Teacher’s Job Satisfaction in the AI Era – Do Organisational Support and AI Self-Efficacy Matters?

Teacher’s Job Satisfaction in the AI Era – Do Organisational Support and AI Self-Efficacy Matters?

¹Xiao, Zhang, ^*2Lai-Kuan, Kong

^{1, 2}Faculty of Education and Liberal Studies, City University Malaysia, Petaling Jaya 46100, Malaysia

² Universiti Teknologi MARA (UiTM) Pahang Branch, Raub Campus, Raub 27600, Malaysia

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.908000539

Received: 16 August 2025; Accepted: 23 August 2025; Published: 19 September 2025

ABSTRACT

Purpose: This study examines the influence of organisational support towards teachers’ job satisfaction, emphasizing the mediating effect of AI self-efficacy among primary and secondary school teachers in Wuhan, China.

Design/Methodology: The study utilised a quantitative, cross-sectional design. Data were gathered through an online questionnaire administered to primary and secondary school teachers in Wuhan. Data analysis was conducted using structural equation modelling (PLS-SEM).

Findings: The findings indicated significant positive relationships between organisational support and both AI self-efficacy and job satisfaction. Furthermore, AI self-efficacy was identified as a significant mediator in the relationship between organisational support and job satisfaction.

Originality/Value: This study contributes to the body of knowledge by empirically showed the relatively unexplored psychological pathway (AI self-efficacy) linking organisational support to job satisfaction within AI-intensive educational contexts. The findings enhance understanding of how organisational support can positively influence teachers’ attitudes and job-related outcomes.

Implications: Educational policymakers and administrators can utilise these findings to develop specialised AI-related support, thereby improving teacher AI self-efficacy, job satisfaction and overall effectiveness in responding to technology changes.

Keywords: Organisational support, AI Self-Efficacy, Job satisfaction, Teachers, Artificial intelligence

INTRODUCTION

Job satisfaction is a popular topic of research in industrial and organisational psychology (Judge et al., 2017). While extensive research has explored its antecedents across various sectors, the rapid proliferation of artificial intelligence (AI) in the workplace introduces novel dynamics that are yet to be fully understood, particularly in the educational sector. For instance, how’s current trend of AI influence job satisfaction of employees, in particularly school teachers is still under study.

The integration of AI in education has transformed traditional pedagogical landscapes, offering innovative tools such as adaptive learning platforms, automated grading systems, and intelligent tutoring applications (Luckin et al., 2016). These technologies are designed to enhance instructional efficiency, personalize student learning, and reduce administrative burdens. However, alongside these benefits, educators face new challenges related to technological adaptation, increased cognitive load, and changing professional roles (Zawacki-Richter et al., 2019). As a result, the impact of AI integration on teachers’ job satisfaction has become an emerging area of concern.

Within this context, organisational support for AI adoption and individual self-efficacy in using AI tools have emerged as salient predictors of how well educators navigate these changes. Drawing upon Social Cognitive Theory (Bandura, 1986), it is posited that supportive organisational environments can bolster individuals’ beliefs in their capabilities, known as AI self-efficacy, which in turn may enhance job satisfaction. Yet, empirical research examining this triadic relationship in education, particularly within the Chinese context, remains limited.

This study aims to investigate the roles of AI-related organisational support and AI self-efficacy in predicting job satisfaction among school teachers in Wuhan, China.

As one of China’s major urban education hubs, Wuhan has experienced a robust digital transformation push in recent years, necessitating the upskilling of teaching staff and the integration of AI across curricula and administrative tasks. The Annual Report of Wuhan Municipal Education Bureau stated that up to 2024, they are 85000 teachers in normal primary and secondary schools in Wuhan (Wuhan Municipal Education Bureau, 2024). Looking at this huge number of school teachers, it is interesting to study the specific factors related to AI that influence their job satisfaction especially those living in a megacity like Wuhan. Furthermore, job satisfaction among teachers is a well-established predictor of retention, instructional quality, and student outcomes (e.g., Brown & Shields, 2016). Thus, insight from this research could help in developing and implementing effective organisational supports and AI strategies that enhance teacher well-being and motivation in evolving educational environments.

LITERATURE REVIEW

Job satisfaction is a critical construct in organizational research, significantly influencing employee performance, retention, and overall workplace well-being (Judge et al., 2020). While definitions of job satisfaction vary, it broadly encompasses an individual’s emotional response towards their job (Locke, 1969). Concurrently, AI-related organizational support is defined as the extent of encouragement and resources provided by organizations, particularly educational institutions, to facilitate employees’ use of artificial intelligence (AI) tools in their professional activities (Zheng et al., 2018). AI self-efficacy, another pertinent construct, represents an individual’s belief in their capability to effectively use AI applications to achieve desired outcomes (Hong, 2022).

Grounded in Bandura’s Social Cognitive Theory (Bandura, 1986), self-efficacy is understood to develop through mastery experiences, vicarious learning, and social persuasion. According to this theoretical perspective, supportive organizational environments foster the development of self-efficacy, subsequently enhancing positive work attitudes, including job satisfaction (Bandura, 1997). Therefore, the present study adopts Social Cognitive Theory as its theoretical foundation to investigate the relationships among AI-related organizational support (independent variable), job satisfaction (dependent variable), and AI self-efficacy (mediating variable). This framework posits that organizational support enhances AI self-efficacy, which in turn positively influences employee job satisfaction.

The relationship between organisational support, AI self-efficacy and job satisfaction

Oganisational support, self-efficacy, and job satisfaction is a significant area of research in organizational behaviour, as it highlights how supportive workplace environments can enhance employee outcomes. Research consistently showed that perceived organizational support positively affects self-efficacy and job satisfaction among employee. For example, Siagian (2024) highlighted that organisational support has the potential to motivate employees and improve job satisfaction, especially in contexts where AI is viewed as a threat. Conoras et al. (2021) found that employees perceiving higher levels of organisational support exhibit greater self-efficacy, subsequently enhancing their work engagement. This finding is consistent with the findings of Rockow et al., (2016) that POS influence employees’ perceptions of their competence, thereby enhancing their self-efficacy. This indicated that when employees perceive support, they are more inclined to have confidence in their capabilities, resulting in enhanced job performance and pleasure. Besides, Jiao et al. (2022) research also showed that organizational support significantly impacts preschool teachers’ self-efficacy, suggesting that supportive environments foster confidence in one’s abilities. These studies collectively underscore the notion that when employees feel supported, they are more likely to believe in their capabilities, leading to improved performance outcomes.

The mediating effect of self-efficacy on the relationship between organisational support and job satisfaction was also demonstrated. Musenze et al. (2020) observed that self-efficacy is a significant explanatory mechanism connecting perceived organisational support (POS) to work engagement, indicating that employees with higher self-efficacy are more inclined to actively engage in their work when they perceive robust organisational support. Aulia et al. (2022) later indicate that perceived organisational support has a positive correlation with work engagement, highlighting the significance of self-efficacy as a mediating factor. Cheng et al. (2020) also noticed that self-efficacy mediates the effects of POS on job satisfaction among paediatric nurses, indicating that supportive work environments enhance self-efficacy, which subsequently leads to higher job satisfaction. This underscores the importance of fostering a supportive organizational culture that enhances employees’ self-efficacy to improve job satisfaction. Additionally, Kim and Jang (2018) demonstrated that organisational support has a positive effect on self-efficacy, which in turn enhances the quality of life for maritime workers. They propose that self-efficacy serves as a mechanism by which organisational support is converted into job satisfaction. This aligns with the broader understanding that supportive environments enhance positive employee attitudes and behaviours.

In sum, the literature consistently demonstrates that perceived organizational support is a significant predictor of self-efficacy among employees and the relationship among organizational support, self-efficacy, and job satisfaction also presented by previous researches that employees who perceive high levels of support from their organization tend to exhibit greater self-efficacy, which in turn enhances their job satisfaction. However, up to date, although the mediation effect of self-efficacy is tested, but the specific mediation effect of AI self-efficacy between the above-mentioned relationship is under explored. Thus, this research aims to fill in this research gap by proposing the below hypotheses:

H1: There is a positive relationship between organisational support and AI self-efficacy among school teachers in Wuhan, China.

H2: There is a positive relationship between organisational support and job satisfaction among school teachers in Wuhan, China.

H3: AI self-efficacy mediates the relationship between organisational support and job satisfaction among school teachers in Wuhan, China.

METHODOLOGY

This research employs a quantitative cross-sectional design utilising a questionnaire survey method. The dependent variable, job satisfaction, was measured using the Chinese version of the 3-item Job Satisfaction Scale, as adopted from Liu et al. (2007). This scale assesses employees’ self-perceived levels of satisfaction or fulfilment derived from their work. Liu et al. (2007) utilise it to assess the impact of work environment stress on employees in China and the United States. The internal consistency coefficients for the job satisfaction scales were .67 for the Chinese version and .82 for the American version. The organisational support scale is adopted and adapted from the work of Zheng et al. (2018) that refers to the support and encouragement provided by the school for using AI in work. The original study reported a Cronbach alpha (α) value of 0.76, indicating that the measurement scales demonstrate adequate reliability. Finally, a 10-item measure of AI self-efficacy was utilised, as derived from Hong (2022), with a reliability coefficient of α = .87. The instrument is a modified technology self-efficacy scale created by Holden and Rada (2011). Both the measurement for job satisfaction and AI self-efficacy was using 7-likert scale, 1=very disagree to 7 = very agree, while organisational support used 5-point Likert scale to indicate the degree of agreement or disagreement.

This study utilised a non-probability purposive sample method, focussing on primary and secondary school educators in Wuhan. Data were gathered using an online questionnaire conducted on the Wenjuanxing platform. A total of 803 responses were initially collected; following a trimming method to improve reliability and exclude disengaged individuals, 655 valid responses were preserved for data analysis. Results indicate that just over half of the respondents are female (58.5%), and the majority possess a bachelor’s degree or higher (95.7%). Seventy-six percent of respondents possess five or more years of work experience, and just over half of them are aged between 25 and 35 years (55.30%).

FINDINGS

The statistical data analysis for this study was conducted using SPSS 29 and Smart PLS 4.1.  The partial least squares structural equation modelling (PLS-SEM) method was employed to analyse the data. The PLS Algorithm was chosen to assess the reliability and validity of the measurement model following the initial phase of the evaluation process, which entailed reviewing the measurement model. In the subsequent phase, the structural model underwent validation, and the bootstrapping method was selected to assess the significance of the indirect effect path coefficients.

Table 1 presents the measuring model, which analyses item factor loadings, Cronbach’s alpha (CA), composite reliability (CR), and convergent validity as assessed by average variance extracted (AVE).  This methodology has been employed in previous studies, as evidenced by the works of Jiang et al. (2025) and Li et al. (2023).

Table 1: Measurement model for reliability and validity

Notes: AISE – AI self-efficacy, JS – Job Satisfaction, OS – Organisational Support

The data presented in Table 1 indicate that Cronbach’s alpha (CA) and composite reliability (CR) values surpass the 0.70 threshold suggested by Hair et al. (2019), thereby affirming the establishment of construct reliability in this study, with values ranging from 0.857 to 0.956.  Table 1 presents item factor loadings ranging from 0.816 to 0.908. Items with loadings exceeding 0.500 may be retained if the average variance extracted (AVE) for the construct is above 0.500 (Li et al., 2024).  This is despite Hair et al. (2019) indicating that factor loadings should ideally exceed 0.708.  Table 1 indicates that the AVE values for all constructs range from 0.684 to 0.782, exceeding the threshold of 0.500.  Consequently, all items were retained for additional analysis.  The convergent validity of each construct is evidenced by AVE values exceeding 0.500.

Table 2: Heterotrait-Monotrait Ratio (HTMT)

Notes: AISE – AI self-efficacy, JS – Job Satisfaction, OS – Organisational Support

This study employed the Heterotrait–Monotrait Ratio of Correlations (HTMT) method to assess and confirm the discriminant validity of the instrument (Henseler et al., 2015), adhering to the HTMT threshold of less than 0.85 (Henseler et al., 2015; Li et al., 2024). Table 2 indicates that all HTMT values are below 0.85, confirming that the necessary criterion is fully met and that discriminant validity is established in this data set.

Table 3: Path coefficients for direct and indirect effects

Notes: OS – Organisational Support: AISE – AI self-efficacy, JS – Job Satisfaction,

The analytical method employs bootstrapping with 10,000 data resamples and utilises a one-tailed test to assess directional hypotheses. The two tested hypotheses were statistically significant and supported. The association between (i) organisational support and AI self-efficacy (β = 0.444, t = 13.420, p < 0.00), and (ii) organisational support and job satisfaction (β = 0.335, t = 8.175, p < 0.00) is established. Consequently, Hypotheses 1 and 2 are supported. A detailed explanation of the graphical presentation illustrated in Figure 1.

The study utilised a bootstrap procedure with 10,000 resamples to test the indirect effect hypothesis in the structural model, as recommended by Guenther et al. (2023). Table 3 results demonstrated that AI self-efficacy served as a mediator in the relationship between organisational support and job satisfaction (β = 0.127, t = 6.362, p < 0.00). Consequently, Hypothesis 3 is also supported.

Figure 1. Hypothesis testing

DISCUSSION AND CONCLUSION

Current research finding consistent with previous researches that showed perceived organizational support positively impacts self-efficacy (Conoras et al., 2021; Rockow et al., 2016). This indicated that the support, training and encouragement provided by the school for using AI in work has successfully enhanced the effectiveness and confidence of school teachers in using AI in their work. Besides, this study also suggested that AI self-efficacy mediates the relationship between organisational support and job satisfaction among school teachers in Wuhan, China. The rational of the finding is that in the era of AI where many industries including education is facing transformation, organizational support such as encouragement and providing training on AI application itself is insufficient to boost school teachers’ job satisfaction, instead AI self-efficacy could enhance this relationship because training effect by providing the teachers confidence and ability to effectively deploy AI in teaching and learning activities which consequently lead to job satisfaction. Current research findings supported the findings of Siagian (2024) that organisational support motivates employees and strengthen their job satisfaction, particularly in environments where AI is perceived as a threat.

In conclusion, current research indicates that organisational support and AI self-efficacy are essential factors influencing job satisfaction, especially in the context of the expanding technological landscape in education. These findings underscore the strategic significance of organisational support in the AI transition for school administrators and policymakers, especially in Wuhan and comparable urban educational environments. Implementing supportive measures, like regular AI training and acknowledgement of AI application in education, is essential to enhance the confidence and competencies of educators, ultimately leading to increased job satisfaction.

Future research could investigate moderating variables that may affect the intensity or direction of these relationships. For example, AI anxiety or technological optimism may influence the relationship between AI self-efficacy and job satisfaction, providing more understanding of individual variations in emotional and behavioural reactions to AI integration.

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