Поведение сотрудников и отказ от инвестиций в банковском секторе: данные, полученные от работников лицензированных государственных и частных банков Шри-Ланки

DOI:

As an introduction to real turnover, Mobley [4] developed the idea of turnover intentions and defined withdrawal behavior as the outward expression of these intentions in the form of tardiness, absenteeism, and voluntary turnover. Building on this approach, Steel and Ovalle [5] added physical and psychological components to the understanding of withdrawal behavior.

They characterized WBS as internal cognitive processes like disengagement and job unhappiness, and WBP as observable behaviors like absenteeism and turnover. Absenteeism, tardiness, low effort, and daydreaming are examples of withdrawal behaviors that can have a big impact on worker productivity, job satisfaction, and the efficiency of the business [4]. Organizations must comprehend the elements driving withdrawal behavior to create strategies that effectively address these problems and foster a pleasant work environment.

The sector in which people work is one important aspect that could affect their withdrawal behavior. The experiences, perceptions, and behaviors of employees can be influenced by the sector type (public or private), which is defined by the industry, organizational structure, and work environment [6]. Particularly, the banking industry, with its reputation for fierce competition, high standards for performance, and stressful work environments, may put more pressure on staff members, which could affect how they withdraw.

A vast array of financial institutions that offer a range of services, including taking deposits, making loans, and assisting with financial transactions, are included in the banking industry. It consists of both private and public sector banks. The public sector is made up of government-owned and run businesses that play a crucial role in providing necessities including public administration, healthcare, and education. In elaborating on this definition, that the public sector is made up of state-funded and -controlled government agencies and organizations that prioritize serving the public interest over private gain.

The private sector is made up of companies and organizations that are privately held and run, with the main goals being market competition and profit maximization. In a further elaboration of this term, the private sector as privately held companies and firms motivated by competitive dynamics, profit incentives, and market forces.

Employee withdrawal patterns in the banking industry are heavily influenced by the type of sector, whether it be public or private. Employees in public sector banks may have more job security, but they may also encounter bureaucratic roadblocks and fewer prospects for professional progress, which could result in job discontent and withdrawal behavior. However, there's a chance that workers in private sector banks experience increased performance standards and competitive pressures, leading to stress and burnout and perhaps influencing withdrawal patterns. In the banking industry, workers' job experiences and withdrawal patterns are largely shaped by the sector type.

Researchers attempting to comprehend the intricate relationship between organizational context and employee outcomes have focused more and more attention on the effect of sector type on employee withdrawal behavior. Although several factors, such as leadership style, organizational commitment, and work satisfaction, have been researched about withdrawal behavior [7-9], the significance of sector type has not received as much attention.

In addition, the banking industry in the Northern Province functions within a unique cultural and socioeconomic milieu that is defined by efforts for economic development and post-conflict transition. Employee withdrawal behavior may be influenced differently by these contextual elements as compared to other industries or regions, depending on how they interact with sector-specific traits. Thus, to better understand how sector type affects the withdrawal patterns of licensed commercial bank employees in the Northern Province, empirical research is required.

By examining the effect of sector type on employee withdrawal behavior among staff-level employees in licensed commercial banks in the Northern Province, this study seeks to close this gap. By concentrating on the banking industry in a particular area, this study aims to shed light on the particular difficulties and dynamics influencing withdrawal behavior in this setting. The Northern Province of Sri Lanka offers a distinctive context for researching the relationship between sector type and employee behavior because of its post-conflict transition and economic development programs. To fill the existing gap, this research sought to respond to the following investigational research questions:

• •    RQ1: Does the sector significantly impact the physical withdrawal behavior of employees in listed commercial banks?

• •    RQ2: Does the sector significantly impact the psychological withdrawal behavior of employees in listed commercial banks?

• •    RQ3: Is there a significant mean difference between the public sector and private sector on the withdrawal behavior of employees in listed commercial banks?

The primary objective is to examine the relationship between the sector and the withdrawal behavior of employees. The following are the secondary objectives of the study:

• •    Examine whether there is a significant relationship between sector and physical withdrawal behavior.

• •    Examine whether there is a significant relationship between sector and psychological withdrawal behavior.

• •    Investigate whether the withdrawal behavior significantly differs between the public and private sector banking staff.

LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT

Theoretical review

Some theories are interconnected with this study. Based on the idea of reciprocal interactions, social exchange theory proposes that workers trade in inputs like dedication and effort for outputs like rewards and recognition [10]. Therefore, when workers feel that there is unfairness or a lack of reciprocity in this trade connection, withdrawal behaviors could appear [11]. On the other hand, the Job Demands-Resources (JD-R) Model presents an alternative perspective, stating that the demands and resources associated with a job have a major influence on the behavior and well-being of employees [12]. For example, in industries such as banking, where there are high job expectations, such as workload and time pressure, workers may get more stressed and burn out, which could lead to withdrawal behaviors [13]. On the other hand, these pressures may be balanced by organizational resources like as autonomy and social support, which serve as barriers against withdrawal behaviors [12].

Going forward, distributive, procedural, and interactional justice are all included in the Organizational Justice Theory, which emphasizes the significance of fairness views in the workplace [14]. Employees in the public sector, which is defined by bureaucratic procedures and restricted autonomy, may experience more procedural injustice than their counterparts in the private sector, which can lead to higher levels of withdrawal behavior. Furthermore, goalsetting theory supports the idea that creating clear, difficult goals is a good way to boost worker motivation and output [15]. On the other hand, employees may experience goal ambiguity or unreasonable standards in industries such as the public sector, where strict performance criteria are the norm. This can lead to withdrawal behaviors as employees struggle to satisfy expectations.

Hackman and Oldham [16] established the work characteristics model, which highlights essential job aspects that impact employee motivation and satisfaction. Especially in the public sector, where job functions are frequently bureaucratic and standardized, workers do not have access to these essential qualities, which could lead to higher rates of withdrawal behavior than in the private sector. Moreover, the dynamic interaction between personal coping strategies and environmental stressors is emphasized by the Transactional Model of Stress and Coping [17]. Workers may turn to coping mechanisms like withdrawal as a way to manage stress, especially in high-stress industries like healthcare or public education.

Finally, according to Organizational Support Theory, employee attitudes and behaviors are greatly influenced by their views of organizational support [18]. Because of bureaucratic restrictions, workers in industries like the public sector, where they feel lower levels of organizational support, may be more likely to engage in withdrawal behaviors as a form of passive resistance.

Empirical review

Physical withdrawal behavior

Numerous scholars have looked closely at the field of withdrawal behavior in the workplace, especially WBP. Observable behaviors taken by workers to disengage from their jobs, such as absenteeism, tardiness, and turnover, were classified as WBP by Mobley [4]. Expanding on this concept, Steel and Ovalle [5] defined WBP as overt actions reflecting an employee's wish to disengage, such as prolonged breaks or frequent sick days. Hom et al. [7] expanded the scope to include missing shifts, leaving early, or taking unapproved breaks. A thorough definition of WBP was given by Griffeth et al. [19] who said that it includes any type of absence from work, whether it be temporary or permanent, which shows a lack of dedication to the company. The importance of job discontent in predicting physical withdrawal behavior was underlined by Hanisch and Hulin [20], who also suggested that unfavorable attitudes toward one's employment can show up in behaviors like absenteeism.

In terms of determinants, a meta-analysis carried out by Hom et al. [7] revealed that perceived alternatives, organizational commitment, and work satisfaction were important predictors of WBP across several research. According to Meyer et al. [21], there are variations in WBP between the public and private sectors, with workers in the former having a higher absence rate than those in the latter. This discrepancy can be explained by elements that are frequently greater in the public sector, such as perceived organizational support and job security [22].

Additionally, the type of sector an individual works in greatly influences how they withhold information. Employee work dissatisfaction and burnout may be more common in the public sector, which is marked by bureaucratic systems and hierarchical management. This might result in a rise in withdrawal behaviors including absenteeism and turnover [8]. On the other hand, the private sector, which is motivated by profit and market rivalry, can provide more chances for financial gain and career progression, which might discourage people from withdrawing from their jobs [23].

Withdrawal behaviors in a variety of sectors are also influenced by leadership behavior. In both public and private sector firms, transformational leadership was linked to reduced levels of WBP, according to Podsakoff et al. [9], while transactional leadership produced inconsistent outcomes. Likewise, lower levels of WBP in both sectors were linked to an organizational climate that prioritizes justice, trust, and open communication [24].

On the other hand, higher rates of withdrawal behaviors were associated with an unfavorable organizational climate. Research on cross-cultural variations and withdrawal behaviors has shown that higher levels of withdrawal behaviors are fostered in cultures with high degrees of power distance and uncertainty avoidance [25]. Moreover, regardless of the industry, new research has highlighted the significance of job design in reducing employee withdrawal behaviors. It has been demonstrated that job crafting, which entails making proactive adjustments to employment duties, responsibilities, and relationships, improves job satisfaction, autonomy, and purpose at work, all of which lower WBP [2].

H1: There is a significant relationship between the sector and the physical withdrawal behavior of banking staff.

H2: There is a significant difference in the mean physical withdrawal behavior between the public and private sector banking staff.

Psychological withdrawal behavior

Numerous studies have examined the issue of WBS syndrome, in great detail, each of them illuminating a distinct facet of this idea. WBS, which was first described by Steel and Ovalle [5] as actions that indicate a worker's disengagement from their job, can take many different forms, such as diminished effort, apathy, or daydreaming. Blau [26] explores this further, highlighting how mental disengagement from work responsibilities lowers commitment, drive, and satisfaction. Tett and Meyer [27] contribute by emphasizing the emotional and cognitive disengagement from work that leads to a decrease in motivation and involvement in tasks. According to Baillien et al. [28], WBS is caused by internal sensations of stress, burnout, and emotional weariness that eventually result in depersonalization and disengagement. Halbesleben [29] further emphasizes the part that interpersonal problems, role ambiguity, and perceived job stress play in promoting WBS, which causes workers to psychologically distance themselves from work-related duties.

Blau [26] focuses on psychological contracts and perceived fairness in forming employees' opinions of the business, influencing their withdrawal intentions, while investigating the psychological aspects of withdrawal behavior. Tett and Meyer [27] present the concept of continuity commitment, which postulates that workers may remain in an organization because they perceive the costs of quitting, leading to WBS like decreased effort and dedication. Bullying at work harms employee well-being, as demonstrated by Baillien et al. [28] investigation of the relationship between bullying and WBS. Halbesleben [29] puts forth a perspective known as the conservation of resources, which argues that when faced with stressful work situations, individuals should disengage to protect their psychological resources. To forecast WBS, including decreased effort and disengagement, Griffeth et al. [20)] develop a complex model that takes into account individual differences, organizational factors, and job characteristics.

Employee WBS is significantly impacted by sector type. According to Rainey and Steinbauer's [30] research, employees working in the public sector may experience higher levels of job stress and discontent because of bureaucratic restrictions and restricted autonomy. This can result in WBS-like presenteeism and lower levels of job engagement. Employees in the private sector, on the other hand, might enjoy greater job autonomy and performance-based incentives, which would lead to lower levels of WBS [31].

Additionally, research by Spector and Jex [32] suggests that whilst the private sector's adaptability and performance-driven culture may lessen WBS, the public sector's hierarchical structures and procedurally strict practices may exacerbate such behaviors.

H3: There is a significant relationship between the sector and the psychological withdrawal behavior of banking staff.

H4: There is a significant difference in the mean psychological withdrawal behavior between public and private sector banking staff.

RESEARCH METHOD

Conceptual model

This conceptual model explores the relationship between the type of sectors and withdrawal behavior, whether it be psychological or physical, in a range of sectors, including public and private ones. It sheds light on how organizational contexts influence worker behaviors and attitudes and provides an understanding of the complex relationship between withdrawal tendencies and work environments.

Withdrawal behavior

Sector Type

• 1.    Public sector

• 2.    Private sector

• 1.    Physical withdrawal behavior (WBP)

• 2.    Psychological withdrawal

behavior (WBS)

Figure 1. Conceptual framework

Sampling and data collection

The study uses a quantitative research design to gather and examine sector-specific data and employee withdrawal behavior. Respondents are from Northern Province-licensed commercial banks using a convenience sampling technique. The validated measures for measuring psychological and physical withdrawal behaviors, along with factors linked to job and demographics, comprise the research instrument. 131 staff members from listed commercial banks make up the sample.

Data analysis

Descriptive statistics is used to show the characteristics of data is used in this study. Mainly, to assess the association between sector type and withdrawal behavior, inferential statistics, such as regression analysis and t-tests are used with the help of SPSS (Statistical Package for the Social Sciences) software.

Demographic profile of the respondents

The study sample of 131 employees from licensed public and private sector commercial banks in the Northern Province of Sri Lanka is young, predominantly female, with significant public sector representation. Most have 6-10 years of experience, are married, hold higher education degrees, and earn over Rs. 55,000 annually, indicating financial stability.

Research model

Two statistical models are formulated and tested in this study, which are:

Model 1

WBP = β0 + β1SEC + β2AGE + β3GEN + β4EXP + β5MS + β6EDU + β7INC + εi

Model 2

WBS = β0 + β1SEC + β2AGE + β3GEN + β4EXP + β5MS + β6EDU + β7INC + εi

Where:

WBP: Physical Withdrawal Behavior

WBS: Psychological Withdrawal Behavior

SEC: Sector (public or private)

AGE: Age

GEN: Gender

EXP: Year of Experience

EDU: Education/ Qualification

INC: Level of Income

RESULTS AND DISCUSSION

Descriptive statistics

Table 1. Descriptive statistics
	WBP Q1	WBP Q2	WBP Q3	WBP Q4	WBS Q1	WBS Q2	WBS Q3	WBS Q4
Mean	1.85	1.79	1.67	1.69	1.79	1.95	1.85	1.91
Std. Deviation	1.268	1.209	1.180	1.227	1.183	1.211	1.190	1.255
Skewness	1.304	1.428	1.665	1.619	1.443	1.054	1.230	1.241
Std. Error of Skewness	.212	.212	.212	.212	.212	.212	.212	.212
Kurtosis	.467	.956	1.605	1.324	1.037	-.017	.418	.389
Std. Error of	.420	.420	.420	.420	.420	.420	.420	.420

Kurtosis

The range of mean value of tested 4 statements regarding WBP and WBS is 1.69 to 1.85 and 1.79 to 1.91 respectively. These mean values show that WBS is higher than WBP, however, all these are at a low level.

Table 2. Group Statistics

	Sector	N	Mean	Std. Deviation	Std. Error Mean
WBP	Private	50	2.1200	1.25889	.17803
	Government	81	1.5216	.96841	.10760
WBS	Private	50	2.1800	1.19847	.16949
	Government	81	1.6852	.93020	.10336

Employees in the private sector (N = 50) score higher on WBP (2.1200) and WBS (2.1800) than do those in the public sector (N = 81), with mean scores of 1.6852 and 1.5216, respectively. In comparison to the public sector, the private sector exhibits increased variability, as evidenced by the higher standard deviations for both WBS (1.19847) and WBP (1.25889). According to this table, workers in the private sector differ more from one another in terms of withdrawal habits and show higher degrees of withdrawal than those in the public sector.

Reliability analysis

Table 3. Reliability Statistics

Cronbach's Alpha	N of Items
.955	8

Table 3 displays the results of a reliability analysis conducted using Cronbach's Alpha, a measure of internal consistency. The Cronbach's Alpha value is 0.955, which is exceptionally high, indicating excellent reliability. This suggests that the items within the scale are highly correlated and consistently measure the same underlying construct.

Correlation analysis

Table 4. Correlation Matrix

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Sector (1) Pearson Correlation	1	.174*	-.211*	.175*	- .255**	.363**	-.003	- .260**	- .227**
Sig.     (2 tailed)		.047	.016	.046	.003	.000	.974	.003	.009

Информатика. Экономика. Управление// Informatics. Economics. Management [(cc) ©J 2024; 3(3)

	N	131	131	131	131	131	131	131	131	131
Age (2)	Pearson	.174*	1	.109	.378**	-	.122	.173*	-.126	-.104
	Correlation					.377**
	Sig.     (2 tailed)	.047		.216	.000	.000	.166	.048	.152	.239
	N	131	131	131	131	131	131	131	131	131
Gender (3)	Pearson	-.211*	.109	1	-.036	-.019	.159	.006	.252**	.261**
	Correlation Sig.     (2 tailed)	.016	.216		.683	.831	.070	.945	.004	.003
	N	131	131	131	131	131	131	131	131	131
Year   of Pearson		.175*	.378**	-.036	1	-	.141	.124	-.015	.029
Experience	Correlation					.289**
(4)	Sig.     (2 tailed)	.046	.000	.683		.001	.109	.160	.864	.739
	N	131	131	131	131	131	131	131	131	131
Marital	Pearson	-	-	-.019	-.289**	1	-.243**	-	.079	.035
Status (5)	Correlation	.255**	.377**					.246**
	Sig.     (2 tailed)	.003	.000	.831	.001		.005	.005	.371	.695
	N	131	131	131	131	131	131	131	131	131
Education	Pearson	.363**	.122	.159	.141	-	1	.004	-.008	.061
(6)	Correlation					.243**
	Sig.     (2 tailed)	.000	.166	.070	.109	.005		.966	.926	.487
	N	131	131	131	131	131	131	131	131	131
Level   of Pearson		-.003	.173*	.006	.124	-	.004	1	-.040	-.031
Income (7)	Correlation					.246**
	Sig.     (2 tailed)	.974	.048	.945	.160	.005	.966		.648	.729
	N	131	131	131	131	131	131	131	131	131
WBP (8)	Pearson	-	-.126	.252**	-.015	.079	-.008	-.040	1	.886**
	Correlation	.260**
	Sig.     (2 tailed)	.003	.152	.004	.864	.371	.926	.648		.000
	N	131	131	131	131	131	131	131	131	131
WBS (9)	Pearson	-	-.104	.261**	.029	.035	.061	-.031	.886**	1
	Correlation	.227**
	Sig.     (2 tailed)	.009	.239	.003	.739	.695	.487	.729	.000
	N	131	131	131	131	131	131	131	131	131

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

The sector has a significant negative correlation with both WBP (p = 0.01) and WBS (p = 0.01). The negative correlation between the sector and both WBP and WBS indicates that as the sector changes or varies, there is a corresponding decrease in both WBP and WBS scores. In other words, as individuals belong to different sectors, their tendencies to engage in WBP and WBS change inversely.

T-Test

Table 5. Independent Samples Test

		Levene's
		Test      for Equality of Variances F       Sig.		t-test for Equality of Means Significance One- Two-Side Side T     Df     d p   d p				Mean Differenc e	Std. Error Differenc e	95%  Confidence Interval   of   the Difference Lower  Upper
WB P	Equal variance s assumed Equal variance s     not assumed	8.40 9	.00 4	3.05 8 2.87 7	129 84.43 5	.001 .003	.003 .005	.59840 .59840	.19566 .20802	.2112 8 .1847 5	.98551 1.0120 4
WB S	Equal variance s assumed Equal variance s     not assumed	6.82 3	.01 0	2.64 5 2.49 3	129 85.01 8	.005 .007	.009 .015	.49481 .49481	.18709 .19852	.1246 5 .1001 1	.86498 .88952

Levene's Test results for WBP demonstrate that there are unequal variances between the groups, showing a violation of the assumption of equal variances (F = 8.409, p = 0.004). With a mean difference of 0.59840 and a 95% confidence interval from 0.21128 to 0.98551, the t-test finds a significant difference in means, assuming equal variances (t = 3.058, df = 129, p < 0.001) and not assuming equal variances (t = 2.877, df = 84.435, p = 0.005). Levene's Test for WBS likewise shows unequal variances (F = 6.823, p = 0.010). With a mean difference of 0.49481 and a 95% confidence interval from 0.12465 to 0.86498, the t-test reveals a significant difference in means, assuming equal variances (t = 2.645, df = 129, p = 0.009) and not assuming equal variances (t = 2.493, df = 85.018, p = 0.015). These findings show that employees in the private and public sectors differ significantly in their levels of psychological and physical withdrawal behavior (WBS and WBP, respectively), with private sector workers displaying higher levels of both forms of withdrawal behavior. Therefore, H2 and H4 are accepted in this study.

Regression analysis

Table 6. Model 1 Summary

Model R R Square Std. Error of the Adjusted R Square Estimate 1 .360a .129 .080 1.07656 a. Predictors: (Constant), Sector b. Dependent Variable: WBP

The sector and WBP have a weakly positive association, as indicated by the model's R-value of 0.360. According to the R Square value, the sector explains 12.9% of the variation in WBP.

Table 7. Model 1 ANOVA

Model Sum of Squares Df Mean Square F Sig. 1       Regression 21.197 7 3.028 2.613 .015b Residual 142.553 123 1.159 Total 163.750 130 a. Dependent Variable: WBP b. Predictors: (Constant), Sector

The regression means square (3.028) and regression sum of squares (21.197) in Model 1 suggest that the regression analysis has some explanatory power. Still, the residual sum of squares (142.553) indicates that a sizable portion of the variation in WBP is not explained. The F-statistic (2.613) is used to assess the statistical significance of the regression model, and a p- value of 0.015 is used to indicate statistical significance. This implies that a portion of the variation in WBP scores can be explained by the sector variable, offering important new information about the connection between the sector and actual withdrawal behavior.

Table 8. Model 1 Coefficient

Model	Unstandardized Coefficients Std.			Standardized Coefficients			Collinearity Statistics Tolerance VIF
	B		Error	Beta	T	Sig.
1     (Constant)		2.350	1.518		1.549	.124
Sector		-.505	.223	-.219	-2.260	.026	.752	1.331
Age		-.268	.182	-.143	-1.474	.143	.753	1.328
Gender		.499	.207	.218	2.413	.017	.869	1.151
Year	of	.137	.154	.083	.889	.376	.818	1.222
Experience Marital Status		.001	.228	.001	.005	.996	.751	1.331
Education		.046	.102	.043	.451	.653	.786	1.272
Level	of	-.105	.330	-.028	-.317	.752	.923	1.083
Income
a. Dependent Variable: WBP

There were several factors assessed in the regression model that predicted WBP. The independent variables' VIF values, which varied from 1.083 to 1.331, showed no multicollinearity and a weak correlation between the predictors. The expected WBP score in the case where all predictors are zero is represented by the constant term, 2.350. There is a noteworthy inverse correlation between the sector and WBP (β = -0.219, p =.026), indicating that workers in the public sector are less likely than those in the private sector to engage in WBP. Therefore, H1 is accepted in this study.

Table 9. Model 2 Summary

Model R R Square Adjusted R Square Std.   Error Estimate of  the 1 .363a .132 .082 1.01931 a. Predictors: (Constant), Sector b. Dependent Variable: WBS

The correlation coefficient (R) of 0.363 indicates a weak positive link between the sector and WBP. The sector can explain around 13.2% of the variance in WBP, according to the R Square value of 0.132.

• Table 10.    Model 2 ANOVA

Model Sum of Squares Df Mean Square F Sig. Regression 19.375 7 2.768 2.664 .013b Residual 127.797 123 1.039 Total 147.172 130 a. Dependent Variable: WBS b. Predictors: (Constant), Sector

According to the regression model, the sector, the predictor, has a statistically significant effect on WBS, as shown by the F-statistic of 2.664 and the p-value of 0.013, both of which are below the conventional alpha criterion of 0.05. The p-value indicates substantial evidence in favor of the theory that the sector influences WBS. As a result, hypothesis 2 is accepted.

• Table 11.    Model 2 Coefficient

Model Unstandardized Coefficients Std. Standardized Coefficients Sig. Collinearity Statistics Tolerance VIF Beta t B Error 1 (Constant) Sector 2.207 -.475 1.437 .211 -.218 1.536 -2.244 .127 .027 .752 1.331 Age -.264 .172 -.149 -1.539 .126 .753 1.328 Gender .476 .196 .219 2.430 .017 .869 1.151 Year of .179 .146 .114 1.230 .221 .818 1.222 Experience Marital -.048 .215 -.022 -.222 .825 .751 1.331 Status Education .105 .097 .102 1.080 .282 .786 1.272 Level of -.095 .313 -.026 -.303 .762 .923 1.083 Income a. Dependent Variable: WBS

The predicted value of WBS when all other predictors are zero is represented by the constant term in the model, which is 2.207. Sector (private or public) and WBS have a negative association, as indicated by the Sector coefficient of -0.475. More specifically, WBS scores are typically lower for public sector employees than for private sector employees. Given the statistical significance of this link (p = 0.027), Sector may be a predictor of WBS. With a tolerance of 0.752 and a variance indicator factor (VIF) of 1.331, multicollinearity diagnostics show acceptable levels, confirming the validity of the model and the predictors' independence in explaining WBS variation.

CONCLUSION

This study examined how employees' withdrawal behavior in licensed commercial banks in the Northern Province of Sri Lanka was affected by the sector type. It was discovered via quantitative study that among bank employees, sector type has a significant impact on both WBP and WBS. The findings show that private-sector workers have greater levels of WBP and WBS than public-sector workers. Particularly, workers in the private sector show higher mean scores for both WBP and WBS, suggesting a higher likelihood of withdrawal behaviors such as disengagement, diminished effort, and absenteeism. These results are supported by regression analysis, which revealed that sector type significantly predicts both WBP and WBS. Compared to workers in the private sector, public-sector employees show a lower level of WBP and WBS. These findings advance our knowledge of the intricate interactions that exist between employee behavior and sector type especially in the banking sector, particularly in the context of Sri Lanka's Northern Province's distinct socioeconomic setting.

This work has yielded interesting insights; however, it is important to acknowledge numerous limitations. First off, the study's exclusive emphasis on licensed commercial banks in Sri Lanka's Northern Province limited the applicability of its conclusions to other areas or categories of financial institutions. Second, the study relied on employee self-reported data, which might contain errors in reporting withdrawal behavior and be influenced by social desirability bias. Thirdly, the study's cross-sectional design makes it more difficult to determine the causal links between withdrawal behavior and sector type. A more complete understanding of how sector type affects employee behavior over time may be obtained through a longitudinal study. Lastly, the study did not take into account any mediating or moderating factors like corporate culture, leadership style, or job qualities that may have an impact on the link between sector type and withdrawal behavior.

Future studies might solve these shortcomings and investigate the influence of sector type on employee withdrawal behavior in more detail. A more complex knowledge of how sector dynamics affect employee outcomes could be obtained by longitudinal studies that monitor changes in withdrawal behavior over time. A deeper knowledge of the mechanisms underlying the association between sector type and withdrawal behavior can also be obtained through qualitative research techniques like focus groups and interviews, which also help to better understand the experiences and perceptions of the employees. To further support employee well-being and organizational effectiveness in the banking sector, organizational leaders and policymakers may find it useful to look into possible interventions or strategies to reduce withdrawal behavior in both public and private sector banks.