Satisfied and High Performing? A Meta-Analysis and Systematic Review of the Correlates of Teachers’ Job Satisfaction

  • META-ANALYSIS
  • Open access
  • Published: 07 December 2023
  • Volume 35 , article number  114 , ( 2023 )

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  • Gyde Wartenberg   ORCID: orcid.org/0000-0002-5586-4565 1 ,
  • Karen Aldrup 1 ,
  • Simon Grund 2 &
  • Uta Klusmann 1  

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Job satisfaction has long been discussed as an important factor determining individual behavior at work. To what extent this relationship is also evident in the teaching profession is especially relevant given the manifold job tasks and tremendous responsibility teachers bear for the development of their students. From a theoretical perspective, teachers’ job satisfaction should be negatively related to turnover intentions and absenteeism, and positively to high-quality teacher-student interactions (i.e., emotional support, classroom management, and instructional support), enhanced student motivation, and achievement. This research synthesis provides a comprehensive overview of the relationship between teachers’ job satisfaction and these variables. A systematic literature search yielded 105 records. Random-effects meta-analyses supported the theoretically postulated relationships between teachers’ job satisfaction and their turnover intentions, absenteeism, teacher-student interactions, and students’ outcomes. Effects were significant not only for teachers’ self-reports of their professional performance, but also for external reports. On the basis of the research synthesis, we discuss theoretical, conceptual, and methodological considerations that inform future research and prospective intervention approaches.

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Introduction

Job satisfaction represents a key indicator of occupational well-being and has gained widespread interest in both research and practice as an important factor for predicting occupational behavior (Judge et al., 2001 ; Spector, 2022 ; Weiss, 2002 ; Wright et al., 2007 ). Across different occupational groups, job satisfaction is positively associated with general productivity, more satisfied recipients, a higher commitment to the job, and enhanced engagement (Judge et al., 2001 ; Meyer et al., 2002 ; Whitman et al., 2010 ).

The question of whether teachers’ job satisfaction is, as in other occupational groups, crucial for their professional performance seems particularly important given the great responsibility teachers bear for the cognitive-motivational development of their students (Hamre & Pianta, 2001 ; Matteucci et al., 2017 ; Tymms et al., 2018 ). At the same time, the teaching profession belongs to a professional group that faces high dropout rates, especially among those entering the profession (den Brok et al., 2017 ; Ingersoll, 2001 ), increasing teacher shortage worldwide (OECD, 2005 ; UNESCO Institute for Statistics, 2016 ), generally low occupational well-being (Iriarte Redín & Erro-Garcés, 2020 ), and frequent incapacity to work due to mental and physical illness in the teaching profession (Seibt et al., 2009 ). Therefore, the urgent question arises as to which psychological characteristics might play a role in these phenomena. Studies across different occupational groups that show associations with reduced turnover intentions, attrition rates, and increased mental and physical health suggest that job satisfaction could be an answer to this question (Baker, 2004 ; Faragher et al., 2013 ; Wright et al., 2007 ). However, it is not self-evident that those results transfer to the teaching profession. Above all, general studies do not allow any conclusions about whether there are certain aspects of the teaching profession for which job satisfaction is particularly relevant.

Against this background, it seems particularly important to investigate the job satisfaction–performance link for the teaching profession and systematically review previous findings. Previous meta-analyses that refer specifically to the teaching profession suggest that job satisfaction might also be important for teachers and link it to general outcomes such as turnover intentions (Li & Yao, 2022 ; Madigan & Kim, 2021 ). In addition to general performance indicators (e.g., turnover intentions and absenteeism), examining clearly defined job requirements specific to the teaching profession, such as the quality of teacher-student interactions and students’ cognitive and motivational development, allows for a more differentiated insight into the specific areas of teachers’ work that benefit more or less from job satisfaction. Accordingly, the present meta-analysis aims to address this research gap and to summarize the various studies investigating the relationship between job satisfaction and specific performance outcomes in the teaching profession in addition to more general outcomes.

The Concept of Job Satisfaction

The concept of job satisfaction has been the subject of research for decades. It can be defined as an attitude towards the job resulting from a cognitive evaluation of specific job aspects (Spector, 2022 ; Weiss, 2002 ). In this sense, job satisfaction indicates what people think about their job (Spector, 2022 ), whether they perceive their needs to be satisfied at work (Dinham & Scott, 1998 ), and whether they experience a balance between rewards received and energy invested (Scarpello & Campell, 1983 ). As a cognitive evaluation of the work, job satisfaction is one of the domain-specific aspects of subjective well-being, which is divided into cognitive and affective experiences (Diener et al., 1999 ). Likewise, Weiss ( 2002 ) clearly distinguished the cognitive evaluation of job aspects from the emotional experiences and argued that both job satisfaction and affect are reciprocally related but distinct constructs (Ellsworth & Scherer, 2003 ; Judge & Ilies, 2004 ).

In addition to the global evaluation of one’s own work, job satisfaction can be operationalized by satisfaction with specific aspects of one’s work (Spector, 2022 ). Different frameworks emphasize various facets, of which the following four have often been identified empirically (Lester, 1987 ; Spector, 2022 ): nature of work (e.g., satisfaction with work content and work itself), general context factors (e.g., satisfaction with leadership, supervision, and autonomy), rewards (e.g., satisfaction with recognition, pay, and promotion), and social aspects of the job (e.g., satisfaction with colleagues and cooperation).With regard to the teaching profession, initial evidence also points to the multidimensionality of job satisfaction, with the factors “nature of work” and “context” emerging most clearly (Dicke et al., 2019 ). The different satisfaction facets correlate moderately with each other as well as with global measures of job satisfaction (Highhouse & Becker, 1993 ; Spector, 2022 ). Accordingly, it is reasonable to consider both global job satisfaction and specific facets separately because they are not congruent (Bowling & Hammond, 2008 ; Spector, 2022 ).

Whether general or facet-specific, job satisfaction is not only discussed in the context of individuals’ mental and physical health (Benevene et al., 2018 ; Faragher et al., 2013 ; Simone et al., 2016 ), but also as an important predictor of general performance and success in the workplace (Diener, 2012 ; Judge et al., 2001 ).

Job Satisfaction and Professional Performance: Theoretical Considerations

Theoretical ideas describe different psychological processes through which job satisfaction could be related to individual behavior at work. These processes are summarized in the affective-events theory (Weiss & Cropanzano, 1996 ), which distinguishes two pathways explaining the link between job satisfaction and professional performance, one involving cognition-driven behavior and the other mediated through affect-driven behavior (Weiss, 2002 ). The assumption underlying cognition-driven behavior postulates that attitudes, such as job satisfaction, determine behavioral tendencies, which result in behavioral consequences such as approach or avoidance behaviors (Ajzen, 1991 ). For instance, job satisfaction likely leads to the desire to maintain the positively evaluated situation in contrast to thoughts about leaving the job or frequent absenteeism (Siegrist, 2002 ; Weiss, 2002 ). This experience facilitates more autonomous forms of motivation and enhances the ability to direct, regulate, and energize individual behavior (Kumari et al., 2021 ), likely resulting in higher engagement and further investment of individual resources.

In addition to the cognitive pathway, job satisfaction can also influence behavior mediated by affective experiences (Weiss, 2002 ; Weiss & Cropanzano, 1996 ). This assumes that job satisfaction is associated with positive affective experiences (Ellsworth & Scherer, 2003 ; Judge & Ilies, 2004 ; Weiss, 2002 ; Wright et al., 2007 ). Positive affect, in turn, potentially increases an individual’s thought-action repertoire and strengthens personal resources (Fredrickson, 2001 ; Fredrickson & Branigan, 2005 ; Scherer & Moors, 2019 ). As a result, basic cognitive processes, such as problem solving or executive functioning, are enhanced, which, in turn, translates into individual behavior and professional performance (Eysenck & Calvo, 1992 ; Forjan et al., 2020 ).

Empirical research suggests that job satisfaction is associated with a variety of behaviors in the professional context, linking it to general productivity and performance at work, lower turnover intentions, and higher work attendance across different occupational groups, because general job satisfaction not only enhances prosocial behaviors, work engagement, and commitment to the job, but is also associated with reduced health problems and negatively related to somatic symptoms, such as sleeping problems, headaches, and tachycardia (Benevene et al., 2018 ; Simone et al., 2016 ; Tett & Meyer, 1993 ; Whitman et al., 2010 ). For this reason, satisfied teachers should be less likely to think about leaving their job. Likewise, these teachers should be less likely to be absent, not only because job satisfaction is associated with reduced health problems, but also because satisfied teachers are less likely to stay home with minimal health complaints. Against this background, the question of how strong the relationship between job satisfaction and professional performance is in the teaching profession and for which aspects of teachers’ professional performance it is most decisive seems particularly compelling.

Job Satisfaction and Professional Performance in the Teaching Profession

The following explains what we mean by professional performance and what aspects can be distinguished in the various job tasks of teachers.

Defining Teachers’ Professional Performance

In general, turnover intentions and absenteeism represent relevant indicators of professional performance across occupational groups. After all, a cognitive withdrawal from work and frequent sick-leaves impede successful performance of job tasks. In the teaching profession, the most important job tasks include creating effective learning environments through supportive teacher-student interactions, increasing student motivation, and facilitating successful student learning (Bardach & Klassen, 2020 ; Kim et al., 2019 ; Zee & Koomen, 2016 ).

Various conceptual approaches describe emotional support, classroom management, and instructional support as key dimensions of teacher-student interactions (Hamre et al., 2013 ; Kunter & Voss, 2013 ; Praetorius et al., 2018 ). Emotional support indicates the generation of a supportive learning environment by the teachers that acknowledges students’ academic, social, and emotional needs (Strati et al., 2017 ). By contrast, effective classroom management is needed to maximize instructional learning time through the proactive management of classroom disruptions and the establishment of behavioral rules and routines (Clunies-Ross et al., 2008 ). Lastly, instructional support describes the facilitation of student interest, motivation, and higher-order thinking through a variety of teaching strategies (Pianta et al., 2012 ; Scherer et al., 2016 ). Empirical research suggests that these three dimensions are central for both students’ motivation and achievement, which can be defined as follows (Allen et al., 2013 ; Bosman et al., 2018 ; Downer et al., 2010 ; Jennings & Greenberg, 2009 ).

Student motivation is described as the central force that drives specific actions, decisions, and intensity of behavior. Self-determination theory (Ryan & Deci, 2017 ) and expectancy-value theory (Wigfield & Eccles, 2000 ) are among the most influential psychological theories on motivation and help define this comprehensive construct. According to these theories, the experience of autonomy (e.g., self-efficacy) and competence (e.g., self-concept) are central determinants of students’ expectancy to successfully accomplish a task and, consequently, of students’ behavioral engagement in learning activities. Furthermore, the value students attach to their tasks, whether they perform them out of inherent interest and enjoyment (i.e., autonomous motivation) or for external incentives and to avoid punishment (i.e., controlled motivation), is seen as a relevant part of students’ motivation (Ryan & Deci, 2020 ; Wigfield & Eccles, 2000 ). Intrinsic motivation is related to learning goals, promotes academic engagement, and manifests itself in interest, all of which reflect motivational constructs (Howard et al., 2021 ; Spinath & Steinmayr, 2012 ). Therefore, we considered all of these variables as indicators of students’ motivation in our meta-analysis.

S tudent achievement is closely related to students’ motivation (Howard et al., 2021 ) and indicates the extent to which instructional strategies and learning activities have been successful in enhancing students’ knowledge, understanding, and skills. Students’ achievement is often assessed by grades, test scores, or general teacher appraisals.

Linking Teachers’ Job Satisfaction to Their Professional Performance

Arguably, job satisfaction is also positively related to the effective completion of the various job tasks in the teaching profession, as has been suggested for general performance measures in different occupational groups (Meyer et al., 2002 ; Tett & Meyer, 1993 ; Whitman et al., 2010 ). The prosocial classroom model (Jennings & Greenberg, 2009 ) provides a teaching-specific rationale and suggests that teachers’ well-being determines the quality of teacher-student interactions, which, in turn, are perceived by their students and thus affect students’ motivation and achievement.

Accordingly, job satisfaction should result in more effective teacher-student interactions. Satisfied teachers are thought to invest more resources (e.g., time and effort) in both lesson planning and implementation (Granziera & Perera, 2019 ; Siegrist, 2002 ) and are more effective at problem solving and managing unexpected situations due to an enhanced thought-action repertoire (Burić & Moè, 2020 ; Forjan et al., 2020 ; Fredrickson & Branigan, 2005 ). Cognitive capacities are not used on negative thoughts such as turnover intentions. This should, on the one hand, be apparent in more effective classroom management. On the other hand, enhanced cognitive processes should enable teachers to create cognitively activating and engaging lessons and to respond to student questions more flexibly. Likewise, the positive affect associated with job satisfaction facilitates social interactions (Forgas, 2002 ; Frenzel et al., 2021 ). Hence, teachers might have more cognitive and emotional resources available to show empathy, care, and sensitivity for students’ needs (Isen, 2001 ; Nezlek et al., 2001 ). Additionally, satisfied teachers show more enthusiasm while teaching (Burić & Moè, 2020 ), which is, on the one hand, positively associated with emotional support, classroom management, and instructional support (Kunter et al., 2008 ; Lazarides et al., 2021 ) and, on the other hand, likely facilitates students’ motivation and achievement through both students’ experience of autonomy and competence (Allen et al., 2013 ; Moè & Katz, 2020 ; Ruzek et al., 2016 ) and emotional contagion (Frenzel et al., 2021 ; Hatfield et al., 1993 ). Lastly, teachers’ job satisfaction likely impacts students’ learning through reduced absenteeism (Miller et al., 2008 ).

Complementing the assumptions outlined above, established theoretical models suggest that the constructs under consideration are reciprocally related (Frenzel et al., 2021 ; Jennings & Greenberg, 2009 ; Judge et al., 2001 ). Accordingly, teachers who experience success in accomplishing work tasks as indicated by both students’ motivation and achievement and positive teacher-student interactions in class are more likely to be satisfied with their work (Jennings & Greenberg, 2009 ; Judge et al., 2001 ). For instance, it might be easier to provide effective teaching with motivated and high-performing students, which, in turn, could foster teachers’ job satisfaction (Frenzel et al., 2021 ; Jennings & Greenberg, 2009 ).

The Role of Specific Facets of Job Satisfaction in Teacher Performance

As outlined above, job satisfaction can be classified into four overarching dimensions (Spector, 2022 ), that is, nature of work (e.g., satisfaction with teaching, student accomplishment, student behavior, and working with students), social aspects (e.g., collegial support, supervision, communication, and cooperation), general context factors (e.g., satisfaction with school management, operating procedures, school environment, amount of administrative work, autonomy, and professional development), and rewards (e.g., pay, fringe benefits, contingents rewards, and promotion). The distinction between different facets of job satisfaction is important with regard to the assumption that they might be differentially associated with teachers’ professional performance. For instance, in evaluating their professional situation, teachers might place particular emphasis on one specific facet of the job, which, in turn, might particularly influence their professional performance.

Central reasons why teachers choose their profession include the variety of social interactions (e.g., with students, colleagues, and parents) and the responsibility for the social-emotional development of students in addition to the mission of teaching and knowledge transfer (Watt et al., 2012 ). This is also reflected in teachers’ professional goals because establishing positive teacher-student relationships and contributing to student learning seem increasingly important in this profession (Butler, 2012 ). Hence, aspects regarding the nature of work (e.g., teacher-student relationship, interactions, and students’ learning achievements) may play a major role in teachers’ evaluation of their job. The resulting satisfaction with teaching-related aspects, in turn, might be particularly relevant for their professional performance.

The Present Review

Job satisfaction is an important aspect of teachers’ occupational well-being. Additionally, job satisfaction could be an important factor in reducing teacher attrition and absenteeism, promoting high-quality teacher-student interactions and thus obtaining positive student development. Thus, teachers’ job satisfaction might not only be critical for the individual’s health and well-being (Simone et al., 2016 ), but also for student development (Jennings & Greenberg, 2009 ). The present research synthesis is the first to provide a comprehensive overview of prior research on the relationship between teachers’ job satisfaction and professional performance, both generally, in terms of turnover intentions and absenteeism, and profession-specific, with regard to the quality of teacher-student interactions and students’ motivation and achievement. By considering this broad set of variables, we go beyond previous research syntheses that were either based on a more general conceptualization of well-being (i.e., several well-being indicators combined into one variable) and did not allow for a differentiated investigation of job satisfaction and its facets, or considered only general outcomes such as turnover intentions (Bardach et al., 2022 ; Madigan & Kim, 2021 ; Maricuțoiu et al., 2023 ).

In addition to summarizing what we can learn from prior research, our goal was to uncover areas with insufficient evidence and discuss where more research is needed to obtain a more nuanced understanding of the potentially complex relationship between teachers’ job satisfaction and their performance and to approach the question of which specific facets of teachers’ job satisfaction are especially important for teachers’ performance. Having a reliable basis from which to draw general conclusions is particularly important for different stakeholders in the teaching profession to assess the importance of promoting teachers’ job satisfaction.

The heuristic working model (Fig.  1 ), which is based on the theoretical assumptions outlined above, summarizes the hypothesized relationships between teachers’ job satisfaction and turnover intentions, absenteeism, quality of teacher-student interactions, and students’ educational development. Accordingly, we expected teachers’ job satisfaction to correlate negatively with their turnover intentions and absenteeism while assuming a positive relationship with the quality of teacher-student interactions because general job satisfaction should enhance teachers occupational commitment, engagement, and the investment of individual resources (Granziera & Perera, 2019 ; Siegrist, 2002 ), as well as expand the thought-action repertoire (Burić & Moè, 2020 ; Forjan et al., 2020 ; Fredrickson & Branigan, 2005 ).

figure 1

Heuristic working model

Ultimately, teachers’ job satisfaction likely relates to students’ academic development through high-quality teacher-student interactions (Allen et al., 2013 ; Jennings & Greenberg, 2009 ), the transfer of positive affect in class (Hatfield et al., 1993 ), and less teacher absenteeism. However, as students’ motivation and achievement are more distal to teachers’ well-being, less pronounced relationships are expected for these variables (Bardach & Klassen, 2021 ).

Second, we also hypothesized stronger effects for satisfaction with the nature of work compared to other facets of job satisfaction (i.e., context factors, rewards, and relationship with colleagues) because interacting with students and contributing to students’ development represent important reasons for teachers’ career choices and professional goals (Butler, 2012 ; Watt et al., 2012 ).

Third, we examined study and sample characteristics that could explain the expected variability of effect sizes between studies. Because the use of self-reported questionnaires alone carries the risk of inflated observed correlations due to common method bias (Podsakoff et al., 2012 ), we examined whether significant correlations could also be found for other sources of reports. For instance, teachers’ current affectivity might influence both their recall of experiences, such as their general satisfaction experienced during the last school year, and the recall of information on the outcome, such as the number of days absent, or the occurrence of negative teacher-student interactions (Tourangeau, 2000 ). Accordingly, teachers’ self-reports are likely to lead to an overestimation of the relationships as they are based on shared variance rather than on true relationships. We further controlled for teachers’ gender, years of teaching experience, and grade level taught. Empirical evidence suggests that these sample characteristics account for different experiences of job satisfaction and influence the development of the observed performance outcomes (Ettekal & Shi, 2020 ; Scherrer & Preckel, 2019 ; Toropova et al., 2021 ).

Lastly, we also reviewed longitudinal studies to verify whether effects are evident across different time spans and to provide an insight into the stability of job satisfaction and its reciprocity with professional performance. Longitudinal studies are thought to be a more appropriate approach to the question of causality than cross-sectional studies. However, smaller effect sizes are expected; on the one hand, because of the time interval between the assessment of the constructs under consideration and, on the other hand, because of controlling for the baseline levels of the criterion to predict changes in the criterion. This likely reduces the size of the relationship because a lot of variance can already be explained by the stability of the construct (e.g., predicting students’ end-of-year grade with teachers’ job satisfaction at the beginning of the school year while controlling for students’ baseline achievement).

Literature Search

We conducted a systematic literature search in November 2020 and updated the search in December 2022 (Fig.  2 ). We initially searched the databases PsycINFO, Web of Science, and OpenGrey to identify both published and unpublished work on teachers’ job satisfaction. For the literature search conducted in PsycINFO, we combined the thesaurus search terms for teacher with the thesaurus terms for job satisfaction leaving the outcomes of interest out in the first step to obtain a comprehensive literature review and avoid studies that only incidentally reported the association between teachers’ job satisfaction and their professional performance from not appearing in our literature search. This search had 692 results. Subsequently, we confined the search in Web of Science by including the outcome variables of interest using a combination of related terms with the following constructs: job satisfaction, absenteeism, turnover intentions, teacher-student interactions, emotional support, classroom management, instructional support, students’ achievement, and students’ motivation. This resulted in 279 additional records after removing duplicates. We conducted an additional search in OpenGrey to search specifically for unpublished work, theses, and dissertations. This revealed three additional records. The detailed search terms and strategies that we implemented in the different databases are listed in the Online Supplement (Table S1 ). Among other aspects, we searched titles, abstracts, and keywords for the specified terms.

figure 2

PRISMA flow diagram for the literature search process

In addition to the database searches we reviewed the reference lists of the identified studies as well as previous meta-analyses that investigated similar relationships for relevant titles (Bardach et al., 2022 ; Madigan & Kim, 2021 ; Maricuțoiu et al., 2023 ). We also searched the titles citing the identified studies. In total, the backward search, citation search, and review of previous meta-analyses identified 163 new records.

Inclusion Criteria

With the support of three student research assistants, the first and second authors screened titles and abstracts of the records identified by the aforementioned search strategies. All coders successfully completed training based on the coding manual prior to the prescreening. We included studies for the subsequent full-text coding, that (1) assessed teachers’ job satisfaction, (2) reported on turnover intentions, absenteeism, teacher-student interactions, or students’ motivation and achievement, (3) investigated a sample of teachers currently teaching at pre-, elementary, or secondary schools, (4) reported quantitative data, and (5) were written in a language with a Latin alphabet. We implemented these inclusion criteria rather liberally and considered studies for full-text coding where it was not apparent from the title and abstract whether they would fulfill the criteria to minimize the risk of erroneously excluding relevant studies. Nevertheless, 813 studies that did not meet these criteria were excluded based on the screening of title and abstracts, as illustrated in the PRISMA flow diagram (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; Page et al., 2021 ) in Fig.  2 . We applied the following refined inclusion and exclusion criteria in the subsequent full-text coding.

Criteria Regarding Teachers’ Job Satisfaction

We included both studies measuring job satisfaction across a variety of established instruments (e.g., Job Satisfaction Scale, Warr et al., 1979 ; Teacher Job Satisfaction Questionnaire, Lester, 1987 ) and studies using self-designed items (e.g., “Overall, how satisfied are you with teaching as a job?”). Studies measuring general positive affect (e.g., “When I’m at work, I feel pretty happy.”) or career optimism (e.g., “I get excited when I think about my teaching career.”) were excluded from the analysis. Studies were also excluded if they operationalized job satisfaction with one item that asked only about teachers’ career decisions (e.g., “Would you choose teaching again?”) because this item is conceptually close to intention to stay. Likewise, scales confounding job satisfaction with other aspects, such as self-efficacy, turnover intentions, or job characteristics, were excluded (e.g., Mogilevsky, 2019 ).

Criteria Regarding Teachers’ Occupational Performance

We included studies that assessed teachers’ professional performance via teachers’ self-reports as well as via student ratings, classroom observations, principal reports, and school records.

With regard to teacher-student interactions, we excluded studies using scales that asked teachers to appraise their own abilities, such as scales on teachers’ self-efficacy or teachers’ educational beliefs and attitudes (e.g., Vidić & Miljković, 2019 ) because the mindset about one’s own abilities does not necessarily translate into actual behavior. In contrast, we considered both scales that captured teachers’ interaction behavior (e.g., being sensitive for students’ individual problems, anticipating student misbehavior, and providing cognitively challenging tasks) and scales that depict student behaviors as an indicator of the quality of teacher-student interactions (e.g., approaching the teacher with questions or individual problems, and frequency of disruptive or off-task behavior).

Additionally, we included a category for general teacher-student interactions to include scales combining different aspects and thus impeding a clear assignment to the categories of emotional support, classroom management, or instructional support.

Because we focused on turnover intentions, we did not consider studies that investigated actual turnover (e.g., Cha & Cohen-Vogel, 2011 ). On the one hand, we decided to target cognitive withdrawal in contrast to behavioral withdrawal because of the paucity of studies that did not only investigate post-hoc assessments of teachers who had already left the teaching profession. On the other hand, turnover intentions are discussed as the most likely valid predictor of actual turnover (Ajzen, 1991 ; Wong & Cheng, 2020 ).

Generally, we excluded studies that measured the dependent variable at the school level (e.g., Dicke et al., 2019 ). Such measures preclude an unambiguous assignment of the outcome to the specific teacher. Studies reporting on students’ misbehavior in class were included in the analysis because the presence or absence of students’ behavioral problems in class can be categorized as an indicator of classroom management (Pianta et al., 2012 ).

Criteria Regarding Teacher Sample

Because we were interested in teachers currently teaching at general pre-, elementary, middle, or high schools, we excluded studies that surveyed preservice, special education, university, or college teachers. Those contexts are not readily comparable to general school contexts (Bettini et al., 2019 ) and could thus reduce the comparability of the samples. Similarly, samples of principals and administrative school staff were excluded from the meta-analyses because these professional groups are less involved in interactions with students. However, if a mixed sample comprised more than 50% general preschool, elementary, middle school, or high school teachers, studies were included in the analysis.

Criteria Regarding Effect Sizes

We included studies that reported either correlation coefficients or other statistics that can be converted to correlation coefficients, such as statistics for mean differences (Thalheimer & Cook, 2002 ). We also considered longitudinal studies or intervention studies. Of the longitudinal studies, we only included cross-sectional effect sizes in the meta-analyses and additionally reviewed longitudinal effect sizes systematically. For intervention studies, we only drew on effect sizes from the baseline measurement or the control group to exclude the possibility of biased correlations based on intervention effects. Studies that did not report the bivariate correlation coefficients and, for example, only reported results from multiple regression analyses or from structural equation models were excluded from the meta-analyses because these analyses control for a variety of covariates that differ between studies, which can change the interpretation of effect sizes. In these cases, we contacted the authors of the respective studies to ask for the correlation coefficients. Of the 58 authors contacted via email for this purpose, 15 provided the requested coefficients. Still, 43 studies had to be excluded because the requested correlation coefficients could not be obtained.

A total of 105 studies were included in the research synthesis based on the more refined criteria, thus excluding a further 203 records after full-text coding. Figure  2 provides a detailed overview of the reasons for why studies were excluded.

For the meta-analyses, three additional studies were excluded because they only reported longitudinal effect sizes. One additional study was excluded because it only measured on specific job satisfaction facet (i.e., pay satisfaction), which is too specific to indicate general job satisfaction. However, these studies were considered in the systematic review of longitudinal and facet-specific research.

Agreement between coders on deciding whether to include or exclude a study after full-text coding was κ = 0.82, based on double coding of 92 studies (30% of total studies coded). Any discrepancies in the decisions about inclusion or exclusion were resolved through discussion. An overview and references of included studies are available in the Online Supplement (Tables S2 and S3 ).

The first and second authors independently conducted the aforementioned full-text coding of primary studies. We retrieved central study characteristics (i.e., authors, publication year, publication type, journal, design, and response rate), sample characteristics (i.e., sample size, country, percentage of female teachers, mean age, mean teaching experience, subject taught, grade level taught, student composition, and mean class size), and information on the investigated variables (i.e., type of construct, instrument used, number of items, response scale, rater perspective [i.e., self-reports vs. other], mean, standard deviation, and reliability). For a detailed overview of the definition and operationalization of constructs considered as correlates of teachers’ job satisfaction, see Table S4 in the Online Supplement. Additionally, the following information was coded for the effect size: type of effect size (e.g., correlation, and mean difference), level of significance, information on whether manifest or latent effect sizes were retrieved, and source of the effect size (i.e., reported in article, requested from authors, and calculated based on primary data).

Regarding the classification of the dependent variables into the associated coding category (i.e., turnover intentions, absenteeism, emotional support, classroom management, instructional support, general interaction, student motivation, and student achievement), the agreement between coders was κ = 0.92 based on the double coding of 35% of the included studies ( k  = 35). To verify the reliability of the coding process, either the first or second author or a trained student research assistant double-coded studies depending on who had conducted the preliminary coding. Any discrepancies in coding decisions were resolved through discussion. The data are available open access at PsychArchives ( https://doi.org/10.23668/psycharchives.13691 ).

Data Processing

We were interested in the relationship between teachers’ job satisfaction and professional performance. For most effect sizes included, we directly extracted and coded the respective correlation coefficients. Only two studies reported mean differences (Lambert et al., 2012 ; Patrick, 2007 ), which we converted to correlation coefficients (Borenstein et al., 2009a ; Thalheimer & Cook, 2002 ). Afterwards, we recoded effect sizes for the sake of interpretability so that positive correlations indicated that higher job satisfaction was associated with higher professional performance. Although most studies reported manifest effect sizes, six studies reported latent correlations. In order to increase the comparability of effect sizes, we estimated the uncorrected correlation for these studies following the formula of Hunter and Schmidt ( 2004 , p. 96). Furthermore, we considered dependencies that emerged in the data of the primary studies before meta-analytic aggregation of effect sizes. Including multiple effect sizes based on the same sample might bias standard errors and distort the overall results of a meta-analysis (Borenstein et al., 2009b ; Hunter & Schmidt, 2004 ). Three types of dependencies existed in primary studies: dependent effect sizes due to multiple subsamples reporting on one association, dependent effect sizes due to the application of multiple subscales and dependent effects due to the use of self-reports and other reports of the observed constructs within one sample. We did the following to deal with these dependencies. First, when studies included multiple reports of the same relationship based on dependent subsamples, we considered only the effect size from the largest subsample. Second, if articles reported more than one effect size for each association because they used multiple subscales for the same construct, we calculated the correlation for the corresponding composite score, following the recommendations of Hunter and Schmidt ( 2004 , pp. 435–439). This approach not simply averages effect sizes within studies but makes it possible to correctly estimate composite effect sizes based on the number of different subscales, the covariance of constructs from different subscales, and the dependent effect sizes. Third, because we were interested in potential differences in the effect sizes that were due to the rater perspective, we did not aggregate self-reports and other reports. This concerned one study (Stahl Lerang et al., 2021 ). Although the error in the aggregated effect sizes is reasonably small if the number of effect sizes that are based on the same sample is minor compared to the total number of effect sizes included in the synthesis (Hunter & Schmidt, 2004 ), we applied robust variance estimation to address the dependencies in sampling errors within this study (Hedges et al., 2010 ).

Following the preparation of effect sizes, we conducted univariate meta-analyses for each performance outcome for which at least five primary studies were available (Higgins et al., 2009 ). We specified random-effects models because of the expected variability of effect sizes between studies. We applied the Hunter-Schmidt estimator for heterogeneity in all analyses with untransformed correlation coefficients using the R package metafor (Field, 2001 ; Viechtbauer, 2010 ). Additionally, we conducted psychometric meta-analyses with artifact distributions using the R package psychmeta to account for the impact of measurement error on the effect sizes (Dahlke & Wiernik, 2021 ; see also Hunter & Schmidt, 2004 ).

We systematically reviewed studies that assessed individual job satisfaction facets to explore the question of whether different facets of job satisfaction are especially closely associated with specific performance outcomes in the teaching profession. Due to the paucity of studies that measured job satisfaction on a facet-specific basis, we only calculated univariate meta-analyses for the association between different job satisfaction facets (i.e., satisfaction with nature of work, relationships with colleagues, and context factors) and turnover intentions ( k  ≥ 5).

The total amount of heterogeneity was estimated based on τ 2 , which represents an absolute measure of heterogeneity between studies (Borenstein et al., 2017 ). We also computed the Q -test for heterogeneity and the relative proportion of variance in the effect sizes that can be attributed to between-study differences with the I 2 statistic (Borenstein et al., 2017 ). We ran meta-regression models using the R package metafor (Field, 2001 ; Viechtbauer, 2010 ) to examine potential moderators possibly explaining heterogeneity in effect sizes between studies. The following study and sample characteristics were considered in the meta-regression models: publication year, type of publication, teachers’ gender, years of teaching experience, grade level (i.e., elementary, secondary, and mixed), and the rater perspective (i.e., self-reports or other). Continuous moderators were entered directly into meta-regression models while categorical moderators were dummy-coded for these analyses. We considered moderators in separate meta-regression models and did not include them simultaneously because the number of studies with complete information on all moderators would be too small for the analysis. In addition to meta-regression models, we conducted subgroup analyses to further explore the differences between teacher self-reports and external reports for teacher-student interactions.

To consider the possibility of biased estimates of the average effect size (Carter et al., 2019 ), we investigated potential publication bias visually using funnel plots and statistically by conducting Egger’s regression tests and computing precision-adjusted effects sizes (Egger et al., 1997 ).

Except for considering measurement error (through psychometric meta-analyses) and publication bias, we did not assess any other risk of bias, such as the risk of bias in the studies included with standardized risk-of-bias tools. Risk of bias is undoubtedly an important issue (Higgins et al., 2019 ). While approaches to the assessment of risk of bias are well established in meta-analyses of randomized and nonrandomized trials (Sterne et al., 2019 ), there are currently no clear recommendations on how to assess risk of bias in correlational studies. Therefore, we decided to reveal potential sources of risk of bias with the help of meta-regression analyses and to investigate whether effect sizes differed as a function of the study and sample characteristics. With the exception of the risk-of-bias assessment, the research synthesis adhered to the PRISMA checklist (Page et al., 2021 ).

Study and Sample Characteristics

Although we specifically searched for unpublished work, the vast majority of the 101 studies included in the meta-analyses represented published journal articles ( k  = 78). However, 23 studies were either un- or informally published, including two master theses, one book chapter and 20 dissertations. Studies were published between 1973 and 2022 ( Md.  = 2015). Most studies were conducted in the United States ( k  = 39), followed by Norway ( k  = 7) and Canada ( k  = 7). Six studies were conducted in China, four in Germany and Spain respectively, and three studies each in Belgium, Ghana, Israel, New Zealand, and Spain.

Overall, the 101 studies included in the meta-analysis were based on a total sample size of 323,035 teachers, varying from 14 to 154,959 teachers per sample. However, excluding two studies with an extremely large sample size of 104,358 and 154,959 teachers (Bellibaş et al., 2020 ; Blömeke et al., 2021 ) left a total sample size of 63,718, ranging between 14 and 4,208 teachers per study. The percentage of female teachers ranged between 13.3% and 100.0% with an average of 70% female teachers per sample. The overall mean teaching experience was 11.75 years ( SD  = 5.28), with a minimum of less than one year and a maximum of more than 22 years of teaching experience. The data set contained 14 studies conducted in primary schools, 33 studies with secondary school teachers, and 47 studies based on mixed grade levels.

Most of the studies measured general job satisfaction. Only 15 studies considered different facets of job satisfaction. Moreover, most studies used self-designed items to measure job satisfaction, with 17 studies using single-item scales (e.g., “Overall, how satisfied are you with teaching as a job?”). The validated instruments that were used most frequently included the Job Satisfaction Scale ( k  = 4; Warr et al., 1979 ), the Job Diagnostic Survey ( k  = 4; Hackman & Oldham, 1975 ), the Teacher Job Satisfaction Questionnaire ( k  = 3; Lester, 1987 ), and the Teacher Job Satisfaction Scale ( k  = 3; Skaalvik & Skaalvik, 2011 ).

Overall Effects

We examined the relationship between teachers’ job satisfaction and professional performance separately for each outcome of interest (Table 1 ). Forest plots are provided in the Online Supplement (Figs. 1 – 8 ). While 61 studies reported on teachers’ turnover intentions, only 14 studies investigated the relationship with absenteeism. Regarding teacher-student interactions, most studies considered instructional support ( k  = 14), general teacher-student interactions ( k  = 14), and classroom management ( k  = 13), while only seven studies assessed emotional support. Associations with students’ achievement ( k  = 8) and motivation ( k  = 6) were also rarely studied.

As hypothesized, results indicated a significant negative association of teachers’ job satisfaction with both turnover intentions ( r  =  − 0.46, CI r  = [–0.52, –0.40]) and absenteeism ( r  =  − 0.18, CI r  = [–0.25, –0.10]). These results suggest that teachers who are generally satisfied with their job are, on average, less likely to consider changing their job and absent less frequently, according to both their self-reports and school records.

Overall, the relationships between teacher job satisfaction and teacher-student interactions were small but positive, with a correlation of r  = 0.14, CI r  = [0.08, 0.20] for emotional support, r  = 0.18, CI r  = [0.10, 0.25] for classroom management, r  = 0.10, CI r  = [0.05, 0.15] for instructional support, and r  = 0.28, CI r  = [0.21, 0.35] for general teacher-student interaction. Accordingly, teachers who are satisfied with their job seem more effective in establishing caring, structured, and cognitively activating learning environments, as measured by teacher self-report, student ratings, classroom observations, and principal evaluation.

The findings regarding the relationship between teachers’ job satisfaction and students’ motivation also indicated a positive relationship between the two ( r  = 0.29, CI r  = [0.18, 0.40]). Accordingly, teachers who are generally satisfied with their job perceive their students as more motivated. Students of satisfied teachers also experienced greater satisfaction of their basic psychological needs and reported being engaged in school more actively. Similarly, a positive relationship also emerged for students’ achievement ( r  = 0.10, CI r  = [0.02, 0.17]), indicating that students taught by satisfied teachers were more successful in achieving their learning goals in terms of school grades, test scores, and general teacher evaluation.

In general, the psychometric meta-analyses substantively confirmed the results described above. Overall, correlations corrected for measurement error tended to be somewhat larger, but similar in size, with negative relationships between teachers job satisfaction and turnover intentions ( r adj  =  − 0.59, CI radj  = [− 0.65, − 0.54]), absenteeism ( r adj  =  − 0.21, CI radj  = [− 0.27, − 0.14]), and positive relationships with emotional support ( r adj  = 0.18, CI radj  = [0.08, 0.29]), classroom management (r adj  = 0.22, CI radj  = [0.15, 0.29]), instructional support ( r adj  = 0.14, CI radj  = [0.12, 0.17]), general interactions ( r adj  = 0.33, CI radj  = [0.30, 0.36]), students’ motivation ( r adj  = 0.37, CI radj  = [0.20, 0.54]), and students’ achievement ( r adj  = 0.12, CI radj  = [0.01, 0.23]).

Relations between Specific Facets of Job Satisfaction and Professional Performance

A total of 14 studies examined specific facets of teachers’ job satisfaction. Overall, the number of studies that considered facet-specific relationships was usually too low to conduct meta-analyses ( k  < 5). The only exception to this was the association between different job satisfaction facets (i.e., satisfaction with nature of work, relationships with colleagues, and context factors) and turnover intentions (Table 2 ). An overview of the relationship between specific job satisfaction facets and the remaining performance outcomes is provided in the Online Supplement (Table S5 ).

For the job satisfaction facets nature of work ( k  = 7, r  =  − 0.34, CI = [− 0.53, − 0.15]), relationship with colleagues ( k  = 6, r  =  − 0.32, CI = [− 0.53, − 0.12]), and context factors ( k  = 5, r  =  − 0.36, CI = [− 0.41, − 0.31]), the meta-analytic relationships with turnover intentions were moderate and comparable in size. Similar patterns of results emerged for satisfaction with pay and benefits. Regarding the remaining performance outcomes, a few isolated relationships were only reported for satisfaction with colleagues and context factors, providing very little empirical evidence to identify patterns of results.

Heterogeneity and Moderating Effects

There was substantial heterogeneity in effect sizes between studies (Table 1 ), except for the relationship between teachers’ job satisfaction and emotional support. The few studies that examined emotional support were quite similar with regard to the conceptualization, which might explain the small heterogeneity between studies. The greatest total amount of variability among the true effects was observed for turnover intentions (τ 2  = 0.023) and students’ motivation (τ 2  = 0.012). For most relationships, a moderate to large proportion of the total variability ( I 2  = 46.06 – 96.19) could be attributed to the true variance of effect sizes between studies (Higgins & Thompson, 2002 ). Against this background, we investigated study and sample characteristics that might explain heterogeneity (Table 3 ). Generally, the meta-regression results should be interpreted with caution as some analyses were based on a very small number of effect sizes.

Study Characteristics

Results from meta-regression analyses revealed a significant increase in effect size over time for the relationship between job satisfaction and general teacher-student interactions (β = 0.01, p  < 0.001) and a significant decrease in effect size over time for the relationship between job satisfaction and turnover intentions, suggesting publication year as a significant moderator. Additionally, stronger effect sizes were found in meta-regression analyses for published (vs. unpublished) studies for associations between teachers’ job satisfaction and both instructional support (β = 0.18, p  = 0.005) and general teacher-student interactions (β = 0.09, p  = 0.001).

From a methodological perspective, we investigated whether effect sizes varied as a function of the rater perspective (teacher self-reports vs. external reports). The rater perspective was a significant moderator of the relationships between teachers’ job satisfaction and absenteeism, general teacher-student interactions (β = 0.16, p  < 0.001), and students’ motivation (β = 0.24, p  < 0.001). Effect sizes were significantly larger when absenteeism was estimated based on school records ( k  = 2, r  =  − 0.37, CI r  = [− 0.47, − 0.26]) as compared to teachers’ retrospective self-reports ( k  = 12, r  =  − 0.17, CI r  = [− 0.22, − 0.13]). For general teacher-student interaction, the effect sizes were significantly larger when they were based on self-reports ( k  = 7, r  = 0.25, CI r  = [0.21, 0.29]) as compared with reports by students or external observers ( k  = 7, r  = 0.14, CI r  = [0.08, 0.19]). The same was true for students’ motivation, where the effect sizes were also larger for teachers’ self-reports ( k  = 2, r  = 0.35, CI r  = [0.29, 0.41]) than for reports by others ( k  = 6, r  = 0.12, CI r other  = [0.04, 0.19]). However, in either case, the correlations were positive and statistically significant regardless of how they were assessed. Detailed results of subgroup analyses and the separate effect sizes for self-reports and external reports can be found in Figures S10 – S13 in the Online Supplement.

Sample Characteristics

Results suggested that the relationships between teachers’ job satisfaction and both instructional support ( Q M  = 14.68, p  = 0.001) and general teacher-student interactions ( Q M  = 50.94, p  < 0.001) varied depending on the grade level taught. The relationships with general teacher-student interactions were weaker in primary school samples (β =  − 0.37, p  < 0.001) compared to mixed samples. Larger effect sizes emerged in secondary school samples (β = 0.09, p  = 0.001) compared to mixed samples for instructional support, while smaller effect sizes were found in secondary school samples (β = 0.09, p  = 0.001) compared to mixed samples for general teacher-student interactions. A closer look revealed that studies based on mixed grade levels relied solely on teacher self-reports, as well as studies conducted in secondary schools assessing instructional support. In contrast, barely any study conducted in primary schools used teacher self-reports, instead using classroom observations, student reports, or principal ratings. Additionally, the meta-regression results revealed that the size of the correlation between job satisfaction and general teacher-student interactions varied as a function of the proportion of female teachers in the sample; a smaller correlation was found for samples with a higher proportion of female teachers (β =  − 0.01, p  < 0.001). However, the proportion of female teachers was higher in primary schools compared to secondary schools or mixed samples. As a result, studies with a higher proportion of female teachers were also more likely to apply more objective measures (i.e., classroom observations, student reports, and principal ratings) for the quality of general teacher-student interactions. Finally, teaching experience emerged as a significant moderator for the relationships with turnover intentions (β = 0.01, p  = 0.047), general teacher-student interactions (β = 0.01, p  = 0.035), and students’ motivation (β =  − 0.01, p  = 0.025).

Relations between Teachers’ Job Satisfaction and Professional Performance over Time

The literature search identified 16 longitudinal studies of which nine reported longitudinal effect sizes for the relationship between job satisfaction and performance among teachers, including eight studies that implemented two measurement points with a mean time interval of 349.62 days ( SD  = 209.61, range = 152–730) and one study that involved three measurement points with a time interval of 152 days. All studies reported longitudinal correlations or regression coefficients between job satisfaction at the prior measurement point and the corresponding performance outcome at the subsequent measurement point without controlling for the baseline level of the performance outcome. In summary, these longitudinal correlations, though smaller, appear to confirm the effects found for turnover intentions and students’ motivation but not for absenteeism, quality of teacher-student interactions, and students’ achievement (a more detailed overview is depicted in Table S6 in the Online Supplement). These simple longitudinal correlations can be taken as a first approximation of the longitudinal nature of the relationship and provide insights into whether effects are evident across different time spans. However, longitudinal correlations obtained without controlling for prior levels of the outcome variable cannot be used to meaningfully estimate direct effects of job satisfaction. Likewise, primary studies did not provide any insights into reciprocal relationships.

Publication Bias

First, graphical exploration of funnel plots (Fig.  3 ) suggested the possibility of publication bias for some relationships, as the plots did not appear to be fully symmetrical (Sterne et al., 2005 ), with a lack of small studies with small effects that trended toward zero. Results of Egger’s regression tests for funnel plot asymmetry (Table 4 ) confirmed this impression, yielding significant results for instructional support (β = 2.34, p  < 0.001), general teacher-student interactions (β =  − 1.69, p  = 0.008), and students’ motivation (β =  − 9.08, p  < 0.001). Other than that, there was no evidence of publication bias. Precision-adjusted effect size estimates are reported in Table 4 , which were comparable or even larger than the estimates from the meta-analyses. Though Egger’s regression tests do not provide a strong indication of publication bias, results regarding the three doubtful relationships should be interpreted with caution especially as the publication status explained variability in effect sizes for instructional support and general teacher-student interactions.

figure 3

Funnel plots for job satisfaction and professional outcomes

Despite the great relevance of job satisfaction that is evident across different occupational groups (Judge et al., 2001 ; Spector, 2022 ), job satisfaction receives little attention in teacher-specific theoretical models explaining teachers’ professional performance. This research synthesis is the first to integrate the empirical evidence on the link between job satisfaction and professional performance for teaching-specific performance outcomes that also considers different facets of teachers’ job satisfaction and includes longitudinal evidence.

The Job Satisfaction–Performance Link in the Teaching Profession — Main Findings and Avenues for Future Research

Overall, the findings from our research synthesis verified the theoretically postulated association between job satisfaction and professional performance, which has been demonstrated across different occupation groups, also for teaching-specific performance outcomes (Judge et al., 2001 ; Li & Yao, 2022 ; Madigan & Kim, 2021 ; Meyer et al., 2002 ; Tett & Meyer, 1993 ; Whitman et al., 2010 ). More specifically, results suggest that satisfied teachers report lower turnover intentions and are less likely to be absent. In turn, satisfied teachers seem more effective in establishing positive teacher-student interactions and caring, structured, and activating learning environments, as perceived by teachers and others (i.e., students, principals, and external observers). Likewise, students of satisfied teachers are more likely to show higher motivation and achievement. However, the association between teachers’ job satisfaction and students’ achievement was small. This can be attributed to several reasons. First, achievement is the most distal outcome in the hypothesized process (Bardach & Klassen, 2021 ). Therefore, smaller effect sizes are expected. Second, the effect on achievement tends to be smaller for subjects other than math (Authors, 2022; Nye et al., 2004 ). Yet, different subjects were combined in the analyses. In summary, most of the observed correlations are rather small in size and only partly moderate.

Does Job Satisfaction Represent a Relevant Psychological Characteristic?

One main research objective is to identify relevant psychological characteristics that are related to teachers’ professional performance, especially to the quality of teacher-student interactions and students’ academic development. Compared to other disciplines, the role of job satisfaction in determining job performance seems less salient in educational psychology, where established theories primarily saw cognitive abilities and knowledge or stable personality traits as central (for an overview of these traditions see Kunter et al., 2013 ).

The correlations found in our research synthesis are comparable in size to effects found in meta-analyses that examined other psychological characteristics in the context of teachers’ professional performance, which further emphasizes that job satisfaction seems to play a significant role alongside other characteristics. Significant associations were also found in meta-analyses for teachers’ personality (Kim et al., 2019 ), professional knowledge (D’Agostino & Powers, 2009 ), and self-efficacy (Kim & Seo, 2018 ; Klassen & Tze, 2014 ; Zee & Koomen, 2016 ). These syntheses found comparable effect sizes for the general evaluation of teaching, teacher-student interactions, and classroom processes, but somewhat smaller effect sizes for students’ achievement compared to results from our meta-analyses. In contrast, teachers’ cognitive (e.g., intelligence and basic skills) and verbal abilities seemed unrelated to the quality of teacher-student interactions and students’ educational outcomes (Aloe & Becker, 2009 ; Bardach & Klassen, 2020 ). Although comparable effect sizes were obtained for different psychological characteristics, previous research did not investigate the relative importance of job satisfaction in the context of other characteristics. To further address the question of whether job satisfaction has an effect beyond other psychological characteristics, future research should consider various characteristics simultaneously (Caprara et al., 2006 ; Pekrun, 2021 ; Wright & Bonett, 2007 ).

The longitudinal studies included in our research synthesis provide preliminary insights into the longitudinal nature of the relationship between teachers’ job satisfaction and subsequent professional performance and suggest that effects are evident across different time spans. Future research should implement longitudinal or experimental studies more frequently to further investigate the nature of the link between job satisfaction and professional performance in the teaching profession. For this purpose, longitudinal studies should capture both job satisfaction and performance outcomes at multiple time points and control for baseline levels of the considered constructs (for an illustration of such a design see Benita et al., 2018 ; Dicke et al., 2015 ). A longitudinal observation over one school year would be ideal with regard to teacher-student interactions and students’ characteristics. It is more difficult to interpret the results, for instance, if teachers change the classes they teach after one school year. In contrast, a longer time period would be appropriate for longitudinal studies targeting turnover because teachers might not immediately consider changing their profession if they do not experience their work to be satisfying in the short term (as, e.g., in Voss et al., 2023 ). Intervention studies implementing a randomized controlled design would make it possible to investigate whether enhancing teachers’ job satisfaction also leads to improved teacher-student interactions or enhanced educational outcomes for students (for examples of studies investigating other teaching characteristics see Dicke et al., 2015 ; Jennings et al., 2013 ).

Despite the open questions, the correlations found in our meta-analyses, while small to moderate, are both comparable in size to effects of other psychological characteristics and evident across different time spans suggesting that job satisfaction represents a relevant characteristic.

Under Which Conditions is the Job Satisfaction–Performance Link Most Pronounced?

Previous research provides limited information on whether the partially small effect sizes found in our meta-analyses might be larger for certain subgroups. Identifying sample characteristics under which the relationship between teachers’ job satisfaction and their performance is particularly close is especially important to further understand theoretical mechanisms underlying the association and to identify who benefits most from the association (Gill, 2021 ). Meta-regression analyses suggested no differences in effect size depending on teacher sample characteristics because all differences seemed attributable to a specific methodological moderator, namely, the rater perspective, which emerged as an important source of possible bias in primary studies. Smaller correlations also remained significant for external reports of teachers’ instructional support and general teacher-student interactions, emphasizing the relevance of these results. For instance, drawing on teacher self-reports to measure both job satisfaction and indicators of professional performance has the risk of obtaining effect sizes that are too large due to common method bias (Podsakoff et al., 2012 ). Teachers who are generally satisfied with their job might be more likely to recall positive events when asked about their turnover intentions, absences, classroom interactions, or experiences with students (Tourangeau, 2000 ). Accordingly, valid conclusions about the relationships can only be derived if teachers’ professional performance is either assessed through more objective measures (e.g., school records, actual turnover, or standardized tests) or reported through other sources (e.g., students, colleagues, classroom observations, or principals). This allows an assessment of the extent to which job satisfaction elicits changes in teachers’ professional behavior also perceived by others, as evident in our findings. Therefore, future studies are encouraged to combine teachers’ self-reports with other perspectives more frequently.

Although results from meta-regression analyses suggest that effect sizes are robust across different samples and that the satisfaction of all teachers, regardless of their demographic characteristics should be addressed equally, further psychological characteristics remain of particular interest. Investigating the interplay between job satisfaction and other psychological characteristics, such as self-efficacy, personality, professional competence, or other well-being characteristics, would provide insights into the relative importance of job satisfaction for teachers’ professional performance and investigate whether different psychological characteristics have an additive, buffering, or conflicting effect. Although the effect sizes found in our meta-analyses are partially small, job satisfaction might still be relevant because it interacts with other characteristics (e.g., positive affectivity). For instance, future studies should target the questions of whether teachers might be satisfied despite experiencing high demands and exhaustion and whether job satisfaction can counteract the negative effects of burnout for teachers’ professional performance (e.g., studies investigating the interaction between different psychological characteristics of teachers; see Braun et al., 2022 ; Seiz et al., 2015 ).

Lastly, the question remains as to which students can benefit most from the association. In particular, the class composition (e.g., class size and number of students with learning disabilities or social-emotional demands) and students’ background variables (e.g., socio-economic status, migration, and ethnicity) seem relevant characteristics determining the size of the relationship because the teacher generally plays a more important role in the development of students with less favorable manifestations of these variables (Hamre & Pianta, 2005 ; Klusmann et al., 2016 ). Arguably, whether teachers are more or less satisfied and highly effective in creating a positive learning environment and providing individualized support makes a critical difference, particularly for the development of students at risk of failing school (Sirin, 2005 ). Unfortunately, we were unable to include these student characteristics in the meta-regression analyses due to insufficient data. Future studies should therefore consider additional moderators (e.g., student characteristics) to further explore the question of for whom and under which conditions the job satisfaction–performance link is most pronounced.

Are Specific Job Satisfaction Facets Especially Important?

Interacting with students and contributing to students’ learning and development are important reasons why teachers choose the profession and largely determine teachers’ professional goals. Therefore, these aspects might be particularly crucial for teachers’ evaluation of their job (i.e., their job satisfaction) and, in turn, for their professional performance. Only few studies measured job satisfaction regarding different facets of the job considering the theoretically postulated multidimensionality of the construct. While differentiating between facets may not be as important in demonstrating an effect of job satisfaction because teachers think of the aspect of their job most important to them when they report on their overall job satisfaction, using multidimensional instruments assessing satisfaction with specific aspects of the teaching profession would provide a better understanding of the differential effects that satisfaction with specific facets may have on similar outcomes. This research synthesis offers a first insight into this question: Contrary to our expectations, no differential findings emerged for specific facets of teachers’ job satisfaction. Satisfaction with different aspects of the job (i.e., nature of work, relationships with colleagues, and context factors) seem equally important, at least in relation to turnover intentions. Due to the small number of studies investigating absenteeism, the quality of teacher-student interactions, and students’ outcomes in the context of specific job satisfaction facets, no meaningful conclusions about differential effects can be drawn for these outcomes.

Consequently, future research should measure job satisfaction as a multidimensional construct to allow a more differentiated view on which job satisfaction facets (i.e., which characteristics of the teaching profession) are most significant for the association with teachers’ professional performance (Dicke et al., 2019 ). This would not only facilitate a more targeted approach to promoting teachers’ satisfaction but it would also open up the debate about whether raising teacher salaries is a useful means to keep teachers in the profession or improve teaching.

Limitations

The present research synthesis offers a comprehensive summary of previous studies on teachers’ job satisfaction and reviews relevant evidence on the job satisfaction–performance link for the teaching profession. However, our research synthesis is not without limitations that must be considered when interpreting these results.

First, we cannot rule out the possibility that unpublished papers, which we could not find via our literature search, would reveal a different pattern of results. We attempted to mitigate the influence of publication bias by specifically searching for unpublished work (i.e., Open Grey), considering theses, dissertations, and book chapters. However, a visual investigation of the funnel plots and results of Egger’s regression tests for funnel plot asymmetry indicated a possible influence of publication bias for instructional support, general teacher-student interactions, and students’ motivation (Sterne et al., 2005 ). Additionally, stronger effect sizes were found in meta-regression analyses for published (vs. unpublished) studies for these associations. Although precision-adjusted effect sizes tended to be comparable in size, future work could attempt to further address the limitations of publication bias by requesting unpublished analyses from authors in the respective field of research.

Second, one major challenge of job satisfaction research is the conceptualization of the construct, both theoretically and empirically. A considerable number of studies included in the meta-analyses used single-item measures and or self-constructed scales, although job satisfaction is a long-studied construct for which various validated instruments exist. This makes it difficult to assess the validity and comparability of constructs across studies, although single-item measures are moderately correlated with established satisfaction scales (Wanous et al., 1997 ). Several studies claiming to measure job satisfaction had to be excluded from the meta-analyses because related but distinct constructs such as enthusiasm, stress, career choice, or a description of job characteristics were merged into the scale (e.g., Johnson et al., 2012 ).

Third, the operationalization methods for the performance outcomes used in the studies included in our meta-analyses differed not only in the rater perspective and type of measure but also in the underlying conceptualization, which entails the risk of limited comparability between studies and potential bias. Turnover intentions were mostly operationalized as cognitive withdrawal from work, although some studies asked about the intention to remain in the profession (reverse coded). Due to the small number of studies assessing actual turnover, we were unable to include turnover as an outcome. However, having more studies prospectively examining actual turnover behavior would be interesting (e.g., Cha & Cohen-Vogel, 2011 ; Grissom, 2011 ). Like turnover intentions, the vast majority of studies measured absenteeism by retrospective teachers’ self-reports, differing greatly in the time period queried and the categorization of response scales. In contrast, some studies determined the number of days absent by surveying school records. The use of these more objective measures would be desirable for future research. With regard to the quality of teacher-student interactions, studies included in the meta-analyses considered both teacher practices and student behavior as indicators of classroom management. Due to the small number of studies, it was not possible to further differentiate between students’ outcomes. For this reason, different motivational constructs were included to determine the effect size for students’ motivation (i.e., engagement, basic need satisfaction, and self-concept). Likewise, students’ achievement was summarized across different subjects (i.e., reading and math), although the subject might account for variability in effect sizes (Klusmann et al., 2022 ; Nye et al., 2004 ). Consequently, it is not certain if all studies synthesized measured the same underlying construct. This is also illustrated by the remaining heterogeneity that could not be explained by the considered moderators. Although it would be desirable for future meta-analyses to focus on more comparable effect sizes, our research synthesis provides preliminary evidence for the relationship between teachers’ job satisfaction and a variety of teacher-specific performance outcomes.

Fourth, no causal inferences can be made based on findings from our meta-analyses because we focused on cross-sectional effect sizes for reasons of comparability. Established theoretical models suggest that job satisfaction is likely part of a complex system with bidirectional relationships to performance indicators (Frenzel et al., 2021 ; Jennings & Greenberg, 2009 ; Judge et al., 2001 ). For example, when teachers have a sense of accomplishment in completing their tasks, it likely increases their job satisfaction (Burić & Moè, 2020 ; Judge et al., 2001 ). This idea is also discussed in the literature, which suggests that positive interactions with students, highly motivated students who actively participate in class, and high student achievement may enhance teachers’ well-being (Ruiter et al., 2020 ; Spilt et al., 2011 ). Unfortunately, there is no empirical evidence providing insights into the reciprocity of the relationships. Additionally, shared third variables (e.g., school leadership and teachers’ self-efficacy) influencing both teachers’ job satisfaction and instructional quality (Bellibaş et al., 2020 ; Caprara et al., 2006 ) could underly the correlative pattern of results. As previously addressed, the task of future research is to further investigate the issue of causality or reciprocity through longitudinal or experimental designs that consider possible third-party variables.

Finally, in addition to the univariate associations established in the present research synthesis, a model-based meta-analysis (e.g., metaSEM) represents a promising approach to further investigate the psychological processes through which teachers’ professional well-being affects their performance in establishing effective learning environments and fostering student social-motivational and cognitive development (Becker & Aloe, 2019 ). This approach makes it possible to fit mediation models on a pool of correlation matrices (Cheung, 2020 ). However, due to the paucity of suitable primary studies, we were not able to test the theoretically hypothesized mediation process between teachers’ job satisfaction, teacher-student interactions, and students’ outcomes meta-analytically (Jennings & Greenberg, 2009 ).

Nonetheless, our research synthesis represents a comprehensive overview of research on the relationship between job satisfaction and both general and teaching-specific professional performance, including a large proportion of studies not directly interested in the relationship. This makes our research synthesis particularly valuable because it provides an overview of studies from different lines of research not readily found in a basic literature search, thus offering initial insights into the nature of the relationship and the basis for theoretical, conceptual, and methodological considerations informing future research and practice.

Practical Considerations

In light of the importance that teachers’ job satisfaction appears to have for their professional performance, it might be promising to further develop and establish interventions that aim to promote teachers’ job satisfaction. Given the effects on the diverse aspects of their professional performance found in our meta-analyses, job satisfaction seems an important characteristic of teachers’ professional well-being, especially for the central tasks of teaching: creating effective learning environments and supporting students in their cognitive and motivational development. Interventions that aim to improve teachers’ working conditions appear promising, as satisfaction results from cognitive evaluation of these aspects (Spector, 2022 ; Weiss, 2002 ). Current research suggests that the school environment (e.g., principal leadership, cooperation with colleagues, school climate, and students’ composition) plays a particularly important role in teachers’ job satisfaction (Cansoy, 2018 ; Toropova et al., 2021 ). For instance, a reduction of teachers’ workload, that is, reducing working hours and focusing on time spent teaching, slightly increased teachers’ job satisfaction (Butt et al., 2005 ). At the same time, collaboration with and support from colleagues could also reduce the workload. This might be achieved by targeting school leadership and school climate (Berger et al., 2022 ).

Individual perceptions also play a relevant role in the evaluation of the context (van Droogenbroeck et al., 2021 ). Hence, intervention approaches should not only target the school level but also consider factors on the individual level. These approaches represent promising alternatives especially when the context can be changed only to a limited extent, first by providing cognitive strategies to reappraise situational factors, and second by strengthening individual resources to better cope with maladaptive working conditions and meet job demands. For instance, interventions that help teachers develop or strengthen important competencies and beliefs, such as social-emotional competencies, self-efficacy, or strategies to reduce stress experiences, appear promising with regard to job satisfaction, life satisfaction, and general well-being (Ashley et al., 2013 ; Oliveira et al., 2021 ; Zee & Koomen, 2016 ).

However, when planning and implementing individual interventions, it is still important to take school-level variables into account: Intervention approaches targeting the individual experience can only be effective in environments that support the practice and implementation of newly learned competencies, strategies, or skills (Walton & Yeager, 2020 ). Consequently, optimal intervention to promote teachers’ job satisfaction should strengthen resources at the school level (e.g., leadership, workload, student composition, cooperation with colleagues, school climate, and sense of belonging), and at the individual level, targeting, for example, self-efficacy or social-emotional competencies (Herman et al., 2020 ).

This research synthesis offers a comprehensive summary of the empirical evidence on the relationship between job satisfaction and professional performance in the teaching profession, which has long been proposed both theoretically and empirically across different occupational groups (Diener, 2012 ; Judge et al., 2001 ; Weiss & Cropanzano, 1996 ; Whitman et al., 2010 ). Meta-analytic findings emphasize the relevance of teachers’ job satisfaction for their professional performance both generally, in terms of turnover intentions and absenteeism, and profession-specific, with regard to the quality of teacher-student interactions and students’ motivation and achievement, yielding significant effects for both teachers’ self-reports and external evaluations.

Data Availability

The data will be available upon publication open access at PsychArchives ( https://doi.org/10.23668/psycharchives.13691 ).

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Are job satisfaction and role breadth self-efficacy the links to proactive work behavior?

Nanank syamsudin.

a Politeknik STIA LAN Jakarta, Indonesia

Anis Eliyana

b Universitas Airlangga, Indonesia

Nurliah Nurdin

Agus sudrajat.

c Lembaga Administrasi Negara, Indonesia

Bambang Giyanto

Alvin permana emur.

d PT Usaha Mulia Digital Indonesia (PT UMDI), Indonesia

Marziah Zahar

e Social Security Management Center of Excellent, College of Business, Universiti Utara Malaysia, Malaysia

Associated Data

Data will be made available on request.

This research is based on a phenomenon that occurs in State Detention Centers in Indonesia. It attempts to test the relation among proactive personality (PP), proactive work behavior (PWB), job satisfaction (JS) and role breadth self-efficacy (RBSE) variables. With a quantitative approach using AMOS, this study took data from 455 respondents from Detention Centers in Indonesia. The results show that PP, JS and RBSE have direct effect on PWB. Further findings will be discussed. The results are expected to increase the understanding of PWB and can be the basis for the human resource management team to decide better approach to build PWB in the organization and eventually implement appropriate policy.

Proactive personality; Proactive work behavior; Job satisfaction; Role breadth self-efficacy; Responsive institution; Effective institution; Quality job; Productive employment.

1. Introduction

A State Detention Center is a facility where a suspect is held while the case is investigated, prosecuted, and examined in court. It is an institution that provides services to community members who are incarcerated in cells while also preserving security and order. Currently, the average population of State Detention Center throughout Indonesia is overcrowded which increases the urgency of PP in the workplace since it is an integral part of the maintaining order and security process. This process includes providing protection, prevention and prosecution against any threats and interference from outside the State Detention Center.

Increased order and security disturbances and the lack of quality of detention officers have widened the gap in State Detention Centers throughout Indonesia. In order to answer these challenges, an effective human resource management system is needed to ensure that the organization can carry out its duties through human resources who are motivated, proactive, professional and high-performing towards the tasks being carried out. In order to build and sustain PWB in Detention Centers, integrative, collaborative, and motivating action toward tasks by adopting a larger role is required ( Parker et al., 2006 ). Additionally, Parker (1998) stated that employees with RBSE appear to possess these kinds of actions and motivations.

Moreover, based on Judge (1993) , individuals also need an affective disposition related to JS to increase proactive action in the workplace. As a result, the management of State Detention Centers must encourage those with PP to carry out responsibilities at State Detention Centers, either directly to PWB or mediated by RBSE and JS.

2. Literature review

2.1. proactive personality.

Individuals with PP are capable of considering all potential risks and chances ( Parker and Collins, 2010 ). This personality tends to have a stable position, and is not easily affected by situational challenges and environmental changes ( Bateman and Crant, 1993 ). According to Akgunduz et al. (2018) those with PP are usually goals-oriented and have initiative to seize potential chances. Thus, they are able to trigger changes within themselves as well as their environment ( Presbitero, 2015 ).

2.2. Proactive work behavior

This behavior generally involves challenging the status quo ( Crant, 2000 ), by taking the initiative to improve current circumstances or create new things, rather than passively adapt to current conditions. It entails actions which are self-directed and future-oriented to change or improve oneself and situations ( Unsworth and Parker, 2008 ). Individuals that exhibit this behavior can go above and beyond the specified work, establish targets, and take a long-term approach to avoid conflicts ( Frese and Fay, 2001 ). According to Bateman & Crant (1993) and Buss (1987) , PWB is able to transform conditions, restructure the mindset, and change the existing state, both social and non-social. To highlight, proactive idea enactment and problem solving are two traits that receive the most attention ( Parker et al., 2006 ).

2.3. Job satisfaction

Employees who are satisfied in their work are the source of job happiness (Luthans, 2011 in Diana et al., 2020 ). Methodologically, JS is employees ‘affective response to their work, which is a contrast between real and intended results, according to Mosadegh Rad A.M (2003). As a result, there may be both satisfaction and dissatisfaction with what is done ( Eliyana and Sridadi, 2020 ). Employment satisfaction may also be the outcome of a positive evaluation of one's job or work experience ( Locke, 1969 ).

2.4. Role breadth self-efficacy

People's opinion of their own capacity to do specific tasks is referred to as self-efficacy ( Gist and Mitchell, 1992 ). It is particularly the perceived capacity to engage in a variety of proactive, social, and blended activities, beyond assigned duties ( Parker, 1998 ). In other terms, self-efficacy that is significant as a job motivation factor is referred to as RBSE ( Gist and Mitchell, 1992 ; Parker, 1998 ). RBSE refers to people's confidence in their ability to take on a larger and more proactive role ( Parker et al., 2006 ). They are confident in their ability to complete a set of activities and are driven to do so whether or not they are permitted ( Parker, 1998 ; Bandura, 1982 , 2986).

2.5. Proactive personality on proactive work behavior

It is a personality that sees possibilities and seizes them, takes initiative, acts, and perseveres until significant changes take place ( Crant, 2000 ). This personality is also not limited by situational forces ( Parker and Collins, 2010 ), meaning that this internal control may lead to behavior. Some literature in psychology as well as organizational behavior have also stated that PWB can be controlled both internally or externally (Schneider, 1983 in Bateman and Crant, 1993 ). Therefore, it can be concluded that PP can influence PWB ( Parker et al., 2006 ). Similar researches were also conducted by McCormick et al. (2019) and Wu et al. (2018) which stated that PP has a significant effect on PWB. The nature of the PP will create change and control that can support PWB in the workplace regardless of the work context because of its natural tendency to be a self-starter and take initiative ( McCormick et al., 2019 ).

Proactive Personality significantly influences Proactive Work Behavior

2.6. Proactive personality on job satisfaction

Individuals with proactive personalities have a steady stance that is not impacted by environmental changes and is not constrained by situational pressures ( Bateman and Crant, 1993 ). This is one aspect of personality traits that leads to JS (N. Li and Crant, 2010 ; Wang, 2010 ). An individual with a PP will be more likely to take action to alter and attain an ideal self or circumstance, and will potentially lead to better JS over time due to three key characteristics: self-initiative, change orientation, and future focus ( Kuo et al., 2019 ). Because proactive people prefer to create situations that are favourable to personal work achievement, PP is linked to JS (N. Li and Crant, 2010 ).

Proactive Personality significantly influences Job Satisfaction

2.7. Proactive personality on role breadth self-efficacy

PP is a character that can initiate and act on it, in other terms, it has a more flexible role-taking style. It also has a strong drive to change, that is comparable to mastery or control ( Bateman and Crant, 1993 ). Moreover, individuals with RBSE believe they are able to perform a larger role in the workplace ( Parker, 1998 ). This notion, in addition to the source of motivation, must be impacted by one's talents ( Parker et al., 2006 ). For example, the ability to have a more adaptable role orientation as well as control ( Parker and Sprigg, 1999 ). According to Andri et al. (2020) , PP is connected to RBSE in a substantial way. When people with PP feel they can effectively start an organization, they are more inclined to do so ( Travis and Freeman, 2017 ).

Proactive Personality significantly influences Role Breadth Self-Efficacy

2.8. Job satisfaction on proactive work behavior

According to Strauss et al. (2013) , JS is a resource that allows individuals to continue the efforts needed to maintain proactive action. However, based on ( Parker et al., 2006 ), it is a construction that is more closely related to compliance than with proactivity. In particular, through proactive goal setting and achievement, individuals tend to fulfil their needs when they are satisfied with their work ( Weigelt et al., 2019 ). Individuals who experience positive affective states associated with JS tend to change their situation proactively ( Judge, 1993 ) and to exhibit higher levels of innovative behavior ( George and George, 1990 ).

Job Satisfaction significantly influences Proactive Work Behavior

2.9. Role breadth self-efficacy on proactive work behavior

Individuals who believe they are capable of performing a task are more likely to do it efficiently ( Barling and Beattie, 1983 ). RBSE refers to employees’ belief on their ability to engage in proactive, social, and integrative activities beyond their standardized tasks ( Parker, 1998 ). RBSE can inspire each individual to believe that they can perform a broader and more proactive role, one that goes beyond the usually specified technical criteria, resulting in PWB ( Peariasamy et al., 2020 ). As indicated earlier by Parker et al. (2006) that there are two crucial attributes of Proactive Behavior in the workplace: proactive enactment of ideas and problem solving.

Role Breadth Self-efficacy significantly influences Proactive Work Behavior

2.10. Mediating role of job satisfaction

According to Bateman and Crant (1993) , individuals who have PP have control from within and can influence their environment in the workplace. Likewise, individuals who experience positive affective states associated with JS tend to change their situation proactively ( Judge, 1993 ). By establishing a positive cycle, JS may be a key concern in organizational behavior and occupational health ( Kuo et al., 2019 ). In short, individuals with PP who have control through JS or a situation where they have an affective disposition will better influence the PWB.

Job Satisfaction significantly mediates Proactive Personality and Personality Work Behavior

2.11. Mediating role of role breadth self-efficacy

Employees' perceived ability to carry out different proactive, social, and integrative actions that go beyond specific technical responsibilities is referred to as RBSE ( Parker, 1998 ). RBSE, on the other hand, is not as stable as PP since it evolves with experience and organizational situation ( Crant, 2000 ). PP rather emphasizes more on future changes ( Unsworth and Parker, 2008 ). As a result, in order to engage in PWB, PP requires RBSE, which indicates the ability to start, play a greater role, and have social and integrative qualities ( Crant, 2000 ).

Role Breadth Self-efficacy significantly mediates Proactive Personality and Personality Work Behavior

All hypotheses are conceptualized in Figure 1 (Research Model)

Figure 1

Research model.

The data collection was done on a total of 455 respondents at the State Detention Centers in Indonesia, an organization that plays a crucial role in society that is ensuring security and order. The number of officers from the State Detention Center (RUTAN) in Indonesia is very limited, especially compared to the number of inmates which are over capacity. This situation increases the need for a form of personality from State Detention officers that can affect their support for institutional effectiveness through efficacy and satisfaction ( Whiteacre, 2019 ). For this reason, this study uses officers from the State Detention Centers in Indonesia to be the population in measuring the variables used, because they have equality with the phenomena that occur. In this study, Akgunduz et al. modified 10 items to measure PP (2018). Following that, 8 items from Parker et al. were developed to measure PWB (2006). There were 7 items developed from Parker et al. for RBSE (2006). Finally, JS was measured using 10 items modified from Jaiswal and Fit (2017) . Responses were then gathered using a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The study's control variables included gender, education, status, length of work, and age. To acquire an assessment of the research variables, this study conducted a data collecting method using a developed questionnaire that was distributed to respondents using Google Form. All employees and managers who were sampled in this study were given questionnaires. The data was then evaluated with Amos Software's SEM (Structural Equation Modeling). For examining diverse phenomena of society, corporations, organizations, and other groupings that incorporate humans as direct research objects, a mix of quantitative methodologies is considered appropriate ( Saebani and Sutisna, 2018 ).

Table 1 shows the demographics of the respondents in this survey. A total of 383 male responses out of 455 total respondents gains the majority. Then, 398 respondents (or 87.3 percent) with a high school degree become the majority in this survey. 307 respondents who are married make up the bulk of this study's participants. In this survey, the majority of individuals who have worked at Indonesian Detention Centers (RUTAN) for more than 8 years were 229 respondents, accounting for 50.2 percent of the total. Finally, respondents ranging from 20 and 30 years old cover 231 respondents.

Table 1

Demographics.

4.1. An overview of the validity and reliability

The validity test is used to establish the extent to which the statement items may measure each variable, and the results of the validity test are reported in Table 2 . Validity measurement uses corrected item-total correlation (r corrected), for the correlation value greater than 0.30 or significant at the real level α 5%, then the statement item is declared valid. Validity testing was carried out with the help of the SPSS version 24 program. The results of the validity test can be seen in Table 2 :

Table 2

Validity statistics.

As shown on Table 2 , the correlation value for each item on all variables has a range between 0.503-0.823, so that all statement items have a correlation value greater than 0.30 and are also significant at the real level α 5%. Thus, it can be concluded that all statement items used to measure all variables are valid and can be used for further analysis.

The next test is the reliability test presented in Table 3 , which is used to determine the reliability or consistency of variable measurements. Reliability testing was carried out using the cronbach's alpha technique, according to Malholtra, the questionnaire was declared reliable if it produced a cronbach's alpha value greater than 0.60 ( Solimun et al., 2017 : 40).

Table 3

Reliability statistics.

Table 3 shows that the Cronbach's alpha value for all variables has a value greater than 0.60, meaning all statement items used to measure all variables can be declared reliable and believed to be a consistent measuring tool.

4.2. Construct validity

Construct Validity determines the extent to which indicators measure constructs. Convergent validity is used in the construct validity test in SEM. If the indicators in the construct have a standardized regression weight (lambda/factor loading) value larger than 0.50 and an Average Variance Extracted (AVE) value greater than 0.50, the construct has convergent validity. Table 4 shows the findings of the concept validity evaluation:

Table 4

Construct validity.

Table 4 shows that each indicator in each construct yields an AVE value more than 0.50 and has a factor loading value higher than 0.50. As a result, the indicators are valid in building constructs and may be utilized to develop models ( Table 5 ).

Table 5

Construct reliability.

4.3. Construct reliability

The construct reliability value in Table 5 is used to assess the construct reliability test; if the construct reliability value is more than 0.70, the construct is considered to be reliable ( Solimun et al., 2017 : 78). According to Hair et al. (2014: 605), the construct value reliability must be larger than 0.70 as a rule of thumb, and a construct reliability value greater than 0.60 is acceptable as long as each indication meets the convergent validity. Table 5 shows the findings of evaluating construct reliability for each construct:

Table 5 shows that each construct produces a construct reliability value greater than 0.70, so it can be concluded that these indicators are reliable in reflecting the constructs of all variables.

4.4. Structural model fit

The structural model analysis stage follows after the measurement model analysis stage is completed. This stage begins with an assessment of the structural model fit (goodness of fit), which ensures that the model created is accurate and consistent with the data (fit). Figure 2 shows the structural model's estimation results as well as the value of the goodness of fit criteria:

Figure 2

Assessing the structural model.

The structural model suitability test in Table 6 shows that all the criteria for absolute fit indices, incremental fit indices, and parsimony fit indices have met the requirements (marginal fit and good fit), so that the structural model is acceptable, and then testing the significance of the interplay between variables, both direct influence and indirect effect. The explanation of each fit indices is presented below Table 6 .

Table 6

Fit measure for the structural model.

4.5. Chi-square

The chi-square statistic (χ2) is the simplest basic test tool for determining model fit, and it is quite sensitive to sample size. According to Hair et al. (2014: 584), in a model with a sample size of <250 and the number of indicators <30, the expected chi-square criterion is to produce a probability> 0.05 (insignificant p-values expected), while in models with a sample size of> 250 or the number of indicators >30, then the expected chi-square criterion is to produce a probability≤0.05 (significant p-values expected). The model in this study, the number of samples is 427 (>250) and the number of indicators is 35 (>30), so that the good fit model if the chi-square criteria yield a probability≤0.05. The structural model's estimate result yields a chi-square probability of 0.000, which is less than the real level of 5%, indicating that the structural model is a good fit.

4.6. Normed Chi-square (Cmin/Df)

The recommended values are 1.0 for the lower limit and 2.0 or 3.0 for the higher limit. The structural model's estimation result is a cmin/df of 2.362, which is less than 3, indicating that the structural model is good-fit.

4.7. Goodness of fit index (GFI)

The GFI is a suitability indicator for estimating a systemized population covariance matrix's weighted proportion of variance. GFI ratings range from 0 to 1, with higher scores indicating better performance. A GFI of more than 0.90 denotes a good fit, whereas a GFI of 0.80–0.90 denotes a moderate fit (marginal fit). The GFI value obtained from the structural model estimate is 0.839, which is within the allowed range of 0.80–0.90, showing that the structural model is still acceptable (marginal fit).

4.8. Root mean square error of approximation (RMSEA)

RMSEA calculates the difference between a model's parameter values and its population covariance matrix. If the RMSEA value is less than or equal to 0.05, the model is considered to be close fit, and if the RMSEA value is between 0.05-0.08, the model is said to be good fit. The structural model's estimate results provide an RMSEA value of 0.057, which is within the range of 0.50–0.08, indicating that the structural model is determined to be a good fit.

4.9. Standardized root mean residual (SRMR)

SRMR is the residual average between the observed covariance/correlation matrices and the estimated results. The model is considered good fit if the SRMR value is less than 0.05. The estimation result of the structural model produces an SRMR value of 0.046, this value is less than 0.05, which indicates that the structural model is good (good fit).

4.10. Comparative fit index (CFI)

The CFI value varies from 0 to 1. If a model's CFI value is more than or equal to 0.95, it is considered to be good fit, and if it is between 0.80-0.95, it is said to be marginal fit. The structural model's estimate findings provide a CFI value of 0.934, which is within the allowed range of 0.80–0.95, indicating that the structural model is still acceptable (marginal fit).

4.11. Tucker Lewis index (TLI)

The TLI value ranges from 0 to 1, and it is also known as a non-normed fit index. A model is deemed good fit if its TLI value is more than or equal to 0.95, and marginal fit if it is between 0.80-0.95. The structural model's estimation result yields a TLI value of 0.930, which is within the allowed range of 0.80–0.95, indicating that the structural model is still accepted (marginal fit).

4.12. Normed fit index (NFI)

The NFI value varies from 0 to 1. If the NFI value of a model is more than or equal to 0.90, it is considered to be good fit, and if the value is between 0.80-0.90, it is said to be marginal fit. The structural model's estimation results generate an NFI value of 0.892, which is within the permitted range of 0.80–0.90, indicating that the structural model is still acceptable (marginal fit).

4.13. Relative fit index (RFI)

If the RFI value of a model is more than or equal to 0.90, it is considered to be good fit, and if the RFI value is between 0.80-0.90, it is said to be marginal fit. The structural model's estimation result yields an RFI value of 0.884. This number falls between 0.80-0.90, indicating that the structural model is still appropriate (marginal fit).

4.14. Adjusted goodness of fit index (AGFI)

For the degree of freedom (df) in the model, AGFI is a variation of GFI. When an AGFI is more than or equal to 0.90, a model is considered to be good fit, and when it is between 0.80-0.90, it is said to be marginal fit. The structural model's estimation results generate an AGFI value of 0.817, which is within the range of 0.80–0.90, indicating that the structural model is still acceptable (marginal fit).

4.15. Parsimony normed fit index (PNFI)

PNFI values greater than 0.50 indicate parsimony which indicates a better fit, and is only used for comparisons between alternative models. The results of the structural model estimate yield a PNFI value of 0.832, which indicates that the structural model is good fit.

4.16. Hypothesis testing

4.16.1. testing structural relationships (direct effect).

Furthermore, in Table 7 , it can be seen that the next stage of structural model analysis is the testing of structural relationships in the direct effect path, namely examining the estimated parameters of the relationship between variables that represent each theoretical hypothesis. The hypothesis can be accepted if the path parameters are statistically significant with the direction of influence predicted, meaning that the path parameters must be greater than zero for the positive direction and less than zero for the negative direction ( Hair et al., 2014 : 589).

Table 7

Summary of the direct effect hypothesis.

∗Significant at the 0.05 level

∗∗Significant at the 0.01 level

In testing structural relationships, a hypothesis is tested to test the significance of the influence between variables, using the critical ratio (CR) and probability values (p-value). If the CR value is ≥1.96 or the p-value ≤ 5% significance level is significant or not, it is decided that there is a significant influence between these variables.

Following are the results of testing structural relationships in order to test each research hypothesis based on the SEM output:

The estimate findings of the influence of PP on PWB demonstrate a substantial effect with a CR value of 2.025 (more than 1.96) and a significance value (p-value) of 0.043 (smaller than the 5 percent real level), as shown in Table 7 . The resultant coefficient of effect is 0.122 (positive), indicating that the more proactive one's personality is, the more proactive one's work behavior is. As a result, the first hypothesis is accepted.

With a CR value of 10,963 (more than 1.96) and a significance value (p-value) of 0,000, the estimate findings of the PP influence parameter on JS likewise demonstrated a significant effect (less than the 5 percent real level). The resultant coefficient of influence is 0.675 (positive), indicating that the more proactive a person is, the more satisfied they are at work. As a result, the second hypothesis is accepted.

The influence of PP on RBSE was also shown to have a significant effect, with a CR value of 13,298 (higher than 1.96) and a significance value (p-value) of 0.000 in the estimation results (less than the 5 percent real level). The resulting coefficient of effect is 0.807 (positive), indicating that the more proactive a person is, the greater their RBSE. As a result, the third hypothesis is accepted.

With a CR value of 7,046 (higher than 1.96) and a significance value (p-value) of 0,000 (less than the 5% actual level), the estimate result of the influence of JS on PWB demonstrates a significant effect. The estimated coefficient of effect is 0.311 (positive), indicating that JS correlates with PWB. As a result, the fourth hypothesis is accepted.

With a CR value of 9,472 (more than 1.96) and a significance value (p-value) of 0,000, the effect of RBSE on PWB is similarly significant (less than the 5 percent real level). The resulting coefficient of effect is 0.575 (positive), indicating that the greater the role breadth of self-efficacy, the more PWB is displayed. As a result, the fifth hypothesis is accepted.

PP has more dominant influence on role breadth of self-efficacy, then on JS, and finally on PWB. PWB is more dominantly influenced by RBSE, then JS, and finally PP. This shows that PP affects PWB more through indirect channels.

4.16.2. Testing structural relationships (indirect effect)

Then in Table 8 , it can be seen that the next stage of structural model analysis is the testing of structural relationships in the path of the indirect effect. Hypothesis testing to test the significance of this indirect effect is carried out in the same way, namely using the critical ratio (CR) value and the probability value (p-value). If the CR value is ≥1.96 or the p-value ≤ 5% is significant, it is decided that there is a significant effect.

Table 8

Summary of the indirect effect hypothesis.

The nature of the mediation must be determined once the importance of the mediation effect has been determined. According to Ghozali (2011: 248), detecting the nature of mediation can be seen in the effect of the mediation; if the direct effect of exogenous variables on endogenous variables is significant, and the indirect effect through intervening variables is also significant, then it is partially mediation. In contrast, fully mediation or perfect mediation occurs when the direct influence of exogenous variables on endogenous variables is minor, but the indirect effect through intervening variables is significant.

Following are the results of testing structural relationships in order to test each research hypothesis of the indirect effect based on the SEM output:

Based on Table 8 above, it can be explained as follows: The results of the indirect path significance test PP → JS → PWB showed a significant effect with a CR value of 5,891 (greater than 1.96) and a significance value (p-value) of 0.000 (less than the 5% real level). Thus, the sixth hypothesis is accepted. The nature of the mediator is known to be partially mediation, meaning that increasing the PWB of employees can only be done by increasing the employee's PP, but if it is also supported by high JS, then the employee's PWB can be even more improved.

The results of the indirect path significance test PP → RBSE → PWB also showed a significant effect with a CR value of 7,689 (greater than 1.96) and a significance value (p-value) of 0,000 (less than the 5% real level). Thus, the seventh hypothesis is accepted. The nature of the mediator is known to be partially mediation, meaning that increasing the PWB of employees can only be done by increasing the employee's PP, but if it is also supported by high role breadth of self-efficacy, then the employee's PWB can be even more improved. The mediation level of RBSE is stronger than JS, in mediating the influence of PP on PWB.

5. Discussion

PP has a considerable beneficial influence on PWB, according to the findings, which is consistent with several previous studies ( Crant, 2000 ; Parker et al., 2006 ). PP is found to influence PWB, which focuses on enacting ideas and solving problems at the workplace. PP is described as a personality trait that contributes to proactive behaviors such as taking initiative, taking action, and not depending on external factors.

Furthermore, there is evidence that PP has a beneficial impact on JS. This is consistent with past researches that have shown comparable results (M. Li et al., 2017 ; N. Li and Crant, 2010 ). As can be observed, a proactive individual tends to establish conditions in the workplace that are beneficial to personal achievement as well as an atmosphere that leads to JS (N. Li and Crant, 2010 ).

Similarly, PP has an impact on RBSE, which is similar to some previous studies ( Parker, 1998 ; Parker et al., 2006 ). It suggests that employees with PP are more likely to be motivated or confident in expanding their responsibilities. Confidence in oneself when doing a task extends beyond the allotted technical core and is integrated and coordinated ( Parker, 1998 ). This notion will be advantageous in positions that demand collaboration and disclosure of information, such as in Indonesian state detention centers.

Furthermore, JS shows significant positive results to PWB. Those who are satisfied with their work will influence their behavior in the workplace. It is supported by research by Strauss et al. (2013) , which stated that JS is a resource that enables individuals to continue with the effort necessary to maintain proactive action in the workplace.

Then, when it comes to PWB, RBSE reveals a strong positive effect. Those who have a strong belief in their ability to carry out tasks with a larger scope will be more proactive. When no one feels capable or understands the position, it leads to taking over behavior ( Parker et al., 2006 ). This conclusion is consistent with earlier research that shows RBSE has a large favorable impact on proactive work personality ( Parker et al., 2006 ).

The importance of JS as a mediator between PP and PWB has shown to be beneficial. It appears that JS plays a role as a partial mediator. Previously, PP has a direct influence on PWB, and PP is also possessed by employees who have an affective work disposition, in other words, JS will support PWB ( Judge, 1993 ; Parker et al., 2006 ).

In this study, the significance of RBSE as a mediator between PP and PWB is found to be important. These findings back with prior research that shows RBSE can considerably positively moderate the relationship between PP and PWB ( Parker et al., 2006 ). Partial mediation is also stated in the findings. RSBE plays a function in behaving confidently while also optimizing their broader role in proactive and integrative action. PP had previously supported this function with initiative and steadiness in order to create future adjustments. Having a steady personality and the confidence to take on a larger role in the job leads to proactive professional activities, which are also advantageous for future changes.

6. Conclusion and suggestion

6.1. conclusion.

This study found that: (1) PP has a positive and significant effect on PWB with a p-value of 0.043 (<0.05), (2) PP has a positive and significant effect on JS with a p-value of 0.000 (<0.05), (3) PP has a positive and significant effect on RBSE with a p-value of 0.000 (<0.05), (4) JS has a positive and significant effect on PWB with a p-value of 0.000 (<0.05), (5) RBSE has a positive and significant effect to PWB with a p-value of 0.000 (<0.05), (6) There is a positive and significant effect of PP on PWB mediated by JS because of the significant direct effect of PP on JS and JS on PWB with a value positive value of 0.210. (7) There is a positive and significant effect of PP on PWB mediated by RBSE because of the significant direct effect of PP on RBSE and RBSE on PWB with a positive value of 0.464. This is consistent with the findings by Nurjaman et al. (2019) , who discovered that PWB is important for organizations since it is likely to enhance present work situations and open up new chances. The State Detention Center (RUTAN) in Indonesia need something comparable. Individuals will be encouraged to participate in proactive activity when they feel in control of their activities, according to Hua et al. (2020) .

6.2. Suggestion

Several suggestions for improving PWB may be offered based on the findings. Employees at Indonesian State Detention Centers should be encouraged to engage in PWB. This is especially true for employees that have a PP. Then, in order to increase PWB, employees want good sentiments associated to JS. Employees with PP must also be given the confidence to take on greater roles and be placed in appropriate positions, which will foster PWB. Similarly, developing or bringing forth a PP in other employees is critical. Preventative actions in state prisons will be easier to adopt this way. As a result, it is suggested that officers be willing to take initiative and persevere in completing job responsibilities in order to improve the working environment and better deal with problems.

For further research, it is suggested that PP be compared to PWB directly, as well as indirectly through JS and RBSE, using a variety of sampling methods and numbers of samples, or with the addition of several other antecedents. Because there is still a scarcity of study on this subject. As a result, variables with diverse types of responders will be developed. So that future research in human resource management and organizational research can focus on PP, JS, RBSE, and PWB. Furthermore, it is preferable to study broader factors in future studies in order to affect PWB, or to employ a new research object. Because proactive conduct may be developed in a variety of ways, including PP, work happiness, and RBSE, it is not limited to Detention Center officers.

Declarations

Author contribution statement.

Nanank Syamsudin: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.

Anis Eliyana: Conceived and designed the experiments; Analyzed and interpreted the data.

Nurliah Nurdin: Performed the experiments.

Agus Sudrajat: Performed the experiments.

Bambang Giyanto: Contributed reagents, materials, analysis tools or data.

Alvin Permana Emur: Analyzed and interpreted the data.

Marziah Binti: Contributed reagents, materials, analysis tools or data.

Funding statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability statement

Declaration of interests statement.

The authors declare no conflict of interest.

Additional information

No additional information is available for this paper.

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  • Open access
  • Published: 27 October 2022

Determining dimensions of job satisfaction in healthcare using factor analysis

  • Dimitris Karaferis 1 ,
  • Vassilis Aletras 2 &
  • Dimitris Niakas 1  

BMC Psychology volume  10 , Article number:  240 ( 2022 ) Cite this article

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A Correction to this article was published on 23 December 2022

This article has been updated

Background:

Job satisfaction in health care has a great impact as it affects quality, productivity, effectiveness, and healthcare costs. In fact, it is an indicator of the well-being and quality of life of the organization’s employees, as it has been variously linked with increased performance and negatively to absenteeism and turnover. Better knowledge of healthcare employees’ job satisfaction and performance can directly contribute to the quality of the services provided to patients and is critical for the success of organizations.

The Cronbach’s alpha coefficient, split-half reliability, exploratory factor and confirmatory factor analysis were employed to assess the reliability and validity of JSS.

Six underlying dimensions were extracted (benefits and salary, management’s attitude, supervision, communication, nature of work, and colleagues’ support). Internal consistency reliability was satisfactory since Cronbach’s alpha for the overall scale was 0.81 and for the various dimensions ranged from 0.61 to 0.81, respectively. Exploratory factor analysis showed a KMO value of 0.912. The confirmatory factor analysis indicated good fit: SRMR = 0.050, RMSEA = 0.055, IFI = 0.906 and CFI = 0.906.

Conclusion:

Job satisfaction is a multidimensional construct that encompasses different facets of satisfaction. There is a lack of consensus as to which factors are more important and a researcher may find satisfaction with some factors while at the same time dissatisfaction with others. Our findings are significant for improving our understanding of the nature and assessment of job satisfaction in the Greek healthcare context, providing a more stable ground in a rapidly changing environment. A short JSS developed that could be much more widely used in the future.

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Introduction

As employee knowledge and skills are intangible assets of any service organization,

employee satisfaction has become an issue of utmost importance. It has been defined as the positive emotional state resulting from the evaluation of one’s work or work experience [ 1 ]. Hoppock [ 2 ] was the first who brought forth the concept of job satisfaction in limelight and described it as “the employees’ subjective reflections or subjective feelings about their working conditions and working environment”. Since then, many researchers have recognized that satisfied employees are a key asset to an organization [ 3 ]. While the importance of job satisfaction is generally recognized, additional and ongoing investigations of satisfaction levels are necessary as external conditions and societal values are constantly changing. In this respect, job satisfaction has a significant role in the operation and performance of organizations.

An essential prerequisite for the development and long-term success of an organization is in fact the utilization of employee’s capabilities and the improvement of their working conditions [ 4 ]. The degree of job satisfaction is actually the overall level of satisfaction on a number of different dimensions of work and affects the behavior of employees that, in turn, impacts upon organizational functioning [ 5 , 6 , 7 ]. Swamy et al. [ 3 ] stated that satisfied employees are the key asset of an organization. Therefore, the issue of job satisfaction is very important especially for non-profit public organizations like hospitals, which are essential for a country’s provision of healthcare services and the population itself.

Employee satisfaction also affects patient satisfaction. As patients are the external customers and employees are the internal customers of the organization they form the current working environment and are willing to cooperate with the community to achieve organizational goals. Previous studies have documented associations between job fulfillment of health workforce and patient contentment with the type of health care services provided in health care facilities [ 8 , 9 ]. Moreover, there seems to exist a positive correlation between the increase in job satisfaction and quality of care [ 10 , 11 ]. Conversely, a low level of job satisfaction would create negative behaviors, including absenteeism, grievances, high level of stress, turnover, exhaustion, low morality, worse patient-provider ratios, longer wait times, psychological distress and increased medical errors [ 12 , 13 , 14 ].

Hospital managers have responsibilities to both patients and staff. It has been suggested that if you want to attain higher job productivity and efficiency, you should comprehend the domains of work which are decisive for job satisfaction amongst healthcare providers. In order to get employees contented with their job; the underlying factors that influence job satisfaction in that particular facility must be examined to guide proper managerial action [ 15 , 16 ].

Measurement of job satisfaction

Due to its importance, a wide range of instruments have been designed to quantify and conceptualize job satisfaction during the past decades. They were developed to capture the entirety of various aspects of job satisfaction be it personal, social, environmental, organizational, and the nature of the job itself. A valuable and widely used measure of job satisfaction is the Job Satisfaction Survey (JSS) that was originally developed by Spector [ 17 ]. JSS provides sufficient reliability and validity and is available for researchers free of charge for use for non-commercial purposes. The instrument contains 36 items expressed on a Likert scale measuring nine dimensions of job satisfaction, as mentioned below:

Pay includes salaries and wages. Unfair distribution can negatively affect employees’ emotions and therefore their behavior in the organization [ 18 ].

Promotion is an important aspect of a employee’s career. It refers to progression to a higher position with more challenges, authority and responsibilities [ 19 ]. Only a meritocratic promotion system with evaluation conditions known in advance can lead to satisfaction.

Fringe Benefits, can be financial or non-financial compensations. Financial compensations consist of direct (e.g. bonuses) and indirect compensation (e.g. retirement plans). Non-financial compensations consist of the job itself (e.g. autonomy), job environment (e.g. working conditions) and workplace flexibility (e.g. part-time work) [ 20 ].

Contingent Rewards, are referred to as promises and exchanges of rewards and recognition for good work. Is a valuable tool for motivating employees because they want to be paid well for the job they perform both for their self-esteem and as useful means of a living [ 21 ].

Supervision, is defined as the perception of employees regarding the support received from supervisors in an organization besides coworkers. Usually, employees are satisfied when they are supported to achieve their goals [ 22 ].

Operating Procedures, are described as steps of finishing tasks that have to follow a certain standard based on regulations, provincial laws, policies, procedures and standards. Inadequacy of equipment and resources, lighting, ventilation, and cleanliness can result in a stressful work environment that leads to job dissatisfaction among employees [ 23 ].

Co-workers, are referred as people working in an organization (besides supervisors). Employees with the same values, attitudes and philosophies can improve satisfaction in an organization [ 24 ]. Support from colleagues can enhance job satisfaction and decrease job stress and burnout.

Nature of Work, is defined as the variability of the given work. It refers to the daily and non-daily tasks carried out as part of the job scope and includes job challenges, feedback, autonomy, and skill variety [ 25 ]. Further, this can increase the motivational level of employees which will ultimately raise their internal happiness of employees, and the internal happiness will cause satisfaction.

Communication, is referred as informing the current employees. Communication between supervisors or the managerial level with employees consistently enables managers to know whether their staff is satisfied and happy with its employment or not [ 26 ]. There is a positive association between communication and job satisfaction. Effective communication at the workplace is essential in ensuring organizational objectives, social support.

Every dimension incorporates four items. Several previous studies have shown that JSS has high internal consistency and validity [ 27 , 28 ].

This research aimed to explore (a) the underlying factorial structure of the JSS when applied to Greek hospital employees, (b) its psychometric properties. Undoubtedly, job satisfaction is a complex concept, so there is always a need to research this phenomenon and related factors to explore the development of optimal human resources strategies in the context of healthcare institutions. Moreover, there is a compelling need for developing constructs in the field of management rather than adapting the constructs that have been developed already.

Materials and methods

Research instrument translation and adaptation.

The JSS has been translated in several languages and found to be valid and reliable among different categories of employees. Spector’s original JSS tool was translated into the Greek language and adapted by Tsounis and Sarafis [ 27 ] to be administered to employees of the Greek Therapy Centre for Dependent Individuals. In this context, the JSS was translated into Greek using the forward-backward translation process. Firstly, the original English of the JSS was translated into the Greek language by two experienced translators. The assessment of forwarding translation drafts was performed by two other researchers who worked independently and asked to review each translated item and choose the most adequate in terms of clarity, common language, and cultural diversity. Secondly, a retranslation of the agreed Greek text to the English language was held by a researcher who had not previously seen the original version. Thirdly, the backward translation was compared with the original version of the survey, and judgments about potential inaccuracies were made by two other researchers. Finally, the resulting differences were checked by another scientist who made the necessary adjustments.

The reliability and validity of the tool has been documented worldwide in a variety of settings. Reliability coefficients of prior and current research are presented in Table  1 . The measures whose Cronbach’s Alpha exceeds 0.6 are considered to be the reliable ones and indicates an acceptable level of reliability [ 29 , 30 , 31 ]. Schmitt [ 32 ] has suggested that there is no general level (such as 0.7) where alpha becomes acceptable. In reality, a key feature of the alpha coefficient is that it is highly dependent on the number of items involved. Thus, if we wish to reduce the items in our survey (e.g. EFA), because of this, a small number of well-correlated items may have a fairly low alpha coefficient. Conversely, since there are more items, the value of alpha can be quite high despite the low correlation between many of these items. Addionnaly, Ursally [ 33 ] showed that important differences in the values of Cronbach Alpha are possible due to indirect influences from external factors - respondents’ age, gender, level of study, religiousness, rural/urban living, and survey type of the research subject for the participants to the survey [ 34 , 35 ].

Prior reliability analysis of the translated and adapted Greek version of the instrument seems to have some issues. First of all, one facet of job satisfaction had Cronbach’s alpha below 0.6 (i.e., 0.48 for “Operating procedures”). Second, the JSS was applied and evaluated on 239 employees of various specialties in drug addiction treatment of one only medical center with common structure. This implies that the sample size might be rather small for factor analysis and that its findings might not even be generalizable [ 31 , 36 ].

Additionally for this research, Split-half reliability analysis (Table  2 ) was assessed by dividing the instrument into two halves; Part 1: consisted of the first 18 items, and Part 2: consisted of the remaining 18 items of the scale. The findings showed that JSS had good split-half reliability as assessed through the Guttman Split-Half Coefficient (0.77).

Research design and procedure

The survey was carried out in the region of Attiki with its capital Athens, with around 3.75 million inhabitants or approximately 35% of the total Greek population. The 1st Regional Health Authority of Attica has the responsibility for 27 public hospitals. Our survey was conducted between July 2019 and December 2020 in thirteen of those who provided healthcare services to 438,745 patients. The main criteria for the selection of these hospitals were (Table  3 ): (a) the categories of hospitals; for this reason, the survey was introduced into four different categories (general, pediatric, maternity, oncology), (b) a large number of different clinics, (c) hospitals with a large number of beds but without ignoring the role of smaller hospitals, (d) the large number of patients treated in these hospitals, (e) the large number of health care employees who work in these hospitals, and (f) the necessary approval of the research by hospital committees.

The researchers distributed the printed questionnaire along with a consent form to the participants in person at their workplaces. They were adults (over 18 years), health care professionals belonging to medical, nursing, administrative, and technical departments serving public healthcare. The main aim of selecting employees from various fields is to get the opinions of a diverse group of people so that the results can be generalized on s vast group of the overall population. They had worked for more than six months in the respective hospital facilities at the time of the research and consented to the study. The study excluded interns, volunteers, and those declining to consent to the study. The participants had one week to complete the questionnaire. All employees had the right to refuse or discontinue their participation in the survey at any time. The researcher guaranteed the anonymity and confidentiality of all data collected. We remained considerate of the names, safety, and well-being of participants, and also the organizations remained anonymous by using codes, such as H01, H02, and so on (Table  3 ). Finally, of the 4,000 questionnaires distributed, 3,278 (81.95%) were returned.

Statistical analysis

The data collected were analyzed using SPSS software (version 24.0). The mean (M) and standard deviation (SD) of each JSS item were determined. The reliability coefficient was examined. As a rule of thumb, values of Cronbach’s α ≥ 0.6 are thought to be acceptable [ 31 ]. Validity was evaluated using convergent and discriminant validity, as well as factor analysis consisting of exploratory factor analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory factor analysis (EFA) was conducted by utilizing principle component analysis (PCA) with the varimax rotation method, which had applied an Eigenvalue of > 1 for this purpose. For EFA we used the Kaiser-Meyer-Olkin (KMO) statistic was employed to assess whether the sample data are suitable for factor analysis. According to Kaiser [ 37 ], a value above 0.5 is considered acceptable; between 0.5 and 0.7 is moderate; between 0.7 and 0.8 is good; between 0.8 and 0.9 is very good; and 0.9 and above is superb. Also, Bartlett’s Test was applied to verify if the data was appropriate for factor analysis and indicated that correlations between items were sufficiently large for PCA. Retained and excluded factors were also explored visually on a screen plot along with the parallel analysis. Many studies reported that factor loadings should be greater than 0.5 for better results [ 38 , 39 , 40 ]. Principal Component Analysis was chosen as the suitable extraction method for obtaining the initial factor solution and reducing the number of factors. PCA is a robust method that is psychometric and less complex conceptually than other methods and is also preferred because it resembles many aspects of discriminate analysis. Varimax rotation of the factors was also applied to produce the factor structure. The advantage of Varimax rotation is that maximizes distribution within the factors, thus introducing a small number of variable loads and more easily interpretable factor clusters into each factor load. Cross-loaded statements also were deleted [ 38 , 39 , 40 , 41 ].

After using EFA to identify the factor structure present in a set of variables, the model fit was then assessed by using Confirmatory Factor Analysis (CFA). A CFA with a maximum likelihood method (ml) in AMOS (version 24.0) was also performed. The fit of the CFA model was assessed using the incremental and absolute indexes, namely: the comparative fit index (CFI), incremental fit index (IFI), the standard mean root square residual (SRMR) and the root mean square error of approximation (RMSEA). The following cut-off values were assumed: CFI, and IFI ≥ 0.900, SRMR and RMSEA ≤ 0.800 [ 42 , 43 ].

Study sample

Among the sample participants 612 (18.67%) were male and 2,666 (81.33%) were female. Regarding their age, 1.49% was under 25 years old, 15.86% were 26–35, 33.25% were between 36 and 45, 38.16% between 46 and 55. The remaining 11.23% were older than 56 years. As far as the educational level is concerned, the majority was university graduates (59.55%), 19.37% had post-graduate studies, only 1.53% had compulsory education and the remaining 19.55% had secondary education. Concerning employment status, the majority worked as permanent staff (80.99%). As regards length of service, 19.37% had less than 5 years, 11.90% of study participants had worked from 6 to 10 years, 17.63% from 11 to 15 years, 22.45% from 16 to 20 years, while 28.65% had worked for more than 20 years. With respect to income, the majority of employees stated that they managed without having much money left aside (see Table  4 ).

Normality analysis

The Kolmogorov-Smirnov and Shapiro-Wilk normality tests were performed and showed that the data was not normally distributed (p < 0.05).

Descriptive statistics results

Descriptive statistics for the items of the questionnaire are shown in Table  5 . The results indicate that the minimum value of the items is 1 while the maximum is 6.

The highest mean values were found for Item–7 and Item–17 while the lowest ones for Item–10 and Item–28. The average variability of the items around mean values was relatively small.

  • Exploratory factor analysis

According to the analysis result, the KMO (Kaiser-Meyer-Olkin) statistic of 0.912 confirmed that the sample used was quite sufficient. We can therefore be confident that the factor analysis fits into our data set. Next, Barlett’s test of sphericity ( χ 2  = 31831.572, df  = 528, p  = 0.000) demonstrated that the correlation matrix is not an identity matrix, therefore providing justification for the use of factor analysis [ 37 , 44 ]. In PCA the eigenvalue provides the fraction of the variation accounted for by the corresponding component (eigenvector). We adopted a combined criteria method as suggested by Lings and Greenley [ 45 ], and Larose [ 46 ] to identify items and factors for inclusion in the final factorial solution. More specifically, to evaluate the factor structures, we used four criteria. First, items factor loadings should be at least equal to or greater than 0.5. Second, a scale should have more than two items or if it has only two they should be strongly correlated. Third, if an item loads more than one dimension and their difference is lower than 0.02, it will be deleted. Moreover, the difference in loadings, equal to or greater than 0.2, implies the item’s inclusion in the dimension with the highest factor load. Finally, in order to maintain an item, it would also have to conceptually match the factor [ 47 , 48 , 49 ].

Based on an eigenvalue greater than one, as one eigenvalue represents a significant amount of variation, factors considered in subsequent analyses. Hence, another eigenvalue-based approach was used to examine Cattell’s “scree” plot, by looking for a spot in the plot where it abruptly levels out. By employing both methods, a six-factor model was identified (see Table  6 ) [ 50 ]. The final factorial structure explains 56.23% of the total variance of the dataset. According to the results obtained, the first factor had 23.78% of the total variance, the second factor 11.52%, the third factor 6.64%, the fourth factor 6.30%, the fifth factor 4.17%, and the sixth factor 3.81%. The total variance explanatory rates of the factors after rotation were as follows: 14.13%, 10.53%, 10.49%, 8.19%. 6.92% and 5.97%.

Varimax rotation was used for the rotation of the original solution as our sampling has a heterogeneous population [ 51 , 52 ]. Twenty variables were included within six factors. The resulting six factors were: Factor 1 which indicates employees’ benefits and salary includes items: 11, 20, 28, 33. Factor 2 represents the management’s attitude and includes items: 14, 19, 24, 29. Factor 3 supervision and includes items: 3, 12, 21, 30. Factor 4 represents employees’ communication, includes items: 18, 26 and 36. Factor 5 mainly indicates the nature of work and includes items: 17, 27,35 and finally Factor 6 consists colleagues support and includes items: 7 and 25 (Table  7 ).

The reliability coefficient Cronbach’s alpha of new construction of scales after application of factor analysis for the overall scale was 0.81 and we concluded that the questionnaire has very good reliability. The results showed that obtained reliability figures (Alphas) range from 0.60 to 0.81 for the various job satisfaction dimensions. These findings provide support for the internal consistency of the sub-scales, so we can state that the scale of the survey questions used in the analysis was acceptable (Table  8 ).

  • Confirmatory factor analysis

Confirmatory Factor Analysis ( CFA) is a statistical technique used to evaluate the measurement models that represent hypotheses about relations between indicators and factors. The CFA assessed the fit of the six-factor structure and the model fitted the data well as defined from the SRMR, RMSEA, CFI and IFI values that were equal to 0.050 (≤ 0.800), 0.055 (≤ 0.800), 0.906 (≥ 0.900) and 0.906 (≥ 0.900) respectively. It was suggested that the fitting optimization index was acceptable and the structure of the model was designed reasonably (Fig.  1 ).

figure 1

Result of confirmatory factor analysis (CFA)

To sum up the discussion, the basic purpose of this study was to validate Spector’s JSS instrument and develop a valid, short and reliable instrument that can measure employee job satisfaction for public hospitals in Athens, Greece. There were 3,278 responses received from the employees of thirteen different hospitals. Factor analysis was conducted due to anticipated dimensionality of factors that are involved in measuring job satisfaction. The values of Cronbach’s Alpha coefficients were computed in order to assess the internal consistency reliability.

Overall, the job satisfaction scale developed in this research illustrates valid and reliable measures for assessing hospital employees’ satisfaction levels with their work. Yet in reality, job satisfaction is a complex multidimensional concept. The study is based on the premise that an organization’s intellectual capital is its most important asset. For this purpose, our survey used a personalized “bottom-up” approach, which studied the properties of employees, their behavior at the workplace, motivators, dissatisfiers, and other properties of the job environment. Satisfied human resource is the most valuable asset for high productivity, commitment, efficiency, and quality of care in a healthcare organization [ 53 ]. Aiming we get answers to basic questions: “How do employees feel in their workplace? What makes them behave in the workplace the way they do? What would motivate them to perform well and according to the hospital’s goals?“ The employees are motivated (or not) to perform as they do because of a combination of internal and external factors, which should be investigated, measured and improved as much as possible.

The statistical analyses identified six predominant components to quantify job satisfaction, namely Benefits and Salary (F1), Management’s attitude (F2), Supervision (F3), Communication (F4), Nature of work (F5), Colleagues Support (F6). Meanwhile, among the affecting factors of job satisfaction, monetary benefits have the most influence, relationships with superiors and colleagues, training and enhancement of employee skills, the perceived fairness of the promotion system, the quality of the working conditions, and a sense of belonging are vital to the development of job satisfaction.

An important strength of this study is that a short JSS questionnaire was developed for healthcare organizations that can be used much more widely in a rapidly changing environment. This newly developed questionnaire will prove very useful in providing continuous feedback to top management as well as health policy makers regarding the level of job satisfaction. Such feedback provided by the existing health workforce will immediately alert them to any adverse working conditions that present themselves as factors leading to job dissatisfaction.

In Greece, the results of this study are important in terms of determining factors that should be considered for success within organizations. This research is valuable because it has both a practical and humanitarian application, as it gives a better understanding of employee satisfaction which in turn will lead to improved organizational behavior and employee attitudes that directly affect the improvement of health quality. Gaining employee’s commitment to their organization’s goals is believed to unlock their potential and achieve heightened levels of performance. Opposite results can lead employees to dissatisfaction or tend to lose interest in their work, higher levels of burnout and stress, absenteeism, intention to quit, and consequently suboptimal healthcare delivery and poor clinical outcomes [ 54 ]. Managers of health services organizations in cooperation with the Ministry of Health (MoH) must elicit cooperation and performance of the employees to ensure the quality of care and the morbidities and mortalities may be improved undoubtedly. Most researchers agree that employees with high job satisfaction levels have improved mental and physical health, job involvement, and improved quality of life. Eliciting such commitment from employees is not easy to obtain especially under uncertain or difficult working conditions [ 55 , 56 , 57 , 58 ].

More than ever, due to the globalization evolution of the Covid-19 pandemic, health systems need satisfied employees who can cope with very difficult conditions, refine health care services, and up surging the level of patient satisfaction. The study of job satisfaction is gaining more and more importance with the passage of time because of its nature and impact on society. The need to understand employee satisfaction resurfaces as everyone understands that they serve the ultimate human good, health [ 59 ].

In total, this study applied quantitative methods to determine factors affecting job satisfaction. So, is important in terms of determining the specific factors that should be considered for job satisfaction, organizational engagement, managerial success, and high performance within hospitals. A short 20-item study for all healthcare staff can benefit hospitals to monitor employee satisfaction across all levels without overburdening employees and analysts with multiple or fielding several non-comparable types of research.

The findings suggest that effective communication and support from managers or supervisors to employees or among employees themselves will reduce stress and conflicts in the workplace. Additionally, it can be recommended that employee empowerment and training, collaboration in teamwork, and a systematic approach regarding innovative types of promotional opportunity, recognition, reward, and evaluation of hospital staff can lead to better results and benefits employees, quality of patient care, and healthcare organizations. Consequently, we believe that empowerment of management, achievement, promotion and evaluation should significantly improve job satisfaction respectively. This study showed that obtained factors are aligned with the findings of the prior studies in the literature [ 60 , 61 ].

The results of this study should not be generalized extensively since the participants of the study come from a single geographical region of the country, only in hospitals in Athens, Greece. Nevertheless, the sample cannot be characterized as homogenous due to the fact that participants were working in different departments in the hospitals, so they deal with different tasks and procedures. Therefore, the findings and related conclusions may not be able to be generalized and compared with the rest regions of the country.

Data availability

The data can be accessible from the corresponding author when the Ethics Committee of the National and Kapodistrian University of Athens and the Scientific Council of Primary Health Care of the 1st Regional Health Authority of Attica provide data access permission.

Change history

23 december 2022.

A Correction to this paper has been published: https://doi.org/10.1186/s40359-022-01026-w

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Acknowledgements

We hereby express our gratitude to all of the employees of the hospitals participating in this research, who aided us in the course of this research.

The study was conducted with no funding, and the authors have not received any financial support for this study.

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Karaferis, D., Aletras, V. & Niakas, D. Determining dimensions of job satisfaction in healthcare using factor analysis. BMC Psychol 10 , 240 (2022). https://doi.org/10.1186/s40359-022-00941-2

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BMC Psychology

ISSN: 2050-7283

job satisfaction literature review 2022

job satisfaction literature review 2022

EMPLOYEE MOTIVATION, JOB SATISFACTION, AND EMPLOYEE PERFORMANCE: A LITERATURE REVIEW

  • Imam Hidayat Universitas Trisaksi, Jakarta, Indonesia
  • Endi Supardi Universitas Trisaksi, Jakarta, Indonesia
  • Alvis Anwar Universitas Trisaksi, Jakarta, Indonesia
  • Sarfilianty Anggiani Universitas Trisaksi, Jakarta, Indonesia

The purpose of this paper is to provide a structured literature review on the constructs of employee motivation, job satisfaction, employee performance, and empirical evidence on the relationship between motivation, job satisfaction, and performance. 20 (twenty) papers published during 2017-2021 that investigates employee motivation, job satisfaction, employee performance, and the relationship between employee motivation, job satisfaction, and employee performance were reviewed. The results of the review show that employee motivation and job satisfaction have positive and significant effect on employee performance or in other word employee motivation and job satisfaction are the determinants of employee performance.

Astuti, W., and Amalia, L. 2021. The Relationship Between Work Motivation, Job Satisfaction, and Employee Performance: The Moderating Role of Psychology Capital and the Mediating Role of Organizational Commitment. Journal of Theory & Applied Management, Vol. 14. No. 2, pp. 102-128.

Buchanan, D.A., and Huczynsky, A.A. 2019. Organizational Behaviour, 10th Edition, Harlow: Pearson Education Limited,

Carvalho, A.D.C., Riana, I.G., and Soares, A.D.C. 2020. Motivation on Job Satisfaction and Employee Performance. International Research Journal of Management, IT & Social Sciences, Vol. 7 No. 5, pp. 13-23. https://doi.org/10.21744/irjmis.v7n5.960 .

Cetin, F., and Askun, D. 2018. The Effect of Occupational Self-Efficacy on Work Performance through Intrinsic Work Motivation. Management Research Review, Vol. 41 No. 2. https://doi.org/10.1108/MRR-03-2017-0062 .

Colquitt, J.A., Lepine, J.A., and Wesson, M.J. 2019. Organizational Behavior: Improving Performance and Commitment in The Workplace. 6th Edition. New York: McGraw-Hill Education.

Dharma, Y. 2018. The Effect of Work Motivation on the Employee Performance with Organization Citizenship Behavior as Intervening Variable at Bank Aceh Syariah. Emerald Reach Proceedings Series, Vol. 1 pp. 7-12. https://doi.org/10.1108/978-1-78756-793-1-00065 .

DuBrin, A.J. 2019. Fundamentals of Organizational Behavior, 6th Edition. Academic Media Solutions.

Egenius, S., Triatmanto, B., and Natsir, M. 2020. The Effect of Job Satisfaction on Employee Performance Through Loyalty at Credit Union (CU) Corporation of East Kutai District, East Kalimantan. International Journal of Multicultural and Multireligious Understanding, Vol. 7, Issue 10, pp.: 480-489.

Endang T., and Sari, E. 2019. The Effect of Motivation and Discipline on Employee Performance at the Ministry of Transportation's Directorate of Ports. Ilomata International Journal of Social Science, Vol. 1 No. 1, pp. 1-9.

Girdwichai, l., and Sriviboon, C. 2020. Employee Motivation and Performance: Do the Work Environment and the Training Matter?. Journal of Security and Sustainability Issues, Vol. 9, pp. 42-64.

Griffin, R.W., Phillips, J.M., and Gully, S.M. 2019. Organizational Behavior: Managing People and Organizations, 13th Edition. Boston: Cengage Learning, Inc

Hariati, Muis, M., and Amar, Y. 2021. The Effect of Job Motivation and Job Satisfaction on Employee Performance through Organizational Citizenship Behavior. Hasanudin Journal of Business Strategy, Volume 3 Nomor 4, pp. 93-104.

Kinicki, A. 2021. Organizational Behavior: A Practical, Problem Solving Approach, 3rd Edition. New York: McGraw-Hill Education.

Kuswati, Y. 2020. The Effect of Motivation on Employee Performance. Budapest International Research and Critics Institute-Journal Vol. 3, No 2, pp. 995-1002.

Lin, Y. 2021. A Study on the Relationship Between Project Management Competency, Job Performance and Job Motivation in e-Commerce Industry. Measuring Business Excellence, Vol. 25 No. 1. https://doi.org/10.1108/MBE-10-2020-0144 .

Luthans, F., Luthans, B.C., and Luthans, K.W. 2021. Organizational Behavior: An Evidence-Based Approach, 14th Edition. Charlotte: Information Age Publishing, Inc.

Mubarok, T.M.S., Lindayani, L., Farizah, S.N. 2021. The Relationship between Job Satisfaction and Employee Performance. Advances in Economics, Business and Management Research, Volume 657, 6th Global Conference on Business, Management, and Entrepreneurship (GCBME 2021), pp. 459-464.

Nurdiansyah, R., Mariam, S., Ameido, M.A., and Ramli, A.H. 2020. Work Motivation, Job Satisfaction, and Employee Performance. Business and Entrepreneurial Review Vol. 20, No.2, pp. 153-162.

Ouakouak, M.L., Zaitouni, M.G., and Arya, B. 2020. Ethical Leadership, Emotional Leadership, and Quitting Intentions in Public Organizations: Does Employee Motivation Play a Role?. Leadership & Organization Development Journal, Vol. 41 No. 2, pp. 257-279. https://doi.org/10.1108/LODJ-05-2019-0206 .

Pawirosumarto, S., Sarjana, P.K., and Muchtar, M. 2017. Factors Affecting Employee Performance of PT. Kiyokuni Indonesia. International Journal of Law and Management, Vol. 59 No. 4. https://doi.org/10.1108/IJLMA-03-2016-0031 .

Rita, M., Payangan, O.R., Rante, Y., Tuhumena, R., and Erari. 2018. Moderating Effect of Organizational Citizenship Behavior on the Effect of Organizational Commitment, Transformational Leadership and Work Motivation on Employee Performance. International Journal of Law and Management, Vol. 60 No. 4, pp. 953-964. https://doi.org/10.1108/IJLMA-03-2017-0026 .

Riyanto, S., Endri, E., and Herlisha, N. 2021. Effect of Work Motivation and Job Satisfaction on Employee Performance: Mediating Role of Employee Engagement. Problems and Perspectives in Management, Vol. 19, Issue 3, pp. 162-174.

Robbins, S.P., and Judge, T.A. Organizational Behavior, Update 18th Edition. Harlow: Pearson Education Limited.

Safitri, R.D., Suratno, A., and Sulistiyani, E. 2018. The Influence of Job Satisfaction and Motivation on Employee Performance at PT Chakra Naga Furniture Jepara. Jurnal JOBS, Vol. 4, No. 1, pp. 45-55.

Sidabutar, E., Syah, T.Y.R., and Anindita. R. 2020. The Impact of Compensation, Motivation, and Job Satisfaction on Employee Performance. Science, Engineering and Social Science Series, Vol. 4, No. 1, pp. 1-5.

Suardhita, N., Rafik, A., and Siregar, O. Analysis of The Effect of Motivation and Job Satisfaction on Employee Performance in PT Gagas Energi Indonesia Jakarta. Journal of Industrial Engineering & Management, Vol. 1 No. 3, pp. 209-217.

Sartika, L., Fatimah, F., and Asiati, D.I. 2022. The Effect of Competence, Job Placement and Job Satisfaction on Employee Performance at the Regional Office VII BKN. International Journal of Business, Management, and Economics, Vol. 3 No. 3, pp.257-270.

job satisfaction literature review 2022

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job satisfaction literature review 2022

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  1. Systematic Literature Review of Job Satisfaction: an Overview and Bibliometric Analysis

    This study aims to dig deeper into job satisfaction variable. In achieving this goal, the researchers used a systematic review using PRISMA method and bibliometric analysis techniques which took ...

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    Research Paper Job Satisfaction: A Literature Review EriaMuwanguzi School of Education, Humanities and Social Sciences, Bugema University Abstract When an employee is satisfied with the job, then such an employee will be more productive and creative and is more likely to be retained by the organization.

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    Job satisfaction reflects on overall life quality involving social relationships, family connection and perceived health status, affecting job performances, work absenteeism and job turnover, leading, in some cases, to serious psychological condition such as burnout [ 2, 3, 4, 5, 6 ].

  4. Job satisfaction and turnover decision of employees in the Internet

    Job satisfaction and turnover decision of employees in the Internet sector in the US Victor Chang , Yeqing Mou , Qianwen Ariel Xu & Yue Xu Article: 2130013 | Received 13 Sep 2021, Accepted 26 Sep 2022, Published online: 07 Oct 2022 Cite this article https://doi.org/10.1080/17517575.2022.2130013 In this article Full Article Figures & data References

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    Section 2 reviews the literature on job satisfaction and employee organizational commitment. Section 3 presents the conceptual framework, the hypotheses to be tested, ... Our results are useful for managers to address perils of the Great Resignation of late 2021 and early 2022, with high number of resignations and high labour market tightness ...

  6. Satisfied and High Performing? A Meta-Analysis and Systematic Review of

    Job satisfaction represents a key indicator of occupational well-being and has gained widespread interest in both research and practice as an important factor for predicting occupational behavior (Judge et al., 2001; Spector, 2022; Weiss, 2002; Wright et al., 2007).Across different occupational groups, job satisfaction is positively associated with general productivity, more satisfied ...

  7. The impact of the COVID-19 pandemic on job satisfaction: A mediated

    Job satisfaction has been the focus of management research, as it significantly affects employees' job performance, as well as other managers' performance indicators, such as customer satisfaction, perceived service quality, customer loyalty, and satisfaction ( O'Donoghue and Tsui, 2015 ).

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    The test revealed that job satisfaction and perceived work autonomy have a moderate positive correlation, r (200) = 0.424, p < .001; the correlation between work-family conflict and job satisfaction came out to be moderately negative with r (200) = −0.484, p < .001 and anxiety related to COVID-19 pandemic and job satisfaction appeared to have ...

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    Abstract This research is based on a phenomenon that occurs in State Detention Centers in Indonesia. It attempts to test the relation among proactive personality (PP), proactive work behavior (PWB), job satisfaction (JS) and role breadth self-efficacy (RBSE) variables.

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    Job satisfaction in health care has a great impact as it affects quality, productivity, effectiveness, and healthcare costs. ... Sandhya MN. Quality of Work Life Components: A Literature Review. Int J Indian Psychol. 2016;3(4):12-36. Google Scholar ... 28 May 2022. Accepted: 29 September 2022. Published: 27 October 2022.

  11. Employee Motivation, Job Satisfaction, and Employee Performance: a

    The results of the review show that employee motivation and job satisfaction have positive and significant effect on employee performance or in other word employee motivation and job satisfaction are the determinants of employee performance. References Astuti, W., and Amalia, L. 2021.

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    SYSTEMATIC LITERATURE REVIEW OF JOB SATISFACTION: AN OVERVIEW AND BIBLIOMETRIC ANALYSIS Rosana Oktaviani, Sopiah Published in JURNAL EKONOMI KREATIF DAN… 23 November 2022 Business JURNAL EKONOMI KREATIF DAN MANAJEMEN BISNIS DIGITAL Job satisfaction is the main variable that must be considered in managing human resource practices.

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    This study aimed to clarify the mixed conclusions regarding work engagement and job satisfaction in the nursing research literature by conducting a systematic review and meta-analysis. Design and Methods. This meta-analytic review synthesized 15 independent studies published between 2007 and 2021. Findings

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    2022 : Job satisfaction is a pivotal topic of the schools of management or HRM. There is no doubt that study the Job Satisfaction is one of the most successful approach to lead a company to reach their… Expand PDF Different levels of job satisfaction by educational organization motivators Aida Mehrad Mohammad Hossein Tahriri Zangeneh

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    Res. Public Health 2022, 19, 14214. https://doi.org/10.3390/ ijerph192114214 Academic Editor: Paul B. Tchounwou Received: 4 October 2022 Accepted: 28 October 2022 Published: 31 October 2022 Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in

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    1. DEFINITION AND IMPORTANCE OF JOB SATISFACTION Despite its vide usage in scientific research,as well as in everyday life,there is still no general agreement regarding what job satisfaction is. In fact there is no final definition on what job represents.

  18. PDF A literature review: Antecedents of Job Satisfaction

    The past nearly a century have seen many groups of studies on job satisfaction, whose focus were various. Some scholars try to analyze the importance of job satisfaction consented by connecting with the area of Psychology, like Ziwei Zhang Mentioned, employee satisfaction has always been a hot topic in the field of human resource management.

  19. PDF Impact of Job Satisfaction on Employee Performance: A Literature Review

    Mr. Vikram Jograna Research Scholar, Abstract Job satisfaction for which he or she indirectly. Employee job pleasure is crucial to face the dynamic and ever-increasing challenges of keeping productiveness of the organization by keeping their staff continuously engaged and motivated.