Causes and consequences of academics’ emotions in private higher education institutions: implications for policy and practice through the lens of affective events theory

Depósito de investigación de la Universidad de Sevilla https://idus.us.es/ Esta es la versión aceptada del artículo publicado en: This is a accepted manuscript of a paper published in: Educational Research for Policy and Practice (2021) DOI: https://doi.org/10.1007/s10671-020-09288-0 Copyright: The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature 2021 El acceso a la versión publicada del artículo puede requerir la suscripción de la revista. Access to the published version may require subscription. “This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10671-020-09288-0” 1 Causes and consequences of academics’ emotions in private higher education institutions: implications for policy and practice through the lens of affective events theory Abstract: This study aims at testing a few tenets of Affective Events Theory (AET) from a predictive perspective in the context of Malaysian private higher education sector. Specifically, we are interested in examining the impact of workload and autonomy on job satisfaction of academics through interpersonal conflict and affective states. Additionally, the impact of affective states on job satisfaction via job performance is considered. We gather data from 325 academics and analyzed them through Partial Least Squares (PLS) methodology. Our findings corroborated AET tenets considerably. The importance of the joint consideration of workload and autonomy in positively contributing to job satisfaction is highlighted. Lastly, positive affect construct has been identified as the strongest construct in increasing job satisfaction of academics. Arguably, the findings indicate policy relevance at both the macro and institutional levels and have implications for future research direction in the area of human resource management in the private higher education sector. Keywords: Affective Events Theory, FIMIX-PLS, Job satisfaction, Malaysian Private Universities, PLS-SEM 2 1 Introduction Universities, especially those operating in a neoliberal market environment, are coping with their new roles – ensuring societal cohesion, harmony, and sustainability – as demanded by higher education systems. With the launch of the Sustainable Development Goals in 2015, many universities are also adapting to the present and future settings in the context of societal well-being and sustainability. Arguably, these entities have been the indicators and tools of unifying people in a society to the extent that they can boost the nation’s pride by displaying their scholarly achievements and potentials (Wan, Morshidi, & Dzulkifli, 2015). Universities’ economic role and their need for improvement and growth have led many Asian countries to adopt the western academic models in establishing their higher education such as patterns of institutional governance, the ethos of academic profession, the rhythm of academic life, and the procedures of examination and assessment (Lee, Wan, & Morshidi, 2017). In Malaysia, as an example of a higher education system that comprised of the public and the private sector, improvement in the global university ranking seems to be attributed to the contribution of the academic staff and graduate candidates in one important criteria:, that is publications in high level (high impact) internationally recognized journals. Interestingly, the studies in Malaysian public universities have shown that publication and research are believed to have higher weightage in the academic promotion system in comparison with the contribution to the teaching and learning (Norzaini, Ibrahim, Aida Suraya, & Ahmad Nurulazam Md et al., 2016). In contrast, private universities are not under intense pressure from the government to increase their research output (publications). Nonetheless, they feel the pressure in competing with public universities for their own market share of students and international academic staff (Chapman, Hutcheson, Wan, Lee, & Md. Zainet al., 2017). This situation would necessarily give rise to very stressful institutions for academic staff in terms of achieving their key personal performance indicators and institutions’ mission and vision (Ghasemy et al., 2018). This implies the importance of promoting a human resource environment based on positive 3 emotions and job satisfaction, capable of generating elements of differentiation and sustainable competitive advantages. According to the Malaysian Ministry of Education in June 2019, the private system has been made up of 53 universities and university branches, 10 foreign branch campuses, 39 university colleges, and 347 colleges. In addition, as of 2017, the number of the students and academics in these institutions have been reported to be 666,617 and 48,643, respectively. Similar to public universities, private universities offer an extensive range of programs in engineering and business (Wan, 2018). Successful private higher education institutions have demonstrated sustainable business (Azlan et al., 2019). Based on such a business model coupled with a flexible academic admission criterion and learning environment, Malaysian private institutions were able to fully utilize their capacity to enrol students of diverse post-secondary academic qualifications and levels of affordability. Notwithstanding, while the role of private institutions in the national economy in terms of positive contribution to the national revenue are widely researched and well-presented, from the literature, studies have a tendency to focus on job performance and job satisfaction of academics in higher education institutions from behavioral or demographic perspectives ((As a few examples, see Eyupoglu and Saner (2009), Du, Lai, and Lo (2010), and Sabharwal and Corley (2009)). However, emotional reactions which are considered as the essential determinants of job performance and job satisfaction of employees (Weiss & Beal, 2005; Weiss & Cropanzano, 1996), have not been given due attention in research. More importantly, while decreasing work stress and increasing health of the employees through both reducing negative events and increasing positive events in organizations have been suggested (Bono, Glomb, Shen, Kim, & Koch et al., 2013), there is still a gap in the literature on the importance of psychological and mental state of academics in higher education research. In order to explore this under-researched topic in higher education literature, the present study aims at testing a few tenets of Affective Events Theory (AET) developed by Weiss and Cropanzano (1996) in the context of Malaysian private higher education institutions. The study, guided by AET, is premised on the notion that the work environment features impact academics’ job satisfaction both directly and indirectly through work events and affective reactions. In Comentado [DMR1]: It is positioned in higher education 4 addition, the impact of job performance on job satisfaction ias another relevant research topic (Schermerhorn, Hunt, Osborn, & Uhl-Bien et al., 2010) that need deeper understanding. Given that job satisfaction, that is negatively influenced by the perceived strain as an stressful work event (Fuller et al., 2003), is also related to the psychological withdrawal behaviors such as daydreaming, cyber loafing, and excessive socializing that are forms of work disengagement (Schermerhorn et al., 2010), focusing on this construct appears to be critical and meaningful. In addition, as quoted by Tillman, Gonzalez, Crawford, and Lawrence (2018), researchers have been encouraged to carry out more research work focusing on the affective process of work outcomes (Walter & Bruch, 2009), thereby providing a substantial rationale for this study. Notably, while the results seem to be useful to the diverse stakeholders in higher education, policy makers would benefit immensely from this study. This is so for it enables policymakers to engage in data-driven evidence-based policy making processes involving the private higher education institutions that continuously explore sustainable business models (Azlan et al., 2019). This business model would determine workplaces situations in terms of academics’ job performance and job satisfaction. 2 Theoretical framework This study draws upon Affective Events Theory or AET (Weiss & Cropanzano, 1996). This theory, with a focus on the structure, causes and consequences of affective experiences at work, was offered as a roadmap for future research on emotions in organizational context (Weiss & Beal, 2005). In fact, AET has been an important guiding framework on how specific work events, caused by work environment features, act as emotional stimuli resulting in positive and negative affective states, that are seen as the drivers for the affect-driven behaviors, attitudes, and judgment-driven behaviors (Weiss & Beal, 2005; Weiss & Cropanzano, 1996). Additionally, personality traits have been considered as the variables playing a moderating role over the relationship between events and affect (Weiss & Beal, 2005). It is noteworthy that fundamental to AET is the idea that, over time, the levels of affect fluctuate (Weiss & Cropanzano, 1996). 5 Due to the complexity of the theory, the focus of this study is on the relationships among work environment features, events, affective states, attitudes and affect-driven behaviors. In addition, based on the controversies over the direction of job performance-job satisfaction linkage (Judge, Thoresen, Bono, & Patton et al., 2001; Schermerhorn et al., 2010), the relationship between these two constructs is assessed. Figure 1 shows the conceptual framework of the study. Figure 1. Theoretical framework It is important to highlight that job satisfaction, that is influenced by different internal and external elements such as interpersonal relationships and working conditions (Rosa-Díaz, Martín Ruiz, & Cepeda Carrión, 2019), is the positive or negative evaluative judgment of an individual’s job or job situation (Weiss & Beal, 2005); job performance is the total anticipated value added to the organization by the discrete behavioral episodes that an individual carries out over a standard course of time Work Environment Feature (Autonomy & Workload) Work Event (Interpersonal Conflict) Affect-driven Behavior (Job Performance) Affective Reactions (Positive Affect & Negative Affect) Attitude (Job Satisfaction) Control Variables 1- Nationality 2- Gender 3- Marital Status 4- University Type H1 (+): AutonomyJob Satisfaction H2 (-): WorkloadJob Satisfaction H3 (+): AutonomyInterpersonal ConflictPositive AffectJob Satisfaction H4 (+): AutonomyInterpersonal ConflictNegative AffectJob Satisfaction H5 (-): WorkloadInterpersonal ConflictPositive AffectJob Satisfaction H6 (-): WorkloadInterpersonal ConflictNegative AffectJob Satisfaction H7 (+): Positive Affect Job PerformanceJob Satisfaction H8 (-): Negative Affect Job PerformanceJob Satisfaction 6 (Motowidlo & Kell, 2003); affect is the range of positive and negative emotions and moods that is experienced by people in their life context (George, 1996); interpersonal conflict is defined as having experiences of bad relationships or working with others with whom one does not get along (Schermerhorn et al., 2010); autonomy implies providing employees with a margin of freedom to carry out their tasks (Patterson et al., 2005); and workload (or pressure to produce) refers to the extent to which the employees are pressured to achieve objectives (Taira, 1996). 2.1 Work environment features-attitude linkage Not only the examination of the impact of employee affective reactions on workplace situations has been an important and promising area of social research (Porath & Pearson, 2012), but also different empirical studies have been focusing on work environment features-attitude linkage within the AET’s framework. For instance, White & Spector (1987) in one study, it was found that older workers are more satisfied with their work characteristics due to the consistency between their jobs and their agerelated needs as well as the feeling that they can determine what happens to them in their job (White & Spector, 1987). Another study Zacher, Jimmieson, & Bordia (2014) addressing the diversity and job satisfaction (Zacher, Jimmieson, & Bordia, 2014) found that while the level of time pressure and coworker support are higher among workers in mid-career, the relationship between age and job satisfaction is mediated by the diversity and job satisfactionboth constructs. Moreover, Besen, Matz- Costa, Brown, Smyer, and Pitt-Catsouphes et al. (2013) examined the relationship between three work environment features namely autonomy, skill variety, and social support with job satisfaction and concluded that the positive relationship between these constructs with job satisfaction was more stronger among younger workers. Lastly, there is empirical evidence that workload can cause time constraints or work-life balance difficulties, reducing job satisfaction (Donovan, 2018). In alignment with the previous research findings and drawing upon AET, the following two hypotheses were developed: H1: Academics’ autonomy in Malaysian private institutions of higher learning positively influence their job satisfaction while controlling for the effect of the selected demographic features. 7 H2: Academics’ workload in Malaysian private institutions of higher learning negatively influence their job satisfaction while controlling for the effect of the selected demographic features. 2.2 Work environment features-work event-affective states-attitude linkage With respect to work environment features-work events linkage, Lam and Chen (2012) found empirical evidence for the positive linkage between supervisory support, as a work environment feature, and supervisory interactional justice, as a work event. This approach connects directly with the philosophy of servant leadership through which, leaders promote positive affect, autonomy, selfefficacy and constructive attitudes (Liden, Wayne, Zhao, & Hendersonet al., 2008; Rosa-Díaz et al., 2019). In addition, Matthews, Bulger, and Barnes-Farrell (2010) found empirical evidence the negative causal relationship between social support (workplace feature) and work-family conflict (work event), with the effect being stronger among the older workers. Moreover, focusing on open-plan offices, Ashkanasy, Ayoko, and Jehn (2014) elaborated the relationship between work environment features related to privacy, identity, and crowding on events namely distractions/ noise and invasions. Regarding event-affect linkage, Zhao, Wayne, Glibkowski, and Bravoet al. (2007) found that perceived psychological contract breaches lead to the negative affective states such as violation and mistrust. In this same line of argumentSimilarly, Tillman et al. (2018) demonstrated that negative emotional reactions of employees are elicited by the experience of abusive supervision, as a negative work event, which consequently leads to negative attitudes about the workplace and exhibiting Counterproductive Work Behaviors, or CWB (Shoss, Jundt, Kobler, & Reynolds et al., 2016). LastlyRecently, Cho and Yang (2018) found that the relationships between perceived organizational politics, as work events, and the intrinsic motivation, as attitude, are partially mediated by depression. In terms of affect-attitude linkage, Volmer, Richter, and Syrek (2018) found a strong empirical evidence for the positive relationship between positive affect and creativity, as a multidimensional attitude that generates self-efficacy, job satisfaction, extra value and competition advantages, and which is empowered by transformational and servant leaders (Rosa-Díaz et al., 2019; Wang, Tsai, & Tsai, 8 2014). In the same line, Yan, Yang, Su, Luo, and Wen (2018) identified a correlation between high levels of emotional intelligence with greater job satisfaction and work engagement, mainly among female employees. Finally, Fuller et al. (2003) examined the linkage between affective states and job satisfaction and concluded a likely causal effect of daily mood on both concurrent and next-day job satisfaction. Indeed, as highlighted by Ashkanasy et al. (2014), workplace environment features and situations are viewed as the immediate causes of events and their associated affective states which may ultimately influence employees’ immediate and long-term behaviors and attitudes. Consistent with AET tenets and in line with the previous research findingsBuilding upon previous arguments, the following hypotheses were considered: H3: Interpersonal conflict and positive affect mediate the relationship between academics’ autonomy and job satisfaction in Malaysian private institutions of higher learning while controlling for the effect of the selected demographic features. H4: Interpersonal conflict and negative affect mediate the relationship between academics’ autonomy and job satisfaction in Malaysian private institutions of higher learning while controlling for the effect of the selected demographic features. H5: Interpersonal conflict and positive affect mediate the relationship between academics’ workload and job satisfaction in Malaysian private institutions of higher learning while controlling for the effect of the selected demographic features. H6: Interpersonal conflict and negative affect mediate the relationship between academics’ workload and job satisfaction in Malaysian private institutions of higher learning while controlling for the effect of the selected demographic features. 2.3 Affective states-affect driven behavior-attitude linkage The impact of affective states on attitude and affect-driven behaviors have grabbed the attention of many social science researchers (Porath & Pearson, 2012). In this context, it is interesting to refer to organizational citizenship behavior (OCB), which is as an affect-driven behavior that can be defined as 15 The results of significance testing of hypotheses as well as path coefficients (direct effects) with percentile confidence intervals have been displayed in Table 3. According to Table 3, H1 to H6 are supported in our analysis, but empirical evidence was not provided for H7 and H8. Table 3. Hypothesis testing and path significance and relevance results Hypotheses and Effects Path Coefficient PCI Supported? / Significant? H1(+) AutonomySatisfaction 0.154 [0.078, 0.229] Yes H2(-) WorkloadSatisfaction 0.100 [0.022, 0.170] Yes H3(+) AutonomyInterpersonal ConflictPositive AffectSatisfaction 0.031 [0.013, 0.054] Yes H4(+) AutonomyInterpersonal ConflictNegative AffectSatisfaction 0.031 [0.014, 0.052] Yes H5(-) WorkloadInterpersonal ConflictPositive AffectSatisfaction -0.052 [-0.079, -0.031] Yes H6(-) WorkloadInterpersonal ConflictNegative AffectSatisfaction -0.052 [-0.080, -0.030] Yes H7(+) Positive AffectPerformanceSatisfaction 0.011 [-0.007, 0.038] No H8(-) Negative AffectPerformanceSatisfaction -0.004 [-0.015, 0.003] No Direct Effects AutonomyInterpersonal Conflict -0.202 [-0.292, -0.112] Yes Interpersonal ConflictNegative Affect 0.546 [0.453, 0.635] Yes Interpersonal ConflictPositive Affect -0.294 [-0.395, -0.195] Yes Negative AffectPerformance -0.098 [-0.210, 0.005] No Negative AffectSatisfaction -0.283 [-0.367, -0.189] Yes PerformanceSatisfaction 0.037 [-0.028, 0.114] No Positive AffectPerformance 0.291 [0.181, 0.403] Yes Positive AffectSatisfaction 0.524 [0.443, 0.597] Yes Workload->Interpersonal Conflict 0.337 [0.272, 0.416] Yes Effects of Control Variables GenderSatisfaction 0.029 [-0.046, 0.107] No Marital StatusSatisfaction 0.068 [-0.010, 0.146] No NationalitySatisfaction -0.015 [-0.094, 0.060] No University TypeSatisfaction 0.014 [-0.062, 0.088] No PCI: Percentile Confidence Interval; Bootstrapping based on n= 10,000 bootstrap samples; Paths based on hypothesized effects and direct effects assessed by applying a one-tailed test at 5% of significance level [5%, 95%]; Effects of the control variables assessed by applying a two-tailed test at 5% of significance level [2.5%, 97.5%]; Regarding the effects of the two work environment features on job satisfaction, while both H1 and H2 were supported, the effect of workload on job satisfaction was positive though the zero-order correlation between these two constructs was negative (See Appendix 2). Interestingly, the magnitude of the effects represented by H3 and H4 were equal, implying that regardless of the type of the affect, the influence of autonomy on job satisfaction through the two mediating mechanisms were similar. Additionally, the effects of workload on job satisfaction based on H5 and H6 were equal, indicating the same finding as the finding with respect to H3 and H4. Regarding the direct effects and focusing on interpersonal conflict, the results showed that while both effects of workload and autonomy were significant and relevant, as evident by the magnitude of the effects, the impact of workload on interpersonal conflict was larger. With respect to job 16 performance, the results indicated that only the impact of positive affect on job performance was significant and relevant. Additionally, focusing on job satisfaction, the results showed that positive affect was the strongest job satisfaction’s predictor in comparison with other constructs within the model. Specifically, its magnitude was nearly two times of the magnitude of the effect of negative affect on job satisfaction. Notably, the effect of interpersonal conflict on negative affect was the strongest effect within the model with a path coefficient of 0.546. As the last issue related to the path coefficients and considering the guiding principles proposed by Nitzl, Roldán, and Cepeda Carrión (2016) with respect to the direction of direct and indirect effects, we concluded that the type of the partial mediation with regard to H3 and H4 was complementary and with respect to H5 and H6, it was competitive. Table 4. In-sample predictive power Construct Satisfaction (R2= 0.569) Positive Affect 0.355 Negative Affect 0.150 Performance 0.010 Workload -0.016 Autonomy 0.060 Gender 0.000 Nationality -0.001 University Type -0.001 Marital Status 0.011 As the last step in evaluating the structural model, we ran PLSpredict analysis (Shmueli et al., 2019), albeit with the default settings, to evaluate the out-of-sample predictive power of the model. To hit this target, we focused on Q²_predict values for the PLS results as well as the Root Mean Squared Error (RMSE) values for the PLS and the Linear Model (LM) results. Notably, while the Q²_predict values for the PLS results should be positive, the prediction errors (e.g., RMSE values) based on PLS results should be smaller than the errors based on LM results, implying that a theoretically established model either improves or doesn’t worsen the predictive performance of the available indicator data (Shmueli et al., 2019). We have presented the results of this analysis for the items of job satisfaction in Table 5. Table 5. Out-of-sample predictive performance based on RMSE values 17 Item PLS Results LM Results RMSEPLS-RMSELM RMSE Q²_predict RMSE SAT3 0.824 0.075 0.829 -0.005 SAT8 0.923 0.071 0.932 -0.009 SAT9 0.678 0.0899 0.680 -0.002 SAT10 0.775 0.070 0.783 -0.008 As displayed in this table, the Q²_predict values were all positive and the RMSE values of all the items in the PLS result section were smaller than RMSE values in the LM section, implying a high predictive power of the model (Shmueli et al., 2019). The final model with factor loadings, path coefficients, and R2 values of the endogenous constructs have been presented in Figure 2. Figure 2. Final Model 18 3.4.3 Structural model robustness check To further validate the results and increase the methodological rigor, we ran Finite Mixture Partial Least Squares (FIMIX-PLS) analysis (Hair et al., 2018). To run this analysis, we set the number of irritations to 5000, the number of repetitions to 10, and the stop criterion to 10-10. In addition, we considered Cohen (1988)’s guidelines with respect to the power analysis to determine and fulfil the minimum sample size requirements per segment. Given that the minimum effect size of interest in the model was the R2 of positive affect being predicted by only one construct, the results of a power analysis assuming an effect size of 0.086 and a power level of 80% suggested that the minimum sample size requirement was 84. Hence, we considered the extraction of 3 segments and ran FIMIX-PLS for one to three-segment solutions, albeit with the same settings. The results have been presented in Table 6. Table 6. FIMIX-PLS results Fit indices No. of Segments 1 2 3 AIC (Akaike's Information Criterion) 4,084.75 3,957.41 3,894.29 AIC3 (Modified AIC with Factor 3) 4,104.75 3,998.41 3,956.29 AIC4 (Modified AIC with Factor 4) 4,124.75 4,039.41 4,018.29 BIC (Bayesian Information Criteria) 4,160.24 4,112.17 4,128.32 CAIC (Consistent AIC) 4,180.24 4,153.17 4,190.32 HQ (Hannan Quinn Criterion) 4,114.89 4,019.20 3,987.72 MDL5 (Minimum Description Length with Factor 5) 4,622.20 5,059.19 5,560.40 LnL (LogLikelihood) -2,022.37 -1,937.71 -1,885.15 EN (Entropy Statistic (Normed)) 0.682 0.853 No of Segments Relative Segment Size Seg 1 Seg 2 Seg 3 1 segment 1 2 segments 0.803 0.197 3 segments 0.823 0.124 0.052 Numbers in bold indicate the best outcome per segment retention criterion. The info in the shaded area show the failure in terms of meeting relative segment size requirement. In terms of interpretation of the results, we followed the rules of thumb by Hair et al. (2018). While in general a solution with fewer segments than indicated by AIC and more segments than indicated by MDL5 should be chosen, the selection of the solution with 2 segments was not feasible and realistic due to three reasons: 1) the joint consideration of AIC3 and CAIC did not result in selection of a 2-segment solution; 2) the joint consideration of AIC3 and BIC did not result in selection of the 2- 19 segment solution; 3) the failure in meeting sample size requirement for a 2-segment solution. Therefore, based on the unclear picture of the presence of unobserved heterogeneity within the data, we concluded that unobserved heterogeneity wasthis is not a threat for the validity of our model, highlighting the methodological and analytical soundness of the model. 4 Discussion and conclusion Our study aims at testing a few tenets of Affective Events Theory (AET) developed by Weiss and Cropanzano (1996) in the context of Malaysian private higher education institutions. As displayed in Table 5, while H1 to H6 were supported, empirical evidence was not provided for H7 and H8. Notably, we formulated H1 to H6 based on AET (Weiss & Beal, 2005; Weiss & Cropanzano, 1996) and hence, the results with respect to these hypotheses were in full alignment with the theory. Regarding H7 and H8, we formulated the hypotheses based on the literature focusing on job performance-job satisfaction linkage, as highlighted by Schermerhorn et al. (2010). Specifically, while based on the theory, there is no direct relationship between job performance and job satisfaction, we hypothesized a relationship between these two constructs based on the debates on the existence and direction of this linkage (Judge et al., 2001). In other words, the lack of imperial evidence for H7 and H8 provided more substantial support for AET in higher education context. An important finding in our analysis reflect the fact that regardless of the type of affect, the impacts of autonomy and workload on job satisfaction through the mediation mechanism were the same in terms of size and direction. In addition, it must be taken into account that the effect of workload on interpersonal conflict is stronger than the effect of autonomy, which gives workload a strategic role superior to that of autonomy within our context of study. Moreover, we found that while positive affect and negative affect were related to job satisfaction, these constructs were truly distinct constructs, as evident by discriminant validity. Lastly, our analysis highlighted the fact that looking at workload as a permanent job dissatisfying factor may not be correct and in fact, the combination of the factors influencing job satisfaction should be considered in a bigger picture. Specifically, while our model offers that balancing between workload 20 and autonomy should work well in hitting the target of achieving academics’ job satisfaction in private higher education institutions, the role of affective states and particularly positive affective states are more vital, as evident by the unique contribution of these constructs to the model’s predictive accuracy. It is noticeable that our results were considerably in line with the previous research findings reviewed in the theoretical framework section, thereby providing a substantial support for the pertinence, relevance, and significance of AET in the context of private higher education system. As a result, guided by AET, the process of making theory-based policies focusing on work environment features, work events, affective states, attitudes, and behaviors in private institutions of higher learning is facilitated, thereby highlighting the strategic importance of strengthening a philosophy that gives the affective states of academics a central role. 5 Practical and theoretical implications of the findings One of the major practical implications of the findings in this study relates to the perfect out- of-sample predictive performance of the developed model. In fact, policymakers in higher education domain will be able to make relevant policies that are underpinned not only by the collected data in this study but also based on the data that are not exposed to our model during its development and validation. In addition, the robustness check results in our study show that the model is highly valid and generalizable since based on FIMIX-PLS results, unobserved heterogeneity is not a matter of concern regarding its validity. More precisely, among the lessons learned in this study having meaningful implications for the policymakers, remains the considerable role of autonomy as its joint consideration with workload leads to academics’ job satisfaction increase in the context of private higher education institutions. In consequence, academic leaders and officials must pay special attention to the design of jobs – depending on the characteristics of the human and technical resources available, as well as those of the socioeconomic and cultural environment in which they carry out their activity – so that the autonomy and workload corresponding to each of them is perfectly known by the employees (academics in our case), understood and perceived as fair and balanced. In fact, the results of our study lead us to recommend to 21 the managers of private higher education institutions the adoption of a service philosophy to enhance job satisfaction through autonomy, self-efficacy, and the appropriate design of workloads. This will enhance the commitment of academics to the institutions to which they belong, as well as their organizational citizenship behavior and job performance, which will ultimately result in the satisfaction of the needs of internal and external clients (e.g. academics, students, and society as a whole), and will allow a sustainable growth of private higher education institutions. Therefore, it is a question of investing more in creating a good working climate (positive affect) than in generating measures and policies to neutralize and compensate for the negative affect generated by factors such as the lack of autonomy, the inadequate design of workloads and interpersonal conflicts. As discussed in the result section, the most important effect in our model corresponds to the influence of interpersonal conflict on negative affect. In terms of theoretical implications, this paper substantiated the applicability of AET in higher education domain. In fact, the nonsignificant path running from job performance to job satisfaction was one of the major findings in this study corroborating AET. More pacifically, as quoted by Weiss and Cropanzano (1996), the correlation between job satisfaction and job performance is negligible (Podsakoff & Williams, 1986) though in general, it makes sense that when the employees perform well, they should feel good and be satisfied about their job (Schermerhorn et al., 2010). 6 Limitation and recommendations In terms of limitations, we did not collect data from academics in private colleges. Also due to the complexity of the theory and the possibility of introducing many different variables in each placeholder box in the macro-structure of the theory, we focused on a few tenets of the theory. As the last limitationIn addition, our study is a cross-sectional survey design which does not address the fluctuation of affective states over time. Regarding future research, the researchers are recommended to verify the tenets of AET in the context of private colleges as well as other higher education sectors and socio-cultural contexts through 22 a bigger picture. In addition, introducing other relevant work environment features, attitudes, affectdriven behaviors, and personality traits into the current validated model is encouraged. Lastly, while this study has contributed to the literature, longitudinal designs in future research endeavors are likely to provide more precise findings and enable policy recommendations. 7 References Aguirre-Urreta, M. I., & Rönkkö, M. (2018). Statistical inference with PLSc using bootstrap confidence intervals. Mis Quarterly, 42(3), 1001-1020. doi:10.25300/MISQ/2018/13587 Ashkanasy, N. M., Ayoko, O. B., & Jehn, K. A. (2014). Understanding the physical environment of work and employee behavior: An affective events perspective. Journal of Organizational Behavior, 35(8), 1169-1184. doi:10.1002/job.1973 Azlan, A., Morshidi, S., Yusnidah, I., Russayani, I., Angelina Yee, S. V., Wan, C. D., & Singh, K. (2019). A study of model trends and changes in corporations that have established private higher education institutions (IPTS). Retrieved from Penang, Malaysia: Bentler, P. M. (2006). EQS 6 structural equations program manual Encino, CA: Multivariate Software, Inc. Besen, E., Matz-Costa, C., Brown, M., Smyer, M. A., & Pitt-Catsouphes, M. (2013). Job characteristics, core self-evaluations, and job satisfaction: What's age got to do with it? Int J Aging Hum Dev, 76(4), 269-295. doi:10.2190/AG.76.4.a Bono, J. E., Glomb, T. M., Shen, W., Kim, E., & Koch, A. J. (2013). Building positive resources: Effects of positive events and positive reflection on work stress and health. Academy of Management Journal, 56(6), 1601-1627. doi:10.5465/amj.2011.0272 Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3 ed.). New York: Routledge. Cepeda Carrión, G., Cegarra-Navarro, J.-G., & Cillo, V. (2018). Tips to use partial least squares structural equation modelling (PLS-SEM) in knowledge management. Journal of Knowledge Management, 0(0), null. doi:10.1108/JKM-05-2018-0322 Chapman, D. W., Hutcheson, S., Wan, C. D., Lee, M. A., Ann, & Md. Zain, A. N. (2017). The pursuit of excellence in Malaysian higher education: Consequences for the academic workplace. International Research in Higher Education, 2(2), 56-66. doi:10.5430/irhe.v2n2p56 Chen, G., Gully, S. M., & Eden, D. (2001). Validation of a new general self-efficacy scale. Organizational research methods, 4(1), 62-83. doi:10.1177/109442810141004 Código de campo cambiado Con formato: Español (España) 23 Chin, W. W. (2010). How to write up and report PLS analyses. In V. E. Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 655-690). Berlin, Heidelberg: Springer. Cho, H.-T., & Yang, J.-S. (2018). How perceptions of organizational politics influence self-determined motivation: The mediating role of work mood. Asia Pacific Management Review, 23(1), 60-69. doi:10.1016/j.apmrv.2017.05.003 Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2 ed.). Hillsdale, NJ: Lawrence Erlbaum Associates Publishing. Creswell, J. W. (2012). Educational research: Planning, conducting, and evaluating quantitative and qualitative research (4 ed.). Boston: Pearson Education, Inc. Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. Mis Quarterly, 39(2), 297-316. Donovan, R. A. (2018). Perceptions of stress, workload, and job satisfaction among HSS faculty in KSU. Retrieved from Du, P., Lai, M., & Lo, L. N. K. (2010). Analysis of job satisfaction of university professors from nine Chinese universities. Frontiers of Education in China, 5(3), 430-449. doi:10.1007/s11516-010- 0109-8 Eyupoglu, S. Z., & Saner, T. (2009). The relationship between job satisfaction and academic rank: a study of academicians in Northern Cyprus. Procedia - Social and Behavioral Sciences, 1(1), 686-691. doi:https://doi.org/10.1016/j.sbspro.2009.01.120 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50. doi:10.2307/3151312 Fuller, J. A., Stanton, J. M., Fisher, G. G., Spitzmüller, C., Russell, S. S., & Smith, P. C. (2003). A lengthy look at the daily grind: Time series analysis of events, mood, stress, and satisfaction. Journal of applied psychology, 88(6), 1019-1033. doi:10.1037/0021-9010.88.6.1019 George, J. M. (1996). Trait and state affect. In K. R. Murphy (Ed.), Individual differences and behavior in organizations (pp. 145-171). San Francisco: Jossey-Bass. Ghasemy, M., Hussin, S., Megat Daud, M. A. K., Md Nor, M., Ghavifekr, S., & Kenayathulla, H. B. (2018). Issues in Malaysian higher education: A quantitative representation of the top five priorities, values, challenges, and solutions from the viewpoints of academic leaders. SAGE Open, 8(1), 1-15. doi:10.1177/2158244018755839 Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2-24. doi:10.1108/EBR-11-2018-0203 Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2018). Advanced issues in partial least squares structural equation modeling. Thousand Oaks, CA: Sage. Henseler, J. (2018). Partial least squares path modeling: Quo vadis? Quality & Quantity, 52(1), 1-8. doi:10.1007/s11135-018-0689-6 Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. doi:10.1007/s11747-014-0403-8 Hult, G. T. M., F, H. J., Proksch, D., Sarstedt, M., Pinkwart, A., & Ringle, C. M. (2018). Addressing endogeneity in international marketing applications of partial least squares structural equation modeling. Journal of International Marketing, 26(3), 1-21. doi:10.1509/jim.17.0151 Judge, T. A., Thoresen, C. J., Bono, J. E., & Patton, G. K. (2001). The job satisfaction–job performance relationship: A qualitative and quantitative review. Psychological Bulletin, 127(3), 376-407. doi:I0.1037//0033-2909.I27.3.376 24 Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10. doi:10.4018/ijec.2015100101 Koning, L. F., & Van Kleef, G. A. (2015). How leaders' emotional displays shape followers' organizational citizenship behavior. The Leadership Quarterly, 26(4), 489-501. doi:10.1016/j.leaqua.2015.03.001 Lam, W., & Chen, Z. (2012). When I put on my service mask: Determinants and outcomes of emotional labor among hotel service providers according to affective event theory. International Journal of Hospitality Management, 31(1), 3-11. doi:10.1016/j.ijhm.2011.04.009 Lee, M., Wan, C. D., & Morshidi, S. (2017). Hybrid universities in Malaysia. Studies in Higher Education, 42(10), 1870-1886. doi:10.1080/03075079.2017.1376871 Li, Y.-F., Chao, M., & Shih, C.-T. (2018). Nurses’ intention to resign and avoidance of emergency department violence: A moderated mediation model. International Emergency Nursing, 39, 55- 61. doi:10.1016/j.ienj.2017.09.004 Liden, R. C., Wayne, S. J., Zhao, H., & Henderson, D. (2008). Servant leadership: Development of a multidimensional measure and multi-level assessment. The Leadership Quarterly, 19(2), 161- 177. doi:10.1016/j.leaqua.2008.01.006 Macdonald, S., & Maclntyre, P. (1997). The generic job satisfaction scale. Employee Assistance Quarterly, 13(2), 1-16. Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519-530. doi:10.1093/biomet/57.3.519 Mardia, K. V. (1974). Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhyā: The Indian Journal of Statistics, Series B (1960- 2002), 36(2), 115-128. Matthews, R. A., Bulger, C. A., & Barnes-Farrell, J. L. (2010). Work social supports, role stressors, and work–family conflict: The moderating effect of age. Journal of vocational behavior, 76(1), 78- 90. doi:10.1016/j.jvb.2009.06.011 Miller, J. S., & Cardy, R. L. (2000). Self-monitoring and performance appraisal: Rating outcomes in project teams. Journal of Organizational Behavior, 21(6), 609-626. doi:10.1002/1099- 1379(200009)21:6<609::AID-JOB42>3.0.CO;2-K Motowidlo, S. J., & Kell, H. J. (2003). Job performance. In I. B. Weiner (Ed.), Handbook of Psychology (pp. 39-53): Wiley. Nitzl, C., Roldán, J. L., & Cepeda Carrión, G. (2016). Mediation analysis in partial least squares path modeling: Helping researchers discuss more sophisticated models. Industrial Management & Data Systems, 116(9), 1849-1864. doi:10.1108/IMDS-07-2015-0302 Norzaini, A., Ibrahim, C. O., Aida Suraya, M. Y., & Ahmad Nurulazam Md, Z. (2016). Academic promotion in Malaysian public universities: a critical look at issues and challenges. Oxford Review of Education, 42(1), 71-88. doi:10.1080/03054985.2015.1135114 Patterson, M. G., West, M. A., Shackleton, V. J., Dawson, J. F., Lawthom, R., Maitlis, S., . . . Wallace, A. M. (2005). Validating the organizational climate measure: links to managerial practices, productivity and innovation. Journal of Organizational Behavior, 26(4), 379-408. doi:10.1002/job.312 Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology, 63(1), 539- 569. doi:10.1146/annurev-psych-120710-100452 Podsakoff, P. M., & Williams, L. J. (1986). The relationship between job performance and job satisfaction. In E. A. Locke (Ed.), Generalizing from laboratory to field settings (pp. 207-253). Lexington, MA: Lexington Press.