The effect of overtime working on burn out, engagement and intention to leave of manufacturing workers in Vietnam

In addition, the study confirmed both the positive and negative roles of over time hour: Working overtime hour has positive relationship with burn out which lead to negati[r]

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VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY

-

NGUYEN THI HAI YEN

THE EFFECT OF OVERTIME WORKING ON BURN OUT, ENGAGEMENT AND

INTENTION TO LEAVE OF

MANUFACTURING WORKERS IN VIETNAM

MASTER'S THESIS

BUSINESS ADMINISTRATION

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VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY

-

NGUYEN THI HAI YEN

THE EFFECT OF OVERTIME WORKING ON BURN OUT, ENGAGEMENT AND

INTENTION TO LEAVE OF

MANUFACTURING WORKERS IN VIETNAM

MAJOR: MASTER OF BUSINESS ADMINISTRATION CODE: 8340101.01

RESEARCH SUPERVISORS: Assoc.Prof Dr KODO YOKOZAWA

Dr DO XUAN TRUONG

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ACKNOWLEDGMENT

I would like to express my deep gratitude to Dr Do Xuan Truong and Assoc Pro Dr Kodo Yokozawa, my research supervisors, for their patient guidance, enthusiastic encouragement and useful advice of this research work

I would also like to thank VJU, YNU and JICA for giving me the opportunity to study, practice and research at YNU, where there are excellent lecturers and adequate facilities to study My grateful thanks are also extent to other lecturers in the MBA faculty for listening and giving me critiques to complete the research further

I am particularly grateful for the assistance given by Ms Huong - MBA program assistant, IPO office staff - YNU and volunteer support team in Japan to prepare and support us during all presentation and follow up research as well as study activities I would also like to extend my thanks to Mr Hao for sharing us his experience and also basic knowledge for doing a thesis

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ABSTRACT

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Table of Contents

CHAPTER 1: INTRODUCTION

1.1 Rationale:

1.2 Research objective

1.3 Research scope

1.4 Research structure

CHAPTER 2: LITERATURE REVIEW

2.1 Working overtime hour

2.1.1 Definition

2.1.2 Related Research

2.2 Intention to leave

2.2.1 Definition

2.2.2 Related research 11

2.3 Job Demand Resource model 15

2.4 Research question: 18

2.5 Hypothesis development and conceptual model 18

2.5.1 Hypothesis development 18

2.5.2 Conceptual Model 20

CHAPTER 3: RESEARCH METHODOLOGY 22

3.1 Research process 22

3.2 Sample design 22

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CHAPTER 4: DATA ANALYSIS 27

4.1 Data description 27

4.2 Reliability analysis 28

4.2 Confirmatory Factor Analysis (CFA) 30

4.2.1 CFA analysis of Engagement 30

4.2.2 CFA analysis of Burn out 34

4.2.3 CFA analysis of ITL: 36

4.3 Creating a representative variable: 38

4.4 Pearson correlation analysis 38

4.5 Regression Analysis 40

4.5.1 Regression Analysis of the impact of Working overtime hour on Burn out 40

4.5.1 Regression Analysis of the impact of Working overtime hour on Engagement 41

4.5.3 Regression Analysis of the impact of Engagement on Burnout 43

4.5.4 Regression Analysis of the impact of Burnout on Engagement 44

4.5.5 Regression Analysis of the impact of Burn out on ITL 45

4.5.6 Regression Analysis of the impact of Engagement on ITL 46

4.6 Hypothesis tested results 48

CHAPTER 5: CONCLUSION AND DISCUSSION 49

5.1 Conclusion 49

5.2 Discussion 50

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5.4 Limitation and future research direction 54

REFERENCE 55

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LIST OF TABLES

Table 2.1: Definition of ITL 10

Table 3.1: The content of measuring items 24

Table 3.2: Likert scale of ITL 25

Table 3.3: Likert scale of Burn out and Engagement 26

Table 4.1: Data description 27

Table 4.2 Reliability analysis 29

Table 4.3: KMO and Bartlett's Test of Virgo 31

Table 4.4: Total Variance Explained of Virgo 31

Table 4.5: Component Matrix of Virgo (Rotated) 31

Table 4.6 : KMO and Bartlett's Test of Dedication 32

Table 4.7: Total Variance Explained of Dedication 32

Table 4.8 : Component Matrix of Dedication (Rotated) 32

Table 4.9: KMO and Bartlett's Test of Absorption 33

Table 4.10: Total Variance Explained of Absorption 33

Table 4.11: Component Matrix of Absorption (Rotated) 33

Table 4.12 : KMO and Bartlett's Test of Burn out 34

Table 4.14 : Component Matrix of Burn out (Rotated) 35

Table 4.15: Total Variance Explained of Burn out (after removing B9) 35

Table 4.16: Component Matrix of Virgo (Rotated, after remove B9) 36

Table 4.17: KMO and Bartlett's Test of ITL 36

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Table 4.19: Component Matrix of ITL (Rotated) 37

Table 4.20: Pearson Correlation statistic 39

Table 4.21: Regression Analysis Summary of Working overtime hour and Burn out………… 40 Table 4.22: Regression Analysis Summary of the impact of Working overtime

on Engagement 42

Table 4.23: Regression Analysis Summary of impact of Engagement on Burn out……….… 44 Table 4.24: Regression Analysis Summary of impact of Burn out on

Engagement 45

Table 4.25: Regression Analysis Summary of impact of Burn out on ITL 46

Table 4.26: Regression Analysis Summary of impact of engagement on ITL 47

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LIST OF FIGURES

Figure 2.1: Relationship between Human Resource Value, ESE and Employee

ITL (Tzafrir et al., 2015) 11

Figure 2.2: Turnover intention model (Muliawan, 2009) 12

Figure 2.3: Model of Personality and Turnover Intention (Jeswani & Dave, 2012) 13

Figure 2.4: Theoretical model of turnover and INL among psychiatric nursing personnel (Alexander, 1998) 14

Figure 2.5: JDR model (Bakker & Demerouti, 2007) 16

Figure 2.6: Conceptual model 20

Figure 3.1: Research Process 22

LIST OF GRAPH Graph 4.1: Linear graph of the impact of Working overtime on Burn out 41

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LIST OF ABBREVATIONS

CFA Confirmatory Factor Analysis

DCM Demand-control model

ERI Effort-reward imbalance model

JDR Job Demand Resource Model

ITL Intention to leave

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CHAPTER 1: INTRODUCTION

1.1 Rationale:

During the current period of economic development, working hours of workers are a very serious social issue The situation that workers have to work overtime exceeding policy is very common Therefore, in many factories in Vietnam, there have been many strikes of workers to claim labor rights The last days of May 2018, due to forced overtime 74 hours per month and having to work in an unsecured environment, 500 garment workers in Tam Dan industrial cluster (Phu Ninh district, Quang Nam) quit their jobs to claim benefits (Trung Kien, 2018) Many workers reported that, fin the period of time after Lunar new year, they were forced to work overtime from Monday to Friday, adding 3.5 hours a day, including Sundays This makes them extremely tired and exhausted By the end of March 2018, nearly 4,000 workers of Yamani Dynasty Co., Ltd located in Nam Hong Industrial Cluster (Nam Truc District, Nam Dinh) simultaneously quit their job, asking the company's leaders to improve the working conditions, including non-overtime work over 300 hours/year (Van Dong, 2018)

The leaving of workers greatly affect the business The interrupted factories and production lines cause production stagnation and significant damage Recruiting new workers and retraining also cause a lot of loss of time and money

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According to the results of the salary, income, expenditure and life survey of employees in 2018 announced by the Vietnam General Confederation of Labor and the Institute of Workers - Union, the basic monthly salary of employees (if they work full time, full working days) received an average of 4.67 million dong/month However, workers have to spend a lot of money to ensure their life, while with many people the fixed salary is not enough to cover their own lives and their families so they need to work overtime and earn extra income In addition to basic wages (accounting for 84.4%), workers also receive overtime pay, attendance money and other allowances, supports from businesses With this additional amount and basic salary, the average income of workers (excluding meals) increases to nearly 5.53 million VND / month Many workers have given up unstable outside jobs to apply for jobs in industrial parks and have worked with the company for a long time because of stable salary, having conditions to increase their income if they work hard On the other hand, they are regularly involved in activities to take care of their spiritual life organized by unions In addition to income, some people also feel that having more overtime will reduce the time pressure to achieve the target Thus, Increasing maximum overtime hour is desired by both workers and businesses

This fact would suggests that working long hours may be the reason for factory workers to leave However, how that effects workers‟ decision to leave is far from clear Overtime working provide workers with additional income and usually at higher pay rates So why workers oppose overtime working and even leave? It can be seen that may overtime is affecting the employee‟s intention to quit in both negative and positive ways simultaneously

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the above studies only looked at individual effects, either negative or positive on employees intention to leave, but were not generalized when both had simultaneous impacts on ITL

This thesis investigates the effect of overtime working on Vietnamese worker‟s intention to leave through cause-effect relationship between working overtime, burn out, engagement and intention to leave

1.2 Research objective

The objective of this research is to explain how working overtime will affect employees‟ intention to leave organization

The expected result can be implication for human resource departments or managers to apply and organize overtime hour appropriately to retain employees and achieve efficiency in their work for long term The government also can refer this information to discuss and make more reasonable rules

1.3 Research scope

The study was conducted in Vietnam and focused on the human resources who are workers at manufacturing companies Currently, the issue of strikes and turnover of workers occurs mainly in manufacturing companies, the increase in overtime causing controversy for the government also revolves around this object

1.4 Research structure

Chapter 1: Introduction

This chapter introduces practical motivation, social problem that have inspired this research, research objective, research question and research scope

Chapter 2: Literature review

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Chapter 3: Research methodology

This chapter describes method to conduct research in detail, including research process, sample design, questionnaire design, and data collection method

Chapter 4: Data analysis

This chapter presents the data analysis steps, description of data collected, the index results obtained when analyzing data by SPSS software This section will also show which hypothesis is accepted

Chapter 5: Conclusion and discussion

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CHAPTER 2: LITERATURE REVIEW

2.1 Working overtime hour

2.1.1 Definition

Pursuant to the laws of Vietnam:

According to Article 104 of Law No.10/2012/QH13 - Ministry of Labor: Regulations on normal working hours as follows:

“ - Employers have the right to set working hours by day or week

- If it is calculated by day, the normal working time shall not exceed hours per day and 48 hours per week

- If it is calculated by week, the average working time must not exceed 10 hours per day and not more than 48 hours in a week

- However, the government encourages employers to implement a 40-hour working per week”

 In addition to normal working hours, it is counted as overtime

According to Article of Decree No 45/2013/ ND-CP stipulating overtime:

“- Overtime hours must not exceed 50% of normal working hours in a day - When applying the weekly working regulations, the total number of normal working hours and overtime hours shall not exceed 12 hours/day, 30 hours/month or 200 hours/year

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2.1.2 Related Research

The problem of working time of workers occurs not only in Vietnam but also in most economies This issue has been of interest for a long time so there has been some research in the past about the effects of working hours on employees

Recently, Chiara Dall‟Ora (2016) studied about characteristics of shift work and their impact on employee performance and well being This research was conducted between January and March 2015 in all sectors including health-care The author wanted to identify how the features of shift work that affect employee's performance and well being Results show that many element of shift work impact on compromised employee's performance and well being such as: Shifts of 12 hours or longer can cause jeopardized outcomes, there is a relationship between working more than 40 hours per week and adverse events Not only focusing on working hours, but the study also exploited deeply many aspects of shift work and points out effects of each aspect including working rotating shifts, fixed night shifts, timely breaks, quick returns Through this paper Chiara Dall‟Ora also studied the impact of overtime and mentioned it as a small characteristic of shift work His research concludes that working overtime was related to decreased job performance

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increase the intention to quit In Model 2, wage-related characteristics illustrate that a low wage or low wage satisfaction decreases employee‟s intention to stay When applying the combination of factors above in Model 3, the impact of them still works Approved relationships in model also demonstrate that consideration about working and commuting times acts as a supplement to wage satisfaction to increase employees‟ intention to stay

Rubery et al (2005) explored together two issues including working time and industrial relations His research exploited the flexibility aspect of working hours The results of qualitative fieldwork in six major UK-based organizations indicate that new working-time distribution blear the previously clear frontier between work and non-work time and influencing the salary negotiation Supporting the findings of Rubery on the positive effects of the flexibility of working time, Kelliher and Anderson (2009) offered evidence proving that who can work with flexible time having higher levels of job satisfaction and organizational commitment than their other colleagues More specifically, analytical results of both the interview and questionnaire illustrate that those who work flexibly in this study were not only generally satisfied with both their jobs and their work-life balance but also having committed tendencies to the organizations they are working for Remote workers confessed that being able to exercise autonomy about where they worked to make them satisfied In addition to the impact on satisfaction, interviewees who reduced hours reported lower levels of strain than those who did not work flexibly This is explained that if working hours are not flexible and limited, they will be under pressure from forcing themselves to complete the work in a short amount of time

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commitment This study also addresses the moderate role of gender and the type of work that adjusts the level of mentioned relationship above Specifically, for female employees or part-time employees, the negative effect of mismatch will be shown more strongly

Different from above studies, Johnson and Lipscomb did not focus on the impact of time-related issues on the organizational outcomes, but rather the impact on workers themselves Their research in 2006 indicated that if employer extend working time or arrange many irregular hours, laborer easily come to stress, fatigue, adverse health behavior and chronic outcomes such as cardiovascular and musculoskeletal disorders

While the study of Johnson and Lipscomb provides general conclusions about the impact of working hours on the health and morale of employees Later then, there have been many studies on this relationship, but going into more detail Lee and Lee (2016) proved that a reduction in working hours significantly decreases the risk of accidents and decreases the injury rate at the workplace Their research is quite favorable and highly reliable because the Korean government has changed the law on working time during that time The new law in which the standard weekly hours were reduced from 44 to 40 has been gradually applied at different times according to industry and facility size from 2004 to 2011 This change has helped them getting data and evidence to draw a conclusion that a one-hour cut down in actual working hours per week (equivalent to 14 minutes per day) lower the injury rate significantly, by about 8% There have been more in-depth studies combining both economic and medical factors related to working hours Typically, a study by Pradhan and Shrestha (2011) about the impact of working hours on backache from gender perspective

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increasing work time, overtime, and number of hours working per week raise the number of errors If nurses work in shifts of 12.5 hours or more will triple the rate of making mistakes compared to normal working hours

Many previous studies have shown that long working hours or irrationalities in time arrangements will lead to negative impacts on both work and health of employees But most of these studies have not yet focused on exploring the role of overtime hours and its both positive and negative effect on employees' intention to leave an organization

2.2 Intention to leave

2.2.1 Definition

Intention to leave (ITL) is a concept that has appeared and been studied for a long time, because this problem emerged very early It is also known under a number of other names such as: intention to quit, turnover intention This concept is classified into two separate folds: Organization and Profession In this paper, we only focus on ITL organization

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There are also many people who think that “intention to leave” and “intention to stay” are two concepts that have opposite meanings, so these two concepts can be measured with the same scale This ambiguity led Nancarrow et al (2014) to an interesting study of the nature, implications and measures of “intention to stay” and “intention to leave” The results showed that they are not measuring the same construct Although there are overlaps in measurement items, using these structures interchangeably will lead to errors and misunderstandings

Table below are herewith some definition of ITL:

Table 2.1: Definition of ITL

Author Definition

Fishbein and Ajzen‟s (1975)

“An individual‟s behavioural intention or conation to leave the employ of the organisation.”

Tett and Meyer (1993) “ The conscious and deliberate wilfulness to leave the organization.”

Vandenberg and Nelson (1999)

“Individual‟s own estimated probability (subjective) that they are permanently leaving the organisation at some point in the near future.”

Boshoff, Van Wyk, Hoole and Owen (2002)

“The strength of an individual‟s view that they not want to stay with their employer.”

Lyer and

Rudramuniyaiah (2008)

“The extent to which an employee plans to leave the organization.”

Cho, Johanson and Guchaitv (2009)

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2.2.2 Related research

Retaining employees is a long-standing problem, but it still doesn't lose its necessity for today's companies Therefore, when the ITL is an important signal to predict the turnover behaviour of employees, there has been a lot of research around this topic to provide useful implications for human resources management policies

Recently, Tzafrir et al (2015) conducted a study and approached this topic from the perspective of human resource management He conducted a study and approached this topic from the perspective of human resource management Specifically, he proposed a model to explore the role of Human Resource value and employment social environment in relation to the intention to quit With Data collected in 2011 from 567 employees and regression analysis, the results help Tzafrir claiming that Human Resource values is a major factor account for employment social environment and employment social environment play a dominated role on employee‟s ITL

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With this model, author tested the influence of three different components of the employment social environment, which might be considered hygienic factors that reduce dissatisfaction (Herzberg, 1966) and directly reduces employee‟s ITL a organization But they did not explore other possible factors that are actually related to employee‟s growth and development in order to improve their intention to stay In addition, From the author's research perspective, this model can suggest some implication to reduce employee's ITL, but this model will not be able to show the direct determinants impacting on the intention to quit

Muliawan et al (2009) also presented a model that emphasizes the key role of organizational commitment and job satisfaction to turnover intention In particular, the indirect factors affecting quitting intention are role conflict, satisfaction with pay, and fulfillment of growth needs However, his model does not mention any direct factor that increases the employee's intention to quit

Figure 2.2: Turnover intention model (Muliawan, 2009)

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(2010) suggested that both work engagement and organizational citizenship behaviour have unfavorable association to turnover intention, while work alienation and burnout have positive impact Stone (2006) demonstrated that organizational climate factors were associated with nurse‟s turnover intention depending on working conditions Djurkovic et al (2008) also asserted the mediating role of perceived organizational support on the relationship between workplace bullying and victims‟ ITL Elangovan (2001) tested many different models, tried to replace the position of variables and direction of relationship The results shows that there is significant cause-effect correlation between commitment and turnover intention (high commitment reduces intentions to quit) and the casual pattern of commitment is satisfaction

Different from above authors, Jeswani and Dave (2012) focused on exploiting influence of personality to the turnover intention The study clarifies that extroversion and agreeableness are two personalities, which negatively influences turnover intention Studies of Salgado (2002) and Zimmerman (2008) also have the similar results

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Contributing for influence of factors come from personal resource, Jacobs (2005) discussed by different model of quitting intention, where contrastive perceptions of organizational culture lead to disparate turnover intentions

The problem of quitting is quite prominent in the nursing industry Therefore, a series of studies on intention to quit have been conducted on this subject Alexander et al (1998) also built up a model of the relationship between the characteristic of the individual, the features of the job affecting the ITL and the turnover behavior The model below describes general direction of these relationships (Figure 2.4) Specifically, satisfaction with professional growth opportunities, autonomy, workload, and role clarity are strong indicators of ITL This model is quite general and covers the relationships of many factors including both personal factors, factors derived from the organization However, the limitation of this research is that this model also includes some specific characteristics of the nursing industry like relationship with patients Moreover, characteristics of work is measured in the view of attitude Meanwhile the standpoint of laborer to their job and the work-place can be affected by training activities, experience, and other group

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Bothma (2011) supposed that thinking of quitting a job may not just come from within employees themselves The decision to withdraw from current company is also influenced by external environment such as potential to be employed and labour-market demand He argued that an turnover intention depends on perceived opportunity and the ability of getting other jobs, the role of mobility cognition, as well as dissimilarity of each person in searching behaviour

Loi et al (2006) also claimed the role of employees‟ justice perceptions, perceived organizational support, organizational commitment toward ITL by testing regression relationship on 514 samples in Hong Kong Through this study, he confirmed that “both procedural and distributive justice contributed to the development of perceived organizational support, and perceived organizational support mediated their effects on organizational commitment and ITL”

In general, there are many studies conducted around ITL Many models have been launched but none have generalized both the negative and positive effects of job characteristics on ITL

2.3 Job Demand Resource model

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The shortcomings in the above models have made model of Baker and Demerouti (2006) become so prominent Two authors above built a general model to study the interrelation between job characteristics and its impact on organizational outcome called Job Demands-Resources model (JDR) The framework of JDR model can shows both negative and positive impact of the characteristics affecting organizational outcomes through two mediators: burnout and engagement The remarkable assumption of JDR model is: “Every occupation has its own specific risk factors associated with job-related stress These factors can be classified in two general categories: Job demands, Job resource” Through strain/burnout Job demand have negative impact on organizational outcome, mediated by motivation/engagement Job resource have positive impact on organizational outcome Based on this model, researchers can apply to examine or explore the role of many characteristics on outcomes In addition, applying JDR model can compacts multifarious occupation, working environment, and explores both negative and positive aspects of organizational outcomes

Figure 2.5: JDR model (Bakker & Demerouti, 2007)

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Jourdain and Chênevert (2010) also used JDR model in order to predict ITL the nursing profession Bakker et al (2011) proved that the JDR model is a conceptual model that can be fruitfully applied to the work–family interface Contributing for application of JDR model on organizational outcomes, Schreurs et al (2011) claimed that “job resources are associated negatively with early retirement intention through work enjoyment”, Carlson et al (2017) considered technology as a predictor of turnover intentions through JDR model In 2018, Ahyoung Lee et al used JDR model to conduct research about work engagement among child-care providers There are many variables belong to job demand, job resource, and organizational outcome, in turn, which are included in the model for research, but the mediate effect of burnout and engagement is not changed

Both job demand and job resource are belong to characteristic of an organization Therefore, as soon as the JDR model was born, many people were interested in the role of characteristics come from within person in this model Xanthopoulou (2007) extended the above model and added the impact of personal resources (including self-efficacy, organizational-based self-esteem and optimism) on relationships in JDR model These studies clarified that personal resources did not make up for the effect of job demands on burn out However, personal resources acts as a mediator on the correlation of job resources and engagement Additionally, it also affect the cognizance of job resources Baker (2017) even develops this model further become JDR theory, adding the roles of self-undermining and job crafting JDR theory covers many factors and much more complicated, but organizational outcomes of this framework are limited to job performance

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and motivated to engage with the company because they can increase their income and reduce the time pressure of the work So what is the role of working overtime hour? None of previous study apply JDR model to research about the role of overtime hour with ITL through burnout and engagement

2.4 Research question:

How working overtime hour impact on intention to leave through burnout and engagement ?

2.5 Hypothesis development and conceptual model

2.5.1 Hypothesis development

The model of this research will focus on clarifying the role of working overtime on ITL through burnout and engagement The relationship between burn out, engagement and ITL are adaptive with JDR model Not only JDR model, there have also been many studies that have shown similar relationships of them For example: the negative impact of engagement on ITL has been proved by Du Plooy and Roodt (2010), Halbesleben and Wheeler (2008); crossover interrelation between engagement and burn out was discussed by Bakker, Emmerik, and Euwema (2006); physical and mental burn out leading to the intention of quitting is an old topic that has long been studied by Weisberg, and Sagie (1999), Leung and Lee(2006) But there have been no studies that applied the JDR model to clarify the role of working overtime on burnout, engagement and organizational outcome

Relationship between working overtime and burnout

Leiter (1997) viewed burnout in terms of exhaustion, cynicism and reduced professional efficacy Similarly, Pines and Aronson (1988) defined burnout as “a state of physical, emotional, and mental exhaustion” Burnout measurement scale by Pines (2005) also developed based on this definition

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pointed to the negative role of working overtime on employees' well being Luther et al (2017) concluded that clinicians those working overtime are much more burnout and facing to stronger work–life conflict than those not working overtime Kok et al (2016) claimed that working around 45 hours per week or more can lead to heavier burnout among military mental health providers Likewise, Rupert, Hartman and Miller (2013) pointed out a strong positive relationship between the average working hours per week and the emotional exhaustion (a dimension of burnout) Supporting for above hypothesis, Yoder (2010) demonstrated that working overtime worked as a trigger for burnout, which is a reaction of chronic work related stress (Leiter & Maslach, 1988; Maslach, Schaufeli & Leiter, 2001) presented by emotional exhaustion, depersonalization When considering the opposite direction, Peterson et al (2008) found that exhausted employees described more frequency of overtime than workers who were non-burned-out and disengaged Imai et al (2004) suggested a similar issue that working overtime hours is one of contributions to burnout

Surprisingly, there are also many studies proving the opposite Richter et al (2014) asserted that decrease in working time in a hospital could not lead to a related reduced risk of burnout Study of Shirom, Nirel, and Vinokur (2010) also indicates that work hours not influence burnout directly Similarly, Schaufeli, Taris and van Rhenen (2008) also concluded that overtime did not cause burnout of employees However, with given the current situation in the context of workers working at manufacturing companies in Vietnam, the hypothesis of relationship between burnout and working overtime in this paper is still positive relationship

Relationship between working overtime and engagement

Work engagement is defined as follows (Schaufeli, Salanova, González-Romá & Bakker, 2001)

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specific state, engagement refers to a more persistent and pervasive affective-cognitive state that is not focused on any particular”

The problem of workers who were forced to work overtime too much leading to exhaustion, protests and even turnover decision, is an indisputable practice While engagement is an important indicator for predicting well being, it may seems that working overtime has a negative impact on engagement Watanabe and Yamauchi (2018) argued that involuntary overtime work described a negative impact on mental health and work engagement, whilst voluntary overtime work bring a positive effect on well‐ being On the other hand, Beckers et al (2004) founded that both compulsive drive and engagement are positively associated with working overtime But based on the reality from interviews with workers and even the government's controversy over the desire to increase maximum overtime hour , it can be seen that, from another perspective, the workers themselves may want to work overtime more More overtime makes them more satisfied because they can increase their income or reduce the pressure on time to meet the productivity targets This evokes an idea that not only does working overtime have a negative effect on engagement, but, to some extent, can have a positive impact on engagement Therefore, this research hypothesizes that working overtime has quadratic (inverted U-shape) relationship with engagement

2.5.2 Conceptual Model

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H1: Working overtime hour has positive impact on burnout of employee

H2: Working overtime hour has inverted U-shape influential relationship with employee's engagement

H3: Employee's burnout has negative impact on employee's engagement

H4: Employee's engagement has negative impact on employee's burn out

H5: Employee's burnout has positive impact on employee's ITL organization

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CHAPTER 3: RESEARCH METHODOLOGY

3.1 Research process

The research process includes steps shown as below:

Figure 3.1: Research Process

3.2 Sample design

Data collection instrument: Online questionnaire survey

Data collection method: Non-probability sampling, snow ball sampling Review the literature (related research and fundamental knowledge)

Based on previous research and secondary data to raise conceptual model, develope hypothesis

Identifying research objective, research scope, research question, research methodology

Designing questionnaire for survey, designing sample population

Collecting data

Analyzing data, identifying accepted hypothesis

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Target respondent: The questionnaire targeted at the subjects of the study, who are workers working at manufacturing company in Vietnam and receiving the

overtime compensation in accordance with the law of Vietnam However, to prevent the effects of demographic factors, this paper will narrow the study‟s subject based on age and gender Specifically, subjects that are female, under 30 years old will be selected for data analysis

Sample size: According to Hair et at (1998), the minimum number of samples should be equal to the number of items measuring multiplying The survey has all variables measured by 25 questions Therefore, based on this theory, the

minimum number of valid samples in the survey should be 125

Questionnaire design: The questionnaire consists of main parts The first part is to introduce the purpose and summarize the content of the questionnaire so that respondents can easily understand the problem and implement the questionnaire The next section is the most important, including questions that measure variables The last part is the demographic information of the respondent

Data analysis method: Statistic analysis by using SPSS software, including:

 Reliability analysis

 Confirmatory Factor analysis

 Pearson correlation analysis

 Linear and quadratic regression analysis

3.3 Measuring

The Working overtime hour variable will include options, representing the actual amount of time averaged over weeks and months as below:

1 Less than 2.5 hours per week (Less than 10 hours per month)

2 From 2.5 - hours per week (From 10 -20 hours per month)

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4 From 7.5 - 10 hours per week (From 30 -40 hours per month)

5 From 10 - 12.5 hours per week (From 40 -50 hours per month)

6 Over 12.5 hours per week (Over 50 hours hours per month)

All of variables Burn out, Engagement, ITL are measured by Likert scale (5 level) The content of measuring items and scale details are described in the table below:

Table 3.1: The content of measuring items

Variables Items Coded

label Source

Engagement

Vigor

Wilmar Schaufeli & Arnold Bakker

(2004)

“At my work, I feel bursting with energy.” E1

“At my job, I feel strong and vigorous.” E2

“When I get up in the morning, I feel like going to

work.” E3

Dedication

“I am enthusiastic about my job.” E4

“My job inspires me.” E5

“I am proud on the work that I do.” E6

Absorption

“I feel happy when I am working intensely.” E7

“I am immersed in my work.” E8

“I get carried away when I‟m working.” E9

Burn out “When you think about your work overall, how

often you feel tired ?” B1

Malach-Pines (2005)

“When you think about your work overall, how

often you feel disappointed with people ?” B2

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often you feel trapped ?” B4

often you feel helpless ?” B5

often you feel depressed ?” B6

often you feel physically weak/Sickly ?” B7

often you feel worthless/like a failure ?” B8

often you feel difficulties sleeping ?” B9

often you feel “I‟ve had it ?” B10

Intention to leave

“As soon as I can find a better job, I‟ll leave my

organization ?” I1

Wayne et al (1997)

“I am actively looking for a job outside my

place of employment.” I2

“I am seriously thinking about quitting my job.” I3

“I often think of quitting my job at my

organization.” I4

“I think I‟ll still be working at my place of

employment years from now.” I5

Table 3.2: Likert scale of ITL

Strongly disagree ==========> Strongly Agree

1

(37)

Table 3.3: Likert scale of Burn out and Engagement

Weakest Frequency ==========> Strongest Frequency

1

Never

Rarely (about several

times a year)

Sometimes (about several times a month)

Often (several

(38)

CHAPTER 4: DATA ANALYSIS

4.1 Data description

Table 4.1: Data description

Item N Minimum Maximum Mean Standard

deviation

WO 139 3,151 1,215

E1 139 3,245 0,833

E2 139 3,288 0,810

E3 139 3,345 0,968

E4 139 3,871 0,824

E5 139 2,957 0,970

E6 139 3,094 1,089

E7 139 3,108 1,159

E8 139 3,475 0,958

E9 139 3,331 0,959

B1 139 2,813 0,848

B2 139 2,370 0,889

B3 139 2,223 0,948

B4 139 2,532 0,973

B5 139 2,388 0,897

B6 139 2,079 0,860

B7 139 2,230 1,023

B8 139 2,201 0,910

B9 139 2,058 0,875

B10 139 2,266 1,004

I1 139 2,806 1,056

I2 139 1,993 0,803

I3 139 2,065 0,911

I4 139 2,173 0,963

(39)

A total of 465 questionnaires were answered, of which 354 respondents were the target objects of the survey, who are workers in manufacturing, assembling, processing companies and have compensation for overtime according to Viet Nam labor law However, only 139 responses were taken for data analysis These are answers from female workers and under 30 years old To avoid the effects of demographic factors, the subjects for data analysis were scaled based on gender and age According to the study of Luekens et al (2004), women are more likely to quit their jobs than men Moreover, according to the Ministry of Labor, Invalids and Social Affairs, in the formal economic sector, women account for a high proportion in the intermediate, low-skilled occupations, typically worker (among those who not require high knowledge, skills, over 50% are female) On the other hand, a study by Ahuja et al (2007) or Collins (2014) also showed that age is related to employees' ITL their jobs The younger employee, the less engaged with the business and the greater the intention to quit Therefore, this study has selected this object to analyze data In addition to age and gender, education and income level is also associated with the intention to quit (Kelly, 2004; Stockard and Lehman, 2004; Johnson and Birkeland, 2003) However, research is only aimed at workers who are low-educated, have similar low-income and not diverse

4.2 Reliability analysis

In order to assess the reliability of the scale and eliminate the unreliable measuring items, this study use Cronbach„s Alpha test for scales of both independent and dependent variables, respectively

If measurement items have Corrected Item-Total Correlation ≥ 0.3, these items reach standard

Cronbach‟s Alpha coefficient value level:

(40)

• From 0.6 and above: eligible measurement scale Table 4.2 Reliability analysis

Item-Total Statistics Item Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Cronbach's Alpha if Item Deleted Cronbach's Alpha N of Items

E1 6.633 2.509 0.684 0.730

0.815

E2 6.590 2.563 0.690 0.727

E3 6.532 2.222 0.640 0.786

E4 6.050 3.787 0.551 0.877

0.825

E6 6.827 2.419 0.775 0.662

E5 6.964 2.832 0.754 0.682

E7 6.806 2.955 0.662 0.757

0.812

E8 6.439 3.494 0.704 0.705

E9 6.583 3.665 0.641 0.766

B1 20.353 34.708 0.536 0.871

0.878 10

B2 20.791 34.326 0.545 0.871

B3 20.942 32.895 0.642 0.864

B4 20.633 33.422 0.570 0.869

B5 20.777 33.189 0.656 0.863

B6 21.086 33.355 0.672 0.862

B7 20.935 32.583 0.611 0.866

B8 20.964 33.528 0.609 0.866

B10 20.899 31.917 0.692 0.859

B9 21.108 34.836 0.501 0.874

I1 9.576 7.811 0.692 0.813

0.850

I2 10.388 9.500 0.578 0.840

I3 10.317 8.174 0.769 0.790

I4 10.209 7.833 0.788 0.782

(41)

Test results show that all observed items have Corrected Item-Total Correlation > 0.3, Cronbach‟s Alpha of each group of items > 0.815 so this is a very good measurement scale

However, Cronbach's Alpha if Item E4 deleted = 0.877 > 0.825 (Cronbach‟s Alpha of group of items representing for Dedication), so we will remove item E4 to improve reliability of this scale Cronbach's Alpha if Item I5 Deleted = 0.858> 0.850 (Cronbach‟s Alpha of group of items representing for ITL), so we will remove item I5 to improve reliability of this scale

In summary, after analyzing reliability, items were rejected, including: E4 and I5 Now the Engagement scale has items, the ITL scale has items, the Burn out scale still has 10 items

4.2 Confirmatory Factor Analysis (CFA)

The Kaiser-Meyer-Olkin coefficient (KMO) is an indicator used to consider the suitability of factor analysis The achieved results must meet the following conditions: 0.5 ≤ KMO ≤ for factor analysis is appropriate The larger the KMO, the greater the common part between variables

Bartlett's test is used to see if observed items are correlated with each other If Sig Bartlett‟s Test <0.05, it shows that the observed items are correlated with each other in a factor

Total Variance Explained ≥ 50% shows that group of these items is suitable Considering the variance to be 100%, this value shows how much extracted items can be condensed and how many percentage of the observed items will be lost

4.2.1 CFA analysis of Engagement

 CFA analysis of Vigor

(42)

Table 4.3: KMO and Bartlett's Test of Virgo

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.715 Bartlett's Test of Sphericity Approx Chi-Square 145.608

df

Sig 0.000

Total Variance Explained = 73.54% ≥ 50%, extracted items are condensed to 73.54% of the observed variable

Table 4.4: Total Variance Explained of Virgo

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance

Cumulative

% Total

% of Variance

Cumulative %

1 2.206 73.540 73.540 2.206 73.540 73.540

2 0.441 14.703 88.243

3 0.353 11.757 100.000

The result of the Component Matrix (Rotated) table shows that the items below are only grouped into one factor Factor loading of each item ≥ 0.7, so the observed items is statistically very good

Table 4.5: Component Matrix of Virgo (Rotated)

Component

E2 0.870

E1 0.867

(43)

 CFA analysis of Dedication

KMO = 0.5, factor analysis is accepted Sig Bartlett‟s Test = 0.000 <0.05, indicating that the observed items are correlated with each other in a factor

Table 4.6 : KMO and Bartlett's Test of Dedication

Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.500

Bartlett's Test of Sphericity

Approx Chi-Square 131.225

df

Sig 0.000

Table 4.7: Total Variance Explained of Dedication

Component

Total % of Variance

Cumulative

% Total

% of Variance

Cumulative %

1 1.786 89.295 89.295 1.786 89.295 89.295

2 0.214 10.705 100.000

Table 4.8 : Component Matrix of Dedication (Rotated)

Component

E5 0.945

(44)

 CFA analysis of Absorption

KMO = 0.712 > 0.5, so the common part between the items is very large, factor analysis is accepted Sig Bartlett‟s Test = 0.000 <0.05, indicating that the observed items are correlated with each other in a factor

Table 4.9: KMO and Bartlett's Test of Absorption

df

Sig 0.000

Table 4.10: Total Variance Explained of Absorption

Component

Total % of Variance

Cumulative

% Total

% of Variance

Cumulative %

1 2.198 73.259 73.259 2.198 73.259 73.259

2 0.447 14.907 88.167 0.355 11.833 100.000

Table 4.11: Component Matrix of Absorption (Rotated)

(45)

E8 0.876

E7 0.851

E9 0.840

4.2.2 CFA analysis of Burn out

KMO = 0.886> 0.5, so the common part between the items is very large, factor analysis is accepted Sig Bartlett‟s Test = 0.000 <0.05, indicating that the observed items are correlated with each other in a factor

Table 4.12 : KMO and Bartlett's Test of Burn out

df 45

Sig 0.000

Total Variance Explained = 48.154% < 50%, so we removed item B9 with the smallest loading factor to improve Total Variance Explained

Table 4.13 : Total Variance Explained of Burn out

Component

Total % of Variance

Cumulative

% Total

% of Variance

Cumulative %

1 4.815 48.154 48.154 4.815 48.154 48.154

2 0.977 9.775 57.928

3 0.808 8.083 66.011

4 0.676 6.755 72.766

5 0.636 6.363 79.130

6 0.576 5.764 84.894

(46)

8 0.366 3.655 93.623

9 0.345 3.449 97.072

10 0.293 2.928 100.000

Table 4.14 : Component Matrix of Burn out (Rotated)

Component

B10 0.768

B6 0.752

B5 0.740

B3 0.727

B7 0.713

B8 0.700

B4 0.667

B2 0.634

B1 0.626

B9 0.589

After removing item B9, Total Variance Explained = 50,217% > 50%, the extracted items are condensed 50,217% of the observed variable

Table 4.15: Total Variance Explained of Burn out (after removing B9)

Total Variance Explained

Component

Total % of Variance

Cumulative

% Total

% of Variance

Cumulative %

(47)

2 0.909 10.100 60.318

3 0.767 8.522 68.839

4 0.649 7.207 76.046

5 0.612 6.797 82.843

6 0.512 5.691 88.534

7 0.373 4.145 92.679

8 0.364 4.042 96.720

9 0.295 3.280 100.000

The result of the Component Matrix (Rotated) table shows that the items below are only grouped into one factor Factor loading of each item ≥ 0.5, so the observed items is statistically good and very good

Table 4.16: Component Matrix of Virgo (Rotated, after remove B9)

Component

B10 0.761

B6 0.754

B5 0.750

B3 0.724

B8 0.718

B7 0.703

B4 0.683

B2 0.645

B1 0.626

4.2.3 CFA analysis of ITL:

KMO = 0.793> 0.5, so the common part between the items is very large, factor analysis is accepted Sig Bartlett‟s Test = 0.000 <0.05, indicating that the observed items are correlated with each other in a factor

(48)

df

Sig 0.000

Table 4.18 : Total Variance Explained of ITL

Component

Total % of Variance

Cumulative

% Total

% of Variance

Cumulative %

1 2.828 70.701 70.701 2.828 70.701 70.701

2 0.562 14.044 84.745

3 0.393 9.815 94.560

4 0.218 5.440 100.000

Table 4.19: Component Matrix of ITL (Rotated)

Component

I3 0.894

I4 0.893

I1 0.811

(49)

In summary, after CFA analysis, items B9 were rejected Now the Burn out scale has items, the ITL scale has items, the Engagement scale has items

4.3 Creating a representative variable:

We create representative variables by the average of the accepted items:

Burn out (BO)=mean(B1,B2,B3,B4,B5,B6,B7,B8,B10)

Intention to leave (ITL)=mean(I1,I2,I3,I4)

The engagement value is equal to the average of the factors Vigor, Dedication, Absorption

Vigor(VI)=mean(E1,E2,E3)

Dedication(DE)=mean(E5,E6)

Absorption(AB)=mean(E7,E8,E9)

Engagement(ENG)=mean(VI,DE,AB)

4.4 Pearson correlation analysis

There is a correlation between two variables if value of Sig (2-tailed) <0.05 The Pearson correlation values (r) range from -1 to 1:

• If r toward to or -1: the linear correlation is more significant Positive r indicates positive correlation while negative r indicates negative correlation

• If r goes to 0: the linear correlation is weaker

• If r = 1: absolute linear correlation, when presented points on the Scatter plot, the points represented will merge into a straight line

(50)

Table 4.20: Pearson Correlation statistic

WO ENG BO ITL

WO

Pearson Correlation -0.046 0.410 0.143

Sig (2-tailed) 0.594 0.000 0.093

N 139 139 139 139

ENG

Pearson Correlation -0.046 -0.475 -0.712

Sig (2-tailed) 0.594 0.000 0.000

N 139 139 139 139

BO

Pearson Correlation 0.410 -0.475 0.611

Sig (2-tailed) 0.000 0.000 0.000

N 139 139 139 139

ITL

Pearson Correlation 0.143 -0.712 0.611

Sig (2-tailed) 0.093 0.000 0.000

N 139 139 139 139

Between WO and BO : Sig (2-tailed) <0.05 and r = 0.41, so there is a positive correlation between working overtime hour and burn out

Between WO and ENG : Sig (2-tailed) >0.05 , so there is no linear correlation between working overtime hour and engagement

Between ENG and BO : Sig (2-tailed) <0.05 and r = -0.475, so engagement and burn out are negatively correlated

Between ENG and IIL : Sig (2-tailed) <0.05 and r = -0.712, so engagement and ITL are negatively correlated

(51)

4.5 Regression Analysis

4.5.1 Regression Analysis of the impact of Working overtime hour on Burn out

Adjusted R Square = 0.162 shows that the Working overtime hour affects 16.2% on the change of the Burn out Durbin – Watson (DW) = 1.972 so there is a high probability that there is no first-order auto correlation

Sig (F-test) = 0.000<0.05, Sig ( t-test) = 0.000 <0.05, Standardized Coefficients Beta = 0.41, indicates that the Working overtime hour has the positive relationship with Burn out The equation describing relationship between Working overtime hour and Burn out is as below: BO = 0,221*WO + 1.647

Table 4.21: Regression Analysis Summary of Working overtime hour and Burn out

Model Summary

Model R R Square Adjusted R Square

Std Error of the

Estimate Durbin-Watson

1 0.410 0.168 0.162 0.600 1.972

ANOVA

Model Sum of

Squares df

Mean

Square F Sig

1 Regression 9.992 9.992 27.749 0.000b

Residual 49.333 137 0.360

Total 59.325 138

Coefficients

Model

Unstandardized Coefficients

Standardized

Coefficients t Sig B Std Error Beta

1

(Constant) 1.647 0.142 11.610 0.000

(52)

Graph 4.1: Linear relationship between working overtime hour and burn out

4.5.2 Regression Analysis of the impact of Working overtime hour on Engagement

Adjusted R Square = 0.36 shows that there is quadratic relationship between Working overtime hour and Engagement, Working overtime hour affects 36% on the change of the Engagement

Sig (F-test) = 0.000<0.05, Sig (t-test) = 0.000 <0.05, Standardized Coefficients Beta (WO) = 2,648, Standardized Coefficients Beta (WO2) = -2,761, the quadratic equation describing relationship between Working overtime hour and Engagement is as below:

(53)

Table 4.22: Regression Analysis Summary of the impact of Working overtime on Engagement

Model Summary

R R Square Adjusted R Square

Std Error of the Estimate

0.608 0.370 0.360 0.619

ANOVA

Sum of

Squares df Mean Square F Sig

Regression 30.531 15.266 39.867 0.000

Residual 52.077 136 0.383

Total 82.608 138

Coefficients

Standardized

Coefficients t Sig

B Std Error Beta

WO 1.686 0.197 2.648 8.540 0.000

WO ** -0.254 0.029 -2.761 -8.904 0.000

(54)

Graph 4.2: Quadratic graph of the impact of Working overtime on Engagement

4.5.3 Regression Analysis of the impact of Engagement on Burnout

Adjusted R Square = 0.220 shows that the Engagement affects 22% on the change of the Burn out Durbin – Watson (DW) = 1.936 so there is a high probability that there is no first-order auto-correlation

Sig (F-test) = 0.000<0.05, Sig (t-test) = 0.000 <0.05, Standardized Coefficients Beta = -0.475, indicates that the Engagement has the negative impact on Burnout The equation describing relationship between Engagement and Burn out is as below:

(55)

Table 4.23: Regression Analysis Summary of impact of Engagement on Burn out

Model Summary

Durbin-Wats on

1 0.475 0.226 0.220 0.579 1.936

ANOVA

Model Sum of

Squares df

Mean

Square F Sig

1

Regression 13.407 13.407 40.002 0.000b

Residual 45.918 137 0.335

Total 59.325 138

Coefficients

Model

Standardized

1 (Constant) 3.637 0.210 17.310 0.000

ENG -0.403 0.064 -0.475 -6.325 0.000

4.5.4 Regression Analysis of the impact of Burnout on Engagement

Adjusted R Square = 0.22 shows that the Burn out affects 22% on the change of the Engagement Durbin – Watson (DW) = 1.250 so there is a probability that there is no first-order auto correlation

(56)

The equation describing relationship between Burn out and Engagement is as below:

ENG = -0.561*BO + 4.523

Table 4.24: Regression Analysis Summary of impact of Burn out on Engagement

Model Summary

Durbin-Wats on

1 0.475a 0.226 0.220 0.683 1.250

ANOVA

Model Sum of

Squares df Mean Square F Sig

1

Regression 18.669 18.669 40.002 000b

Residual 63.939 137 0.467

Total 82.608 138

Coefficients

Model

Standardized

1

(Constant) 4.523 0.216 20.948 0.000

BO -0.561 0.089 -0.475 -6.325 0.000

4.5.5 Regression Analysis of the impact of Burn out on ITL

(57)

Sig (F-test) = 0.000<0.05, Sig ( t-test) = 0.000 <0.05, Standardized Coefficients Beta = 0.611, indicates that the Burn out has the positive impact on ITL The equation describing relationship between Burn out and ITL is as below:

TIL = 0.731*BO + 0.544

Table 4.25: Regression Analysis Summary of impact of Burn out on ITL

Model Summary

Durbin-Wats on

1 0.611 0.373 0.368 0.624 1.520

ANOVA

Model Sum of

Squares df

Mean

Square F Sig

1

Regression 31.717 31.717 81.472 0.000b

Residual 53.334 137 0.389

Total 85.051 138

Coefficients

Model

Standardized

1

(Constant) 0.544 0.197 2.760 0.007

BO 0.731 0.081 0.611 9.026 0.000

(58)

Adjusted R Square = 0.503 shows that the Engagement affects 50,3% on the change of the ITL Durbin – Watson (DW) = 1.520 so there is a probability that there is no first-order autocorrelation

Sig (F-test) = 0.000<0.05, Sig ( t-test) = 0.000 <0.05, Standardized Coefficients Beta = -0,712, indicates that the Burn out has the positive impact on ITL The equation describing impact of Engagement on ITL is as below:

ITL = -0.722*ENG + 4.575

Table 4.26: Regression Analysis Summary of impact of engagement on ITL

Model Summary

Model R R

Square

Adjusted R Square

Durbin-Wat son

1 0.712 0.506 0.503 0.554 1.513

ANOVA

Model Sum of

Squares df

Mean

Square F Sig

1

Regression 43.069 43.069 140.550 0.000b

Residual 41.982 137 0.306

Total 85.051 138

Coefficients

Model

Standardized

1

(Constant) 4.575 0.201 22.771 0.000

(59)

4.6 Hypothesis tested results

Table 4.27: Hypothesis tested results

Hypothesis Result

Hypothesis 1: Working overtime hour has positive impact on burnout

of employee Supported

Hypothesis 2: Working overtime hour has inverted U-shape influential

relationship with employee's engagement Supported

Hypothesis 3: Employee's burnout has negative impact on employee's

engagement Supported

Hypothesis 4: Employee's engagement has negative impact on

employee's burn out Supported

Hypothesis 5: Employee's burnout has positive impact on employee's

ITL organization Supported

Hypothesis 6: Employee's engagement has negative impact on

employee's ITL organization Supported

(60)

CHAPTER 5: CONCLUSION AND DISCUSSION

5.1 Conclusion

Based on the foundation of JDR model on the relationship of job characteristics and organizational outcomes (Bakker & Demerouti,2007), related research and practical issues, this research has built a model to focus on exploring the impact of working overtime hour on burnout, engagement and ITL The study focused on the workers who work at a manufacturing company in Vietnam and get compensation for overtime accordance with Vietnamese labor law, which has caused controversies in making the law on maximum overtime in Vietnam A total of 465 questionnaires were answered, of which 139 questionnaires were appropriate to the study subjects and qualified for data analysis The analysis results show that all hypotheses are accepted

Hypothesis 1: Working overtime hour has positive impact on burnout of employee

Hypothesis 2: Working overtime hour has inverted U-shape influential relat ionship with employee's engagement

Hypothesis 3: Employee's burnout has negative impact on employee's engagement

Hypothesis 4: Employee's engagement has negative impact on employee's burn out

Hypothesis 5: Employee's burnout has positive impact on employee's ITL organization

Hypothesis 6: Employee's engagement has negative impact on employee's ITL organization

(61)

work through exhaustion and has both negative and positive effects on the intention to leave work through engagement

5.2 Discussion

Each of the relationships in Hypothesis 3,4,5,6 is accepted This show that the relationship between burnout, engagement and ITL are adaptive with the corresponding relationships in in JDR model Specifically, the research results show that engagement affects 50.3% on the change of the ITL, proving that the role of engagement contributes greatly to ITL Accepted H6 also supports the results of previous studies on the relationship of engagement and ITL by Du Plooy and Roodt (2010), Halbesleben and Wheeler (2008) Burn out has a positive effect on the intention to quit, but burn out only explains 36,8% of the change in ITL, lower than engagement This is understandable, as previous studies also showed that burn out leads to health outcomes problems much more clearly than outcomes about motivational outcome, like ITL The fact also shows that exhausted workers will lead to health problems and errors in the working process as well as work efficiency Malnourished workers often suffer from malnutrition, weak resistance, easy attack, especially in polluted and unsafe working environment, increasing pressure of hard work According to Doctor Huynh Tan Tien, Director of Ho Chi Minh City Center for Occupational Health and Environmental Protection (2019), workers are at a high risk of diseases, mainly from ear, nose and throat diseases (31%), eyes (23.11%) and maxillofacial (18%) Although the impact on the intention to quit is not really great but decreasing burn out also increases employee engagement with work, while engagement is a key factor in reducing employees' intention to quit The results of the reciprocal relationship between burn out and engagement coincide with study of Bakker, Emmerik and Euwema (2006) as well as JDR model

(62)

we can see that employees are burn out from working more than 40 hours per month This level is still quite high compared to the maximum level prescribed by the government However, the study‟s target respondent are those under 30 years old, so this is understandable Because this age often has good health and ability to work at higher intensity than older ages However, the results showed that overtime hour affected only 16.2% of burn out This demonstrates that there is also many other factors dominate the exhaustion of workers, not merely number of hours For example: distributed or concentrated overtime arrangement, breaks time for employees to regain strength, whether there are organizational support in nutrition or the work environment to improve health for workers or not

(63)

jobs of workers when the salary is calculated by working time, overtime is the optimal solution If they can get more money from overtime, they can meet their demand and feel more engaged to their work However, when the overtime is too much, it will lead to an imbalance in life, They not have enough time to take care of yourself and your family, leading to dissatisfaction and disengagement to work Moreover, when the overtime is too high, it cause exhaustion and also contribute to reduce engagement Based on the graph 4.2 we can see that engagement increases when employees work around from less than 2.5 hours/week to less than 7.5 hours/week (from less than 10 hours/month to less than 30 hours/month) Beyond this time, engagement will decrease Amazingly, this is in line with the current situation where the government stipulates that organizations can conduct working overtime within 30 hours a month However, overtime hour only explains 36% of engagement According to the interviews with workers, not only the income that makes them want to stay in company, but other welfare policies also greatly influence the worker‟s decision to work at the company For example: Policy for health insurance, social insurance, the care for the spiritual life of trade unions, moreover, long-term job stability compared to free precarious jobs outside

5.3 Implication

Research results show that to reduce workers' intention to quit, we can: increase worker engagement with businesses, reduce worker exhaustion and adjust working overtime reasonably:

To reduce the burnout and ITL of workers:

 Looking at the graph 4.1, you can see that working around over 40 hours per

month and more will make employees physically and mentally exhausted Therefore, overtime should not exceed 40 hours per month to ensure employee health

(64)

To overcome malnutrition and disease among workers, many health experts say that it is necessary to improve the quality of meals and improve the working environment In Vietnam, there is currently no law on meals for workers There are no specific standards for a meal for workers to have enough energy to work during the day, so when poisoning occurs, there is no legal basis to bind the responsibility for anyone

 Organizing periodic health examinations, to ensure workers are detected disease promptly and take measures to treat and recover soon Avoiding workers having to work in exhausted health conditions, which will affect not only workers themselves and the quality of work as well as the benefits of the business in the long term

 Arranging overtime appropriately Organization should allocates overtime

scatteredly so that workers can have time to recover energy Having reasonable break time during shift work also important

To improve engagement and decrease ITL of worker:

 The government may consider the results of this study and keep the law for workers' maximum overtime of 30 hours per month Because working less than 30 hours a month will make workers retain high engagement with the company

 There should be a clear benefit system for all types of insurance as well as pensions for workers Organizations should show their long-term commitment and job stability to workers and explain clearly their benefit in the future

 Organizations like unions or managers themselves should have activities that

(65)

5.4 Limitation and future research direction

The scope of the research is quite narrow The research aims to worker in Vietnamese manufacturing companies, so the research results may not be accurate in other contexts, when the economy is more developed or more primitive, or conducted in companies that have a different business model than manufacturing Other author can conduct similar studies in other contexts to retest the above relationships and compare with these results

Research objects is aim for workers This object has feature different from other high-qualified employees in terms of demands, perceptions, education, mind set, behavior, etc Therefore, research results when applied to other objects may be biased.The sample is only 139 respondents Even it reach minimum standard for measurement, it is still quite small and narrow To avoid the impact of demographic factors on the study subjects, target respondent are female workers, under 30 years old Therefore, when the research object is expanded, the results may change The further research direction may be to test the model with a wider respondent to see how the demographic factor affects the relationships in the model

(66)

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APENDIX

QUESTIONNAIRE ABOUT WORKING OVERTIME

Hello everybody I am a graduate student of the Master of Business Administration Program of Vietnam Japan University I am currently doing a research related to overtime hours of workers at manufacturing companies in Vietnam So I made this survey and hope to get everyone's help in answering the questions below

I assure that the survey results are completely confidential and only serve for scientific research purposes, not for commercial purposes Your response will be of great help to sooner complete my research topic Thank you very much!

1 What is your average overtime hours?

1 Less than 2.5 hours per week (Less than 10 hours per month)

2 From 2.5 - hours per week (From 10 -20 hours per month)

3 From - 7.5 hours per week (From 20 -30 hours per month)

4 From 7.5 - 10 hours per week (From 30 -40 hours per month)

5 From 10 - 12.5 hours per week (From 40 -50 hours per month)

6 Over 12.5 hours per week (Over 50 hours hours per month)

From questions to 26, When thinking about your work, evaluate your thoughts on the following statements by choosing a scale from to (ascending with agree level or frequency level) as below:

Level of agree

Strongly disagree ==========> Strongly Agree

1

Strongly

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Level of frequency

Weakest Frequency ==========> Strongest Frequency

1

Never

Rarely (about several

times a year)

Sometimes (about several times a month)

Often (several

times a week) Always

2 My job inspires me

1 Never 2.Rarely Sometimes Often Always

3 I am actively looking for a job outside my place of employment

1 Strongly disagree 2.Disagree Neutral 4.Agree Strongly agree

4 I am proud on the work that I

5 I am enthusiastic about my job

6 When you think about your work overall, how often you feel physically weak/sickly?

7 When you think about your work overall, how often you feel helpless?

8 When you think about your work overall, how often you feel trapped?

9 When you think about your work overall, how often you feel tired?

10 When I get up in the morning, I feel like going to work

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11 As soon as I can find a better job, I’ll leave my organization

12 I get carried away when I’m working

13 When you think about your work overall, how often you feel hopeless?

14 I feel happy when I am working intensely

15 I often think of quitting my job at my organization

16 At my job, I feel strong and vigorous

17 When you think about your work overall, how often you feel disappointed with people?

18 When I get up in the morning, I feel like going to work

19 I think I’ll still be working at my place of employment years from now

20 When you think about your work overall, how often you feel worthless/like a failure?

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22 I am seriously thinking about quitting my job

23 When you think about your work overall, how often you feel depressed?

24 When you think about your work overall, how often you feel difficulties sleeping?

25 I get carried away when I’m working

26 I am immersed in my work

27 .What is your gender?

A Male B Female

28 How old are you?

A Under 30 B 30-45 years old

C Over 45 years old

29 What is your occupation position?

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30 What is your current marital status ?

A Married B Not married

31 What kind of company you work for?

A Manufacturing / assembling / processing company

B Not a manufacturing / assembly / processing company

32 According to Vietnamese law, normal working hours are hours per day, days per week, exceeding that time is overtime The regular overtime wage is 150% of the normal working hour wage The night shift salary is added 30% compared to normal Do you receive overtime pay under the law?