FACULTY OF ECONOMICS AND BUSINESS MSc IN HUMAN RESOURCE MANAGEMENT
Relationship between happiness, job satisfaction and
productivity
MASTER THESIS 2015
Maria Balatoglou
Student number: S2521512
Ptolemeon 12, 546 30
Thessaloniki, Greece
Phone: +30 6984534710
E-mail:
m.balatoglou@student.rug.nl
Supervisor: P.H. van der Meer
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ABSTRACT
Happiness for many people is the main achievement during their life. Someone is happy when he or she experiences satisfaction in life, feels frequent joy, is satisfied with his/her job and only sometimes feels unpleasant emotions like sadness and anger. Happiness seems to be a broader concept than just job satisfaction. This research examines how both happiness and job satisfaction of employees affect their productivity. It also presents relationships between these concepts separately. The research was conducted among 43.816 people in 27 different countries (mostly European Union Member States). The findings of this study reveal that happiness and job satisfaction have a negative relationship with productivity.
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TABLE OF CONTENTS
1. INTRODUCTION ... 4
2. THEORETICAL FRAMEWORK ... 6
2.1. Productivity (Absenteeism) ... 6
2.2. Productivity – Job satisfaction ... 7
2.2.1. Job satisfaction ... 7
2.2.2. Relationship between productivity and job satisfaction ... 8
2.3. Productivity – Well-being ... 10
2.3.1. Well-being ... 10
2.3.2. Relationship between productivity and well-being ... 10
3. METHODOLOGY ... 12
4. RESULTS ... 15
4.1. Descriptive statistics ... 15
4.2. Relationship between absenteeism and well-being ... 16
4.3. Relationship between absenteeism and job satisfaction ... 18
4.4. Relationship between absenteeism and the independent variables of well-being and job satisfaction ... 20
4.5. Summary of hypothesis testing ... 22
5. DISCUSSION OF RESULTS ... 23
6. CONCLUSION ... 25
6.1. Limitations ... 26
6.2. Further research ... 26
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1. INTRODUCTION
Happiness is an issue of major importance for everyone. It is often considered as the ultimate goal in life, everybody wants to be happy (Frey and Stutzer, 2002). Feeling happy is fundamental to human experience, and most people are at least mildly happy much of the time (Diener & Diener, 1996). The pursuit of happiness is even listed in the United States Declaration of Independence (1776) between the right to life and the right to liberty. The interest in happiness has also extended to workplace experiences (Fisher, 2010).
An increasing body of literature about happiness shows that employment makes people happy (Clark & Oswald, 1994; Frey & Stutzer, 2002). Happiness of workers can be influenced by many factors such as income, hours of work or job characteristics either in a positive or in a negative way (Meer & Wielers, 2013). Happiness is often labeled subjective being (Dolan, Peasgood & White, 2008). The term ‘subjective well-being’ will also be used in this study. One aspect that is often mentioned to be positively related to subjective well-being is the job. It is often argued that working, and in particular the satisfaction that the job offers, increases the subjective well-being of a person.
The rise of positive psychology has increased the attention towards studying the importance of happiness and the effects on productivity and job satisfaction, but happiness as a concept is more than job satisfaction (Fisher, 2010). Job satisfaction is only one component of the overall subjective well-being of a person since life is more than just the job. A comprehensive measure of individual level happiness might include work engagement, job satisfaction, and organizational commitment (Fisher, 2010).
It is common known that the main goal of firms is to achieve the performance outcomes, they desire. According to the Human Resource Management field, a firm without employees, cannot exist (Boxall & Purcell, 2011). Therefore, HR Managers attempt to employ qualified workers, motivate them and give them the opportunity to perform - AMO model - (Boxall & Purcell, 2011). According to this model, employees perform well, when they have the (A) abilities to do so, when they are (M) motivated to do so and when they are given the (O) opportunity to do so (Boxal & Purcell, 2011). Throughout the literature, a link between HRM and productivity growth has found (Gerhart & Milkovich 1992; Huselid 1995; Weitzman & Kruse 1990).
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job satisfaction. As mentioned before, job satisfaction is a component of happiness. Therefore, I will also examine how it influences productivity of the employees. Based on the literature, productivity is difficult to be measured. According to Herrmann and Rockoff (2011), absenteeism can be a measure of productivity. In the survey used in this study, there are no statistical data on productivity but there are statistical data on absenteeism. Therefore, it will be used as a measure of productivity. Absenteeism indicates that the worker has become an ‘economic man’ which is what sociologists have always wanted. Absenteeism is a consequence of the workers’ new-found economic rationality: it shows that the worker can value work and leisure in monetary terms (Hone, 1968). The concept of absenteeism will be developed more in the theoretical framework.
After testing the relationships between happiness and productivity and job satisfaction and productivity, I will examine which relationship of these two, is stronger than the other one. The main question of the study will be about how both of them (happiness and job satisfaction) affect productivity. According to the literature, when an employee is happy and feels satisfied with his/her job, he/she will be more productive (Cropanzano & Wright, 2001). This results in the following research question:
Do happiness and job satisfaction of employees increase their productivity?
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2. THEORETICAL FRAMEWORK
2.1. Productivity (Absenteeism)
The level of productivity is defined as the ratio of output to input of a single firm (Rogers, 1998). However, in the survey that is used in this study, there are no exact questions that can measure productivity. One challenge in measuring productivity is that productivity measured in real time will be revised due to revision to its source data (Hara & Ichiue, 2011). There are many different ways to measure productivity such as worker hours, materials, energy per unit etc (Schreyer, 2005). In this case, I measure productivity using the term of absenteeism. Missed work days have an economically important negative impact on productivity (Herrmann & Rockoff, 2011). In other words, absenteeism leads to productivity loss. This relationship will be used throughout this research.
Employee absenteeism is a worldwide phenomenon which, due to the financial impact on a nation’s economy, is an important subject on the international agenda. It is a costly yet poorly understood organizational phenomenon (Gellatly, 1995), attracting the attention of theoreticians and practitioners alike (Hackett, 1989). Considerable research on this topic has linked absence work and work-related attitudes such as job satisfaction (Cheloha & Farr, 1980; Hackett & Guion, 1985). Hanisch and Hulin (1991) supported that absenteeism reflects ‘invisible’ attitudes such as low level of organizational commitment, job dissatisfaction or an intention to quit. In other words, an employee who is often absent from work has a negative attachment to the organization, either consciously or unconsciously. Surprisingly, absence can also have a positive role for a dissatisfied or a lowly committed employee (Rosse & Miller, 1984). Specifically, an employee who is absent can avoid the negative emotions associated with work. Conversely, employees who are highly satisfied with their jobs or strongly committed to the organization, will also be highly attached to their work (Blau & Boal, 1989).
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(Hackett & Guion, 1985). According to March and Simon (1958), voluntary absences include vacation, uncertified sickness etc. They are under the direct control of the employee and are also frequently utilized for personal aims such as testing the market for alternative employment prospects (Miller, 1981). On the other hand, involuntary absences include certified sickness, funeral attendance etc. (March & Simon, 1958). Conversely, these are beyond the employee’s immediate control (Sagie, 1998). Comparatively, voluntary absence from work may reflect job dissatisfaction and lack of commitment to the organization, rather than involuntary absences. In other words, work attitudes will be more negatively related to voluntary absence than to involuntary absence (Sagie, 1998).
2.2. Productivity – Job satisfaction 2.2.1. Job satisfaction
Job satisfaction has been an important focal point for organizational and industrial psychology for at least two reasons. First, job satisfaction is strongly caused by working conditions such as responsibility, task variety or communication requirements (Hackman & Oldman, 1980). Second, job satisfaction is supposed to be a major cause of outcome variables such as absenteeism (Keller, 1983; Tharenou, 1993), fluctuation (Rusbult & Farrell, 1983), or organizational inefficiency such as counterproductive behavior (Gottfredson & Holland, 1990) or sabotage (Chen & Spector, 1992). Therefore, job satisfaction mediates the relation between working conditions on the one hand and organizational and individual outcomes on the other hand (Dormann & Zapf, 2001).
In defining job satisfaction, there are countless definitions given throughout the literature (Mullins 2002, Weis 2002, Baron & Greenberg 2003). The reference is often made to Locke’s (1976) description of job satisfaction as a “pleasurable or positive emotional state resulting from the appraisal of one’s job or job experiences”. There are different facets to job satisfaction and the challenge to understand job satisfaction and its effects in an organisation is easier said than done. Personally, I would define job satisfaction as a collection of attitudes, feelings, beliefs and behaviour one has towards his or her job.
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The JCM can be depicted on terms of five core dimensions: (1) skills variety, defined as the degree to which the job involves a range of activities and talent, (2) task variety, defined as the degree to which a worker completes a particular job from the beginning to the end, (3) task significance, defined as the extent to which the job has an impact on people concerned, (4) autonomy, defined as the degree to which the job provides freedom, independence and discretion to the worker in the planning and execution of tasks, and (5)
feedback, defined as the extent to which clear and direct information is provided to the
worker to evaluate his or her performance. Consequently, if a job includes all these characteristics, then the incumbent will interpret the job as more valuable and worthwhile. In addition, Baron and Greenberg (2003) states more factors that lead workers to hold positive or negative perceptions of their jobs. Such factors are pay, the work itself, promotions, supervision and working conditions.
2.2.2. Relationship between productivity and job satisfaction
One of the most controversial topics in the history of industrial/organizational psychology is the relationship between job satisfaction and job performance. (Judge, Thoresen, Bono, & Patton, 2001). Job satisfaction has been defined as ‘feelings or affective responses to facets of the (workplace) situation’ (Smith, Kendall, & Hulin, 1969). Job performance, on the other hand, is of interest to organizations because of the importance of high productivity in the workplace (Hunter & Hunter, 1984). The level of productivity is defined as the ratio of output to input of a single firm (Rogers, 1998).
More than two decades of research have been devoted to understanding the job satisfaction – job performance relationship; yet, the controversy has remained alive (Petty, Mcgee & Cavender, 1964). According to Brayfield and Crockett (1955), the first meta-analysis was performed in regard to the relationship between job satisfaction and job performance and only a weak correlation was obtained. Thirty years later, another meta-analysis was conducted by Petty et al (1984), which demostrated a slightly higher correlation. Moreover, the most extensive meta-analysis (Iaffaldano & Muchinsky, 1985) resulted in an also high average correlation.
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satisfied with their job will also be better performers on those jobs. Despite the emotional flavor of lay conceptions of ‘happiness,’ job satisfaction scales do not typically focus on emotions, instead asking employees to rate their satisfaction with their pay, working conditions, job as a whole, etc. (Brayfield & Rothe 1951). Wright and Cropanzano (2004) argue that the relationship between happiness and productivity would be stronger if happiness were operationalized more broadly than job satisfaction. They state, ‘recent research has consistently demonstrated that high levels of… well-being can boost performance on the job’’ (p. 341). They also argue (2001) that happiness has been inconsistently operationalized as the presence of positive affect, the absence of negative affect, lack of emotional exhaustion, and as job satisfaction. They suggest that although some of these constructs are meaningfully associated with performance, others may not be as central.
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The first hypothesis that is derived about the relationship between job satisfaction and productivity is:
H1: Employees who are more satisfied with their job, they will be less absent from their job.
2.3. Productivity – Well-being 2.3.1. Well-being
Happiness for many people is the main achievement during their life. It has been found to be a highly valued goal in most societies (Diener, 2000). Thus, subjective well-being (in this research synonym with happiness) can be defined as a broad concept that includes experiencing pleasant emotions, low levels of negative moods, and high life satisfaction. The positive experiences embodied in high subjective well-being are a core concept of positive psychology because they make life rewarding (Diener, Lucas & Oishi, 2009).
Because the various components of subjective well-being are only moderately intercorrelated (and because the size of these correlations may vary across different populations), SWB researchers recommend assessing at least four different constructs to get a relatively complete view of a person’s subjective quality of life (Diener & Lucas, 2008). Diener, Suh, Lucas and Smith (1999) recommend assessing positive affect, negative affect, life satisfaction and satisfaction with specific life domains (eg. one’s health, one’s job etc).
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2.3.2. Relationship between productivity and well-being
For decades, organizational scientists and practitioners have examined the relationship between happiness (subjective well-being) and employee productivity. Evidence showed that happy employees exhibit higher levels of productivity than do unhappy workers (Cropanzano & Wright, 2001). However, despite years of research, the results remain ambiguous. This results from the variety of ways in which happiness has been operationalized. Researchers have operationalized happiness as job satisfaction, presence of positive affect, absence of negative affect, lack of emotional exhaustion and psychological well-being. Some of these measures exhibit appreciable associations with productivity, others do not (Cropanzano & Wright, 2001). Nevertheless, according to DiMaria, Peroni and Sarracino (2014), subjective well-being is an ingredient of productivity, which also confirms the results of the previous literature. In this study, this theory will be supported. Keep in mind the negative relationship between productivity and absenteeism as mentioned in the beginning of the theoretical part and the fact that, in this study, productivity is measured in terms of absenteeism. This results in the following hypothesis:
H2: Employees who feel happy, they will be less absent from their job.
The third and main hypothesis of this study is about the relationship between well-being and productivity and the relationship between job satisfaction and productivity. As for the first relationship, several micro-econometric, psychological and experimental studies support that subjective well-being fosters productivity (DiMaria, Peroni & Sarracino, 2014). On the other hand, job satisfaction and productivity are also related but literature has concluded that there is no strong pervasive relation between these two variables (Iaffaldano & Muchinsky, 1985). Therefore, I state that the relationship between subjective well-being and productivity is stronger than the one between job satisfaction and productivity, since job satisfaction is only a part of the more broad concept of subjective well-being. This issue will be examined more within the study.
This results in the following main hypothesis:
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3. METHODOLOGY
For this research, the data is derived from the 5th European Survey on Working Conditions of Eurofound (EWCS). This data set can be downloaded freely after a registration procedure at the UK Data Archive on the website
http://discover.ukdataservice.ac.uk/catalogue?sn=6971. In an effort to provide comparable and reliable data on working conditions across Europe, the survey covers 27 European Union Member States, Turkey, Croatia, the Former Yugoslavian Republic of Macedonia, Norway, Albania, Kosovo, and Montenegro.
The target population is all residents of the countries mentioned above, that were aged 15 or older (aged 16 or older in Spain, the UK and Norway), and that were in employment at the time of the survey. In total, 43,816 people were interviewed in 2010. The target sample size in most countries was 1000. Exceptions were Germany and Turkey (target sample size of 2000), and Italy, Poland and the United Kingdom (target sample size 1500). Moreover, three countries decided to finance bigger national samples resulting in a target sample size of 4000 in Belgium, 3000 in France and 1400 in Slovenia.
The type of this survey is based on questionnaires with interviews conducted in people’s homes in the national language(s) of the country. The questionnaire includes 77 questions about working conditions and 12 demographic questions. The type of the interview is face to face, at home (i.e. outside the workplace), with average duration 44 minutes. As soon as all the data from the 5th European Survey on Working Conditions are gathered, they will be analysed by the use of SPSS.
Variables
The variables that are going to be used in this research from the 2010 dataset are:
1. Well-being
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been recorded, ranging from 1 (all of the time) to 6 (at no time), with also an option to answer ‘Do not know’ or ‘Refusal’. In order to check which one to use or how I can combine these five subquestions, I run a reliability test to test if the Cronbach’s Alpha is (> .70). The revealed values suggested that the different scales are reliable with Cronbach’s alpha = 0.883.
2. Job Satisfaction
Job satisfaction is measured with the question ‘On the whole, are you very satisfied, satisfied, not very satisfied or not at all satisfied with working conditions in your main paid job?’ (Q76). The question should be answered rating on a four point scale, where 1 stands for ‘very satisfied’ and 4 for ‘not at all satisfied’. Respondents had the option to answer ‘8’, which stands for ‘Do not know/No opinion’ or ‘9’ for ‘Refusal’ as well.
3. Absenteeism
It is worth mentioning that this specific survey does not include exact data about employee productivity. According to Miller, Murname and Willett (2008), productivity is highly influenced by worker absence. Therefore, I will approximate productivity using the term of absenteeism for which data are given in this survey. The question which is used in this research to measure the absenteeism is: ‘Over the past 12 months did you work when you were sick?’ (Q74). This question seems a better indicator of employee commitment to the job than just the number of days the employees are absent. In this case, I recoded the variable in the way that makes the analysis meaningful. Specifically, 0 for ‘No, I did not work when I was sick’, 1 for ‘Yes I did work when I was sick’ and 3 for ‘I was not sick’.
Control Variables
The control variables which will be used in this research are the demographic variables: age (HH2b), gender (HH2a), education (EF1) and subjective income (EF6).
Age
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are important.’ For instance, for work tasks where experience and verbal abilities matter more, there is less or no reduction in productivity among elderly workers. Furthermore, the elderly’s productive potential has been increased over time. Jobs do not rely only on strength. Cognitive abilities and health of the elderly are improved, more training is offered, there is better work organisation, and more flexible earnings systems could allow the elderly to benefit from their comparative advantages and effectively extend the working life. The question that indicates the age of the respondent is: ‘Starting with yourself, how old are you?’ (HH2b).
Gender
Gender is coded 1 or 2, 1 stands for male and 2 stands for female.
Education
The question that indicates the level of education of the respondent is : ‘What is the highest level of education or training that you have successfully completed?’ (EF1). The answers are measured on a seven point scale, ranging from 1 which stands for ‘No education’ to 7 for ‘Tertiary education – advanced level’. Respondents could also refuse to answer.
Subjective income
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4. RESULTS
In this section, I present the analysis of the data from the 5th European Survey on Working Conditions of Eurofound (2010) with the use of SPSS. As a reminder, the research question of this study is: ‘Do happiness and job satisfaction of employees increase their productivity?’ First, I will give information about the descriptive statistics such as mean, standard deviation (SD) and correlations (Table 1,2) of the three main variables (absenteeism, well-being and job satisfaction). Second, I will examine the relationship between well-being and absenteeism,both excluding and including the control variables (Table 3). Further, in the same way, the relationship between job satisfaction and absenteeism will be presented, without and with the control variables (Table 4). Lastly, I will compare the effects of both well-being and job satisfaction on absenteeism, including and excluding the control variables (Table 5). Finally, there will be a short summary of the confirmation of the hypotheses (Table 6).
4.1. Descriptive statistics
The first step in the analysis is to provide a basic description of the three main variables. In particular, the table below (Table 1) outlines the means and standard deviations of these variables (well-being, job satisfaction, absenteeism) in use.
Table 1: Descriptive statistics of main variables
Mean SD N
Well-being 2.74 1.06 43024 Job Satisfaction 2.01 .74 43268 Productivity
(Absenteeism) 0.74 .96 43053
SD: Standard Deviation; N: Number of respondents
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In the Table 2 below, the correlations of these main variables are provided. The correlation between Absenteeism and Job satisfaction is significantly positive (r=.09). The correlation between Absenteeism and Well-being is significant and positive (r=.03) as well but rather weak in comparison with the correlation between Absenteeism and Job satisfaction (r= .09). Additionally, it is interesting to note that there is a significant and positive correlation between Job satisfaction and Well-being (r= .36).
Table 2: Correlations of main variables
Well-being Job satisfaction Absenteeism
Well-being 1
Job satisfaction .36** 1
Absenteeism .03** .09** 1
** Correlation is significant at the 0.01 level (2-tailed)
4.2. Relationship between absenteeism and well-being
The findings concerning the relationship between the dependent variable of absenteeim and the independent variable of well-being can be found in Table 3.
Model 1 represents the relationship between absenteeism and well-being and how well-being affects absenteeism without any control variables. The results of this model are presented under the heading Model 1 in Table 3.
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Table 3: Effects of control variables on the relationship between well-being and absenteeism
Model 1 Model 2
Parameter Estim
ate
Std. Error Sig. Estimate Std. Error Sig.
Intercept .65 .01 .00 .52 .03 .00 Control Age - .00 .00 .00 Gender - .07 .01 .00 Education 1.94 .00 .00 Subj Income .04 .00 .00 Main effects Well-being .03 .00 .00 .02 .00 .00 R Square .00 .95 .06 .92 Residual variance .91 .00 .86 .00
a. Dependent variable: Absenteeism b. p<.05
The analysis above shows that well-being has a significant positive effect on absenteeism. In Model 1, concerning well-being, B= .03 and p= .00 (p< .05) which means that there is a positive and significant relationship with absenteeism. This means that the happier one is, the more absent he/she will be from his/her job.
Similarly, in Model 2, concerning well-being, B= .02 and p= .00 (p< .05) which means that there is also a positive and significant relationship with absenteeism including the control variables (age, gender, education and subjective income).
As Table above shows, the control variables have different effects on the relationship between well-being and absenteeism. Specifically, age affects neagatively and to a small extent the relationship between absenteeism and well-being with B= - .00 and p= .00 (p< .05). Moreover, this effect seems rather weak. Concerning the gender, there is a negative effect of it on the relationship between well-being and absenteeism with B= -.07 and p= .00 (p< .05).
It is interesting to note how education affects the relationship between the dependent and independent variables. It is obvious that there is a very strong significant and positive effect with B=1.94 and p=.00 (p< .05). It seems that education plays a very important role upon this relationship. Lastly, subjective income has a significant positive effect upon the relationship between well-being and absenteeism with B=.042 and p=.00
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Concerning the relationship between absenteeism and well-being, the hypothesis H2 has to be rejected (H2: Employees who feel happy, they will be less absent from their job). The analysis above shows that there is a positive relation between them. In other words, employees who feel happy, they will be more absent from their job. This statement disagrees with the second hypothesis.
4.3. Relationship between absenteeism and job satisfaction
The findings concerning the relationship between the dependent variable of absenteeim and the independent variable of job satisfaction can be found in Table 4.
Model 1 represents the relationship between absenteeism and job satisfaction and how job satisfaction affects absenteeism without any control variables. The results of this model are presented under the heading Model 1 in table 4.
Model 2 represents the relationship between the dependent variable of absenteeism and the independent variable of job satisfaction taking into account the control variables of age, gender, education and subjective income. The results of this model are presented under the heading Model 2 in Table 4.
Table 4: Effects of control variables on the relationship between job satisfaction and absenteeism
Model 1 Model 2
Parameter Estim
ate
Std. Error Sig. Estimate Std. Error Sig.
Intercept .51 .01 .00 .47 .03 .00 Control Age - .00 .00 .00 Gender - .07 .01 .00 Education 1.89 .00 .00 Subj Income .03 .00 .00 Main effects Job satisfaction .11 .01 .00 .06 .01 .00 R Square .01 .94 .06 .92 Residual variance .90 .00 .86 .00
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The analysis above shows that job satisfaction has a significant positive effect on absenteeism. It should be noted that all the relations presented in this table proved to be significant (p< .05). In Model 1, concerning job satisfaction, B= .11 which means that there is a positive and significant relationship with absenteeism. This means that the more satisfied one is, the more absent he/she will be from his/her job.
Similarly, in Model 2, concerning job satisfaction, B= .06 which means that there is also a positive and significant relationship with absenteeism including the control variables (age, gender, education and subjective income).
As the table above shows, the control variables have different effects on the relationship between job satisfaction and absenteeism. Specifically, age affects neagatively and to a small extent the relationship between absenteeism and job satisfaction with B= - .00 and p= .00 (p< .05). Moreover, this effect seems rather weak. Concerning the gender, there is also a negative effect of it on the relationship between job satisfaction and absenteeism with B= -.07 and p= .00 (p< .05).
It is interesting to note how education affects the relationship between the dependent and independent variables. It is obvious that there is a very strong significant and positive effect with B=1.89. It seems that education plays a very important role upon this relationship. Lastly, subjective income has a significant positive effect upon the relationship between job satisfaction and absenteeism with B=.03 and p=.00 (p< .05).
According to the results from the table above about the relationship between absenteeism and job satisfaction, the hypothesis H1 has to be rejected (H1: Employees
who are more satisfied with their job, they will be less absent from their job). The analysis
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4.4. Relationship between absenteeism and the independent variables of well-being and job satisfaction
In this part, after testing the relationship between well-being and absenteeism and job satisfaction and absenteeism separately, I will now examine how the two (independent) variables of well-being and job satisfaction together affect the dependent variable of absenteeism. The findings concerning this relationship can be found in the following table.
Model 1 represents the relationship between absenteeism and well-being, job satisfaction and how these two independent variables affect absenteeism without any control variables. The results of this model are presented under the heading Model 1 in Table 5.
Model 2 represents the relationship between the dependent variable of absenteeism and the independent variables of well-being and job satisfaction taking into account the control variables of age, gender, education and subjective income. The results of this model are presented under the heading Model 2 in Table 5.
Table 5: Effects of control variables on the relationship between well-being, job satisfaction and absenteeism
Model 1 Model 2
Parameter Estim
ate
Std. Error Sig. Estimate Std. Error Sig.
Intercept .49 .02 .00 .46 .03 .000 Control Age - .00 .00 .00 Gender - .07 .01 .00 Education 1.88 .00 .00 Subj Income .03 .00 .00 Main effects Well-being .01 .00 .17 .01 .00 .07 Job satisfaction .11 .01 .00 .05 .01 .00 R Square .01 .94 .06 .92 Residual variance .89 .00 .85 .00
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The analysis above shows that both well-being, job satisfaction have a positive effect on absenteeism. It worths mentioning that all the relations presented in the table above proved to be significant (p< .05) except for the one between absenteeism and well-being in both Model 1 and 2.
Specifically, in Model 1, concerning well-being, B= .01 and p= .17 (p> .05) which means that it is not significant. Similarly, Model 2 gives for well-being B= .01 and
p= .07 (p> .05) which means that it is not significant, as well. As above mentioned, the
rest of the relations proved to be significant (p< .05).
Concerning job satisfaction, Model 1 gives B= .11 whereas model 2 gives B= .05, which means in both cases that there is a positive and significant relationship with absenteeism, excluding and including the control variables (age, gebder, education and subjective income).
As the table above shows, the control variables affect the relationship of the dependent and independent variables in a similar way. Namely, age affects negatively and to a small extent again the relationship between them, with B= - .00. Moreover, this effect seems also rather weak. Concerning the gender, there is also a negative effect of it on the relationship with B= -.07 and p= .00 (p< .05).
It is interesting to note how education affects the relationship between the dependent and independent variables. It is obvious that there is a very strong significant and positive effect with B=1.88. It seems that education plays a very important role upon this relationship. Lastly, subjective income has a significant positive effect upon the relationship between well-being, job satisfaction and absenteeism with B=.03 and p=.00
(p< .05).
According to the results from the table above about the relationship between the well-being, job satisfaction as independent variables and absenteeism as the dependent variable, the hypothesis H3 has to be rejected (H3: The relationship between productivity
and well-being is stronger than the relationship between productivity and job satisfaction). Contrarily, the relationship between absenteeism and job satisfaction seems
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4.5. Summary of hypothesis testing
Table 6 presents a summary of whether the hypotheses were confirmed or rejected. It is interesting to note that all the hypotheses are rejected.
Table 6: Summary of hypothesis testing
Hypothesis Confirmation
H1: Employees who are more satisfied with their job, they will be less absent from their job.
Rejected
H2: Employees who feel happy, they will be less absent from their job.
Rejected
H3: The relationship between productivity and well-being is stronger than the relationship between productivity and job satisfaction.
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5. DISCUSSION OF RESULTS
The purpose of this research is to find an answer to the research question: ‘Do happiness
and job satisfaction of employees increase their productivity?’. In order to formulate the
answer of this question, first, the three hypotheses will be discussed to finally ends up with an answer to the research question.
To achieve this goal, I tried to find the interaction between absenteeism, well-being and job satisfaction. It has to be kept in mind that, in this study, absenteeism measures employee productivity. According to Herrmann and Rockoff (2011), absenteeism can be a measure of productivity. Missed work days have an economically important negative impact on productivity (Herrmann & Rockoff, 2011). In other words, absenteeism leads to productivity loss. This relationship will relate the results of absenteeism presented in tables, to productivity throughout this research.
In this part, I will try to compare the results of the hypotheses with the theoretical framework given in this study. The first hypothesis is : ‘H1: Employees who are more
satisfied with their job, they will be less absent from their job’. This hypothesis is rejected.
The analysis presented, shows that employees who are more satisfied with their job, they will be more absent from them. This result partially contradicts some prior findings. According to early studies, studies have conducted that absenteeism is negatively related to overall job satisfaction (Brayfield & Crockett, 1955; Muchinsky, 1977; Vroom, 1964). This theory is in direct contrast to the result of the analysis.
However, some researchers have cautioned that the relationship is likely to be indirect and/or tenuous (Steers & Rhodes, 1978; Vroom 1964). Specifically, only a weak relationship between job satisfaction and absenteeism was found (Nicholson,Brown & Chadwick-Jones, 1976; Ilgen & Hollenback, 1977; Chadwick-Jones, Nicholson & Brown, 1982). It was also found that frequency of absence was significantly related to overall job satisfaction (Waters & Roach, 1971; Roteman, 1973).
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The second hypothesis is: ‘H2: Employees who feel happy, they will be less absent
from their job’. This hypothesis is rejected. The analysis shows that there is a positive
relation between absenteeism and well-being. In other words, employees who feel happy, they will be more absent from their job, thus less productive. This finding is highly unexpected. According to DiMaria, Peroni and Sarracino (2014), subjective well-being fosters productivity. Evidence showed that happy employees exhibit higher levels of productivity than do unhappy workers (Cropanzano & Wright, 2001). However, despite years of research, the results remain ambiguous.
The relation between the two relationships ‘well-being and productivity’ and ‘job satisfaction and productivity’ was also researched. The hypothesis here is: ‘H3: The
relationship between productivity and well-being is stronger than the relationship between productivity and job satisfaction’. According to the literature, subjective
well-being fosters productivity (DiMaria, Peroni & Sarracino, 2014). On the other hand, job satisfaction and productivity are also related but literature has concluded that there is no strong pervasive relation between these two variables (Iaffaldano & Muchinsky, 1985). Moreover, job satisfaction is only a part of the more broad concept of subjective well-being.
Contrarily, the results of this study show that the relationship between absenteeism and job satisfaction seems highly stronger than the relationship between absenteeism and well-being. Furthermore, the relationship between well-being and absenteeism is not only weaker but also not significant (p> .05). Consequently, the third hypothesis is also rejected.
Finally, based on this information above, the research question ‘Do happiness and
job satisfaction of employees increase their productivity?’ can be answered. According
to the literature on the relation between productivity and absenteeism and after testing the relationship between well-being and absenteeism and the relationship between job satisfaction and absenteeism, it can be concluded that both happiness and job satisfaction of employees do not increase their productivity.
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research to be done in order to give clear answers on how happiness and job satisfaction of employees affect their productivity.
6. CONCLUSION
The goal of this research was to investigate how happiness and job satisfaction of employees affect the productivity of them. This leads to the following research question: ‘Do happiness and job satisfaction of employees increase their productivity?’. For this research, the data from the 5th European Survey on Working Conditions of Eurofound (EWCS) was used. This survey covers 27 European Union Member States and 7 non-European Union Member States.
In order to formulate the answer of the research question, first, three hypotheses were discussed. It worths mentioning that, in this study, productivity was measured by absenteeism. Literature shows that absenteeism leads to productivity loss (Herrmann & Rockoff, 2011).
The first hypothesis was that employees who are more satisfied with their job, they will be less absent from their job. This hypothesis was expected to be proved right according to the literature. Specifically, studies have conducted that absenteeism is negatively related to overall job satisfaction (Brayfield & Crockett, 1955; Vroom, 1964). Contrarily, the analysis showed that employees who are more satisfied with their job, they will be more absent from it. Therefore, the first hypothesis was rejected.
The second subject was that employees who feel happy, they will be less absent from their job (Hypothesis 2). This hypothesis was also rejected. The analysis showed that there is a positive relation between absenteeism and well-being. On the other hand, evidence showed that happiness increases the levels of productivity (Cropanzano & Wright, 2001) and not the opposite.
After examining the relation between ‘well-being and productivity’ and ‘job satisfaction and productivity’, the relation between these two was also researched. Contrary to my expectations, the third hypothesis (H3: The relationship between
productivity and well-being is stronger than the relationship between productivity and job satisfaction) was rejected, as well. Indeed, the relation between job satisfaction and
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which indicates that well-being relation is expected to be stronger (Cropanzano & Wright, 2001).
To conclude, based on these results, the answer for the research question seems to be negative although it appears to need more further research.
6.1. Limitations
It is important to note the limitations involved in this thesis. The first limitation is the generalization of the current research. The survey used in this research includes both 27 European Union and 7 non-European Union countries (Turkey, Croatia, the Former Yugoslavian Republic of Macedonia, Norway, Albania, Kosovo and Montenegro). Therefore, the outcomes of this study cannot be reflected as an European Union Member State.
Another limitation is the usefulness of the study outside Europe. As previously mentioned, the study reveal the results of 27 European countries. But other non-European countries may not give the same results.
The most important limitation is that the data set used, was not specifically designed for this research purpose. Therefore, many variables were not completely suited to measure the different themes of the research. In this vein, it is inevitable to mention that there might be some misrepresentations due to different data problems.
6.2. Further research
The analysis seems to be a significant point for further research. It shows that it is highly difficult to draw clear conclusion about the relationship between well-being, job satisfaction and productivity. I can only assume a possible explanation for this difficulty. It might be due to the fact that both concepts of ‘well-being’ and ‘job satisfaction’ are not clear but already complex and are influenced by various variables which may differ from time to time.
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