Academy of Accounting and Financial Studies Journal (Print ISSN: 1096-3685; Online ISSN: 1528-2635)

Research Article: 2022 Vol: 26 Issue: 1

Organizational Communication Satisfaction on Performance: Testing a Mediated- Moderated Accounting and Financial Model in Jordanian Public Hospitals

Yousef Ali Mohammad Alrefai, Universiti Sains Malaysia

Hazril Izwar Ibrahim, Universiti Sains Malaysia

Ghaith Abdulraheem Ali Alsheikh, Amman Arab University

Citation Information: Alrefai, Y.A.M., Ibrahim, H.I., & Alsheikh, G.A. (2022). Organizational communication satisfaction on performance: testing a mediated- moderated accounting and financial model in jordanian public hospitals. Academy of Accounting and Financial Studies Journal, 26(1), 1-17.

Abstract

Researchers tie communication satisfaction within organizations to job satisfaction, productivity, and quality of work-life, and yet supporting communication satisfaction remains a challenge for workplaces. This research study examines the effect of organizational communication satisfaction, employee engagement, quality of work-life, and job performance as observed in Jordanian public hospitals. To achieve this goal, A descriptive methodology was also applied, which comprised a review of published and unpublished scientific research material, as well as a survey in the form of a questionnaire including nurses from Jordanian public hospitals to get their thoughts and ideas on the subject. 370 questionnaires were nurses of public hospitals in Jordan. The data were analyzed using Structural Equation Modelling. Findings indicate that twenty-four hypotheses were rejected and the rest were accepted. The findings triggers future researchers to conduct similar studies for other sectors in Jordan, possibly in different contexts and perspectives.

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.

Figure 1 Research 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  

Figure 2 PLS Measurement Model

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.

Figure 3 PLS Structural Model

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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