The literature review as already discussed in the previous section revealed that, terms such as “subjective wellbeing,” “wellbeing,” “job satisfaction” and “Happiness” have been synonymously used. Wellbeing has been operationalized by a variety of complex constructs touching one of the element of happiness, for example, affect, satisfaction, work engagement, quality of working life (Cropanzano and Wright, 2001; Hosie et al., 2007; Sender et al., 2020) and more commonly as job satisfaction in the work performance link (Wright, 2005). Even though, when various investigations utilize the same construct, the measurement scale was different. Diener (1984) took a more rational approach to the problem. He proved that virtually all scientific explanations of happiness could be boiled down to three key points that set them apart. According to Diener (1994), happiness is, first and foremost, a uniquely individual experience contingent on each individual’s point of view. In addition to both affect (positive and negative), a greater number of positive emotions in combination with a relatively lower number of negative emotions is a significant component in determining levels of happiness (Argyle, 1987). As a final point, happiness is a universal evaluation. As a result, a person’s entire life is taken into account when judging them. He added that happiness exhibits consistency over time (Myers, 1993; Myers and Diener, 1995). Overall, Diener classified subjective wellbeing (SWB) as the term for happiness. He explained that the evaluation of a person’s life on a cognitive and emotional level is subjective wellbeing. Most academician recognized this happiness paradigm as the hedonic wellbeing model, or the hedonic wellbeing theory. As a matter of fact, happiness is defined as the wellbeing that can be experienced by an individual (Ryan and Deci, 2001). Diener et al. (1998) found that the absence of bad moods, the existence of good moods, and the satisfaction with life can significantly impact happiness levels.

The other prospect given into contrary to hedonic is of eudemonia wellbeing/happiness. The eudemonic perspective shows engagement in activities that foster human growth, such as autonomy, personal growth, self-acceptance, life purpose, mastery, and positive relatedness, as essential to wellbeing (Ryff and Keyes, 1995; Ryff, 2013). While Ryff’s (2013) model centers on eudemonic elements, researchers stated that hedonic presents an important part in wellbeing. On the hedonic SWB conception and its measurement scale for assessing an individual’s level of happiness, the vast majority of academics have reached a consensus (Lent and Brown, 2008). In the same way, some studies on wellbeing in term of happiness had been conducted from a hedonistic point of view (Lorente et al., 2019). Although the subjective wellbeing (SWB) framework is generally thought of as context-free, In the workplace wellbeing paradigm, there has only been a relatively limited amount of research undertaken (Houge Mackenzie and Hodge, 2020).

By employing the hedonic paradigm in the context of the workplace, the study presented employee wellbeing model referred as “work-related subjective wellbeing” or “employee subjective wellbeing,” respectively (refer to Figure 1). It is defined as “employee affective and cognitive appraisal of work-life,” The definition is line with the suggestions from the previous scholars researchers (such as Bakker and Oerlemans, 2011). An employee’s emotional response and feelings at work, are considered as the affective evaluation, whereas, the cognitive evaluation and judgment refers to what employees think about their work or employment.

The justification of proposing the new definition lies in the fact that this definition considers both the cognitive and affective component in forming the composite employee wellbeing construct that has been identified in the previous literature as one of the reasons for its misinterpretation by tapping only one of the components/facet, i.e., either affective or cognitive. Another plausible justification for proposing this definition is that the operationalization of the complex wellbeing construct has now become simpler and easily understandable among the scholar of same interest from various field. It proposes that wellbeing as a multidimensional construct, will be measured as a high order composite wellbeing, with an affective and cognitive component on a single order. These arguments are based on the recommendations and empirical evidences from the previous scholarly works. For example, various attitude scholars suggested that to achieve construct (wellbeing) correspondence with job performance, considering both component (affective and cognitive) as a whole is the most appropriate approach (e.g., Norman, 1975, Fazio and Zanna, 1978; Bagozzi and Burnkrant, 1979; Schleicher et al., 2004). Similarly, Judge et al. (2001), concluded that when different facets of job satisfaction (affective and cognitive) are formed into the composite measure, there is a notable corrected correlation of r 0.30 among satisfaction and performance. Moreover studies on human brain also suggested that emotional and cognitive regions influence one another via a complex web of connections in ways that jointly contribute to behavior (e.g., Okon-Singer et al., 2015). Similarly some scholars have also demonstrated the neural integration of emotion and cognition in the brain via imaging (Shackman et al., 2011; Raz et al., 2012). As a result, the proposed definition is consistent, not only conceptually and intellectually, but also experimentally, with the earlier thoughts and research.

The term “wellbeing” refers to a state of mental and physical fitness that can be affected by a variety of factors, depending on the setting. Further, studies have also conducted on the outcomes of wellbeing. As discussed earlier, limited studies have shown that there is a positive correlation between an employee’s level of happiness and their level of performance on the job (e.g., Zheng et al., 2015; Krekel et al., 2019; Mardanov, 2020, 2021). Several different antecedents of happiness have been uncovered by other researchers. These antecedents include emotional intelligence (Carmeli, 2003), organizational rewards (Nthebe et al., 2016), perceived organizational support (Ahmed and Nawaz, 2015), organizational virtuousness (Asad et al., 2017), and personality (Higgs and Dulewicz, 2014). It has been stated by Nanda and Randhawa (2019) that, in order for organizations to manage their employees’ emotional states at work, they must create an environment in which workers can feel happy while at work and thus be able to manage their emotional states effectively. Even though wellbeing is a highly relevant variable, it has, for the most part, been ignored in work place context. Furthermore, very few studies have investigated the question of whether or not wellbeing mediates the relationship between the individual difference in both IRP and ERP by identifying several factors in the workplace that triggers happiness (Haryono et al., 2019).

When it comes to reaching different results and accomplishments, organizations place a strong emphasis on their employees’ job performance. It was Campbell (1990) who first formulated the concept of job performance and describe job performance as “synonymous with behavior” (pg. 40). This is something that people really do and can be observed. By definition, it includes behaviors or actions that are related to the organization’s goals. Work that an organization hires people to do and expects them to accomplish successfully is referred to as performance. Campbell et al. (1993) further stated that the only action that can be restrained could be considered as performance. Besides, this behavior was pointed out as manageable and will be drawn toward the organization’s objectives (Campbell et al., 1993). It is a widely held belief that employee performance on the job cannot be reduced to a single dimension, even though different academician has come up with varied definitions of what constitutes good job performance. Therefore, it ought to be understood and conceived of as a variable comprising various behaviors in their many manifestations.

Although many attempts have introduced different performance frameworks, Campbell’s definition of performance has been widely accepted as the basic definition of job performance Scholars have put forward different dimensions of job performance based on the multi-dimensional nature of human behavior, however, A closer inspection reveals that they only focus on two or three unique dimensions, namely In-role performance (IRP) or task and Extra-role performance (ERP) or contextual performance.

The notion of task performance was first introduced by Williams and Anderson (1991) and then Borman and Motowidlo (1997). Based on the behaviors they observed, they classified work performance into two categories: task performance and contextual performance. Task performance, on the other hand, is defined as behaviors specific to a job, which are formal and fundamental to the job as a whole. While, employee behaviors are normally categorized as contextual performance when they have the potential to impact the whole context in which they performs work as well as the overall psychological and organizational context in which it exists.

In addition, a review of the literature in the field confirmed that some other labels for in-role performance had also been used previously. These include task behavior (Murphy, 1989); job-specific task proficiency (Campbell et al., 1990; Griffin et al., 2007; Wisecarver et al., 2007); technical proficiency (Lance et al., 1992; Campbell et al., 2001); task performance (Borman and Motowidlo, 1993) in-role performance (Bakker et al., 2004; Maxham et al., 2008) and more recently individual work performance (Koopmans et al., 2011). The ERP dimension had also been interchangeable with several other labels in the literature. These include: contextual performance (Borman and Motowidlo, 1993);extra-role performance (Maxham et al., 2008); citizenship Performance (Organ, 1997; Organ and Paine, 1999; Coleman and Borman, 2000); non–job-specific task proficiency (Campbell, 1990); organizational citizenship behavior (Smith et al., 1983; Williams and Anderson, 1991); non-prescribe behavior (Orr et al., 1989); interpersonal relations (Murphy, 1989); organizational spontaneity (George and Brief, 1992) among others.

Concluding, all of these terms overlaps each other conceptualization and are mostly used interchangeably through the performance literature. The fact that managers or employers use ERP in addition to IRP when evaluating an employee’s performance is one of the main justifications for distinguishing the two types of job performance. Further, because both of these factors contribute to the success of the companies in their own different ways, it is essential to investigate factors affecting both the performance, even though previous researchers have largely neglected this aspect. By focusing on the factors that affect employee wellbeing, this study hopes to close this gap and improve the performance of the organization as a whole.

In organizational literature, the concept remained neglected by scholars and did not draw the practitioner’s attention. Nevertheless, due to emerging financial and moral crises around the globe, prevalent business press and business community tend to believe that nurturing virtuousness both at the organizational and individual level can enhance efficiency and performance (Cameron, 2010; Rego et al., 2011; Magnier-Watanabe et al., 2020) therefore, virtues should be considered in the agenda of business and management researches. Positive organizational scholarship is where the concept of organizational virtuousness (henceforth abbreviated as OV) first emerged. OV has been formally defined as “individuals’ actions, collective activities, cultural attributes, or processes that enable dissemination and perpetuation of virtuousness in an organization” (Cameron et al., 2004, p. 768). It is characterized as the presence of an atmosphere in which trust, humanism, forgiveness, optimism, and integrity flourished, maintained, and spread across the members of the organization (Cameron et al., 2004). Employees may develop a more optimistic outlook on their jobs due to their exposure to the organization’s core values, which, in turn, will likely affect their attitudes and behaviors (Rego et al., 2010).

As a concept, OV has received limited attention since its origin (Cameron et al., 2004), and only a small number of empirical studies have examined its consequences and determinants. Although research on this topic is sparse, some researchers have discovered that organizational and individual-level effects can be brought about through the perception of OV (for example, Bright et al., 2006; Cameron et al., 2004; Rego et al., 2015; Ahmed et al., 2018). It is argued that employees are likely to represent great performance in their job and put most of their efforts for the organization and their work when they experience their organization to be good and virtuous (Magnier-Watanabe et al., 2017). Moreover, when employee perceive organizational support (in the form of virtuous environment) they will be motivated to be grateful to their employers by engaging in ERP and IRP (Ahmed et al., 2018). Similarly, when employees perceive OV, they form emotions, organizational image, and self-construal, making their relationship stronger with their employer (Rhee et al., 2006).

Though some scholars have provided empirical evidences of OV significant direct relationship with the job performance (Ahmed et al., 2018; Pires and Nunes, 2018; Sun and Yoon, 2022) and also with the wellbeing (Rego et al., 2011; Nikandrou and Tsachouridi, 2015; Asad et al., 2017; Singh et al., 2018; Salas-Vallina, 2020), however, only a handful number of studies have focused on the psychological mechanism through which OV influences workplace outcomes and warrants further investigations (Ahmed et al., 2018; Dubey et al., 2020). Therefore, this study has considered the wellbeing as mediating mechanism for linking OV and job performance in the workplace to bridge this gap.

The basic idea of Emotional Intelligence (henceforth abbreviated as EI) can be found in the noticeable study by Thorndike (1920). According to Thorndike (1920), intelligence is basically the junction of three aspects, which are abstract intelligence (capability to manage and understand ideas), mechanical intelligence (associated with real objects) and social intelligence (related to people). He further defined social intelligence as “the ability to understand and manage men and women, boys and girls—to act wisely in human relations.” (p. 228). Later, Salovey and Mayer (1990) first defined EI and gave the true meaning of EI in their developed theory which is also called the ability model. Emotional intelligence is comprised of four different competencies that are all intertwined with one another. These competencies are: recognizing one’s own feelings as well as the feelings of others; using emotion to facilitate thought; recognizing emotional information, and controlling one’s own feelings and those of others (Mayer et al., 1990). According to the notion of EI, workers who have a high level of EI will have better success in their personal and professional lives (Carmeli, 2003).

There is considerable evidence in favor of EI, showing that EI is more critical than job-specific (technical) skills and knowledge or IQ. Brotheridge and Grandey (2002) regarded EI as twice as important as IQ and technical expertise and four times as important in overall success. Sternberg (1996), in another analysis, stated that IQ only explains 4 to 10 percent of success at work. Further, EI is double as important as intellectual intelligence, and IQ only explains twenty percent of the factors that cause life success (Goleman, 1996), whereas emotional intelligence accounted for 80% of the determinants of individual success (Martinez-Pons, 1997; Boyatzis et al., 2000). Moreover, Dulewicz and Higgs (2000) found EI as a vital success construct that directly contributed more than managerial intelligence and intelligence quotient (IQ) in job performance. Therefore, EI has become the focus of the interest of several researchers in examining the EI and job performance relationship.

In a famous study conducted by Wong and Law (2002), who used empirical research as a basis for the study on emotional intelligence, discovered a significant correlation between EI and outcomes related to the workplace. In addition, Gong et al. (2019) and Prentice (2019) have demonstrated that EI influence a number of attitudes and positive behaviors related to the job, including job satisfaction and job performance. It is argued that in contrast to someone with a lower EI score, an individual with a high EI score would be expected to be better capable of comprehending, perceiving, and managing their emotions in a way that might affect their success at work. This is why Mayer et al. (2000) suggest that EI may be an important variable in determining job performance. This is because employee with high EI are better able to understand, perceive, and have the ability to regulate their emotions in a way that contributes to their success in their careers.

More importantly, large number of studies have empirically determined EI as an important construct for increasing job performance (i.e., Sendaro and Baharun, 2020; Alheet and Hamdan, 2021; Furnham and Treglown, 2021; Miao et al., 2021) and also the wellbeing of the employees (i.e., Akhtar et al., 2017; Fida et al., 2019; Xu et al., 2021; Peláez-Fernández et al., 2022; Sha et al., 2022). However, limited studies have explored the mechanism by which EI leads to better IRP and ERP. Thus, this study aims to address this gap by considering wellbeing as a key intervening variable that is proposed to cause EI enhancing the job performance.

The Affective Events Theory (AET) theory gives us a good theoretical reason to look into the mediating processes via which background information and personality traits influence behavior in the workplace. AET provides an extremely useful theoretical framework for the investigation of these mechanisms. The purpose of this is to understand how people behave in various situations in order to better predict their behavior. From a theoretical point of view, there is a possibility that, within the context of AET, a work-related SWB may play an important role in mediating between dispositional factors (such as personality traits or emotional intelligence) and external factors (Organizational Virtuousness) in the relationship between performance behaviors and performance factors. A work-related SWB is one that is related to the workplace (i.e., the use of IRP or ERP) and therefore has a greater impact. Behavioral, affective, and emotional aspects may be crucial intervening explanations for how people behave at work and how these elements influence their behavior (Devonish, 2016). An affective state is proximal to affective events, which are important to wellbeing on the basis of AET. The impact of the effect on work behavior is determined by the impact of the effect on job attitude (Weiss and Cropanzano, 1996). As the holistic concept of wellbeing ties into cognitive and affective constructs (Greenidge et al., 2014; Devonish, 2016), this study examines how an external factor (organizational virtuousness) may contribute to IRP and ERP since virtuousness is seen as a cognitive and affective construct.

In this study, we demonstrate that according to the assumptions of AET, work-related SWB may enhance or hinder job performance (i.e., IRP and ERP depending on both cognitive and affective dimensions of the employee), considering observed levels and quality of employees (EI). This is true regardless of whether or not the employee is engaged in their work (Devonish, 2016). As a result, the study’s hypothesis claimed that work-related subjective wellbeing would mediate between individual factors (EI) and job performance. In addition to this, Organizational support theory (OST) is another theory that supports the relationship between the study’s variables. In accordance with the tenets of the OST, an employee is expected to demonstrate positive behavior to demonstrate gratitude for their benefits from their employer. This is because the employee feels obligated to show gratitude (Eisenberger et al., 1986). According to this presumption, the assistance provided by employees comes from working in an environment that exemplifies virtue. When people have the impression that they are being supported, they experience increased levels of happiness and satisfaction, which contributes to employees’ strong sense of wellbeing. Because of this wellbeing, the employee will have the feeling that they are compelled to provide favorable treatment to the organization in return for the favorable treatment they have got, and they will participate in IRP and ERP in order to help the business realize its goals.

The following conceptual model is presented in light of the research conducted for this study, which combines theoretical underpinnings, empirical findings, analytical support, and an examination of relevant literature. All of these elements help form the model’s foundation.

On the basis of the literature review and theoretical underpinning discussed in the previous sections, the following hypotheses are formulated as shown in Figure 2:

A five-point Likert scale was used to acquire the respondents responses and validated and well-established. In order to measure the variables following well established and validated scales are used. The 15-item measure that Cameron et al. (2004) established for OV’s evaluation was used. As, there is not yet a single scale capable of measuring both the emotional (including both positive and negative sensations) and the cognitive aspects of wellbeing, subjective wellbeing was examined using two distinct scales (satisfaction with life). In addition, in order to get a sense of the respondents’ levels of SWB in relation to their work, we asked them to frame their comments in terms of their work. This allowed us to get a better sense of the respondents’ SWB. The 12 item Scale of Positive and Negative Experience (SPANE) that was developed by Diener et al. (2010) was used in this concern to measure the affective wellbeing, and the 5-item SWLS (Satisfaction with Life scale) that was developed by Diener et al. (1985) was adapted to measure the cognitive component of wellbeing. Both of these scales were developed by Diener et al. (2010). Diener et al. (2010) are responsible for the development of both of these scales. In addition, the Wong Law EI Scale (WLEIS) (Wong and Law, 2002) was applied to individuals to ascertain the amount of EI they possessed. In addition, the IRP was evaluated utilizing a scale that consisted of 7 items and was established by Williams and Anderson (1991). In contrast, the ERP was evaluated utilizing a scale that consisted of 8 items and was developed by Koopmans et al. (2013). In this investigation, trustworthy and valid measurements were used to establish the variables that were to be introduced, and the measures themselves were all regarded as acceptable and useful based on the findings of the study.

It is important to stress that as this study was conceived as a construct of both reflective and formative components, it is important to focus on each separately. We chose to use PLS-SEM, as recommended by prominent scholars in the field of measurement (Sarstedt et al., 2017a,b). This section examines the second-order formative measurement which combines first-order reflections along with second-order (higher-order) formative measures.

Validity and reliability were determined using the first-order reflective measurement model. In order to accomplish this goal, the research investigated the reliability of individual items, as well as the consistency of the dependability within itself, convergent validity, and discriminant validity. In Figure 3, you can see an image depicting the PLS algorithm diagram, which was used in order to assess the measurement model. Additionally, the results of their evaluation can be found in Table 1 as well as a report on their evaluation process. Each construct’s outer loading was examined as part of the work undertaken to assess its dependence on individual items. This was done to determine whether those outer loads corroborated with the dependability of individual items (Hair et al., 2014). Hair et al. (2014) conducted a study detailing that a maximum value of outer loading was acceptable as determined by the maximum value of 0.700, which was determined by the research. As a direct consequence of this, the item loadings in the current study ranged anywhere from 0.822 to 0.962. Furthermore, we investigated the composite reliability (CR) coefficient and Cronbach’s alpha (CA) coefficient to determine the degree of internal consistency dependability as proposed by Peterson and Kim (2013). This CA result exceeded the threshold value of 0.70, which was 0.872–0.912. Additionally, the CR values were 0.876–0.916, which are higher than the cutoff value of 0.7 (Hair et al., 2010). This resulted in significant internal consistency reliability for every construct in the study. Thirdly, CV was analyzed using the values obtained from the Average Variance Extracted (AVE) statistic. According to the recommendations made by Chin (1998), an AVE value that is larger than 0.5 should be regarded as satisfactory. According to the suggestions made by Chin (1998), the findings showed that the convergent validity was good because the AVE was greater than 0.50 on their important constructs. In conclusion, the current research utilized the Fornell-Larcker Criterion in order to evaluate the discriminant validity of the results (Hair et al., 2010). Fornell and Larcker (1981. p. 11) recommend that, for all reflective constructs, “the square root of the AVE (diagonal) needs to be higher than the correlations (off-diagonal) in order to achieve sufficient discriminant validity.” In Table 2, it appears that the study met the Fornell-Larcker Criterion as well.

Measuring the formative construct in conjunction with the measurement model, Hair et al. (2014) developed a set of criteria related to the overlap and significance of outer weights as well as the collinearity issue. Figure 4 depicts the level of the partial least squares diagram that corresponds to the second-order formative measurement model. Additionally, the results of the assessment of the formative measurement model are presented in Table 3, which shows that for the constructs within the model, the outer weights have been found to be significant, along with the t-values associated with the formative constructs. Despite the fact that the emotional intelligence indicator known as “use of emotion” was not relevant (t-value = 0.338) to the formative construct of emotional intelligence. Despite this, we will continue to use this signal because the outer loading it contributes is significantly more significant than 0.50. In addition, the VIF numbers were used to investigate the collinearity issues. This was done to determine whether or not the formative indicators have a strong correlation with one another. A VIF value of 5 or higher indicates that there may be a problem with collinearity (Hair et al., 2014). As illustrated in Table 3, the results of this study indicate that for each formative indicator, the VIF values fell below the threshold value of 5 in light of the findings. Consequently, all the VIFs for the constructions did not mention any issues with multicollinearity appearing in the VIFs of the constructions.

There are three main evaluation metrics for assessing the robustness of structural models: the collinearity concerns test for the structural model, the coefficients of determination (R2) and their statistical significance explain the variance.

For the first step, we measured the VIF and tolerances of the structural model in order to find out if it was multicollinear. Based on the study material and rules of thumb, we were able to conclude that there was no collinearity issue in Table 4 since both values fell inside the range that is indicated by a tolerance value more than 0.2 and a VIF value less than 5, as stated by Hair et al. (2014).

An endogenous construct’s predictive ability was measured by its R2 value (coefficient of determination) in the research model by Henseler et al. (2009). It was then determined that there was a level and significance associated with the path coefficient so that the PLS algorithm could be run to test the hypothesis and bootstrapping could be done. As can be seen in Table 5, the study’s R2 findings can be summarized as follows.

As a rule of thumb, Henseler et al. (2009) recommend R2 values of 0.75, 0.50, and 0.25 for endogenous latent constructs relate to substantial, moderate, and weak constructs, respectively. 94.4% of the variance in the WB was accounted for by independent variables, including OV and EI, as evidenced in Table 6. There was also a substantial R2, with a value of 86.5 and 88.4% for the R2 of ERP and IRP, respectively.

We took certain steps according to the guidelines that were described in Preacher and Hayes (2004) in order to test the mediation hypothesis, which is a path coefficient, in this study. As a result of this, bootstrapping was performed in the PLS-SEM model based on these assumptions. To determine the “P-value” and “T-Value” of the bootstrapping process, two-tailed tests of significant level 5% were run in Smart-PLS, followed by 2-tailed test significance. Hence, if a t-value of 1.96 is considered to be a critical value, as a result, this should serve as a threshold value in our current study when evaluating the hypotheses. The correlation coefficient for structural models along the measurement path can be seen in Figure 5.

The following table gives the results of the structural model, which are shown in Table 7. As it turns out, the first hypothesis H1 suggests that “wellbeing mediates the relationship between OV and IRP” at a level of significance of 0.05 was significant (β = 0.367, t = 5.925, p < 0.01). Based on the results of the analysis, the second hypothesis H2, which states that wellbeing mediates the correlation between OV and ERP, has also been confirmed (β = 0.370, t = 5.906, p < 0.01). There was also a significant correlation between EI and IRP when H3 was assumed to be the mediator (β = 0.539, t = 8.293, p < 0.01). In the final hypothesis H4 we also found that an association between EI and ERP is mediated by wellbeing (β = 0.545, t = 8.387, p < 0.01). It can be concluded that the proposed hypotheses of the study were all supported by the findings of the study.