Social Cognitive Career Theory Case Study


Despite a recent increase in the number of adults who work past traditional retirement age, existing theories of vocational behavior have not yet received adequate empirical support. In a large sample of adults age 60–87, we evaluated the relationship between theorized predictors of work satisfaction proposed by Social Cognitive Career Theory (SCCT), work satisfaction as a predictor of continued work, as proposed by the Theory of Work adjustment (TWA), as well as the influence of reported experiences of discrimination on these relationships. While the results supported most of the predicted relationships, the effects of discrimination were stronger than the variables proposed by either SCCT or TWA for the present sample.

Keywords: Social Learning and Cognitive Theory, others (Career Theory), elderly employees, career/vocational education/guidance

Workforce statistics in the past two decades have indicated ever-increasing numbers of individuals who continue to work past retirement age (Authors, this issue). The majority of research regarding the application of vocational theory has focused on job choice, person-organization fit, and more recently mid-life career transitions. However, there is scant empirical evidence within existing vocational theory that can adequately explain vocational behavior and work satisfaction among older adults who remain in the workforce past traditional retirement age. Even Super’s (1980) life-span developmental theory, which does directly address retirement as the final stage of vocational development, has viewed the primary tasks when reaching retirement age as disengagement and transition from worker to leisurite.

Although for some older workers, the choice to continue working is driven by a desire to remain engaged in a personally fulfilling career or to transition to a new career beyond retirement, for others this decision is driven by financial need (Authors, this issue). Thus, theoretical explanations of work/retirement decisions must consider issues of volition. As workers age, health and healthcare concerns may factor more prominently into their retirement decisions. Further, as the U.S. becomes more demographically diverse, it is important to ensure that theoretical models consider issues of race, gender, sexual orientation, culture, and discrimination, as well as age.

The goal of the present study is to explore selected components of both Social Cognitive Career Theory (SCCT: Lent, Brown, & Hackett, 1994; Lent & Brown, 2006, 2013) and the Theory of Work Adjustment (TWA: Dawis, England, & Lofquist, 1964) to determine their applicability to work satisfaction with older adults, with specific consideration of racial group membership and experiences of discrimination as personal and contextual determinants of both work and life satisfaction.

Theoretical Predictions

Social Cognitive Career Theory was initially developed to address the role of background variables, self-efficacy, and outcome expectations in the development of vocational interest, career choice, and work performance, and it has recently been extended to both work and educational satisfaction (Lent & Brown, 2006, 2013). The SCCT model of work satisfaction builds on Lent’s (2004) initial integration of both domain-specific and global life satisfaction. This grew from an earlier study, in which Lent et al. (2005) found that for a sample of college students, self-efficacy predicted academic satisfaction, and that positive affectivity predicted both academic and social self-efficacy as well as life satisfaction but was not significantly related to academic satisfaction. Outcome expectations, contrary to theoretical prediction, did not significantly affect either academic or social satisfaction, although both types of satisfaction positively predicted life satisfaction. It was not clear, however, how strongly academic satisfaction is related to work satisfaction, regardless of age. In 2006, Lent and Brown cited an increased interest among vocational researchers in the relationship between dispositional factors in job satisfaction, and they proposed a model predicting that the following variables directly affect work satisfaction: personality/affective traits (extraversion, neuroticism, and conscientiousness), self-efficacy expectations, participation in and progress at goal-directed activity, work conditions and outcomes, and relevant environmental supports, resources, and obstacles. The model also predicted that personality/affective variables and environmental conditions directly affect self-efficacy expectations. To date, this model has not been directly tested with working adults, either in mid-life or of retirement age.

In 2013, Lent and Brown suggested that the SCCT self-management model could be applied to work transitions associated with the retirement process (i.e., bridge employment or the decision to delay or gradually retire), and the same predictive variables associated with work satisfaction may also be applicable to retirement outcomes. Specifically, conscientiousness and extroversion are traits that could influence an older worker to choose bridge employment over delaying retirement whereas work satisfaction may impact the decision to phase into retirement. Aside from personality traits, older workers may need adaptive skills to cope with changing life roles or self-efficacy to adjust their career plans based on potential barriers such as discrimination, economic concerns or health and healthcare issues.

While the recent expansion of SCCT has added proposed influences on work satisfaction, the current theory does not indicate effects of either high or low work satisfaction on persistence in the current job, or on decisions to stop working. The TWA does directly address the impact of both the worker’s satisfaction with the workplace and the employer, as well as the employer’s satisfaction with the worker. This theory proposes that when both are high, the employment relationship will continue as it is, but when one of these components is low, either the worker or the employer will make adjustments. While it may seem obvious that individuals who are satisfied with their jobs and whose employers are satisfied with them will tend to remain with the same employer, for older workers it is possible that additional factors become more salient. For example, the decision to continue working may be driven by financial needs or the need to retain healthcare benefits, or the decision to leave may be involuntary, related to financial stress on the employer, as in downsizing. In a study of the relationship between job satisfaction and delayed retirement across countries in the European Union (EU), Aristovnik and Jaklič (2013) found that the relationship between job satisfaction and workforce participation of older adults was significant though weak, indicating that many other factors influence this decision. For example, in Slovenia an extremely generous early retirement benefit appeared to have encouraged larger numbers of workers to retire early there than in other countries. Job satisfaction also varied by job type, with highly qualified white-collar workers indicating higher levels of satisfaction than those in lower-level blue-collar jobs. Consistent with the predictions of SCCT, self-efficacy was related to job satisfaction, as older workers who did not anticipate being able to fulfill the requirements of their jobs in the future indicated lower satisfaction. The area in which older workers indicated the lowest level of satisfaction was the possibility of career advancement. Similarly, in a sample of 421 registered nurses aged 50 and older in Ontario, Canada, Armstrong-Stassen and Ursel (2009) found that perceived organizational support was related to opportunities for new training as well as advancement and was positively related to both job satisfaction and intent to stay. Thus, although the last stage of working life has been characterized as a period of slowing down and possibly lower ambitions for advancement and increased challenge, this may not be the case.

The effects of demographic variables can also affect job satisfaction in older workers. Aristovnik and Jaklič (2013) found that men in general across the EU were more satisfied than were women, though this was not the case in all countries. Further, the factors influencing job satisfaction varied by age, with younger workers more focused on recognition and older workers more focused on finding meaning in their work. It is important to note that the measure of job satisfaction used in this study also included questions about harassment and discrimination. In a large sample of employees aged 50–75 across two Australian companies, von Hippel, Kalokerinos, and Henry (2013) found that stereotype threat related to age negatively affected job satisfaction, work mental health, and organizational commitment and increased intentions to resign or retire. The relationship between work satisfaction and retirement intentions may also vary by race. Based on the 2002 Health and Retirement Study, Burr and Mutchler (2007) found that similar percentages of Whites (90.7%), Blacks (88.9%), and Hispanics (91.2%) reported that they enjoyed going to work, although in reporting their plans for retirement, Blacks (25.4%) and Hispanics (23.4%) were more likely to report that they planned to stop work altogether, as compared to 18.7% of White respondents.

In the present study, we evaluated the following predictions of Social Cognitive Career Theory:

  • H1

    Life satisfaction and work satisfaction are positively related.

  • H2a

    Personality traits including extraversion, neuroticism, and conscientiousness will predict self-efficacy.

  • H2b

    Experiences of discrimination, as learning experiences, will predict self-efficacy beyond what is predicted by personality.

  • H3

    Self-efficacy will predict work satisfaction, after controlling for experiences of discrimination.

  • H4

    Though not directly addressed by existing models of SCCT, we also proposed that experiences of discrimination as learning experiences would predict both life satisfaction and work satisfaction.

Further, we tested the following prediction, based on the Theory of Work Adjustment:

  • H5

    Those who reported that they were currently looking for a new job will report lower levels of work satisfaction with their present jobs.

Although the motivation to work because of financial rewards and benefits including healthcare are consistent with the TWA, it is likely that this relationship may change as individuals begin to contemplate retirement. Although many younger people feel that they work because they “have to” rather than because they want to, those who reach an age at which they are expected to stop working may become less satisfied with their work but nonetheless remain in their jobs. Our final hypotheses accounts for this:

  • H6

    Work satisfaction will be lower for those individuals with lower levels of work volition; i.e., those who reported in that they would like to leave work altogether but were unable to do so because they needed either the money or health insurance.



Data are from the Health and Retirement Study (HRS) 2008 and 2010 interview waves (HRS 2008b, HRS 2010, HRS 2012a, 2013a). For the purposes of this study, we used the HRS Core 2008 and 2010 Enhanced Fat Files from RAND (2013, for more information about this data see The HRS is a nationwide longitudinal dataset of older adults in the United Sates that is primarily funded by the National Institute on Aging (NIA) with additional funding from the Social Security Administration (HRS, 2012a, 2013a). The NIA and the University of Michigan’s Institute for Social Research collaboratively manage this ongoing study (HRS, 2008a; NIA, 2007).

In addition to using a multi-stage national area probability sampling methods, the HRS was designed to oversample Black, Hispanic, and residents of Florida to supplement the data (Heeringa & Connor, 1995). The Asset and Health Dynamics among the Oldest Old (AHEAD; individuals born in 1923 or earlier) and Early Baby Boomer (EBB; born between 1948 and 1953) cohorts were also oversampled for Black and Hispanic individuals; however, due to financial reasons the War Baby (WB; born between 1942 and 1947) and Children of the Depression Age (CODA; born between 1924 and 1930) cohorts were not oversampled (HRS, 2011). Respondents were over 50 years of age when they began participating, and surveys are completed every two years (HRS, 2011). Data are collected by telephone as well as in person interviews, and proxy informants are interviewed when respondents are unable to complete the interview due to cognitive or physical limitations (HRS, 2008a). The 2008 data was collected from 17,217 respondents between February 2008 and February 2009 and the 2010 data was collected from 22,037 respondents between February 2010 and November 2011 (HRS, 2013b, 2013c). The response rate for the 2008 data ranged from 86.3% – 90.7% depending on the cohort (HRS, 2011), and the response rate for the 2010 data is not yet available. Additional information about the sample and methodology has already been reported (see Heeringa & Connor, 1995).

From an initial sample of all adults who participated in both the 2008 and 2010 waves of the HRS, we selected those who were at least 60 years old, completed the Psychosocial and Lifestyle Survey (the “leave-behind” questionnaire, Smith, Fisher, Ryan, Clarke, House & Weir, 2013) during either wave, and reported that they were working at least 20 hours per week at the time they completed the leave-behind questionnaire. The rationale for this was that age 59–1/2 is the earliest age at which individuals may begin withdrawing benefits from retirement savings plans, which may help a phased transition to either full retirement or to a bridge career. Further, although the earliest Social Security retirement age is 62, we wanted to include the largest possible sample of African American and Latino/Hispanic respondents, particularly given lower U.S. Bureau of Labor statistics (2013) indicated lower employment levels for older Blacks (43.1% of Blacks/African Americans aged 60–64 and 14.8% aged 65+) compared to Whites (53.4% aged 60–64 and 17.5% aged 65+), which is consistent with the full HRS dataset. This resulted in a final sample of 1,858, ranging in age from 60 to 87 (M = 66.11, SD = 5.29, median = 65.00). The genders were about evenly split, with 901 (48.5%) male and 899 (48.4%) female; 58 (3.12%) did not report their gender. Years of education ranged from 0 to 17 or more, with a mode of 12 (n = 557). Slightly less than half (n=772, 41.6%) of the sample had completed 12 years of education or fewer, with 428 (23.0%) completing one to three years of college, 243 (13.1%), reporting that they had completed 4 years of college, and 312 (16.79%) completing more than 16 years of education; 103 (5.5%) did not provide this information. The participants were predominantly White (n=1,506, 81.2%), 241 (13.0%) were Black, 52 (2.8%) reported some other race, and 58 (3.1%) did not respond to this question. This represents a slight over-representation of Blacks compared to the U.S. civilian workforce age 60 or older, in which 86.30% are White, and 7.97% are Black (U.S. Bureau of Labor Statistics, 2013). Of the White participants, 79 (5.4%) reported Hispanic ethnicity, while 3 Black participants (1.5%) and 17 participants who identified as some other race (39.5%) reported their ethnicity as Hispanic. The mean weekly hours of work for this sample was 38.02 (SD=12.05, median = 40.00). Note that although the HRS used the term “Hispanic” in interviews and reports, for the remainder of this paper we have substituted the term “Latino,” which is more consistent with current practice and has been used by other authors in a study using a prior HRS dataset (Ayalon & Gum, 2011).


Except as indicated, the measures are taken from the HRS Psychosocial and Lifestyle Questionnaire (Smith et al., 2013), which represents a set of both existing instruments and measures that were created specifically for the HRS based on prior studies.

Job Stressors and Job Satisfaction Scale

Following Lent and Brown (2002), we defined work satisfaction as both overall satisfaction with one’s work and satisfaction with individual job facets, such as pay, job security, and opportunity for promotion. We measured this using the 7-item job satisfaction items from the HRS Job Stressors and Job Satisfaction scale. The items are coded from 1 = strongly disagree to 4 = strongly agree, with 5 = does not apply (Smith et al., 2013). This scale was modeled after the Quinn and Staines (1979) Quality of Employment Survey (QES), which measures both satisfaction with specific job facets as well as overall satisfaction. Psychometric analysis done with participants from the 2006, 2008 and 2010 HRS indicated the alpha coefficient for the Job Satisfaction scale has remained .80 through these waves of data (Smith et al., 2013). The Job Stress and Job Satisfaction scale as well as the Job Content Questionnaire (JCD) were both developed using QES items (Karasek, Brisson, Kawakami, Houtman, Bongers, & Amick, 1998; Mezuk, Bohnert, Ratliff, & Zivin, 2011). Concurrent validity of the JCD was evaluated by correlating it with similar scales as well as with participants’ age and education using data from the QES, and the two-factor loading pattern provides evidence of factorial validity (Karasek et al., 1998; Karasek & Theorell, 1992). The developers of the Survey of Health Ageing and Retirement in Europe (Siegrist, Wahrendorf, von dem Knesebeck, Jürges, & Börsch-Supan, 2006) and the National Study of the Changing Workforce (Beutell, 2013) used the QES to create job satisfaction measures, which have been used with older adults.

Satisfaction with Life Scale

The variable life satisfaction is defined as an individual’s evaluation of the quality of her or his life (Diener, Emmons, Larson, & Griffin, 1985). We measured this using the Satisfaction with Life Scale (SWLS), and the purpose of the SWLS is to assess how individuals evaluate the quality of their lives (Diener, et al., 1985). This 5-item scale is coded from 1 = strongly disagree to 7 = strongly agree with higher scores suggesting greater satisfaction. Diener and colleagues reported the SWLS had a coefficient alpha of .87 with a sample of college students and the coefficient alphas ranged from .88 – .89 for the 2006, 2008, and 2010 waves of HRS (Smith et. al, 2013). The SWLS correlated with the Life Satisfaction Index, (LSI, Adams, 1969), suggesting convergent validity (Diener et al., 1985). Additional evidence for reliability, convergent validity, and construct validity were provided in a review conducted by Pavot and Diener (1993) using diverse samples, including older adults. They found that the SWLS had a positive correlation with similar measures and the SWLS is associated with self-esteem, marital status, and health (Pavot & Diener, 1993). Further, the SWLS has been used to assess life satisfaction and quality of life among older adults across cultures (McAuley, Konopack, Motl, Morris, Doerksen, & Rosengren, 2006; Utsey, Payne, Jackson, & Jones, 2002).

Current Ability to Work

Lent and Brown (2006), defined self-efficacy as “personal beliefs about one’s capability to perform particular behaviors necessary to achieve valued school or work goals or, more generally, to perform tasks requisite to success in one’s work or school context” (p. 239). We measured self-efficacy using the participants’ responses to four separate questions about their perceived ability perform requirements of their current jobs, and to specifically manage the physical, cognitive, and interpersonal demands of their work. Participants responded to each question using a 10-point scale, with 0 indicating that they were unable to work in the specified area at their current job, and 10 indicating high confidence in their ability to perform the related tasks. Scores from the four scales are summed to create an overall index of perceived work ability. These items were taken from the Work Ability Index (WAI) developed by Ilmarinen and Rantanen (1999) to promote work ability among older adults. The purpose of the WAI is to assess “How good is the worker at present and in the near future, and how able is he or she to do his or her work with respect to work demands, health, and mental resources?” (Ilmarinen & Tuomi, 1992, p. 8). An international study of the WAI provided evidence of reliability with coefficient alphas that ranged from .54 to .79 depending on the country, with a mean alpha of .72 (Radkiewicz & Widerszal-Bazyl, 2005). This sample consisted of approximately 38,000 nurses, but specific demographic data was not provided. Coefficient alpha in both HRS 2008 and 2010 was .96 (Smith et al., 2013). Lastly, Radkiewicz and Widerszal-Bazyl (2005) provide evidence of construct validity by correlating the WAI with physical and mental health assessments.

Midlife Developmental Inventory

Personality/affective variables are defined as the extraversion, neuroticism, and conscientiousness scales from the Midlife Developmental Inventory (MIDI: Lachman & Weaver, 1997), which is based on the “Big 5” components of personality. The MIDI items are coded from 1 = a lot to 4 = not at all (Smith et al., 2013). This instrument also includes openness, agreeableness, and agency but these are not included as they were not part of Lent and Brown’s (2006) model. Using a sample of adults between the ages of 30 and 70, the internal consistency reliability of the MIDI subscales ranged from .72 to .81; extraversion, neuroticism, and conscientious scales correlated with the NEO short form (.75, .70, and .81, respectively), suggesting convergent validity; and divergent validity was tested by comparing the subscales of the MIDI and NEO short form (Lachman & Weaver, 1997). Psychometric analysis done with participants of from the 2006, 2008 and 2010 HRS waves indicated the alpha coefficients for extraversion, neuroticism, and conscientiousness ranged from .74–.75, .70–.72, and .66–.73, respectively (Smith et al., 2013). Further, the MIDI was used with middle age and older adults in the Midlife in the United States (MIDUS) study (Lachman, 2001).

Experiences of Discrimination

To measure discrimination, we used four scales from the HRS Psychosocial and Lifestyle Questionnaire, as described below:

Chronic Work Discrimination

The Chronic Work Discrimination Scale indicates the frequency with which participants perceived unfair treatment in a range of work situations, consisting of 6 items on a 6-point scale ranging from 1= never to 6= almost every day. The development of this scale was influenced by the research of Williams, Yu, Jackson, and Anderson (1997). According to Williams (2014), his Chronic Work Discrimination and Harassment scale was modeled after the Perceived Racism Scale (PRS) developed by McNeilly and colleagues (1996). Unlike prior measures of discrimination, Williams and colleagues modified the items to capture perceived workplace discrimination defined as unfair treatment, regardless of racial group. Consistent with findings of prior studies focused on racism, however, this study found that Blacks reported higher levels of work place stress than did White participants. The coefficient alpha for the 2010 HRS Chronic Work Discrimination Scale is .81 (Smith et al., 2013). Utsey (1998) reported that the PRS was found to have a direct correlation with other racism scales, thus providing further evidence of convergent validity. In addition, the factor loadings of .58 to .74 for the Racism on the Job subscale is also evidence of construct validity.

Everyday Discrimination

Everyday discrimination is represented by a 5-item scale indicating how often the participant experiences discrimination in her or his daily life, also on a 6-point scale, ranging from 1= almost every day to 6 = never, and reverse coded. This scale was also developed from the Everyday Discrimination subscale of the Detroit Area Study Discrimination Scale (Williams et al., 1997). The purpose of Everyday Discrimination scale was to assess minor, chronic, and routine experiences of unfairness. As with workplace discrimination, Williams et al. found that Blacks reported higher levels of everyday discrimination than did Whites, and that this variable moderated the relationship between race and health status. Coefficient alpha for this scale using HRS data is .80 (Smith et al., 2013). Taylor, Kamarck, and Shiffman (2004) reported a coefficient alpha of .80 for older Black adults, and Krieger, Smith, Naishadham, Hartman, and Barbeau (2005) reported a Cronbach alpha of .88 for Black and Latino individuals between the ages of 25 and 64. Krieger et al. also used the Everyday Discrimination scale to validate their Experiences of Discrimination scale, and reported a correlation of .56 with the frequency subscale and a correlation of .61 with the situation subscale, suggesting convergent validity. Taylor et al. (2004) found that the Everyday Discrimination scale also correlated with negative affect, social conflict, and perceived stress suggesting construct validity.

Attributions of Everyday Discrimination

The purpose of the Attributions of Discrimination Scale is to allow participants to report the perceived reasons for their experiences, including ancestry or national origin, gender, race, age, weight, physical disability, other aspect of physical experience, sexual orientation, and other. Participants may check as many as apply. These questions were taken from Kessler, Michelson, and Williams (1999) and are discrete variables that have been used in conjunction with Everyday Discrimination and Major Experiences of Lifetime Discrimination scales.

Major Experiences of Lifetime Discrimination

To capture the cumulative effects of discrimination, we used the Major Experiences of Lifetime Discrimination scale, which allows participants to report whether they have ever been unfairly treated in six specific areas (yes or no), including: employment (unfairly dismissed, not hired, or denied a promotion), housing, bank loan, or treatment by police. The score on this measure is the number of affirmative responses. Items were drawn from the Major Experiences (i.e., lifetime discrimination) scale from the Williams et al. (1997) Detroit Area Study Discrimination Scale (Smith, et al., 2013). The Major Experiences subscale correlates with the Everyday Discrimination scale and has a coefficient alpha of .63 for older Black adults (Taylor, et al, 2004). The coefficient alpha ranged from .52 to .71 for Latino and Black adults, respectively (Krieger et al., 2005). In addition, Krieger et al. (2005) reported correlations of .65 and .61 between the Major Experiences scale and their Experiences of Discrimination frequency and situation subscales, suggesting convergent validity.

Statistical Analyses and Results

The descriptive statistics, including means, standard deviations, and correlations among the variables are provided in Table 1. About half (n = 992, 53.4%) of the participants reported experiencing some type of everyday discrimination. Of these, 106 (10.7%) reported experiencing discrimination based on ancestry or national origin, 164 (16.5%) based on gender, 88 (8.9%) based on race, 341 (34.4%) based on age, 18 (1.8%) based on religion, 33 (3.3%) based on weight, 40 (4.0%) based on physical disability or other physical characteristic, 4 (0.4%) based on sexual orientation, and 47 (4.7%) based on financial status.

Table 1

Correlations among study variables

Results of Hypothesis Tests

As shown in Table 1, the bivariate relationships were in the directions predicted by theory, as described by hypotheses 1–4. Work satisfaction had a small but positive bivariate relationship with life satisfaction. The personality variables of extroversion and conscientiousness had small positive relationships with self-efficacy, while neuroticism had a small negative relationship. Self-efficacy, as predicted, was positively related to work satisfaction, as well as to life satisfaction. However, the three discrimination variables (chronic work discrimination, everyday discrimination, and lifetime discrimination) had stronger bivariate relationships with job satisfaction than did any other variable. Further, discrimination was more strongly associated with job satisfaction than with life satisfaction. Chronic work discrimination and everyday discrimination had small negative relationships with self-efficacy, while the relationship between lifetime experiences of discrimination and self-efficacy were not significant. All three types of discrimination had small negative relationships with life satisfaction.

To explore whether experiences of discrimination affected self-efficacy after controlling for the effects of personality (Hypothesis 2a), we conducted a hierarchical multiple regression predicting self-efficacy from the personality variables extroversion, neuroticism, and conscientiousness in the first step and the three discrimination variables in the second step. Hierarchical or sequential regression is an appropriate analysis when the order of entry is determined by theoretical considerations and allows the researcher to determine the contribution of one or more variables after controlling for those entered earlier in the model (Tabachnik & Fidell, 2007). The set of personality variables accounted for a significant but small proportion of the variance in self-efficacy, F(3, 1617) = 59.84, p < .001. The addition of the discrimination variables accounted for an additional 4% of the variance, F(3, 1614) = 21.92, p < .001. When all variables were in the model, extroversion and neuroticism were significant among the personality variables, and chronic work discrimination and everyday discrimination both had small effects. Neither conscientiousness nor lifetime experiences of discrimination affected self-efficacy.

Table 3 shows the results of a hierarchical multiple regression predicting work satisfaction from self-efficacy, after controlling for the three discrimination variables (Hypothesis 3). The set of discrimination variables accounted for a significant proportion of the variance in work satisfaction R2 = .23, F(3, 1616) = 159.61, p < .001, with chronic work discrimination most strongly affecting job satisfaction. Self-efficacy, while a significant predictor, accounted for only an additional 2% of the variance in work satisfaction after controlling for experiences of discrimination.

Table 3

Hierarchical Multiple Regression Predicting Job Satisfaction from Self-Efficacy after Controlling for Experiences of Discrimination

To further explore whether the effects of discrimination varied by race and ethnicity, we conducted a MANOVA comparing the experiences of the three types of discrimination by racial and ethnic group. We found no significant main effects for chronic work discrimination or everyday discrimination, but as may be expected, Black participants reported significantly greater levels of discrimination over their lifetimes than did White participants, F(2, 1586) = 7.89, p < .001. There was also a significant interaction by ethnicity for lifetime discrimination, F(2, 1586) = 3.61, with Latino ethnicity increasing the lifetime incidence of discrimination for White respondents and lowering the reported incidence for Blacks who reported Hispanic ethnicity. There were no interaction effects for the other measures of discrimination.

To determine the effect of work satisfaction on plans to change jobs (Hypothesis 5), we compared the levels of work satisfaction between those who reported in 2010 that were currently looking for a new job and those who were not. The results were consistent with predictions, with the mean work satisfaction of those who were currently looking a new job (M = 2.67, SD = .51) significantly lower than that of those who were not (M = 3.12, SD = .56), t(1, 343) = −7.10, p < .001. The mean difference in reported chronic work discrimination was also significant, with those looking for another job reporting higher levels of work discrimination (M = 1.99, SD = 1.08) than those who were not looking (M = 1.55, SD = .77); Levene’s test was significant for this comparison, indicating unequal variances between the two groups, t(88.20) = 4.94, p < .001.

Hypothesis 6 predicted that those with lower work volition would report lower work satisfaction. In the 2010 cohort, 692 (75.0%) participants reported that they would like to stop working altogether but needed the money, while 231 (25.0%) said that this was not true. As expected, those in the first group reported significantly lower work satisfaction (M = 3.08, SD = .60) than those in the second group (M = 3.37, SD = .55), t(880) = −5.56, p < .001. A somewhat smaller percentage of White participants (79.8%) reported staying in their jobs because they needed money, compared with 82.9% of Black participants, χ2 = 11.62, p < .009. Among individuals over age 65, this difference was no longer significant (67% of Whites and 73.9% of Blacks).

In the same cohort, 424 (52.4%) individuals reported that they continued working because of the need for health insurance, while 385 (47.6%) said this was not the case. Those who kept working for health insurance also reported somewhat lower work satisfaction (M = 3.01, SD = .56) than those who did not have this need (M = 3.29, SD = .58), t(773) = −6.92, p < .001. A significantly lower percentage of White participants (48.4%) reported that they were staying in their jobs because of the need for health insurance than did Blacks (72.7%), χ2 = 26.41, p < .001. Somewhat surprisingly, this difference remained statistically significant among those who would be eligible for Medicare, with 29.4% of Whites and 56.7% of Blacks age 65 or older reporting this reason for remaining in their jobs, χ2 = 9.66, p < .01.

A hierarchical multiple regression predicting work satisfaction based on the stated need to keep working either for money or health insurance was also significant at the initial step, F(2,726) = 27.28, p < .001. However, this model accounted for only 7% of the variance in work satisfaction. The addition of self-efficacy and chronic work discrimination to the model accounted for an additional 20% of the variance in job satisfaction, F(4, 724) = 68.38, p < .001. Nonetheless, chronic work discrimination (β = .41, p < .001) continued to be a stronger predictor than self-efficacy (β = .14, p < .001) after controlling for the effects of “job lock,” or a stated need to keep working despite a desire to stop.


The results of this study do provide some support for the applicability of both SCCT and TWA to older working adults, particularly regarding work satisfaction. As predicted, self-efficacy and life satisfaction were positively related to work satisfaction, and the personality variables extroversion and neuroticism had the expected relationships with self-efficacy. Contrary to predictions, conscientiousness was not significantly related to self-efficacy. Also, though significant, the relationship between the other two personality variables and self-efficacy was small. Thus, while it is possible that these traits affect confidence in one’s ability to meet the requirements of work, other factors are more salient, at least for older working adults.

The variable showing the strongest relationship with work satisfaction for older adults in the present study was chronic work discrimination, which was not part of the SCCT model. Although the participants were 81.2% White, over half of the participants in this sample reported some type of daily discrimination, with the largest proportion of the total sample attributing this discrimination to age. This suggests a need to broaden models of work satisfaction and work behavior to more directly consider the effects of discrimination based on a wide range of factors. For those who have faced lifelong discrimination based on gender, race, ethnicity, sexual orientation, disability, or other factors, adding age discrimination creates an additional dimension of multiple minority status. For older White men, in particular, it is possible that the experience of age discrimination may be the first time they have faced identity-based discrimination. Using data from the 2006 HRS, Ayalon and Gum (2011) found that the effect of everyday discrimination on life satisfaction, though significant for all groups, was higher for Whites. The authors proposed that this was because Black older adults, and to a lesser extent Latinos, had greater resilience in the face of discrimination because they had a lifetime to develop coping skills.

As predicted by the TWA, work satisfaction was positively related to intentions to leave one’s current job. This suggests that although retirement age is associated with planning to either decrease working hours or to stop work altogether, older adults who are happy in their work may in fact tend to work longer. Further, although a perceived need to continue working for either money or health insurance slightly decreased work satisfaction, the effect size was small compared to self-efficacy and particularly chronic work discrimination. Thus, even though older adults may feel that they have to keep working, this may be only somewhat related to whether or not they enjoy their work, and in fact this may not be much different from the feelings of many younger workers. As noted by Blustein (2006), work has long been viewed as necessary for survival but only those in the higher social classes may have full volition to choose not only how long to work but also the type of work that is available to them. It therefore is possible that the perceived need to work is viewed as constant, for all but the most privileged groups.

It is not clear why even for those of full Medicare retirement age the need for health insurance remained a common reason for staying in a job despite the desire to stop working, or why this reason was significantly more frequently cited by Black participants age 65 or older. U.S. Census data (2013) indicate that the rate of poverty among Blacks and Latinos remains approximately twice those of non-Latino Whites. Therefore, it is possible that those with lower incomes are more likely to be financially unable to match employer-provided healthcare benefits through a combination of Medicare and supplemental insurance coverage. It remains to be seen whether the Affordable Care Act will affect the perceived need of older adults to remain in the workforce for the purposes of retaining health insurance.

The findings of the present study suggest that although both SCCT and the TWA have at least preliminary support for their application in cross-cultural groups (Authors, this issue), the experience of discrimination for older adults may become more universal. We therefore recommend the inclusion of measures of discrimination for all groups, regardless of race, ethnicity, or gender. The participants in the present study additionally reported discrimination based on religion, weight or other physical characteristic, sexual orientation, and financial status. The finding that particularly chronic work discrimination affects both self-efficacy and work satisfaction indicate a need for additional study to further explore the nature of workplace discrimination and factors that may either increase or prevent these experiences for older adults in particular. Although the popular press and some empirical study has recently focused on the issue of workplace bullying, an exhaustive literature review found only very little addressing this issue with older adults (Powell, 2010). As the working population ages, this becomes more urgent. The present study addressed only the SCCT model of work satisfaction, but additional study may continue to evaluate the other SCCT models with older workers.

The results provide preliminary support vocational psychologists and counselors who conceptualize cases from the theoretical frameworks of SCCT and the TWA, but they may also consider incorporating specifically asking their clients about perceptions of ageism as well as workplace discrimination based on other personal attributes. This may be of particular importance for White men, who may be having their first encounter with discrimination and may ironically be less prepared to cope with the effects of discrimination on their sense of ability and work satisfaction than are women and members of other minority groups. Further, ageism may compound the negative effects of other types of discrimination, based on factors including but not limited to gender, race, ethnicity, and sexual orientation.

This study was limited by the self-report nature of the instruments, as well as by the availability of variables, which were already defined by the existing data set. Further, the publicly available data from the HRS masks detailed information regarding race, ethnicity, and occupational details. This limited the demographic analysis that was possible, and it did not allow further analysis of the data by occupational category, which may have revealed differences between those in blue-collar compared to white collar occupations or those requiring substantial physical effort, for example. We also were not able to determine based on the available data whether individuals who planned to leave their jobs were searching for another job in the same field or whether they were changing fields, for example searching for bridge employment or an encore career. Although the data set includes individuals with a wide range of educational levels, the average level of education of those in the present study is somewhat higher than that of the general population, which might be expected for individuals who agree to participate in longer-term research projects. Thus, the results may not generalize to groups with lower average levels of education, who may also perceive more limited occupational opportunities. Finally, we were particularly interested in evaluating the theoretical proposals of SCCT and the TWA with individuals of retirement age, but our results may not represent the predictors of job satisfaction and persistence of adults who are nearing retirement age but for whom leaving the workplace completely would not be a near-term consideration.

In conclusion, the increased numbers of older working adults have challenged the fields of counseling and vocational psychology to expand their scope to address the needs of older adults who remain in the workforce beyond traditional retirement age. As noted in the other two papers in this special section (Authors, this issue), the forms of continued work vary from full-time to part-time, and older workers may remain in the same vocation with a current employer or may change jobs, type of work, or both. Understanding the sometimes unique needs of this workforce is important for both researchers and practitioners, and the results of ongoing study may further support both continued productivity of these older workers as well as their satisfaction and retention.

Contributor Information

Pamela F. Foley, Seton Hall University.

Megan C. Lytle, University of Rochester Medical Center.


  • Aristovnik A, Jaklič K. Job satisfaction of older workers as a factor of promoting labour market participation in the EU: The case of Slovenia. Revija Za Socijalnu Politiku. 2013;20(2):123–148. doi: 10.3935/rsp.v20i2.1126.[Cross Ref]
  • Armstrong-Stassen M, Ursel ND. Perceived organizational support, career satisfaction, and the retention of older workers. Journal of Occupational & Organizational Psychology. 2009;82(1):201–220.
  • Ayalon L, Gum AM. The relationships between major lifetime discrimination, everyday discrimination, and mental health in three racial and ethnic groups of older adults. Aging & Mental Health. 2011;15(5):587–594. doi: 10.1080/13607863.2010.543664.[PubMed][Cross Ref]
  • Beutell NJ. Generational differences in work-family conflict and synergy. International Journal of Environmental Research and Public Health. 2013;10(6):2544–2559.[PMC free article][PubMed]
  • Blustein DL. The psychology of working. Mahwah, NJ: Lawrence Erlbaum; 2006.
  • Burr J, Mutchler J. Employment in later life: A focus on race/ethnicity and gender. Generations. 2007;31(1):37–44.
  • Diener E, Emmons RA, Larsen RJ, Griffin S. The satisfaction with life scale. Journal of Personality Assessment. 1985;49:71– 75.[PubMed]
  • Health and Retirement Survey. Sample evolution 1992–1998. 2008a Retrieved from
  • Health and Retirement Study. 2008 HRS core fat files (Final) RAND (v.B) University of Michigan with funding from the National Institute on Aging; Ann Arbor, MI: 2008b. public use dataset. Produced and distributed by the. (grant number NIA U01AG009740)
  • Health and Retirement Study. 2010 HRS Core Fat Files (Final) RAND (v.1.0) University of Michigan with funding from the National Institute on Aging; Ann Arbor, MI: 2010. public use dataset. Produced and distributed by the. (grant number NIA U01AG009740)
  • Health and Retirement Study. Sample size and response rates. 2011 Retrieved from
  • Health and Retirement Study. Health and retirement study 2008 core final, version 2.0: Data description and usage. 2012a Retrieved from
  • Health and Retirement Study. Health and retirement study 2010 core final, version 3.0: Data description and usage. 2013a Retrieved from
  • Health and Retirement Study. Data products: What’s available: HRS 2008 core. 2013b Retrieved from
  • Health and Retirement Study. Data products: What’s available: HRS 2010 core. 2013c Retrieved from
  • Ilmarinen J, Tuomi K. Work ability of aging workers. Scandinavian Journal of Work Environment and Health. 1992;18(2):8–10.[PubMed]
  • Ilmarinen J, Rantanen J. Promotion of work ability during ageing. American Journal of Industrial Medicine. 1999;36:S1, 21–23. doi: 10.1002/(SICI)1097-0274(199909)36.[PubMed][Cross Ref]
  • Heeringa SG, Connor J. Technical description of the health and retirement study sample design: HRS/AHEAD documentation report DR-002. 1995 Retrieved from
  • Karasek R. Job demands, job decision latitude, and mental strain: Implications for job re-design. Administrative Science Quarterly. 1979;24:285–306.
  • Karasek R, Brisson C, Kawakami N, Houtman I, Bongers P, Amick B. The Job Content Questionnaire (JCQ): An instrument for internationally comparative assessments of psychosocial job characteristics. Journal of occupational health psychology. 1998;3(4):322.[PubMed]
  • Karasek R, Theorell T. Healthy work: stress, productivity, and the reconstruction of working life. Basic books; 1992.
  • Kessler RC, Mickelson KD, Williams DR. The prevalence, distribution, and mental health correlates of perceived discrimination in the United States. Journal of Health and Social Behavior. 1999;40(3):208–230.[PubMed]
  • Krieger N, Smith K, Naishadham D, Hartman C, Barbeau EM. Experiences of discrimination: validity and reliability of a self-report measure for population health research on racism and health. Social Science & Medicine. 2005;61(7):1576–1596.[PubMed]
  • Lachman ME. Handbook of midlife development. Vol. 4. New York: Wiley; 2001.
  • Lachman ME, Weaver SL. Unpublished Technical Report. Brandeis University; 1997. Midlife Development Inventory (MIDI) personality scales: Scale construction and scoring. Retrieved from
  • Lent RW. Toward a unifying theoretical and practical perspective on well-being and psychosocial adjustment. Journal of Counseling Psychology. 2004;51(4):482–509. doi: 10.1037/0022-0167.51.4.482.[Cross Ref]
  • Lent RW, Brown SD. Integrating person and situation perspectives on work satisfaction: A social-cognitive view. Journal of vocational behavior. 2006;69(2):236–247.
  • Lent RW, Brown SD. Social Cognitive Model of Career Self-Management: Toward a unifying view of adaptive career behavior across the life span. Journal of Counseling Psychology. 2013 doi: 10.1037/a0033446.[PubMed][Cross Ref]
  • Lent RW, Brown SD, Hackett G. Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of vocational behavior. 1994;45(1):79–122.
  • Lent RW, Singley D, Sheu H, Gainor KA, Brenner BR, Treistman D, Ades L. Social cognitive predictors of domain and life satisfaction: Exploring the theoretical precursors of subjective well-being. Journal of Counseling Psychology. 2005;52(3):429–442. doi: 10.1037/0022-0167.52.3.429.[Cross Ref]
  • McAuley E, Konopack JF, Motl RW, Morris KS, Doerksen SE, Rosengren KR. Physical activity and quality of life in older adults: influence of health status and self-efficacy. Annals of Behavioral Medicine. 2006;31(1):99–103.[PubMed]
  • Mezuk B, Bohnert AS, Ratliff S, Zivin K. Job strain, depressive symptoms, and drinking behavior among older adults: results from the health and retirement study. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2011;66(4):426–434.[PMC free article][PubMed]
  • Moody Ayers SY, Stewart AL, Covinsky KE, Inouye SK. Prevalence and correlates of perceived societal racism in older African American adults with type 2 diabetes mellitus. Journal of the American Geriatrics Society. 2005;53(12):2202–2208.[PubMed]
  • National Institute on Aging. Growing older in America: The health and retirement study. Washington, DC: US Department of Health and Human Services; 2007. Retrieved from
  • Pavot W, Diener E. Review of the satisfaction with life scale. Psychological Assessment. 1993;5(2):164.
  • Powell M. Ageism and abuse in the workplace: A new frontier. Journal of Gerontological Social Work. 2010;53:7, 654–658. doi: 10.1080/01634372.2010.508510.[PubMed][Cross Ref]
  • Quinn RP, Staines GL. The 1977 quality of employment survey: Descriptive statistics, with comparison data from the 1969–70 and the 1972–73 surveys. Ann Arbor, MI: Survey Research Center, Institute for Social Research, University of Michigan; 1979.
  • Radkiewicz P, Widerszal-Bazyl M. International Congress Series. Vol. 1280. Elsevier; 2005. Jun, Psychometric properties of work ability index in the light of comparative survey study; pp. 304–309.
  • RAND. Data products of the center for the study of aging. 2013 Retrieved from
  • Siegrist J, Wahrendorf M, von dem Knesebeck O, Jürges H, Börsch-Supan A. Quality of work, well-being, and intended early retirement of older employees—baseline results from the SHARE Study. The European Journal of Public Health. 2007;17(1):62–68.[PubMed]
  • Smith J, Fisher G, Ryan L, Clarke P, House J, Weir DR. Psychosocial and lifestyle questionnaire 2006 – 2010: Documentation Report. 2013 Retrieved from
  • Super DE. A life-span, life-space approach to career development. Journal of Vocational Behavior. 1980;16:282–298.
  • Tabachnik BG, Fidell LS. Using multivariate statistics. 5. Boston, MA: Allyn and Bacon; 2007.
  • Taylor TR, Kamarck TW, Shiffman S. Validation of the Detroit area study discrimination scale in a community sample of older African American Adults: The Pittsburgh healthy heart project. International Journal of Behavioral Medicine. 2004;11(2):88–94.[PubMed]
  • U.S. Bureau of Labor Statistics. Employment status of the civilian noninstitutional population by age, sex, and race. 2013 Retrieved from
  • U.S. Census Bureau. Income, poverty, and health insurance coverage in the United States, 2012. 2013 Retrieved from
  • Utsey SO. Assessing the stressful effects of racism: A review of instrumentation. Journal of Black Psychology. 1998;24(3):269–288.
  • Utsey SO, Payne YA, Jackson ES, Jones AM. Race-related stress, quality of life indicators, and life satisfaction among elderly African Americans. Cultural Diversity and Ethnic Minority Psychology. 2002;8(3):224.[PubMed]
  • von Hippel C, Kalokerinos EK, Henry JD. Stereotype threat among older employees: Relationship with job attitudes and turnover intentions. Psychology and Aging. 2013;28(1):17–27. doi: 10.1037/a0029825.[PubMed][Cross Ref]
  • Williams DR. Measuring discrimination resource. 2014 Retrieved from
  • Williams DR, Yu Y, Jackson JS, Anderson NB. Racial differences in physical and mental health: socio-economic status, stress and discrimination. Journal of Health Psychology. 1997;2:335–351.[PubMed]

Table 2

Hierarchical Multiple Regression Predicting Self-Efficacy from Personality and Discrimination Variables.

Social Cognitive Theory

Social cognitive theory was originally proposed by Neal Miller and John Dollard in 1941. This theory is also known as the social learning theory. This theory focuses on the cognitive, behavioral, individuals and environmental factors that affect how people behave and how people are motivated. There is no single reason that can determine our thoughts or behaviors. Social cognitive theory is also referred to as a theory of theories, or a meta theory. It is primarily divided into four processes of goal attainment: 1) self-observation, 2) self-evaluation, 3) self-reaction, and 4) self-efficacy. Our lesson focuses on the 4th idea, self-efficacy.

Self-Efficacy Theory

A person’s ability to succeed is based on the individuals own belief of their ability to achieve goals, these beliefs affect their motivation and performance. This is referred to as self-efficacy. Self-efficacy is measured in two scales magnitude and strength and there are four main factors that help us create our self-efficacy ideas of what we can succeed at, and they are: 1) performance outcomes, 2) vicarious experiences, 3) verbal persuasion and 4) physiological feedback.

In order to succeed, people need a sense of self-efficacy, to struggle together with resilience to meet the inevitable obstacles and inequities of life. (Albert Bandura)

In the case below we will evaluate how social cognitive theory and self-efficacy theory is applied:

Case Study

A typical day at Metro One Telecommunications in Philadelphia was extremely fast paced but the atmosphere was laid back.  Before everyone owned a smart phone and the information from Google was at their fingertips, customers dialed 411 for directory assistance using their cellular phones.  The directory assistance operators where charged with providing cellular customers with directory information.

The Problem

The company offered great pay, a generous vacation policy, excellent health benefits and all the overtime an employee could handle.  The call center was open 24 hours a day/ 7 days a week.  They didn’t close for holidays or inclement weather.  At the time Metro-One held the contract to provide directory assistance for a majority of the cell phone carriers (Including Sprint, Verizon and Nextel).  The Philadelphia office did not have a problem with the Operators meeting (quality, speed or call fulfillment) goals.   Management did have a problem with the high turnover rates of new hires.  At times the job could be very stressful and most new employees did not make it through their 90 day probationary period.  The company was investing a lot of money training new employees to make them proficient in various operator tasks and systems.  In order to get a return on that investment the administrators were charged with increasing the rate of new staff retention.

Operator Goals

Directory assistance operators had three set goals:

  1. Speed- Average call completion time of 6 seconds per call.  New staff was given the goal of having an average of 10-15 seconds per call.
  2. Inquiry Fulfillment - The center was given a goal of 90% call fulfillment.  This was extremely important because the carriers offered refunds to customers who calls weren’t fulfilled. 
  3. Operator Quality – How well did the operator handle problems? Was the operator professional? (operators were given a script to read from and weren’t allowed to diverge from the script)

Management Goal

  1. Increase new hire retention.

The Solution

In order to choose candidates who could meet the rigorous goals every prospective employee was required to take a timed spelling and typing test at their interview.  Of the initial thirteen applicants, only 8 of us left after the typing test.  Next candidates had to correctly spell common business names that customer frequently requested.  Applicants were required a score of at least 90% to successfully complete this level in the interview process and had to type a minimum of 40 WPM.  Six of us successfully completed both test and were offered employment with the telecommunications company.   

Performance Outcome

During the initial training session (which was given on a computer) the trainers informed the new staff that the new job entailed exactly what everyone had successfully performed at the interview.   In fact if you received over 85% on the test you would have any problem performing directory assistance duties because it is essential very similar in nature. Exit interviews revealed that most new staff felt they wouldn’t have any problems handling the job because they had aced the test. A trainees’ expectation of success may have led to better performance in the testing. Furthermore, passing the testing serves as a benchmark for the employees who can now look back on successfully passing the requirements that were directly related to the job. 

Vicarious Experience

After the first week training session new staff is assigned to a mentor that was to assist them in their 90 day probationary period.   During the second week of training; mentees performed their duties with a mentor. They would sit at the same station and take turns answering calls. This was helpful because it offered real time experience and also gave new staff the ability to compare their performance with another coworker.  Another example of vicarious experiences in regards to this case would be if the new hires knew others that already work for Metro One Communication. Additionally, if the people that they know were successful with the company, then those new hires could use their experiences to expect success.

Verbal Persuasion

Around week three new staff would begin to take calls on their own. However the mentor is not far away. Typically the mentor sat either directly behind or adjacent to the new staff and offered encouragement and help as needed. They would listen to the mentee perform duties and give immediate feedback. When giving a critique mentors were instructed to give two positive statements for every negative statement. For example: “I was listening to your last call and you handled the hostile customer very professionally. Just watch your speech and make sure your language matches that of the given script. I was looking over your numbers and your goals are on par with those of seasoned employees’ good job!!” Managers giving employees positive feedback in the form of a pep talk may also improve a new hire’s chances of success.

Physiological Feedback

The demands of Metro One Telecommunications, and rigorous testing that it put its potential new hires through likely spawned physiological feedback in those that were tested. Physiological feedback such as anxiety or agitation may have had negative impacts on those being tested. In the tests for speed and accuracy, being nervous may have caused some potential employees to fail. Additionally, knowing that a failed test results in not being employed may have added to the stress.


While this system did not help management achieve their goal of a 95% retention rate for new hires, they were able to boost the retention rate by 25%.  The new staff that remained employed with Metro beyond their 90 day probation period felt that the training and mentors helped them perform their jobs better.

Two Basic Measuring Scales for Self-Efficacy:

Self-Efficacy is an important part of who we are and how we believe we can achieve certain task. This belief is how we approach situations and handle challenges (goals) (Cherry, 2011). Self-Efficacy is the idea of whether or not a person is able to perform a specific task. According to Siegle (2000) self-efficacy, “…is a person's judgment about being able to perform a particular activity” additionally as Siegle (2000) continued, “… self-efficacy reflects how confident students are about performing specific tasks. For example, if a person has high self-efficacy in math they will typically succeed, however if a person has low self-efficacy in math they are more than likely not to do as well. We can measure self-efficacy judgments with two basic scales self-efficacy magnitude, and self-efficacy strength.

Self-Efficacy Magnitude according Appelbaum & Hare (1996), “… refers to the level of task difficulty a person believes he or she can attain” (p. 35). These levels can be categorized as simple, moderate, or difficult. For example, can you run a 5k race, a half marathon, or run a full marathon? We see different levels of self-efficacy with the following video:

Self-Efficacy Strength according to Appelbaum & Hare (1996), “…refers to the degree of conviction that a given level of task performance is attainable (p. 35). For example, is your confidence high or low that you can complete a 5k race, a half marathon, or a full marathon? We see high self-efficacy (strength) with the following video:


Metro One Telecommunication is a company that works fast and has high demands on its employees. The company is having issues with high turnover with new employees due to a very high stress environment. Metro One Telecommunication has developed an intensive training system to weed through the applicants and find the ones that keep up with the stress. We can apply the self-efficacy magnitude scale to determine what level of difficulty each applicant believes they can perform. We can do this by creating a survey that each applicant could take and have them describe what level they feel they can perform their upcoming task. We relay this level of belief to their mentor, so the mentor can help develop the areas where the applicant is the weakest. This additional mentoring will help the applicant through the program at a pace that coincides with the applicant’s survey. In addition, we can develop the self-efficacy strength through a similar survey, so that the company can understand the applicant’s individual confidence. Again, take the results of the survey to the applicant’s mentor, so that the mentor can monitor the applicant’s confidence and adjust accordingly. For example, if the new employee is struggling with verbal skills over the phone the mentor can make a note about the issue and discuss it through verbal persuasion. This discussion would address not only the weak area of the new employee, but also reinforce confidence. I believe with these two basic scales of self-efficacy Metro One Telecommunication will not only reduce their high turnover rate, but also begin a new way of training more effective employees. 

High Self-efficacy, Over-confidence and Possible Negative Repercussions

Vancouver, Thompson, Tischner, and Putka did two studies to examine how high self-efficacy would relate to a person’s performance.  The findings of these studies were reported in the Journal of Applied Psychology in 2002.  What they found was that when a person had a high level of self-efficacy, this did not mean they had a high level of performance.  In fact, it could lead to a low level of performance. 

The studies were done on western college students using the Mastermind game which is a game that participants must put four colored squares in the correct order and they have ten attempts to do so.  With each attempt, the participant would get feedback to use for their next attempt.  46 participants were in the experimental group and 41 in the control group.  In the experimental group, during a few of the games, the participant would automatically get their third attempt correct in order to increase self-efficacy.  The control group did not get any manipulations at all. 

The way that they determined a person’s level of self-efficacy and self-confidence was through questionnaires given between each set attempts to arrange the blocks in the correct order.  One question for self-efficacy involved having the participant state how many attempts it would take them to find a solution based on a scale of 1, extremely unlikely to 6, extremely likely.  The question for self-confidence involved having the participant state how confident they were in the arrangement choice they were making based on the feedback that they had received based on a scale of 0, not confident, to 100, very confident.

What the experiment found was that in the experimental groups, the manipulated games did increase the self-efficacy of the person and on some levels it also decreased the performance of the person on the next game.  Once the person did not have a couple of the games manipulated, the self-efficacy lowered and the person’s performance once again increased.  Vancouver in 2001 found that by looking at the change with-in an individual, there was a negative affect between high self-efficacy and performance as a whole but he also felt that there needs to be more research on this for there could also be other reasons that the study did not show for these changes.

In the second study they did similar testing but this time they were looking at what the level of confidence had on the performance and the self-efficacy of the individuals.  What they found surprised them.  They found that there was a positive effect of self-efficacy and confidence, the higher the level of self-efficacy the higher the level of confidence and vice-versa.  What they also found was that there was no effect on confidence and performance and this also did not explain the lower performance of participants with the higher levels of self-efficacy.

Powers in 1973 and 1991 also found a negative between self-efficacy and performance but these studies did not take a look at the confidence of the individuals.  He feels that having high levels of self-efficacy may cause a person to set higher goals, but it can also reduce the motivation to reach the goals (Vancouver et el, 2002).

Stone in 1994 also found that a person that was over-confident in their abilities were high is self-efficacy and that these individuals also had less motivation and contributed less to reaching these goals.  In 1991, Bandura and Jourdon found similar results in studies that they preformed and stated “complacent self-assurance creates little incentive to expend the increased efforts needed to attain high levels of performance” (Vancouver et el, 2002).

After looking at these studies, one may conclude that high levels of self-efficacy may not be as good as Bandura once thought.  Before making this conclusion, one must realize that this is what seems to happen over time and not in a short length of time.  It must also be considered that people in this group are also more likely to set higher goals and to push on when the going gets tough.  These individuals are less likely to stop or quit a task where as a person with low self-efficacy is more likely to set lower goals and to quit or give up when things get tough.  It must also be considered that there may be other factors that have not been researched that are leading to the lower performance levels with high levels of self-efficacy and high self- confidence.  This is just a few of the points that need to be considered when trying to use high levels of self-efficacy to get more and better production out of workers.

Characteristics of Self-Esteem and Self-Efficacy

According to our lesson commentary “Although somewhat similar, self-efficacy is distinct from self-esteem in that self-esteem refers to a more general level of self-confidence and feelings of adequacy, whereas self-efficacy refers to a person’s belief that he or she can successfully perform a specific task (Gist, Schwoerer, & Rosen, 1989).” (L7, p.5) While these concepts can inter-relate, it is not necessarily true that a positive relationship will always exist between these two very similar constructs.  See table below for separate characteristics that are true of self-efficacy and self-esteem. 


High Self-Efficacy

High Self-Esteem

Low Self-Efficacy

Low Self-Esteem



Fear of Risks


Accurate Self-evaluation

Goal Commitment

Fear of Uncertainty


Willingness to take risks


Feelings of Failure

Inferiority or Superiority

Sense of accomplishment


Impression Management

Impatience or Irritability

Internal Values

Externally oriented goals




(Frank, 2011)

An individual may have a high opinion of themselves in general and be satisfied with the person that they are, but still know on a given task they may not be well equipped to handle it, just as easily as one can be confident on a given task but not be very satisfied with themselves in general.  As we look at our case study, where the company in question was having trouble retaining new hires, in response to that potential employees were given a tests that very strongly mimicked what they would be doing on a daily basis.  By informing the new hires who made it through that they have already proven that they can do the job because the test was essentially the same as the work, they started them out with a good dose of self-efficacy, by looking at the traits in the table above, the process also may have touched on increasing their self-esteem as well, knowing they were able to do the job already may contribute to goal commitment out of pride and maintaining the standard they set for themselves, as well as positivity; however the process was primarily a way to give the new hire a sense of high self-efficacy. Based on the definitions of self-esteem and self-efficacy, this employer improved self-efficacy for this particular job but it wouldn’t necessarily hit all of the points of self-esteem therefore showing that the two are not directly related.

 Social Facilitation and Social Loafing Phenomena

Sanna (1992) investigates how self-efficacy theory provides an integrative framework for social facilitation and social loafing phenomena.  The researcher conducted two experiments. In the first experiment, the researcher manipulated efficacy expectancies and outcome expectancies. Efficacy expectancies (high vs low) were manipulated by providing false performance feedback (successfully vs unsuccessfully) to the participants who worked on the preliminary task (the vigilance test). Outcome expectancies were manipulated by having participants work in one of three group conditions: alone, in coaching pairs (when performance was evaluated individually), and in collective pairs (performance was not evaluated individually).  The results of the first experiment demonstrate that efficacy expectancy and outcome expectancy jointly affected performance on a vigilance task. Particularly, participants with high efficacy expectancy (positive feedback) and high outcome expectancy (when they were evaluated individually) performed better than participants with low efficacy expectancy (negative feedback) and low outcome expectancy (they were not evaluated individually). In the second experiment, the researcher manipulated the difficulty of the task. The hypothesis is that an easy task predicts high-efficacy expectancies, whereas a difficult task predicts developing low-efficacy expectancies. The results proved the hypothesis. The researcher argues that the participants may loaf because they believe that they are not evaluated individually by others. This research supports the idea that self- efficacy expectancy and valence of evaluation affect performance.


In conclusion, the candidates who made it through the interview process were reassured that they had the skills and qualifications to succeed at their new positions.  This reassurance came in the form of a “pep talk” or “verbal persuasion” delivered by a company administrator.  This “pep talk” helped the new employees develop a sense of high self-efficacy going into the beginning of their training.  In the second week of training management continued to reinforce the new employees’ high sense of self-efficacy by assigning them to a mentor.  The role of the mentors in helping their mentees maintain a high sense of self-efficacy was twofold.  First, they would provide “vicarious experiences” where a mentee would observe their performance and then be able to compare it to their own.  Second, mentors were asked to provide a constant level of feedback or “verbal persuasion” where they encouraged and discouraged specific behavior by identifying two positives to every one negative item they would point out.  This constant level of “verbal persuasion” helped the new employees develop and maintain a high sense of self-efficacy by providing them examples of successful previous experiences in a short period of time.  By providing new employees a mentor   Metro One Telecommunication helped reduce negative “physiological feedback” throughout the 90 day probation period and beyond which also helped the company increase their new employee retention rate by 25%. (PSU WC, 2014, L.7)


Espinoza, Maestra. (2013, Nov., 3). Social Cognitive Career Theory. Retrieved from

Appelbaum, Steven H. & Hare, Alan. (1996). Self-Efficacy as a mediator  of goal setting and performance. Retrieved February 27, 2014.

Cherry, Kendra. (2011). Self-Efficacy Psychology definition of the week. Retrieved February 27, 2014.

dgamer84. (2010, July, 19). So you’re saying there’s a chance. Retrieved from

Morelock, Kayla. (2012, July, 3). Teacher Self-Efficacy Example. Retrieved from

Siegle, Dell. (2000). An Introduction to Self-Efficacy. Retrieved February 27, 2014.

Vancouver, J.B., Thompson, C.M., Tischner, E.C., & Putka, D.J. (2002). Two studies examining the negative effect of self-efficacy on performance. Journal of Applied Psychology, 87(3), 506-516. doi:

Frank, PH.D, M. (2011). The pillars of the self concept: Self-esteem and self-efficacy. Retrieved from

Gist, M. E., Schwoerer, C., & Rosen, B. (1989). Effects of alternative training methods on self-efficacy and performance in computer software training. Journal of Applied Psychology, 74, 884-891.

Berkelhammer, Larry. (2012, Aug., 1). Biofeedback Builds Self-efficacy, Hope, Health, & Wellbeing. Retrieved from

Pennsylvania State University World Campus (2011). PSYCH 484 Lesson 7: Self Efficacy Theory: Do I Think I can succeed at work. Retrieved from

Sanna, L. (1992). Self-efficacy theory: Implications for social facilitation and social loafing. Journal of Personal and Social Psychology, 62(5), 774-786.

Walsh, Jeffrey. (2014, Feb., 10). Social Facilitation & Social Loafing. Retrieved from


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