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Journal of Psychology in Africa
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Burnout and Work Engagement for Different Age
Groups: Examining Group-Level Differences and
Predictors
Lize-Mari Haley
a, Karina Mostert
a& Crizelle Els
a aNorth-West University, Potchefstroom, South Africa
Published online: 01 May 2014.
To cite this article: Lize-Mari Haley, Karina Mostert & Crizelle Els (2013) Burnout and Work Engagement for Different Age
Groups: Examining Group-Level Differences and Predictors, Journal of Psychology in Africa, 23:2, 283-295
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http://dx.doi.org/10.1080/14330237.2013.10820625
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Burnout and Work Engagement for Different Age Groups: Examining
Group-Level Differences and Predictors
Lize-Mari Haley Karina Mostert Crizelle Els
North-West University, Potchefstroom, South Africa
Address correspondence to Prof. K. Mostert, School for Human Resources Sciences, WorkWell: Research Unit for Economic and Management Sciences, North-West University, Private Bag X6001 (Internal 202), Potchefstroom, South Africa, 2520. E-mail: Karina.Mostert@nwu.ac.za
This study examined age-related differences of burnout and engagement levels among employees in the South African financial sector, including influences of perceived job characteristics and personal sense of coherence. Data on burnout and work engagement, job characteristics and sense of coherence were collected from a convenience sample of 582 of junior managers (females = 55.50%, majority language was English, 54.1%). The results from this study reveal that young and middle-aged employees experience higher levels of exhaustion when compared to older employees, while older employees seemed to be more dedicated than their younger counterparts. Different job demands and resources seemed to predict burnout and work engagement of the three age groups. SOC was a significant predictor of burnout and work engagement across the three age groups. To conclude, the results of this study highlight the differences in the antecedents of employee well-being. These differences can therefore not be ignored in the workplace.
Keywords: Job demands, job resources, sense of coherence, burnout, work engagement, Job Demands-Resources (JD-R) Model, age
In the literature, two concepts of well-being that are widely researched are burnout and work engagement (Schaufeli, Salanova, González-Romá, & Bakker, 2002). Many research-ers emphasise the fact that there are certain demographic fac-tors that can play a role in the development of burnout or work engagement, specifically age (Akkermans, Brenninkmeijer, Blonk, & Koppes, 2009; Garner, Knight, & Simpson, 2007). For example, older workers seem to experience higher levels of en-gagement than younger workers do (James, McKechnie, & Swanberg, 2011), whereas the younger workforce seems to ex-perience higher levels of burnout than their older counterparts (e.g., Jackson & Rothmann, 2005; Randall, 2007). Similarly, lit-erature also suggests that there may be different predictors that can have an influence on burnout and work engagement in older and younger employees, including job demands and re-sources, and personal resources such as sense of coherence (Kalimo, Pahkin, Mutanen, & Toppinen-Tanner, 2003; Korunka, Kubicek, Schaufeli, & Hoonakker, 2009).
James et al. (2011) state that the well-being of younger em-ployees is just as important to consider as the well-being of older employees is. The majority of literature findings however focus on the well-being of older employees, ignoring the well-being of younger employees to a large extent. Few studies could be found that investigate burnout and work engagement differences between age groups in the South African context, or that investigate if important predictors (including job demands, job resources and sense of coherence) are the same for differ-ent age groups in the financial sector.
Burnout and Work Engagement
Burnout can be defined as “a psychological syndrome in re-sponse to chronic interpersonal stressors on the job” (Maslach,
Schaufeli, & Leiter, 2001, p. 399). Burnout comprises of two core dimensions, namely exhaustion and cynicism (Salanova, Schaufeli, Llorens, Peiro, & Grau, 2001; Schaufeli & Bakker, 2004). When employees experience extensive levels of ex-haustion to such an extent that they cannot function optimally within their job (exhaustion), a detachment from the job (cyni-cism) can result (Maslach et al., 2001). On the other hand, work engagement can be defined as a positive, fulfilling, work-related state of mind that is characterised by two core dimensions, vig-our and dedication (Schaufeli et al., 2002; Schaufeli & Bakker, 2004). Vigour is characterised by very high levels of energy ex-perienced within the work place as well as an enthusiasm within themselves to perform their job the best way they know how, re-gardless of any downfalls which may occur. When an employee experiences a sense of “significance, enthusiasm, inspiration, pride and challenge” within their job environment, it is known as dedication (Schaufeli et al., 2002, p. 74).
Age Effects on Burnout and Work Engagement
Employees of all ages are included in the workforce of or-ganisations and are therefore in different phases of their ca-reers, ranging from entry level employees (young adulthood) to employees that have reached retirement age (James et al., 2011). A number of studies have established that age does seem to play a role in the burnout and engagement levels of em-ployees (e.g., Garner et al., 2007; James et al., 2011). Younger employees seem to experience higher levels of burnout than their older colleagues (e.g., Antoniou, Polychroni, & Vlachakis, 2006; Brewer & Shapard, 2004; Jackson & Rothmann, 2005; Patrick & Lavery, 2007). Burnout therefore appears to develop in the early career stage of employees and consequently seems
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to be more prevalent in younger employees than in older em-ployees.
Possible reasons for younger employees’ higher burnout levels seem to include a lack of skills to deal with everyday prob-lems arising in the workplace, a “reality shock” when just enter-ing the workplace, a lack of copenter-ing skills as result of less experi-ence in a working environment and transitional shock (Ahola et al., 2006; Duchscher, 2009; Ghorpade, Lackritz, & Singh, 2007; Patrick & Lavery, 2007). Older workers seem to be more likely to have higher levels of engagement than younger or middle aged employees (e.g., James et al., 2011; Pitt-Catsouphes & Matz-Costa, 2008). For instance, James et al. (2011) found that older workers displayed significantly higher levels of engage-ment than their younger colleagues. These findings call for rep-lication in the financial sector. Employees in the financial sector have been found to experience high levels of burnout as a result of their demanding work due to a decline in their job resources and an increase in their job demands (Ten Brummelhuis, Ter Hoeven, Bakker, & Peper, 2011). More specifically, employees in the banking sector seem to experience high levels of burnout as a result of their work load and working hours (Khattak, Khan, Haq, Arif, & Minhas, 2011). However, little research was found on work engagement within the financial sector. Also, few stud-ies could be found that investigate burnout and work engage-ment differences between age groups in the South African con-text, or that investigate if important predictors (including job demands, job resources and personal resources) are the same for different age groups in the financial sector.
Job Characteristics and Personal Resources
The Job Demands-Resource (JD-R) model is a popular means to explain the process of employee well-being (engage-ment and burnout). It is a heuristic model that explains the role of two working conditions, namely job demands and job re-sources, in the well-being process (Bakker, Demerouti, & Schaufeli, 2003). Bakker et al. (2003) define job demands as “those physical, social, or organisational aspects of the job that require sustained physical and/or psychological (i.e., cognitive or emotional) effort on the part of the employee and are there-fore associated with certain physiological and/or psychological costs (e.g., exhaustion)” (p. 395). Job demands therefore repre-sent any characteristic connected to the job that can have a negative impact on the employee. Job resources on the other hand, are defined as “those physical, psychological, social, or organisational aspects of the job that (a) are functional in achieving work goals, (b) reduce job demands and the associ-ated physiological and psychological costs, or (c) stimulate per-sonal growth and development” (Bakker, Demerouti, & Euwema, 2005, p. 170).
The role of job demands and job resources in well-being can be explained through two processes, i.e., the energetic process and the motivational process. Having continuously high job de-mands, such as high work pressure and emotional dede-mands, ul-timately leads to burnout and ill-health over the long term. This is known as the energetic process (Bakker et al., 2003; Bakker, Demerouti, & Verbeke, 2004). On the other hand, the motiva-tional process involves ensuring high job resources to meet those demands, such as autonomy, social support, job clarity and supervisory support, to name just a few, can ultimately pre-dict work engagement and commitment within the organisation (Bakker, Van Emmerik, & Euwema, 2006; James et al., 2011). Xanthopoulou, Bakker, Demerouti, and Schaufeli (2007) ex-plain that personal resources also play a significant role in the
JD-R model. It is argued that personal resources (e.g., self-es-teem, self-efficacy and optimism) can act as buffer against the negative effects of excessive job demands on burnout, and also increase work engagement. One such a personal resource is sense of coherence (SOC). SOC is a “stress resistant resource” which protects the employee from the harmful effects that nega-tive stressors may have on their well-being and ultimately helps to improve well-being (Antonovsky, 1979, 1987). In support of this, for example, Van der Colff and Rothmann (2009) found in their study amongst registered nurses in South Africa that a strong SOC predicted work engagement. Antonovsky (1987) proposed that in theory SOC will develop over an individual’s life span and will stabilise by the age of 30, only changing if circum-stances of the individual’s life changes dramatically. Feldt, Leskinen, Kinnunen, and Ruoppila (2003) tested Antonovsky’s theory over a span of five years and found that older employees did not differ in their stability of SOC from younger employees. Age did not seem to be a factor in the stability, level or mean changes in SOC (Feldt et al., 2003).
Goals of the Study
Currently no research was found on how age, job demands, job resources and personal resources predict work related well-being in the South African financial services sector. The fol-lowing hypotheses were tested.
H1: Young employees will experience higher levels of burnout
compared to their middle-aged or older counterparts.
H2: Older employees will experience higher levels of
engage-ment compared to their middle-aged or younger counter-parts.
H3a: Job demands which predict burnout and work
engage-ment will not differ for the different age groups.
H3b: Job resources which predict burnout and work
engage-ment will not differ for the different age groups.
H4: SOC levels which predict burnout and work engagement
will not differ for the different age groups.
Method
Research Participants and Procedure
The participants consisted of 582 junior managers in the banking industry within South Africa (majority language was English, 54.1%, followed by Afrikaans, 33.2% and other African languages, 12.7%). Participants were divided into three groups, which consisted of young employees (18-30 years of age), mid-dle-aged employees (31-50 years of age) and older employees (51-65 years of age). The participants were a representation of all nine provinces and comprised mostly of female participants (55.50%) who resided in the Gauteng province (77.00%). The majority of the participants were in the middle-aged, 31-50 years old group (48.10%) and were married (57.70%). With re-gard to formal qualifications, 38.70% of the participants ob-tained a Grade 12 qualification or a three-year Degree/Diploma (30.40%).
Procedure
A letter was sent to participating banks explaining the nature of the study. Permission was granted by all the general manag-ers from the respective banks, after which the data were then collected. Data were gathered by means of a self-report ques-tionnaire which was completed online using a secure website. Informed consent was received from all the participants prior to them completing the questionnaires and before the tests were
administered the participants was assured that the question-naires would be kept confidential. To ensure that this project was conducted in an ethical manner, ethical issues such as in-formed consent, confidentiality and deception were considered (Struwig & Stead, 2001).
Measures
Participants provided their personal characteristics includ-ing gender, age, marital status, qualification and home lan-guage. They also completed the South African Employee
Health and Wellness Survey (SAEHWS: Rothmann &
Rothmann, 2006), a self-report measure of work-related well-being. It is a Likert scale measure of the following aspects of work-related well-being: job demands, job resources, burnout and work engagement. Internal consistency reliabilities with a multi-ethnic sample for the different scales are reported in fol-lowing tables.
Job characteristics. Three items were used to measure
job pressure (e.g., “Do you have too much work to do?”), three items for mental load (e.g., “Do you have to give continuous at-tention to your work?”) and three items for emotional load (e.g., “Does your work put you in emotionally upsetting situations?”). These were measures using a Likert scale (1 = never; 3 = al-ways). The overall reliability coefficient of overload is .83 (Rothmann & Rothmann, 2006).
Perceived organisational support was measured on a Likert scale (1 = never; 4 = always) for colleague support, supervisory support, role clarity, job information and participation in deci-sion-making. An example item for each dimension was as fol-lows: colleague support (“If necessary, can you ask your col-leagues for help?”), supervisory support (“Can you count on your direct supervisor when you come across difficulties?”), role clarity (e.g., “Do you know exactly what your responsibilities are?”); job information (e.g., “Do you receive sufficient informa-tion on the results of your work?”); and participainforma-tion in decision making was measured by three items (e.g., “Can you participate in decisions about the nature of your work?”). The overall reli-ability coefficients of social support is .83 and of organisational support (including role clarity, job information, participation in decision making) is .90 (Rothmann & Rothmann, 2006).
Personal resources. The most common way of measuring
sense of coherence is to use the SOC scale developed by Antonovsky (Antonovsky, 1987). The measure comprises 13 Likert scale type items (0 = never; 6 = always). A typical ques-tion would be: “Do you have the feeling that you don’t really care about what goes on around you?”. Previous studies have con-firmed the reliability of this instrument, with Cronbach alpha
co-efficients ranging from .85 (Feldt, Lintula, Suominen,
Koskenvuo, Vahtera, & Kivimäki, 2007) to .89 (Olsson, Gassne, & Hansson, 2009).
Burnout and work engagement. This was measured
us-ing five items Likert scale type t items (0 = never; 6 = always).
Items included “I feel tired before I arrive at work”,a = .83;
ex-haustion); “I have become less enthusiastic about my work”,a =
.81; cynicism), “I am full of energy in my work”,a = .80; vigour)
and “I am passionate about my job”;a = .86; dedication).
Statistical Analysis
Multivariate analysis of variance (MANOVA) was used to determine whether there were differences between the age groups regarding their well-being. MANOVA is a statistical pro-cedure used to determine whether there are any group differ-ences (Salkind, 2009). Hierarchical multiple regression analysis
was used to determine which job demands, job resources and personal resources predict burnout and work engagement for different age groups.
Results
Descriptive statistics and reliabilities, as well as the correla-tions between the dimensions are reported in Table 1.
There was considerable multi-colinearity among the de-pendent measures. For instance, exhaustion, cynicism and vig-our were statistically and practically related, with a medium ef-fect, to emotional load, while dedication was only statistically significantly related to emotional load. The multi-colinearity among the dependent variable measures constrain the confi-dence in the findings so that the results need to be interpreted with caution.
Age Difference Effects
MANOVA was used to determine the differences between age and burnout (exhaustion and cynicism) and work engage-ment (vigour and dedication). The analysis of the Wilks’ Lambda values showed that there were indeed statistically sig-nificant differences (F(8, 1152) = 2.09; p = .03, with partial eta
squared = .01) (differences are significant at p£.05 level)
be-tween the age groups regarding burnout and work engagement. Specific differences were analysed further using ANOVA. As a result of the differing sizes of the samples, the Games-Howell procedure was used in order to determine whether there were any statistically significant differences between the age groups. The results of the ANOVA analysis are presented in Table 2.
Predicting exhaustion. As illustrated by Table 2, the three
age groups experienced significantly different levels of
exhaus-tion (p£.05), where young and middle-aged employees seemed
to experience the highest levels of exhaustion and older em-ployees the lowest exhaustion levels. However, no significant differences were found for cynicism. Therefore, Hypothesis 1 is partially accepted. Furthermore, although the p-values for vig-our and dedication were higher than .05, the post-hoc analysis did show significant differences for dedication between young and older employees, with older employees experiencing signif-icantly higher levels of dedication compared to younger employ-ees. Partial support was therefore found for Hypothesis 2.
In order to establish which predictors (job demands, job re-sources, SOC) significantly predicted burnout and engagement across the three age groups, hierarchical multiple regression analysis was used. The results of the regression analysis are il-lustrated in Tables 3, 4, 5, and 6 and a summary of all the signifi-cant findings across the age groups is illustrated in Table 7.
Predicting exhaustion. Table 3 summarises the
regres-sion analyses with job demands, job resources, and SOC as predictors of exhaustion for young, middle-aged and older
em-ployees. For young employees, job pressure (b = .21; t = 2.10; p
£.05), and emotional load (b = .25; t = 2.84; p £ .05) predicted
exhaustion; for middle-aged employees, job pressure (b = .27; t
= 4.25; p£ .05), emotional load (b = .18; t = 3.29; p £ .05) and
supervisory support (b = -.17; t = -2.16; p £ .05) predict
exhaus-tion; and for older employees, mental load (b = .30; t = 4.38; p £
.05), emotional load (b = .15; t = 2,21; p £ .05), supervisor
sup-port ( = -.23; t = -2.55; p£ .05) and lack of participation in
deci-sion making (b = .18; t = 2.11; p £ .05) seemed to predict
ex-haustion. Lower levels of SOC was a significant predictor of
exhaustion for younger employees (b = -.27; t = -3.23; p £ .05);
Table 1 Descriptive Statistics, Cronbach A lpha Coefficients o f the Measuring Instruments and Correlation Coefficients Item M SD a 123456789 1 0 1 1 1 2 1. Job p ressure 7.85 2.21 .77 2. Mental load 9.56 2.12 .79 .63+** 3. Emotional load 7.10 2.12 .72 .41+* .41+* 4. Supervisor s upport 9 .05 2 .41 .81 -.10+ -.04 -.26+ 5. Colleague support 9 .16 1 .95 .83 -.19+ -.12+ -.27+ .51+** 6. Role clarity 9 .29 1 .96 .79 -.04 .07 -.14+ .61+** .39+* 7. Job information 8.52 2.23 .74 -.04 .08 -.12+ .65+** .40+* .78+** 8. Participation in decision 8 .61 2 .25 .84 -.07 .01 -.19+ .71+** .45+* .61+** .66+** making 9. Sense o f c oherence 6 .40 12.81 .76 -.19+ -.12+ -.33+* .35+* .31+* .33+* .32+* .38+* 10. Exhaustion 14.45 6.44 .89 .39+* .32+* .45+* -.33+* -.32+* -.23+ -.20+ -.28+ -.53+** 11. Cynicism 8.08 5.40 .86 .12+ .02 .31+* -.39+* -.34+* -.37+* -.34+* -.42+* -.51+** .52+** 12. Vigour 2.68 5.19 .84 -.14+ -.04 -.31+* .42+* .38+* .31+* .34+* .42+* .54+** -.59+** -.67+** 13. Dedication 21.70 5.86 .88 .08 .11+ -.18+ .41+* .32+* .36+* .38+* .45+* .48+* -.35+* -.73+** .78+** Note . + Statistically significant (p £ .05); * Correlation is practically significant r ³ .30 (medium effect); ** Correlation is practically significant r ³ .50 (large e ffect) Table 2 ANOVA Differences in Burnout Based o n A ge Middle aged Older Partial Variable Y oung employees employees employees p Eta S quared Exhaustion 15.25 a 14.97 a 12.95 b .00* .02 Cynicism 8.70 8.20 7.38 .09 .01 Vigour 20.04 20.59 21.36 .08 .01 Dedication 20.79 a 21.73 22.38 b .06 .01 Note. * S tatistically significant difference: p £ .05; Means with d ifferent superscripts differed s ignificantly at p £ .05; a = G roup differs statisticall significantly from type (in row) where b is indicated.
Table 3 Hierarchical M ultiple R egression Analysis with E xhaustion as Dependent Variable Young Middle-aged Old Model Standardised b tp R 2 Standardised b tp R 2 Standardised b tp R 2 1 (Constant) 1 .84 .07 .24 1 .74 .08 .25 -2.63 .01 .33 Job p ressure .27 2 .53 .01** .29 4.22 .00** .08 .96 .34 Mental load -.07 -.61 .54 -.06 -.85 .39 .28 3 .59 .00** Emotional load .36 4 .10 .00** .34 5.96 .00** .34 4.62 .00** 2 (Constant) 3 .89 .00 .32 4 .30 .00 .31 1 .44 .15 .40 Job p ressure .24 2 .27 .03* .28 4 .19 .00** .06 .71 .48 Mental load -.03 -.27 .79 -.02 -.23 .82 .30 3 .86 .00** Emotional load .33 3 .79 .00** .25 4.29 .00** .24 3.16 .00** Supervisor s upport .04 .32 .75 -.20 -2.35 .02* -.17 -1.65 .10 Colleague support -.14 -1.58 .12 -.04 -.61 .54 -.21 -2.70 .01** Role Clarity -.09 -.79 .43 -.10 -1.16 .25 .04 .39 .70 Job information -.00 -.02 .99 .13 1.39 .17 -.03 -.32 .75 Participation in -.18 -1.67 .10 -.07 -.83 .41 .06 .66 .51 decision m aking 3 (Constant) 5 .15 .00 .37 7 .18 .00 .41 4 .50 .00 .55 Job p ressure .21 2 .10 .04* .27 4 .25 .00** .05 .75 .46 Mental load -.02 -.22 .83 -.02 -.28 .78 .30 4 .38 .00** Emotional load .25 2 .84 .01** .18 3.29 .00** .15 2.21 .03* Supervisor s upport .06 .54 .59 -.17 -2.16 .03* -0.23 -2.55 .01** Colleague support -.13 -1.51 .13 -.03 -.48 .63 -0.10 -1.42 .16 Role Clarity -.12 -1.09 .28 -.04 -.44 .66 0.11 1.34 .18 Job information .04 .35 .73 .12 1.43 .15 -0.04 -.46 .65 Participation in -.11 -1.10 .28 -.02 -.31 .76 0.18 2.11 .04* decision m aking SOC -.27 -3.23 .00** -.36 -6.77 .00** -0.47 -7.38 .00** Note. ** Statistically significant p £ .01; * S tatistically significant p £ .05; For the young group, Model 1 (F = 13.43; R = .49; D R 2 = .22); M odel 2 (F = 7.37; R = .57; D R 2 = .28); Model 3 (F = 8.21; R = .61; D R 2 = .33); F or the m iddle g roup, Model 1 (F = 30.06; R = .50; D R 2 = .24); M odel 2 (F = 15.14; R = .56; D R2= .29); M odel 3 (F = 20.77; R = .64; D R 2 = .39). F or the o lder group, Model 1 (F = 26.51; R = .57; D R 2 = .31); M odel 2 (F = 13.23; R = .63; D R 2 = .37); M odel 3 (F = 21.77; R = .74; D R 2 = .53).
middle-aged employees (b = -.36; t = -6.77; p £ .05); as well as
older employees (b = -.47; t = -7.38; p £ .05).
Predicting cynicism. Table 4 summarises the regression
analyses with job demands, job resources, and SOC as predic-tors of cynicism for young, middle-aged and older employees. Entry of job pressure, work load and emotional load at the first step of the regression analysis and supervisor support, col-league support, role clarity, job information and participation in decision making entered in the second step of the regression analysis, both produced a statistically significant model for young employees, middle-aged employees and older
employ-ees. For younger employees, emotional load (b = .22; t = 2.53;
p£ .05), role clarity (b = -.21; t = -1.97; p £ .05) and a lack of
in-clusion in decision making (b = -.28; t = -2.72; p £ .05) predicted
cynicism, for middle-aged employees job pressure (b = .15; t =
2.22; p £.05), mental load (b = -.21; t = -3.18; p£ .05) and a lack
of participation in decision making (b = -.18; t = -2.20; p £ .05)
and for older employees only emotional load (b = .17; t = 2.22; p
£.05) and colleague support (b = -.15; t = -1.94; p £ .05). Lower levels of SOC seem to be a significant predictor of cynicism for
younger employees (b = -.31; t = -3.83; p £ .05); middle-aged
employees (b = -.34; t = -6.11; p £ .05) and for older employees
(b = -.39; t = -5.31; p £ .05).
Predicting vigour. Table 5 summarises the regression
analyses with job demands, job resources and SOC as being predictors of vigour for young, middle-aged and older employ-ees. Entry of supervisor support, colleague support, role clarity, job information and participation in decision making in the first step of the regression analysis and job pressure, work load, and emotional load entered in the second step of the regression analysis, produced a statistically significant model for young, middle-aged and older employees. For young employees
emo-tional load (b = -.21; t = -2.58; p £ .05) and inclusion in the
deci-sion making process (b = .20; t = 1.97; p £ .05) seemed to
pre-dict vigour; for middle-aged employees colleague support (b =
.15; t = 2.67; p£ .05), sufficient participation in decision making
(b = .17; t = 2.15; p £ .05), mental load (b = .20; t = 3.25; p £ .05)
and emotional load (b = -.14; t = -2.50; p £ .05) seems to predict
vigour and for older employees role clarity (b = -.23; t = -2.26; p
£ .05) and job information (b = .28; t = 2.65; p £ .05) seems to be sufficient predictors of vigour. Further, higher levels of SOC seems to be a significant predictor of vigour for younger
em-ployees (b = .25; t = 3.10; p £ .05); middle-aged employees (b =
.40; t = 7.67; p£ .05); as well as older workers (b = .47; t = 6.30;
p£ .05).
Predicting dedication. Table 6 summarises the regression
analyses with job demands, job resources, and SOC as predic-tors of dedication for young, middle-aged and older employees. Entry of supervisor support, colleague support, role clarity, job information and participation in decision making in the first step of the regression analysis and entry of job pressure, work load, and emotional load in the second step of the regression analy-sis, did produce a statistically significant model for young, mid-dle-aged employees and older employees. For younger
em-ployees participation in decision making (b = .37; t = 3.85; p
£.05), less job pressure (b = .21; t = 2.23; p £ .05) and less
emo-tional load (b = -.20; t = -2.47; p £ .05) predicted higher
dedica-tion. As seems to predict dedication, for middle aged employees
participation in decision making (b = .20; t = 2.61; p £ .05) and
mental load (b = .17; t = 2.61; p £ .05) and for older workers job
information (b = .23; t = 2.19; p £.05) and job pressure (b = .29; t
= 2.19; p£ .05). According to the results, it seems that a higher
SOC seems to be a significant predictor of dedication for
youn-ger employees (b = .22; t = 2.86; p £ .05); middle-aged
employ-ees (b = .35; t = 6.42; p £ .05); as well as older workers (b = .48; t
= 6.60; p £ .05). Based on the above results no support was
found for the hypotheses stated above and therefore Hypothe-sis 3a is partially accepted and Hypotheses 3b and 4 are re-jected.
Table 7 constitutes a summary of all the significant findings across the three age groups. The most significant finding of the study was that SOC levels consistently predict all four well-be-ing dimensions for all three age groups.
Discussion
The results of this study reveal differences in the experience of exhaustion by young, middle-aged and older workers, whereas no age differences were found for cynicism between these age groups. Therefore, hypothesis 1 was partially con-firmed. This finding is supported by the results of a study by Brewer and Shapard (2004) which found that younger employ-ees experienced higher levels of exhaustion than their older col-leagues. These results also partially coincide with Jackson and Rothmann’s (2005) findings on educators in South Africa, who found that exhaustion levels seems to be higher for younger employees than middle-aged or older employees. However, they also found significant differences on the cynicism dimen-sion in educators which was not the case for this sample of ju-nior banking managers. A possible reason for these young managers experiencing higher levels of exhaustion can be the fact they are yet to develop coping skills as they are just enter-ing the workforce and do not have the necessary experience to deal with the challenges posed by their new job (Brewer & Shapard, 2004).
Significant differences regarding dedication levels were found between younger and older employees, with older em-ployees experiencing higher levels of dedication than their younger counterparts. This result corresponds with the findings of previous studies that older employees seemed to exhibit higher levels of engagement than the younger employees (Pitt-Catsouphes & Matz-Costa, 2008; Schaufeli, Bakker, & Salanova, 2006). A plausible explanation for younger employ-ees experiencing lower dedication levels than their older coun-terparts could be that young employees are just entering the workplace and may not have had sufficient time to create a meaningful relationship with the organisation yet. Peeters and Van Emmerik (2008) assert in their review article, that older em-ployees seem to be more satisfied with their work, which can also be a reason why they tend to be more dedicated to their or-ganisation than the young employees.
The results of this study found that different job demands predict burnout and engagement in the three age groups. Emo-tional load predicted exhaustion and cynicism for older employ-ees, exhaustion and lower vigour for middle-aged employees and exhaustion, cynicism, lower vigour and lower dedication for younger employees. Mental load predicted exhaustion for older employees, cynicism, vigour and dedication for middle-aged employees, but failed to predict any one of the four well-being dimensions for younger employees. Job pressure predicted dedication for older employees, exhaustion and cynicism for middle-aged employees and exhaustion and dedication for younger employees. As can be seen from the above, in certain instances job demands tend to be negatively appraised, which
Table 4 Hierarchical M ultiple R egression Analysis with C ynicism a s D ependent Variable Young Middle-aged Old Model Standardised b tp R 2 Standardised t p R 2 Standardised b tp R 2 1 (Constant) 2 .59 .01 .11 4 .02 .00 .11 .48 .63 .14 Job p ressure .06 .55 .58 .18 2.44 .02* -.07 -.74 .46 Mental load -.20 -1.69 .09 -.28 -3.68 .00** .02 .20 .84 Emotional load .37 3 .92 .00** .30 4.72 .00** .40 4.77 .00** 2 (Constant) 5 .59 .00 .33 7 .52 .00 .26 5 .01 .00 .30 Job p ressure .02 .15 .88 .16 2.32 .02* -.14 -1.62 .11 Mental load -.13 -1.17 .24 -.21 -2.96 .00** .08 .99 .32 Emotional load .31 3 .59 .00** .18 2.90 .00** .24 3.02 .00** Supervisor s upport -.00 -.01 .99 -.13 -1.41 .16 -.02 -.15 .88 Colleague support -.08 -.96 .34 -.07 -1.19 .23 -.25 -2.95 .00** Role Clarity -.18 -1.57 .12 -.13 -1.40 .16 -.14 -1.31 .19 Job information .06 .53 .60 .10 .98 .33 -.10 -.88 .38 Participation in -.35 -3.33 .00** -.22 -2.57 .01** -.04 -.36 .72 in decision m aking 3 (Constant) 7 .02 .00 .40 9 .90 .00 .35 7 .10 .00 .41 Job p ressure -.01 -.14 .89 .15 2.22 .03* -.14 -1.81 .07 Mental load -.12 -1.15 .25 -.21 -3.18 .00** .08 1.01 .32 Emotional load .22 2 .53 .01** .11 1.91 .06 .17 2.22 .03* Supervisor s upport .02 .24 .81 -.10 -1.17 .25 -.06 -.63 .53 Colleague support -.07 -.86 .39 -.07 -1.11 .27 -.15 -1.94 .05* Role Clarity -.21 -1.97 .05* -.07 -.75 .45 -.07 -.76 .45 Job information .11 .98 .33 .09 .98 .33 -.11 -1.02 .31 Participation in -.28 -2.72 .01** -.18 -2.20 .03* .06 .59 .55 decision m aking SOC -.31 -3.83 .00** -.34 -6.11 .00** -.39 -5.31 .00** Note . ** S tatistically significant p £ .01; * S tatistically significant p £ .05; For the young group, Model 1 (F = 5.50; R = .34; D R 2 = .09); M odel 2 (F = 7.54; R = .57; D R 2 = .28); Model 3 (F = 9.06; R = .63; D R 2 = .35); F or the m iddle g roup, Model 1 (F = 11.60; R = .34; D R 2 = .10); M odel 2 (F = 11.72; R = .51; D R 2 = .24); M odel 3 (F = 15.96; R = .59; D R 2 = .33). F or the o lder group, Model 1 (F = 9.01; R = .38; D R 2 = .13); M odel 2 (F = 8.66; R = .55; D R 2 = .27); M odel 3 (F = 12.14; R = .64; D R 2 = .38)
Table 5 Hierarchical M ultiple R egression Analysis with V igour as Dependent Variable Young Middle-aged Old Model Standardised b tp R 2 Standardised b tp R 2 Standardised t p R 2 1 (Constant) 3 .07 .00 .30 5 .63 .00 .24 4 .46 .00 .20 Supervisor s upport .22 2.05 .04* .23 2 .66 .01** .07 .56 .57 Colleague support .14 1.57 .12 .19 3.06 .00** .27 3.16 .00** Role Clarity .01 .05 .97 .04 .38 .70 -.15 -1.37 .17 Job information .01 .08 .94 -.08 -.78 .44 .24 2 .03 .04* Participation in .29 2.74 .01** .20 2.29 .02* .08 .77 .44 decision m aking 2 (Constant) 3 .87 .00 .38 5 .45 .00 .30 4 .35 .00 .23 Supervisor s upport .18 1.74 .08 .16 1.80 .07 .04 .38 .71 Colleague support .12 1.44 .15 .16 2.61 .01** .23 2.62 .01** Role Clarity -.01 -.05 .96 .03 .31 .76 -.15 -1.35 .18 Job information .06 .52 .60 -.07 -.69 .49 .28 2 .31 .02* Participation in .26 2.51 .01** .22 2.56 .01** .05 .51 .61 decision m aking J o b p res s u re -.14 -1.40 .16 -.12 -1.81 .07 .08 .88 .38 Mental load .19 1 .88 .06 .20 2 .92 .00** -.12 -1.30 .19 Emotional load -.29 -3.50 .00** -.22 -3.63 .00** -.13 -1.57 .12 3 (Constant) 1 .51 .13 .42 1 .94 .05 .42 1 .90 .06 .38 Supervisor s upport .16 1.60 .11 .12 1.56 .12 .10 .98 .33 Colleague support .11 1.36 .18 .15 2.67 .01** .12 1.46 .15 Role Clarity .02 .21 .83 -.05 -.58 .57 -.23 -2.26 .03* Job information .02 .19 .85 -.06 -.68 .50 .28 2 .65 .01** Participation in .20 1.97 .05* .17 2 .15 .03* -.06 -.60 .55 decision m aking J o b p res s u re -.12 -1.21 .23 -.10 -1.68 .09 .09 1 .05 .30 Mental load .19 1 .88 .06 .20 3 .25 .00** -.11 -1.38 .17 Emotional load -.21 -2.58 .01** -.14 -2.50 .01** -.04 -.54 .59 SOC .25 3.10 .00** .40 7.67 .00** .47 6.30 .00** Note. ** S tatistically significant p £ .01; * S tatistically significant p £ .05; For the young group, Model 1 (F = 10.86; R = .55; R2= .27); M odel 2 (F = 9.43; R = .61; R 2 = .34); Model 3 (F = 10.02; R = .65; D R 2 = .38); F or the m iddle g roup, Model 1 (F = 17.65; R = .50; D R 2 = .23); M odel 2 (F = 14.20; R = .54; D R 2 = .28); M odel 3 (F = 21.84; R = .65; D R 2 = .40). F or the o lder group, Model 1 (F = 8.15; R = .45; D R 2 = .18); M odel 2 (F = 5.86; R = .48; D R 2 = .19); M odel 3 (F = 10.88; R = .62; D R 2 = .35).
Table 6 Hierarchical M ultiple R egression Analysis with D edication as Dependent Variable Young Middle-aged Old Model Standardised b tp R 2 Standardised b tp R 2 Standardised t p R 2 1 (Constant) 2 .22 .03 .36 4 .69 .00 .22 4 .17 .00 .19 Supervisor s upport .12 1.18 .24 .11 1.28 .20 .07 .58 .56 Colleague support .06 .75 .46 .11 1 .75 .08 .14 1 .63 .11 Role Clarity .06 .59 .56 .07 .75 .45 .01 .06 .95 Job information -.05 -.41 .68 .02 .25 .80 .24 1 .99 .05* Participation in .48 4.78 .00** .23 2.65 .01** .08 .72 .47 decision m aking 2 (Constant) 1 .32 .19 .43 2 .77 .01 .25 1 .79 .08 .24 Supervisor s upport .13 1.29 .20 .07 .82 .41 .01 .09 .93 Colleague support .08 1.00 .32 .11 1.79 .07 .18 2.09 .04* Role Clarity .08 .80 .43 .04 .39 .70 .06 .51 .61 Job information -.04 -.33 .74 .04 .37 .71 .22 1 .87 .06 Participation in .42 4.35 .00** .25 2.84 .01** .09 .89 .37 decision m aking Job p ressure .19 1 .96 .05* .04 .61 .55 .29 3.19 .00** Mental load .08 .84 .40 .17 2.41 .02* -.07 -.75 .45 Emotional load -.26 -3.34 .00** -.15 -2.46 .02* -.08 -.95 .34 3 (Constant) -.52 .61 .47 -.22 .83 .35 -.82 .41 .41 Supervisor s upport .11 1.14 .26 .04 .53 .60 .07 .68 .50 Colleague support .07 .91 .37 .10 1 .75 .08 .07 .84 .40 Role Clarity .11 1.06 .29 -.03 -.35 .73 -.02 -.23 .82 Job information -.07 -.66 .51 .04 .46 .64 .23 2 .19 .03* Participation in .37 3.85 .00** .20 2.48 .01** -.02 -.23 .82 decision m aking Job p ressure .21 2 .23 .03* .06 .91 .37 .29 3.66 .00** Mental load .08 .81 .42 .17 2.61 .01** -.06 -.77 .44 Emotional load -.20 -2.47 .02* -.08 -1.40 .16 .01 .19 .85 SOC .22 2.86 .01* .35 6 .42 .00** .48 6.60 .00** Note . ** S tatistically significant p £ .01; * S tatistically significant p £ .05; For the young group, Model 1 (F = 14.45; R = .60; D R 2 = .34); M odel 2 (F = 11.80; R = .66; D R 2 = .40); Model 3 (F = 11.99; R = .68; D R 2 = .43); F or the m iddle g roup, Model 1 (F = 14.99; R = .46; D R2= .20); M odel 2 (F = 11.36; R = .50; D R 2 = .23); M odel 3 (F = 16.17; R = .59; D R2= .33). F or the o lder group, Model 1 (F = 7.62;R = .44; D R 2 = .17); M odel 2 (F = 6.36; R = .49; D R2= .20); M odel 3 (F = 12.00; R = .64; D R 2 = .37).
Table 7 Summary of Significant Predictors o f W ell-being Across the T hree Age G roups Young Middle-Aged Old b tp b tp b tp EXHAUSTI ON R2 = 37% R2 =41% R2 =55% SOC -.27 -3.23 .00** S OC -.36 -6 .77 .00** S OC -.47 -7.38 .00** Emotional load .25 2 .84 .01** E motional load .18 3 .29 .00** E motional load .15 2 .21 .03* Job p ressure .21 2 .10 .04* Job p ressure .27 4 .25 .00** -S upervisor s upport -.17 -2.16 .03* Supervisor s upport -.23 -2.55 .01** -Mental load .30 4 .38 .00* -Participation in .18 2.11 .04* decision m aking CYNI CI SM R2 =40% R2 =35% R2 =41% SOC -.31 -3.83 .00** S OC -.34 -6 .11 .00** S OC -.39 -5.31 .00** Participation in -.28 -2.72 .01** P articipation in -.18 -2.20 .03* -decision m aking decision m aking -Emotional load .22 2 .53 .01** -Emotional load .17 2 .22 .03* Role clarity -.21 -1.97 .05* -M ental load -.21 -3.18 .00** -J ob pressure .15 2 .22 .03* -Colleague support -.15 -1.94 .05* VIGOUR R2 =42% R2 =42% R2 =38% SOC .25 3.10 .00** S OC .40 7 .67 .00** S OC .47 6 .30 .00** Participation in .20 1.97 .05* Pa rticipation in .17 2.15 .03* -decision m aking decision m aking Emotional load -.21 -2.58 .01** E motional load -.14 -2.50 .01** -Role clarity -.23 -2.26 .03* -Job information .28 2 .65 .01** -C olleague support .15 2.67 .01** -M ental load .20 3 .25 .00** -DEDI CATI ON R2 =47% R2 =35% R2 =41% SOC .22 2.86 .01** S OC .35 6 .42 .00** S OC .48 6 .60 .00** Participation in .37 3.85 .00** P articipation in .20 2.48 .01** -decision m akin decision m akin Job p ressure .21 2 .23 .03* -Job p ressure .29 3 .66 .00* Emotional load -.20 -2.47 .02* -M ental load .17 2 .61 .01** -Job information .23 2 .19 .03 Note . *** Statistically significant p £ .01; * S tatistically significant p £ .05
is in line with the results of other studies suggesting that job de-mands are associated with negative outcomes such as burnout (Bakker, Demerouti, & Schaufeli, 2003; Demerouti et al., 2001; Schaufeli & Bakker, 2004). On the other hand, the results also suggest that in some cases job demands are seen as contribut-ing factors to positive outcomes (i.e., vigour and dedication). This also corresponds with the findings of previous studies (Lepine, Podsakoff, & Lepine, 2005, Rothmann & Jordaan, 2006).
This hypothesis is rejected since the results in this study sug-gest that different job resources predict different dimensions of burnout and engagement for the three age groups. Supervisor support predicted lower exhaustion for older and middle-aged em-ployees but did not predict any of the four well-being dimensions for younger employees. Colleague support predicted lower cyni-cism for older employees, vigour for middle-aged employees but did not predict any of the four well-being dimensions for younger employees. Participation in decision-making predicted exhaustion for older employees, lower cynicism, vigour and dedication for middle-aged employees and lower cynicism, vigour and dedica-tion for younger employees. Role clarity predicted vigour for older employees and lower cynicism for younger employees. However, it did not predict well-being for middle-aged employees. Job infor-mation predicted vigour and dedication for older employees, but did not predict any of the well-being dimensions for middle-aged or younger employees. Again, the results reveal that young, mid-dle-aged and older employees experience certain job resources differently in the workplace.
The results reveal that older employees seem to experience higher levels of SOC, compared to their younger colleagues. These higher levels of SOC could serve as a means for older employees to effectively deal with stressors, which in turn could result in increased levels of well-being and lower burnout (Love, Goh, Hogg, Robson, & Irani, 2011; Van der Colff & Rothmann, 2009). Having a strong SOC “enables the worker to evaluate potential stressors as benign or irrelevant and thus supports problem-solving in stressful situations, which prevents mental breakdown at work” (Kalimo et al., 2003, p.119). Furthermore, hypothesis 4 stated that SOC levels which predict burnout and work engagement will not differ for the different age groups. This hypothesis is confirmed. The results of this study suggest that SOC was a significant predictor of exhaustion, cynicism, vigour and dedication for all three the age groups. These results correspond with findings of previous studies that have found SOC to be related to lower levels of burnout (Bezuidenhout & Cilliers, 2010; Feldt, Kinnunen, & Mauno, 2000) and increased work engagement (Naudé & Rothmann, 2006; Rothmann, Steyn, & Mostert, 2005).
Limitations of the Study
The main limitation of the study is that it made use of cross sectional design when collecting the data, whereas a longitudi-nal study could have shown a causal relationship regarding age and its implications. The second limitation is that self-report measures were used. The use of self-report measures is often stated to lead to “common method variance problems”, al-though consensus has not yet been reached on whether or not this is problematic (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003; Spector 2006; Tremblay & Messervey, 2011). A larger sample may have also yielded more significant results as the re-sults would be more reliable when generalised. A final limitation is that this study only focused junior bank managers.
Conse-quently, the results of this study cannot be generalised across different occupations or sectors.
Conclusion
In conclusion, the following main findings for the present study were produced. Young and middle-aged employees ex-perienced higher levels of exhaustion compared to older em-ployees. The post-hoc analysis on work engagement revealed that older employees seemed to be more dedicated than their younger counterparts. However, there were no significant differ-ences found across the three groups regarding cynicism and vigour. It was also found that different job demands and re-sources seemed to predict burnout and work engagement with the exception of emotional load which was a significant predic-tor for exhaustion across all three age groups. An important finding for South African literature, individuals and organisa-tions, was that SOC across the three age groups was a signifi-cant predictor of burnout and work engagement. The present study yielded important results that can be very beneficial for or-ganisations to take into consideration when exploring well-be-ing further in other occupations as well as for the junior manag-ers in the banking industry.
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