Testing a Social Cognitive Model of Well-Being With Women Engineers

Abstract

The present study tests the utility of the Social Cognitive Model of Well-Being (SCWB) in the context of work, with a sample of 348 women engineers. Using structural equation modeling, we examined the relations of positive affect, self-efficacy, work conditions, goal progress, and environmental supports and barriers that were assumed to account for job satisfaction and life satisfaction of women engineers. Overall, the model provided a good fit to the data, and SCWB predictors accounted for a significant amount of variance in job satisfaction (63%) and life satisfaction (54%) with our sample of women engineers. As expected, most paths of the SCWB model were significant; however, we also found nonsignificant relations among variables in the model. In particular, goal progress did not play a critical role in the present study. In addition, we examined the indirect effects of environmental variables (e.g., supports and barriers) on job satisfaction via sociocognitive variables (e.g., self-efficacy and perceived work conditions) in the engineering work domain. Implications for practice, theory, and future vocational and organizational research in engineering are discussed.

Keywords

social cognitive model of well-being engineering women job and life satisfaction

The gender gap in engineering has been a concern for educators and policy makers in the United States. The Society of Women Engineers (SWE, 2007) reported that 25% of women who enter engineering careers leave their job after the age of 30 due to low job satisfaction. The loss of talented women engineers is a personal loss for women who exhibit talent in this area and has a negative impact on the economic growth and global competitiveness of the United States. In spite of government efforts to increase the representation of women in engineering, only 14.5% of professional engineers are women (NSF, 2015), and this proportion has not changed for 20 years (NSF, 2011). Given the concerns of women’s low representation in engineering, continued attention is needed to understand factors that may enhance the job satisfaction of women engineers.

Several studies have explored key factors that negatively impact job satisfaction. Previous studies have mainly focused on an unfriendly organizational culture in engineering (Ahuja, 2002; Callister, 2006; Tattersall, Keogh, Richardson, & Adam, 2006; Trauth, Quesenbery, & Huang, 2009), work–home conflict (Bacharach, Bamberger, & Conley, 1991), and gender discrimination and inequality (Powell, Bagilhole, & Dainty, 2008) as predictors of women engineers’ low job satisfaction. These studies highlighted the importance of supportive environments at home and in the workplace and reducing discrimination for women engineers to increase their job satisfaction. More attention and efforts are needed to examine key factors, including sociocognitive, affective, and behavioral factors, to increase job and eventually general life satisfaction of women engineers; this aligns with vocational psychology’s emphasis on positive adjustment and well-being in the workplace. We also need to understand the interplay between environmental factors and individual psychological factors.

Lent and Brown (2008) proposed a comprehensive theoretical framework in the context of work, Social Cognitive Career Theory of Well-Being (SCWB), to explore multiple factors including cognitive, environmental, affective, and behavioral aspects to understand domain-specific satisfaction and general life satisfaction. This framework provides a holistic view to understand women engineers’ domain specific well-being (e.g., engineering job satisfaction) and their general life satisfaction. Thus, the present study aims to explore the relations and structure of cognitive, behavioral, affective, and environmental aspects to women engineers’ job and life satisfaction using the SCWB model. We expect that findings of this study can provide practical implications for supervisors, career counselors, and mental health professionals about interventions to enhance women’s job and life satisfaction in engineering, both at the individual and organization levels.

Theoretical Framework: SCWB

Among various vocational psychology theories, SCWB (Lent, 2004) provides a solid framework to understand individuals’ domain-specific satisfaction and overall life satisfaction through a comprehensive approach by capturing affective, sociocognitive, behavioral, and environmental aspects. Using SCWB theory, vocational psychologists have examined engineering students’ academic satisfaction (Flores et al., 2014; Lent, Singely, Sheu, Schmidt, & Schmidt, 2007) and academic persistence (Lent et al., 2013, 2015, 2016; Navarro, Flores, Lee, & Gonzalez, 2014) in engineering. Generally, the previous findings of the SCWB model provided good fit of the data. For example, engineering self-efficacy (Flores et al., 2014; Lent et al., 2007; Navarro et al., 2014) and engineering goal-related variables (Flores et al., 2014; Lent et al., 2007) were significant predictors of engineering students’ academic satisfaction. The direct path from environmental supports to academic satisfaction was significant with samples of engineering students (Lent et al., 2007; Navarro et al., 2014). Inconsistent findings were reported in relation to outcome expectations. Lent, Singely, Sheu, Schmidt, and Schmidt (2007) reported that the paths from outcome expectations to goal progress and academic satisfaction were not significant. However, Flores et al. (2014) presented significant relations for these paths. In spite of a few inconsistent findings, most predictors of the model were significant and accounted for a significant amount of variance in engineering students’ academic satisfaction. Given the narrow pipeline of women in the engineering workforce, we test the utility of this model in the context of work with a sample of employed adults to inform efforts to increase job and life satisfaction of women engineers via social cognitive predictors.

Lent and Brown (2008) extended the SCWB model (Lent, 2004) to the specific context of vocational settings. Their model includes five predictors: (a) affective traits, (b) goal-directed activity, (c) self-efficacy, (d) work conditions, and (e) environmental supports and barriers that are assumed to account for (f) job satisfaction and eventually link to (g) overall life satisfaction. Lent and Brown (2006, 2008) conceptualized perceived work conditions as domain-specific outcome expectations to be the match between what an individual wants and what an individual receives in the work setting. Also, they conceptualized work-relevant environmental supports (e.g., supports from supervisors or mentors) and obstacles (e.g., discouragement related to their job from significant others) as domain-specific environmental supports and barriers in the context of work. Job satisfaction, work-related self-belief about ones’ ability to perform their work, and work-related goal progress were conceptualized as domain-specific well-being, self-efficacy, and goal progress, respectively, in the extended work model. This extended SCWB model hypothesized that job and life satisfaction are partly determined by sociocognitive, affective, and behavioral variables. The hypothesized paths among the variables are presented in Figure 1.

Figure 1.
Social cognitive model of well-being (SCWB) in the context of work (Lent & Brown, 2008).

To our knowledge, only two studies have tested the SCWB model with adult workers, specifically teacher samples in the United States (Duffy & Lent, 2009) and Italy (Lent et al., 2011). The SCWB work model accounted for 75% (Duffy & Lent, 2009) and 41% (Lent et al., 2011) of the variance in job satisfaction of teachers. The findings of these two studies were consistent in terms of reporting significant paths from work conditions and positive affect to job satisfaction, self-efficacy to goal progress and work conditions, and environmental support to goal progress and work conditions. Contrary to the hypotheses, both studies consistently reported that goal progress did not directly predict job satisfaction. The direct path from self-efficacy to job satisfaction was not significant for a sample of Italian teachers (2011), but it was significant for a sample of U.S. teachers (Duffy & Lent, 2009). Also, there were inconsistent findings related to the path between supports and job satisfaction. One study reported that the direct path from support to job satisfaction was not significant (Duffy & Lent, 2009), and another reported a significant path between these variables (Lent et al., 2011). Given the inconsistent findings from previous applications of the SCWB model to teachers and the different job characteristics between teachers and workers in other professional fields, more research is needed to test the SCWB model with samples of workers in other work domains.

Purpose of the Study

The primary purpose of the present study is to test the applicability of SCWB model in the context of work (Lent & Brown, 2008), with a sample of women engineers. We extend previous SCWB studies (Duffy & Lent, 2009; Flores et al., 2014; Lent et al., 2007; Navarro et al., 2014) by exploring overall life satisfaction as well as job satisfaction. We hypothesized that the affective, cognitive, behavioral, and social environmental contributors would enhance life satisfaction based on the SCWB model (Lent & Brown, 2008; see Figure 1). Accordingly, we expect that affective traits (e.g., positive affect) are directly related to job and life satisfaction. We expect affective traits will have indirect effects on job satisfaction via its effects on self-efficacy and environmental supports and barriers. Also, we expect environmental supports and barriers to be directly associated with job satisfaction and to have indirect effects on job satisfaction via self-efficacy, work condition, and goal progress. We assumed that the paths from self-efficacy to goal progress and work condition will be significant, and positive work conditions are likely to enhance work-related goal progress in the workplace. Regarding the conceptualization of key constructs, consistent with previous key SCWB studies in the context of work (Duffy & Lent, 2009; Lent et al., 2011), we conceptualized work conditions as domain-specific outcome expectations in the present study as indexed by a measure of perceived organizational support. In addition, we measured positive affect as a construct of affective traits because prior SCWB studies (Lent et al., 2005, 2011, 2013) have supported significant relationships between positive affect and social cognitive variables in the model.

Moreover, prior studies noted that it is important to understand the role of environmental supports and barriers on career-related goals and outcome variables (Fouad et al., 2010; Lent et al., 2003, 2005, 2007). However, inconsistent findings related to the path between environmental variables and job satisfaction were reported in previous SCWB studies (Duffy & Lent, 2009; Lent et al., 2011). Thus, a secondary purpose of the present study is to assess the direct and indirect effects of environmental supports and barriers on job satisfaction via sociocognitive variables (e.g., self-efficacy and work conditions).

Method

Participants

Participants were 348 current women engineers who were employed in engineering for more than 1 year. Among them, 255 (77.7%) identified as White, 30 (9.2%) as Asian American, 11 (3.4%) as Latina, 13 (4%) as African American, 7 (2.1%) as multiracial, 11 (2.1%) as Asian international, 2 (0.6%) as European international, and 3 as “other” (1.3%). Participants ranged in age from 22 to 65 years (mean = 35.82; SD = 10.34). Of these participants, 115 (33%) were in their 20s, 106 (30%) were in 30s, 62 (18%) were in 40s, 40 (11%) were in 50s, 6 (2%) were in 60s, and 19 (5%) did not report age. They averaged 45.4 hr of work per week (SD = 8.69). The following engineering areas were represented: 72 (20.7%) were civil, 63 (18.1%) were mechanical, 56 (16.1%) were chemical, 37 (10.6%) were electrical and computer, 14 (4%) were industrial, 9 (2.6%) were aerospace, 5 (1.4%) were in engineering physics, and 69 (19.8%) were in other engineering areas (19 had missing data). Most participants (n =183, 55.3%) were married, 88 (26.8%) were single, 36 (10.9%) were partnered, and 9 (2.7%) were divorced. Just over 40% (n = 135, 41.8%) reported that they had children (age range of child(ren) 1–34 years). Seventy-five (22.8%) had a bachelor’s degree, 58 (17.63%) had a master’s, 55 (16.72%) had a doctoral, and 141 (42.86%) indicated “other.”

Instruments

Life satisfaction

Life satisfaction was assessed with the Satisfaction with Life Scale (SWLS; Diener, Emmons, Larsen, & Griffin, 1985), a 5-item scale (e.g., “So far I have gotten the important things I want in life”) that is rated on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). A high score indicates strong satisfaction with life. The coefficient αs ranged from .72 to .88 (Diener et al., 1985; Lent et al., 2011; Utsey, Payne, Jackson, & Jones, 2002) for scale scores. Convergent validity has been established by positive correlations with measures of support, goal progress, and self-efficacy (Lent et al., 2011), and discriminant validity has been supported with negative correlations with measures of negative affect (Lucas, Diener, & Suh, 1996). For the present study, the coefficient α was .88.

Job satisfaction

Job satisfaction was measured with Brayfield and Rothe’s (1951) Index of Job Satisfaction (Judge, Locke, Durham, & Kluger, 1998), which consists of 5 items (e.g., “I feel fairly satisfied with my present job”) rated on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). The coefficient α scores ranged from .80 to .86 (Duffy & Lent, 2009; Lent et al., 2011). Job satisfaction scores were positively correlated with life satisfaction (Judge et al., 1998; Lent et al., 2011). Job satisfaction was significantly correlated with positive affectivity, goal progress, support, self-efficacy, and work conditions (Lent et al., 2011). With our sample of women engineers, a coefficient α of .88 was obtained.

Positive affect

The construct of affective traits was measured with the Positive Affect (PA) subscale of the Positive and Negative Affect Scale (Watson, Clark, & Tellegen, 1988) because it has been shown to be significantly related to the key variables of the model such as job satisfaction (Connolly & Viswesvaran, 2000; Duffy & Lent, 2009; Lent et al., 2011), self-efficacy (Duffy & Lent, 2009;; Lent et al., 2013, 2015, 2016; Navarro et al., 2014), and environmental support (Duffy & Lent, 2009; Lent et al., 2011). The PA is a 10-item scale that assesses individuals’ tendency to experience various positive emotions (e.g., “excited, strong, proud”) using a 5-point scale ranging from 1 (very slightly or not at all) to 5 (extremely). High scores indicate a strong tendency to experience positive emotions across various situations. The coefficient α from previous studies ranged from .76 to 92 (Duffy & Lent, 2009; Lent et al., 2011). Discriminant validity has been demonstrated through negative correlations with state anxiety and depressive symptoms (Watson et al., 1988). For the present study, the coefficient α was .91.

Goal progress

We assessed goal progress with a 7-item scale that was used in a prior SCWB study (Lent, Singley, et al., 2005). This scale was slightly adapted from academic tasks related to goal progress to job tasks related to goal progress. The measure assesses how much progress participants were making toward each described goals related to their job tasks (e.g., “Working effectively on all of your tasks”). Participants responded to each goal using a response scale ranging from 1 (no progress at all) to 5 (excellent progress). Lent, Singley, et al. (2005) reported a coefficient α of .86. Scale scores were associated with core SCWB variables of outcome expectations, self-efficacy, positive affect, domain satisfaction, and life satisfaction (Lent et al., 2005). The coefficient α in the present study was .88.

Self-efficacy

Consistent with prior SCWB engineering studies (Lent et al., 2007, 2013, 2015, 2016), we measured two aspects of self-efficacy: coping self-efficacy and domain-specific self-efficacy. For coping efficacy, we used the coping efficacy measure developed by Lent et al. (2003) and tested in previous SCCT or SCWB engineering studies (Lent et al., 2007, 2013, 2015, 2016; Navarro et al., 2014). Participants indicate the confidence level in their capability to cope with various barriers related to engineering career (e.g., how confident are you that you could find ways to work effectively despite having competing demands for your time) based on seven statements. Participants responded to each statement using a 10-point scale ranging from 1 (no confidence) to 10 (complete confidence), where high scores reflect strong confidence levels in one’s ability to cope with barriers in engineering. Coefficient α of this measure’s scores has demonstrated adequate ranges from .89 to above .90 in the SCWB engineering studies (Lent et al., 2003, 2007, 2013, 2015; Navarro et al., 2014).

For domain-specific self-efficacy, we used a task-specific self-efficacy striving scale from career development strivings assessment (Dik, Sargent, & Steger, 2008), which used an open-ended format to capture participants’ confidence level in the specific tasks related to the goals of participants. The scale asked participants to indicate their confidence level in their top three written task-specific statements using a 5-point scale that ranged from 1 (not at all confident) to 5 (completely confident). High scores reflect strong belief in their capability of task-related goals attainment. Dik, Sargent, and Steger (2008) reported a coefficient α of .72 for self-efficacy strivings. The researchers noted that it was not surprising to see a relatively low coefficient of this scale because self-efficacy striving is theoretically based on a specific task, which is what we aim to measure in our present study. For the present sample, a coefficient α of coping self-efficacy was .82 and a coefficient α of task-specific self-efficacy striving was .61. In terms of validity, engineering self-efficacy positively correlated with key variables of the model including goal progress (Flores et al., 2014; Lent et al., 2003, 2007), outcome expectations (Flores et al., 2014; Lent et al., 2003, 2007, 2013, Navarro et al., 2014), environmental supports (Lent et al., 2007, 2013), positive affect (Lent et al., 2013, 2015, 2016), and academic satisfaction (Flores et al., 2014; Lent et al., 2007, 2013, 2015, 2016; Navarro et al., 2014) in prior SCWB engineering studies.

Work conditions

Consistent with prior SCWB studies in the context of work (Duffy & Lent, 2009; Lent et al., 2011), we used the Perceived Organizational Support Scale–Short Form (Eisenberger, Huntington, Hutchison, & Sowa, 1986) to assess work conditions. Participants indicate their perception of work condition with 16 items (e.g., The organization takes pride in my accomplishments at work) using a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). The coefficient αs from previous SCWB studies were .95 (Duffy & Lent, 2009) and .91 (Lent et al., 2011). In terms of validity, scores on this measure were positively correlated with job satisfaction, positivity, self-efficacy, and goal progress (Duffy & Lent, 2009; Lent et al., 2011) and moderately correlated with job and organizational commitment (Rhoades & Eisenberger, 2002). The coefficient α for the scale scores in the present study was .94.

Environmental supports and barriers

Environmental supports and barriers were measured with 14 items that were used by Lent, Brown, et al. (2005) in the engineering domain. This measure was slightly modified to assess environmental supports and barriers in engineering work settings. For environmental supports, participants indicated how much they agree on the likelihood of experiencing supports related to their engineering career with 9 items (e.g., I feel support from important people in my life for pursuing my professional in engineering) using a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). For environmental barriers, participants indicated how much they agree on likelihood of experiencing barriers related to their engineering career with 5 items (e.g., I feel pressure from my parents or other important people to change my job to some other fields). High scores of environmental supports reflect strong positive experiences related to pursuing an engineering career. High scores of environmental barriers reflect strong negative or discouraging experiences related to pursuing an engineering career. The coefficient α was .86 for both environmental supports and barriers in engineering in a prior study (Lent, Brown, et al., 2005). In terms of validity, environmental supports and barriers were related to social cognitive variables including self-efficacy and goals (Lent & Brown et al., 2005). For the present sample, coefficient αs of .81 and .63 were obtained for the environmental supports and barriers scores, respectively.

Procedures

Data were collected through an online survey using Qualtrics. Participants were recruited through e-mail announcements sent to women alumni from engineering colleges at 10 large U.S. universities. Members of professional organizations for women engineers (e.g., Society of Women Engineers) were also invited to participate. A snowball approach was utilized by asking participants to forward information about the study to other female engineers. A donation ranging from US$50 to US$500 was made to the engineering college based on the participation rates of women alumni from each school.

Plan of Analysis

Prior to testing the fit of the hypothesized SCWB model with our sample, an analysis of variance (ANOVA) was conducted to test for the presence of age-group differences in the major dependent variables among women engineers. After this, structural equation modeling was conducted to test the fit of the hypothesized SCWB model with a sample of women engineers using Mplus Version 6.11 and the maximum likelihood estimation method. Prior to examining the structural model, latent variables were created for the unidimensional constructs of work conditions, positive affect, environmental supports and goal progress, using item parceling procedures (Russell, Kahn, Spoth, & Altmaier, 1998). To create the item parcels, data were screened using maximum likelihood exploratory factor analysis (EFA) to check that each scale had a unidimensional factor structure; this was accomplished by examining scree plots, eigenvalues, and factor loadings (Little, Cunningham, Shahar, & Widaman, 2002). According to guidelines for parceling items (Bandalos & Finney, 2001), each latent variable was indexed by two to five indicators. Item parcels were created by using the results of the EFAs to balance loadings among high, medium, and lower factor loadings for latent variables. In the present study, four parcels were created for work conditions, and three parcels were created for positive affect, environmental supports, and goal progress, respectively. As we described in the instruments, we used two indicators to assess self-efficacy. Also, for scales with less than 6 items, we used each item as an indicator based on guidelines (Bandalos & Finney, 2001). The results of the EFA results to create item parcels are available through the first author. See Table 1 for the means, standard deviations, ranges, and factor loadings for the study’s measured variables.

Table 1.
Means, Standard Deviations, Score Range, and Factor Loading of Measured Variables.

Variable M SD Score Range Factor Loading

Self-efficacy

Indicator 1 6.99 1.32 1–10 .81

Indicator 2 3.93 .64 1–5 .81

Work conditions

Indicator 1 4.98 1.22 1–7 .91

Indicator 2 5.06 1.03 1–7 .91

Indicator 3 5.04 1.08 1–7 .92

Indicator 4 4.81 1.19 1–7 .92

Goal progress

Indicator 1 4.19 .58 1–5 .89

Indicator 2 4.22 .65 1–5 .89

Indicator 3 3.93 .66 1–5 .91

Positive affect

Indicator 1 3.47 .81 1–5 .92

Indicator 2 3.51 .77 1–5 .92

Indicator 3 3.59 .71 1–5 .93

Environmental supports

Indicator 1 4.18 .60 1–5 .85

Indicator 2 3.96 .71 1–5 .87

Indicator 3 4.04 .67 1–5 .92

Environmental barriers

Indicator 1 4.59 .68 1–5 .75

Indicator 2 3.44 1.24 1–5 .56

Indicator 3 3.60 1.12 1–5 .46

Indicator 4 4.53 .65 1–5 .84

Indicator 5 4.70 .53 1–5 .73

Job satisfaction

Indicator 1 5.44 1.35 1–7 .87

Indicator 2 5.27 1.41 1–7 .91

Indicator 3 5.01 1.47 1–7 .61

Indicator 4 5.41 1.19 1–7 .88

Indicator 5 5.74 1.21 1–7 .85

Life satisfaction

Indicator 1 5.35 1.20 1–7 .89

Indicator 2 5.65 1.13 1–7 .84

Indicator 3 5.81 1.08 1–7 .88

Indicator 4 5.75 1.14 1–7 .85

Indicator 5 5.05 1.49 1–7 .71

Note. All factor loadings are statistically significant at the p < .001 level.

The SEM analysis consisted of several steps. For the first step, a confirmatory factor analysis (CFA) was conducted to determine whether the latent variables in the model were adequately measured. Second, a structural model analysis was conducted to determine whether the data fit the hypothesized SCBW model. The ratio of χ² statistic to degree of freedom (χ²/df) as well as specific indices including root mean square error approximation (RMSEA) and comparative fit index and (CFI), were used to assess both the measurement and structural model because these fit indices are less susceptible to bias by extraneous factors such as sample size (Hu & Bentler, 1999). An adequate fit to the data is reported when χ²/df ≤ 3, CFI values ≥.90, and RMSEA ≤.08. When χ²/df ≤3, CFI values ≥.95, and RMSEA ≤.06, the data represent a very close fit to the model (Kline, 2005). Finally, a bootstrapping method was used to examine the proposed indirect effects of environmental supports and barriers on job satisfaction through self-efficacy and work conditions using 10,000 random samples. The 95% bias-corrected confidence intervals tested for statistical significance. If confidence intervals do not include zero, it indicates it has an indirect effect (Mallinckrodt, Abraham, Wei, & Russell, 2006).

Results

Preliminary Analyses

Data screening

The initial data set included 485 women engineers. According to Schlomer, Bauman, and Card (2010), the 20% missing data rule on each variable of our SCWB model was used to determine case deletion. This estimate has been suggested for studies in which deletion of a large number of participants could adversely affect statistical power. Using this criterion, 99 cases were deleted due to more than 20% missing data. Little’s missing completely at random test was not significant (χ² = 373.11, p > .05), suggesting the data were missing completely at random (Schlomer, Bauman, & Card, 2010).

In the next step, the expectation maximization procedure was used to replace the missing values; this method is superior to other common missing value imputations (Schlomer et al., 2010). After imputing missing data, the data were examined to ensure they met multivariate assumptions (Tabachnick & Fidell, 2007). Data were screened to identify univariate and multivariate outliers. Twenty-two univariate outliers that exhibited z-scores above the critical value of 3.3 were deleted, and 16 additional cases were deleted as multivariate outliers. The data met the assumption of normal distribution, and no violation of linearity was revealed. The multicollinearity of the data was tested by using tolerance values and variance inflation factor; there was no collinearity among the variables. The final data set included 348 cases. The means, standard deviations, and bivariate correlations for the study’s variables are reported in Table 2.

Table 2.
Means, Standard Deviations, and Correlations Among the Main Variables.

Variable M SD 1 2 3 4 5 6 7 8 9

1. Life satisfaction 5.52 1.00 — .61 .58 .40 .30 .41 .40 −.37 .33

2. Job satisfaction 5.37 1.09 — .64 .53 .35 .36 .37 −.33 .40

3. Positive affect 3.52 .70 — .42 .36 .43 .39 −.25 .38

4. Work conditions 4.98 1.04 — .19 .31 .47 −.26 .30

5. Coping self-efficacy 6.99 1.32 — .31 .17 −.29 .39

6. Self-efficacy striving 3.93 0.64 — .47 −.33 .37

7. Environmental supports 4.06 0.58 — −.36 .33

8. Environmental barriers 4.17 0.56 — .24

9. Goal progress 4.11 0.56 —

Note. N = 348.

**p < .01 level.

Primary Analyses

First, an ANOVA test was conducted to ensure our sample had no significant group differences by age for outcome variables. The results of the ANOVA indicated no significant differences across 20s, 30s, 40s, 50s, and 60s age groups in job satisfaction, F(4, 324) = 2.23, p > .05, and life satisfaction, F(4, 324) = .64, p > .05. Second, the measurement model was tested to ensure the plausibility of eight factor representations of the latent constructs. One factor loading for each construct was fixed to 1, and all other loadings and paths among the latent constructs were freely estimated using confirmatory factor analysis. The results of the measurement model (χ²/df ≤ 3; Tucker–Lewis index and CFI >.90; RMSEA >.06) suggested that the data were a good fit for the model (see Table 3). The means, standard deviations, and factor loadings for the measured variables are reported in Table 1. Next, a structural model analysis was conducted to examine the fit of the data to the hypothesized SCBW model. Table 3 shows the results of the structural model analysis, which suggested that the data adequately fit the hypothesized model (χ²/df ≤ 3, CFI values ≥ .90, and RMSEA ≤ .06). An examination of the parameter estimates of the hypothesized model indicated that most of the paths were statistically significant (see Figure 2). For example, the paths from positive affect to environmental supports, barriers, self-efficacy, job and life satisfaction were all significant (p < .05). The path from job satisfaction to life satisfaction was also significant (p < .05). Self-efficacy was directly associated with work conditions and goal progress. Contrary to expectations, goal progress did not play a key role in the hypothesized model with this sample. For example, the paths from environmental supports and barriers to goal progress, work conditions to goal progress, and goal progress to job satisfaction were not significant. SCWB predictors accounted for a significant amount of variance in job satisfaction (63%) and life satisfaction (54%) with our sample of women engineers.

Table 3.
Goodness-of-Fit Indicators for Measurement and Structural Models.

Model χ² df χ²/df CFI RMSEA 90% CI for RMSEA

Measurement model 775.07 377 2.06 .94 .06 [0.05, 0.06]

Structural model 842.99 384 2.20 .93 .06 [0.05, 0.06]

Note. CFI = comparative fit index; RMSEA = root mean squared error approximation; CI = confidence interval.

Figure 2.
Parameter estimates for the hypothesized SCWB model in the work context of engineering. The bold lines and * indicate significant paths at level of p < .05.

Bootstrapping estimates were conducted to test the indirect effects of environmental supports and barriers on job satisfaction via self-efficacy and work conditions. The 95% bias-corrected confidence intervals did not include zero for both indirect effects, indicating that environmental supports and barriers had indirect effects on job satisfaction via self-efficacy and work conditions (see Table 4 for the results of the bootstrapping estimates).

Table 4.
Summary of Indirect Effects.

Path/Effect Bootstrap Estimates

Mean B Mean SE 95% CI

Supports → SE → WC → Job satisfaction .29 .04 [.22, −.38]

Barriers → SE → WC → Job satisfaction .24 .04 [.16, −.32]

Note*. Estimates of indirect effects are based on standardized coefficients. SE = self-efficacy; WC = work conditions; CI = confidence interval.

Discussion

This study was the first to test the predictive utility of a full version of the SCWB model (Lent & Brown, 2008) for women engineers. Our findings expand the SCWB literature in two aspects. First, the findings of this present study extend previous findings of the SCWB model with a sample of teachers (Duffy & Lent, 2009; Lent et al., 2011) into another occupation, engineering. Second, this study expands the applicability of the SCWB model from engineering educational settings (Flores et al., 2014; Lent et al., 2013, 2015, 2016; Navarro et al., 2014) to engineering work settings by showing that the data fit the model well with a sample of employed women engineers. We expect our finding may provide useful information to develop SCWB-based interventions and various support systems or workshops for women engineers.

As expected, most paths of the SCWB model were significant with our sample. However, we found a few nonsignificant direct paths among variables that were similar with previous findings. First, consistent with findings of a previous SCWB study with a sample of teachers (Lent et al., 2011), we found that the direct path from self-efficacy to job satisfaction was not significant, and self-efficacy was only indirectly related to job satisfaction via work conditions with this sample of women engineers. Our findings suggest that women engineers who have stronger self-confidence in managing their work tend to perceive more favorable work conditions and, in turn, report increased job satisfaction.

Also, goal progress did not play a key role in the SCWB model in the engineering work domain. The results indicated that goal progress was not related to environmental factors (e.g., supports and barriers), work conditions, or job satisfaction. The latter is consistent with previous SCWB studies in the work domain, which also found no association between perceptions of goal progress and satisfaction at work (Duffy & Lent, 2009; Lent et al., 2011; Wiese, 2007; Wiese & Freund, 2005). However, our findings were inconsistent with previous SCWB engineering studies in the educational domain reporting the significant path from goal progress to academic satisfaction. A possible explanation for these inconsistent results can be due to sample characteristics. The previous SCWB study (Lent et al., 2007) tested the model with a college sample of engineering students, where students are more likely to be exposed to short-term and concrete goals and to receive immediate feedback on the outcomes of goal-directed activities through quizzes, exams, and semester grades. In contrast, employees may have longer project timelines and may not see immediate progress on work-related goals. As such, the time gap between outcomes and goal progress from long-term goals (e.g., more than 1 year for a long-term project) may not influence job satisfaction.

Contrary to the findings of Lent et al. (2011), work conditions were not associated with goal progress in our study. Inconsistent findings related to this construct in the previous SCCT engineering educational settings have been reported. Some studies reported that outcome expectations (work conditions in the work domain model) was a useful predictor of interests, academic satisfaction, and major choice (Flores et al., 2014; Lent et al., 2013) in prior SCWB engineering studies, but in others, outcome expectations were not significantly related to these variables (Lent et al., 2003; Navarro et al., 2014). Given these inconsistent findings, more studies are needed to further examine the relations between this variable and other SCWB variables in both work and educational settings in engineering.

Environmental supports and barriers had no direct effects on goal progress and job satisfaction but indirectly affected job satisfaction via self-efficacy and work conditions. These are consistent with findings of a previous study (Fouad, Singh, Cappaert, Chang, & Wan, 2016) that found that workplace supports in the form of developmental opportunities played a greater role in shaping efficacy beliefs and outcome expectations with sample of women engineers. It implies that women engineers who experience high environmental supports and few barriers are more likely to have strong confidence in performing the work-related tasks and perceive favorable work conditions, and these, in turn, increase job satisfaction.

Also, it is noticeable that the two strongest direct predictors of job satisfaction from Lent et al.’s study (2011) and the present study were work conditions and positive affect. We need to consider the nature of occupations when interpreting these findings. It may be more important for both teachers and engineers to have favorable work conditions because they are more likely work in a collaborative organization or school system with frequent interactions with coworkers or students compared to artists or freelancers who are independent in terms of work conditions. Thus, further study is needed to test this model with workers in various occupations. Positive affect was significantly associated with self-efficacy, environmental supports and barriers, and job satisfaction in engineering with our sample. These are consistent findings of SCWB studies with other adult workers (Duffy & Lent, 2009; Lent et al., 2011) and college students (Lent et al., 2005, 2013). These results suggest that women engineers who have a tendency to experience positive affect across situations are more likely to have high confidence levels in their ability, perceive more environmental support and less environmental barriers, and to feel satisfied with their job. To our knowledge, only one study (Lent et al., 2011) tested the full version of the SCWB model with employees, including life satisfaction. Our findings were consistent with their findings by showing that both job satisfaction and positive affect were directly associated with life satisfaction. However, contrary to Lent et al. (2011), the path from goal progress to life satisfaction was not significant in our study. These findings imply that women engineers’ general life satisfaction may be more directly impacted by positive affect traits across situations and job satisfaction rather than the progress of their task-related goals.

Practical Implications

Our results provide suggestions for interventions targeted at increasing the job and life satisfaction of women engineers. First, the findings have implications for mental health professionals, career counselors, and supervisors who work with women engineers who report low levels of life and job satisfaction. Considering the critical role of self-efficacy in our findings and previous SCWB studies (Duffy & Lent, 2009; Lent & Brown, 2006), mental health professionals, career counselors, and supervisors might increase the job satisfaction and psychological well-being of women engineers by providing programs or interventions to build self-efficacy beliefs. For example, supervisors and career counselors may boost the confidence of women engineers by highlighting their unique strengths and contributions to projects. In addition, supervisors can provide mentoring programs, preferably ones that match women engineers with women mentors to fit each individual’s learning needs and life/work stages. Moreover, career counselors or mental health professionals might provide workshops or skill-building seminars for women engineers to help them to navigate and deal with unexpected challenges as women engineers and to manage negative emotions related to work. A potential workshop topic can be an assertive communication skills building to advocate for their unique needs and experiences as women in a male-dominated field to increase their self-efficacy to deal with challenging issues in the workplace (e.g., gender discrimination, feeling of isolation, and a lack of child care system) that they might face.

Second, given previous findings (Lent et al., 2011) and our findings related to the association of positive affect to increased self-efficacy, job satisfaction, and life satisfaction, vocational psychologists and leaders of engineering organizations need to consider various ways to promote positive affect among women engineers in the workplace. More scholars in psychology fields are attentive to the crucial role of positive affect and emotions. For example, Fredrickson (2003) highlighted the effect of positive emotions on cognition by explaining that positive emotions could broaden our scope of attention and thought–action repertories. The increased general cognitive functions by positive emotions may lead to increased confidence levels in women engineers’ ability to complete work tasks. Also, Fredrickson (2003) suggested that positive emotions may lead to employees’ optimal functioning in the workplace by generating “upward spirals.” To promote positive affect in the workplace, both individual- and organizational-level approaches need to be discussed among vocational psychologists, supervisors, engineers, and administrators. In terms of individual levels, it might be useful to utilize strategies to promote positive affect (Sheldon & Lyubomirsky, 2006) such as daily gratitude journaling and self-care activities. Also, efforts to foster positive organizational climates need to be made by providing caring and constructive feedback rather than critical or discouraging feedback.

The findings of our study and previous SCWB studies (Duffy & Lent, 2009; Lent et al., 2011) noted that work conditions played an essential role to increased job satisfaction. Administrators and organizations need to be aware of the importance of creating work conditions, where women feel that they are treated fairly, that their opinions are heard, and their well-being is taken into consideration. It might be helpful to develop family-friendly policies for all engineers (e.g., flexible working hours, working from home, and family leave) and cultivate gender-friendly work conditions (e.g., inclusive work climates for both women and men engineers).

Limitations and Future Directions

Our findings of the present study should be interpreted in light of its limitations. First, we tested the SCWB model with women engineers because our research questions were focused on examining sociocognitive, affective, and behavioral predictors of increasing job and life satisfaction of women engineers. However, it might be useful to test this model with male engineers and examine the group differences between female and male engineers to provide an understanding of the general applicability of the SCWB model in engineering. Second, this study involved a sample of women engineers who were mostly European American. Thus, the generalizability of this study’s finding to women engineers in other countries or to other U.S. racial/ethnic groups is limited. It is worth examining racial/ethnic group differences among women engineers in future studies because White women and women of color may have different experiences in the same work setting. Third, our sample included various levels and positions of women engineers. As Lent et al. (2011) discussed, job satisfaction itself and the impact of sociocognitive variables on job satisfaction might be influenced by the security of job position or job tenure status. Thus, future studies might need to consider other variables related to job tenure status or job position level as moderators to job and life satisfaction. Finally, the participants in the current study were employed in various engineering fields. Thus, generalizations of the findings to every engineering subfield may be limited because each engineering subfield may have a unique work culture and varied representations of women engineers. Further studies are needed to examine the applicability of this model with each subfield of engineering.

To conclude, the findings of this study provide strong support for Lent and Brown’s (2008) extended version of the SCWB model to a sample of women engineers. Environmental supports and barriers did not directly affect job satisfaction, but they were indirectly associated to job satisfaction via key cognitive variables. Also, positive affect was a critical predictor to both job and general life satisfaction among women engineers. Our findings suggest that women engineers can effectively increase their job and life satisfaction by actively engaging in promoting their positive affect, self-efficacy building up activities, and creating or perceiving favorable work conditions. Additional studies are needed to further explore the group differences across gender, job positions, various ethnic groups, and subengineering fields with a consideration of potential cultural differences.

Variable	M	SD	Score Range	Factor Loading
Self-efficacy
Indicator 1	6.99	1.32	1–10	.81
Indicator 2	3.93	.64	1–5	.81
Work conditions
Indicator 1	4.98	1.22	1–7	.91
Indicator 2	5.06	1.03	1–7	.91
Indicator 3	5.04	1.08	1–7	.92
Indicator 4	4.81	1.19	1–7	.92
Goal progress
Indicator 1	4.19	.58	1–5	.89
Indicator 2	4.22	.65	1–5	.89
Indicator 3	3.93	.66	1–5	.91
Positive affect
Indicator 1	3.47	.81	1–5	.92
Indicator 2	3.51	.77	1–5	.92
Indicator 3	3.59	.71	1–5	.93
Environmental supports
Indicator 1	4.18	.60	1–5	.85
Indicator 2	3.96	.71	1–5	.87
Indicator 3	4.04	.67	1–5	.92
Environmental barriers
Indicator 1	4.59	.68	1–5	.75
Indicator 2	3.44	1.24	1–5	.56
Indicator 3	3.60	1.12	1–5	.46
Indicator 4	4.53	.65	1–5	.84
Indicator 5	4.70	.53	1–5	.73
Job satisfaction
Indicator 1	5.44	1.35	1–7	.87
Indicator 2	5.27	1.41	1–7	.91
Indicator 3	5.01	1.47	1–7	.61
Indicator 4	5.41	1.19	1–7	.88
Indicator 5	5.74	1.21	1–7	.85
Life satisfaction
Indicator 1	5.35	1.20	1–7	.89
Indicator 2	5.65	1.13	1–7	.84
Indicator 3	5.81	1.08	1–7	.88
Indicator 4	5.75	1.14	1–7	.85
Indicator 5	5.05	1.49	1–7	.71

Variable	M	SD	1	2	3	4	5	6	7	8	9
1. Life satisfaction	5.52	1.00	—	.61**	.58**	.40**	.30**	.41**	.40**	−.37**	.33**
2. Job satisfaction	5.37	1.09		—	.64**	.53**	.35**	.36**	.37**	−.33**	.40**
3. Positive affect	3.52	.70			—	.42**	.36**	.43**	.39**	−.25**	.38**
4. Work conditions	4.98	1.04				—	.19**	.31**	.47**	−.26**	.30**
5. Coping self-efficacy	6.99	1.32					—	.31**	.17**	−.29**	.39**
6. Self-efficacy striving	3.93	0.64						—	.47**	−.33**	.37**
7. Environmental supports	4.06	0.58							—	−.36**	.33**
8. Environmental barriers	4.17	0.56								—	.24**
9. Goal progress	4.11	0.56									—

Model	χ²	df	χ²/df	CFI	RMSEA	90% CI for RMSEA
Measurement model	775.07	377	2.06	.94	.06	[0.05, 0.06]
Structural model	842.99	384	2.20	.93	.06	[0.05, 0.06]

Path/Effect	Bootstrap Estimates
Supports → SE → WC → Job satisfaction	.29	.04	[.22, −.38]
Barriers → SE → WC → Job satisfaction	.24	.04	[.16, −.32]

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded in part by the University of Missouri’s Joseph Johnston Research Award and the American Psychological Association Science Directorate’s Dissertation Research Award.

References

Ahuja

(2002). Women in the information technology profession: A literature review, synthesis and research agenda. European Journal of Information System, 11, 20–34.

Bacharach

S. B.

Bamberger

Conley

(1991). Work-home conflict among nurses and engineers: Mediating the impact of role stress on burnout and satisfaction at work. Journal of Organizational Behavior, 12, 39–53.

Bandalos

D. L.

Finney

S. J.

(2001). Item parceling issues in structural equation modeling. In Marcoulides

G. A.

& Schumacker

R. E.

(Eds.), New developments and techniques in structural equation modeling (pp. 269–295). Mahwah, NJ: Erlbaum

Brayfield

A. H.

Rothe

H. F.

(1951). An index of job satisfaction. Journal of Applied Psychology, 35, 307–311.

Callister

R. R.

(2006). The impact of gender and department climate on job satisfaction and intentions to quit for faculty in science and engineering fields. The Journal of Technology Transfer, 31, 367–375.

Connelly

J. J.

Viswesvaran

(2000). The role of affectivity in job satisfaction: A meta-analysis. Personality and Individual Differences, 29, 265–281.

Diener

Emmons

R. A.

Larsen

R. J.

Griffen

(1985). The satisfaction with life scale. Journal of Personality Assessment, 49, 71–75.

Dik

B. J.

Sargent

A. M.

Steger

M. F.

(2008). Career development strivings assessing goals and motivation in career decision-making and planning. Journal of Career Development, 35, 23–41.

Duffy

R. D.

Lent

R. W.

(2009). Test of a social cognitive model of work satisfaction in teachers. Journal of Vocational Behavior, 75, 212–223.

10.

Eisenberger

Huntington

Hutchison

Sowa

(1986). Perceived organizational support. Journal of Applied Psychology, 71, 500–507.

11.

Flores

L. Y.

Navarro

R. L.

Lee

H. S.

Addae

D. A.

Gonzalez

Luna

L. L.

… Mitchell

. (2014). Academic satisfaction among Latino/a and White men and women engineering students. Journal of Counseling Psychology, 61, 81–92.

12.

Fouad

N. A.

Hackett

Smith

P. L.

Kantamneni

Fitzpatrick

Haag

Spencer

(2010). Barriers and supports for continuing in mathematics and science: Gender and educational level differences. Journal of Vocational Behavior, 77, 361–373.

13.

Fouad

N. A.

Singh

Cappaert

Chang

W. H.

Wan

(2016). Comparison of women engineers who persist in or depart from engineering. Journal of Vocational Behavior, 92, 79–93.

14.

Fredrickson

B. L.

(2003). Positive emotions and upward spirals in organizations. In Cameron

K. S

Dutton

J. E

, & Quinn

R. E

(Eds.), Positive organizational scholarship: Foundations of a new discipline (pp. 163–175). San Francisco, CA: Berrett-Koehler.

15.

Bentler

P. M.

(1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

16.

Judge

T. A.

Locke

E. A.

Durham

C. C.

Kluger

A. N.

(1998). Dispositional effects on job and life satisfaction: The role of core evaluations. Journal of Applied Psychology, 83, 17–34.

17.

Kline

R. B.

(2005). Principles and practice of structural equation modeling (2nd ed.). New York, NY: Guilford Press.

18.

Lent

R. W.

(2004). Toward a unifying theoretical and practical perspective on well-being and psychosocial adjustment. Journal of Counseling Psychology, 51, 482–509.

19.

Lent

R. W.

Brown

S. D.

(2006). Integrating person and situation perspectives on work satisfaction: A social-cognitive view. Journal of Vocational Behavior, 69, 236–247.

20.

Lent

R. W.

Brown

S. D.

(2008). Social cognitive career theory and subjective well-being in the context of work. Journal of Career Assessment, 16, 6–21.

21.

Lent

R. W.

Brown

S. D.

Schmidt

Brenner

Lyons

Treistman

(2003). Relation of contextual supports and barriers to choice behavior in engineering majors: Test of alternative social cognitive models. Journal of Counseling Psychology, 50, 458–465.

22.

Lent

R. W.

Brown

S. D.

Sheu

Schmidt

Brenner

B. R.

Gloster

C. S.

… Treistman

. (2005). Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically Black universities. Journal of Counseling Psychology, 52, 84–92.

23.

Lent

R. W.

Miller

M. J.

Smith

P. E.

Watford

B. A.

Hui

Lim

R. H.

(2015). Social cognitive model of adjustment to engineering majors: Longitudinal test across gender and race/ethnicity. Journal of Vocational Behavior, 86, 77–85.

24.

Lent

R. W.

Miller

M. J.

Smith

P. E.

Watford

B. A.

Lim

R. H.

Hui

(2016). Social cognitive predictors of academic persistence and performance in engineering: Applicability across gender and race/ethnicity. Journal of Vocational Behavior, 94, 79–88.

25.

Lent

R. W.

Miller

M. J.

Smith

P. E.

Watford

B. A.

Lim

R. H.

Hui

… Williams

. (2013). Social cognitive predictors of adjustment to engineering majors across gender and race/ethnicity. Journal of Vocational Behavior, 83, 22–30.

26.

Lent

R. W.

Nota

Soresi

Ginevra

M. C.

Duffy

R. D.

Brown

S. D.

(2011). Predicting the job and life satisfaction of Italian teachers: Test of a social cognitive model. Journal of Vocational Behavior, 79, 91–97.

27.

Lent

R. W.

Singley

Sheu

H. B.

Gainor

K. A.

Brenner

B. R.

Treistman

Ades

(2005). Social cognitive predictors of domain and life satisfaction: Exploring the theoretical precursors of subjective well-being. Journal of Counseling Psychology, 52, 429–422.

28.

Lent

R. W.

Singley

Sheu

H. B.

Schmidt

J. A.

Schmidt

L. C.

(2007). Relation of social-cognitive factors to academic satisfaction in engineering students. Journal of Career Assessment, 15, 87–97.

29.

Little

T. D.

Cunningham

W. A.

Shahar

Widaman

K. F.

(2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9, 151–173.

30.

Lucas

R. E.

Diener

Suh

(1996). Discriminant validity of well-being measures. Journal of Personality and Social Psychology, 71, 616–628.

31.

Mallinckrodt

Abraham

W. T.

Wei

Russell

D. W.

(2006). Advances in testing the statistical significance of mediation effects. Journal of Counseling Psychology, 53, 372–378.

32.

National Science Foundation. (2011). Science and Engineering Indicators 2011. Arlington, VA: Author.

33.

National Science Foundation, Division of Science Resources Statistics. (2015). Women, minorities, and persons with disabilities in science and engineering: 2015. Science and Engineering Indicators 2015. Arlington, VA: Author. Retrieved from http://www.nsf.gov/statistics/2015/nsf15311/digest/theme2.cfm

34.

Navarro

R. L.

Flores

L. Y.

Lee

H. S.

Gonzalez

(2014). Testing a longitudinal social cognitive model of intended persistence with engineering students across gender and race/ethnicity. Journal of Vocational Behavior, 85, 146–155.

35.

Powell

Bagilhole

Dainty

(2008). How women engineers do and undo gender: Consequences for gender equality. Gender, Work & Organization, 16, 411–428.

36.

Rhoades

Eisenberger

(2002). Perceived organizational support: A review of the literature. Journal of Applied Psychology, 87, 698–714.

37.

Russell

D. W.

Kahn

J. H.

Spoth

Altmaier

E. M.

(1998). Analyzing data from experimental studies: A latent variable structural equation modeling approach. Journal of Counseling Psychology, 45, 18–29.

38.

Schlomer

G. L.

Bauman

Card

N. A.

(2010). Best practices for missing data management in counseling psychology. Journal of Counseling Psychology, 57, 1–10.

39.

Sheldon

K. M.

Lyubomirsky

(2006). How to increase and sustain positive emotion: The effects of expressing gratitude and visualizing best possible selves. The Journal of Positive Psychology, 1, 73–82.

40.

Society of Women Engineers. (2007). Where are all the women going? Press release. Retrieved from http://www.swe.org

41.

Tabachnick

B. G.

Fidell

L. S.

(2007). Experimental designs using ANOVA. Belmont, CA: Thomson/Brooks/Cole.

42.

Tattersall

Keogh

Richardson

H. J.

Adam

(2006). Women and the workplace-issues from North West England. In Trauth

E. M.

(Ed.), Encyclopedia of gender and information technology (pp. 1252–1257). Hershey, PA: Idea Group.

43.

Trauth

E. M.

Quesenberry

J. L.

Huang

(2009). Retaining women in the U.S. IT workforce: Theorizing the influence of organizational factors. European Journal of Information Systems, 18, 476–497.

44.

Utsey

S. O.

Payne

Y. A.

Jackson

E. S.

Jones

A. M.

(2002). Race-related stress, quality of life indicators, and life satisfaction among elderly African Americans. Cultural Diversity and Ethnic Minority Psychology, 8, 224–233.

45.

Watson

Clark

L. A.

Tellegen

(1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1064–1070.

46.

Wiese

B. S.

(2007). Successful pursuit of personal goals and subjective well-being. In Little

Salmela-Aro

Nurmi

Phillips

(Eds.), Personal project pursuit: Goals, action, and human flourishing (pp. 301–328). Hillsdale, NJ: Lawrence Erlbaum.

47.

Wiese

B. S.

Freund

A. M.

(2005). Goal progress makes one happy, or does it? Longitudinal findings from the work domain. Journal of Occupational and Organizational Psychology, 78, 287–304.

Path/Effect	Bootstrap Estimates
Path/Effect	Mean B	Mean SE	95% CI
Supports → SE → WC → Job satisfaction	.29	.04	[.22, −.38]
Barriers → SE → WC → Job satisfaction	.24	.04	[.16, −.32]