




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 10(4) (2025), 30-41 

30 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 10 NO 4 (2025) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

PERSON-ORGANIZATION FIT AND JOB BURNOUT AS 

PREDICTORS OF QUIET QUITTING IN VOCATIONAL 

EDUCATION                            
                                               

 Jing Zhou (a)1     Hooi Sin Soo (b)   Azelin Binti Aziz (c)   
 

(a) PhD Candidate, School of Business Management, Universiti Utara Malaysia, Sintok 06010, Kedah, Malaysia; Guizhou Communications 

Polytechnic University, Guiyang City 551400, Guizhou Province, China; E-mail: 13994932icepoint@gmail.com 
(b) Associate Professor, School of Business Management, Universiti Utara Malaysia, Sintok 06010, Kedah, Malaysia; E-mail: 

jennies@uum.edu.my  
(c) Lecturer, School of Business Management, Universiti Utara Malaysia, Sintok 06010, Kedah, Malaysia; E-mail: azelin@uum.edu.my 
        

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 25th February 2025 

Reviewed & Revised: 25th February 

to 16th June 2025 

Accepted: 18th June 2025 

Published: 20th June 2025 

 

 
Keywords: 
 

Quiet Quitting, Vocational Education, Job 

Burnout, Work Overload, China 

 

 
JEL Classification Codes: 

 

      M5, M540, C3, I2 

       
 

 

      Peer-Review Model:  

 

      External peer review was done through  

      double-blind method.        

 
A B S T R A C T      

 
In recent years, the phenomenon of quiet quitting, characterized by employees' psychological 

disengagement while continuing to meet only the minimum job requirements, has garnered increasing 
attention in organizational research. Despite its growing relevance, empirical studies examining this 

behavior within the context of vocational education remain limited, particularly in China. This study 

examines the relationships between person-organization fit (P-O fit), work overload, job burnout, and 

quiet quitting among faculty members in Chinese vocational colleges. A cross-sectional survey design 

was employed, and data were collected from 588 teaching staff members using structured 

questionnaires. Partial least squares structural equation modeling (PLS-SEM) was applied to analyze 

the hypothesized relationships among the variables. The results show that P-O fit is significantly and 

negatively associated with quiet quitting, both directly (β = –0.274, p < 0.001) and indirectly through 
the reduction of job burnout (β = –0.191, p < 0.001). Although work overload does not have a significant 

direct effect on quiet quitting (β = 0.105, p = 0.113), it exhibits a significant indirect effect through job 

burnout (β = 0.202, p < 0.001), which acts as a key mediating variable. Additionally, job burnout 

demonstrates a strong, positive relationship with quiet quitting behavior (β = 0.462, p < 0.001). These 

findings suggest that alignment between individuals and organizations, along with effective management 

of job-related strain, is key to understanding quiet quitting among vocational lecturers. This research 

contributes to the limited empirical literature on quiet quitting in Chinese vocational education and 

provides a quantitative assessment of the underlying organizational and psychological mechanisms 
involved. The study contributes theoretically by extending the Conservation of Resources (COR) theory, 

validating burnout as a mediating mechanism between organizational stressors and withdrawal 

behaviors in the underexplored context of Chinese vocational education. Practically, it offers actionable 

insights for reducing quiet quitting through improved P-O fit, balanced workload management, and early 

intervention for burnout. 

 
 

© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 

terms and conditions of the Creative Commons Attribution (CC BY) license 

(http://creativecommons.org/licenses/by/4.0).  
            

 

INTRODUCTION 

Quiet quitting has evolved into an observable yet subtle dilemma across various professional domains, including education 

(Serenko, 2024). Within Chinese vocational colleges, quiet quitting is emerging as an increasingly prominent concern 

among teaching staff, who continue to dissipate their psychological behaviour in the workplace, while only exercising the 

very basics of their role. Several features energize this event, behavior, and phenomenon (Li et al., 2025). In the context of 

vocational education in China, vocational educators are often expected to work high workloads, often in a stationary 

position, with administrative tasks and limited professional development opportunities, which adds layers of occupational 

stress and dissatisfaction (Zhang et al., 2024). Vocational teachers may also face more professional limitations in terms of 

institutional validation, professional prestige, and opportunities for career advancement. According to Wang and Huang 

(2025), the emergence of reflection, emotional drain, and management of administrative chaos, as well as limited workplace 

identity and forced workload expectations, creates a sense of numbness among teachers. Where educators retain draining 

expectations to relieve emotional strain and frustration, this can lead to job dissatisfaction and even quitting the profession 

                                                      
1Corresponding author: ORCID ID: 0009-0009-6090-7804 

© 2025 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  
https://doi.org/10.46281/c9d2ab72 

 

To cite this article: Zhou, J., Soo, H. S., & Aziz, A. B. (2025). PERSON-ORGANIZATION FIT AND JOB BURNOUT AS PREDICTORS OF QUIET 
QUITTING IN VOCATIONAL EDUCATION. Bangladesh Journal of Multidisciplinary Scientific Research, 10(4), 30-41. 

https://doi.org/10.46281/c9d2ab72 

 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/c9d2ab72
https://orcid.org/0009-0009-6090-7804
https://orcid.org/0000-0001-5283-593X
https://orcid.org/0000-0003-0946-121X


Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

31 

(Li et al., 2021). While vocational education plays a significant role in fulfilling national economic priorities and human 

capital imperatives for the future, a disengaged educator workforce poses a serious threat to ensuring the quality of 

education, developing institutions, and student learning (Bernuzzi et al., 2025). Therefore, it is essential to systematically 

examine the elements and conditions that contribute to quiet quitting in this sector. 

Quiet quitting among vocational college teaching personnel presents substantial, multidimensional, and serious 

challenges (Pan et al., 2025). It creates not only personal dissatisfaction and decreased morale, but it ultimately decreases 

teaching quality, student engagement, and institutional performance. When teaching staff disengage from their professional 

obligations, they may refuse to accept responsibilities for curriculum development, student oversight and mentoring, and 

various academic initiatives that are crucial to establishing and sustaining a vibrant and encouraging learning environment 

(Lu et al., 2023). According to Lv et al. (2023), quiet quitting can also contribute to a culture of mediocrity that has the 

potential to undermine team cohesiveness and collective professional expectations across an institution. For institutions 

focused on providing supplementary learning experiences to develop students' applied skills and knowledge, widespread 

quiet quitting can ultimately hinder the alignment of learning objectives with labour market needs (Wei et al., 2021). At the 

institutional level, persistent individual quiet quitting behaviours collectively contribute to higher levels of voluntary 

turnover intention and absenteeism, as well as reduced retention of experienced teaching staff (J. Lu et al., 2023). These 

efforts exacerbate the burden on an institution's resources and ability to administrate its workforce, while limiting its ability 

to sustain educational quality and viability (Xueyun et al., 2023). As vocational colleges in China continue to expand in line 

with national policy priorities, finding solutions to the evolving quiet quitting problem is critical to protecting future 

institutional sustainability and educational quality. 

In response to this issue, this study brings together three variables — P-O Fit, work overload, and job burnout —

to examine the relationships these variables have with quiet quitting behavior among vocational college teaching staff. By 

examining these variables, the present study addresses the critical need for empirical evidence on the organizational and 

psychological factors contributing to quiet quitting in Chinese vocational education. Meanwhile, investigating how these 

relationships interrelate is valuable for institutional leadership. In addition, the results of this study and analysis provide 

leaders with valuable insights into improving institutional alignment, assessing workload expectations, and fostering a 

working environment that enables staff to thrive. 

The purpose of this study was to empirically explore the direct and indirect relationships involving P-O Fit, work 

overload, job burnout, and quiet quitting among teaching staff in vocational colleges in China. By applying a structured 

empirical framework, the study aims to illustrate how P-O Fit and work overload contribute to burnout and quiet quitting 

and identify the processes that these two factors follow. The study contributes to an understanding of quiet quitting by 

exploring it within the unique context of Chinese vocational colleges, which include different institutional and socio-cultural 

dimensions compared to systemic issues present within corporate sectors or higher education university settings. By 

considering both organization-level and individual-level psychological variables, the study provides practitioners with a 

comprehensive perspective on employee disengagement, rather than simply an employee displaying job dissatisfaction. 

Furthermore, the discussion section presents the findings in the context of previous studies, while the conclusion outlines 

the study's implications in both theoretical and practical terms. 

 

LITERATURE REVIEW 

P-O Fit plays a critical role in shaping employee attitudes and behaviors in today’s dynamic work environment (Tsemach 

& Barth, 2023). When employees perceive alignment between their personal values, skills, and the organization’s culture 

and expectations, they are more likely to remain engaged and committed to their roles. Conversely, a lack of fit can result 

in decreased motivation, job dissatisfaction, and difficulty performing effectively (Serenko, 2024). In such cases, quiet 

quitting—characterized by minimal discretionary effort and emotional detachment—often emerges as a passive form of 

disengagement. This behavior, although subtle, can have significant implications for organizational performance, 

particularly when no immediate replacements are available (Foltz et al., 2025). Recognizing this, many organizations have 

begun prioritizing the alignment of individual capabilities with organizational needs to reduce quiet quitting  (Xueyun et al., 

2023). When employees experience a strong sense of fit, their engagement increases, and the likelihood of quiet quitting 

diminishes. 

Work overload is widely recognized as a key factor that negatively affects employee motivation and performance 

(Wang et al., 2022). When employees face excessive workloads, they often experience stress and disengagement, which 

may lead to quiet quitting—a behavior where individuals reduce their effort and psychological investment without formally 

resigning. Although training and support from human resources are essential, it is challenging to address each employee’s 

needs individually under high-pressure conditions (Kothari et al., 2021). Conversely, when work is distributed fairly and 

remains within manageable limits, employees tend to show higher engagement and improved performance (Liu, 2020). 

However, unequal or excessive workloads may push employees toward burnout and withdrawal (Prentice et al., 2025). 

Therefore, work overload may significantly contribute to the occurrence of quiet quitting. 

When employees perceive a poor fit between their attributes and the organization's expectations—such as 

mismatched skills, values, or goals—they are more likely to experience job burnout(Xueyun et al., 2024). A lack of 

alignment between employee competencies and job demands often leads to frustration, reduced motivation, and emotional 

exhaustion. In contrast, when employees feel supported and experience a strong sense of person-organization fit, they are 

more motivated to improve their skills and contribute meaningfully to their roles (Gabelaia & Bagociunaite, 2024). Low 

motivation and disengagement not only diminish individual performance but also heighten the risk of burnout over time (M. 

Lu et al., 2023). Therefore, fostering a positive work environment that promotes alignment between employee capabilities 

and organizational expectations is crucial to preventing burnout and supporting sustainable performance (Perveen et al., 



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

32 

2025). 

Work overload is a significant factor contributing to reduced employee productivity and increased job burnout 

(Fang et al., 2024). When employees are consistently assigned tasks that exceed their capacity or fall outside their job 

descriptions, their motivation tends to decline, leading to emotional exhaustion and a decrease in performance. In contrast, 

maintaining reasonable workload expectations enables employees to stay focused and productive. Additionally, motivation 

plays a crucial role in helping employees cope with demanding work conditions by encouraging skill development and 

resilience (Thu Trang & Thi Thu Trang, 2024). When employees are empowered and supported through training, guidance, 

and a balanced workload, they are better equipped to manage stress and avoid burnout (Jeilani & Hussein, 2025). Therefore, 

excessive work demands without adequate support can significantly increase the likelihood of job burnout. 

Job burnout, characterized by emotional exhaustion, reduced personal accomplishment, and depersonalization, 

significantly impairs employees’ effectiveness and engagement at work (Soren & Ryff, 2023). Employees experiencing 

burnout are more likely to disengage from their roles, which may manifest as quiet quitting—a passive form of withdrawal 

marked by minimal effort and psychological detachment. This phenomenon presents a growing challenge for organizations, 

as it often occurs without explicit communication, making it difficult for management to respond in a timely and supportive 

manner. Enhancing employee performance through skills development and behavioral support is essential to maintaining 

productivity (Engle et al., 2024). Moreover, fostering positive behavioral change through motivation and organizational 

support can mitigate the adverse effects of burnout  (Farahmandpour & Voelkel, 2025). Thus, job burnout may serve as a 

key predictor of quiet quitting behavior. 

When employees perceive a poor fit between their personal values, skills, and the characteristics of their job, it 

often leads to reduced motivation, lower performance, and diminished productivity (Gone et al., 2025). Such misalignment 

can foster dissatisfaction and emotional exhaustion, which are core components of job burnout (Gundlach, 2025). In turn, 

employees experiencing burnout are more likely to psychologically withdraw from their roles, exhibiting quiet quitting 

behaviors characterized by minimal engagement and effort. While some may eventually leave the organization, others 

remain physically present but disengaged, posing significant management challenges (Henderson et al., 2024; Khan et al., 

2021). This decline in employee commitment and cooperation ultimately affects organizational performance. Given this 

dynamic, job burnout may serve as a key psychological mechanism linking person-organization misfit to quiet quitting. 

Work overload poses a significant challenge to employees, often resulting in reduced productivity and impaired 

job performance. When employees are overwhelmed by excessive demands and lack sufficient motivation or resources, 

they struggle to meet job expectations effectively. Inadequate organizational support further exacerbates this issue, making 

it challenging for employees to maintain their performance under pressure. To address these challenges, management must 

provide targeted support and consider redesigning job roles to align with employee capabilities and reduce unnecessary 

burden  (Gillison et al., 2023). Without such interventions, persistent work overload can lead to emotional exhaustion and 

job burnout, conditions that may prompt employees to adopt quiet quitting as a coping response. Thus, job burnout may 

serve as a mediating mechanism through which work overload influences quiet quitting behavior. 

The review of existing studies highlighted that there are mixed and unclear findings regarding the relationship 

among P-O fit, work overload, job burnout, and quitting. Hence, the purpose of this study was to investigate the relationships 

between person-organization fit, work overload, job burnout, and quitting. To achieve this purpose, the following hypotheses 

are developed. 

 

H1: There is a relationship between P-O Fit and quiet quitting. 

H2: There is a relationship between work overload and quiet quitting. 

H3: There is a relationship between P-O Fit and job burnout. 

H4: There is a relationship between work overload and job burnout. 

H5: There is a relationship between job burnout and quiet quitting. 

H6: Job burnout mediates between P-O Fit and quiet quitting. 

H7: Job burnout mediates between work overload and quiet quitting. 

 

The relationships based on the hypotheses are drawn in the model of this study, shown in Figure 1. 

 

Figure 1. Research Model 

 

 



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

33 

MATERIALS AND METHODS 
In this study, data from the population (lecturers) were collected from 9 vocational colleges from eastern, central, and 

western China, all with differing levels of economic development and distribution of teaching resources. In anticipation of 

the formal survey, a pilot study was conducted further to refine the questionnaire's quality before data collection. The study 

initially collected data from 61 lecturers and then adjusted the data based on the issues raised during the pilot study. 

This study employed cluster sampling, where data were collected randomly. Krejcie and Morgan (1970) established 

the minimum sample size requirement of 384 people in their table for sample size determination when the population 

exceeds 10,000. The data collection for this study occurred from March to May 2025. The teacher development centers of 

the nine vocational colleges were approached via phone and email, and they were requested to distribute the questionnaires. 

Each college contacted by the research team employed a random sampling approach, selecting a list of their own chosen 

number of lecturers based on the minimum sample size of this study. The colleges distributed the link to the online 

questionnaire on the Questionnaire Star platform randomly through on-campus distribution channels. 

During the final data collection, a total of 588 questionnaires were returned in the formal study. The sample size 

for each college was proportionate to the college's representation in the total sample, ensuring the sample's 

representativeness and a reasonable allocation of samples from each college. The Teacher Development Center assisted 

with data collection surveys in each college, and participants (lecturers) were selected randomly from the college's faculty 

list. Additionally, in some colleges, the questionnaire was distributed through WeChat, while others were approached via 

email. Each questionnaire was sent anonymously, allowing for sample diversity and reliability. 

To ensure sample accuracy and relevance, the colleges were the first to identify themselves from the sample, and 

only individual teachers from the recognized colleges were allowed to proceed with the questionnaire. After the 

questionnaire data collection was completed, the research team promptly downloaded and backed up the sample data, 

screened out invalid questionnaires, and maintained the identity and integrity of the sample data. The outlined method of 

operation provided a means of enhancing study data quality and research reliability, laying a firm foundation of unquestioned 

confidence and robustness for all subsequent analysis. 

All measurements were based on a Likert scale. The P-O-Fit scale was adapted from the Person-Organization Fit 

Scale (POFs) proposed by Cable and DeRue (2002). However, the work overload scale was adapted from the ROS scale by 

Peterson et al. (1995). Moreover, the job burnout scale used the MBI-General Survey (MBI-GS), which was more relevant 

to the Chinese cultural context (Wu et al., 2021). Finally, the quiet quitting of lecturers was measured using the QQ scale 

developed by Anand et al. (2024). This study utilized IBM SPSS 26 and SmartPLS 4 for data analysis. First, the demographic 

data was analyzed at the first point. Furthermore, measurement model assessment and structural model assessment were 

conducted to evaluate the reliability of the data and examine the relationship between the research variables. 

 

RESULTS 

This study used IBM SPSS 26 to analyze the demographic data. The demographic profile reported in Table 1 shows that the 

sample was predominantly female, with women comprising 67% (n = 394) of the respondents, while men accounted for 

33% (n = 194). This gender imbalance may reflect broader trends in the vocational education workforce, where female 

educators are more prevalent. Regarding marital status, a substantial majority of the participants (76%, n = 449) reported 

being married, followed by 18% (n = 107) who identified as single, and 5% (n = 32) who selected "Other," potentially 

indicating widowed, divorced, or other non-specified statuses. Educational attainment among respondents was relatively 

high. The majority held a Master's degree (62%, n = 366), while 28% (n = 162) had obtained a Bachelor's degree. A smaller 

proportion had completed doctoral studies, with PhD holders representing 8% (n = 48), and an additional 2% (n = 12) 

reported other qualifications. 

In terms of professional experience, the most common range was 10 to 15 years of teaching, reported by 36% (n = 

211) of participants. Those with less than five years of experience comprised 24% (n = 141), and 22% (n = 129) had between 

five and ten years of experience. Fewer respondents reported longer tenures, with 14% (n = 80) indicating 15–20 years and 

only 5% (n = 27) having 20–25 years of teaching experience. Regarding academic rank, "Senior Lecturer" was the most 

frequently reported title (40%, n = 237), followed by "Associate Professor" (32%, n = 187). Lecturers constituted 15% (n = 

85) of the sample, while Professors made up a relatively small proportion (5%, n = 32). An additional 8% (n = 47) fell into 

the "Other" category, indicating roles that may not align with the conventional academic hierarchy. Finally, the geographical 

distribution of respondents was skewed towards Eastern China, where 65% (n = 381) of the vocational colleges were located. 

Western China accounted for 20% (n = 119) of the sample, and Central China represented the remaining 15% (n = 88). This 

regional imbalance may reflect the institutional focus of the research or the actual distribution of vocational institutions 

across the nation. 

 

Table 1. Demographics 
 

Variable Category Frequency Percentage 

Gender Male 194 33%  
Female 394 67% 

Marriage Status Married 449 76%  
Single 107 18%  
Other 32 5% 

Educational Level PhD (Doctorate) 48 8%  
Master's Degree 366 62%  
Bachelor's Degree 162 28% 



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

34 

 
Other 12 2% 

Years of teaching experience Less than 5 years 141 24%  
5-10 Years 129 22%  
10-15 Years 211 36%  
15-20 Years 80 14%  
20-25 Years 27 5% 

Academic Titles Lecturer 85 15%  
Senior Lecturer 237 40%  
Associate Professor 187 32%  
Professor 32 5%  
Other 47 8% 

The region where your vocational college is located Eastern China 381 65%  
Central China 88 15% 

  Western China 119 20% 

 
The data from this study were analyzed using SmartPLS 4 to assess the measurement model and determine 

convergent validity. It is based on the four-factor assessment. Initially, the investigation of factor loadings was conducted. 

In the reflective measurement model, indicators with loadings below 0.70 are considered for deletion. However, this study 

found that all indicators were above 0.70 (Hair et al., 2011). Hence, the reliability of the individual item was significantly 

established in this study. Secondly, the study analyzed the Cronbach's alpha findings to test the reliability of the instruments. 

Thirdly, composite reliability was also assessed to evaluate the reliability of the individual instruments. The findings of both 

should be above 0.70 (Hair et al., 2011) for significant reliability of the instruments. The study found that the value for all 

variables was less than the recommended threshold. Hence, the reliability of the individual item was significantly achieved. 

Moreover, the study investigated the average variance extracted to confirm convergent validity. According to Hair et al. 

(2011), the value for average variance extracted above 0.50 is considered significant. Hence, the study found that convergent 

validity was significantly established, as the data reported in the study are reliable and significant (see Table 2 and Figure 

2). 

 

 

Figure 2. Measurement Model 
Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 

 

Table 2. Convergent Validity 
 

Construct Indicators Factor Loadings Cronbach's alpha Composite reliability Average variance extracted 

JB JB1 0.851 0.955 0.961 0.735  
JB2 0.860 

   

 
JB3 0.859 

   

 
JB4 0.840 

   

 
JB5 0.850 

   

 
JB6 0.908 

   



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

35 

 
JB7 0.881 

   

 
JB8 0.851 

   

 
JB9 0.812 

   

POF POF1 0.802 0.898 0.921 0.660  
POF2 0.796 

   

 
POF3 0.798 

   

 
POF4 0.824 

   

 
POF5 0.809 

   

 
POF6 0.844 

   

QQ QQ1 0.811 0.936 0.948 0.725  
QQ2 0.732 

   

 
QQ3 0.874 

   

 
QQ4 0.897 

   

 
QQ5 0.864 

   

 
QQ6 0.894 

   

 
QQ7 0.877 

   

WO WO1 0.870 0.955 0.965 0.847  
WO2 0.922 

   

 
WO3 0.955 

   

 
WO4 0.922 

   

  WO5 0.932       

Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 

 

Next, the study performed an analysis for discriminant validity. This analysis is used to assess discrimination in 

the data collected from the research instruments. In this study, two methods are employed to assess discriminant validity. 

Initially, the study employed the Heterotrait-Monotrait (HTMT) method for assessing discriminant validity. According to 

Henseler et al. (2015), an HTMT value below 0.85 indicates acceptable discriminant validity. The findings reported in Table 

3 confirmed that HTMT was significantly achieved, indicating that the variables of this research have data that establish 

significant discriminant validity. 

 

Table 3. Discriminant Validity – HTMT 
 

Construct JB POF QQ WO 

JB 
    

POF 0.575 
   

QQ 0.703 0.599 
  

WO 0.592 0.322 0.470   

Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 

 

Moreover, the study used the findings of cross-loadings to confirm discriminant validity. For the assessment of 

discriminant validity with cross-loadings, the study investigated whether the loadings of one variable's indicators are greater 

than those of other variables correlated with it. The findings shown in Table 4 confirmed that the cross-loadings were 

significantly established (Hair et al., 2011). Hence, the study found no issues related to discriminant validity in the data. 

 

Table 4. Cross-loadings 
 

Indicators JB POF QQ WO 

JB1 0.851 -0.427 0.463 0.692 

JB2 0.860 -0.445 0.463 0.643 

JB3 0.859 -0.442 0.473 0.562 

JB4 0.840 -0.420 0.483 0.516 

JB5 0.850 -0.392 0.534 0.481 

JB6 0.908 -0.529 0.687 0.457 

JB7 0.881 -0.476 0.686 0.385 

JB8 0.851 -0.522 0.671 0.359 

JB9 0.812 -0.589 0.684 0.348 

POF1 -0.373 0.802 -0.446 -0.199 

POF2 -0.379 0.796 -0.424 -0.216 

POF3 -0.381 0.798 -0.382 -0.190 

POF4 -0.404 0.824 -0.409 -0.248 

POF5 -0.450 0.809 -0.466 -0.253 

POF6 -0.630 0.844 -0.567 -0.396 

QQ1 0.527 -0.412 0.811 0.378 

QQ2 0.478 -0.420 0.732 0.357 

QQ3 0.584 -0.452 0.874 0.376 

QQ4 0.622 -0.530 0.897 0.488 

QQ5 0.607 -0.535 0.864 0.400 

QQ6 0.621 -0.550 0.894 0.365 

QQ7 0.553 -0.432 0.877 0.345 

WO1 0.382 -0.157 0.273 0.870 

WO2 0.557 -0.321 0.479 0.922 

WO3 0.553 -0.334 0.448 0.955 



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

36 

WO4 0.498 -0.294 0.380 0.922 

WO5 0.590 -0.327 0.468 0.932 

Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 

 

Meanwhile, the study also examined the findings of the variance inflation factor (VIF) to identify multicollinearity 

issues in the data. These issues are evident in the data when the independent variables are highly correlated with each other. 

However, if the VIF value for all variables is found to be less than 3.3, it confirms that no multicollinearity issues are present 

in the data. The findings of Table 5 confirm that all variables received the VIF values less than 3.3 (Hair et al., 2011). Hence, 

the study found no issues with multicollinearity in the research data. 

 

Table 5. Multicollinearity Assessment 
 

Construct JB QQ 

JB 
 

1.920 

POF 1.115 1.442 

WO 1.115 1.485 

Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 
 

Furthermore, the findings of the structural equation model were used to test the hypotheses and examine the 

relationships between the research variables. First, job burnout was found to have a strong and statistically significant 

positive effect on quiet quitting (β = 0.462, t = 6.156, p < 0.001), indicating that higher levels of burnout are associated with 

increased tendencies toward quiet quitting behaviors. This suggests that burnout plays a critical role in shaping 

disengagement and withdrawal intentions in the workplace. Secondly, P-O Fit exhibited a significant negative relationship 

with both job burnout and quiet quitting. Specifically, P-O Fit negatively predicted job burnout (β = -0.413, t = 10.068, p < 

0.001), implying that better alignment between individual and organizational values reduces burnout. Additionally, P-O Fit 

had a significant adverse direct effect on quiet quitting (β = -0.274, t = 5.552, p < 0.001), suggesting that a strong P-O Fit 

may directly mitigate tendencies toward disengagement, beyond its indirect effect via burnout. On the other hand, work 

overload showed a significant positive relationship with job burnout (β = 0.439, t = 11.152, p < 0.001), supporting the idea 

that excessive workload substantially contributes to employee burnout. However, its direct path to quiet quitting was not 

statistically significant (β = 0.105, t = 1.583, p = 0.113). This indicates that while work overload may not directly lead to 

quiet quitting, its influence on such behavior may be mediated through job burnout. The findings of direct paths are reported 

in Table 6 and Figure 3. 

 

Figure 3. Structural Model 
Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 

 

Table 6. Direct Paths 
 

Direct Paths Original sample Standard deviation  T statistics P values 

JB -> QQ 0.462 0.075  6.156 0.000 

POF -> JB -0.413 0.041  10.068 0.000 



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

37 

POF -> QQ -0.274 0.049  5.552 0.000 

WO -> JB 0.439 0.039  11.152 0.000 

WO -> QQ 0.105 0.066  1.583 0.113 

Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 

 

Furthermore, the mediating hypotheses were tested with structural equation modeling. The indirect path from P-O 

Fit to quiet quitting via job burnout was found statistically significant and negative (β = -0.191, t = 5.177, p < 0.001). This 

result suggests that part of the influence of P-O Fit on quiet quitting is mediated through its effect on job burnout. 

Specifically, when individuals perceive a more substantial alignment between their values and those of the organization, 

they experience lower levels of burnout, which in turn reduces their propensity to engage in quiet quitting. This highlights 

job burnout as a significant mediator, suggesting that improving organizational fit can indirectly mitigate disengagement by 

alleviating burnout.  

Similarly, the indirect path from work overload to quiet quitting via job burnout was positive and statistically 

significant (β = 0.202, t = 4.296, p < 0.001). This finding demonstrates that work overload contributes to quiet quitting 

behaviors indirectly by increasing job burnout. While the direct effect of work overload on quiet quitting was not significant, 

this result confirms that its impact is entirely mediated through burnout. In other words, an excessive workload may not 

directly cause employees to disengage, but it significantly raises burnout levels, which subsequently lead to quiet quitting. 

The findings of indirect paths are reported in Table 7. 

 

Table 7. Indirect Paths 

 
Indirect Paths Original sample Standard deviation T statistics P values 

POF -> JB -> QQ -0.191 0.037 5.177 0.000 

WO -> JB -> QQ 0.202 0.047 4.296 0.000 

Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 
 

The study ultimately analyzed the data to assess its predictive relevance. The findings in predictive relevance 

confirm the predictive power of the research model. For this purpose, the study used the PLS blindfolding method to assess 

Q2. The value of Q2 above 0 is considered significant for appropriate predictive relevance. The findings reported in Table 

8 and Figure 4 confirm that the Q2 value for dependent variables was above 0. Hence, it was confirmed that the model of 

study has a significant predictive power. 

 

 

Figure 4. Predictive Relevance 

Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 
 

 

 

 



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

38 

Table 8. Predictive Relevance 
 

Construct SSO SSE Q² 

JB 5292 3449.111 0.348 

POF 3528 3528 0.000 

QQ 4116 2612.643 0.365 

WO 2940 2940 0.000 

Note. POF = Person Organization Fit, WO = Work Overload, JB = Job Burnout and QQ = Quiet Quitting 

 

This study systematically addressed its research objectives through direct path testing and mediation effect analysis. 

A total of five direct hypotheses and two indirect hypotheses were proposed. Except for H2, all were statistically supported, 

demonstrating the model’s strong explanatory power and validating the hypothesized relationships among the variables. 

Table 9 summarizes the results of the hypothesis testing. 

 

Table 9. Testing Hypotheses  
 

Hypotheses 
Path Relationships 

Results 

H1 P-O Fit → Quiet Quitting Supported 

H2 Work Overload → Quiet Quitting Not Supported 

H3 P-O Fit → Job Burnout Supported 

H4 Work Overload → Job Burnout. Supported 

H5 Job burnout → Quiet Quitting Supported 

H6 P-O Fit →Job Burnout→ Quiet Quitting Supported 

H7 Work Overload→ Job Burnout → Quiet Quitting Supported 

 

DISCUSSIONS 

This study aimed to investigate the relationships among P-O Fit, work overload, job burnout, and quiet quitting among 

lecturers in Chinese vocational colleges. Drawing on COR theory and employing PLS-SEM, the study tested seven 

hypotheses, yielding results that offer both theoretical insights and practical implications. The findings provided strong 

support for most of the hypothesized relationships, with H1, H3, H4, H5, H6, and H7 being statistically confirmed, while H2 

was not. 

H1 posited a negative relationship between P-O Fit and quiet quitting. The results confirmed a significant and 

negative direct effect (β = -0.274, p < 0.001), indicating that higher alignment between individual and organizational values 

directly reduces the likelihood of disengagement. This finding aligns with Farahmandpour and Voelkel (2025), who noted 

that well-matched skills and values enhance employee attitudes and reduce withdrawal behaviors.  

H2, which hypothesized a positive relationship between work overload and quiet quitting, was not statistically 

supported (β = 0.105, p = 0.113). This contrasts with Fernández-Arias et al. (2024), who found that increased workload can 

escalate withdrawal behavior. One possible post hoc explanation is that in the vocational education context, high workloads 

may be normalized, and faculty may not perceive them as sufficient cause for immediate disengagement unless accompanied 

by emotional exhaustion. Alternatively, institutional pressure or concerns about job security may suppress overt withdrawal 

responses. 

H3 and H4 explored antecedents of job burnout. P-O Fit was negatively related to job burnout (β = -0.413, p < 

0.001), supporting H3 and reinforcing findings by Wang et al. (2022) and Gabelaia and Bagociunaite (2024), who suggested 

that alignment enhances intrinsic motivation and buffers emotional strain. H4 was also supported, as work overload 

significantly predicted higher burnout levels (β = 0.439, p < 0.001), confirming studies by Fang et al. (2024) and Dong et 

al. (2025) on the psychological cost of excessive demands. 

H5 was strongly supported, showing that job burnout positively predicts quiet quitting (β = 0.462, p < 0.001). This 

highlights burnout as a key proximal antecedent of psychological disengagement. The result confirms prior research by 

Emir et al. (2023) and Fang et al. (2024), who found that burnout is a precursor to reduced emotional commitment and 

discretionary effort. 

The findings also affirmed the mediating role of job burnout in both paths: H6 confirmed that job burnout mediates 

the relationship between P-O Fit and quiet quitting (β = -0.191, p < 0.001). This suggests that P-O Fit not only directly 

reduces disengagement but also does so indirectly by lowering emotional exhaustion. This is consistent with Drouin-

Rousseau et al. (2024), who emphasized the motivational benefits of value alignment. H7 revealed that work overload 

indirectly contributes to quiet quitting through job burnout (β = 0.202, p < 0.001). Although the direct path was non-

significant, this mediation path suggests a full mediation effect, meaning that the experience of burnout is the channel 

through which excessive workload leads to disengagement. This supports Drüge et al. (2021) and  Dymecka et al. (2024), 

who observed that the psychological toll of overload often manifests through burnout symptoms before behavioral 

withdrawal. 

Overall, these findings suggest that P-O Fit serves as a protective factor, directly and indirectly reducing quiet 

quitting, while work overload primarily influences job burnout. The central role of burnout as a mediating mechanism aligns 

with the Conservation of Resources theory, which posits that resource depletion—whether caused by misfit or 

overexertion—leads individuals to withdraw as a psychological coping mechanism. The mixed support for the direct effect 

of work overload suggests that workload alone may not be sufficient to trigger quiet quitting unless it threatens psychological 

resources. Conversely, P-O Fit and organizational support appear critical in preserving employee engagement. These results 



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

39 

contribute to a growing body of literature on disengagement behaviors in non-Western academic contexts, providing 

practical insights for human resource policies. Vocational institutions may mitigate quiet quitting by enhancing 

organizational-personal alignment, promoting realistic workload management, and addressing signs of burnout through 

proactive interventions. 

 

CONCLUSIONS  

The purpose of this study was to investigate the relationships between person-organization fit, work overload, job burnout, 

and quitting. Therefore, this research sheds light on the existing empirical literature on the antecedents of quiet quitting by 

outlining the importance of P-O Fit, work overload, and job burnout as predictor variables of quiet quitting behaviors of 

teaching staff in the context of vocational colleges in China. The findings illustrate the role of P-O Fit in terms of a significant 

pessimistic prediction of quiet quitting, both directly and indirectly through job burnout. However, these findings 

demonstrate that values congruence between an employee and an organization can be a necessary factor in reducing 

disengagement. On the other hand, this study highlights that the role work overload plays in quiet quitting is also noteworthy. 

While it was not a statistically significant direct predictor of quiet quitting, it had a significant indirect effect through job 

burnout, illustrating that burnout serves as a mediator between excessive workload and withdrawal. Moreover, the research 

also supports existing literature by demonstrating that P-O Fit has a buffering effect on job burnout while work overload 

has the opposite effect. According to the findings, what is perhaps even more important, however, is that job burnout was 

also the most significant predictor of quiet quitting, emphasizing its salient role in vocational education contexts as opposed 

to traditional higher education settings. Therefore, the implications of this research for institutional management and policy 

are significant, as it illustrates that the quality of organizational aspects, namely P-O Fit, and effective management of 

workload demands can potentially reduce burnout and, hence, disengagement. 

From a theoretical perspective, this research makes valuable contributions to the literature on employee 

disengagement by adding to the discussion on quiet quitting in the limited studied context of vocational education, 

particularly in China. Before this research, most studies on quiet quitting have focused on company employees and 

healthcare staff contexts. This study has contributed to the literature by highlighting the lack of research on teaching staff 

in vocational colleges. Nevertheless, the involvement of teaching staff in vocational college institutions raises the possibility 

that work stressors and organizational reactivity differ substantially. The findings enhance the literature by identifying P-O 

Fit and job burnout as predictors of quiet quitting tendencies among Chinese vocational college teaching staff, presenting 

evidence on how both work organizational fit and employee wellbeing are related to the phenomenon of quiet quitting. 

Additionally, the study identifies a more straightforward indirect path from work overload to quiet quitting via job burnout. 

Therefore, this study provides a finding that reinforces job burnout as a critical intermediary psychological response and 

mediating variable when examining withdrawal behaviors and cognitions. Furthermore, by contextualizing these 

relationships in Chinese vocational institutions, the research has produced culture-specific values to address the lack of 

international literature on employee disengagement in non-Western educational contexts. 

For the practical implications, the negative relationship between P-O Fit and quiet quitting suggests that institutions 

should focus their recruitment, selection, and retention strategies on personnel who align with the organization. Furthermore, 

initiatives that include structured onboarding processes, value-based hiring practices, and ongoing organizational 

socialization would reinforce P-O Fit and decrease disengagement behaviour. Moreover, the study identified job burnout as 

an important variable that influences quiet quitting. It is essential to note that work overload was an indirect contributor to 

quiet quitting, with burnout serving as a mediating factor. Consequently, vocational colleges in China should introduce 

workload management strategies that ensure expectations do not exceed the teaching and administrative load capacity of 

the staff. Moreover, policies that support the workplace environment, such as flexible scheduling, mental health initiatives, 

and professional development opportunities, can help better manage burnout issues that may be occurring in the workplace. 

Moreover, these findings confirm that educational leaders need to be aware of employee wellbeing. Additionally, 

it is essential to foster an open communication environment in academic settings that enables leaders to engage in candid 

dialogue with employees when signs of burnout or disengagement emerge. The practical implications of this study suggest 

that to eliminate quiet quitting in vocational colleges, educational institutions should address both organizational and 

psychological issues affecting employees simultaneously.  

This study focused on teaching staff at Chinese vocational colleges. It examined the effects of P-O Fit and work 

overload on quiet quitting, with job burnout as a mediating variable. Although the research provides both theoretical and 

practical insights, several limitations should be noted. First, the sample was drawn exclusively from vocational colleges in 

China, which may limit the generalizability of the findings to other educational settings, such as universities or primary and 

secondary schools. Second, the study employed a cross-sectional design, which restricts the ability to capture the dynamic 

and causal relationships among variables over time. Third, although the mediating role of job burnout was confirmed, other 

potential moderators, such as perceived organizational support, psychological resilience, or professional identity, were not 

examined.  

 
Author Contributions:  Conceptualization, J.Z., H.S.S. and A.B.A.; Methodology, H.S.S.; Software, J.Z.; Validation, J.Z., H.S.S. and A.B.A.; Formal 
Analysis, J.Z.; Investigation, J.Z.; Resources, J.Z.; Data Curation, J.Z.; Writing – Original Draft Preparation, J.Z., H.S.S. and A.B.A.; Writing – Review 

& Editing, J.Z.; Visualization, J.Z.; Supervision, H.S.S. and A.B.A.; Project Administration, H.S.S. and A.B.A.; Funding Acquisition, J.Z. All authors 

have read and agreed to the published version of the manuscript. 
Institutional Review Board Statement: Ethical review and approval were waived for this study, as the research does not involve vulnerable groups or 

sensitive issues. 

Funding: Authors received no funding for this research.  



Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

40 

Acknowledgment: We extend our sincere appreciation to all participants who contributed to this study. The School of Business Management at Universiti 

Utara Malaysia is highly acknowledged. 

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   
 

REFERENCES 
Anand, A., Doll, J., & Ray, P. (2024). Drowning in silence: a scale development and validation of quiet quitting and quiet firing. International Journal of 

Organizational Analysis, 32(4), 721–743. https://doi.org/10.1108/IJOA-01-2023-3600  
Bernuzzi, C., Paganin, G., Petrilli, S., & Margheritti, S. (2025). Old trends in new clothing? Exploring the quiet quitting phenomenon through a scoping 

review. Current Psychology, 44, 10990-11010. https://doi.org/10.1007/s12144-025-07920-x  

Cable, D. M., & DeRue, D. S. (2002). The convergent and discriminant validity of subjective fit perceptions, 87(5), 875-884. https://doi.org/10.1037/0021-
9010.87.5.875 

Dong, X., Guo, X., Fu, Y., & Fu, T. (2025). Impact of work-family conflict on work engagement among female university teachers: Evidence from China. 

PloS one, 20(3), e0319785. https://doi.org/10.1371/journal.pone.0319785  
Drouin‐Rousseau, S., Morin, A. J., Fernet, C., Blechman, Y., & Gillet, N. (2024). Teachers' profiles of work engagement and burnout over the course of a 

school year. Applied psychology, 73(1), 57–92. https://doi.org/10.1111/apps.12465  

Drüge, M., Schladitz, S., Wirtz, M. A., & Schleider, K. (2021). Psychosocial burden and strains of pedagogues—using the job demands-resources theory 
to predict burnout, job satisfaction, general state of health, and life satisfaction. International Journal of Environmental Research and Public 

Health, 18(15), 7921. https://doi.org/10.3390/ijerph18157921  

Dymecka, J., Gerymski, R., Walczak, R. B., & Jania, A. (2024). The effect of risk of contracting and fear of COVID-19 on burnout and job satisfaction in 
polish teachers. Current Issues in Personality Psychology, 12(4), 234-242. https://doi.org/10.5114/cipp/185864  

Emir, G., Saral, N. Ç., Saçan, B. M., & Özmen, K. S. (2023). The stressors affecting teacher burnout in emergency remote teaching context. Teaching and 

Teacher Education, 134, 104313. https://doi.org/10.1016/j.tate.2023.104313  
Engle, J., Xia, J., & Butler, S. J. (2024). Teacher Leadership, Wellbeing, and Intent to Leave in US Rural Schools: Evidence from the 2020–21 National 

Teacher and Principal Survey. Education Sciences, 14(7), 758. https://doi.org/10.3390/educsci14070758  

Fang, Z., Bao, Y., & Hua, M. (2024). Social media use for work during non-work hours and turnover intention: the mediating role of burnout and the 
moderating role of resilience. Frontiers in Psychology, 15, 1391554. https://doi.org/10.3389/fpsyg.2024.1391554  

Farahmandpour, Z., & Voelkel, R. (2025). Teacher Turnover Factors and School-Level Influences: A Meta-Analysis of the Literature [Review]. Education 

Sciences, 15(2), 219. https://doi.org/10.3390/educsci15020219  
Fernández-Arias, P., Antón-Sancho, Á., Antona, C. J., & Vergara, D. (2024). Post-pandemic Work Motivation, Work Behavior and Psychic Structure in 

University Professors. Psychology in Russia: State of the Art, 17(4), 126-141. https://doi.org/10.11621/pir.2024.0407  

Foltz, K., Kromka, S. M., & MacNeil, T. (2025). Toxic university: the relationships between instructor job satisfaction, burnout, conflict resolution styles, 
and intentions to quit. Communication Quarterly, 73(3), 312–330. https://doi.org/10.1080/01463373.2025.2478848  

Gabelaia, I., & Bagociunaite, R. (2024). The Impact of “Quiet Quitting” on Overall Organizational Behavior and Culture. In: Kabashkin, I., Yatskiv, I., 

Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2023. Lecture Notes in Networks and Systems, 
vol 913. Springer, Cham. https://doi.org/10.1007/978-3-031-53598-7_33 

Gillison, S. T., Beatty, S. E., Northington, W. M., & Vivek, S. (2023). FLEs' concerns with misbehaving customers in the time of COVID and beyond. 

Journal of Service Theory and Practice, 33(6), 771–795. https://doi.org/10.1108/JSTP-02-2023-0035  
Gone, Y. S. P., Naim, M. F., & Peethambaran, M. (2025). Sounding the silence: examining the antecedents and outcomes of quiet quitting. International 

Journal of Organizational Analysis, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJOA-09-2024-4794  

Gundlach, H. A. (2025). What really influences teacher attrition, migration, and retention?. The Australian Educational Researcher, 1–21. 
https://doi.org/10.1007/s13384-025-00842-4  

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a Silver Bullet. Journal of Marketing Theory and Practice, 19(2), 139-152. 

https://doi.org/10.2753/MTP1069-6679190202  
Henderson, R., Lehman, J., & Sunder, A. (2024). Clock‐out: What explains “Quiet Quitting” in financial services?. Financial Planning Review, 7(3-4), 

e1192. https://doi.org/10.1002/cfp2.1192  

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. 
Journal of the Academy of Marketing Science, 43, 115-135. https://doi.org/10.1007/s11747-014-0403-8  

Jeilani, A., & Hussein, A. (2025). Impact of digital health technologies adoption on healthcare workers’ performance and workload: perspective with DOI 
and TOE models. BMC Health Services Research, 25(1), 271. https://doi.org/10.1186/s12913-025-12414-4  

Khan, N., Palepu, A., Dodek, P., Salmon, A., Leitch, H., Ruzycki, S., ... & Lacaille, D. (2021). Cross-sectional survey on physician burnout during the 

COVID-19 pandemic in Vancouver, Canada: the role of gender, ethnicity and sexual orientation. BMJ open, 11(5), e050380. 
https://doi.org/10.1136/bmjopen-2021-050380  

Kothari, B. H., Chandler, K. D., Waugh, A., McElvaine, K. K., Jaramillo, J., & Lipscomb, S. (2021). Retention of child welfare caseworkers: The role of 

case severity and workplace resources. Children and Youth Services Review, 126, 106039. https://doi.org/10.1016/j.childyouth.2021.106039  
Krejcie, R. V., & Morgan, D. W. (1970). Determining Sample Size for Research Activities. Educational and Psychological Measurement, 30(3), 607-610. 

https://doi.org/10.1177/001316447003000308  

Li, H., Chen, J., Wei, Q., & Chen, H. (2025). Knowledge, attitudes, and practices of vocational college teachers towards occupational burnout. Frontiers 
in Public Health, 13, 1513170. https://doi.org/10.3389/fpubh.2025.1513170  

Li, R., Liu, H., Chen, Y., & Yao, M. (2021). Why teachers want to leave? The roles of achievement goals, burnout and perceived school context. Learning 

and Individual Differences, 89, 102032. https://doi.org/https://doi.org/10.1016/j.lindif.2021.102032  
Liu, H. L. (2020). Decision-making Process in Quitting Journalism - A Study on the Resignation of Journalists in the Digital Era. Mass Communication 

Research, 144, 49–96. https://doi.org/10.30386/MCR.202007_(144).0002  

Lu, J., Guo, S., Qu, J., Lin, W., & Lev, B. (2023). “Stay” or “Leave”: Influence of employee-oriented social responsibility on the turnover intention of 

new-generation employees. Journal of Business Research, 161, 113814. https://doi.org/https://doi.org/10.1016/j.jbusres.2023.113814  

Lu, M., Al Mamun, A., Chen, X., Yang, Q., & Masukujjaman, M. (2023). Quiet quitting during COVID-19: the role of psychological empowerment. 

Humanities and Social Sciences Communications, 10, 485. https://doi.org/10.1057/s41599-023-02012-2  
Lv, X., Kexin, Z., Yue, L., Caicai, W., & and Wang, Y. (2023). Why Chinese hospitality management undergraduates give up a hotel career: the effects 

of perceived occupational stigma and perceived work dirtiness. Current Issues in Tourism, 26(17), 2863-2882. 

https://doi.org/10.1080/13683500.2022.2101437  
Pan, Z., Wang, Y., & Liu, Z. (2025). Over-Education, Job Satisfaction, and Intention to Quit: Evidence from China. Social Indicators Research, 176(1), 

287-307. https://doi.org/10.1007/s11205-024-03462-0  

Perveen, Y., Raza, A., Khan, M. J., Matloob, S., & Jaboob, A. S. (2025). Burnout and Beyond: Exploring How Work Environment, Self-Efficacy, and 
Emotional Exhaustion and Job Security Drive Quitting Intentions Among Newly Recruited Teachers in Sindh, Pakistan. Psychology in the 

Schools. https://doi.org/10.1002/pits.23564  

Peterson, M. F., Smith, P. B., Akande, A., Ayestaran, S., Bochner, S., Callan, V., ... & Viedge, C. (1995). Role conflict, ambiguity, and overload: A 21-
nation study. Academy of Management Journal, 38(2), 429–452. Retrieved from https://journals.aom.org/doi/abs/10.5465/256687 

https://doi.org/10.1108/IJOA-01-2023-3600
https://doi.org/10.1007/s12144-025-07920-x
https://doi.org/10.1371/journal.pone.0319785
https://doi.org/10.1111/apps.12465
https://doi.org/10.3390/ijerph18157921
https://doi.org/10.5114/cipp/185864
https://doi.org/10.1016/j.tate.2023.104313
https://doi.org/10.3390/educsci14070758
https://doi.org/10.3389/fpsyg.2024.1391554
https://doi.org/10.3390/educsci15020219
https://doi.org/10.11621/pir.2024.0407
https://doi.org/10.1080/01463373.2025.2478848
https://doi.org/10.1108/JSTP-02-2023-0035
https://doi.org/10.1007/s13384-025-00842-4
https://doi.org/10.2753/MTP1069-6679190202
https://doi.org/10.1002/cfp2.1192
https://doi.org/10.1007/s11747-014-0403-8
https://doi.org/10.1186/s12913-025-12414-4
https://doi.org/10.1136/bmjopen-2021-050380
https://doi.org/10.1016/j.childyouth.2021.106039
https://doi.org/10.1177/001316447003000308
https://doi.org/10.3389/fpubh.2025.1513170
https://doi.org/https:/doi.org/10.1016/j.lindif.2021.102032
https://doi.org/10.30386/MCR.202007_(144).0002
https://doi.org/https:/doi.org/10.1016/j.jbusres.2023.113814
https://doi.org/10.1057/s41599-023-02012-2
https://doi.org/10.1080/13683500.2022.2101437
https://doi.org/10.1007/s11205-024-03462-0
https://doi.org/10.1002/pits.23564


Zhou el al., Bangladesh Journal of Multidisciplinary Scientific Research 10(4) (2025), 30-41

 

41 

Prentice, C., Dominique-Ferreira, S., Wang, X., Tuominen, J., Duarte, M., & Rocha, H. (2025). Work-life imbalance, burning out, feeling down, I will 

quit, but quietly–the case of hospitality employees. Journal of Hospitality Marketing & Management, 34(1), 24–45. 

https://doi.org/10.1080/19368623.2024.2389074  

Serenko, A. (2024). The human capital management perspective on quiet quitting: recommendations for employees, managers, and national policymakers. 
Journal of Knowledge Management, 28(1), 27–43. https://doi.org/10.1108/JKM-10-2022-0792  

Soren, A., & Ryff, C. D. (2023). Meaningful work, wellbeing, and health: Enacting a eudaimonic vision. International Journal of Environmental Research 

and Public Health, 20(16), 6570. https://doi.org/10.3390/ijerph20166570  
Thu Trang, P., & Thi Thu Trang, N. (2024). Job burnout and quiet quitting in Vietnamese banking sector: the moderation effect of optimism [Article]. 

Cogent Business and Management, 11(1), 2371549. https://doi.org/10.1080/23311975.2024.2371549  

Tsemach, S., & Barth, A. (2023). Authentic leadership as a predictor of organizational citizenship behaviour and teachers’ burnout: What's ‘quiet 
quitting’got to do with it?. Educational Management Administration & Leadership, 17411432231212288. 

https://doi.org/10.1177/17411432231212288  

Wang, H., Lee, S. Y., & Hall, N. C. (2022). Coping profiles among teachers: Implications for emotions, job satisfaction, burnout, and quitting intentions. 
Contemporary Educational Psychology, 68, 102030. https://doi.org/10.1016/j.cedpsych.2021.102030  

Wang, W., & Huang, X. (2025). Transitioning from secondary vocational school to university: A case study of first-year students from two Chinese 

universities. Asia Pacific Journal of Education, 45(2), 614–627. https://doi.org/10.1080/02188791.2023.2183489  
Wei, Y., Wang, L., Tan, L., Li, Q., & Zhou, D. (2021). Occupational commitment of Chinese kindergarten teachers during the COVID-19 pandemic: 

Predictions of anti-epidemic action, income reduction, and career confidence. Early childhood education journal, 49, 1031-1045. 

https://doi.org/10.1007/s10643-021-01232-y  
Wu, F., Ren, Z., Wang, Q., He, M., Xiong, W., Ma, G., ... & Zhang, X. (2021). The relationship between job stress and job burnout: the mediating effects 

of perceived social support and job satisfaction. Psychology, health & medicine, 26(2), 204–211. 

https://doi.org/10.1080/13548506.2020.1778750  
Xueyun, Z., Al Mamun, A., Masukujjaman, M., Rahman, M. K., Gao, J., & Yang, Q. (2023). Modelling the significance of organizational conditions on 

quiet quitting intention among Gen Z workforce in an emerging economy. Scientific reports, 13(1), 15438. https://doi.org/10.1038/s41598-023-

42591-3  
Xueyun, Z., Yang, Q., & Al Mamun, A. (2024). Predicting the Quiet Quitting Intention Among the Generation Z Workforce in Hotel Industry. Journal of 

Quality Assurance in Hospitality & Tourism, 1-30. https://doi.org/10.1080/1528008X.2024.2393336  
Zhang, H., Shi, Y., & Teng, L. S. (2024). Exploring relationships of job satisfaction and burnout with turnover intention among Chinese English language 

teachers. The Asia-Pacific Education Researcher, 33(3), 587-601. https://doi.org/10.1007/s40299-023-00755-9  

 

APPENDICES 

Appendix A: Measurement Questionnaire 

 
Person-Organization Fit 

There is congruence between my own values and the values of the university I work for. 

In this university, there is congruence between my own values and the values of the other lecturers. 

In this university, there is congruence between my own values and the values of the head of department. 

In this university, there is congruence between my own values and the characteristics of the work I perform. 

In this university, there is congruence between my personal expectations and provided opportunities. 

In general, I am highly fit with my university. 

Work Overload 

I have been given too many responsibilities at work.  

The amount of work I have to do interferes with the quality of life I want to maintain. 

I feel overburdened by my work. 

I feel there is a need to reduce some aspects of my work. 

In general, my workload is severely overloaded. 

Job Burnout 

I feel emotionally drained from my work. 

I feel used up at the end of the workday. 

I feel tired when I get up in the morning and have to face another day at work. 

Working all day is really a strain for me. 

I feel burned out from my work. 

I have become more callous toward work since I took this job. 

I have become less enthusiastic about my work. 

I doubt the significance of my work. 

I have become more and more indifferent to the contribution of my work. 

In general, I am feeling increasingly burned out in my work. 

Quiet Quitting 

I am doing the bare minimum work to avoid being fired. 

I feel that I lack interest in attending meetings. 

I often avoid working more hours unless there is additional pay. 

I feel there is a lack of passion and enthusiasm in me to work above and beyond. 

I feel there is a lack of opportunities to learn and grow at my university. 

I feel there is a lack of meaningfulness at work. 

Generally, I prefer quiet quitting at work. 

 

 

Publisher’s Note: CRIBFB stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. 

 

 
© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the terms and conditions of the Creative Commons 

Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0). 

Bangladesh Journal of Multidisciplinary Scientific Research (P-ISSN 2687-850X   E-ISSN 2687-8518) by CRIBFB is licensed under a Creative 
Commons Attribution 4.0 International License. 

https://doi.org/10.1080/19368623.2024.2389074
https://doi.org/10.1108/JKM-10-2022-0792
https://doi.org/10.3390/ijerph20166570
https://doi.org/10.1080/23311975.2024.2371549
https://doi.org/10.1177/17411432231212288
https://doi.org/10.1016/j.cedpsych.2021.102030
https://doi.org/10.1080/02188791.2023.2183489
https://doi.org/10.1007/s10643-021-01232-y
https://doi.org/10.1080/13548506.2020.1778750
https://doi.org/10.1038/s41598-023-42591-3
https://doi.org/10.1038/s41598-023-42591-3
https://doi.org/10.1080/1528008X.2024.2393336
https://doi.org/10.1007/s40299-023-00755-9
http://creativecommons.org/licenses/by/4.0).
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/
http://creativecommons.org/licenses/by/4.0/

