A Study Into the Impact of Occupational Burnout on the Job Performance of Enterprises’ Employees

A Study into the Impact of Occupational Burnout on the Job Performance of Enterprises’ Employees

Dr. Jia-Chern Chen, Health Behavior and Exercise Science Research Center, YMCA, Taiwan Dr. Chin-Hsien Hsu, Department of Leisure Industry Management, National Chin-Yi University of Technology, Taiwan Dr. Tu-Kuang Ho, Corresponding Author, Taiwan Hospitality & Tourism University, Taiwan

ABSTRACT

This study aims to investigate the impact of employee health on occupational burnout and the impact of occupational burnout on job performance. The study used a questionnaire which was distributed to employees in Taiwanese enterprises employing 100 full-time employees as its research method. A variety of enterprise types were approached including public agencies, private companies, as well as for-profit and non-profit organizations. The questionnaires were distributed in northern, central, and southern Taiwan from June 2014 to May 2015. A total of 530 questionnaires were sent out and 493 were returned, giving a return rate of 93.0%. After deducting 60 invalid questionnaires which were not completed, the total number of valid questionnaires was 433, resulting in an effective return rate of 87.8%. The results of this study showed that: 1. Employee health has a clear positive influence on occupational burnout; and 2. Occupational burnout has a notable positive influence on job performance. In the conclusion, this study proposes some recommendations for further research as well as some implications for management based on the results obtained. Keywords: Employee health, occupational burnout, job performance

INTRODUCTION

From the labor-intensive economy in the industrial age to the current knowledge-intensive economy in the information age today, the global economy has become an environment that is highly competitive, rapidly changing, and experiencing labor shortages (Fry & Slowm, 2008). This means that within the modern workplace an increasing number of workers are experiencing health problems such as anxiety, exhaustion, nervousness, and stress as a result of their jobs. According to Pestonjee (1999), workplace- and job-related stress come from the job itself and the organization. Areas including the workplace environment, relationships with colleagues, and leadership styles can all either directly or indirectly cause employee health problems or affect production. When employees experience stress in their workplace, their loyalty to the organization is reduced, which even results in contradictions and conflicts within their family life (Kinjerski & Skrypnek, 2006), as well as posing a threat to their health. Berry, Mirabito, & Baun (2010) also pointed out that depression, nervousness, and stress among employees in the workplace have been proven to be important elements which affect companies’ productivity. When an appropriate channel for grievances to be aired does not exist, it leads to feelings of alienation, detachment, and anger within the workplace. It can also cause lower self-esteem, indifference in attitudes and behavior, a lack of enthusiasm for work, and a decrease in efficiency. Cordes & Dougherty (1993) identified that it can also lower both engagement and the amount of effort put in at work, while increasing employee no-shows.

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Other scholars identified that workplace stress can reduce commitment to organizations (Burke & Richardsen, 1993; Kahill, 1988), and cause other negative consequences such as poor efficiency (Hom, Caranikas-Walker, Prussia, & Griffeth, 1992). All of this leads to financial losses or a drop in operational performance for enterprises, and the disintegration of the family structure. In turn, this has an impact upon a country’s wider economic development. The scholars Myers & Sweeney (2008) put forward their theory on wellness, which stated that when enterprises’ employees enjoy good physical, intellectual, social, spiritual, and emotional health it has been proven to be an important element in increasing the enterprises’ productivity. The scholars stressed that when enterprises pay attention to employee health it helps to boost job satisfaction, reduce burnout, and increase performance. This in turn strengthens employee loyalty toward companies, while also reducing absences, no-shows, and resignations. It can also lead to increased production performance, thereby improving the enterprises’ performance and competitiveness. Data from previous literature has also confirmed that employee satisfaction and burnout affect enterprises’ competitive advantages and development (Clark, Peters & Tomlinson, 2005; Cotton & Tuttle, 1986; Shaw, 1999; Lam, Baum, & Pine, 2001; Lambert, Lynne, & Barton, 2001; Tett & Meyer, 1993; Van Dick et al., 2004). For these reasons, this study investigates the correlation between employee health, occupational burnout, and job performance. The results of this study may be used as a reference of managerial practice for industry.

RESEARCH METHOD

Research Structure In accordance with the aims of this study and other relevant literature, this study proposes a framework for the relationship between employee health, occupational burnout, and job performance. This framework is shown in Figure 1:

Figure 1: Research Framework: Employee health, occupational burnout, job performance

Research Subjects This study issued questionnaires to specifically chosen enterprises in Taiwan which employ more than 100 full-time employees. A variety of organization types, including public agencies, private companies, as well as for-profit and non-profit organizations, were approached. The questionnaires were then distributed in northern, central, and southern Taiwan, and adopted a cluster sampling method which ran from June 2014 to May 2015. A total of 530 questionnaires were sent out and 493 were returned, giving a return rate of 93.0%. After deducting 60 invalid questionnaires which were incomplete, the total number of valid questionnaires was 433, resulting in an effective return rate of 87.8%.

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Research Tools The content of the questionnaire used in this study was mainly compiled by combining the areas covered in the literature review. First, the Employee Wellness Scale referred to the 5F-Wellness scales proposed by Myers & Sweeney (2008), and a total of 23 statements in accordance with the scales edited and used by Porter, Claycomb & Kraft (2008) were translated for the questionnaire. Secondly, The Occupational Burnout Scale referred to the theories put forward by Maslach (1982), and were edited in accordance with those used by Almer & Kaplan (2002) to create 9 statements. Third, the Job Performance Scale referred to the theories on job performance proposed by Choo (1986), and 9 statements were formed and included in the questionnaire according to an edited version of the scales used by Fisher (2001). Each of these three areas used the Likert Scale for measurement, with each statement using a score from 1 (strongly disagree) to 5 (strongly agree).

Research Hypotheses This study proposes the following hypotheses in accordance with the aims of this study and other relevant literature: Hypothesis 1: Employee health has a notable positive impact on occupational burnout. Hypothesis 2: Occupational burnout has a notable negative impact on job performance.

Data Analysis This study first took all of the data from returned valid questionnaires and encoded it, removed any errors, organized it, dealt with missing values, converted reverse-wording questions, as well as examining and combining the scales. After a final check to ensure no errors were present, the data was analyzed and collated using SPSS for Windows 19.0.

RESULTS AND DISCUSSION

Descriptive Statistics of the Samples

1. Factor Analysis 1.1 Employee Wellness Scale The corrected values of total correlation for each of the statements on the Employee Wellness Scale are all greater than .5. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy for this scale is .875, which is a value approaching 1. The chi-square value of Bartlett’s Sphericity Test is 5465.700 and is therefore statistically significant. All of these figures demonstrate that a good level of sampling accuracy is present within the data, making it suitable for performing factor analysis. This study used Principal Component Analysis (PCA) to carry out factor analysis. After using the Varimax Method to perform orthogonal rotation, the factor loading values which were extracted were all greater than .5. These figures are shown in Table 1.1.1.

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Table 1.1: Employee Wellness Factor Analysis Aspect Statement Factor LoadingEigenvalue Explained Variation (%) I care about my health and make healthy activity and dietary choices. .710 A healthy lifestyle is one of my main goals in life. .683 Staying I try to avoid contracting illnesses in my daily life. .599 3.339 14.516 Healthy When sick, I follow the courses of treatment recommended by medical .506 practitioners so I can recover as quickly as possible. I believe that relaxation techniques are beneficial to my mental and physical health. .713 Coping I use stress-relieving techniques to relieve the stress in my life. .743 2.521 10.959 Mechanisms I enjoy participating in activities as they help me to manage stress. .697 I spend some time each day trying to relieve my stress. .647 I take note of the nutritional values of the food I consume each day. .617 I take note of and read the nutritional information on the packaged food that I buy. .548 Health Routine exercise is an important part of my day. .825 2.917 12.683 Awareness I try to exercise for at least 30 minutes per day, three times a week. .859 I schedule aerobic exercise (e.g. walking or fitness walking) as one of my daily tasks. .815 I will ask my boss for help when my job becomes difficult. .811 My boss is willing to listen to me talk about my work. .830 Social My partner, friends, and parents are willing to hear about my problems at work. .792 3.323 14.446 Support People at home (e.g. my partner, parents, or brothers and sisters) support me when I .794 have difficulties at work. My friends and relatives are willing to hear about my problems at work. .766 I am aware of the significance of my job. .707 I can motivate myself at work. .734 Individual My job is an important part of my life. .659 3.177 13.816 Values My values affect the choices I make in life. .636 I believe that I have a soul. .529

1.2 Occupational Burnout Scale The corrected values of total correlation for each of the statements on the occupational burnout scale are all greater than .5. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy for this scale is .806, which is a value approaching 1. The chi-square value of Bartlett’s Sphericity Test is 1998.355 and is therefore statistically significant. The factor loading explained variation that was carried out for the factor analysis shows that a good degree of sampling accuracy is present within the data. The results of this factor analysis are shown in Table 1.2.

Table 1.2: Occupational Burnout Factor Analysis Aspect Statement Factor LoadingEigenvalue Explained Variation (%) My job leaves me feeling in low spirits. .697 I feel drained after a day at work. .581 I feel like my job sucks all the life out of me. .693 I can effectively solve clients’ problems. 0.736 Occupational My job has a positive impact on other peoples’ lives. .660 6.110 67.889 Burnout I can easily understand my clients’ issues. .744 I like to maintain an objective attitude when dealing with clients. .660 Since starting this job, I have become more detached toward people. .637 This job makes me feel down. .703 Note:  indicates a reverse-wording statement

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1.3 Job Performance Scale The corrected values of total correlation for each of the statements on the job performance scale are all greater than .5. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy for this scale is .925, which is a value approaching 1. The chi-square value of Bartlett’s Sphericity Test is 2286.556 and is therefore statistically significant. The factor loading explained variation that was carried out for the factor analysis shows that a good degree of sampling accuracy is present within the data. The results of this factor analysis are shown in Table 1.3.

Table 1.1.3: Job Performance Factor Analysis Aspect Statement Factor LoadingEigenvalue Explained Variation (%) I am satisfied with the quantity and quality of our products at work. .563 I am very satisfied with my ability to guide others. .550 I am satisfied with my ability to adapt to new situations. .568 I am satisfied with all of the responsibilities I shoulder and take a positive .727 approach to them. Job I am satisfied with my professional capabilities and duties at work. .702 5.464 60.709 Performance I am satisfied that I carry out my responsibilities in accordance with company .621 policies and processes. I am satisfied with my implementation and coordination abilities at work. .690 I am satisfied with my ability to get on with other people where I work. .583 I am satisfied with my ability to get on with external clients. .559

2. Reliability Analysis 2.1 Employee Health Scale This study adopted the concept of wellness proposed by Myers & Sweeney (2008), with the final scale based on the work of Porter, Claycomb & Kraft (2008) to measure the recognition of employee health. The internal consistency coefficients from this study (using Cronbach’s α value) are as follows: Staying Healthy: .830, Coping Mechanisms: .816, Health Awareness: .858, Social Support: .806, Individual Values: .841. These figures are shown in Table 2.1.

Table 2.1: Employee Health Reliability Analysis Aspect Statement Average Standard Deviation Analysis of individual statements and total scores Cronbach’s α Coefficient 1 1.77 0.651 .577** 2 1.74 0.648 .610** Staying Healthy .830 3 1.79 0.670 .556** 4 1.71 0.645 .525** 5 1.68 0.630 .476** Coping 6 1.92 0.678 .554** .816 Mechanisms 7 1.94 0.784 .562** 8 2.25 0.775 .574** 9 2.45 0.829 .561** 10 2.44 0.906 .470** Health Awareness 11 2.55 0.924 .562** .858 12 2.69 1.048 .525** 13 2.64 1.035 .500** 14 2.37 0.820 .458** Social Support 15 2.34 0.857 .436** .806 16 2.00 0.691 .477**

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17 2.02 0.762 .454**

18 2.13 0.762 .397** 19 2.00 0.680 .564** 20 2.17 0.762 .533** Individual Value 21 2.15 0.826 .439** .841 22 1.93 0.676 .462** 23 2.18 0.812 .449** Note: * refers to a significance level greater than .5 (p< .05); ** refers to a significance level greater than .01 (p< .01); *** refers to a significance level greater than .001 (p< .001)

2.2 Occupational Burnout Scale This study used the theory of occupational burnout proposed by Maslach (1982), with the final scale based on the editing work carried out by Almer & Kaplan (2002) to measure occupational burnout. The internal consistency coefficients from this study (using Cronbach’s α value) are as follows:

Table 2.2: Occupational Burnout Reliability Analysis Aspect Statement Average Standard Deviation Analysis of individual statements and total scores Cronbach’s α Coefficient 1 3.47 0.895 .648** 2 3.02 0.930 .513** 3 3.25 0.929 .558** 4 3.59 0.696 .415** Occupational Burnout 5 3.45 0.778 .323** 0.823 6 3.58 0.687 .459** 7 3.77 0.673 .534** 8 3.72 0.976 .619** 9 3.66 0.983 .664** Note: * refers to a significance level greater than .5 (p< .05); ** refers to a significance level greater than .01 (p< .01); *** refers to a significance level greater than .001 (p< .001)

2.3 Job Performance Scale This study used the concept of job performance proposed by Choo (1986), with the final testing scale based on the usable scales and edited versions put forward by Fisher (2001) to measure job performance. The internal consistency coefficient from this study (using Cronbach’s α value) was .918, as shown in Table 2.3.

Table 2.3: Job Performance Reliability Analysis Aspect Statement AverageStandard Deviation Analysis of individual statements and total scores Cronbach’s α Coefficient 1 2.31 0.714 .680** 2 2.34 0.739 .670** 3 2.18 0.653 .686** 4 2.14 0.672 .798** Job Performance 5 2.22 0.686 .778** .918 6 2.18 0.665 .721** 7 2.25 0.681 .771** 8 2.08 0.654 .619** 9 2.30 0.676 .676** Note: * refers to a significance level greater than .5 (p< .05); ** refers to a significance level greater than .01 (p< .01); *** refers to a significance level greater than .001 (p< .001)

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3. Correlation Analysis Table 3.1 shows the results of the correlation analysis that was carried out. A description of the correlations for each of the variables is as follows: The product-moment correlation between employee health and occupational burnout is r (431) = -.399, p < .01. This shows that when employee health is better, levels of occupational burnout are lower. The product-moment correlation between job performance and occupational burnout is r (431) = -.525, p < .01. This shows that when employee job performance is higher, levels of occupational burnout are lower.

Table 3.1: Correlation Analysis for the Employee Health, Job Satisfaction, Occupational Burnout, and Job Performance Scales (n=433) 1 2 3 4 5 6 7 8 1. Gender 1.000 2. Age -.241** 1.000 3. Educational Background -.076 -.213** 1.000 4. Length of Service -.126** .825** -.268** 1.000 5. Employee Health .066 -.138** .010 -.183** 1.000 6. Job Satisfaction .066 -.219** .075 -.234** .539** 1.000 7. Occupational Burnout .036 .169** -.016 .183** -.399** -.589** 1.000 8. Job Performance -.018 -.145** -.030 -.208** .615** .722** -.525** 1.000 Average 1.56 2.89 3.05 3.22 48.92 20.51 31.49 20.01 Standard Deviation .497 1.024 .516 1.538 10.125 5.815 4.911 4.775 Note: * refers to a significant correlation when the level of significance is .05; ** refers to a significant correlation when the level of significance is .01; *** refers to a significant correlation when the level of significance is .001.

4. Regression Analysis 4.1 Employee Health and Occupational Burnout Regression Analysis In accordance with the returned questionnaires, the variables were entered into the regression analysis in two stages. The five aspects used as control variables for the analysis were: gender, age, education level, length of service, and the independent variable. These were used to examine the influence on job performance which served as the dependent variable. The results are shown in Table 4.1. In the first stage, the control variables were inserted to understand the impact of these variables on the dependent variable – occupational burnout. When Model 1 (M1) was produced the F value was 4.604, meaning that the results were statistically significant (p< .001). The R2 value for M1 was .41, AdjR2 was .32, andR2 increased by .41. In the second stage of the analysis, employee health was included as an independent variable and Model 2 (M2) was produced. From this it can be seen that there is a clear positive link between employee health and job performance, with an F value of 19.047 meaning that the data is statistically significant (p< .001). The R2 value was .182, AdjR2 was .173, and R2 increased by .141. The regression β coefficient for employee health was -.383, meaning that the level of significance was reached (p< .001). This result shows that when employees are healthier, they are less likely to suffer occupational burnout. Therefore, the hypothesis proposed by this study that employee health has a noticeably positive impact on occupational burnout is supported.

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Table 4.1: Employee Health and Occupational Burnout Regression Analysis Dependent Variable: Occupational Burnout β Coefficient (Model 1) β Coefficient (Model 2) Control Variables Gender .077 .096 Age .088 .112 Education Level .044 .030 Length of Service .132 .041 Independent Variable Employee Health -.383*** F Value 4.604** 19.047*** R2 .041 .182 AdjR2 .032 .173 R2 .041 .141 Note: * refers to a significance level greater than .5 (p< .05); ** refers to a significance level greater than .01 (p< .01); *** refers to a significance level greater than .001 (p< .001)

According to Table 4.2, the tolerance figures for Models 1 and 2 are both greater than 0.1. As the variance inflation factor (VIF) is also under the standard value of 10, this shows that there are no repeated collinearity issues within the models. Examining for unique error terms was carried out using the Durbin-Watson (D-W) statistic. With all of the values achieved being between 1.000 and 2.000 which is close to the value of 2, this shows that there are no instances of autocorrelation.

Table 4.2: Collinearity Diagnosis as part of a Regression Model for Employee Health and Occupational Burnout Model ResearchVariable Tolerance VIF Durbin-Watson Gender 0.913 1.095 Age 0.301 3.325 M 1 1.643 Education Level 0.916 1.092 Length of Service 0.307 3.258 Gender 0.911 1.097 Age 0.300 3.329 M 2 EducationLevel 0.914 1.094 1.712 Length of Service 0.301 3.317 Employee Health 0.962 1.039

4.2 Occupational Burnout and Job Performance Regression Analysis In accordance with the returned questionnaires, the variables were entered into the regression analysis in two stages. The five aspects used as control variables for the analysis were: gender, age, education level, length of service, and the independent variable. These were used to examine the influence on job performance which served as the dependent variable. The results are shown in Table 4.3. In the first stage, the control variables were inserted to understand the impact of these variables on the dependent variable – job performance. When Model 1 (M1) was produced the F value was 6.289, meaning that the results were statistically significant (p< .001). The R2 value for M1 was .056, AdjR2 was .047, and R2 increased by .056. In the second stage of the analysis, occupational burnout was included as an independent variable and Model 2 (M2) was produced. From this it can be seen that there is a clear positive correlation between occupational burnout and job performance, with an F value of

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36.200 meaning that the data is statistically significant (p< .001). The R2 value was .298, AdjR2 was .289, and R2 increased by .242. The regression β coefficient for employee health was -.503, meaning that the level of significance was reached (p< .001). This result shows that lower occupational burnout leads to higher job performance. Therefore, the hypothesis proposed by this study that occupational burnout has a notable impact on job performance can be supported.

Table 4.3: Occupational Burnout and Job Performance Regression Analysis Dependent Variable: Job Performance β Coefficient (Model 1) β Coefficient (Model 2) Control Variables Gender -.047 -.009 Age .064 .109 Education Level -.099 -.077 Length of Service -.293 -.227 Independent Variable Occupational Burnout -.503*** F Value 6.289*** 36.200*** R2 .056 .298 AdjR2 .047 .289 R2 .056 .242 Note: * refers to a significance level greater than .5 (p< .05); ** refers to a significance level greater than .01 (p< .01); *** refers to a significance level greater than .001 (p< .001)

According to Table 4.4, the tolerance figures for Models 1 and 2 are both greater than 0.1. As the variance inflation factor (VIF) is also under the standard value of 10, this shows that there are no repeated collinearity issues within the models. Examining for unique error terms was carried out using the Durbin-Watson (D-W) statistic. With all of the values achieved being between 1.000 and 2.000 which is close to the value of 2, this shows that there are no instances of autocorrelation.

Table 4.4: Collinearity Diagnosis as part of a Regression Model for Occupational Burnout and Job Performance Model Research Variable Tolerance VIF Durbin-Watson Gender 0.913 1.095 Age 0.301 3.325 1.823 M 1 Education Level 0.916 1.092 Length of Service 0.307 3.258 Gender 0.908 1.101 Age 0.300 3.333 M 2 Education Level 0.914 1.094 1.842 Length of Service 0.305 3.277 Occupational Burnout 0.955 1.043

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CONCLUSION AND SUGGESTIONS

Conclusion This study used research design, the gathering of data, and the results of empirical analysis to examine the relationship between employee health, occupational burnout, and job performance. The results of this empirical analysis are as follows: (1) Employee health has a notable positive impact on occupational burnout. The better the health condition of employees, the greater their ability to overcome difficulties at work. Furthermore, when employees have a greater ability to face up to challenges and to deal with adversity, they are less likely to quit. (2) Occupational burnout has a significant negative impact on job performance. When employees are unable to find a suitable outlet to release their work pressures, are unable to accomplish their work goals, or lose the feeling of accomplishment in their job, it will result in a feeling of boredom at work. This can also result in other negative impacts such as a lack of commitment or loyalty toward the enterprise. As previous scholars identified that occupational burnout negatively impacts job performance, this study once again demonstrates their contribution.

Suggestions (1) As Oldridge’s (2008) study proved, when enterprises ignore their employees’ health it results in economic loses and increases employee healthcare costs by between 1.5 and 3%. This increases financial costs for the enterprises. Longenecker, Yonker, & McGoldrick, (2009) confirmed that when enterprises improve the health of their employees, it can reduce their financial outlay on employee healthcare. This is similar to the fact that when enterprises value employees’ knowledge and increase in functional performance in its human capital it results in a greater return on investment (ROI) for the organization. This study once again confirms the theories of previous scholars who stated that healthy employees within enterprises can increase production efficiency (Hansson, 2007; Lindberg, 2006; Oxenburghet al., 2004; Wolf, 2008), and results in increased creativity, efficiency, productivity, and profits for the organizations. (2) When managers look at the wellness of their employees, they are able to understand their health condition. This can then be used to forecast the behavior and performance of these employees, as well as predict the risks which may arise as a result of poor health. Furthermore, the use of rewards or preventative measures at appropriate times can be used to increase the enterprises’ production efficiency and reduce the consequences of employee burnout. All of this will establish a better culture and atmosphere within the organization, create a sense of pride within employees, and form a sense of partnership between employees and the organization.

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