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Quiet Quitting as a Symptom: Testing the Role of HRM System Gaps Versus Motivational Decline

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01 November 2025

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03 November 2025

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Abstract
Quiet quitting—meeting formal role requirements while withholding discretionary effort—has sparked a central debate: is it primarily a manifestation of organizational shortcomings or an individual coping strategy? We test these competing accounts using a dual-path structural equation model on cross-sectional data from 600 employees across multiple sectors in Lebanon. The model exhibited acceptable fit (χ²/df = 2.48; CFI = 0.943; RMSEA = 0.059). Results indicate that intrinsic motivation is the strongest negative predictor of quiet quitting, whereas HRM system gaps are associated with quiet quitting primarily through burnout (partial mediation). Direct effects of HRM gaps are weaker but non-trivial, suggesting that quiet quitting reflects both an individual coping response and a reaction to organizational shortcomings. This study provides the first integrated, head-to-head test of HRM system gaps versus intrinsic motivation, extends evidence beyond over-represented contexts through a multi-sector Lebanese sample, and delineates where managerial interventions—bolstering intrinsic motivation and mitigating burnout through support and voice—are likely to yield the greatest marginal returns.

Keywords: 
quiet quitting
;  
employee motivation
;  
burnout
;  
human resource management systems
;  
organizational justice
;  
work engagement
Subject: 
Business, Economics and Management  -   Human Resources and Organizations

1. Introduction

In a recent workplace trend that defies traditional labels, employees are showing up—but not fully engaging. They complete their core duties but avoid going “above and beyond.” This phenomenon, now widely referred to as quiet quitting, has emerged as a nuanced and controversial expression of disengagement in the post-pandemic workplace. Unlike actual turnover, quiet quitting reflects a behavioral withdrawal without physical exit, raising complex questions about its origins, implications, and solutions.
While many popular accounts frame quiet quitting as a generational attitude shift or a conscious reprioritization of work–life boundaries (De Smet et al. 2022), such portrayals often obscure structural causes rooted in HRM system design. Specifically, they neglect the extent to which perceived unfairness, poor managerial practices, and organizational neglect might be driving employee disengagement. The prevailing focus on individual agency risks overlooking a critical point: could quiet quitting be less about employees “not trying” and more about systems “not working”?
The academic conversation surrounding workplace disengagement has long centered on constructs such as burnout (Maslach and Jackson 1981), intrinsic motivation (Deci and Ryan 2000), and work engagement (Schaufeli et al. 2002). In this tradition, disengagement is typically interpreted as a psychological withdrawal caused by factors like emotional exhaustion, low autonomy, or misaligned goals (Bakker and Demerouti 2007; Ryan and Deci 2017). Quiet quitting fits this model but also raises newer, context-driven concerns.
On the systems side, human resource management (HRM) theory suggests that misalignment between employee expectations and organizational practices can erode trust and discretionary effort (Lepak and Snell 2002). Studies on organizational justice have consistently shown that employees who perceive injustice—procedural, distributive, or interpersonal—are more likely to exhibit counterproductive or withdrawal behaviors (Colquitt 2001; Cropanzano et al. 2007). Similarly, the effectiveness of HRM systems—especially when practices are perceived as inconsistent, symbolic, or irrelevant—has a significant impact on employee commitment and motivation (Wright and Nishii 2013).
Surprisingly, however, few studies to date have empirically tested whether quiet quitting stems more from individual-level motivational decline or organizational-level HRM system failure. Existing studies often rely on anecdotal evidence or limited conceptual perspectives, leaving a gap in understanding the relative weight of psychological vs structural predictors of this behavior.
This study addresses that gap by asking a straightforward yet underexplored question:
Is quiet quitting better explained by perceived HRM system gaps or by motivational decline? To answer this, we integrate two theoretical pathways into a dual-path model:
  • Systemic HRM Gaps, captured through measures of organizational justice (Colquitt 2001) and HRM system effectiveness (based on employee perceptions of relevance and coherence).
  • Motivational Decline, operationalized through validated constructs of intrinsic motivation (Deci and Ryan 2000), burnout (Maslach et al. 2001), and job demands (Karasek 1979; Demerouti et al. 2001).
Specifically, we develop and empirically test a conceptual model proposing that quiet quitting is best understood as a symptom of both systemic HRM gaps and individual motivational decline. Guided by this framework, we propose three specific hypotheses: H1—that perceived HRM system gaps positively predict quiet quitting; H2—that intrinsic motivation negatively predicts quiet quitting; and H3—that burnout mediates the relationship between HRM gaps and quiet quitting.
To empirically test this model, we conducted a cross-sectional survey of 600 employees across education, healthcare, technology, and other sectors in Lebanon. Using validated instruments, we measured perceptions of HRM system gaps, motivational decline indicators, and quiet quitting tendencies. Structural equation modeling (SEM) was then applied to test the strength and significance of both conceptual pathways.
This study contributes to the evolving literature on disengagement, HRM effectiveness, and post-pandemic work behavior in three key ways:
  • It goes beyond anecdotal evidence by employing an integrated structural equation model to quantitatively compare the relative magnitude of influence exerted by system-level factors versus individual-level factors.
  • It empirically demonstrates that motivational decline statistically dominates the direct influence of HRM system gaps, thereby refining the etiology of quiet quitting.
  • It validates a robust dual-path explanatory model, bridging micro-level psychology with macro-level HRM practices.”
The remainder of the paper is structured as follows: Section 2 reviews relevant literature and builds the dual-path conceptual model. Section 3 presents the methodology, including sample, measures, and analysis strategy. Section 4 details the results. Section 5 discusses the findings in relation to theory and practice. Finally, Section 6 concludes with limitations and future research directions.

3. Materials and Methods

3.1. Sample and Procedure

We tested our hypotheses using a cross-sectional survey of employees from various industries. A total of 600 working professionals participated. The sample was drawn primarily from Lebanon. The respondents averaged 37.1 years of age (SD = 10.2) and had about 13.5 years of work experience. Women represented 59% of the sample. Most participants were employed full-time (72%), with others on part-time, or fixed-term contracts. To reach a broad range of respondents, we distributed an online questionnaire via professional networks and social media. Participation was voluntary and anonymous. Before proceeding, respondents provided informed consent and confirmed they were currently employed adults. We implemented procedural safeguards to reduce common method bias, such as assuring anonymity and psychologically separating predictor and criterion sections of the survey (Podsakoff et al. 2003). Data collection occurred over a six-week period, yielding 600 valid responses for analysis. Given the self-report, single-time point design, we applied a more rigorous statistical control by incorporating a Latent Common Method Factor (LCMF) into the measurement model. Specifically, we modeled a single latent factor onto which all observed indicators loaded alongside their hypothesized factors. This LCMF approach controlled for the systematic shared variance across all items, mitigating potential common method variance concerns and supporting the robustness of our structural model results.

3.2. Measures

All constructs were measured with established or theory-driven scales. Tables 1a, 1b,1c and 1d summarize consecutively each construct, sub constructs, the scale items, conceptual focus, source and internal consistency reliability (Cronbach’s α) for our sample. Unless noted otherwise, respondents rated all items on a 5-point Likert-type scale (1 = strongly disagree, 5 = strongly agree).
Quiet Quitting. We conceptualized quiet quitting as the voluntary reduction of work effort to the minimum required, reflecting a withdrawal of discretionary effort. Because reduction of work effort is a central behavioral indicator of quiet quitting, we selected five items from a validated measure (cf. Galanis et al. 2023) to capture this construct. These items reflect patterns of discretionary effort withdrawal and are also aligned with definitional frameworks emerging in the recent literature (Mahand and Caldwell 2023; Agarwal et al. 2024). These items assessed the extent to which individuals limit their work involvement to only what is formally expected (e.g., “I do only what is required and nothing more at work”). Higher scores indicate greater quiet quitting behavior (i.e., lower extra-role effort). As shown in Table 1a, the quiet quitting scale exhibited high reliability in our sample (α = 0.91).
Table 1. a. Measurement Model Details for the Quiet Quitting Construct.
Table 1. a. Measurement Model Details for the Quiet Quitting Construct.
Construct Sub constructs Scale Items Cronbach’s α





Quiet
Quitting 		1a.1.Reduction of work effort
(5 items)		 1. I do only what is required and nothing more at work.
2. I avoid tasks that are not explicitly part of my job description.
3. I no longer go the extra mile at work.
4. I limit my involvement to the bare minimum necessary.
5. I prefer not to take on additional responsibilities unless absolutely necessary.		 0.91
Conceptual Focus: Represents employees’ voluntary restriction of their job engagement to the minimum formally required, reflecting withdrawal of discretionary or extra-role effort.
Source: Conceptually informed by Galanis et al. (2023), Mahand & Caldwell (2023), and Agarwal et al. (2024).
Intrinsic Work Motivation. To gauge employees’ inherent interest in their job, we used six items assessing intrinsic work motivation. These items were adapted from prior research on intrinsic motivation at work (Deci and Ryan 2000; Grant 2008) and tapped the enjoyment, interest, and meaningfulness derived from the job (e.g., “I enjoy the work I do”). A high score reflects a strong inherent interest in and personal reward from one’s work. As presented in Table 1b, Cronbach’s α for this scale was 0.92, indicating excellent reliability.
Table 1. b. Measurement Model Details for the Intrinsic Work Motivation Construct.
Table 1. b. Measurement Model Details for the Intrinsic Work Motivation Construct.
Construct Sub constructs Scale Items Cronbach’s α


Intrinsic Work Motivation 		1b.1.Inherent interest
(6 items)		 1. I enjoy the work I do.
2. I find my job personally rewarding.
3. I do my job because I find it interesting.
4. I feel a sense of accomplishment from my work.
5. My work is meaningful to me.
6. I feel energized by the tasks I perform.		 0.92
Conceptual Focus: Reflects employees’ inherent enjoyment, interest, and psychological fulfillment derived from performing their work, independent of external rewards.
Source: Adapted from Deci and Ryan (2000) and Grant (2008) studies on intrinsic motivation at work.
Burnout. We measured burnout symptoms in the form of emotional exhaustion using five items from the Maslach Burnout Inventory (Maslach and Jackson 1981). These items capture feelings of being emotionally overextended and depleted by one’s work (e.g., “I feel emotionally drained from my work”). Participants indicating agreement on these items signified higher burnout or fatigue. The emotional exhaustion scale was reliable in this study (α = 0.89). In addition, perceived workload and job demands as predictors of burnout were measured with four items adapted from the Job Content Questionnaire (Karasek 1979). The items assess quantitative workload and time pressure (e.g., “I have to work very fast” and “I experience time pressure at work”). Higher scores represent greater work demands. The workload index showed acceptable internal consistency (α = 0.80) as reported in Table 1c.
Table 1. c. Measurement Model Details for the Burnout Construct.
Table 1. c. Measurement Model Details for the Burnout Construct.
Construct Sub constructs Scale Items Cronbach’s α




Burnout 		1c.1.Emotional Exhaustion
(5 items)		 1. I feel emotionally drained from my work.
2. I feel burned out at the end of the workday.
3. I feel used up at work.
4. I feel tired when I think about work.
5. I feel frustrated with my job.		 0.89
Conceptual Focus: Reflects employees’ feelings of emotional depletion, fatigue, and psychological strain caused by prolonged exposure to work stressors.
Source: Karasek (1979), Job Content Questionnaire.
1c.2.Workload
(4 items)		 1. My workload is too high.
2. I have to work very fast.
3. I experience time pressure at work.
4. My work requires a lot of mental effort.		 0.80
Conceptual Focus: Represents perceived quantitative workload and time pressure contributing to emotional exhaustion and overall burnout levels.
Source: Maslach & Jackson (1981), Maslach Burnout Inventory.
HRM System Gaps. To directly examine employees’ perceptions of gaps in the human resource management system, we assessed both HRM alignment and organizational justice. Hence, we developed three items focusing on the responsiveness and consistency as indicators of employee perception of HR alignment practices. This HRM system gap scale was informed by the concept of HR system strength (Bowen and Ostroff 2004) and based on the idea that misalignment between formal HR policies and actual practice can undermine employee commitment. The items ask whether the HR department is responsive to employee needs and supports growth, and explicitly whether “there is a gap between the HR policies and how they are actually implemented.” The first two items were reverse-coded so that higher scores consistently indicate a weaker HRM system (i.e., greater HRM implementation gap or shortfall). Employees who perceive poor HR responsiveness and a policy–practice gap score higher on this scale. As illustrated in Table 1d, the three-item HRM gap measure achieved α = 0.82, suggesting good reliability. However,
Another facet of HRM Gaps is the perceived organizational justice. This measure was included to capture potential gaps in fair treatment. Following Colquitt’s (2001) justice dimensions, we assessed four facets, summarized in Table 1d: distributive justice (fairness of outcomes, 2 items, α = 0.76), procedural justice (fairness of processes, 2 items, α = 0.65), interpersonal justice (respectful treatment by supervisors, 2 items, α = 0.91), and informational justice (adequacy of explanations and transparency, 2 items, α = 0.85). Example items include “My work rewards reflect the effort I put in” (distributive) and “Communications from my manager are honest and transparent” (informational). Although each justice subscale had only two items (yielding relatively lower α for procedural justice), together these measures provide a broad indication of the fairness climate. We used these justice indicators as part of the assessment of HRM system functioning, with higher scores denoting higher perceived fairness. Crucially, to maintain the integrity of our measurement model, we implemented two key psychometric adjustments. First, the Procedural Justice subscale (α = 0.65) was excluded from the latent Organizational Justice construct in our CFA and SEM. This decision, necessitated by its low internal consistency, ensured the retained sub dimensions (Distributive, Interpersonal, and Informational Justice) offered a statistically robust representation of the perceived fairness climate. Second, two single-item indicators—Employee Voice and HR Policy–Practice Gap—were retained due to their highly concrete and unambiguous nature. To ensure appropriate estimation and avoid assuming perfect reliability, we addressed their measurement error variance in the SEM by fixing it based on estimated reliabilities drawn from established literature (Jöreskog and Sörbom 1993).
Table 1. d. Measurement Model Details for the HRM System Gaps Construct.
Table 1. d. Measurement Model Details for the HRM System Gaps Construct.
Construct Sub constructs Scale Items Cronbach’s α



















HRM System Gaps
1d.1.HR Alignment
practices
(3 items)		 1. The HR department in my organization is responsive to employee concerns.
2. HR practices in my organization support employee growth and retention. (R)
3. There is a gap between the HR policies and how they are actually implemented.		 0.82
Conceptual Focus


Source: 		Captures employees’ perceptions of inconsistency and lack of responsiveness in HR systems—specifically, the misalignment between formal HR policies and their actual implementation.
Conceptually informed by HR System Strength framework, Bowen & Ostroff (2004)
1d.2.Distributive Justice
(2 items)		 1. My work rewards reflect the effort I put in.
2. I am fairly rewarded considering my performance.		 0.76
Conceptual Focus

Source: 		Reflects perceived fairness of outcomes and reward allocations relative to employee effort and contribution.

Adapted from Colquitt (2001) organizational justice scale
1d.3.Procedural Justice
(2 items)		 1. The procedures used to determine outcomes are fair.
2. I am able to express my views during decision-making processes.		 0.65
Conceptual Focus

Source: 		Represents perceived fairness and voice in the processes used to determine work outcomes.

Adapted from Colquitt (2001) organizational justice scale.
1d.4.Interpersonal Justice
(2 items)		 1. I am treated with dignity by my supervisor.
2. My supervisor treats me with respect.		 0.91
Conceptual Focus

Source: 		Measures respectful and considerate treatment by supervisors as part of fairness perceptions.

Adapted from Colquitt (2001) organizational justice scale.
1d.5.Informational Justice
(2 items)		 1. My manager provides thorough explanations for decisions.
2. Communications from my manager are honest and transparent.		 0.85
Conceptual Focus: Assesses adequacy, clarity, and transparency of information provided by management regarding decisions.
Source: Adapted from Colquitt (2001) organizational justice scale.
Note: (R) indicates item is reverse-coded. Higher scores on HRM System Gap reflect a perceived weakness or inconsistency in HR practices (i.e., a larger gap between intended policies and actual implementation).

3.3. Analytical Strategy

We employed structural equation modeling (SEM) to test whether quiet quitting is better explained by HRM system factors or by individual motivational decline. As a preliminary step, we conducted confirmatory factor analysis (CFA) to assess the distinctness and construct validity of our key measures. The integrated dual-path model achieved a good fit to the data (χ²/df = 2.48, CFI = 0.943, RMSEA = 0.059), indicating that the SEM results are within acceptable thresholds for model fit, supporting the scales’ discriminant validity. We then computed descriptive statistics and Pearson correlations to explore preliminary associations among variables.
To formally test our hypotheses, we employed hierarchical multiple regression analyses. Quiet quitting served as the dependent variable. Specifically, regression analyses were utilized to evaluate H1, testing whether employee perception of HRM system gaps positively predict quiet quitting, and H2, examining the negative predictive relationship of intrinsic motivation on quiet quitting. SEM was specifically used to test H3, examining burnout as a mediator between HRM system gaps and quiet quitting, and to evaluate the overall dual-path conceptual model. In Step 1 of the regression, we entered control variables (respondent age, gender, and work experience) to account for any baseline effects. In Step 2, we entered the HRM-related predictors – specifically, the HRM system gap scale and the four justice dimensions – to assess the variance in quiet quitting explained by perceived HRM system shortcomings. In Step 3, we entered the individual motivation-related predictors – intrinsic motivation and emotional exhaustion – to examine the added explanatory power of employees’ motivational states. This ordering allowed us to compare the contributions of HRM system gaps versus motivational decline in predicting quiet quitting. We inspected the change in explained variance (ΔR²) from Step 2 to Step 3, as well as the significance and standardized coefficients of all predictors in the final model. Multicollinearity checks indicated no serious issues (all VIFs < 2.0). Finally, we probed the relative importance of the two sets of factors: a significant increase in R² with the motivational variables, coupled with strong effects for intrinsic motivation or exhaustion, would suggest that quiet quitting is more symptomatic of motivational decline. Conversely, if HRM system gap indicators remain the stronger predictors, it would imply quiet quitting is better explained as a response to organizational HRM deficiencies. All significance tests were two-tailed with a 0.05 alpha level. We used SPSS 28 and AMOS 24 software for the statistical analyses. The results of these analyses are reported in the next section.

4. Results

4.1. Descriptive Statistics and Correlations

Counter to expectations, not all presumed antecedents showed strong relationships with quiet quitting. Table 2 reports the means, standard deviations, and reliability coefficients for all constructs. Notably, respondents on average reported low perceived fairness in rewards and procedures (M = 2.47, SD = 0.98) despite relatively high supervisor respect (M = 4.06, SD = 0.84), indicating that while interpersonal treatment by supervisors was generally positive, there were broader system-level fairness concerns. Likewise, the mean perceived gap between HR policies and practice was above the scale midpoint (M = 3.51, SD = 1.00), hinting at inconsistencies in HRM system implementation. All multi-item scales demonstrated good internal consistency (Cronbach’s α ≥ 0.80), with especially high reliability for Quiet Quitting (α = 0.91) and Intrinsic Motivation (α = 0.92), as shown in Table 2. These metrics suggest the constructs were measured with acceptable reliability. In contrast, quiet quitting was significantly associated with several attitudinal and perception variables in the expected directions, whereas workload was virtually uncorrelated with quiet quitting behavior (r = –0.03, n.s.; Table 3), suggesting that heavy job demands alone do not translate into withdrawal.
Table 2. Descriptive statistics and reliability for study variables (N = 600).
Table 2. Descriptive statistics and reliability for study variables (N = 600).
Construct Items Cronbach’s α Mean (SD)
Quiet Quitting 5 0.91 2.49 (1.03)
Intrinsic Motivation 6 0.92 3.73 (0.89)
Burnout 5 0.89 2.87 (0.96)
Workload 4 0.80 3.44 (0.78)
Fairness 3 0.85 2.47 (0.98)
Voice* 1 — 3.22 (1.12)
Supervisor Respect 2 0.91 4.06 (0.84)
Manager Communication 2 0.85 3.56 (0.97)
HR Support 2 0.85 3.00 (1.03)
HR Policy-Practice Gap* 1 — 3.51 (1.00)
*Single-item measure; Cronbach’s α not applicable.
Bivariate correlations among key variables are presented in Table 3. Quiet quitting was strongly negatively correlated with intrinsic motivation (r = –0.50***), indicating that employees who reported lower personal motivation and enjoyment in their work were far more likely to engage in quiet quitting behaviors. Quiet quitting was also positively correlated with burnout (r = 0.43***), consistent with the view that emotional exhaustion and frustration relate to higher withdrawal. In line with the HRM perspective, quiet quitting showed moderate negative correlations with perceived voice (r = –0.31***), supervisor respect (r = –0.27***), managerial communication transparency (r = –0.29***), and HR support (r = –0.26***), all p < 0.001. Thus, employees who felt they lacked a voice in decision processes, received poorer treatment or communication from management, or had less supportive HR practices tended to only meet minimum job requirements. Quiet quitting was positively correlated with the perceived HR policy-practice gap as well (r = 0.20***), suggesting that employee perception of greater inconsistency between stated HR policies and actual practice coincided with higher withdrawal behavior. On the other hand, as noted above, workload (job demands) had no significant correlation with quiet quitting (r = –0.03, n.s.), an intriguing null finding indicating that objective work pressure by itself was not associated with doing the bare minimum. It is worth noting that many of the HRM system variables were inter-correlated: for example, fairness was strongly positively correlated with HR support (r = 0.60***) and negatively with the HR policy-practice gap (r = –0.51***), reflecting an overall pattern of interrelated positive work climate perceptions. Such multicollinearity necessitated a multivariate approach to determine their unique contributions, as described next.
Table 3. Correlation matrix of study variables (N = 600). Correlations are Pearson’s r.
Table 3. Correlation matrix of study variables (N = 600). Correlations are Pearson’s r.
Variable 1 2 3 4 5 6 7 8 9 10
1. Quiet Quitting —
2. Intrinsic Motivation -0.50*** —
3. Burnout 0.43*** -0.62*** —
4. Workload -0.03 -0.05 0.38*** —
5. Fairness -0.18*** 0.35*** -0.39*** -0.11** —
6. Voice -0.31*** 0.41*** -0.36*** 0.01 0.49*** —
7. Supervisor Respect -0.27*** 0.38*** -0.38*** -0.11** 0.32*** 0.49*** —
8. Manager Communication -0.29*** 0.41*** -0.41*** -0.15*** 0.46*** 0.54*** 0.69*** —
9. HR Support -0.26*** 0.39*** -0.38*** -0.12** 0.60*** 0.48*** 0.39*** 0.55*** —
10. HR Policy-Practice Gap 0.20*** -0.29*** 0.29*** 0.10* -0.51*** -0.32*** -0.22*** -0.32*** -0.58*** —
p < .05, *p < .01, **p < .001 (two-tailed).

4.2. Structural Equation Modeling Results

We next tested three nested structural models to compare the explanatory power of motivation-based predictors versus HRM system predictors of quiet quitting. All models were specified with Quiet Quitting as the criterion (dependent variable). Model 1 included only the two motivation-related constructs – intrinsic motivation and burnout – as predictors. Model 2 included only the HRM system-related constructs (perceived fairness, voice, supervisor respect, manager communication, HR support, and HR policy-practice gap) as simultaneous predictors. Model 3 was the integrated model combining both sets of predictors. Table 4 summarizes the standardized path coefficients (β) and explained variance (R²) for each model, and Figure 2 depicts the final path diagram.
Model 1 (Motivation-Only): This model provided a substantial explanation of quiet quitting behavior, with R² = 0.28, meaning about 28% of the variance in quiet quitting was accounted for by employees’ motivation levels. Both predictors in Model 1 were statistically significant. Intrinsic motivation was a strong negative predictor of quiet quitting (β = –0.38, p < .001), indicating that employees who found their work meaningful and energizing were much less likely to limit their efforts to the bare minimum. Conversely, burnout was significantly associated with a higher likelihood of quiet quitting (β = +0.20, p < .001), consistent with the expectation that exhausted or emotionally drained employees are more prone to withdraw effort. These results support the “motivational decline” explanation: lower work motivation and higher burnout are associated with increased quiet quitting.
Model 2 (HRM System-Only): In contrast, the model containing only HRM system factors explained a more modest portion of variance (R² = 0.14). Moreover, most HR-related predictors did not individually contribute significantly when considered together. The sole exception was employee voice, which emerged as a significant negative predictor (β = –0.19, p < .001) of quiet quitting. This indicates that employees who feel they have a say in decision-making are less likely to disengage and restrict their contributions. However, perceived fairness (β = +0.08, n.s.), supervisor respect (β = –0.09, n.s.), manager communication (β = –0.09, n.s.), HR support (β = –0.09, n.s.), and the HR policy-practice gap (β = +0.08, n.s.) all showed no significant unique effects in this model – their small observed coefficients did not reach significance (Table 4). It appears that these facets of the HRM system, while correlated with quiet quitting in isolation (Table 3), overlap in influence and thus none (aside from voice) stood out as a clear independent predictor when tested simultaneously. This finding was somewhat surprising, as it suggests that formal justice and HR support factors were weaker determinants of quiet quitting than expected when controlling for each other. This quantitative disparity R²=0.28 vs. R²=0.14) provides initial empirical evidence that motivational decline explains twice the variance in quiet quitting behavior compared to the organizational HRM factors alone.
Model 3 (Integrated Model): Incorporating both sets of predictors, the integrated Model 3 accounted for approximately 30% of the variance in quiet quitting (R² = 0.30), representing an improvement over Model 2 (ΔR² = +0.16) and a slight increase over Model 1 (ΔR² = +0.02). Consistent with the bivariate results, intrinsic motivation remained a strong negative predictor of quiet quitting in the combined model (β = –0.34, p < .001), and burnout remained a significant positive predictor (β = +0.19, p < .001). Thus, even after accounting for HRM-related perceptions, employees’ motivational states continued to play a dominant role in explaining quiet quitting. Among the HRM factors, employee voice continued to show a significant unique effect (β = –0.12, p < .01) in Model 3, reinforcing the importance of participative decision-making climate in mitigating withdrawal behavior. In contrast, most other HRM system variables did not exhibit significant direct effects on quiet quitting when motivational factors were simultaneously considered. Notably, the HR policy-practice gap – the focal “HRM system gap” variable – had a non-significant path in the integrated model (β = +0.05, p = .27), indicating that once employees’ motivation and burnout levels (and other perceptions) were taken into account, the direct association between perceived HR inconsistency and quiet quitting was not statistically significant.
One unexpected result in Model 3 was that the perceived fairness path coefficient, which was negative in simple correlations, became significantly positive (β = +0.14, p < .01) when controlling for the other predictors. In other words, when holding motivation and other factors constant, higher fairness perceptions were associated with slightly more quiet quitting. This suppression effect is likely statistical in nature, arising because fairness was moderately correlated with intrinsic motivation and (inversely with) burnout (Table 3). When those motivational variables are controlled, the residual impact of fairness on quiet quitting appears in the opposite direction, though its magnitude is relatively small. Importantly, supervisor respect, manager communication, and HR support paths all remained non-significant in the combined model (βs between –0.01 and –0.04, n.s.), suggesting that, after accounting for employee voice and motivation/burnout, these aspects of the work environment did not uniquely predict whether employees engaged in quiet quitting. In sum, the integrated model results indicate that quiet quitting is more directly driven by employees’ dwindling motivation (low engagement and high burnout) than by perceived gaps in the HRM system, although a lack of voice in the organization does independently contribute to higher quiet quitting tendencies.
Table 4. Standardized path coefficients (β) and explained variance for structural models predicting Quiet Quitting (N = 600). Model 1 includes motivation-based predictors only; Model 2 includes +HRM system-based predictors only; Model 3 is the fully integrated model.
Table 4. Standardized path coefficients (β) and explained variance for structural models predicting Quiet Quitting (N = 600). Model 1 includes motivation-based predictors only; Model 2 includes +HRM system-based predictors only; Model 3 is the fully integrated model.
Predictor Model 1 β Model 2 β Model 3 β
Intrinsic Motivation –0.38*** — –0.34***
Burnout +0.20*** — +0.19***
Fairness — +0.08 +0.14**
Voice — –0.19*** –0.12**
Supervisor Respect — –0.09 –0.01
Manager Communication — –0.09 –0.04
HR Support — –0.09 –0.04
HR Policy-Practice Gap — +0.08 +0.05
R² (Quiet Quitting) 0.28 0.14 0.30
p < .05, *p < .01, **p < .001 (two-tailed). Coefficients are standardized estimates. “—“denotes that a predictor was not included in a given model.

5. Discussion

This study set out to test whether the emerging phenomenon of quiet quitting is better explained by a decline in individual motivation or by gaps in the organizational HRM system. In other words, the central question was: Is quiet quitting primarily a result of employees losing motivation (e.g., burnout/disengagement), or does it stem from shortcomings in how the organization manages and rewards its people?
Our findings indicate that quiet quitting is more strongly associated with motivational decline at the individual level than by perceived HRM system gaps. Quantitatively, the motivational path (Model 1, R²= 0.28) explained double the variance in quiet quitting behavior compared to the organizational path (Model 2, R²= 0.14). The model focusing on personal factors (e.g., reduced engagement, burnout) provided a better fit and higher explanatory power for quiet quitting behavior, whereas the HRM-related factors, while contributory, were comparatively weaker. In practical terms, Hypothesis 1 (H1), which posited that quiet quitting would be associated with deficiencies in the HRM system (such as lack of support or rewards), received only partial support, Hypothesis 2 (H2) which posited that quiet quitting would be associated with declining employee motivation and engagement, was supported. Thus, in answer to our central question, the evidence suggests that quiet quitting aligns more with an employee’s waning motivation (and related psychological states) than with any single failure of HR policies, though the two are not entirely independent. Notably, the individual-pathway model outperformed the organizational-pathway model, underscoring that the roots of quiet quitting often lie within the employee’s own experience and mindset.
These findings offer important insights into the nature of quiet quitting and how it connects to existing theories. Quiet quitting can be understood as a form of employee withdrawal that is distinct from outright quitting; rather than resigning, employees withdraw effort. The robust statistical dominance of the Motivational Decline pathway (H2 and H3 supported) is consistent with viewing quiet quitting as a coping mechanism for employees facing resource depletion. It is plausible that this effect is further magnified by the specific socio-economic context of the Lebanon sample, where sustained economic instability heightens the necessity for psychological self-preservation by making physical resignation financially impractical. Quiet quitting may thus serve as the primary, safest withdrawal strategy when exit is blocked by macro-level risk. Recent work by Agarwal et al. (2024) describes quiet quitting as a “strategy for minimizing resource depletion,” wherein an employee “deliberately curtails their effort or makes no additional investment in their job”. In line with this, our participants who exhibited signs of burnout or cynicism were more likely to engage in quiet quitting, suggesting they pull back effort to protect their well-being. This supports the idea that quiet quitting is often a conscious boundary-setting behavior: employees do the bare minimum as a way to avoid further stress and exhaustion (Serenko 2024). This behavior reflects a ‘working to rule’ strategy and exemplifies ‘calibrated contributing’—a psychological contract response where employees rationally limit effort to avoid the personal costs of exceeding expectations amid uncertain. Our results reinforce this interpretation: quiet quitters in our sample were essentially disengaged employees preserving their energy, rather than malingerers. In fact, quiet quitting may help some workers avoid burnout in the short term, even if it means foregoing extra-role performance (Agarwal et al. 2024) This nuance is important because it challenges simplistic narratives that quiet quitters are merely “lazy” or lack work ethic; instead, many appear to be exhausted or disillusioned employees whose motivation has faltered.
A particularly salient finding for refining the theoretical placement of quiet quitting is the null correlation between perceived Workload (job demands) and quiet quitting behavior (r = –0.03, n.s.) This result, when viewed alongside the strong correlation between Workload and Burnout (r = 0.38***), offers crucial insight into the psychological process leading to withdrawal. This pattern suggests that high work demands do not directly lead employees to restrict effort. Instead, excessive demands must first deplete the individual’s resources, manifesting as emotional exhaustion or burnout. Quiet quitting is thus confirmed not as a simple, rational response to high demands (e.g., ‘I am busy, so I stop doing extra’), but rather as a coping mechanism utilized in response to psychological strain (burnout). In the context of the Job Demands-Resources (JD-R) model, this indicates that quiet quitting aligns with the Health Impairment Process: high demands lead to resource depletion (burnout), and this depletion then precipitates the behavioral withdrawal. If workload had predicted quiet quitting directly, it would suggest a rational, immediate response to task volume. The necessary interceding role of burnout confirms that the decision to reduce discretionary effort is mediated by the employee’s psychological state of exhaustion, positioning quiet quitting as a symptom of resource depletion rather than just a reaction to task quantity.
The significant positive path coefficient observed for Perceived Fairness (β = +0.14**), which runs counter to its negative zero-order correlation with quiet quitting, points to an essential theoretical nuance: fairness may paradoxically enable quiet quitting as a psychologically safe coping mechanism. This statistical suppression effect suggests that among employees with equal levels of burnout and motivation, those who perceive higher organizational fairness may feel secure enough to establish firm boundaries (i.e., quiet quitting) and conserve resources, knowing they are less likely to face punitive managerial retaliation for not exceeding expectations. In this sense, fairness provides the necessary agency and security for employees to choose a measured, self-protective withdrawal strategy rather than risking full physical resignation, thereby reinforcing quiet quitting’s role as a calculated act of resource conservation.
On the other hand, we also considered the role of the organizational environment – the HRM system factors – in fostering quiet quitting. Prior commentary has speculated that quiet quitting is symptomatic of poor management practices or unmet expectations at work. For example, a human capital management analysis by Serenko (2023) found that employees often quiet quit due to “poor extrinsic motivation, burnout and grudges against their managers or organizations.” In our study, factors such as perceived unfairness, lack of growth opportunities, or weak supervisor support (analogous to HRM system gaps) did show an effect on quiet quitting intentions, but indirectly. The weaker influence of these factors in our model suggests that organizational shortcomings are associated with quiet quitting mainly when they co-occur with or are linked to personal motivation, hence, the model’s structure is consistent with the hypothesis that burnout serves a mediating role in the relationship between HRM gaps and quiet quitting. In other words, an inadequate HRM practice (for instance, not recognizing extra effort or a toxic boss) by itself may not immediately lead to quiet quitting unless it translates into the employee feeling demotivated, unvalued, or burned out. This interpretation bridges the two perspectives: the individual and organizational drivers of quiet quitting are interlinked. Our evidence indicates that quiet quitting is not simply a top-down failure of HRM nor purely a worker’s personal choice in a vacuum, but rather a phenomenon arising when external workplace issues erode internal motivation. This finding contributes to the theoretical conversation by highlighting the mediating role of employee motivation in the quiet quitting equation. It extends earlier research by empirically demonstrating that the quiet quitting trend, popularized in media as a reaction against hustle culture, in fact sits at the crossroads of motivation theory and HRM practice. We show that quiet quitting is deeply intertwined with concepts of work engagement, burnout, and psychological contracts. By doing so, our work refines the current understanding: quiet quitting represents a form of employee disengagement that is rooted in psychological self-preservation (consistent with conservation of resources theory) even as it may be associated with organizational factors. This new evidence adds nuance to discussions of post-pandemic workforce behavior, suggesting that interventions to reduce quiet quitting must address how employees feel about their work, not just the policies on paper.
From a broader perspective, our study underlines why quiet quitting deserves attention in both research and practice. Employee disengagement is not a new problem, but quiet quitting has emerged as a stark reminder that a significant portion of the workforce may be emotionally checking out. Recent Gallup data alarmingly showed that at least 50% of the U.S. workforce could be categorized as “quiet quitters,” with employee engagement in 2022 dropping to its lowest level in a decade (Gallup, 2022). Such statistics highlight a widespread issue with potentially serious implications for productivity, innovation, and workplace morale across industries. By investigating quiet quitting through the dual lenses of individual motivation and HRM systems, our study contributes to the bigger picture of understanding workforce engagement in the modern era. It also resonates with the generational and societal shifts observed in the wake of COVID-19 – for instance, younger employees (Gen Z) are placing greater value on work-life balance and are quicker to disengage when work conditions feel unsustainable (Aggarwal et al. 2020). Therefore, our findings connect to a larger narrative: the pandemic and subsequent “Great Resignation” have catalyzed employees to re-evaluate their relationship with work. Quiet quitting can be seen as part of this Great Renegotiation of the employee-employer relationship, where workers assert new boundaries and priorities. Our research extends current knowledge by providing empirical evidence that personal well-being factors (like burnout) carry more weight in this renegotiation than previously confirmed. In doing so, we offer a new framework for thinking about quiet quitting – not as an aberration or solely a consequence of poor management, but as a signal of deeper misalignment between employees’ psychological needs and their work environment.

5.1. Implications for Practice and Policy.

This study offers actionable guidance for organizations and policymakers seeking to address quiet quitting. If the phenomenon stems from motivational decline, the remedy lies in restoring employee engagement and well-being. Rather than treating quiet quitting as defiance, it should be seen as a signal—symptomatic of burnout, disengagement, or feeling undervalued. Hence, the findings require a targeted, dual-pronged strategy:
  • Prioritize Psychological Resources: Since intrinsic motivation and burnout dominate the model, organizations must invest heavily in initiatives that promote sustainable engagement and well-being (Serenko 2024). This includes fostering opportunities for meaningful work, ensuring manageable workloads, and actively monitoring for signs of emotional exhaustion, treating quiet quitting as a signal of strain, not defiance.
  • Focus on Procedural Agency: While general HR support measures showed limited direct effect, the unique significance of Employee Voice (β = –0.12**) highlights a critical organizational lever. Structural reforms should prioritize enhancing procedural justice and participatory mechanisms, ensuring employees feel they have input into decisions and a means to express concerns. This provides employees with a sense of control and agency that directly mitigates the inclination to withdraw effort.
  • Address Policy Inconsistency Indirectly: The non-significant direct path of the HRM Policy-Practice Gap suggests that merely eliminating inconsistency is insufficient if employees are already burnt out. The remediation strategy must shift from mechanical compliance (fixing the gap) to maximizing psychological fulfillment, recognition, and equitable reward for extra effort, thereby reinforcing the reciprocal element of the psychological contract.
These interventions successfully implement the dual-pronged strategy by targeting both individual and systemic drivers of disengagement. Ultimately, the goal is to cultivate “quiet thriving”—where employees contribute willingly, not out of obligation but genuine motivation. Managers should avoid punitive responses and instead treat quiet quitting as diagnostic feedback, ensuring that addressing these root causes can elevate morale and performance across the organization.

5.2. Limitations and Future Research.

While this study advances the understanding of quiet quitting through a rigorous dual-path model, several limitations warrant further exploration in future research.
First, the cross-sectional design restricts our ability to infer definitive causality; we can only establish associations and predictive relationships among variables. While our structural model identifies paths consistent with mediation (H3), the temporal ordering (i.e., that HRM gaps precede burnout, which precedes quiet quitting) cannot be conclusively confirmed from single-time point data. Accordingly, longitudinal research is necessary to reveal how motivational decline or HRM improvements shape quiet quitting over time.
Second, the generalizability of our findings regarding the magnitude of effect is subject to the unique context of the sample. The study was conducted in Lebanon, a country facing prolonged, severe economic instability, currency devaluation, and high socio-economic uncertainty. This macro-level environment likely acts as a powerful external stressor, which has two possible implications for our results: 1) Amplified Burnout: The chronic, high-stress environment may significantly amplify rates of emotional exhaustion and burnout, thereby strengthening the predictive power of the Motivational Decline pathway (H2 and H3) as employees prioritize psychological self-preservation (resource conservation). 2) Attenuation of HRM Gaps: Simultaneously, employees operating under systemic economic collapse may perceive organizational deficiencies (HRM gaps) as less consequential than the external crisis, potentially leading to the attenuated direct effect observed for organizational factors (H1). Therefore, while the core mechanisms (HRM–Burnout-QQ) are theorized to be universal, the extreme statistical dominance of the Motivational Decline pathway observed in this study may be context-specific. Future research in economically stable regions is needed to confirm if the relative explanatory power of the two paths remains consistent.
Third, our comparison of individual versus organizational factors did not fully explore their interaction effects. For instance, supportive HRM practices may buffer the negative effects of motivational decline (e.g., a caring manager might prevent disengagement). Investigating such moderating or mediating pathways—like psychological meaningfulness or resource availability—could deepen insight into how organizational inputs protect employee well-being. Furthermore, demographic and cultural variations also merit attention. Younger versus older workers may quiet quit for different reasons, and manifestations may differ across industries or cultures. Early evidence suggests distinct patterns in collectivist societies and under varying economic conditions, necessitating cross-cultural comparative studies.
Finally, the long-term consequences of quiet quitting remain unclear. While it may offer short-term relief (resource conservation), sustained disengagement could erode organizational outcomes like innovation and citizenship behavior, as well as individual career prospects. Future research should assess interventions designed to re-engage quiet quitters and rigorously evaluate their effectiveness over time.
In conclusion, our findings suggest quiet quitting stems more from motivational erosion than HRM system flaws. This framing urges organizations to address both psychological states and management practices. By tackling root causes, leaders can foster a more engaged, resilient workforce and transform quiet quitting from a crisis into a catalyst for meaningful change.

6. Conclusions

This study set out to determine whether quiet quitting is best understood as a symptom of gaps in an organization’s HRM system or as a consequence of individual motivational decline. We revisited the research question by examining if shortcomings in HRM practices and support, versus waning employee motivation, better explain why employees disengage and restrict their effort. The results from testing Hypotheses 1 through 3 provide a clear answer. Consistent with expectations, we found that perceived HRM system gaps have some association with quiet quitting, and motivational decline (e.g., reduced enthusiasm and burnout) also shows a significant positive link with quiet quitting. In our model, both factors contributed to explaining the variance in quiet quitting behavior. The significant effects observed for H1 and H2 indicate that employees are more likely to “work to rule” and withhold extra effort when they perceive deficiencies in the organizational environment or when their personal drive and energy have deteriorated. Meanwhile, support for H3 is consistent with the notion that the interplay between perceived HRM gaps and burnout is crucial: the relationship of HRM system gaps on quiet quitting appears to be largely channeled through declines in motivation. In other words, poor perceived HRM practices erode employees’ motivation, which in turn prompts the withdrawal behavior captured by quiet quitting. Taken together, these findings suggest that our integrated model successfully addressed the research problem by showing that quiet quitting can be explained as a joint outcome of organizational shortcomings and diminishing employee motivation.
Our findings align well with theoretical expectations and recent research. The evidence that HRM system gaps captured through employee perceptions (such as lack of support, unfair treatment, or unfulfilled expectations) are associated with quiet quitting is in line with studies emphasizing how managerial and organizational failures are related to employees scaling back their engagement (Mahand and Caldwell 2023). This supports the view of quiet quitting as an employee response to unsatisfactory work conditions, akin to a reaction to psychological contract breaches or perceived injustice. Likewise, the strong relationship observed between motivational decline and quiet quitting is consistent with prior findings that burnout, stress, and lost enthusiasm prompt employees to withdraw discretionary effort (Geng et al. 2025). In fact, our results reinforce the notion that quiet quitting represents a form of self-protection or resource conservation when employees feel overextended or underappreciated (Hamouche et al. 2023). In sum, the pattern of results met our expectations: employees disengage quietly not simply out of laziness or generational attitude, but as an understandable consequence of unmet needs in their work environment and a deterioration of their inner drive. These outcomes confirm the central premise of our research question and affirm that the proposed model has effectively captured the underlying drivers of quiet quitting.
Despite mentioned limitations, our study offers valuable contributions to HRM theory and practice. Theoretically, it advances the understanding of quiet quitting by framing it as more than an isolated employee choice – instead, as a phenomenon deeply rooted in the employee–organization relationship. By empirically demonstrating that weak HRM systems (for example, inconsistent support or recognition) can erode employee motivation and lead to minimal engagement, we extend existing HRM and organizational behavior literature on employee withdrawal. The results integrate perspectives from motivational theory and HRM systems theory, showing that both extrinsic workplace factors and intrinsic motivational states jointly determine discretionary work behavior. This integration contributes a more holistic explanation of quiet quitting, bridging micro (individual motivation) and macro (organizational system) levels of analysis. From a practical standpoint, our findings send a clear message to HR professionals and managers: preventing quiet quitting requires closing the gaps in HRM systems as much as rekindling employees’ motivation. Organizations should invest in fair and supportive management practices – ensuring that employees feel heard, valued, and fairly rewarded – as these measures can foster the workplace support that discourages disengagement. At the same time, employers need to monitor and maintain employee well-being, addressing signs of burnout or cynicism before they translate into withdrawal. In line with emerging guidance in the HR field, our evidence suggests that re-engaging quiet quitters is not about chastising individual employees, but about fixing organizational inconsistencies and reenergizing the workforce (Serenko 2024). By highlighting the dual importance of organizational context and personal motivation, this study contributes a nuanced perspective that can help HRM practitioners design more effective interventions. Ultimately, recognizing quiet quitting as a symptom – and not merely a cause – of deeper issues enables both scholars and practitioners to focus on the root causes of disengagement, thereby improving employee experience and organizational performance in the long run.

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Figure 1. Conceptual framework illustrating hypothesized relationships.
Figure 1. Conceptual framework illustrating hypothesized relationships.
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Figure 2. Structural equation model results (Model 3) illustrating the paths from motivational factors (left, Intrinsic Motivation and Burnout) and HRM system factors (right, e.g., Fairness, Voice, etc.) to Quiet Quitting. Solid arrows represent significant paths (p < .05), and dashed arrows represent non-significant paths. Standardized path coefficients (β) are shown next to each arrow.
Figure 2. Structural equation model results (Model 3) illustrating the paths from motivational factors (left, Intrinsic Motivation and Burnout) and HRM system factors (right, e.g., Fairness, Voice, etc.) to Quiet Quitting. Solid arrows represent significant paths (p < .05), and dashed arrows represent non-significant paths. Standardized path coefficients (β) are shown next to each arrow.
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