The Moderating Effect of Emotional Intelligence on Mentorship, Job Autonomy, Technostress and Employee’s Turnover Intention in Bangladesh Private Banking Sector

 The Moderating Effect of Emotional Intelligence on Mentorship, Job Autonomy, Technostress and Employee’s Turnover Intention in Bangladesh Private Banking Sector

Aklima Begum¹, Raemah Abdullah Hashim², Hishamuddin Md.Som³

¹Department of Management, Putra Business School, University of Putra Malaysia.

²Department of Management, City University, Malaysia

³Department of Management, Putra Business School, University of Putra Malaysia.

DOI: https://doi.org/10.51244/IJRSI.2025.12020038

Received: 25 January 2025; Revised: 02 February 2025; Accepted: 05 February 2025; Published: 05 March 2025

 

ABSTRACT

The purpose of this study is to investigates the moderating effects of emotional intelligence on the relationships among mentorship, job autonomy, technostress and employee’s turnover intention in Bangladesh private banks. In terms of research methodologies, this study adopted cross-sectional study and quantitative research approach. To collect data for this study, the survey questionnaires were distributed by using simple random sampling and 440 samples were collected from the four private banks located in Dhaka city in Bangladesh. By using quantitative research design, there are three hypotheses developed and tested by using Partial Least Squares (PLS-SEM: version 4) and SPSS (version 29) for demographic and descriptive analysis. The study’s empirical results showed that, EI had no moderating effect on the relationship between job autonomy, technostress, and employee turnover intention in the private banking industry of Bangladesh. Based on the thorough literature and results, this study made an effort to contribute from an academic and industry perspective, focusing on the banking sector as one of Bangladesh’s prospective industries. By employing emotional intelligence (EI) as a moderator in private bank organizations in Bangladesh, this study theoretically advances JD-R theory. Regarding the practical implications, the impact of emotional intelligence on the connections among job autonomy, technological stress, and employee turnover intention demonstrated the significance of individual characteristics in lowering the banking industry’s turnover rate. Finally, the study concluded the discussion on findings and implications.

Keywords: Mentorship, Job autonomy, Technostress, Emotional intelligence, Turnover intention

INTRODUCTION

A bank is an institution which creates money with money (Hock,1995).) The banking sector is the most significant sector in the development of the global economy (Anis et al., 2022). The banking sector contributes long-term economic development and poverty reduction for any country by providing technical and financial support to help countries implement reforms or projects, such as building schools, providing water and electricity, fighting disease, and protecting the environment. In 2021, the banking sector’s contribution to the GDP is about 7.7% (Khatun et al.,2023) The World Bank (2022) reports that as measured by total loans as a percentage of GDP, banking sector intermediation is 30%. The banking sector has generated employment to the tune of 1.5 million (BBS,2020). Therefore, the banking sector is the most significant factor in the development of the global economy. Similarly, according to Islam et al., (2022) the most contributing sectors in the economy of Bangladesh is banking industry. Recently, Private banking sector has recognized as a significant industry for achieving the Sustainable Development Goals (SDG) of Bangladesh (Hasan & Sadat,2023; Pradhan, 2020; Sabbir et al., 2017). In 2017, Private Commercial Banks’ (PCBs) share in export finance was the highest (60%) followed by state owned commercial banks (SOCBs (Anis et al.,2022). Banking sector contribution to country GDP is 37.3%   where commercial bank contribution is 22% (Haque, 2021; Khatun et al.,2023). This industry is one of Bangladesh’s most desirable job pathways due to its attractive pay structures and social standing (Islam et al., 2022; Sabbir et al., 2017). However, Bangladesh private banking sector recently facing a problem of employee turnover intention (Anis et al., 2022; Rahman, 2023)

The concept of Turnover intention is the possibility of an employee leaving the current organization with the hope of finding a better position than the existing one (Rasheed et al.,2024). Several researchers stated that turnover intention is one of the best predictors of employees’ actual turnover which badly effect the organization performance and profitability (Giao et al., 2020; Sarma & Tiwari, 2023). Turnover raises the production expenses because when an employee leaves the company, the management must spend more money on hiring new employees, creating a new compensation plan, and funding training sessions (Wang et al.,2023).Some firms have reported astounding figures for turnover costs, such as an IT firm that estimated an average turnover cost of $200,000 per employee (Thakur et al.,2022).In Bangladesh, annual turnover value of 10,373.370 BDT in the banking sector (DSE, 2022). According to Khatun et al., (2023) in Bangladesh few banks turnover costs of other banks are more than 10% of their total recruitment, selection and training cost, which is upsetting. High turnover rate also Disrupts operations and decreased customer service and quality (Jannat et al., 2019). This can hamper their performance in the market, especially in terms of profit margin (Sobhani et al.,2021). Employees are the most valuable assets for a service sector (Emon et al.,2023), banking   sector cannot meet its obligations without retaining employees (Khatun et al.,2023).

Numerous research has been conducted nationally and worldwide on employee turnover intention in banking sector (Shahid & Khalid, 2024; Shanmugam,2022; Nour & Parker,2020). Prior Research focused different effective factors and strategi to reduce turnover intention. In Bangladesh perspective, research has been conducted on the performance, profitability and sustainability of commercial banks (Kabir et al.,2023; Rouf et al.,2020; Bajaba et al., 2022). Employee intentions to turnover in Bangladesh’s private banking organisations seem limited (Khan & Roy, 2020). Similarly, Emotional intelligence has been identified as a predictor of human behaviour (Mayer et al., 2020). Earlier studies investigating the causes of employee turnover have primarily focused on demographic and individual variables (Emon, et al.,2023; Ahad et al.,2021; Hassan et al., 2021). The potential moderating role of emotional intelligence between job demand resources and turnover intention has received scant attention in developing or less developed countries, particularly in Bangladesh (Akhter, 2021). Specific to the financial sector, a banker with high emotional intelligence would perform effectively in enhancing customer enthusiasm and reducing customer frustration (Giao et al.,2020). Thus, understanding employee emotional intelligence is crucial to this research. Therefore, this study aims to fill these gaps by examining the moderating effect of emotional intelligence on the relationships between mentorship, job autonomy, technostress, and turnover intention within the context of private banking organizations in Bangladesh.

LITERATURE REVIEW

Turnover intention

The research on turnover first appeared around 1920 (Hu et al., 2022). The Journal of Applied Psychology was the first to copy turnover research in the second decade of the 20th century. Most researchers and scholars defined the Turnover intention from many different perspectives (Haque, 2021). The underlying idea of turnover begins when a person exhibits psychological responses to challenging work, the manager, and the company (Yang et al., 2022). The employee’s desire to leave their position undoubtedly impact their performance and status within the company (Salleh et al.,2024). There is growing evidence that turnover intentions are the most significant attitudinal predictor of actual turnover behaviour (wang et al.,2023). High rate of turnover creates serious difficulties on organization performance and profitability (Park et al.,2021; Tsen et al.,2022)

It is generally acknowledged that turnover intention is an important area for HRM study. Previous scholars have cited various reason and factors of intended turnover in the banking industry from different angle (Anis et al.,2022; Rahman, 2023; Alom et al., 2019; Islam et.,2022). The primary factor discovered was conventional human resource development strategies, such as seniority-based pay and promotion, and strict regulations and norms that are usually bureaucratic that encourage employees to leave these institutions (Faroqu et al.,2019; Bhardwaj et al.,2020). According to Zhang et al., (2023), employees leave companies for financial reasons. Poor compensation is one of the leading causes of the high employee turnover rate since employees are often looking for high-paying positions, particularly in the private sector (Ray et al.,2021). One further factor for employee turnover, is job dissatisfactions (Kurniawan & Susanto,2023). Employee who are satisfied less likely to leave the current job. Employee discontent at their current workplace is another factor that encourages employees to leave their jobs (Salleh et al.,2024).

Moreover, Past studies show positive association between mentoring and employee turnover rate   in banking sector (Hu et al., 2024; Gio et al.,2022). Daiena et al., (2023) conducted a study where the results of the hypothesis testing showed that the mentoring role had a detrimental impact on intended turnover intention. These conclusions align with the findings of Firzly et al., (2022); Upadhyay& Singh, (2024); and Park et al., (2022), which also demonstrate that the mentoring role hurts intended turnover. In a similar vein, previous studies also demonstrated that the job autonomy has positive influence on employees’ intention to turnover in banking sector (Giao et al.,2020, Tsen et al., 2021; Li et al., 2022). Furthermore, the relationship between techno stress and employee turnover intention has been assessed in various studies (Siddiqi, 2024). Findings of the prior research mentioned that a certain level of techno stress significantly impact the employee’s intention to leave their current job (Bao et al., 2024; Islam et al., 2022). Previous research demonstrated that no single factor could be attributed to turnover intentions and proposed following a holistic approach to studying factors affecting the turnover intention of employees. Therefore, the study makes a new contribution by using mentorship, job autonomy, as organizational factor technostress as technological factors and individuals factors emotional intelligence as a moderator in a frame.

Emotional intelligence as a moderator variable

Emotional intelligence has been identified as a predictor of human behaviour (Giao et al.,2020). Individuals with higher emotional intelligence are believed to perform better due to their capacity to understand, acknowledge, and manage emotions (Laws et al., 2021). Emotionally intelligent employee can create a conducive work environment and improve task performance and productivity (Pirsoul et al.,2023). Most studies used EI as a predictor of employee turnover intentions. Giao, et al., (2020) examined the effect of emotional intelligence on turnover intention in the banking industry of Vietnam.  Kurniawan & Susanto, (2023) conducted an empirical study investigated the effect of emotional intelligence on job performance and turnover intention.  Galanis et al., (2024) mentioned that the emotional intelligence as an antecedent of turnover intention.  Similarly, Islam et al., (2022) a study on commercial banks in Bangladesh Exploring the effect of job satisfaction, employee empowerment, and emotional intelligence on bank employee performance. Akhter, (2021) examined the impact of emotional intelligence, employee empowerment and cultural intelligence on commercial bank employees’ job satisfaction in Bangladesh.

More recently, emotional intelligence has been considered a personal resource (Lo et al.,2023). Many studies have examined personal resources as moderators of the relationship between unfavourable work characteristics and employee outcomes. For example, Chen et al., (2021) examined EI as a moderator of the relationship between job demands (stress appraisal) and quality of life (burnout, compassion satisfaction, compassion fatigue) among rescue workers. Research indicates that personal resources buffer the negative effects of job demands on burnout. Higher levels of emotional intelligence are also associated with a variety of interpersonal outcomes, including more cooperative behaviour (Wang et al.,2023), and higher relationship satisfaction (Suwandana,2024; Kurniawan & Susanto, 2023). Individuals with higher emotional intelligence tend to perceive having more social support and are more satisfied with their social support (Shen et al.,2020). Lipson, (2020) demonstrated significant moderating effect of emotional intelligence on the job demand and employee turnover. From an organisational standpoint, meta-analyses showed that employees with higher emotional intelligence reported better work performance (Kurniawan & Susanto,2023) and tended to perform better in high emotional labour work.

Moreover, Emotional intelligence (EI) has been recognised as a significant factor in workplace dynamics, including mentoring relationships and turnover intentions (Giao et al.,2020). Individuals with high EI are likely to be more effective mentors as they can better understand and respond to their mentees’ emotional needs (Sharma & Tiwari, 2023). This can lead to more satisfying mentoring relationships, reducing turnover intentions (Giao et al.,2020; Ouerdian et al.,2021). According to Suwandana, (2024) EI can also moderate the relationship between mentoring and turnover intentions. Employees with high EI may be better able to leverage the benefits of mentoring to enhance their job satisfaction and commitment, thereby reducing their turnover intentions (Wang et al., 2023; Witcher,2023). Conversely, those with low EI may not derive as much benefit from mentoring, which could result in higher turnover intentions (Pirsoul et al.,2023). In addition to building strong relationships, EI can help mentors provide more targeted and effective guidance to their mentees. By being attuned to their mentees’ emotions and needs, mentors can tailor their advice and support to the specific challenges and goals of the mentees. Whitlock, (2024) highlighted the importance of incorporating emotional intelligence (EI) training into mentorship programs. For formal mentoring programs to be successful, they must be established with specific goals and objectives, requiring foundational work for context.

Secondly, Prior Research has shown that job autonomy has significant relation with emotional intelligence and employee turnover intention (Du et al.,2020). Hwang & Park, (2022) suggested that EI would have more predictive power in high-relationship, high-autonomy occupations, given their ambiguous structure. Zeng et al., (2023) conducted on the effect of leaders’ and followers’ emotional intelligence (EI) on employees’ performance and attitude, and turnover intention finding mentioned significant relationship between EI and job satisfaction and employee turnover intention. The relationship between EI and performance was stronger for high-emotional labour jobs than for low-emotional ones. Jain & Duggal, (2018) studied Empirical analysis on the transformational leadership, organizational commitment, emotional intelligence and job autonomy with the mediation and moderation. Goswami & Mahanta, (2021) Explored the role of emotional labour and job autonomy in the relation between emotional intelligence and job performance. Du et al., (2020) examined the role of emotional intelligence and autonomy in transformational leadership with leader member exchange perspective.  Similarly. Radha & Aithal, (2023) conducted a study on the influence of emotional intelligence interventions in the banking sector. Udod et al., (2020) demonstrated dynamics of emotional intelligence and empowerment with the perspectives of middle managers. The results of these previous studies showed that a positive significant relationship among emotional intelligence, job autonomy and employee turnover intention.

Giao, et al., (2020) examined the effect of emotional intelligence on turnover intention and the moderating role of perceived organizational support in the banking industry of Vietnam.  Kurniawan & Susanto, (2023) conducted an empirical study investigated the effect of emotional intelligence on job performance and turnover intention.  Akhter, (2021) demonstrated the impact of emotional intelligence, employee empowerment and cultural intelligence on commercial bank employees’ job satisfaction in Bangladesh.  Lipson, (2020) examined the moderating role of emotional intelligence on the relationship between job resources (i.e., perceived supervisor support and autonomy) and employee engagement. Hassan et al., (2023) also examined the moderating roles of emotional Intelligence Between Burnout and Employee turnover Intention. The results demonstrated that the significant moderating effect of emotional intelligence. According to earlier research, employee with high emotional intelligence is probably better able to identify and control their own feelings, particularly when it comes to stress and dissatisfaction (Burki, et al., 2020; Düzgün & Çelik,2023).). Employees who are aware of the factors that contribute to stress are better equipped to control it and create long-term coping mechanisms. Furthermore, workers with low emotional intelligence are probably under more stress at work and are less satisfied with their professions (Chen, et al.,2024).

Similarly, Ertiö et al., (2024) conducted research the role of digital leaders’ emotional intelligence in mitigating employee technostress. Sudrajat, (2021) researched the relationship between emotional intelligence (EI) and workplace techno stress among nurses in West Java Province, Indonesia. The findings showed a significant relationship between EI and techno stress. According to Ekwochi et al., (2021) individuals with higher EI levels are more aware of their emotions and possess more effective coping strategies to deal with stress-related emotions, leading to higher levels of well-being. Ghobbeh & Atrian, (2024) studied emotional Intelligence’s role in reducing technostress through Ethical Work Climates. Pagán et al., (2024) explored of stress, burnout and technostress levels in teachers the Prediction of their resilience levels using an artificial neuronal network (ANN). Findings of these above-mentioned previous studies showed significant relationship among emotional intelligence, technostress and employee turnover intentions.

Additionally, Warrier et al., (2024) examined the relationship between emotional intelligence (EI) and stress management, finding a significant relationship between EI and job stress. Burki, et al., (2020) stated that employees’ emotional intelligence not only decreases frustration and stress in the workplace but also helps others to have less intention to quit. Emotional intelligence may be a key component in keeping employees engaged and understanding the emotional reasons for leaving decisions (Abusweilem, et al.,2023). Employees with high EI may be better equipped to manage the stress and challenges associated with technology use, leading to lower turnover intentions (Ghobbeh & Atrian, 2024). Conversely, employees with low EI may struggle to cope with technostress, which could increase their turnover intentions (Warrier, er al.,2023: Pagán et al.,2024).

However, emotional intelligence (EI) has not been examined as a moderator of the relationships between mentorship, job autonomy, technostress, and turnover intention in Bangladesh private bank organization. Bakker & Demouti, (2007) mentioned that   the job demand -resource (JDR) model has 2 physiological processes, stress process a motivation process and that influence important job outcomes (Schaufeli et al.,2017). The JD-R model has the added value of considering negative indicators of technology at work (Mahlasela, et al.,2020; Wan & Duffy, 2022) According to Lipson, (2020) more recent extension of the original JD-R model includes personal resources. Emotional intelligence has been looked at as a personal resource (Lipson,2020; Wang et al.,2024). Research suggests that employees with higher levels of EI are less likely to experience burnout and more likely to demonstrate resilience in the face of occupational stressors (Raza et al.,2020). The current study is situated in these studies. Finally, this research examines the relationship job resources such as mentorship, job autonomy and job demand such as technostress and employee’s intention to leave. Moreover, as a personal resource, emotional intelligence moderates the relationship between job demand resources and employee turnover intention in Bangladesh private bank organizations. Thus, the following hypotheses are proposed:

H1: Emotional intelligence plays a significant moderating role in the relationship between mentoring and employees’ turnover intention in Bangladesh private banks organization

H2: Emotional intelligence plays a significant moderating role in the relationship between job autonomy and ‘employees’ turnover intention in Bangladesh private banks organization.

H3: Emotional intelligence plays a significant moderating role in the relationship between technostress and employee’s turnover intention in Bangladesh private bank organizations

 

 

Figure 1: Research model

METHODOLOGY

This study used a quantitative research approach. The findings from a quantitative study are more generalisable to the population (Sekaran & Bouige,2017). The survey is a popular data collection method since it delivers fast, accurate, valid, and trustworthy information (Dalati & Gómez, 2018). Therefore, Survey was conducted for primary data collection in this study. As a sampling technique, Simple random sampling has been employed where each individual in the population has the same chance of being selected (Creswell,2021). The sample size is 384 in this study, as suggested by Krejcie and Morgan, (1970). To reduce the possibility of non-response bias and refusal to participate in the survey, 440 questionnaires were distributed to the respondents to avoid omission and missing data (Barlett et al.,2001). The primary sampling locations for this study are four central private banks in Dhaka city: BRAC Bank, Dhaka Bank, Dutch Bangla Bank, and Eastern Bank Limited.

Moreover, Data in this study were collected using questionnaires that were distributed through email. The items of the variables in the questionnaire were adopted from previous studies related to turnover intention and other selected variables. In addition, 5-point Likert scale, ranging from “strongly disagree” to “strongly agree,” was also used for respondents to answer the statements (He & Luo, 2020). Out of the 440 questionnaires distributed, 400 responses were received and overall response rate of 92.5%. After calculating missing value and cheeking of outliers the dataset was refined to include 392 observations, which were retained for further analysis. Data analysis takes less time because statistical software such as SPSS and SmartPLS are employed (Hair et al., 2019).

RESULTS AND FINDINGS

The Table no.1 presents that, among the 392 respondent 372 respondents were male which is 94.9% on the other hand 20 respondents were female which is 5.1% of the sample. among them the majority of the respondents were married, with 277 individuals, equating to 70.7% of the sample. The remaining 115 participants were single, representing 29.3% of the respondents. The age distribution among the participants was fairly spread out, with the largest group being those between 30 to 35 years of age, numbering 161 and constituting 41.1% of the sample. The next age group, 36-40 years, comprised 99 individuals, accounting for 25.3%. Those in the 41-45 years age bracket was 73 in number, making up 18.6%, while the 46-50 years age group had 59 respondents, representing 15.1% of the total.

Regarding income level majority of the respondents have monthly income range between 30001 to 35000 BDT which is individuals (44.1%). The next income bracket, between 35001 and 40000 BDT, included 111 respondents, which is 28.3% of the sample. Those earning less than 30000 BDT were 58 in number, making up 14.8%, and the category of participants with an income of 45001 BDT and above comprised 50 individuals, representing 12.8%. In terms of education, bachelor’s degree level, with 290 out of the 392 respondents holding this qualification, representing a substantial 74.4% of the sample. Those with a master’s degree comprised a significant portion as well, with 102 respondents, accounting for 26.0% of the participants.

Table 1.:- Demographic Profile of the Respondents (n=392)

Variable	Category	N	%
Gender	Male	372	94.9
	Female	20	5.1
Marital Status	Married	277	70.7
	Single	115	29.3
Age	30- 35 years	161	41.1
	36-40 years	99	25.3
	41-45 years	73	18.6
	46- 50 years	59	15.1
Monthly Income	Less than 30000 BDT	58	14.8
	Between 30001 to 35000 BDT	173	44.1
	Between 35001 and 40000 BDT	111	28.3
	BDT 45001 and above	50	12.8
Education	Bachelor’s Degree	290	74.4
	Master’s Degree	102	26.0

 Normality test

Normality is a key prerequisite in multivariate analysis. Tabachnick and Fidell, (2013) highlighted that normality can be evaluated by graphical of statistical manner, where skewness and kurtosis are the key components.  when the distribution is normal the value of skewness and kurtosis will be close to zero (Tabachnick & Fidell, 2013). At a 0.05 error level, the widely accepted critical value for detecting skewness and kurtosis is ±1.96 (Hair et al., 2019). Based on the above suggestion, this study applied the statistical method of Skewness and Kurtosis. The findings from Table 2 make it clear that every item falls inside the acceptable threshold. Therefore, the data are normally distributed in this research.

Table:2s Result of normality (Skewness and Kurtosis)

Constructs	Skewness	Std.Error	Kurtosis	Std. Error
Mentorship	-0.731	0.123	0.845	0.246
Job Autonomy	-0.316	0.123	-0.340	0.246
Technostress	-1.356	0.123	0.697	0.246
Emotional Intelligence	-0.288	0.123	-0.615	0.246
Turnover Intention	-0.463	0.123	-0.168	0.246

Measurement Model

The measurement model is assessed based on internal consistency reliability, convergent validity, and discriminant validity. For internal consistency the composite reliability (CR) needs to evaluate. The threshold value for composite reliability is between 0.70 and 0.95 (Hair et al., 2018; Ringle et al ,2018). Based on the Table 3, the CR for all the variables is above .80 which is excellent.

Convergent validity occurs when many items of a certain concept have a significant amount of variation in common with one another (Hair et al., 2019). According to Hair et al. (2019), convergent validity is also referred to as Average Variance Extracted (AVE). As a general guideline, 0.50 is the permitted AVE value (Hair et al., 2019). Every single AVE result for the current research was over this cutoff. However, factor loading of 0.50 or more is generally considered to be the standard for item factor loading (Hair et al., 2019). Every factor loading value above 0. 60, meeting the requirements established by Hair et al., (2019).

Table 3:-Summary Measurement Model Analysis

Variable	Items	Loading	CA	CR	AVE
Emotional Intelligence	EI1	0.745	0.876	0.907	0.619
	EI2	0.857
	EI3	0.815
	EI4	0.804
	EI5	0.787
	EI6	0.706
Job Autonomy	JA1	0.759	0.879	0.908	0.622
	JA2	0.787
	JA3	0.798
	JA4	0.793
	JA5	0.793
	JA6	0.803
Mentorship	MT2	0.869	0.923	0.938	0.686
	MT3	0.874
	MT4	0.883
	MT5	0.788
	MT6	0.739
	MT7	0.888
	MT8	0.738
Technostress	TE1	0.864	0.900	0.930	0.770
	TE2	0.915
	TE3	0.903
	TE4	0.825
Turnover Intention	TI1	0.803	0.887	0.914	0.640
	TI2	0.838
	TI3	0.781
	TI4	0.825
	TI5	0.830
	TI6	0.717

 Discriminant validity refers to the degree the constructs differentiate with one another (Ramayah et al., 2018). There are three criteria to evaluate discriminant validity which are cross-loading criterion, Fornell-Larcker criterion, and Heterotrait Monotrait (HTMT) criteria. According to Ramayah et al. (2018), in the cross-loading criterion the designated construct needs to be higher that the loading of other constructs. This pattern supports the discriminant validity of the constructs within the measurement model.

Table 4. Measurement Model: Discriminant Validity (Cross-loadings)

Items	Emotional Intelligence	Job Autonomy	Mentorship	Technostress	Turnover Intention
EI1	0.745	0.449	0.392	0.078	0.463
EI2	0.857	0.529	0.431	0.078	0.502
EI3	0.815	0.584	0.384	0.031	0.407
EI4	0.804	0.579	0.375	0.034	0.470
EI5	0.787	0.528	0.422	0.039	0.497
EI6	0.706	0.577	0.472	0.078	0.490
JA1	0.502	0.759	0.280	0.072	0.497
JA2	0.624	0.787	0.474	0.113	0.595
JA3	0.536	0.798	0.355	0.131	0.454
JA4	0.522	0.793	0.302	0.068	0.520
JA5	0.543	0.793	0.374	0.054	0.407
JA6	0.503	0.803	0.349	0.008	0.426
MT2	0.557	0.420	0.869	0.055	0.402
MT3	0.458	0.392	0.874	0.055	0.428
MT4	0.501	0.431	0.883	0.057	0.501
MT5	0.348	0.286	0.788	0.056	0.355
MT6	0.349	0.354	0.739	0.067	0.352
MT7	0.485	0.449	0.354	0.888	0.065	0.475
MT8	0.298	0.289	0.317	0.738	-0.026	0.313
TE1	0.058	0.106	0.046	0.043	0.864	0.208
TE2	0.058	0.076	0.060	0.044	0.915	0.189
TE3	0.054	0.067	0.047	0.058	0.903	0.186
TE4	0.088	0.095	0.083	0.062	0.825	0.163
TI1	0.476	0.524	0.223	0.369	0.152	0.803
TI2	0.517	0.550	0.290	0.383	0.188	0.838
TI3	0.432	0.416	0.269	0.423	0.217	0.781
TI4	0.493	0.475	0.250	0.355	0.155	0.825
TI5	0.529	0.549	0.359	0.425	0.176	0.830
TI6	0.438	0.457	0.474	0.408	0.138	0.717

Fornell-Larcker criterion evaluates the square root of AVE of latent variable which should be higher that the correlation of construct and other constructs (T. Ramayah et al., 2018). The Fornell-Larcker criterion results presented in Table indicate that the constructs within the model possess good discriminant validity. all the correlations were smaller than the square root of AVE which presents that the Fornell-Larcker criterion values established the discriminant validity.

Table 5: Measurement Model: Discriminant Validity (Fornell-Larcker)

Constructs	01	02	03	04	05
01. Emotional Intelligence	0.787
02. Job Autonomy	0.688	0.789
04. Mentorship	0.527	0.457	0.451	0.828
05. Technostress	0.073	0.099	0.067	0.059	0.878
06. Turnover Intention	0.603	0.622	0.392	0.493	0.214

According to Ramayah et al., (2018) value below 0.90 on the HTMT ratio is typically indicative of adequate discriminant validity, although a more conservative threshold of 0.85 is often recommended for more closely related construct. The HTMT values presented in the table generally indicate good discriminant validity among the construct, with most values falling well below the conservative threshold of 0.85

 Table 6 Measurement Model: Discriminant Validity (HTMT)

Constructs				01			02		03	04	05
01. Emotional Intelligence				01.00
02. Job Autonomy				0.777
03. Mentorship				0.573			0.495		0.475
04. Technostress				0.083			0.109		0.074	0.073
05. Turnover Intention				0.679			0.690		0.422	0.539	0.239

 Structural model (moderating effect)

In structural model, to identify the moderating effects the bootstrapping technique need to follow to evaluate the results of for indirect effects. Refer to the table 7, there are three indirect moderating effect  analysed by using the bootstrapping technique. Based on the result as per the table 7 and 8 the bootstrapping analysis presented that Hypothesis 1 (H1) investigates the moderating effect of Emotional Intelligence on the relationship between Mentorship and Turnover Intention. The standardized beta value is 0.102, indicating a positive effect, with a standard error of 0.052. The t-value of 1.964 suggests that this relationship is at the threshold of significance (p = 0.050), and the confidence interval (0.007 to 0.212) does not include zero, which supports the hypothesis.

On the other hand, Hypothesis 2 (H2) explores the moderating role of Emotional Intelligence in the link between Job Autonomy and Turnover Intention. The standardized beta is -0.051, which indicates a negative direction of the effect, but with a standard error of 0.047, the t-value is 1.088, which is not statistically significant (p = 0.277). The confidence interval ranges from -0.139 to 0.045, which crosses zero, suggesting that there is no support for the hypothesis.

Similarly, Hypothesis 3 (H3) assesses the moderating effect of Emotional Intelligence on the relationship between Technostress and Turnover Intention. The standardized beta is -0.010, with a standard error of 0.032, leading to a t-value of 0.309, which is far from significant (p = 0.757). The confidence interval (-0.082 to 0.044) includes zero, indicating no support for the hypothesis

Table 7: Structural Model: Bootstrapping Results for Moderating Relationships

Hypothesis	Relationship	Std Beta	Std Error	t-value	P Values	Decision
H1	EI x ME->TI	0.102	0.052	1.964	0.050*	Supported
H2	EI x JA->TI	-0.051	0.047	1.088	0.277	Not Supported
H3	EI x TS->TI	-0.010	0.032	0.309	0.757	Not Supported

** P<0.10, *P<0.05

Table 8: Structural Model: Confidence Level Results for Moderating Relationships

Hypothesis	Relationship	Confidence UCL	Level LCL
H1	EI x ME->TI	0.212	0.007
H2	EI x JA->TI	0.045	0.139
H3	EI x TS->TI	0.044	0.082

** UCL= Upper Confidence Level, LCL= Lower Confidence Level

DISCUSSION

The results provide a nuanced understanding of the role of Emotional Intelligence (EI) plays in the banking sector in Bangladesh.  Firstly, Hypothesis 1 explored that higher levels of EI may enhance the beneficial impacts of mentorship, potentially reducing turnover intentions Giao et al.,2020; Sharma & Tiwari, 2023; Burki et al.,2020; Li et al.,2023; Shuo et al.,2022). This can be interpreted as higher EI enabling employees to understand better and leverage the guidance and support offered by mentors, translating into lower turnover intentions in Bangladesh private bank employees.

In contrast, Hypothesis 2, suggests that regardless of an employee’s level of EI, the degree of autonomy they experience in their job does not differentially affect their intention to leave (Giao et al., 2020; Raza et al., 2018). This result indicates that emotional intelligence does not significantly alter the effect of job autonomy on employee turnover intention in the studied sample Bangladesh private banks. Similarly, Hypothesis 3, demonstrated that an employee’s EI level does not significantly change the way technostress influences their turnover intentions (Harris et al.,2021; Riaz et al.,2018; Bao et al., 2024). This could mean that regardless of an individual’s level of emotional intelligence, technostress does not differentially affect their likelihood to leave their job in Bangladesh private banks.

Overall, the study’s findings on the moderating effects of EI offer valuable insights into the complex interplay between key workplace factors and employee turnover intentions in the Bangladeshi private banking sector. While EI appears to play a significant role in enhancing the positive effects of mentorship, its influence is less pronounced or non-existent in job autonomy and technostress. These insights underscore the importance of considering individual differences in EI when developing strategies to reduce turnover and improve employee retention. Specifically, the findings suggest that interventions to enhance EI could be particularly effective in maximizing the benefits of mentorship programs. However, for aspects like job autonomy and technostress, other strategies may need to be explored, as EI does not significantly moderate their impact on employee’s turnover intentions in Bangladesh private banks.

CONCLUSION

This study focused on the Bangladeshi private banking sector and comprehensively analysed the moderating effect of emotional intelligence on mentorship, job autonomy, technostress and employee turnover intention. The conclusions drawn from this study are significant both for their theoretical contributions and practical implications.

Theoretically, the study contributes to the growing body of knowledge on the importance of emotional intelligence in the workplace, showing its role in moderating the effects of mentorship on turnover intention. However, it also reveals that emotional intelligence does not significantly alter the impact of job autonomy or technostress on turnover intention. This nuanced understanding of the role of emotional intelligence adds depth to existing literature and opens avenues for future research. Practically, the study provides actionable insights for banking sector management, and policy maker to reduce turnover intention before actual turnover occurs (Moore,2021), the better understanding of employee turnover intention can help the banking industry improve strategies to reduce the turnover rate and cost. The study’s insights into the role of emotional intelligence emphasise the need for developing training programs that enhance emotional skills among employees, which could be particularly effective in maximising the benefits of mentorship programs. The findings also suggest that banks should invest in structured mentorship programs where experienced employees guide and support newer or less experienced staff. This not only aids in skill development but also fosters a supportive work environment and reducing turnover intentions.

Finally, the limitation of this study revolves around its scope and generalizability. Explicitly focused on the private banking sector in Bangladesh, the findings offer rich insights into this particular context but may not extend seamlessly to other industries or geographic regions. Each sector, with its unique operational dynamics and cultural contexts, potentially exhibits different patterns in employee turnover intentions. Therefore, applying these results to other sectors, or even to public banks within Bangladesh, should be approached with caution.

ACKNOWLEDGEMENT

I would like to sincerely thank my research supervisors, Associate Professor Dr. Raemah Abdullah Hashim and Prof. Dr. Hishamuddin Md. Som for their understanding advice, passionate support, and insightful criticism of this study. Dr. Raemah’s guidance and help in keeping my progress on track have been greatly appreciated. My gratitude also goes to my   family Specially, I want to express my gratitude to my husband for his unwavering support and encouragement during my studies.  As well as my heartiest thank goes to my two kids for their patience.

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