1. Background
Job burnout is a challenging problem that managers face and affects the organizational outcomes; it is widely observed in organizations and other human service sectors. The term burnout emerged primarily in the 1970s to describe distressing feelings and exhaustion caused by work pressure and stress (1-3). Job burnout is a multi-dimensional construct including emotional exhaustion, depersonalization and lower self-efficacy (4). In the early stages of burnout research, researchers came to this realization that in workplace, the interpersonal nature of human service was the central feature of the job and also a source of emotional and interpersonal stress makers related to reaching job requirements (3). As interest in job burnout and its measurement rose during the late 70s and early 80s, Christine Maslach and Susan Jackson developed the Maslach burnout inventory (4), which is the most commonly used measure of burnout for researchers in the social fields. Job burnout is mostly caused by workers experiencing stress at workplace because of weak occupational interpersonal relations (3, 4). The emotional exhaustion dimension, referring to feelings of being emotionally exhausted is the core of burnout. Depersonalization is the interpersonal aspect of job burnout; it seems necessary to be developed to protect individuals against feelings of exhaustion. When the exhaustion becomes too discomforting for the individuals, they may be isolated from the working force and become skeptical towards their co-workers. Then, personal accomplishment is reduced and their abilities lessen when they experience burnout (5, 6). Therefore, Job burnout is the result of experiencing high stress in the workplace and causes a reduction of the individuals’ emotional resources and personal energy, hence higher job burnout.
One of the major factors affecting job burnout in different organizations is mostly known as psychological well-being, potential capabilities the workers may need to cope with various challenges. The psychological well-being construct is mainly developed when the proponents of positive psychology (7) are focused on understanding the experience of deep happiness, common sense, resilience and psychological, physical and social well-being. A change from negative aspects of life such as damages, disorders, depression, anxiety and sadness toward the positive human qualities such as happiness and hope and well-being is part of the positive psychology movement. The literature review may generally follow two related but distinct perspectives to examine well-being: hedonic or subjective well-being dealing with happiness, and eudemonic well-being, rooted in the views of philosophers such as Aristotle, dealing with human potentials (8-10). On the other hand, eudemonic well-being asserts that there is more to feeling well than feeling happy and satisfied with life. It claims that individuals are always striving toward meeting their true potentials, and pursuing meaningful goals, growing as a person, establishing quality ties to others (9). Psychological well-being focuses on human full development including the concepts such as self-actualization (11) full functioning (12) and individuation (13). This school of thought also suggests that a decrease of negative pitfalls does not spontaneously result in an increase in positive assets (1, 14). Ryff and Keyes presented six aspects of psychological well-being including: positive evaluation of oneself and one’s past life (self-acceptance), a sense of continued growth and development as a person (personal growth), the belief that life is purposeful and meaningful (purpose in life), the possession of quality relations with others (positive relations with others), the capacity to effectively manage life and the surrounding world (environmental mastery), and a sense of self-determination (autonomy) (15). Ruini et al found a negative relationship between the psychological aspects of the well-being and depression, anxiety and hostility (16). The results of research conducted by Keyes et al. confirmed that people with higher psychological well-being had more education, showed more openness to experience, and tended to actualize their potentialities (9).
On the other hand, human beings enjoy some psychological resources that make up a construct as psychological capital. These abilities can motivate them to face life challenges in work settings. Psychological capital is defined as “an individuals’ psychological capital state of development and is characterized by: (a) Having confidence to take on, and do the necessary effort to succeed at challenging tasks (self-efficacy); (b) Making a positive attribution about succeeding, now and in the future (optimism); (c) Persevering toward goals and when necessary, redirect paths to goals to succeed (hope); and (d) When surrounded by problems and adversity, sustain and bounce back up and even beyond to attain success (resiliency)”(17). There is a growing body of empirical evidence that psychological capital has a positive impact on the individuals’ work attitudes and behaviors (18). This is shown in the results of longitudinal research conducted by some researchers (19). Psychological capital represents the positive effective resources that individuals possess, which enable them to move towards flourishing and success, and help them achieve goals and cope better with the difficulties they may face. (20). The researchers found that psychological capital significantly decreases stress symptoms, intentions to quit, and job search behaviors (17); it increases satisfaction, organizational commitment (21), and well-being in workplaces (22). The present study, therefore, tried to investigate the relationship among the research variables and the role of psychological capital in the psychological well-being and job burnout of principals of high schools.
2. Objectives
The present study aimed to explore the correlation between psychological capital and psychological well-being with job burnout of the principals of high schools in Saveh, in 2015.
3. Materials and Methods
3.1. Participants and Procedure
It was a descriptive-correlational study. The statistical population consisted of all principals and their assistants working in girl and boy high schools in Saveh, a city in Markazi province of Iran. The Sample consisted of 130 subjects selected through simple randomized sampling method. Subjects were randomly selected among the principals and their assistants working in high schools in 2015. Fourteen persons were discarded because of incomplete questionnaires. Their age ranged from 25 to 55 years, with a mean of 42 years. At the time of data gathering, the principals had worked in high schools more than 10 years. The participants completed the questionnaires in an educational environment. Before participants complete the measures, written permission from education office was taken. Participants filled out the questioners on their own will; they were told that they were engaged in a psychological investigation in which there were no correct or incorrect answers. Authors distributed 130 questionnaires, out of which 116 were collected and validated through SPSS ver. 16, using descriptive and inferential statistics of Pearson correlation, and step to step regression.
3.2. Measurement Instruments
3.2.1. Psychological Capital Questionnaire
The psychological capital questionnaire (PCQ), developed by Luthans, et al., is a 24-item self-report scale that includes four dimensions, namely, self-efficacy, optimism, resiliency and hope. The items are rated from one (strongly disagree) to six (strongly agree). Some of the items are “I usually take stressful things at work in stride” and “I always look on the bright side of things regarding my job.” Scale scores are the sum of items with reverse coding of relevant items. In order to obtain the total score of psychological capital, first, the score of each subscale was calculated separately; then they were calculated altogether. The ratio of the Chi-square test was equal to 24.6 and the statistics of CFI, root mean square error of approximation (RMSEA), were 97.0 and 0.80, respectively. In this study, the Cronbach’s alpha coefficient for the PCQ was 0.81.
3.2.2. Job Burnout Inventory
In the current study, job burnout is conceptually defined as feelings of emotional exhaustion and depersonalization and is measured by two of the three subscales in the Maslach burnout inventory- human services survey- (MBI-HSS) (4). The scale is comprised of a 22-item Likert scale that includes three subscales each measuring the three aspects of burnout: emotional exhaustion (nine items), depersonalization (five items) and personal accomplishment (eight items). Subscale items pertaining to the personal accomplishment dimension of burnout on conceptual and empirical grounds were excluded from the present study. Conceptually, it is proposed that personal accomplishment is more accurately conceptualized as a personal characteristic rather than a symptom of burnout (23) and empirically previous study findings suggest that personal accomplishment is not strongly related to the emotional exhaustion and depersonalization (24). Developed initially for human service workers, the MBI was found a valid and reliable measure of job burnout across occupations, cultures and languages (4, 25, 26). In Iran, Ahgar (27) the coefficient of burnout scale and its dimensions of Chronbach’s reliability, using the test-retest method reported as follows; job burnout (0.88 and 0.90), emotional exhaustion (and 0.86 0.89), depersonalization (0.84 and 0.86) and individual failure (0.85 and 0.84). The emotional exhaustion scale items used in the study had an overall Chronbach’s reliability coefficient of 0.79 and the depersonalization scale items had a 0.74 reliability coefficient.
3.2.3. The Ryff Scales of Psychological Well-Being
The Ryff scales of psychological well-being is a 54-item questionnaire developed to measure six related constructs of human functioning: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance. In this 54-item version, each dimension is composed of nine items and is measured by a six-point Likert rating scale, (6 = strongly agree, 5 = agree, 4 = agree slightly, 3 = disagree slightly, 2 = disagree, and 1 = strongly disagree). Its internal reliability coefficients were obtained to standardize the sample: self-acceptance (α = 0.93); positive relations with others (α = 91); autonomy (α = 86); environmental mastery (α = 90); purpose in life (α = 90); and personal growth (α = 87). To score the PWB scales, the total score represents the sum of the 54 items. To validate the scale, Ryff (28) found a Cronbach’s alpha, ranging from 0.83 to 0.91 and strong test-retest reliability with coefficients ranging from 0.81 to 0.85 for each of the six factors (28). In Iran, Kalantarkousheh, and Navarbafi (29) administered Ryff’s PWB questionnaire on 860 Iranian people. The scale had good internal consistency. To validate the scale, they found a Cronbach’s alpha of 0.92 and reported high validity and reliability for the questionnaire. Sample items for each dimension were as follows: I am not afraid to voice my opinions, even when they are in opposition to the opinions of most people (autonomy); I am good at juggling my time therefore I can fit everything that needs to be done (environmental mastery); when I think about it, I have not really improved much as a person since I was younger (personal growth); I often feel lonely because I have few close friends with whom to share my concerns (positive relations with others); I enjoy making plans for the future and working to make them a reality (purpose in life); and when I look at my life so far, I am pleased with how things have turned out (self-acceptance).
4. Results
First, Table 1 presents demographics of the gender, age group and education of the subjects in the study.
Table 1.Subjects Demographic Data
| Variables | Male, No. (%) | Female, No. (%) |
|---|---|---|
| Marital Status | ||
| Single | 4 (3.5) | 11 (9.5) |
| Married | 66 (56.8) | 35 (30) |
| Age | ||
| Below 30 | 2 (1.7) | 3 (2.5) |
| 31 - 40 | 23 (19.8) | 18 (15.5) |
| 41 - 50 | 33 (28.5) | 22 (19) |
| 51 - 60 | 12 (10.5) | 3 (2.5) |
| Education | ||
| Associate high school degree | 2 (1.7) | 3 (2.5) |
| Bachelor of art | 46 (37) | 27 (23.5) |
| Master of art | 22 (19) | 16 (13.7) |
As Table 1 indicates, the subjects of the study were 70 (60.34%) males and 46 (39.66%) females. Participants’ age ranged from 25 to 55 years, and the age group for male and female subjects included: below 30 with the frequency of 2 and 2.5 (the lowest, 4.3%), 31 - 40 with the frequency of 23 and 18 (35.35%), 41 - 50 with the frequency of 33 and 22 (the highest, 47.4%) and 51 - 60 with the frequency of 12 and 3 (12.93%) among the subjects. Also, There were five subjects with associate high school degree consisting of % 4.31 with the lowest, 73 bachelor of art (BA) consisting of % 62.93 with the highest frequency, and 38 master of art (MA) consisting 32.75% in the third rank. The majority of the participants had a bachelor degree (62.93 %).
The descriptive statistics of research variables are reported. Table 2 presents means, standard deviation, minimum and maximum values for each of the study variables.
Table 2.Descriptive Indicators of Research Variables
| Statistics Indicators Research Variables | Means (SD) | Minimum Values | Maximum Values |
|---|---|---|---|
| Psychological capital | 88.73 (10.12) | 59 | 108 |
| Efficacy | 23.98 (3.93) | 12 | 30 |
| Hope | 22.66 (3.34) | 15 | 29 |
| Resilience | 19.49 (2.79) | 11 | 26 |
| Optimism | 22.59 (2.98) | 16 | 29 |
| Psychological Well-Being | 82.91 (8.99) | 60 | 101 |
| Self-acceptance | 14.48 (2.39) | 7 | 18 |
| Positive relations with others | 13.32 (2.48) | 9 | 18 |
| Autonomy | 12.49 (2.21) | 6 | 18 |
| Environmental mastery | 14.75 (2.10) | 10 | 18 |
| Purpose in life | 12.97 (2.09) | 5 | 18 |
| Personal growth | 14.87 (2.29) | 8 | 18 |
| Job burnout | 92.18 (12.74) | 57 | 115 |
Since the research variables assessed through Kolmogorov-Smirnov test and distribution of the sample variables were normal, the hypotheses were tested step by step, the results are presented in the following tables.
To assess the relationship between psychological capital and psychological well-being, Pearson correlation test was used, the results are presented in Table 3.
Table 3.Correlation Coefficient between Psychological Capital and Psychological Well Being
| Variables | Psychological Capital | Efficacy | Hope | Resilience | Optimism |
|---|---|---|---|---|---|
| Psychological well-being | 0.418a | 0.389a | 0.372a | 0.045 | 0.446a |
| Self-acceptance | 0.280a | 0.434a | 0.400a | 0.205b | 0.461a |
| Positive relations with others | 0.418a | 0.342a | 0.377a | 0.151 | 0.403a |
| Autonomy | 0.033a | 0.077 | 0.045 | 0.178b | 0.135 |
| Environmental mastery | 0.372a | 0.277a | 0.352a | 0.134 | 0.377a |
| Purpose in life | 0.012 | 0.164 | -0.042 | 0.212b | 0.072 |
| Personal growth | 0.349a | 0.384a | 0.305a | 0.051 | 0.287a |
| Job burnout | 0.419a | 0.451a | 0.411a | 0.077 | 0.438a |
aP ≤ 0.001.
bP ≤ 0.05.
According to Table 3, there was a positive correlation between psychological capital and psychological well-being and its subscales including self-acceptance, positive relationships with others, environmental mastery, and personal growth at 0.0001 as level of significance. But it revealed no significant relationship between psychological capital and purpose in life and autonomy and life of the targeted view.
On the other hand, there was a significant relationship between psychological well-being and subscales of psychological capital including efficacy, hope and optimism at 0.0001 as level of significance. There was no significant relationship between psychological well-being and resilience, however. Considering the subscales of the research variables, there was a significant relationship between efficacy and psychological well-being subscales including self-acceptance, positive relationships with others, environmental mastery and personal growth at 0.0001 as level of significance. Also, there is a significant relationship between psychological well-being subscales of hope, self-acceptance, positive relationships with others, environmental mastery, and personal growth at 0.0001 as level of significance. There was also a significant relationship between psychological resilience, self-acceptance, autonomy and purpose in life at 0.05 as level of significance. Finally, there was a significant relationship between optimism and self-acceptance, positive relationships with others, environmental mastery and personal growth at 0.0001 as level of significance. Totally, there was a weak correlation between autonomy and purpose in life and psychological capital and its subscales. The largest correlation coefficient corresponded to the relationship between self-acceptance and optimism and the smallest correlation coefficient was related to the relationship between psychological capital and purpose in life.
To assess the relationship between psychological well-being and job burnout, Pearson correlation test was also used, the results are presented in Table 4.
According to Table 4, there was a positive correlation between job burnout and psychological well-being and its subscales at 0.0001 as level of significance. There was a significant relationship between job burnout and autonomy and purpose in life at 0.05 as level of significance. There was a significant relationship between job burnout and other subscales of psychological well-being at 0.0001 as level of significance. The largest correlation coefficient corresponded to the relationship between job burnout and psychological well-being and its subscale personal growth, and the smallest correlation coefficient was related to the relationship between job burnout and autonomy and purpose in life.
Then, to assess the relationship between psychological capital and job burnout, Pearson correlation test was used, the results are presented in Table 5.
Table 5.Correlation Coefficient between Psychological Capital and Psychological Well Beinga
| Variables | Psychological Capital | Efficacy | Hope | Resilience | Optimism |
|---|---|---|---|---|---|
| Job burnout | 0.438** | -0.077 | 0.411** | 0.451** | 0.419** |
a**P ≤ 0.0001, *P ≤ 0.0005.
According to Table 5, there was a correlation between job burnout and psychological capital and its subscales, except to resilience at 0.0001 as level of significance. But there was no significant relationship between job burnout and resilience. There was a significant relationship between job burnout and other subscales of psychological well-being at 0.0001 as level of significance. The largest correlation coefficient linked to the relationship between job burnout and psychological well-being and its subscale personal growth, and the smallest correlation coefficient related to the relationship between job burnout, autonomy and purpose in life.
To study the predicting role of the psychological well-being and psychological capital variables on job burnout, as well as the role of gender as moderating variable in the relationship between psychological capital and psychological well-being and job burnout, step by step regression was used; first, it was necessary to assure meeting its assumptions. In addition to normality of the error distribution of job burnout, independence or lack of correlation between errors, and lack of homoscedasticity (constant variance) of the errors in predicting variables should be considered in regression analysis.
Table 6.Regression Analysis
| Indicator | Durbin-Watson Statistic | Status Indicator | |
|---|---|---|---|
| Values | 1.985 | 1 | 1 |
| 2 | 17.32 | ||
| 3 | 18.9 | ||
According to Table 6, since the independence of errors assessed by Watson-Durbin was placed between 1.5 and 2.5, and the value of Index condition assessing non-linear correlations between predicting variables was smaller than 30, regression analysis was used.
To study the predicting role of the psychological well-being and psychological capital variables on job burnout, as well as the role of gender as moderating variable in the relationship between psychological capital and psychological well-being and job burnout, step by step regression method was applied as follows:
Step 1, the psychological well-being and psychological capital variables of the variable; step 2, gender as a moderating variable, and in step 3, the interaction between predicting and moderating variables were entered into the regression equation.
To check the significance of regression analysis, variance analysis was used. Significance of variance analysis was essential to certain interpretation of the results obtained through regression analysis.
Table 7.Analysis of Variance for a Meaningful Analysis of the Regression
| Model | Total Square Roots | Degree of Freedom | Mean of Square Roots | F | Significance | |
|---|---|---|---|---|---|---|
| 1 | Total btw and within groups | 6687.16 | 2 | 3343.58 | 31.485 | 0.0001 |
| 12000.03 | 113 | 106.19 | ||||
| 18687.19 | 115 | |||||
| 2 | Total btw and within groups | 6702.32 | 2 | 2234.10 | 20.878 | 0.0001 |
| 11984.87 | 113 | 107.01 | ||||
| 18687.19 | 115 | |||||
| 3 | btw groups | 7225.66 | 2 | 1445.13 | 13.869 | 0.0001 |
| 11461.53 | 113 | 104.19 | ||||
| 115 | ||||||
If the significance level of statistic F is smaller than 0.05, it can be concluded that the analysis of variance, and subsequently regression analysis is significant.
Table 8.Summary of Regression Models
| Model | R | R2 | Moderated R2 | Std. | R2 Changes | F | Sig. |
|---|---|---|---|---|---|---|---|
| Step1 | 0.598 | 0.358 | 0.346 | 10.30 | 0.358 | 31.48 | 0.000 |
| Step2 | 0.599 | 0.359 | 0.341 | 10.34 | 0.001 | 0.142 | 0.707 |
| Step3 | 0.622 | 0.387 | o.359 | 10.20 | 0.280 | 2.51 | 0.086 |
In Tables 7 and 8, multiple correlation coefficients, multiple square correlation, moderated multiple square correlation adjusted, standard deviation, and meaningfulness of changes in multiple correlation coefficients test among the regression models are presented. According to Tables 7 and 8, in the first step, the psychological well-being and psychological capital variables were entered into the equation, multiple correlation coefficient square was 0.358, indicating that these predicting variables can predict changes of job burnout up to 36%. By adding the gender variable to the model, the value of R2 increased to 39%, though notable, it was not significant. Consequently, correlation coefficients of predicting variables were presented.
The criterion variable: job burnout
According to Table 9, in the first model, the psychological well-being and psychological capital variables as predicting variables were entered into the regression equation, standard correlation coefficient square for psychological capital equals 0.223, and for psychological well-being 0.470; both were more than 0.01 at the level of significance. Hence, it can be concluded that the hypothesis four was verified, so psychological well-being and psychological capital can significantly predict job burnout. Then, these regression coefficients can be used to predict equation of job burnout, based on psychological well-being and psychological capital, which can be presented as following: job burnout 0.281 = (psychological well-being) 0.666 + (psychological capital) 12.604.
Table 9.Regression Coefficients of Predicting Variables
| Model | B | Std. | BETA | t | Sig. |
|---|---|---|---|---|---|
| 1 | |||||
| Stable | 12.064 | 10.316 | 0.223 | 1.169 | 0.245 |
| Psy-capital | 0.281 | 0.105 | 0.470 | 2.685 | 0.008 |
| Psy-well-being | 0.666 | 0.118 | 5.660 | 0.0001 | |
| 2 | |||||
| Stable | 13.909 | 11.457 | 0.216 | 1.214 | 0.227 |
| Psy-capital | 0.273 | 0.107 | 0.470 | 2.547 | 0.012 |
| Psy-well-being | 0.666 | 0.118 | -0.029 | 5.636 | 0.0001 |
| Gender | -0.741 | 1.969 | -0.376 | 0.707 | |
| 3 | |||||
| Stable | 13.822 | 35.311 | -0.297 | 0.391 | 0.696 |
| Psy-capital | -0.374 | 0.384 | 0.964 | -0.974 | 0.332 |
| Psy-well-being | 1.366 | 0.367 | 0.095 | 3.726 | 0.0001 |
| Gender | 2.403 | 21.420 | 1.384 | 0.112 | 0.911 |
| Gender-Psy. C | 0.369 | 0.223 | -1.554 | 1.775 | 0.079 |
| Gender-Psy. W | -0.464 | 0.234 | -1.980 | 0.051 |
Based on Table 6, there was no significant relationship regarding interactive effects of gender among psychological capital, psychological well-being and job burnout (P > 0.05). As a result, gender does not have a moderating role in relationship between psychological capital and job burnout and also between psychological wellbeing and job burnout.
5. Discussion
The purpose of current study is to explore the role of psychological capital on the psychological well-being and job burnout of principals of high schools. Using the analytic technique of ANCOVA, the following hypotheses were examined. H1: There is a significant relationship between psychological capital and psychological well-being. These findings were consistent with research on psychological capital in an organizational context (17). The findings were in line with psychological capital positively regarding job satisfaction and job performance (17). The results also indicate that there is a significant relationship among psychological capital, psychological well-being and job burnout, conforming H2 and H3. Psychological capital of the employee is moderately and positively related to the manager’s positive performance and sales performance; while it is negatively related to skepticism, intentions to quit the job (22). Researches of Luthans et al. (17, 20) showed that an increase in organizational behavior was associated with less skepticism and deviant behaviors by employees, which can positively affect the organizational change. Avey et al. also found that psychological capital is a factor to reduce employee’s stress and job burnout (29). Fred Luthans, and Luthans and Luthans believed that psychological capital plays a paramount role in the development of job burnout and it may effectively reduce the extent of burnout (30). The findings of the present study showed that psychological well-being and psychological capital can significantly predict job burnout. So, it can be said that increasing psychological capital plays an important role in decreasing job burnout among work force. On the other hand, gender does not have a moderating role in relationship between psychological capital and job burnout and also between psychological well-being and job burnout.
Researchers believe that in order to be able to cope with changes in occupational contexts in modern societies, workers need to promote their psychological capital. Thus, managers should be aware of the behaviors, attitudes or interactions that may be hindering the workers’ success. Managers also need help to design programs based on organizational instructions and provide workshops organized and directed by professional facilitators.
Findings of the current study should be interpreted accurately because the geographic area from which the sample was drawn may make the results not generalizable. Samples drawn from different geographic areas would provide more reliable statistical data with broad results. Some results may be affected by the size and properties of the population; a larger body of participants in other places and organizations may provide more reliable findings. Finally, high schools time limitations prevented a pre- and post-test design; so further studies need to investigate the variables in longitudinal method to come up with broad findings. Since the body of research was relatively small, further studies may be helpful to examine the reliability of the current study results under different conditions and larger population. Also, future researches are recommended to investigate broader businesses as well as educational communities.
