence on the relationship between feedback and intellectual engagement.

H22b: Quality of work-life has a moderating influence on the relationship between feedback and social engagement.

H22c: Quality of work-life has a moderating influence on the relationship between feedback and affective engagement.

Research Method

The hypothesised variables and their correlations in the model were derived from previous literature on models and theories, as well as the above-mentioned information. Figure 1 depicts the model's proposed conceptual framework.

Furthermore, because the measurement technique has a higher degree of external dependability, the results and significant discoveries can be generalized and used to a wide range of situations (Johari et al., 2019). When PLS SEM is used as the major technique of analysis, the data acquired using a quantitative approach are regarded as appropriate and suitable when a structured questionnaire design is used (Hair et al., 2018). As a result, for this study, a quantitative research design geared toward PLS SEM is acceptable.

The data was collected by delivering a self-administered questionnaires “in-person” from Jun 2020 till March 2021 to nurses in Jordanian public hospitals. The number of the distributed questionnaires was 500 and the number of the returned sets is 397 of which 270 responses were considered suitable for the analysis. According to Tabachnick et al. (2007), Krejcie & Morgan (1970) the sample size was seen as sufficient. Compared to the relevant literature the 69% response rate of this study is considered very good (Baruch & Holtom, 2008).

Convergent Validity

Convergent validity is used to determine the degree of correlation between measures within a single construct (Sekaran & Bougie, 2010). Convergent validity should be established by factor loading, average variance extracted (AVE), and the composite reliability (CR) (Hair et al., 2014). The AVE, which is the mean variance extracted for the items loaded on a construct was greater than and equal to the standard value of 0.5. (Hair et al., 2010). As a result of this investigation, it was determined that the AVE of all constructions meets the required cut-off value. Additionally, the findings of this investigation revealed a composite reliability range of 0.89 to 0.99, exceeding the cut-off value of 0.7. (Nunnally, 1978). Refer to table 1.

Table 1 Convergent Validity
Variables	Items	Loadings	Cronbach's Alpha	CR	AVE
AE	AE1	0.809	0.849	0.91	0.771
	AE2	0.917
	AE3	0.903
CC	CC1	0.851	0.905	0.929	0.724
	CC2	0.87
	CC3	0.885
	CC4	0.811
	CC5	0.836
FB	FB1	0.9	0.929	0.949	0.824
	FB2	0.889
	FB3	0.923
	FB4	0.918
GOP	GOP1	0.876	0.852	0.899	0.691
	GOP2	0.752
	GOP4	0.818
	GOP5	0.873
HIC	HIC1	0.759	0.875	0.91	0.669
	HIC2	0.857
	HIC3	0.873
	HIC4	0.844
	HIC5	0.751
IE	IE1	0.793	0.815	0.891	0.731
	IE2	0.885
	IE3	0.884
JP	JP3	0.863	0.831	0.898	0.747
	JP4	0.915
	JP5	0.812
MQ	MQ1	0.79	0.885	0.921	0.744
	MQ2	0.87
	MQ3	0.901
	MQ4	0.885
OI	OI1	0.751	0.707	0.82	0.536
	OI2	0.816
	OI3	0.752
	OI4	0.589
SC	SC1	0.802	0.898	0.924	0.709
	SC2	0.788
	SC3	0.849
	SC4	0.905
	SC5	0.861
SE	SE1	0.871	0.879	0.925	0.805
	SE2	0.924
	SE3	0.895
WQ	WQ1	0.931	0.916	0.947	0.856
	WQ2	0.921
	WQ3	0.923
Note= Media Quality (MQ), Organizational Integration (OI), General Organizational Perspective (GOP), Feedback (FB), Horizontal Informal Communication (HIC), Superior Communication (SC), Communication Climate (CC), Quality of Work-life (WQ), Affective Engagement (AE), Social Engagement (SE), Intellectual Engagement (IE), Job Performance (JP)

Discriminant Validity

As shown in Table 2, all measuring items had suitable loadings, meaning that they loaded significantly higher on their major variable than on other variables. The study used the discriminant validity test to see if it was valid (Fornell & Larcker, 1981). According to the Fornell & Larcker (1981), two criteria are used to determine discriminant validity: (1) when the measuring items have an adequate pattern of loadings, meaning they load heavily on their theoretically conceptualized variable and not on any other variable, and (2) when the square root of AVE for each factor is bigger than the correlations with other variables (Gefen & Straub, 2005).

Table 2 Fornell & Larcker Criterion
Constructs	AE	CC	FB	GOP	HIC	IE	JP	MQ	OI	SC	SE	WQ
AE	0.878
CC	0.326	0.851
FB	0.706	0.266	0.908
GOP	0.316	0.433	0.258	0.831
HIC	0.612	0.311	0.684	0.295	0.818
IE	0.529	0.255	0.347	0.139	0.392	0.855
JP	0.415	0.485	0.387	0.34	0.478	0.427	0.864
MQ	0.254	0.2	0.213	0.137	0.294	0.343	0.26	0.863
OI	0.277	0.259	0.219	0.397	0.181	0.2	0.265	0.112	0.732
SC	0.109	-0.045	0.004	0.133	0.023	0.1	0.034	0.023	0.155	0.842
SE	0.569	0.43	0.509	0.382	0.495	0.349	0.441	0.252	0.315	0.161	0.897
WQ	0.542	0.146	0.771	0.109	0.495	0.281	0.281	0.149	0.166	-0.002	0.38	0.925

As a result, the Heterotrait-Monotrait Ratio of Correlations (HTMT) can be used to evaluate discriminant validity more precisely. If the HTMT score is less than 0.90, discriminant validity between two constructs has been established. As a result, the HTMT criterion was used to confirm the discriminant validity concerns. The HTMT values are shown in Table 3. The table's values are all less than the standard value of 0.90. As a result, it's possible to show that discriminant validity has been demonstrated in Table 3 and Figure 2.

Table 3 HTMT Criterion
Constructs	AE	CC	FB	GOP	HIC	IE	JP	MQ	OI	SC	SE	WQ
AE
CC	.372
FB	.795	.292
GOP	.363	.49	.291
HIC	.711	.355	.759	.336
IE	.63	.289	.394	.207	.46
JP	.493	.557	.445	.391	.565	.509
MQ	.289	.214	.231	.15	.333	.402	.289
OI	.351	.327	.261	.508	.223	.255	.339	.155
SC	.136	.081	.053	.144	.069	.13	.09	.066	.207
SE	.656	.478	.561	.429	.563	.409	.511	.283	.398	.185
WQ	.614	.162	.836	.125	.553	.322	.328	.163	.202	.044	.422

The R² value for intellectual engagement in the endogenous construct was 0.254, which suggests that exogenous variables describe 25.4 percent variance on intellectual engagement. The R² value for social engagement in the endogenous construct was 0.429, which suggests that exogenous variables describe 42.9 percent variance on social engagement. The R² value for affective engagement in the endogenous construct was 0.565, which suggests that exogenous variables describe 56.5 percent variance on affective engagement. Similarly, R² value for job performance in the endogenous construct was 0.286, which suggests that exogenous variables describe 28.6 percent variance on job performance. Further study reveals that the R² is much higher than the Cohen (1988) R² recommendation, 0.02 -0.12 is considered low, 0.13 -0.25 is considered moderate, and 0.26 and above is considered significant. The result of the analysis proves there is both significant and non-significant effect of the dimension of organizational communication satisfaction on the dimensions of employee engagement. However, there was significant role of dimensions of employee engagement on the job performance Table 4.

Table 4 Direct Hypothesis Results
Hypothesis	Path	Beta	Std Error	T values	P Values	LL	UL	Decision
H1a	CC -> IE	0.131	0.061	2.156	0.016	0.02	0.228	Accepted
H1b	CC -> SE	0.232	0.049	4.707	0.000	0.151	0.312	Accepted
H1c	CC -> AE	0.087	0.041	2.114	0.018	0.022	0.155	Accepted
H2a	SC -> IE	0.093	0.052	1.779	0.038	0.009	0.181	Accepted
H2b	SC -> SE	0.137	0.042	3.243	0.001	0.07	0.207	Accepted
H2c	SC -> AE	0.089	0.036	2.487	0.007	0.03	0.154	Accepted
H3a	OI -> IE	0.099	0.047	2.099	0.018	0.019	0.179	Accepted
H3b	OI -> SE	0.099	0.04	2.461	0.007	0.032	0.166	Accepted
H3c	OI -> AE	0.071	0.041	1.701	0.045	-0.001	0.14	Accepted
H4a	MQ -> IE	0.23	0.065	3.513	0.000	0.122	0.339	Accepted
H4b	MQ -> SE	0.072	0.044	1.631	0.052	0.001	0.148	Rejected
H4c	MQ -> AE	0.056	0.041	1.356	0.088	-0.012	0.124	Rejected
H5a	HIC -> IE	0.206	0.065	3.151	0.001	0.094	0.305	Accepted
H5b	HIC -> SE	0.168	0.061	2.743	0.003	0.067	0.261	Accepted
H5c	HIC -> AE	0.189	0.065	2.891	0.002	0.083	0.301	Accepted
H6a	GOP -> IE	-0.095	0.053	1.771	0.039	-0.184	-0.003	Accepted
H6b	GOP -> SE	0.094	0.044	2.135	0.017	0.023	0.17	Accepted
H6c	GOP -> AE	0.042	0.041	1.01	0.157	-0.027	0.106	Rejected
H7a	FB -> IE	0.125	0.059	2.129	0.017	0.021	0.217	Accepted
H7b	FB -> SE	0.271	0.055	4.946	0.000	0.175	0.355	Accepted
H7c	FB -> AE	0.516	0.054	9.603	0.000	0.424	0.602	Accepted
H8a	IE -> JP	0.269	0.055	4.885	0.000	0.186	0.361	Accepted
H8b	SE -> JP	0.284	0.065	4.339	0.000	0.167	0.38	Accepted
H8c	AE -> JP	0.111	0.065	1.721	0.043	0.001	0.204	Accepted

H1a states the relationship between CC and IE, the beta value was0.131, the t value was2.156, and the p value was 0.016. Hence the hypothesis was found to be accepted. H1b investigated the relationship between CC and SE, the beta value was 0.232, the t value was 4.707, and the p value was 0.000. Hence hypothesis was found to be accepted. H1c examined the relationship between CC and AE, the beta value was 0.087, the t value was 2.114, and the p value was 0.018. Hence hypothesis was found to be accepted. H2a proposed the relationship between SC and IE, the beta value was 0.093, the t value was 1.779, and the p value was 0.038. Hence hypothesis was found to be accepted. H2b investigated the relationship between SC and SE, the beta value was 0.137, the t value was 3.243, and the p value was 0.001. Hence hypothesis was found to be accepted. H2c examined the relationship between SC and AE, the beta value was 0.089, the t value was 2.487, and the p value was 0.007. Hence hypothesis was found to be accepted. H3a states the relationship between OI and IE, the beta value was 0.099, the t value was 2.099, and the p value was 0.018. Hence hypothesis was found to be accepted. H3b examined the relationship between OI and SE, the beta value was 0.099, the t value was 2.461, and the p value was 0.007. Hence hypothesis was found to be accepted. H3c investigated the relationship between OI and AE, the beta value was 0.071, the t value was 1.701, and the p value was 0.045. Hence hypothesis was found to be accepted. H4a proposed the relationship between MQ and IE, the beta value was 0.230, the t value was 3.513, and the p value was 0.000. Hence hypothesis was found to be accepted. H4b examined the relationship between MQ and SE, the beta value was 0.072, the t value was 1.631, and the p value was 0.052. Hence hypothesis was found to be reject. H4c states the relationship between MQ and AE, the beta value was 0.056, the t value was 1.356, and the p value was 0.088. Hence hypothesis was found to be reject. H5a examined the relationship between HIC and IE, the beta value was 0.206, the t value was 3.151, and the p value was 0.001. Hence hypothesis was found to be accepted. H5b investigated the relationship between HIC and SE, the beta value was 0.168, the t value was 2.743, and the p value was 0.003. Hence hypothesis was found to be accepted. H5c proposed the relationship between HIC and AE, the beta value was 0.189, the t value was 2.891, and the p value was 0.002. Hence hypothesis was found to be accepted. H6a states the relationship between GOP and IE, the beta value was 0.095, the t value was 1.771, and the p value was 0.039. Hence hypothesis was found to be accepted. H6b investigated the relationship between GOP and SE, the beta value was 0.094, the t value was 2.135, and the p value was 0.017. Hence hypothesis was found to be accepted. H6c examined the relationship between GOP and AE, the beta value was 0.042, the t value was 1.010, and the p value was 0.157. Hence hypothesis was found to be reject. H7a investigated the relationship between FB and IE, the beta value was 0.125, the t value was 2.129, and the p value was 0.017. hence the hypothesis was accepted. H7b proposed the relationship between FB and SE, the beta value was 0.271, the t value was 4.946, and the p value was 0.000. Hence hypothesis was found to be accepted. H7c states the relationship between FB and AE, the beta value was 0.516, the t value was 9.603, and the p value was 0.000. Hence hypothesis was found to be accepted. H8a proposed the relationship between IE and JP, the beta value was 0.269, the t value was 4.885, and the p value was 0.000. Hence hypothesis was found to be accepted. H8b investigated the relationship between SE and JP, the beta value was 0.284, the t value was 4.339, and the p value was 0.000. Hence hypothesis was found to be accepted. H8c examined the relationship between AE and JP, the beta value was 0.111, the t value was 1.721, and the p value was 0.043. Hence hypothesis was found to be accepted in Figure 3.

To test the mediating effect of employee engagement, this study follow Preacher & Hayes (2008) approach. Furthermore, because there are no assumptions about the distribution curve, this technique works well with PLS-SEM. The indirect influence between (a) and (b) must be considerable in order to analyze the mediating effect. However, Zhao et al. (2010), on the other hand, claimed that the mediating impact between the independent and dependent variables does not have to be substantial if the mediator variable is not present. To test for path significance, the results of bootstrapping were calculated in Table 5.

Table 5 Mediating Hypothesis Result
Hypothesis	Path	Beta	Std Error	T values	P Values	LL	UL	Decision
H9a	CC -> IE -> JP	0.035	0.018	1.908	0.029	0.005	0.069	Accepted
H9b	CC -> SE -> JP	0.066	0.022	2.942	0.002	0.033	0.105	Accepted
H9c	CC -> AE -> JP	0.010	0.008	1.159	0.124	0.000	0.026	Rejected
H10a	SC -> IE -> JP	0.025	0.015	1.705	0.044	0.002	0.049	Accepted
H10b	SC -> SE -> JP	0.039	0.015	2.636	0.004	0.016	0.065	Accepted
H10c	SC -> AE -> JP	0.010	0.007	1.385	0.083	-0.001	0.022	Rejected
H11a	OI -> IE -> JP	0.027	0.015	1.750	0.040	0.006	0.053	Accepted
H11b	OI -> SE -> JP	0.028	0.013	2.204	0.014	0.008	0.049	Accepted
H11c	OI -> AE -> JP	0.008	0.007	1.204	0.115	-0.002	0.020	Rejected
H12a	MQ -> IE -> JP	0.062	0.023	2.696	0.004	0.030	0.106	Accepted
H12b	MQ -> SE -> JP	0.020	0.014	1.487	0.069	0.000	0.043	Rejected
H12c	MQ -> AE -> JP	0.006	0.006	0.980	0.164	-0.002	0.017	Rejected
H13a	HIC -> IE -> JP	0.055	0.023	2.365	0.009	0.022	0.096	Accepted
H13b	HIC -> SE -> JP	0.048	0.021	2.316	0.011	0.016	0.078	Accepted
H13c	HIC -> AE -> JP	0.021	0.015	1.367	0.086	-0.001	0.047	Rejected
H14a	FB -> IE -> JP	0.034	0.016	2.065	0.020	0.006	0.059	Accepted
H14b	FB -> SE -> JP	0.077	0.023	3.384	0.000	0.040	0.115	Accepted
H14c	FB -> AE -> JP	0.057	0.033	1.712	0.044	0.001	0.107	Accepted
H15a	GOP -> IE -> JP	-0.025	0.015	1.674	0.047	-0.051	-0.001	Accepted
H15b	GOP -> SE -> JP	0.027	0.015	1.749	0.041	0.005	0.054	Accepted
H15c	GOP -> AE -> JP	0.005	0.006	0.754	0.226	-0.002	0.016	Rejected

The results of the hypothesis show that H9c, H10c, H12b, H12c, H13c and H15c is rejected these relationships were mostly depended on the mediating role of affective engagement. This represents that five-mediating hypothesis with affective engagement were rejected whereas only one hypothesis with mediating role of social engagement is rejected. It is noted here that no hypothesis with intellectual engagement was rejected which shows that intellectual engagement is a strong mediator between IV and DV. Lastly, the findings show that H9a, H9b, H10a, H10b, H11a, H11b, H12a, H13a, H13b, H14a, H14b, H14c, H15a and H15b were accepted in Table 6.

Table 6 Moderating Hypothesis Results
Hypothesis	Path	Beta	Std Error	T values	P Values	Decision
H16a	CC*WQ -> IE	0.033	0.060	0.548	0.292	Rejected
H16b	CC*WQ -> SE	-0.045	0.055	0.832	0.203	Rejected
H16c	CC*WQ -> AE	-0.057	0.044	1.313	0.095	Rejected
H17a	SC*WQ -> IE	-0.177	0.047	3.780	0.000	Accepted
H17b	SC*WQ -> SE	-0.001	0.04	0.031	0.488	Rejected
H17c	SC*WQ -> AE	-0.075	0.035	2.149	0.016	Accepted
H18a	OI*WQ -> IE	-0.020	0.043	0.466	0.321	Rejected
H18b	OI*WQ -> SE	-0.107	0.042	2.557	0.005	Accepted
H18c	OI*WQ -> AE	-0.060	0.034	1.773	0.039	Accepted
H19a	MQ*WQ -> IE	-0.011	0.074	0.150	0.441	Rejected
H19b	MQ*WQ -> SE	-0.003	0.055	0.054	0.478	Rejected
H19c	MQ*WQ -> AE	-0.005	0.037	0.122	0.452	Accepted
H20a	HIC*WQ -> IE	-0.058	0.068	0.849	0.198	Rejected
H20b	HIC*WQ -> SE	-0.021	0.071	0.295	0.384	Rejected
H20c	HIC*WQ -> AE	0.045	0.061	0.738	0.230	Rejected
H21a	FB*WQ -> IE	0.095	0.053	1.806	0.036	Accepted
H21b	FB*WQ -> SE	0.092	0.056	1.635	0.051	Accepted
H21c	FB*WQ -> AE	0.045	0.046	0.981	0.164	Rejected
H22a	GOP*WQ -> IE	0.022	0.049	0.456	0.324	Rejected
H22b	GOP*WQ -> SE	0.018	0.047	0.386	0.35	Rejected
H22c	GOP*WQ -> AE	-0.055	0.042	1.299	0.097	Rejected

In examining the interaction effect of moderator using PLS-SEM, the product indicator approach was followed as recommended by Chin et al. (2003).It was applied to detect the moderating effect of quality of work life on the relationship between organizational communication satisfaction and employee engagement. In order to assess the interaction effect is significant, a bootstrap re-sampling with 1000 re-sampling was employed. Table revealed that an insignificant moderating effect of quality of work life in the hypothesis H16a, H16b, H16c, H17b, H18a, H19a, H19b, H20a, H20b, H20c, H21c, H22a, H22b, H22c. However, H17a, H17c, H18b, H18c, H19c, H21a, H21b were significant.

Conclusion

In conclusion, the study's findings found no significant changes in the interactions between components for both subsamples, with the exception of the relationship between job satisfaction and job performance and mediating-moderating roles. These findings led to the conclusion that nurses' opinions were not stable during the crisis and could change as a result of the quality of work life. Employees who believe they did not receive all of the information they required may make up their own facts based on assumptions and hearsay. The bulk of the nurses in this study, however, worked in Jordanian public hospitals, which required them to be fully informed of all events and decisions affecting the institution and its employees. Furthermore, the Coronavirus disease (COVID-19) is regarded as an external catastrophe, with detrimental consequences for all hospitals in the affected area. The majority of hospitals took similar crisis management procedures and stopped hiring new employees. As a result, finding new work during a recession may have been difficult. The findings of this study may have implications for the ministry of health, notably human resources management, due to the importance of communication in the sector, where employees and consumers of various backgrounds interact. As a result, challenges like generalizability and large-scale global industry are constrained. Replicating this study, on the other hand, has some merit. Differences in demographics, cultures, and other significant human resource characteristics must be taken into account, the healthcare sectors of various countries should be the focus of future research on compliance, organizational identification, organizational citizenship behavior, and commitment.

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