The Impact of Financial Benefits and Emotional Intelligence on Employee Productivity in the Readymade Garments Industry of Bangladesh: The Mediating Role of Job Security

The Impact of Financial Benefits and Emotional Intelligence on Employee Productivity in the Readymade Garments Industry of Bangladesh: The Mediating Role of Job Security

Muhit Anwar Chowdhury

Canadian International University, Bangladesh

DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0120

Received: 20 February 2025; Accepted: 24 February 2025; Published: 27 March 2025

ABSTRACT

Readymade garments sector serves as an economic foundation of Bangladesh by generating substantial employment and export revenue. Organizations face ongoing difficulties in preserving their workforce production levels. This study investigates the impact of financial benefits and emotional intelligence on employee productivity in the RMG sector, with job security as a mediating factor. The study focuses on investigating the synergistic effects between these variables to determine performance-enhancing measures for organizations. The research uses questionnaires as part of a quantitative design to gather information from 400 Bangladeshi RMG factory workers. The research team used purposive sampling to gather responses from factory employees holding different positions in the sector. Employee productivity received analysis through SEM which examined how financial rewards as well as emotional intelligence and job security influence job outcomes. The research outcomes confirm that financial rewards and emotional intelligence boost employee productivity because job security acts as an essential link between these variables. Financial benefits complement emotional intelligence to help employees handle workplace stress and work better together as well as drive satisfaction and motivation at work. The results demonstrate job security perception increases the impact of these findings because it plays an essential role in supporting workforce stability while raising productivity. Organizations within the RMG sector should create superior financial compensation plans and train employees in emotional intelligence to maximize their performance levels. Leaders in industry and policymakers should make job security stronger by adopting honest employee communication together with environmentally sustainable employment practices. The implementation of these measures leads to workforce resilience which produces higher productivity and better success for organizations in the fast-paced global competition.

Keywords: Financial Benefits, Emotional Intelligence, Employee Productivity, Job Security, Readymade Garments (RMG) Industry, Workforce Motivation

INTRODUCTION

The ready-made garment (RMG) industry functions as a vital economic driver in Bangladesh by creating substantial growth in the GDP as well as maintaining millions of job opportunities. Employee productivity within this sector continues to be a concern because it does not match up to Chinese and Indian industry performance levels. This scholarly work evaluates how the blend of financial rewards with employee emotional intelligence influences productivity in the Bangladeshi ready-made garment sector through the employment security mechanism. The analysis of these industry dynamics becomes crucial because it helps improve sector output while preserving lasting business expansion.

Employee motivation levels and their productivity improve due to wages and workplace welfare facilities which serve as essential financial incentives. Productivity among garment employees in Bangladesh stands significantly below levels observed in other countries because workers experience deficient financial benefits and insufficient welfare provisions according to Alam et al. (2020). The study by Hoque and Shahinuzzaman (2021) shows health and safety concerns get set aside when organizations try to save costs (Hoque & Shahinuzzaman, 2021). This choice leads to dissatisfied employees who plan to leave their jobs. Emotional intelligence stands as a vital determinant for employee performance and experts now acknowledge this fact increasingly. The performance of employees who display strong emotional intelligence skills enables them to control their stress better and interact properly with others which results in greater productivity (Rubel et al., 2016).

Job security serves as a crucial mediator in the relationship between financial benefits, emotional intelligence, and employee productivity. The security experienced by employees leads them to allocate more effort toward work responsibilities which results in better organizational goal achievement. Job security functions as a protective mechanism against financial stress thus improving employee health according to Nazrul (2023). The RMG sector faces unstable employment conditions because of market fluctuations yet employers who provide job security experience dedicated staff members who perform better (Aktar, 2021).

The relationship between financial incentives and emotional intelligence together with job security exists as a challenging system of multiple components. Employee training programs according to Pervin serve to enhance job security perceptions among staff which establishes a committed work environment (Pervin, 2023). The RMG sector must develop a dual focus on financial compensation together with emotional development and professional growth so employees adopt a stable productive work environment. Empathetic leadership performances help establish team security through understanding which leads to productivity improvement (Choudhury & Rahman, 2017).

Organizations must understand that a supportive workplace culture stands at the core of creating effective organizational management. Organizations that promote open communication together with respect for employee contributions develop positive work environments which boost emotional intelligence and enhance job satisfaction levels (Hasan et al., 2020). The RMG industry faces communication barriers because of its traditional hierarchical structure that hinders innovation. Leadership plays a vital part in how workers view their job security status as well as their emotional intelligence development. Teams under effective leaders who show empathy toward their teams will develop a sense of security which leads to higher productivity. Various studies conducted by Hossain and Mahmood (2018) show that leadership approaches which focus on employee advancement alongside well-being produce reduced employee turnover and elevated work satisfaction.

The Bangladeshi RMG sector’s employee productivity improvement depends on the synergistic relationship between financial benefits and emotional intelligence and job security practices. Organizations need to develop an integrated approach which handles these elements for establishing a better connected workforce with increased productivity. The RMG industry will increase its competitive market position as well as sustainability through financial investments alongside emotional intelligence development and job security assurance measures.

LITERATURE REVIEW

Financial Benefits and Employee Productivity

Readymade garments (RMG) forms an essential economic foundation in Bangladesh by supplying jobs and generating important foreign revenues. Thousands of millions of employees in the second-biggest global apparel exporter work primarily as women and sustain the socio-economic fabric of the nation according to Rahaman & Arefin (2022) and Islam & Roman (2019) and Hossain & Alam (2021). The study examines financial benefits within the RMG industry by evaluating research which demonstrates the relationship between economic growth and worker welfare and staff output.

The majority of Bangladesh’s GDP and 78% of total export values originate from the RMG industry (Islam & Roman, 2019; Hossain & Alam, 2021). Through character and resilience the sector successfully recovered from worldwide economic setbacks such as COVID-19 which caused extensive damage to various industries globally (“The impact of Covid-19 on Readymade Garments’ (RMG; Workers in Bangladesh- A study on Mirpur and Uttara”, 2023) Shahen, 2022). Living conditions for garment workers along with their families improve due to financial benefits from the RMG sector because most garment workers act as the main wage earners in their household (Pervin, 2023; Mahmud et al., 2018). The growth of the industry prompted foreign direct investment so economic development quickens and more employment opportunities emerge according to Ahmed & Faroque (2017) and Hossain & Baars (2022).

Productivity levels within the RMG sector depend on numerous elements like workplace environment and worker health together with employee welfare programs. Labor productivity among garment workers shows a direct correlation with their physical health as well as their psychological state (Billah et al., 2023; Yuan et al., 2022). Several studies demonstrate how health problems such as musculoskeletal disorders coupled with occupational stress cause diminished productivity in the workforce according to Nabi et al. (2021) and Yuan et al. (2022).

H1: There is a significant positive relationship between financial benefits and employee productivity in the Readymade Garments Industry of Bangladesh.

Emotional Intelligence and Employee Productivity

The readymade garments (RMG) industry represents a fundamental sector in Bangladesh where millions are employed and the country generates large export earnings. Productivity among employees depends significantly on their emotional intelligence and directly affects organizational success.

The ability to recognize understand and control personal emotions as well as others’ emotions constitutes emotional intelligence according to Adiguzel & Uygun (2020) and Rahman et al., 2011. Employee performance and productivity levels rise directly with increased emotional intelligence among staff members according to Naqvi (2023) and Igboyi (2023). Employees working in the RMG industry benefit from emotional intelligence because it helps them maintain job satisfaction and reduces their stress levels and develops a supportive workplace atmosphere according to Jahan et al. (2021) and Chen & Dar Brodeur (2020) as well as Naqvi (2023) and Igboyi (2023). Current research shows that emotional intelligence creates positive personal relationships between employees while fostering teamwork at the workplace of the RMG industry (Mostafiz et al., 2019; Li et al., 2019).

H2: There is a significant positive relationship between emotional intelligence and employee productivity in the Readymade Garments Industry of Bangladesh.

Financial Benefits and Job Security

Ready-made garments (RMG) production in Bangladesh functions as a crucial economic driver which generates numerous jobs and supplies substantial financial benefits through foreign currency acquisition.

Bangladesh derives its biggest export revenues from the RMG sector through 84% of global shipments while this sector sustains 33% of GDP value (Majumder & Ferdaus, 2020; Hoque & Maalouf, 2021). Foreign investment and factory establishment throughout Bangladesh is driven primarily by the inexpensive workforce (Mostafiz & Sun, 2023; Islam et al., 2018) and the vast pool of cheap labor (Mostafiz & Sun, 2023).  The RMG sector offers financial gain yet job security emerges as a major problem. Multiple employees work in uncertain job situations and earn minimal pay and labor extensive periods at work without any written contractual provisions (Uddin et al., 2022; Hossain & Mahmood, 2018). Labor insecurity becomes worse because employees do not obtain key benefits including health insurance and retirement benefits due to informal subcontracting work environments (Huber & Schormair, 2019).

The RMG sector faces additional challenges regarding employment stability because workers frequently participate in protests and strikes consisting of wage and workplace condition appeals (Akhter et al., 2017; Choudhury & Rahman, 2017). Labor rights gain temporary improvements from such actions yet they generate unstable circumstances that discourage investment and threaten job security according to Mahmud & Afrin (2017) and Rahim & Islam (2020).

H3: There is a significant positive relationship between financial benefits and employee job security in the Readymade Garments Industry of Bangladesh.

Emotional Intelligence and Job Security

The ready-made garments (RMG) industry in Bangladesh stands essential for the national economy through the creation of substantial employment opportunities and major export revenue generation. The garment industry contains both successful economic aspects as well as specific workforce difficulties regarding employment certainty and staff welfare problems.

Emotional intelligence means understanding the emotions within oneself and also within others and it produces various work-related benefits such as enhanced work performance and job satisfaction according to Manalo (2023) and Kamarozaman et al. (2022). The RMG industrial environment with its elevated stress levels and worker job insecurity makes enhancing employee emotional intelligence a strong method to develop both resilience and satisfaction levels (Dâmbean & Gabor, 2021). People with strong emotional intelligence possess superior capabilities to overcome workplace difficulties because this skill leads to enhanced job outcomes and increased happiness at work (Peng 2023).

In the RMG sector emotional intelligence demonstrates clear significance for job satisfaction levels because employees generally experience low satisfaction due to poor working conditions together with low wages and insecure job status (Alinejad, 2023; Gorji et al., 2016).

H4: There is a significant positive relationship between emotional intelligence and employee job security in the Readymade Garments Industry of Bangladesh.

Job Security and Employee Productivity

A significant part of Bangladesh’s economy rests on the ready-made garments (RMG) industry because millions of people find jobs there and the sector produces substantial export earnings. Job security represents a complex dynamic which affects employee productivity throughout the ready-made garments industry of Bangladesh.

Employees in RMG sector face problematic work stability thanks to their unpredictable employment patterns which include low salary compensation and informal contracts and high staff turnover Haryani & Koestoer (2023), Alam, 2023). Workers without secure employment positions tend to experience both reduced morale and productivity since they lack motivation when they believe their job stability is uncertain (Rahman et al., 2019). Research by Uddin et al. (2021) together with Salam & Senasu (2019) confirms how worker job security creates a positive connection to employee productivity since secure workers exhibit higher engagement and commitment toward their work tasks. The connection between job security and satisfaction proves essential for RMG employees since their employment situation frequently suffers from labor practices and employment contracts (Azim et al., 2020).

H5: There is a significant positive relationship between employee job security and employee productivity in the Readymade Garments Industry of Bangladesh.

Mediating Role of Job Security in between Financial Benefits and Employee Productivity

The Ready-Made Garments (RMG) industry in Bangladesh brings great economic value to the nation through mass employment and sizeable export earnings. This sector shows complex patterns of interrelation between financial benefits and job security when linked to employee productivity. Therefore it requires extensive study.

Financial incentives within the ready-made garments sector consist mainly of compensation and bonus plans that determine worker financial stability. Employee job satisfaction combined with motivational levels grows along with increased financial rewards according to research findings (Hossain & Mahmood, 2018).

Job security serves as a critical mediator in this relationship. The research demonstrates that working people who view their positions as secure show enhanced job involvement and workplace dedication resulting in better productivity measurements (Pervin& Begum, 2022; Pervin, 2023). Workers who experience job insecurity tend to experience worry and reduced drive because of which their potential motivation from financial incentives becomes hindered (Hasan et al., 2020). A research investigation into garment workers demonstrated that job security created conditions where employees showed productive behavior despite harsh workplace circumstances (Abolarinwa, 2023).

H6: Employee job security acts as a mediator in the relationship between financial benefits and employee productivity in the Readymade Garments Industry of Bangladesh.

Mediating Role of Job Security in between Emotional Intelligence and Employee Productivity

Ready-made garments (RMG) production remains a vital economic sector for Bangladesh because it generates millions of jobs and produces substantial export value. Employee productivity in this sector develops through several key factors which combine emotional intelligence with job security.

The term emotional intelligence defines the capability to perceive and control personal emotions together with understanding emotional states within others. Research unveiled how high emotional intelligence-leading performance in the same manner as boosting workplace productivity according to Peng (2023). Pretty high stress and demanding working conditions characterize the RMG industry forcing workers to benefit from emotional intelligence which improves both resilience and operational performance (Kamarozaman et al., 2022).

Job security serves as a critical mediator in the relationship between emotional intelligence and employee productivity. Job security perceptions by workers tend to lead them toward complete role engagement which produces higher productivity outcomes (Gorji et al., 2016). Job security represents a direct influence factor on emotional intelligence since job insecurity creates anxiety which reduces motivation (Doğru, 2022).

H7: Employee job security acts as a mediator in the relationship between emotional intelligence and employee productivity in the Readymade Garments Industry of Bangladesh.

Research Gap

The research field concerning how financial benefits and emotional intelligence affect employee productivity in Bangladesh’s readymade garments sector through job security needs further investigation. Research about financial rewards increasing worker performance exists in Robinah (2023) and Ogunmakin (2023) but lacks investigation into how emotional intelligence affects financial benefits for productivity outcomes. Employee motivation and workplace engagement in Bangladesh are strongly influenced by job security according to Khan (2019) yet scholarly research about this mediation effect remains insufficient. The literature acknowledges financial wellness programs boost employee productivity (Despard et al., 2020; Click & Dobbins, 2020) but researchers have not yet defined the processes by which emotional intelligence works in combination with job security to enhance this connection. Research is needed to combine these variables in one study because the current gap indicates employee productivity needs better analysis in the readymade garments industry.

Problem Statement

Employee productivity faces severe impediments in the Bangladesh readymade garments (RMG) sector which stands as a vital economic foundation for the country. Analysis of productivity factors confirms that job security together with financial benefits and emotional intelligence represent essential components needing intensive review. Employee performance improves through monetary incentives which produce better worker motivation when employees believe their workplace security remains stable (Ariawan et al., 2023). Workplace

productivity benefits from emotional intelligence since this competency helps people handle stress better and improve their communication skills and interpersonal abilities (Amrollahifar, 2023). The RMG sector of Bangladesh requires a comprehensive solution that unifies monetary incentives with emotional competence and secure employment to improve overall staff productivity.

Research Questions

Here are some research questions based on your topic:

1. How do financial benefits and emotional intelligence impact employee productivity in the readymade garments sector in Bangladesh?
2. What is the relationship between financial benefits and employee productivity in the readymade garments sector in Bangladesh?
3. How does emotional intelligence influence employee productivity in the readymade garments sector in Bangladesh?
4. What role does job security play in mediating the relationship between financial benefits and employee productivity?
5. What role does job security play in mediating the relationship between emotional intelligence and employee productivity?

Objectives of the Study

The objectives for the study are as follows:

Main Objective

To examine the impact of financial benefits and emotional intelligence on employee productivity in the readymade garments sector of Bangladesh, with a focus on the mediating role of job security.

Specific Objectives: The specific objectives of this study are as follows:

1. To analyze the relationship between financial benefits and employee productivity in the readymade garments sector of Bangladesh.
2. To explore the influence of emotional intelligence on employee productivity in the readymade garments sector of Bangladesh.
3. To investigate the role of job security as a mediator in the relationship between financial benefits and employee productivity.
4. To examine the role of job security as a mediator in the relationship between emotional intelligence and employee productivity.

Conceptual Framework

The conceptual framework for this study illustrates the relationships among key variables: financial benefits (FB), emotional intelligence (EI), employee productivity (EP), and employee job security (JS) within the context of the RMG Industry in Bangladesh.

Figure 1: Conceptual Framework

RESEARCH METHODOLOGY

Research Approach

Research approach selection stands as a key initial decision during research projects because it determines all data collection strategies and analytic methods and methods for solving research objectives (Creswell & Creswell, 2017). Researchers select fundamental research approaches early on to determine the structure and operationalization of their studies. Our research adopts quantitative research to investigate the study. Research using the quantitative method utilizes numerical data through structured gathering and processing for both hypothesis testing and question answering (Creswell & Creswell, 2017).

Research Population

During research methodology the research population indicates every member of the group or things which demand study or conclusion development. The target group consists of all individuals whose data serves as the basis for drawing samples used in data collection and subsequent analysis. The research population consists of all considered individual entities reaching study eligibility standards according to Cooper and Schindler (2014).

Sampling Technique

Research sampling methods determine which part of the population under study will receive evaluation through data accumulation and analysis. A well-chosen sampling technique stands as a necessary requirement to guarantee accurate representation along with reliable outcomes for any study (Creswell & Creswell, 2017). According to Patton (2002) we executed purposive sampling to pick particular people and cases that directly connected to our research aims. Purposive sampling became our preference since we required participants who comprehensively grasped the subject material according to Palinkas et al. (2015).

Sample Size

Research design requires a pivotal element for determining sample size because the choice impacts both statistical power levels and the development of significant findings (Creswell & Creswell, 2017). The research sample comprised 400 respondents according to recommendations presented in Dillman et al. (2014) regarding statistical precision and data collection practicality. The researchers settled on 400 respondents because this number would offer both a 95% confidence level and a 5% margin of error according to the established criteria of Krejcie& Morgan (1970).

Data Collection

The data collection process is a critical phase of this research, as it involves gathering information from the selected sample to address the research objectives (Saunders et al., 2018). The survey questionnaire consisted of close-ended questions, rated on a Likert scale, covering demographic information and key variables (Dillman et al., 2014).

1. Primary Data: Primary data were collected through surveys and interviews conducted with participants selected from the target population (Creswell & Creswell, 2017). Primary data collection allowed for the collection of specific information tailored to the research objectives, ensuring relevance and accuracy (Bryman, 2016).
2. Secondary Data: Secondary data were obtained from publicly available sources, including government reports and academic studies, to provide context and support primary findings (Dillman et al., 2014). Utilizing secondary data reduced data collection costs and allowed for historical and comparative analyses (Johnson & Christensen, 2019).

Measure

Researchers obtained the measurement scales directly from the analysis of their study. All evaluation scales utilized a response range from 1 (strongly disagree) to 5 (strongly agree). The five-items financial benefits (FB) measurement scale was developed according to Milkovich, G. T., & Newman, J. M. (2008). The Emotional Intelligence (EI) measurement adopted sixteen items from Wong and Law (2002) to assess this variable throughout the research. The Employee Productivity (EP) scale used eight items borrowed from Alam et al. (2020) as part of its development as a dependent variable. Kraimer, et al. (2005) system created a seven-item measurement scale for Job Security (JS) that this research adopted for assessing the variable. The researcher validated the scale specifically for use in the RMG business of Bangladesh.

DATA ANALYSIS AND RESULT

Assessment of Measurement Model

In this study, we rigorously assessed the measurement model to ensure the reliability and validity of the measurement scales used for key variables. The assessment of the measurement model is a crucial step in structural equation modeling (SEM) or other latent variable analyses to evaluate the validity and reliability of the measurement instruments used in the study (Hair et al., 2019; Gefen et al., 2000).

Table 01: Analysis of Indicators’ and Constructs’ Reliability and Convergent Validity

Source: Output from primary data using SmartPLS

Indicators Reliability

We tested the indicators for reliability across each measurement scale because Cronbach (1951) described that construct indicators need consistent and stable measurement across multiple items. Factor loadings exceeding 0.70 serve as solid proof for significant item-factor connections according to Stevens (1996).

The indicators measured in this study (Table 01) provide evidence that demonstrates how well the constructs reflect their core elements while showing measurement steadiness (Table 01). The two Financial Benefits indicators produce reliable results; FB2 exhibits outstanding reliability with 0.938 factor loading while FB3 demonstrates acceptable reliability with 0.741 factor loading. The EI indicators present strong reliability outcomes because EI3 (0.839) stands out as the most contributing dimension yet EI12 (0.786) along with EI10 (0.776) also proved effective at detecting emotional and interpersonal abilities. The Employee Productivity construct receives exceptional reliability from two of its indicators: EP3 (0.881) and EP1 (0.807). The reliable indicator for Job Security (JS) is JS5 (0.875) supported by JS4 (0.794) and JS8 (0.777) demonstrating strong consistency. Research findings validate the usefulness of selected indicators to correctly measure their relevant variables in the study environment.

Constructs’ Reliability

In this study, we evaluated the reliability of the latent constructs represented by the measurement scales to ensure the internal consistency and stability of measurement across multiple indicators (Hair et al., 2019).

Table 02: Analysis of Constructs’ Reliability

Source: Output from primary data using SmartPLS

The study measured construct reliability using Cronbach’s Alpha to evaluate the consistent nature of all variables selected in the research. Different reliability levels exist among the study variables according to the results. The reliability measurements for Emotional Intelligence (0.723) and Job Security (0.755) exceed the established threshold of 0.7 since they both yielded acceptable Cronbach’s Alpha scores. Data reliability for Financial Benefit (FB) and Employee Productivity (EP) was lower than optimal threshold yet acceptable at 0.632 and 0.603. The reliability measurements for Emotional Intelligence and Job Security established consistency but Financial Benefit and Employee Productivity require additional improvement through modifications to their scale items. The analyzed constructs create an adequate research framework to examine the readymade garments sector of Bangladesh through relationships between financial benefits, emotional intelligence and employee productivity while job security acts as a mediating influence.

Convergent Validity

Different measurement scales with the intention of assessing the same construct achieve similar and consistent results through the assessment of convergent validity (Hair et al., 2019). This research evaluated convergent validity because it ensured that measurement scale indicators adequately merged together to show accurate measurement of constructs across their scales. This research took an example study to evaluate the convergent validity of a self-esteem measurement scale. Within this case the evaluation items regarding self-esteem must demonstrate substantial associations between each other. According to Nunnally (1978), researchers consider 0.70 as a significant threshold for acceptable correlation coefficients.

Table 03: Analysis of Convergent Validity

Source: Output from primary data using SmartPLS

The research used convergent validity analysis to verify the appropriate correlations between study constructs and their designated indicators. All research constructs satisfied the evaluation requirements for convergent validity in the analysis results. The indicators of Emotional Intelligence (EI) displayed adequate internal consistency along with sufficient variable loading because its Composite Reliability (CR) was 0.843 and Average Variance Extracted (AVE) was 0.641. The data indicates Employee Productivity exhibited strong convergent validity through its CR value of 0.833 and AVE value of 0.714 which demonstrates that the construct explains more than 70% of its indicator variance. The study found strong evidence of convergent validity between Financial Benefit (FB) and Job Security (JS) as their Composite Reliability scores reached 0.831 and 0.857 and Average Variance Extracted values amounted to 0.714 and 0.666. The valid measurement of constructs through these findings enables meaningful research into relationships between financial benefits, emotional intelligence, employee productivity, and job security in the readymade garments sector of Bangladesh.

Discriminant Validity

The assessment of discriminant validity measures how distinct measurement scales used to measure different constructs should not demonstrate strong correlations with each other (Hair et al., 2019). The study confirmed discriminant validity by testing if the measurement scales correctly separated the constructs from one another. The analysis checked if the construct correlations in this study remained below the square root of their AVE values according to the Fornell&Larcker (1981) approach.

Table 04: Discrimination Validity (HTMT)

Source: Output from primary data using SmartPLS

Discriminant validity analysis verified that study constructs maintain proper separation as elements which show minimal correlations with other research variables. Discriminant validity testing used Fornell-Larcker criteria which requires all constructs to display a higher square root value of Average Variance Extracted than their correlation values towards other constructs. Self-correlation of Emotional Intelligence (1.039) remains higher than correlation values between it and other constructs in this study. Employee Productivity (EP) maintains a higher level of self-correlation at 0.597 when compared to its mutual associations with Financial Benefit (FB) at 0.409 as well as Job Security (JS) at 0.48. Job Security (JS) together with Financial Benefit (FB) present greater internal linkages (0.597 and 0.35, respectively) than their relationships with other constructs. The study results establish discriminant validity because each construct maintains its uniqueness thus validating its appropriateness for examining financial benefit-sharing and employee productivity with job security patterns in the readymade garments sector of Bangladesh.

Table 05: Discrimination Validity (Fornell & Larcker Criterion)

Source: Output from primary data using SmartPLS

The Fornell and Larcker criterion helped verify that each model construct has distinct measures from all other model constructs. The square root values of Average Variance Extracted (AVE) for each construct show on the diagonal and surpass the construct relations reported between cells. The square root of AVE value for Emotional Intelligence reaches 0.801 while exceeding the correlations it shares with Employee Productivity (0.701) as well as Financial Benefit (0.272) and Job Security (0.286). The square root of AVE for EP reaches 0.845 surpassing both of its correlations to FB (0.424) and JS (0.344). The square root of AVE measures confirm distinctiveness for both FB and JS because their values exceed 0.845 and 0.816 respectively when compared to their respective construct links. The results demonstrate appropriate construct separateness which verifies satisfactory discriminant validity between financial benefits and emotional intelligence and employee productivity and job security for the readymade garments sector in Bangladesh.

Figure 2: Measurement Model

Model Fit

Structural equation modeling (SEM) requires essential model fit assessments for determining the alignment between theoretical models and observed data according to Hair et al. (2019). The research utilized different fit indices to evaluate the goodness of fit for our SEM models. SRMR evaluates the overall distance between empirical correlation coefficients and those calculated from the SEM model through an average absolute difference measure. A model fit that accepts data when the value of SRMR reaches less than 0.08 (Hu & Bentler, 1999).

Table 06: Model Fit

Source: Output from primary data using SmartPLS

The structural model testing utilized key fit indices for evaluating its acceptance. A moderate fit exists according to the standardized root mean square residual values of 0.112 both for saturated and estimated models. Discrepancy measures d_ULS (0.691) and d_G (0.286) fall within standard parameters which suggests that the model has a sufficient capacity to explain data variability. Both structured models yield a Chi-square value of 707.657 showing viability of the model fit but a lower figure would provide superior matching between data. Model performance can be improved based on the Normed Fit Index (NFI) value which stands at 0.504 below the accepted threshold of 0.90. Although the model shows an adequate match to the data points there are opportunities to improve its explanatory power by enhancing the definition of model constructs and operational relationships between financial benefits and employee productivity mediated through job security in Bangladesh’s readymade garments segment.

Assessment of Structural Model

The evaluation of the structural model is essential for validating theoretical hypotheses and understanding latent constructs’ relationships (Kline, 2016). The significance of path coefficients, as determined by p-values, aligns with the expectations of the hypothesized relationships (Hair et al., 2019). The presented structural model reveals the following insights: Path Coefficient Estimation, Hypothesis Testing, Model Fit Evaluation, Mediation and Moderation Effects, Effect Size and Direction and Robustness Analysis.

Coefficient of Determination (R²)

Researchers use the statistical tool R² as a vital measurement to evaluate how well the data matches the regression model predictions (Hair et al., 2019). The study used R² to determine how well independent variables explained dependent variable changes when examining regression data. R² values span from 0 up to 1 because they demonstrate how much variation exists in dependent variables that independent variables can explain. The size of dependent variable variance that independent variables explain becomes larger when R² increases. The value of R² indicates how well the variables explain the dependent variable changes while a smaller R² value means a model only explains a small portion of the element change and residual variance persists.

Table 07:Result of R² (Prediction Power)

Source: Output from primary data using SmartPLS

The model demonstrates high predictive capability because the R² values determine how well it explains changes in dependent variables. Around 56.2% of the variation observed in Employee Productivity (EP) can be explained by Financial Benefits (FB) and Emotional Intelligence (EI) together with Job Security (JS) as a mediating variable. Deposit Indexes shows strong capability to predict Employee Productivity results. Job Security (JS) remains 10.9% of explained variance when measured through the R² statistic. The model demonstrates a high capacity for predicting EP yet shows limited accuracy when forecasting JS. More unidentified factors which are not part of the model seem to play an important role in impacting job security levels.

Effect Size ( )

F² is used to indicate the proportion of variance in the dependent variable that can be explained by the independent variable(s) in the model. It is particularly useful when evaluating the effect of multiple independent variables on a dependent variable. A larger F² value suggests a stronger effect, while a smaller value implies a weaker effect. Effect size measures play a crucial role in understanding the practical significance of observed effects. One commonly used effect size measure is F² which quantifies the proportion of variance in the dependent variable accounted for by the independent variable (Cohen, 1988).

Table 08:Result of

Source: Output from primary data using SmartPLS

analysis provided practitioners with information regarding how much the independent variables influence the dependent variables throughout the structural model. The extent to which Emotional Intelligence influences Employee Productivity reaches a large value of 0.728 thus demonstrating its important role in determining EP variation. The influence of Emotional Intelligence on Job Security maintains a moderate strength as represented by an F^2 value of 0.06. The influence of Financial Benefit (FB) on Employee Productivity (EP) has a reasonably small impact with an effect size ( =0.11). The research indicates FB operates with a very small strength of impact ( =0.031) on JS assessments. Job Security (JS) exhibits a minimal effect size of =0.027 on Employee Productivity (EP) since its impact on the financial benefits and emotional intelligence-employee productivity relationship remains brittle.

Multicolinearity (VIF)  

Multicollinearity emerges as a fundamental problem in regression analysis because it occurs when independent variables show high levels of correlation and this weakness coefficient estimates and decreases the understandability of regression results (Kutner et al., 2005). Researchers consider multicollinearity issues present when VIF values surpass 5 or 10 according to Kutner et al. (2004) and Hair et al. (1998). High variable correlations in predictor variables create multicollinearity problems that generate large standard errors and confusing statistical effects that mask accurate predictor effects (Belsley et al., 1980).

Table09: Result of Multicollinearity (VIF)

Source: Output from primary data using SmartPLS

Assessment of Path Coefficient

Understanding dimensions between latent constructs and observed variables depends on evaluating path coefficients when working with a structural equation model (SEM). In order to perform both structural equation modeling (SEM) and regression analysis successfully path coefficients need proper assessment. Within model path coefficients demonstrate both the magnitudes and directions of the variable relationships. The study evaluated path coefficients in a strict manner to understand the strength and statistical importance of these relationships as per Hair et al. (2019). The statistical confirm or rejection of a path coefficient depends on its corresponding p-value. A significant result can be determined when the p-value reaches a value less than 0.05 indicating that the observed relationship would be unlikely to happen randomly. Researcher decisions whether to validate path coefficients as meaningful indicators of associations rely on this criterion (Kline, 2016; Hair et al., 2010).

Table 10: Outcome of Structure Model (Test of Hypothesis)

Source: Output from primary data using SmartPLS

The analysis indicates positive and significant relations between all essential variables in the model structure. Financial benefits effect employee productivity at a β of 0.233 which creates a direct positive impact and yields highly statistically significant results (p = 0.000). Employee productivity receives substantial influence from emotional intelligence since its β value reached 0.603 which demonstrates its essential role in productivity improvement. Job security (JS) receives significant positive influence from FB (β = 0.175) as well as EI (β = 0.242). Both JS relationships are statistically significant (p = 0.000). Research shows that job security creates positive impacts on productivity since employees with job security record higher levels of productivity (β = 0.117, p = 0.002). This study confirms the proposed hypotheses by demonstrating the essential nature of financial benefits and emotional intelligence and job security for boosting readymade garments employee productivity in Bangladesh. These results validate the proposed model and demonstrate the essential relationship between employee productivity factors including financial benefits and emotional intelligence and job security within the readymade garments sector of Bangladesh.

Figure 3: Assessment of Structural Model

Mediating Effect

The research evaluated the mediating effect because it aimed to identify which variable served as an explanation between independent variables and dependent variables. An essential concept in research examines the mediating effect of variables because it reveals how independent variables (IV) affect dependent variables (DV) using one or more intermediate variables known as mediators (Baron & Kenny, 1986). Our evaluation of mediating effects required analysis through established bootstrapping techniques outlined in Preacher and Hayes (2008).

Table 11: Outcome of Mediation Effect

Source: Output from primary data using SmartPLS

Job security serves as a vital mediator that explains how both financial benefits and employee productivity interact and how emotional intelligence impacts employee productivity. The FB → JS → EP mediating effect features a path coefficient value of 0.021 together with a standard deviation of 0.008 at a statistically significant p-value of 0.019. The EI → JS → EP pathway produces a higher mediating impact which shows a β of 0.029 along with a standard deviation of 0.012 and a significant p-value of 0.017. Job security stands as an essential intermediary mechanism which strengthens financial resource and emotional intelligence gains to improve employee productivity throughout Bangladesh’s readymade garments sector. The research reveals that establishing job security systems creates maximum enhancement of financial resources alongside emotional resources which improves work outcomes.

SUMMARY FINDINGS OF THE STUDY

The research study provides these main findings:

The research establishes several vital associations that link financial benefits (FB) with emotional intelligence (EI) and job security (JS) towards employee productivity (EP) within the readymade garments sector of Bangladesh. The research results validated all seven hypotheses to confirm the importance of the proposed model.

1. The readymade garment sector of Bangladesh experiences increased employee productivity (EP) because of proper financial benefits (FB) as confirmed by H1 (H1: FB.>EP).
2. Emotional intelligence demonstrates greater effectiveness than other factors at improving EP (H2) in the Readymade Garments Industry of Bangladesh because it enables stronger productivity development.
3. Financial benefits of FB demonstrate their positive effect on JS in the Readymade Garments Industry of Bangladesh (H3).
4. The Readymade Garments Industry of Bangladesh benefits from emotional intelligence because EI creates better job security according to hypothesis H4.
5. Organizational security programs boost labor productivity in the Readymade Garments Industry of Bangladesh through their positive influence on employee productivity (H5).
6. Job security acts as a mediator which strengthens the effect of financial benefits on employee productivity for the Readymade Garments Industry of Bangladesh (H6).
7. The Readymade Garments Industry of Bangladesh demonstrates that job security (JS) boosts the productivity effects of emotional intelligence (EI) through its association with productivity (EP) (H7).

Research Implications

The implications are as follows:

Theoretical Implications

• The study expands organizational behavior knowledge by analyzing interacted effects between financial incentives and emotional intelligence on employee work output within the Bangladeshi readymade garments sector.
• This research links emotional intelligence to work security and output so it extends current soft skills theories to show their powerful ability in enabling psychological protection and performance advancement.
• Job security acts as a mediator according to research findings thus proving the theory using real data about employee responses and work performance results.

Practical Implications

• Financial benefits should be implemented as productivity motivators that combine with job security programs to build enduring employee loyalty within organizations.
• Organization leaders should make emotional intelligence training part of leadership development programs as well as workforce training because this approach improves workplace relationships to boost productivity.
• Employers should create stable working environment policies including precise performance assessments along with extended contracts and crystal-clear career path strategies which improve both staff morale and operational output.
• These research findings demonstrate the necessity of specialized worker satisfaction enhancement strategies because job and financial stability badly need intervention in the readymade garments sector.

Future Research Direction

Future inquiry should address three essential topics starting with how financial incentives cooperate with non-financial rewards and finishing with how emotional intelligence affects job security perspectives and their wider effects on employee production.

Firstly, The investigation of financial against non-financial rewards must become the primary focus in understanding employee productivity outcomes. Previous studies have indicated that while financial incentives are crucial, non-financial factors such as recognition, job stability, and corporate culture also play significant roles in shaping employee motivation and effectiveness. The research by Kang et al. indicates employee benefits produce positive productivity outcomes suggesting organizations should adopt complete reward systems to achieve enhanced performance results (Kang et al., 2016).

Secondly, Research should be conducted to understand how ready-made garments sector leaders can utilize emotional intelligence to build favorable workplace cultures which increase both employee workplace security and factory production levels. Researchers should investigate the particular leadership approaches which drive maximum employee engagement together with performance improvement.

Thirdly, The analysis needs to include the overall social and economic framework which shapes the ready-made garments industry operation. The commercial and psychological burdens staff members encounter in this field frequently become worse when economic unpredictability and changing labor conditions occur. Future research implementing a macroeconomic investigation should explore external factors which affect productivity levels alongside their impact on financial and emotional support methods.

Lastly, Employee perceptions about both financial compensation and emotional intelligence receive significant influence from organizational culture. Additional research must examine the impact various cultural aspects in work environments play on the performance results of financial investment programs combined with emotional support to enhance employee workplace productivity.

Limitations of the Study

Despite providing valuable insights, this study has certain limitations that should be acknowledged to guide future research efforts:

• The study employs a cross-sectional design, which limits the ability to establish causal relationships between financial benefits, emotional intelligence, job security, and employee productivity. A longitudinal study would offer more robust evidence of causality.
• The research is confined to the readymade garments (RMG) sector in Bangladesh. This sector’s unique characteristics may limit the generalizability of the findings to other industries or geographic contexts.
• The study relies on self-reported data from employees, which may introduce biases such as social desirability bias or response bias, potentially affecting the accuracy of the results.
• While the study focuses on financial benefits, emotional intelligence, and job security, other significant factors influencing employee productivity, such as leadership style, workplace environment, or work-life balance, were not considered.
• The study primarily uses subjective assessments of employee productivity. Incorporating objective performance metrics, such as production output or quality indicators, could provide a more comprehensive analysis.
• Organizational-level variables, such as management practices, organizational culture, or technological adoption, were not addressed, which might also play a significant role in shaping employee productivity.
• The study treats emotional intelligence as a static trait rather than a skill that can be developed. Future research could explore the impact of emotional intelligence training on employee productivity.

CONCLUSION

The exploration of the impact of financial benefits and emotional intelligence on employee productivity in the ready-made garments sector of Bangladesh, with a particular focus on the mediating role of job security, reveals significant insights that can inform both academic research and practical applications within the industry. The findings underscore the importance of integrating financial incentives and emotional intelligence strategies to enhance employee productivity while recognizing the critical mediating influence of job security.

Financial benefits have been consistently shown to correlate positively with employee productivity. Studies indicate that financial incentives, such as bonuses and salary increases, can significantly motivate employees and enhance their performance. In the context of the ready-made garments industry, where many employees face economic challenges, the provision of financial benefits can serve as a crucial motivator. This is particularly relevant in Bangladesh, where the garment sector is a significant contributor to the economy and employs a large workforce. The findings suggest that organizations should prioritize the development of comprehensive financial benefits packages that not only meet the basic needs of employees but also incentivize higher performance levels.

Emotional intelligence also plays a pivotal role in enhancing employee productivity. Employees with high emotional intelligence are better equipped to manage stress, communicate effectively, and foster positive relationships within the workplace. This emotional competency can lead to improved job satisfaction and engagement, which are critical factors in driving productivity. In the ready-made garments sector, where teamwork and collaboration are essential, fostering emotional intelligence among employees can create a more harmonious work environment, ultimately contributing to higher productivity levels.

The mediating role of job security is particularly noteworthy. Job security has been identified as a significant factor influencing employee motivation and productivity. Employees who perceive their jobs as secure are more likely to invest effort into their work, leading to enhanced productivity outcomes. In the context of the ready-made garments industry, where job security can be precarious due to market fluctuations and economic pressures, organizations must implement strategies to enhance employees’ perceptions of job security. This could involve transparent communication regarding company performance, providing opportunities for skill development, and fostering a culture of support and stability. Moreover, the interplay between financial benefits, emotional intelligence, and job security suggests a holistic approach to employee management. Organizations should not only focus on financial incentives but also invest in emotional intelligence training and initiatives that promote job security. This integrated approach can lead to a more motivated and productive workforce, which is essential for the competitiveness of the ready-made garments sector in Bangladesh.

Future research should delve deeper into the specific mechanisms through which emotional intelligence influences job security perceptions and how these, in turn, affect productivity. Longitudinal studies could provide valuable insights into how changes in financial benefits and emotional intelligence training impact employee productivity over time. Additionally, exploring the role of organizational culture in shaping these dynamics could yield important findings that inform best practices for employee management in the ready-made garments industry.

In conclusion, the impact of financial benefits and emotional intelligence on employee productivity in the ready-made garments sector of Bangladesh is profound and multifaceted. By recognizing the mediating role of job security, organizations can develop targeted strategies that enhance employee motivation and performance. This not only benefits the employees but also contributes to the overall success and sustainability of the industry. As the sector continues to evolve, ongoing research and practical applications in these areas will be essential for fostering a productive and engaged workforce.

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