Transforming Labor-Management Conflict Through a Data-Driven Approach: Addressing Dissatisfaction and Motivation Under Uncertainty

Transforming Labor-Management Conflict through a Data-Driven Approach: Addressing Dissatisfaction and Motivation under Uncertainty

Md Sagor Hossain, Rahman Mostafizur*, Sazzad Kadir Zim, Akhmad Riadi

Nanjing University of Information Science and Technology, China

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.909000360

Received: 02 September 2025; Accepted: 10 September 2025; Published: 11 October 2025

ABSTRACT

In increasingly complex public-sector environments, employee dissatisfaction and labor–management conflict pose significant challenges to organizational effectiveness. This study analyzes 823 administrative separation records and 822 survey responses from Queensland, Australia to examine predictors of voluntary turnover and interpersonal conflict. Because several outcomes are rare and the data contain potential separation, Firth penalized logistic regression is employed as the primary estimator to reduce small-sample bias, with standard logistic models reported as sensitivity checks. The Firth models yielded no statistically significant predictors, a finding interpreted as informative evidence of measurement constraints, low variance in outcome variables, and limited explanatory power of dichotomous indicators in administrative datasets. Sensitivity analyses using standard logistic regression revealed associations between lack of recognition, work–life balance, and voluntary exit; however, these results are presented cautiously due to potential bias under rare-event conditions. Interpersonal conflict exhibited only directional (non-significant) relationships with job insecurity and weak supervisory support. Rather than presenting a definitive predictive model, the study contributes methodologically by demonstrating how penalized regression and sensitivity checks can clarify interpretation when null results arise. The paper recommends refined measurement (using multi-item scales), richer longitudinal or mixed-methods designs, and the integration of unstructured data to better capture the drivers of employee dissatisfaction and conflict in future research.

Keywords:   Employee dissatisfaction 1; Interpersonal conflict 2; Voluntary Exit 3; JDR Model 4; Herzberg’s Theory 5;

INTRODUCTION

In an era of accelerating external uncertainties and organizational volatility, employee dissatisfaction and labor-management conflict are increasingly recognized as barriers to effective human capital management. These issues affect leadership effectiveness, employee motivation, and organizational stability—core management concerns that demand evidence-based, technology-enabled solutions. However, traditional literature often underrepresents how these problems manifest in collectivist cultural settings and within the public sector, where power distance, indirect communication, and value-based motivation shape workplace dynamics.

This study responds to that gap by employing a data–driven analytical approach to explore how factors like interpersonal conflict, lack of recognition, and unmet psychological needs lead to voluntary employee exit. Focusing on Queensland’s public sector, the study applies the JDR model, Herzberg’s Two-Factor Theory, and Maslow’s Hierarchy of Needs to examine dissatisfaction drivers through both theoretical and statistical lenses. In doing so, it bridges leadership and motivational theory with practical organizational planning, offering actionable insights into conflict management and retention strategies that align with digital transformation in management.

Employee dissatisfaction and conflict between staff and management pose a threat to organizational effectiveness, lower employee turnover ratio, and workplace well-being(Pandey et al., 2021; Wu and Wu, 2011). Across the world, society is facing unprecedented complexities of organizing diverse workforces under growing mental pressure, but Western individualistic cultures dominate the majority of the literature. Such a narrow perceptual lens overlooks the role of culture in the experience of dissatisfaction and conflict, and its manifestation is shouldered by cultural norms, especially in more collectivist cultural settings, where it is hierarchy, authority and group cooperation which shape the way in which conflict is both experienced and expressed (Taras et al., 2010).

In cultures where it’s not acceptable to have open confrontation, the employees tend to hold in things until they either resign or disengage. Western conventional models of the workplace did not grasp such communications. Employees in the public sector are typically motivated by service-oriented values that may serve as sources of satisfaction in a positive manner and analogous source of dissatisfaction, in the negative sense, when these values are not met (Perry and Wise, 1990). The Job Demands-Resources (JDR) model describes employee burnout as resulting from high levels of job demands, which include interpersonal conflict, workload and ambiguity, as well as low levels of resources in the workplace, such as autonomy, recognition or social support (Bakker and Demerouti, 2007). Public sector workers also have high levels of mismatch (between demands and resources), that could lead to heightened dissatisfaction and turnover (Halbesleben and Buckley, 2004). Nevertheless, the vast majority of empirical studies focusing on the JDR model have not taken into account cultural differences in the way in which employees perceive and react toward these dimensions.

Similarly, Herzberg’s Two-Factor theory describes hygiene factors (such as salary, supervision, and working conditions) as those that remove unhappiness, and motivator factors (such as achievement, recognition, and personal growth) as those that contribute to job satisfaction (Herzberg et al., 1959). The latter may have especially been the case if a certain minimum level of hygiene is provided (Argyle, Michael., 1994) .Although it is frequently formulated as an assumption in organizational research, this theory has received little empirical validation in public sectors and non-Western countries. Additionally, Maslow’s (Maslow, 1943). Hierarchy of Needs provides a stepwise motivational model from basic physiological and safety needs to belongingness and esteem to self-actualization and self-transcendence—all areas underexplored concerning employees who leave their positions because unresolved workplace conflict.

Three gaps in research are most striking. Interpersonal conflict is at times being analysed regardless as independent cause for voluntary leaving, although evidence suggests that it can be hazardous to well-being even taking place in environments where dissatisfaction is not much widespread. Second, although there is abundant research collecting demographic information, very little study has been made into the role of age and gender and conflict related unhappiness despite evidence to indicate that younger and female workers may be particularly vulnerable(Kooij et al., 2010). Women may experience more workplace discontent and psychological distress because of the structural and cultural impediments that exist in organizational systems (Brough, P., & Pears, J, 2004). The impact of traumatic incidents (e.g., harassment, exclusion, or severe stress) has not been well captured as a mechanism for voluntary turnover, especially in countries or cultures where direct complaints are rare (Spector, P. E., 2012).

The current study aims to fill those gaps by taking a conflict-centric, data-driven approach to the analysis of employee dissatisfaction, and voluntary turnover, in the public sector of Queensland. Applying JDR model and borrowing the the HRD and Maslow framework, the study investigated the role of interpersonal conflict, lack of appreciation and traumatic events on worker outcomes and explored the differences in the process across gender and age. This approach aims to further develop understanding of dissatisfaction and discord in culturally heterogeneous public-sector contexts and to inform the effectiveness of organization improvement initiatives.

The study provides new insights into labor-management relations by contextualizing these findings from local and international levels. It offers opportunities for strategic measures that can improve employee engagement and retention – especially where workplaces are managed by cultural hierarchy, indirect communication, and lack of conflict resolution.

LITERATURE REVIEW

Employee Discontent and Voluntary Exit

The dissatisfaction of employees has been accepted as an antecedent of turnover, particularly it is from unfulfilled psychological or organisational requirements (Tett and Meyer, 1993). Fit Recomparation of the concerning organizational commitment is moderated by the organizational commitment fit (Michaels and Spector, 1982). It is also less likely to leave with more committed employees, even if they are experiencing job stressors, than (Allen and Meyer, 1996). Some traditional contributors include salary, hours worked, and job security. The latest literature claims that non-readily noticeable factors, including insufficient acknowledgment, issues of interpersonal relationships, and deficiency of emotional support, can be more substantial determinants when employees make resignation decisions, particularly in the public sector settings(Pandey et al., 2021). Dissatisfaction is being measured in some studies as a broad concept while in some the elements of dissatisfaction such as perceived fairness, support and recognition are investigated separately for better understanding of the reasons led to dissatisfaction(Judge and Bono, 2001). Furthermore, meta-analytic data indicate that job satisfaction is highly negatively correlated with turnover intentions and can be considered to be a net predictor of turnover behavior (Harrison et al., 2006). These studies, particularly in collectivist public sectors where silent dissatisfaction is more common than visible dissatisfaction, may also over-generalize employee dissatisfaction without taking into account cultural or organization-level variations.

Workplace conflict between colleagues.

Although commonly perceived as a manifestation of significant dissatisfaction, interpersonal conflict can independently contribute to employee stress and turnover. (Wu and Wu, 2011) points out that conflict management styles vary across cultures and that collectivist societies are less likely to favor direct confrontation. This deflection can compound unresolved tensions, eventually leading to departure or silent departure. In addition, negative affect resulting from unresolved conflict can lower an employee’s capacity to manage work stressors, subsequent to which they are more likely to become disengaged or to exit the company voluntarily (Chen et al., 2012). The dynamic between conflict and demographic factors such as gender and age has not been studied enough. Some research, though, points to relationship tension as a direct predictor of voluntary turnover. Furthermore, extant conflict models barely account for culturally normative behavior such as indirect communication or conflict avoidance, which may contribute to underreporting of interrelationship stressors in survey research.

Perception of recognition and supervisor support:

Recognition has a two-fold determination on the workplace, namely, it is a motivating factor and it is emotion-related. Supervisory and peer recognition have a significant impact on employee engagement and withdrawal behaviors in particular for public sector employees, whom intrinsic motivation is main motivator of performance(Liu, B., & Wang, X., 2013). Herzberg (Herzberg et al., 1959) explains that recognition as important and in two side view, stimulation that comes from the environment will result to people to be easy to look for the recognition when on it not found immediately, people will not feel dissatisfied. When employees are given consistent recognition and support, they will exchange their loyalty and turnover intentions(Cropanzano and Mitchell, 2005). On the other hand, lack of supervisor support has been associated with emotional exhaustion and reduced attendance (Eisenberger et al., 1986). Perceptions of strong supervisor support is related to higher job satisfaction and organizational commitment among employees, further underscoring the protective nature of relational resources (Jaramillo et al., 2005). Even so, it’s not always readily apparent — nor well-studied in the extant literature — how recognition translates into lower turnover, whether it occurs through emotional validation, performance feedback, social inclusion or something else. Although general theories converge, there are a limited number of empirical works that assess together the effect of recognition and support on the likelihood of le having voluntarily, in the sense of purposi ve le aving, especially within hierarchical, risk-averse environments, such as that occur ring in the public sector domain.

Jobs Demands, Psychological Needs, and Turnover

The Job Demands–Resources (JDR) model provides a complete framework for understanding how the stressful and supportive aspects of the work environment affect workplace outcomes (Bakker and Demerouti, 2007). While the JDR model could be used in other settings, such as the public sector, which is characterized by collectivist values and a hierarchical structure, there too, the model does not exist and thus it’s hard to apply it within other cultural settings. When demands are high (e.g., conflict and workload) and resources low (e.g., appreciation, autonomy), it negatively affects well-being of employees, leading to burnout or quitting. Maslow’s Hierarchy of needs (Maslow, 1943) adds these psychological elements to this concept, suggesting that unsatisfied needs for safety and esteem may also precipitate withdrawal. There are traumatic workplace circumstances that undermine perceived safety and will result in workers leaving, regardless of overall discontent.

Demographic Moderators of the Conflict and 

Juniors feel sometimes more dissatisfied and less committed to the company, which increases their likelihood of quitting under challenging conditions (Kooij et al., 2010). Most research examining demographic moderators look at these factors in isolation, whereas few or no studies examine how they interact with organizational structure or cultural expectations. This study attempts to remedy this shortfall. Similarly, gendered working environments can expose women workers to higher levels of interpersonal stress or fewer chances of career advancement. These two trends are frequently mentioned, but not usually tested in turnover models, and thus we lack an understanding of how demographic characteristics moderate the dissatisfaction-to-exit process.

The literature review demonstrated that dissatisfaction and turnover among employees is a complex phenomena with multiple contributing factors. One thing that is commonly overlooked, however, is context. This is particularly true within collectivist public sectors where hierarchical norms influence how people talk about and resolve conflicts. And there’s simply not enough known about how these psychological needs, or that supportive supervisor, are intertwined with those demographic risks. With placement of the study in these uncovered areas, it is intended to add cultural aspects that would complement the JDR as well as other well-established models; Herzberg (Herzberg et al., 1959), Maslow (Maslow, 1943).

Figure 1. Conceptual model showing key predictors of employee dissatisfaction and voluntary exit, incorporating interpersonal conflict, traumatic experiences, and demographic moderators

RESEARCH METHOD

Research Design

The mixed-method design and the focus on the relation between voluntary exit and labour-management conflicts were considered when drawing on both quantitative data and theoretical reasoning in this study. The quantitative side uses regression analysis to verify the assumptions about the antecedents of dissatisfaction and voluntary exit, and theoretical frameworks (the JDR model(Bakker and Demerouti, 2007), Herzberg’s two-factor theory (Herzberg et al., 1959), and Maslow’s Theory of Hierarchy of Needs (Maslow, 1943) serve for interpretation.

Data Source and sample

The study uses a longitudinal dataset that consists of 823 separate records related to employees and was sourced from Kaggle (“Resigning-Dissatisfaction Analysis,” n.d.), spanning from 1963 to 2013. The sample mainly consists of employees in Queensland, Australia, and is divided into: Central Queensland, Darling Downs South West, Far North Queensland, Metropolitan, North Coast, and South East. Approximately 15% of records categorize the region as “Not Stated” or lack precise geographic information. The sample comprises several roles, including educators, public employees, school administrative personnel, and custodians, with a majority (68%) identifying as female, mirroring the typical gender distribution in the education and public sectors. Variables were available for separation type (e.g. resignation, retirement, poor health), demographic information (age, gender), workplace issues (i.e. workplace difficulties, lack of recognition, interpersonal disputes, traumatic events), and organizational responses (i.e. workplace demands, wellness programmes.

Data Pre-processing

Cleaning and Categorization:

Deleted 12 cases with missing values in crucial variables (e.g., separation date, gender). Age was divided into six categories: ≤20 years, 21–25 years, 26–30 years, 31–40 years, 41–55 years, ≥56 years. Types of separation were categorized as voluntary exit (resignation, voluntary early retirement, career transition) and involuntary exit (age retirement, health-related retirement, termination). Workplace factors (e.g., conflicts, lack of recognition) were dichotomous (2 = present, 1 = absent) indicators that were either self-reported or extracted from personnel files. An additional 48 were dropped from the regression analysis because the values of the predictor variables were not available, leaving a final sample size of 774.

Analytical Approach

Descriptive statistics: We carried out frequency analyses for types of relationships, age, sex, and regional distribution, to investigate demographic and organizational characteristics of the data. Age retirement was behind 45% of all separations and voluntary early retirement (VER) 22%. Employees aged 56 and older accounted for 58% of age-related retirements, while 8% of voluntary departures were ages 21–25.

Correlational Analysis: Correlation Matrix A correlation matrix was calculated to test the relationship between two continuous variables, for example age and voluntary leave (r = -0.475, p < 0.01).

Firth’s penalized logistic regression: To examine the predictors of employee dissatisfaction, we employed Firth’s penalized logistic regression due to the binary nature of the dependent variables and to address potential small-sample bias or separation in the data. Two models were estimated: one with a binary indicator of dissatisfaction and another with a broader binary job dissatisfaction with department index. The general form of the Firth regression model is:

\[
\ln \big(p(y=1)\big) = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 X_3 + \beta_4 X_4 + \beta_5 X_5 + \beta_6 X_6 + \beta_7 X_7 + \beta_8 X_8 + \varepsilon
\]   (1)

where Y is the binary outcome dissatisfaction and job dissatisfaction with the department and independent variables are Interpersonal conflicts (X₁ ), Lack of recognition ( X₂), Lack of job security (X₃ ), Employment conditions (X₄ ), Workload (X₅ ), Work life Balance (X₆ ), Traumatic Incident (X₇ ), Workplace issue score (X₈ ),

Logistic Regression:  Factors Influencing Voluntary Exit:

Dependent Variable:   voluntary exit (Y = 1 if there is a voluntary exit, 0 if not).

Independent Variables: Interpersonal conflicts ( X₁), job dissatisfaction with the(X₂), dissatisfaction ( X₃), Workplace issue score ( X₄)

\[
\ln \left( \frac{P(Y=1)}{1-P(Y=1)} \right) = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 X_3 + \beta_4 X_4 + \varepsilon \tag{2}
\]

Factors influencing Interpersonal Conflicts (0, 1)

Dependent Variable:  Interpersonal conflicts (Y = 1 if there is a voluntary exit, 0 if not).

Independent Variables: Dissatisfaction ( X₁), job dissatisfaction with the department (X₂ ), Employment conditions (X₃ ), score Lack of recognition ( X₄), Lack of job security Workload ( X₅), Work life Balance (X₆ ), Traumatic Incident (X₇),

\[
\ln \left( \frac{P(Y=1)}{1-P(Y=1)} \right) = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \beta_3 X_3 + \beta_4 X_4 + \beta_5 X_5 + \beta_6 X_6 + \beta_7 X_7 + \varepsilon \tag{3}
\]

Theoretical Integration

The findings were analyzed using three theoretical frameworks:

1. JDR Model: Interpersonal conflicts (demands) and insufficient recognition (resource deficiency) correspond with the model’s assertion that imbalance results in burnout.
2. Herzberg’s Two-Factor Theory: Insufficient recognition (motivator) and workplace problems (hygiene factor) both lead to unhappiness, affirming the theory’s dual framework.
3. Maslow’s Hierarchy: Traumatic events (safety needs) and insufficient acknowledgment (esteem needs) indicate unfulfilled lower- and higher-order requirements.

RESULTS AND DISCUSSION

Descriptive Analysis: Table 1 Reports summary statistics for selected measures of employee dissatisfaction. All factors are dichotomously coded as 1= not present, 2= present. The general dissatisfaction has received an average of 1.07 (SD = 0.26), which indicates that the employees did not generally express dissatisfaction. Likewise, the mean for interpersonal conflicts was 1.04 (SD = 0.20), representing low frequencies of reports of conflicts. However, the mean level of satisfaction with workload (M = 1.11 for the sample, SD = 0.31) indicates that it may be a more important issue. Lack of recognition satisfaction (M = 1.07, SD = 0.25) and lack of job security (M = 1.03, SD = 0.18) were infrequent in the same way. That such dissatisfaction is not generally high, but that workload may represent some danger as a form of stress, and generator of concentration conflict.

Table 1 This table reports the descriptive statistics for the main variables, employee dissatisfaction, and personal conflicts of 822 employees. (1=absence, 2=presence)

Descriptive Statistics

Firth Logistic Regression Results

Table 2  presents the Firth logistic regression estimates for predictors of employee dissatisfaction with their department (Model 1) and job dissatisfaction (Model 2). Using Equation (1), we applied Firth logistic regression to address potential small-sample bias. Although most of the estimated odds ratios are positive, none of the coefficients reach statistical significance, indicating that no individual predictor strongly influences dissatisfaction in these models. Notably, higher odds ratios are observed for: Workplace issue score (OR = 1.366 in Model 1; OR = 1.540 in Model 2). Workload (OR = 0.723 in Model 1; OR = 0.647 in Model 2). Lack of recognition (OR = 0.554 in Model 1; OR = 0.565 in Model 2). These suggest potential trends in the relationship between perceived workplace factors and dissatisfaction. However, the large standard errors reflect a high degree of variability, which may imply insufficient statistical power. This indicates the need for a larger sample size, better measurement precision, or alternative modeling strategies to detect significant effects.

Table 2 This table represents the results of the Firth logistic regression models forecasting employee dissatisfaction and job dissatisfaction with the department, using workplace-related variables. Coefficients are reported as odds ratios with standard errors in parentheses.

Correlation Analysis:

The correlation matrix (Table 3) indicates several strong associations between individual workplace factors:

1. Voluntary turnover​ had a significant strong negative correlation with Age (r = -0.475, p < 001), suggesting that aged workers were less likely to leave.
2. ​Job dissatisfaction​ was positively correlated with Workplace issues (r = 0.174, p < 0.01) and Lack of recognition (r = 0.306, p < 0.001).
3. Wellness programs were negatively correlated with Workplace (r = -0.248, p < 0.001), and positively correlated with Health/safety (r = 0.546, p < 001).
4. Gender‎​Gender had small correlations with other variables (all |r| < 0.1), beyond a small association with ‎Voluntary exit (r = −0.075, p = 0.033).

For a full overview, see Table 3 and the accompanying heatmap (Fig. 2)

Figure 2. ​​ Heatmap of correlations between voluntary exit and workplace factors

Note. Colors show Pearson correlation strength (yellow = strong positive, purple = strong negative).  Key findings: Strongest predictors: Age (C = -0.95), Work-life balance (C = +0.42)

​Weaker but significant: Gender (C = -0.07), Job dissatisfaction (C = +0.56)

Logistic regression: (Voluntary Exit)_

The results of a logistic regression on predictors of voluntary employee exit are shown in Table 4. Using equation (2) we analyses the logistic regression. The model was a significant predictor (Chi² = 33.15, p = 0.007), which means that the elements included in the analysis make a useful contribution to the prediction of voluntary turnover decisions. The model accounts for around 4% of the variation in voluntary exit decisions (Pseudo R²=0.040 There were three significant predictors: Gender was a significant negative predictor (β = -0.455, p = 0.017), indicating that men were less prone to voluntarily exit the organization compared to women. Nonrecognition was a significant positive predictor of voluntary turnover (β = 0.755, p = 0.041), indicating that employees who were not recognized were more likely to want to resign voluntarily. Work-life balance was negatively and significantly correlated (β = -0.452, p = 0.023), meaning that reporting a better work-life balance was associated with a reduction in leaving. One significant bivariate relationship was found with employment status (β = -0.327, p = 0.008), indicating that it is less likely for staff to voluntarily leave if their employment is secure (e.g., permanent).

Other stressors, including conflict, overall lack of satisfaction, job insecurity, and workload, were not statistically significantly related, although there was some directional relationship.

Table 3  Correlation Matrix of Workplace Variables. Note: Pearson correlation coefficients (upper triangle) with p-values in parentheses (lower triangle). Bolded values indicate p < 0.05. Variables abbreviated for clarity (e.g., Interpersonalc = Interpersonal Conflict).

Table 4 This table displays the results of a logistic regression analysis examining the effect of various workplace and demographic factors on the likelihood of voluntary employee exit. Significant predictors include gender, lack of recognition, work-life balance, and employment status. Coefficients (β), standard errors, and confidence intervals are reported. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

Logistic regression

Logistic Regression Results: Predicting Interpersonal Conflict

The logistic regression model examining variables predictive of the odds of interpersonal conflict at work is presented in Table 5 using equation (3). The final model is statistically significant (Chi² = 64.65, p < 0.001) and accounts for 31.4% of the variance of the outcome (Pseudo R² =.314), indicating the model to be well fitted. Several predictors were found to be significant:

1. Contract expiration also had a positive and significant impact on IVC (β = 3.16, p = 0.018), meaning that employees with ending contracts are more likely to suffer from this type of conflict.
2. Resignation other showed a marginally significant positive association with resignation (β= 1.65, p = 0.079), which potentially suggests that unspecified resignation was associated with conflict.
3. Overall job dissatisfaction was slightly significant (β = 1.01, p = 0.091) indicating that employees with higher levels of dissatisfaction are more vulnerable to interpersonal issues.
4. Workplace issue score was significantly inversely associated with interpersonal conflict (β = -0.64, p = 0.045): it is possible that higher scores regarding broader workplace issues could supersede individual reports of conflict.
5. Employment status also had a significant inverse coefficient (β = -0.93, p = 0.049), indicating that the presence of better stability in employment could decrease the likelihood of interpersonal
6. Supervisor support was weak and negatively associated (β = -0.60, p = 0.065), which suggests that supportive management might reduce the conflict between work and family.
7. Gender, age, job dissatisfaction, wellness programs, and promotions were also not

Discussion

The results of this study suggest that employee dissatisfaction and interpersonal conflicts are not situational events but rather are rooted in the organizational and psychological landscape. The contribution of these findings is in providing a multidimensional understanding of the causes that make an employee disengage and/or leave an organization, and going beyond the typical considerations of compensation and task load. One important note here is that recognition, often perceived as a “soft capitveator”, actually holds a strong behavioural impact. When people feel that they are not being treating properly, they are more likely to leave a job voluntarily, suggesting that unmet esteem needs play a role in organizational instability. This also adds support to goal-setting theories that promote internal factors and find that even in resource-constrained contexts such as the public sector, non-material aspects can be more relevant than material ones in influencing turnover. Interpersonal conflict reveals them to be more attuned to both structural and immediate causes (e.g., contract non-renewal and poor quality daily supervisory feedback). Perhaps uncertainty and lack of communication within transition periods can contribute to relationship difficulties. It implies that the issue might not even be ‘disagreement’ as such, it’s a symptom of there being a base level of unhappiness and instability. The negative relationship between supervisor support and conflict accentuates the countering effect of leadership intervention at work. Proper management can also prevent discontent from escalating to relational disputes, meaning that the quality of leadership is a concern to both performance and retention. Inconsistent with some earlier predictions, work hours and general dissatisfaction were not consistently large predictors of turnover. This is likely to happen when several employees are under high demands, and can sense both support and appreciation. Crucially,  such dissatisfaction is in terms not only of the volume of work but also the extent to which that job fits with your requirements, position, and support.

Table 5: This table displays logistic regression estimates of factors associated with the likelihood of reported interpersonal conflict among employees. The model includes demographic variables, separation types, dissatisfaction indicators, and workplace conditions. Significant predictors include contract expiration, general dissatisfaction, workplace issue score, employment status, and supervisor support. Coefficients, standard errors, and confidence intervals are presented. Statistical significance is indicated as follows: p < 0.01, p < 0.05, p < 0.10.

CONCLUSION AND RECOMMENDATION

This study investigated the organizational and psychological antecedents of employee dissatisfaction and voluntary turnover, using a data-driven approach rooted in motivational and conflict management theories. Focusing on interpersonal conflict, lack of recognition, and inadequate workplace support, the findings underscore that turnover is not merely the result of generalized dissatisfaction but is driven by specific structural and relational gaps—such as unmet expectations regarding recognition, work-life balance, and employment security.

Importantly, these patterns are magnified in contexts marked by cultural hierarchy, indirect communication, and rigid organizational structures. The research reveals that emotional and social resources—particularly supervisory support and perceived fairness—play a critical role in buffering the effects of workplace stressors. Younger and female employees were found to be more sensitive to these dynamics, highlighting the need for differentiated, inclusive retention strategies.

This study contributes to the transformation of human resource management by demonstrating how data analytics can identify early indicators of disengagement and dissatisfaction. Organizations can use predictive models to develop proactive conflict-resolution strategies and adaptive motivation systems, moving from reactive personnel practices to strategic, technology-enabled management. In an era of heightened uncertainty and labor volatility, these insights are vital for building resilient, equitable, and future-ready organizations. Leadership, therefore, must evolve not only in its practices but also in its mindset—embracing data-informed empathy as a core management competency.

RECOMMENDATION

1. Build mechanisms to avoid and resolve conflict. Settle small personal issues before they become the reasons for discontent or excuses for quitting, using mediation and proactive training
2. Daily recognition practices such as peer recognition every day, public recognition and developmental conversations help rise above just the annual review, thus fulfilling the esteem needs and reducing disengagement.
3. Review the allocation of resources, opportunities, and workload across departments with a focus on fairness, psychological safety, and availability to all employee demographics.
4. Develop retention and engagement programs that address the age and sex-related challenges of working that ensure young and women employees are equally supported, respected and recognized as their co-workers.
5. Equip team leaders with interpersonal and conflict-resolution skills to inspire trust and prevent the spread of disenchantment across departments.

REFERENCES

1. Allen, N.J., Meyer, J.P., 1996. Affective, Continuance, and Normative Commitment to the Organization: An Examination of Construct Validity. J. Vocat. Behav. 49, 252–276. https://doi.org/10.1006/jvbe.1996.0043
2. Argyle, Michael., 1994. The Psychology of Interpersonal Behaviour. Londons, 5th ed. ed. Penguin Book.
3. Bakker, A.B., Demerouti, E., 2007. The Job Demands–Resources model: State of the art. J. Manag. Psychol. 22, 309–328. https://doi.org/10.1108/02683940710733115
4. Brough, P., & Pears, J, 2004. Evaluating the influence of the type of social support on job satisfaction and work-related psychological well-being. Int. J. Organ. Behav. 8(2), 472–485.
5. Chen, X., Zhao, K., Liu, X., Dash Wu, D., 2012. Improving employees’ job satisfaction and innovation performance using conflict management. Int. J. Confl. Manag. 23, 151–172. https://doi.org/10.1108/10444061211218276
6. Cropanzano, R., Mitchell, M.S., 2005. Social Exchange Theory: An Interdisciplinary Review. J. Manag. 31, 874–900. https://doi.org/10.1177/0149206305279602
7. Eisenberger, R., Huntington, R., Hutchison, S., Sowa, D., 1986. Perceived organizational support. J. Appl. Psychol. 71, 500–507. https://doi.org/10.1037/0021-9010.71.3.500
8. Halbesleben, J.R.B., Buckley, M.R., 2004. Burnout in Organizational Life. J. Manag. 30, 859–879. https://doi.org/10.1016/j.jm.2004.06.004
9. Harrison, D.A., Newman, D.A., Roth, P.L., 2006. How Important are Job Attitudes? Meta-Analytic Comparisons of Integrative Behavioral Outcomes and Time Sequences. Acad. Manage. J. 49, 305–325. https://doi.org/10.5465/amj.2006.20786077
10. Herzberg, F., Mausner, B., Snyderman, B.B., 1959. The motivation to work. Wiley.
11. Jaramillo, F., Mulki, J.P., Marshall, G.W., 2005. A meta-analysis of the relationship between organizational commitment and salesperson job performance: 25 years of research. J. Bus. Res. 58, 705–714. https://doi.org/10.1016/j.jbusres.2003.10.004
12. Judge, T.A., Bono, J.E., 2001. Relationship of core self-evaluations traits—self-esteem, generalized self-efficacy, locus of control, and emotional stability—with job satisfaction and job performance: A meta-analysis. J. Appl. Psychol. 86, 80–92. https://doi.org/10.1037/0021-9010.86.1.80
13. Kooij, D.T.A.M., Jansen, P.G.W., Dikkers, J.S.E., De Lange, A.H., 2010. The influence of age on the associations between HR practices and both affective commitment and job satisfaction: A meta‐analysis. J. Organ. Behav. 31, 1111–1136. https://doi.org/10.1002/job.666
14. Liu, B., & Wang, X., 2013. The role of organizational culture in public service motivation: A cross-national study. Int. J. Public Adm. 36(1), 48–60.
15. Maslow, A.H., 1943. A theory of human motivation. Psychol. Rev. 50, 370–396. https://doi.org/10.1037/h0054346
16. Michaels, C.E., Spector, P.E., 1982. Causes of employee turnover: A test of the Mobley, Griffeth, Hand, and Meglino model. J. Appl. Psychol. 67, 53–59. https://doi.org/10.1037/0021-9010.67.1.53
17. Pandey, S.K., Pandey, S., Miller, L., 2021. Public service motivation and employee outcomes: Testing a multilevel model. Rev. Public Pers. Adm. 41, 454–476. https://doi.org/10.1177/0734371X19878412
18. Perry, J.L., Wise, L.R., 1990. The Motivational Bases of Public Service. Public Adm. Rev. 50, 367. https://doi.org/10.2307/976618
19. Spector, P. E., 2012. Industrial and organizational psychology: Research and practice., 6th ed. ed.
20. Taras, V., Kirkman, B.L., Steel, P., 2010. Examining the impact of culture’s consequences: A three-decade, multilevel, meta-analytic review of Hofstede’s cultural value dimensions. J. Appl. Psychol. 95, 405–439. https://doi.org/10.1037/a0018938
21. Tett, R.P., Meyer, J.P., 1993. JOB SATISFACTION, ORGANIZATIONAL COMMITMENT, TURNOVER INTENTION, AND TURNOVER: PATH ANALYSES BASED ON META‐ANALYTIC FINDINGS. Pers. Psychol. 46, 259–293. https://doi.org/10.1111/j.1744-6570.1993.tb00874.x
22. Wu, W., Wu, C.-C., 2011. Leadership and conflict management style among Chinese and American managers. Int. J. Confl. Manag. 22, 144–164. https://doi.org/10.1108/10444061111126646