October, 2014 From buffering to boosting Fleur Verhoeven

Transcription

1 October, 2014 From buffering to boosting The moderating role of supervisory support and selfefficacy in the relation between a strengths intervention and work engagement Fleur Verhoeven Tilburg University

2 Master thesis Human Resources Studies The moderating role of supervisory support and self-efficacy in the relation between a strengths intervention and work engagement Student: Fleur Verhoeven ANR: Project theme: Strengths intervention Project period: January 2014 October 2014 Supervisor: Christina Meyers Second reader: Marianne van Woerkom 2

3 Abstract This study examined the effect of a strengths intervention on work related well-being. In addition, the moderating effect of two different resources (supervisory support and self-efficacy) was studied, in order to add to already existing literature on strengths. On the basis of an experiment, 71 employees of Heijmans participated in four workshops with the aim of testing hypotheses. During the workshops the participants were encouraged to identify their strengths, develop them, and make a plan to use them in their workplaces. Next, three hierarchical multiple regressions were conducted; one for the direct effect, one for supervisory support as a moderator, and one for selfefficacy as a moderator. Unfortunately, no significant effect was found from the strengths intervention on work engagement. Therefore, the first hypothesis was not confirmed. In addition, the moderating effect of supervisory support found no supporting evidence, however, self-efficacy proved to be influential in the relationship between a strengths intervention and work engagement. Remarkable here was the negative effect the intervention had on employees scoring low on selfefficacy. Based on the results and limitations of this research, recommendations for future research on strengths interventions were provided. Keywords: positive psychology, strengths intervention, work related well-being, work engagement, supervisory support, self-efficacy 3

4 Preface The Master Thesis circle Strengths intervention gave me the challenge to execute my first experiment. Starting from the principles of positive psychology, I learned a lot about the strengths based approach, the positive activity model, and work engagement as part of work-related wellbeing. In addition to the theoretical knowledge I gained, I have learned a lot about doing experimental research, working in a team, and about myself. The research process was characterized by ups and downs, which we handled professionally as a team. Taking it all together, it was an enriching road to success. I want to sincerely thank my supervisor Christina Meyers for her close supervision and support during the process of doing research and writing this thesis. Her feedback really challenged me to look critically at my own writing, which improved the quality of my master thesis. Also I would like to thank Marianne van Woerkom for providing critical feedback, and offering support during the whole process. Finally, thanks to my fellow students in this thesis circle for the cooperation, mutually support, and feedback where needed. 4

5 Table of contents 1. Introduction Theoretical framework The relationship between strengths interventions and work engagement Moderating roles Supervisory support Self-efficacy Method Research set up Description of Respondents Instruments Work engagement Supervisory support Self-efficacy Control variables Data analysis Results Correlation analysis Multivariate analysis of variance Mixed between-within subject analysis of variance Hierarchical multiple regression Direct effect Supervisory support Self-efficacy Discussion Limitations and directions for future research Practical implications Concluding remarks References..33 5

6 1. Introduction Former research has shown that employee engagement predicts employee outcomes, organizational success, and financial performance (Harter, Schmidt & Hayes, 2002; Schaufeli & Bakker, 2004). Therefore, organizations should pay attention to valuing the engagement of their employees. One effective way to promote employee engagement is to involve them in positive activities at work, for example by providing workshops for personal development and strengths identification (Lyubomirsky & Layous, 2013). This assumption is based on positive psychology, which generally focuses on understanding and building aspects that make individuals, communities and societies flourish (Seligman & Csikszentmihalyi, 2000). To link this to positive activities at work, Buckingham and Clifton (2001) argued that improvement could best work out when focusing on talents and strengths, instead of the traditional approach which mainly focuses on improving weaknesses of employees. Strengths are defined as potentials for excellence (Biswas-Diener, Kashdan & Minhas, 2011, p.106). To combine this, a strengths intervention is a technique aiming at the identification, development, and use of the participants strengths (Quinlan, Swain, & Vella-Brodrick, 2012). The effect of strengths interventions on engagement can be explained by three theoretical arguments. First, strengths interventions lead to a raised energy level of employees and in turn can cause more invigoration, which is a component of engagement (Schaufeli, Salanova, González-Romá & Bakker, 2002). Second, after the strengths intervention, employees will feel more authentic and true to themselves (Peterson & Seligman, 2004). This could add meaning to the lives of employees, which is a key predictor of engagement in one s work (Schaufeli, et al., 2002). Finally, after participating in a strengths intervention, employees may experience a flow, closely related to absorption, which is also a component of engagement (Csikszentmihalyi, 1990). This study aims to test the effects of a strengths intervention on work engagement as an indicator of employee well-being. In addition, this study will investigate whether job or personal resources can boost the expected positive effects of strengths interventions on work engagement. Based on the job demands-resources model (JD-R), most researchers predict job- and personal resources to act as buffers in diminishing the effect of job demands on negative outcomes such as burnout (e.g. Kirmeyer & Dougherty, 1988; Bakker, Hakanen, Demerouti & Xanthopoulou, 2007). The present 6

7 study looks at resources in a different way, based on positive psychology, by investigating whether job- and personal resources could also boost the effect of another job resource (strengths intervention) on positive outcomes such as work engagement. In more detail, the study focuses on the relationship between positive activities (strengths intervention) and employee well-being (work engagement), and the boosting effect of two moderators; supervisory support and selfefficacy. The positive-activity model (Lyubomirsky & Layous, 2013) indicates that supervisory support and self-efficacy may act as moderators between positive activities and well-being. For self-efficacy the reason may be that employees who believe in their own abilities will develop and use their strengths more, and therefore gain more from positive activities. The boosting effect of high supervisory support can be explained by an increased ability to reap rewards from positive activities. Thus, building on the assumptions of the positive-activity model and positive psychology, this research examines the boosting effect of two moderators on the relation between positive activities and work-related well-being. Until now, existing studies have only looked at whether strengths interventions increase well-being (Quinlan et al., 2012), but not whether they increase well-being for some people to a greater extent than for other people. This research extends the existing research by making a distinction, based on the two moderators, on individuals who benefit more, and individuals who benefit less from a strengths intervention. To add, organizations expect their employees to be energetic, dedicated, and absorbed by their work. Hence, they want their workforce to be engaged with their work (Schaufeli & Salanova, 2008). Therefore, work engagement may be of great interest to organizations aiming at increasing their performance through human capital. Strengths interventions are helpful for organizations aiming to increase employee engagement, because they are easy to implement (Meyers & van Woerkom, in press). To sum up, this research aims at two important goals: Firstly, it implements a strengths intervention and tests its effects on work engagement. Secondly, it aims at extending the JD-R model, by testing a boosting effect of two moderators in the direct relationship between strength intervention and work engagement. Therefore, the following research question is formulated: To what extent does a strength intervention positively affect work engagement, and is this relationship moderated by supervisory support and self-efficacy? 7

8 2. Theoretical framework 2.1 The relationship between a strengths intervention and work engagement Nowadays, organizations foster the mind-set of employees to compare themselves with others in the working context, and focus on what they lack, instead of what they are good at. Based on the assumption that the greatest potential for improvement lies in the weaknesses of employees, most Human Resource Development (HRD) practices offer a deficiency approach (Buckingham & Clifton, 2001). However, HRD theorists have criticised this assumption and started to focus on the opposite idea: focusing on strengths (Buckingham & Clifton, 2001). Strengths are defined in this paper as potentials for excellence (Biswas-Diener et al., 2011, p.106). The strengths-based approach (SBA) focuses on developing talents and strengths, instead of fixing employees weaknesses (Buckingham & Clifton, 2001). Organizations that use SBA in their HRD practices, do not ignore weaknesses, but rather focus on highlighting and building upon talents and strengths (Clifton & Harter, 2003). The shift towards a more positive psychology may be of incremental value to both individuals as organizations, due to the fact that using strengths positively influences engagement, motivation, satisfaction, energy level, and employee health (Linley & Harrington, 2006a; Peterson & Park, 2006; Peterson & Seligman, 2004). Organizations can implement positive activities, such as a strengths intervention, in their HRD practices. A strengths intervention is a technique aiming at the identification, development, and use of the participants strengths (Quinlan et al., 2012). The goal of strengths interventions is to increase employee well-being through identification, development, and use of strengths. Identification of strengths is of value since most of the people are blind to personal strengths because they often view them as ordinary, instead of extraordinary (Biswas-Diener, in press). In addition, these interventions help employees to develop their strengths and make a plan to use them in their work environments (Lyubomirsky & Layous, 2013). Research so far has identified some effects of strengths intervention on general well-being, but not much is found about their effect on positive work-related well-being (Schaufeli, 2014; Fredrickson, Mancuso, Branigan & Tugade, 2000; van Woerkom & Meyers, in progress). 8

9 In order to add to the research gap, this study explores whether strengths interventions lead to higher work engagement. Schaufeli (2014) argued that work engagement is an aspect of workrelated well-being, which is linked to organizational commitment, innovativeness, health, and employee performance (Schaufeli & Taris, 2014). Therefore, examining the effect of strengths interventions on work engagement may be beneficial for organizations. Work engagement can be defined as a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption (Schaufeli, et al., 2002, p.74). They described the three dimensions as follows. Vigor refers to high levels of energy and mental resilience while working, the willingness to invest effort in one s work, and persistence when difficulties occur. Dedication is characterized by a strong involvement in one s work, accompanied by feelings of enthusiasm and significance, and by a sense of pride and inspiration. The final characteristic of engagement, absorption, is in more detail the pleasant state of total immersion in one s work, whereby time passes quickly and one has difficulties detaching oneself from work. Schaufeli, Taris, and Van Rhenen (2008) showed these three aspects correlating with health of employees, indicating that work engagement is part of employee well-being. Therefore, organizations aiming at enhancing employee well-being could foster work engagement through HRD practices. To summarize, engaged employees are characterized by high levels of energy and effort in their work activities, immerse themselves fully in their tasks, and feel challenged at work (Salanova, Del Libano, Llorens, & Schaufeli, 2014; Schaufeli et al., 2008; Schaufeli et al., 2002). Schaufeli and Salanova (2010) have outlined the importance of positive workplace interventions in influencing work engagement. A strengths intervention can be seen as a positive workplace intervention. According to the article of Meyers and van Woerkom (in press) there are three reasons, from a theoretical perspective, for the relation between a strengths intervention and work engagement. First, Peterson and Seligman (2004) emphasized the raising energy level when employees participate in strengths interventions, which caused a more invigorated feeling among employees. This feeling of vigor is one of the components of work engagement (Schaufeli et al., 2002). Second, people who use their strengths feel more authentic and true to themselves, which gives them the chance to behave in accordance with their principles (Peterson & Seligman, 2004). In turn, these employees will experience a feeling of meaning in their lives, which is a key predictor of being engaged in one s work (Schaufeli & Salanova, 2010). Third, flow experiences 9

10 (Csikszentmihalyi, 1990) can be stimulated by identification and use of strengths, and by choosing jobs that match one s strengths (Seligman, 2002). Subsequently, flow experiences are closely related to absorption, which is a component of engagement (Schaufeli et al., 2002). Therefore, it is expected that strengths interventions will have a positive influence on work engagement among employees. A study by Mitchell, Stanimirovic, Klein and Vella-Brodrick (2009), which is similar to the present research, indicated that employees who participated in a strengths intervention showed increases in well-being directly and three months after the intervention. These findings are in line with other earlier research investigating the relationship between strengths interventions and work engagement (e.g. Quinlan et al., 2012; Harter, et al., 2002; Seligman, Ernst, Gillham, Reivich, Linkins, 2009; Harzer & Ruch, 2013). In short, the relationship between strengths intervention and work engagement can be explained by an energizing process, the feeling of meaningfulness for employees, and the possibility of flow experiences. By combining these theoretical findings with the already existing literature to date, the following is expected: Hypothesis 1: Participating in a strengths intervention is positively related to employee work engagement. 2.2 Moderating roles This study investigates whether some people benefit more from positive activities than others. The positive activity model by Lyubomirsky and Layous (2013) stresses that people can increase happiness through simple positive activities. Activity features and person features influence the degree to which these activities improve well-being. Supervisory support is part of the activity features in the model, self-efficacy of person features. In combination with the categorization of these concepts as job- and personal resources in the JD-R model, the moderation effects will be examined in this research Supervisory support The job demands-resources model (JD-R model) states that employees should have at least as much resources as demands to perform well on their jobs (Demerouti, Bakker, Nachreiner & Schaufeli, 2001). Since this study is based on positive psychology, the focus is not on the job 10

11 demands of employees, but rather on the role of resources. These resources can be divided in job and personal resources (Xanthopoulou, Bakkers, Demerouti & Schaufeli, 2009). Job resources are those physical, social, psychological and/or organizational aspects of the job that facilitate accomplishing goals, decrease demands of the job or enhance personal growth (Halbesleben & Buckley, 2004). Demerouti and Bakker (2007) stated that social support is an important resource in jobs. This study focuses on supervisory support, which is a form of social support. Supervisory support is the degree to which employees perceive that supervisors offer support, encouragement and concern (Burke, Borucki & Hurley, 1992). Employees who feel supported by their supervisor receive more feedback, recommendations, recognition and flexible work schedules. These benefits elicit positive feelings and trust from employees (Dirks & Ferrin, 2002). Therefore, employees who experience support from their supervisor, based on positive psychology, will feel supported in developing and using their strengths. These employees, high in supervisory support, are expected to benefit more from the strengths intervention than employees experiencing low supervisory support. A strengths intervention technique contains a workshop, wherein participants identify, develop and use their strengths. Supervisors can play an important role in the period after the strengths intervention. First, strengths interventions are useful techniques to encourage personal growth, development and learning of employees (Schaufeli & Bakker, 2004). Employees experiencing high supervisory support receive for example more personal attention and feedback, which can boost the positive concepts encouraged by the strengths intervention. Therefore, supervisors have an important role in optimizing what employees learn in workshops; they can foster the development of knowledge, skills and attitude (Nijman, Nijhof, Wognum & Veldkamp, 2006). Thus, it is expected that employees who experience feedback, guidance and positivism of their supervisors, will benefit more from the strengths intervention and in the end report greater work engagement. To add, Bandura and Cervone (1986) argued that employees probably will be more successful in their positive activities when experiencing social (and thus supervisory) support. Finally, Lyubomirsky and Layous (2013) argued in the positive activity model the increased ability to reap rewards from positive activities, when employees experience social support. In other words, these studies indicate that high supervisory support may increase the positive effects the strengths intervention can have on employee outcomes. 11

12 In summary, employees experiencing high supervisory support probably benefit more from the positive results of strengths interventions, e.g. increase in work engagement. Based on these findings, it is hypothesized that: Hypothesis 2a: Supervisory support moderates the relationship between a strengths intervention and work engagement in such a way that the effect of a strengths intervention will be stronger when employees experience high supervisory support than when employees experience low supervisory support. Supervisory support Strengths intervention Work engagement Figure 1: Conceptual model of the relationship between strengths intervention and work engagement, moderated by supervisory support Self-efficacy Personal resources are positive self-evaluations that are linked to resiliency and refer to individuals sense of their ability to control and impact their environment (Hobfoll, Johnson, Ennis & Jackson, 2003). Self-efficacy can be considered as one of these personal resources (Lyubomirsky & Layous, 2013), and is explained as the belief of an employee in one s ability to achieve goals (Bandura, 1997). Which group, high or low in self-efficacy, benefit more from the intervention? In other words, how does self-efficacy moderate the relation between a strengths intervention and work engagement? First, according to Schunk (1991), self-efficacy can be seen as a motivator, which indicates a positive influence on activities such as a strengths intervention. Employees who feel more confident about their own abilities will visualize success scenarios that provide positive feelings and supports performance (Bandura, 1993), which adds to the underlying mechanisms of positive psychology. In addition, employees with high self-efficacy might show more effort and persistence in using their strengths (Bandura, 1997), which in combination with the positive feelings described above, may lead to a better motivation of keeping up the development and using 12

13 of their strengths. Second, an explanation for the moderating influence of self-efficacy can be the fact that these employees feel more trust in themselves and are more likely to achieve their goals successfully. In addition, they will feel more committed to achieving these goals (Bandura, 1997), which in turn may be beneficial for the relationship between strengths interventions and work engagement. In other words, employees who feel more committed to their goals will probably invest more on the use and development of their strengths, based on increased persistence (Bandura, 1997), and will therefore benefit more and longer from the intervention. In turn, this will result in an increased positive effect on work engagement. These findings indicate that resources, as described by the JD-R model, are not only helping to overcome negative work related outcomes, but can also boost the positive effect they have on the relationship between positive activities and work-related outcomes. To add, the positive activity model indicates that people s personality, and thus self-efficacy, affect how much is gained from positive activities (Lyubomirsky & Layous, 2013). In other words, employees high in self-efficacy may have an increased gain from the positive activities, and thus a greater influence on work engagement. The latter suggests that employees who score high on self-efficacy will benefit more from the strengths intervention, and will in return report higher levels of work engagement. Based on these theoretical arguments, the following is expected: Hypothesis 2b: Self-efficacy moderates the relationship between a strengths intervention and work engagement in such a way that employees scoring high in self-efficacy will report greater gains in work engagement than employees scoring low in self-efficacy. Self-efficacy Strengths intervention Work engagement Figure 2: Conceptual model of the relationship between strengths intervention and work engagement, moderated by self-efficacy 13

14 3. Method 3.1 Research set-up This study is part of the research which is designed and conducted in cooperation with Heijmans, a Dutch construction company. It is a longitudinal field experiment with three measurement points, which uses both an experimental and a waitlist-control group to test the intervention on its effectiveness. Employees of Heijmans could voluntarily subscribe to one of the four workshops not knowing that two of the groups would form the experimental, and two the waitlist-control group. Hereby, differences between the experimental and control group should be avoided. Due to voluntary subscription of employees, the sampling method was convenience based. Participants were asked to fill in three questionnaires at the three different points in time. These questionnaires were also used for several other researches on strengths interventions, and contain scales that are not used nor mentioned in this study. The experimental group filled in two of the three questionnaires online, and one directly after the workshop with paper and pencil. The control group filled in all three questionnaires online. 3.2 Description of Respondents All respondents recruited work at Heijmans in the Netherlands; employees between 20 and 35 years old were able to participate. In total, 71 employees started participating in the research, 39 in the control group, and 32 in the experimental group. The sample comprised 47 men (66.2%) and 24 women (33.8%). The average age of the respondents was (SD = 3.54). Tenure within the organization ranged from less than one year, to 16 years, with an average of 3.49 years (SD = 3.1). Of all respondents, 27 (36.6%) completed higher academic education, 27 (38.0%) higher vocational education, and 18 (25.4%) secondary vocational education. A small part of the sample had a supervisory role, namely 17 (23.9%), the 54 other respondents (76.1%) were not in this position. 71 respondents filled in the first questionnaire, 62 the second, and 57 filled in all three questionnaires. Therefore, the attrition rate at measurement point three is 19.7%. 14

15 3.3 Instruments In order to test the hypotheses existing scales are used. Supervisory support is only measured at pre-measurement, self-efficacy on all three measurement points, and work engagement at point one and three. The scales were all checked for their factor structure by using a principal component analysis (PCA). To this end, the Kaiser Meyer Olkin (KMO) measure should been higher than.6, and the Bartlett s sphericity test had to be significant (p-value<.05). Based on the eigenvalues and the scree plot the number of components of each scale was determined. Next, to check the reliability of the scale, Cronbach s alpha was computed. In order to conclude reliability of the scale, the alpha should be.7 or higher. However, an alpha of.8 is preferable according to Pallant (2007). In addition, the Corrected item-total correlation (should be greater than.3) and Alpha if item deleted (should be lower than Cronbach s alpha) were checked Work engagement Work engagement was measured with the nine-item version of the Utrecht Work Engagement Scale (UWES-9; Schaufeli, Bakker & Salanova, 2006). The scale included items like My job inspires me and I am proud of the work I do. The respondents could answer on a seven-point Likert scale from one (never) to seven (always). The factor analysis for this scale gave a KMO value of.91, which is significant (p<.05), and both the eigenvalues and the scree plot indicated that all items loaded on one factor. This factor had an eigenvalue of 6.75, which explained 74.94% of the variance. The Cronbach s alpha of this scale at t1 was.96. By deleting item nine, the reliability could be increased with.002, but due to the fact it is not a valid difference, the item was preserved. At t3, the Cronbach s alpha was.95 and thus this scale is interpreted as reliable. In addition, factor analysis for engagement at t3 gave comparable results, namely one factor, with an eigenvalue of 6.47, which explained 71.89% of the variance Supervisory support Supervisory support was measured using the four-item scale of Rhoades, Eisenberger and Armeli (2001), derived from the Survey of Perceived Organizational Support (SPOS). This scale measures perceived organizational support, but in order to measure supervisory support, they replaced the word organization by supervisor. Items in the scale are My supervisor cares about my opinion 15

16 and My supervisor strongly considers my goals and values. The answers have been given on a seven-point Likert scale from one (1=totally disagree) to seven (7=totally agree). One item from the scale had to be reversed because it was negatively formulated. Factor analysis on the scale showed a KMO value of.81, which is significant (p<.05). The scale had one factor with an eigenvalue of 2.98, which explained 74.48% of variance. Therefore, there is concluded that the items of the supervisory support scale loaded on one factor. The Cronbach s alpha of this scale was Self-efficacy We used four items of the New General Self-efficacy scale by Chen, Gully and Eden (2001). Examples of questions are I am confident that I can perform effectively on many different tasks and Even when things are tough, I can perform quite well. Answers were given on a five-point Likert scale from one (1=totally disagree) to five (5=totally agree). Factor analysis showed a KMO value of.73, which is significant according to Bartlett s sphericity test (p<.05). Eigenvalue was 2.32, which explained 58.07%. In combination with the one factor indicated by the scree plot there was concluded all items of the scale loaded on one factor. The reliability analysis indicated a Cronbach s alpha of.75. By deleting item one the reliability will be.76, however the difference is very small and the scale is commonly used, the item was preserved. Therefore, according to Pallant (2007), this scale was considered reliable. At both time measurement two and three, the factor analysis indicated comparable results from measurement point one. The Cronbach s alphas are both.82 for t2 and t3. For this research, measurement point one was used in the analysis. To add, no mention worthy differences between the scales of self-efficacy on the three measurement points has been found Control variables This research controlled for gender (male = 0, female = 1). Wahn (1998) showed in his research the relationship between gender, organizational commitment and job satisfaction, which are closely linked to work engagement. In addition, level of education (from primary = 1, to academic = 5) was also controlled for in this research. Next, this research controlled for baseline engagement (t1) to prevent disturbed results, due to the fact Schaufeli, Bakker, and van Rhenen (2009) showed evidence for this effect in their research. 16

17 3.4 Data analysis In order to test the conceptual model proposed in the theoretical framework, SPSS version 19 has been used. First, the data was checked for missing values, probable mistakes and outliers. During all analyses missing data was excluded pairwise, which means the respondent was excluded only if the required data for the specific analysis was missing. They were included in the analyses for which they have the necessary information (Pallant, 2007). The investigation of outliers indicated for each scale one respondent scoring above the standard deviation of 3.29 (Tabachnick & Fidell, 2007). The results with and without outliers were identical, this was done to make sure these outliers were not influencing the research results. This confirms that the outliers could stay in the data file. Next, all scales were checked for its factor structure and reliability. All scales were reliable and thus a Pearson correlation analysis was conducted for all variables at all measurement points, indicating the strength and direction of the relationships between the variables. Then, it was checked whether there were significant differences between the experimental and control group at pre-measurement, due to the fact this research did not made use of random sampling. These possible differences were checked by making use of a multivariate analysis of variance (MANOVA), with work engagement, self-efficacy and supervisory support as dependent variables. Next, to check whether the strengths intervention has led to increases in engagement over time, a mixed between-within subject analysis of variance (ANOVA) is used, with experimental condition as between-subject factor, and time as within-subject factor (Pallant, 2007). To test the moderation effect, several steps had to be taken. First, a grand-mean centre for the variables Group, Selfefficacy, and Supervisory support was made. In other words, these variables were recoded into new variables by subtracting its overall mean. Next, self-efficacy t1 and group were computed in an interaction term, even as supervisory support and group. Before conducting a hierarchical multiple regression analysis, several assumptions had to be met. There is checked for outliers, normality, homoscedasticity, and independence of residuals. In the regression, the first model contained control variables gender and education, the second model the two interacting variables separately, and the third model the interaction term. Finally, the results from the analysis have been interpreted, which will be described in the next section of this paper. 17

18 4 Results This section presents the findings of the analyses conducted in order to test the hypotheses of this research. Three different regression models are tested, the direct effect, the moderating effect of supervisory support and the moderating effect of self-efficacy. 4.1 Correlation analysis The relationships between strengths intervention (group), self-efficacy, supervisory support, and work engagement were analysed using the Pearson correlation coefficient (Pallant, 2007). As shown in table 1, a positive significant relation was found between work engagement and selfefficacy (r=.49, p<.01) at pre-measurement. Furthermore, self-efficacy t1 was positively related to group (r=.26, p<.05) and work engagement t3 (r=.30, p<.05), and a positive relationship between supervisory support t1 and work engagement t3 (r=.28, p<.05) was found. Finally, three measurement points of self-efficacy show mutually large correlations. Self-efficacy t1 and t2 show a correlation of.61 (p<.001), t1 and t3.72 (p<.001), and t2 and t3 correlate.63 (p<.001). Contrary to expectations made in the theoretical framework, no significant relations were found between the grouping variable, self-efficacy t3 and work engagement t3. Table 1: Means, Standard Deviations, Correlations, and Reliabilities Variable M SD Group Gender Education * - 4. Self-efficacy t * (.75) 5. Supervisory support t (.87) 6. Work engagement t ***.18 (.96) 7. Self-efficacy t ** ***.23.31* (.82) 8. Self-efficacy t ***.35**.41**.63*** (.82) 9. Work engagement t *.28*.88***.30*.40** (.95) N=71, 7 N=62, 89 N=57; t1 = pre-measurement, t2 = post-measurement, t3 = one month follow up measurement; 1 experimental group = 1, control group = 0; 2 male = 0, female = 1; 3 primary = 1, basic = 2, secondary = 3, higher = 4, academic = 5; Cronbach s alphas are displayed between brackets on the diagonal; *p<.05, **p<.01, ***p<

19 4.2 Multivariate analysis of variance Since no random sampling is used in this research, there has to be checked whether there were significant differences on pre-measurement between experimental and control group. This is done by using a multivariate analysis of variance (MANOVA). Sample size, normality, and outliers did not violate any of the assumptions. The results of this analysis indicated no multivariate differences between the groups on work engagement, self-efficacy, and supervisory support (Wilks Lambda=.92, F(3, 67)=1.88, p=.14, partial η2=.08). Investigation of the variables separately showed no differences in work engagement (F(1, 69)=.46, p=.50), and supervisory support (F(1, 69)=.62, p=.43). In contrast, self-efficacy indicated a significant difference between the groups at pre-measurement (F(1, 69)=4.90, p=.03). An inspection of the mean scores showed slightly higher levels of self-efficacy for the experimental group (M=4.18, SD=.38) than the control group (M=3.91, SD=.60). Therefore, the analysis was continued, however there will be critically reflected on these differences in the discussion section. 4.3 Mixed between-within subject analysis of variance In order to investigate whether the strengths intervention has led to increases in work engagement across the three time periods (pre-measurement, post-measurement, and one month follow up), a mixed between-within subject analysis of variance (ANOVA) was conducted. There was no significant interaction between the groups and time for work engagement (Wilks Lambda=1.00, F(1,55)=.06, p=.80, partial η2=.00). Similarly, no significant main effect for time was found, Wilks Lambda =.96, F(1,55)=2.53, p=.12, partial η2=.04. The main effect comparing the experimental and the control group was also not significant, F(1,55)=.09, p=.77, partial η2=.00, suggesting no difference in work engagement scores between the respondents in the experimental and control group. For more details, and a graph plot, see table 2a. In addition, self-efficacy indicated no significant differences between the groups over the three time periods (Wilks Lambda=.94, F(2,52)=1.70, p=.19, partial η2=.06). The main effect of self-efficacy over time was also not significant (F(2,52)=.59, p=.56, partial η2=.02. However, the significance level of the between-subjects effects shows that there is almost a difference between the experimental and control group comparing the three time periods (F(1,2.23)=3.86, p=.06, partial η2=.07). Details and plots can be found in the next tables and figures. 19

20 Table 2a: Work engagement scores for the experimental and control group at two points in time Experimental group Control group n M SD n M SD 1. Pre-measurement (t1) One month follow up (t3) Figure 3. Plot work engagement differences between groups Table 3a: ANOVA table work engagement Sum of Squares df Mean Square F Sig. Between groups Within groups

21 Table 2b: Self-efficacy scores for the experimental and control group at three points in time Experimental group Control group n M SD n M SD 1. Pre-measurement (t1) Post-measurement (t2) One month follow up (t3) Figure 4. Plot self-efficacy differences between groups Table 3b: ANOVA table self-efficacy Sum of Squares df Mean Square F Sig. Between groups Within groups

22 4.4 Hierarchical multiple regression Direct effect Primarily analyses for outliers, normality, linearity, homoscedasticity, and independence of residuals were conducted to ensure there was no violation of the assumptions (Pallant, 2007). The first hierarchical multiple regression conducted tested hypothesis 1, which stated the strengths intervention positively influenced employees work engagement. In step 1, gender, education and work engagement t1 explained 77.5% of the variance in work engagement t3. After entering the variable group, the total variance explained by the model as a whole was 78.8% (F(4,52)=48.28, p=.00). In table 4a there can be seen that group had a negative significant effect on work engagement t3, however this finding is not significant (β=-.12, p=.08). Due to these results, additional paired-sample t-tests are conducted, in order to compare the results on work engagement t1 and t3 for both groups. Both tests indicated no significant result (p=.21 and p=.34), which implies there were no significant differences in both groups between measurement point one and three. So, hypothesis 1 was not supported, the strengths intervention did not positively influence employee work engagement. Table 4a: Regression analysis for direct effect strengths intervention on work engagement Model 1 Work engagement t3 Model 2 Work engagement t3 b SE β b SE Β Gender Education Work engagement t *** *** Experimental or control group R² R².01 F 60.71*** 48.28*** F² 3.25 Note. N=57; b = unstandardized regression coefficient; SE = standard error; β = standardized regression coefficient; *p <.05; **p <.01; ***p <

23 4.4.2 Supervisory support A second hierarchical regression was conducted in order to test hypothesis 2a, which stated supervisory support positively moderates the relationship between a strengths intervention and work engagement. In step 1, gender, education and work engagement t1 explained 77.5% of the variance in work engagement t3. By entering the variables supervisory support and group in step 2, the model as a whole explained 80.6% of the variance, with significant results of supervisory support (β=.14, p=.0.3), and group (β=-.13, p=.04). When adding the interaction term (strengths intervention and supervisory support) in model 3, the model is not significant ( R 2 =.00, p=.53). The latter indicates no significant moderating effect of supervisory support on the relationship between a strengths intervention and work engagement (β=-.05, p=.53). Therefore, hypothesis 2a is rejected. For more details of this regression analysis, see table 4b. Table 4b: Regression analysis for moderation effect supervisory support Model 1 Work engagement t3 Model 2 Work engagement t3 Model 3 Work engagement t3 B SE β b SE β b SE β Gender Education Work engagement t *** *** *** Supervisory support * Experimental or control group * * Interaction effect intervention and supervisory support R² R² F 60.71*** 42.35*** 34.94*** F² 4.11**.39 Note. N=57; b = unstandardized regression coefficient; SE = standard error; β = standardized regression coefficient; *p <.05; **p <.01; ***p <

24 4.4.3 Self-efficacy Third, a hierarchical multiple regression was conducted to test hypothesis 2b, stating self-efficacy will moderate the relationship between a strengths intervention and work engagement positively. First, in step 1 the control variables gender, education and work engagement t1 explained 77.5% of the variance in work engagement t3. Next, self-efficacy t1 and group were added in model 2, which in total explained 80.3% of the variance in work engagement t3 ( R 2 =.03, p<.05). When examining the variables independently, neither self-efficacy nor group showed a significant effect on work engagement t3. Model 3, which includes the interaction effect of the strengths intervention and self-efficacy, explained 84.9% of the variance in work engagement t3, and showed a positive significant effect on work engagement (β=.24, p<.001). See table 4c for more details of the regression analysis, and figure 5 for the interaction plot. Table 4c: Regression analysis for moderation effect self-efficacy Model 1 Work engagement t3 Model 2 Work engagement t3 Model 3 Work engagement t3 B SE Β b SE β b SE β Gender Education Work engagement t *** *** *** Self-efficacy t Experimental or control group ** Interaction effect intervention and selfefficacy *** R² R² F *** 46.71*** F² 3.70* 15.02*** Note. N=57; b = unstandardized regression coefficient; SE = standard error; β = standardized regression coefficient; *p <.05; **p <.01; ***p <

25 Figure 5 graphically shows the moderation effect of self-efficacy in the relationship between the strengths intervention and work engagement. Interactions are plotted for individuals high in selfefficacy and individuals low in self-efficacy, respondents scoring below the mean are indicated as low, and respondents scoring above the mean are indicated as high. As can be seen, respondents high on self-efficacy score higher on work engagement after participating in the intervention, in comparison to employees who did not participate in the intervention. When adding the interaction effect of self-efficacy to the model, its beta changes into a positive relationship and thus this confirms the moderating effect of self-efficacy. In other words, respondents high in self-efficacy who participated in the workshop score higher on work engagement at post-measurement than employees low in self-efficacy, which confirms hypothesis 2b. Figure 5: Plot of the interaction effect between self-efficacy and work engagement. 25

26 However, when looking at the interaction plot, the intervention might be harmful for respondents scoring low in self-efficacy, due to their low scores after participating in the intervention. Therefore, another repeated measures ANOVA was conducted, with four groups (experimental low self-efficacy, experimental high self-efficacy, control low self-efficacy and control high selfefficacy). This was done to test whether the experimental low self-efficacy group really scored lower after participating in the workshop. The time*group interaction indicated no significant result (p=.15), however the interaction plot (see figure 6) indicated indeed the low self-efficacy group decreased in work engagement after the intervention. Also a decrease in work engagement is seen for employees high in self-efficacy in the control group. An additional t-test was conducted to check whether the negative effects were significant. Both the effects were indicated significant by the t-test (p=.04 and p=.02). There will be reflected on these results in more detail in the discussion section of this paper. Figure 6: Plot of the scores of work engagement t1 and t3 for the four groups 26

27 5. Discussion This study aimed at being the first in investigating a boosting effect of resources, to extend the job demands-resources model. Earlier research has been about buffering negative effects of job demands, whereas no research has focused on resources that can potentially boost the positive effect of resources. In contrast, this study aimed to be leading in examining a boosting effect of positive resources (self-efficacy and supervisory support), on positive forms of well-being (work engagement), making use of a strengths intervention. Several theoretical arguments of Meyers and van Woerkom (in press) lead to the expected positive effect of a strengths intervention on employees work engagement. In addition, the positive activity model of Lyubomirsky and Layous (2013) theoretically supported the expected moderating effects of supervisory support and selfefficacy. In order to investigate the research question, to what extent does a strength intervention positively affect work engagement, and is this relationship moderated by supervisory support and self-efficacy?, 71 employees of Heijmans participated in the research, by attending a workshop and filling in three questionnaires. This study found no evidence for the direct relationship between the strengths intervention and work engagement. As indicated in the results section, there were no differences between the experimental and control group according work engagement over time. This finding does not support the expected positive effect of the intervention on employees work engagement (Quinlan et al., 2012; Meyers & van Woerkom, in press; Harter et al., 2002). An explanation might be in the application of strengths in daily working routines. Harzer and Ruch (2013) argued that the application of strengths is dependent on two conditions. The first one is the possession of the strength by the employee, which in this study is what the employees found out and were made aware of in the workshop. Next, situational circumstances should encourage behavior that suits the strengths of individuals. In this study, it might be that condition two of Harzer and Ruch (2013) is not met; employees did find their strengths in the workshop, though were not able to apply these in their daily responsibilities at work. In more detail, employees who work on what they can do best are motivated to do so and feel fulfilled by their tasks (Linley & Harrington, 2006b). Combining this with research by Rath and Conchie (2009), indicating that employees who do not use their strengths at work are only 9% engaged in their jobs, might be the reasoning why these 27

28 employees not significantly increased on their work engagement scores. Another possible explanation for not finding the positive effect of the strengths intervention can be found in research by Biswas-Diener et al., (2011). They discovered, despite the potential benefits of a strengths approach, that employees may feel disappointed, disengaged, or distressed as a result of focusing on strengths. These feelings mostly occur when employees, who work in their areas of strengths, experience failures or setbacks (Biswas-Diener et al., 2011). Biswas-Diener et al. (2011) stressed, this only occurs if employees believe strengths are stable entities that cannot be altered. Therefore, maybe the intervention failed in convincing employees to develop and refine their strengths. Finally, research on general well-being, by for example Quinlan et al. (2012), indicated consistently increases in well-being after respondents participated in a strengths intervention. However, general well-being and work-related well-being are enhanced through different processes and therefore further research is necessary to investigate possible difference in the influence on general or work-related well-being. In addition, the influence of a strengths intervention on work-related well-being can have beneficial effects on both employee and organizational performance (Schaufeli & Taris, 2014) and therefore may require some more in depth research. Next, this research found no support for the moderating effect of supervisory support in the relation between a strengths intervention and work engagement, as expected in the positive activity model (Lyubomirsky & Layous, 2013). Maybe supervisory support in general is not sufficient or specific enough to enhance the expected positive effects of a strengths intervention. A possibility is that employees need close supervision on using their strengths in the workplace, not only supervisory support in general. In addition, Harzer and Ruch (2013) argued that environmental circumstances are of great influence on the application of strengths. It can be that supervisory support, high or low, influenced the application of strengths of the employees. However, due to no significant outcomes for this hypothesis, additional research is necessary in order to draw conclusions about the real causes and mechanisms through which it works. This study, furthermore, found evidence for theoretical arguments from Bandura (1997), the positive activity model of Lyubomirsky and Layous (2013), and the proposed boosting effect of self-efficacy based on the JD-R model by Demerouti et al. (2001). While no significant evidence 28