theory of work adjustment case study

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Social Cognitive Career Theory, the Theory of Work Adjustment, and Work Satisfaction of Retirement-Age Adults

Pamela f. foley.

Seton Hall University

Megan C. Lytle

University of Rochester Medical Center

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.

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 http://www.rand.org/labor/aging/dataprod.html ). 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 e xtraversion, 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.

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 R 2 = .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.

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, χ 2 = 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%), χ 2 = 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, χ 2 = 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.

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

Contributor Information

Pamela F. Foley, Seton Hall University.

Megan C. Lytle, University of Rochester Medical Center.

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The self-initiated work adjustment for learning scale: development and validation

Journal of Managerial Psychology

ISSN : 0268-3946

Article publication date: 4 May 2021

Issue publication date: 14 July 2021

The purpose of this study is to develop the self-initiated work adjustment for learning (SIWAL) scale that measures the adjustments that employees make in their work to enhance learning, based on theories and research on workplace learning, work adjustment and work design.

Design/methodology/approach

The SIWAL scale was validated in two independent studies. Study 1 ( n  = 208) focused on the internal consistency and factor structure of the SIWAL scale. Study 2 ( n  = 178) re-examined the factorial structure using confirmatory factor analysis and investigated scale validity.

In both studies, the SIWAL scale showed good psychometric characteristics, i.e. a clear two-factorial structure and internal reliable sub-scales. The findings also indicated convergent, divergent and concurrent validity.

Research limitations/implications

Using the SIWAL scale, future research could focus on the individual, social and organizational predictors and outcomes of SIWAL, collect supervisor and peer ratings to further validate this self-report scale and investigate lower-educated workers.

Practical implications

Organizations might try to enhance their employees' SIWAL through organizational policies, such as supportive leadership, and a learning climate.

Originality/value

This study provides a first step toward a better understanding of what workers do to enhance their workplace learning. The study findings indicate that employees address two adaptive behaviors: adjusting job responsibilities and adjusting social interactions.

• Workplace learning
• Informal learning
• Work adjustment

van Ruysseveldt, J. , van Wiggen-Valkenburg, T. and Dam, K.v. (2021), "The self-initiated work adjustment for learning scale: development and validation", Journal of Managerial Psychology , Vol. 36 No. 6, pp. 491-504. https://doi.org/10.1108/JMP-04-2020-0198

Emerald Publishing Limited

Copyright © 2021, Joris van Ruysseveldt, Tonnie van Wiggen-Valkenburg, Karen van Dam

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

Workplace learning is of crucial importance for contemporary organizations that operate in dynamic and complex business environments. Adapting to these ever-changing environments requires a continuous update of knowledge, skills and attitudes necessary to maintain and improve the quality and progress of work ( Kyndt et al. , 2016 ). While both formal and informal workplace learning contribute to the development of work-related competencies, informal learning in particular appears essential for adaptation to dynamic work situations ( Nikolova et al. , 2014 ). Informal learning refers to the self-initiated and self-directed learning through work practice (learning by doing) and interactions in the workplace (learning from others) ( Battistelli et al. , 2019 ; Coetzer et al. , 2020 ; Noe et al. , 2017 ). Informal learning has been positively associated with organizational and individual outcomes, such as productivity and competitiveness ( Ellström, 2001 ), performance ( Cerasoli et al. , 2018 ; Noe et al. , 2017 ), innovation ( Battistelli et al. , 2019 ; Holman et al. , 2012 ) and positive work attitudes, such as work engagement ( Cerasoli et al. , 2018 ; Coetzer et al. , 2020 ).

Given these benefits of informal workplace learning, a crucial question is whether and how workers engage in work adjustment behaviors to advance workplace learning and increase opportunities for professional development. According to the theory of work adjustment (TWA) ( Dawis and Lofquist, 1984 ), workers will try to modify aspects of their work environment or themselves to increase the correspondence between their abilities and job requirements, i.e. demands–abilities (D-A) fit ( Bayl-Smith and Griffin, 2018 ; Dawis, 2005 ). We introduce self-initiated work adjustment for learning (SIWAL) as a concept referring to work adjustment efforts of employees that contribute to an increase of the learning potential of their job and a better correspondence between skill requirements and skill abilities ( Dawis, 2005 ) through learning. SIWAL behaviors affect workplace characteristics that advance workplace learning. We identified these characteristics by following Zhang and Parker's (2019) recommendation that meta-analytical research outcomes should affect the selection of specific work characteristics conducive to workplace learning.

To obtain more insight into learning-enhancing work adjusting activities, a scale is needed that measures these behaviors. The goal of this study is to develop a scale for assessing SIWAL and to establish its reliability and validity. As the topic of this study is at the intersection of different research domains, i.e. job design, work adjustment and workplace learning, this study contributes to theory and research in all these areas. In the job design theory, adjusting work to improve its learning potential is often viewed as a management responsibility with workers as rather passive recipients ( Parker and Wang, 2015 ). In the workplace learning and human resource development (HRD) literature, much research focuses on the policies and practices that organizations can develop and apply to promote learning (e.g. Battistelli et al. , 2019 ; Doornbos et al. , 2008 ; Lohman, 2005 ). However, the worker is also an active participant in both work design and learning, and hence, they affect learning conditions in the workplace ( Janssens et al. , 2017 ; Parker, 2014 ). Applying this perspective may inspire future research and enhance our understanding of learning mechanisms and processes of competence development in the workplace.

There is still little information about the work adjustment behaviors that employees exhibit to optimize the learning potential of their workplace. To improve our understanding of employees' work adjustment initiatives for these informal learning activities, this study takes a crucial first step by developing and validating the SIWAL scale that researchers and practitioners can use to assess this behavior. As workplace learning contributes to organizational performance, adaptive potential and innovation ( Battistelli et al. , 2019 ; Cerasoli et al. , 2018 ), organizations have a clear interest in knowing what employees themselves (can) do to increase learning at work. At the same time, workplace learning yields important benefits for workers. Opportunities for workplace learning are positively related to the acquisition and development of work-related competencies ( Cerasoli et al. , 2018 ) and employability ( Froehlich et al. , 2015 ; Martini and Cavenago, 2017 ) and is associated with health and well-being (e.g. Holman and Wall, 2002 ).

Theoretical background

The workplace learning literature specifies different types of learning. An important distinction relates to formal and informal learning ( Coetzer et al. , 2020 ). Formal learning typically takes place within a structure deliberately designed and created for that purpose ( Froehlich et al. , 2015 ), such as a course or training. It is structured and organized in terms of learning context, learning support, learning time and learning objectives ( Janssens et al. , 2017 ). Conversely, informal learning typically takes place outside formally designated learning contexts (e.g. Cerasoli et al. , 2018 ), at or near the workplace and is less structured and organized. As informal learning is strongly embedded in, and interwoven with daily work activities and social interactions, specific workplace characteristics stimulate the engagement in informal learning activities ( Cerasoli et al. , 2018 ; Janssens et al. , 2017 ). This clear link between work and informal learning subsequently prompts the question whether and how workers adjust and mold these workplace characteristics to learn.

Theoretically, SIWAL behaviors can be understood and studied within frameworks such as the TWA ( Dawis and Lofquist, 1984 ; Dawis, 2005 ) and work design approaches ( Bakker and Demerouti, 2007 ; Hackman and Oldham, 1980 ; Parker, 2014 ). In the job design theory, adjusting work to improve its learning potential is often viewed as a management responsibility with workers as rather passive recipients ( Parker and Wang, 2015 ). In this perspective, job design derives from broader organizational and technological choices, and others (e.g. managers, staff, consultants) design jobs. However, from theoretical perspectives such as TWA, employees engage in work adjustment behaviors, which change aspects of their work situation ( Parker, 2014 ) or themselves.

TWA is a prominent person–environment (P-E) fit theory, which deals with how person (P) and environment (E) maintain and increase their level of correspondence through active and reactive adjustment behaviors ( Bayl-Smith and Griffin, 2018 , p. 210). Today's rapidly changing work environments challenge P-E correspondence, calling for a dynamic view of P-E fit ( Bayl-Smith and Griffin, 2018 ). Consequently, we apply the TWA process model as our main theoretical background. This dynamic model explains how P-E correspondence is achieved, maintained and replicated ( Dawis, 2005 ) through adaptive behaviors such as SIWAL. In this model, P and E are not static, but can and do change where dissatisfaction with the level of P-E correspondence drives work adjustment: dissatisfied workers “will do” something to change a dissatisfying situation through engagement in adaptive behaviors ( Dawis, 2005 , p. 4).

The TWA process model distinguishes between two modes of work adjustment ( Dawis, 2005 ): behaviors that affect changes in the environment (activeness) and behaviors that affect changes in the individual (reactiveness). SIWAL includes adaptive behaviors, which changes the person (i.e. learning of new abilities, skills and knowledge) and, hence, should be labeled as reactiveness ( Bayl-Smith and Griffin, 2018 ). More specifically, SIWAL is triggered by, and impacts, the level of correspondence between a person's abilities and the changing demands of the job (D-A fit). In contemporary dynamic work contexts, shifting work requirements resulting in a D-A misfit challenge workers to engage in continuous processes of skill development. SIWAL refers to adaptation by workers to situations of D-A misfit through learning new abilities (reactiveness).

In sum, SIWAL refers to self-initiated work adjustment behavior (e.g. signing up for challenging assignments or asking for feedback), which enhances the learning potential of the workplace. The goal of SIWAL is to advance one's professional and personal growth. Through workplace learning, it subsequently contributes to a better correspondence between skill requirements and skill abilities. This study focusses on adaptive behaviors, which serve this clear end, i.e. to learn.

Because SIWAL focuses on those aspects of work that facilitate workplace learning, we need to identify the work characteristics that are important facilitators or drivers of workplace learning ( Lohman, 2005 ; Van der Klink et al. , 2012 ). Two main sources of informal learning can be distinguished: learning in relation to task performance (learning by doing) and learning in relation to others in the work context (learning through others). This classification mirrors existing distinctions in the literature, e.g. between learning through practice and social interaction ( Coetzer et al. , 2020 ). Both the task environment and the social environment have proven to be important sources for workplace learning (see also Battistelli et al. , 2019 ; Jeon and Kim, 2012 ; Noe et al. , 2017 ; Tannenbaum et al. , 2010 ) and serve as the base for scale development. Next, specific characteristics in these two environments with a strong impact on workplace learning were identified, following Zhang and Parker's (2019) recommendations that meta-analytical research outcomes (e.g. Cerasoli et al. , 2018 ; Wielenga-Meijer et al. , 2010 ) should determine this selection.

SIWAL and the task environment: adjusting job responsibilities

Extensive evidence indicates that the learning potential of the workplace is highly dependent on aspects of the tasks pertaining to the job responsibilities ( Jeon and Kim, 2012 ; Van der Klink et al. , 2012 ), such as the scope, complexity and variety of these tasks ( Holman et al. , 2012 ).

In order to learn, employees can demonstrate adaptive behavior, e.g. applying for challenging assignments, which affects the content of their work in such a way that their job becomes broader and/or more challenging ( Battistelli et al. , 2019 ), i.e. they address the number, scope or type of tasks they perform ( Holman et al. , 2012 ). The beneficial impact of job characteristics, such as task variety and task complexity, is theoretically elaborated in the literature (e.g. Demerouti et al. , 2001 ; Ellström, 2001 ; LePine et al. , 2004 ). For instance, Hackman and Oldham (1980) stressed the motivational role of these characteristics in their Job Characteristics Model. Moreover, the Job Demands-Resources model ( Bakker and Demerouti, 2007 ; Demerouti et al. , 2001 ) shows how job resources, such as task variety, contribute to the motivational process that stimulates outcomes such as personal and professional growth. By attracting more tasks and signing up for challenging assignments, employees engage in a bottom-up task enlargement or enrichment process that benefits their personal and professional development ( Hackman and Oldham, 1980 ).

These theoretical notions have been empirically substantiated by a large number of studies ( Holman et al. , 2012 ; Parker, 2014 ). Meta-analytic research ( Cerasoli et al. , 2018 ; Wielenga-Meijer et al. , 2010 ) found evidence for the positive influence of challenging work settings on workplace learning. Task complexity has been related to enhanced learning and learning motivation ( Holman et al. , 2012 ; LePine et al. , 2004 ). Additionally, Jeon and Kim (2012) noticed that decreasing routinization by adding a new task greatly contributed to informal learning. In conclusion, adaptive behavior adjusting the number, scope or type of tasks comprises changes to the task repertoire itself, modifying workplace characteristics such as task variety and complexity.

SIWAL and the social environment: adjusting social interactions

Interactions with others are a powerful learning source ( Bandura, 2001 ), and this is equally true for learning at work. Some scholars and researchers even focus mainly on the social aspects of workplace learning (e.g. Janssens et al. , 2017 ; Lohman, 2005 ; Schürmann and Beausaert, 2016 ). Through the presence of different work relations, the work context offers numerous learning opportunities, in particular when adaptive behavior aims for a more effective use of these social interactions for professional growth. Employees can seek feedback from colleagues and supervisors, ask them for advice, invite them to work together on challenging projects or simply observe how more experienced co-workers perform their tasks ( Bandura, 2001 ; Lohman, 2005 ; Van der Klink et al. , 2012 ). In all instances, information, knowledge and experiences are exchanged, triggering cognitive and behavioral learning processes such as reflection and experimentation ( Nikolova et al. , 2014 ). Adaptive behaviors, such as asking for feedback, trigger adjustments in social interactions at work, which facilitate reflection, modeling and experimentation. These efforts to build and maintain social relationships and networks with a higher learning potential stimulate the process of learning of new abilities, skills and knowledge enabling the worker to better adapt to shifting job requirements.

The positive impact of interactions with colleagues and supervisors on informal learning was demonstrated in a meta-analysis by Cerasoli et al. (2018) and many other studies. For instance, feedback has proven to be one of the main drivers of informal learning ( Doornbos et al. , 2008 ; Schürmann and Beausaert, 2016 ) and has both a motivational and cognitive function ( Ellström, 2001 ). According to Ashford et al. (2003) , many workers nowadays experience a feedback vacuum, and therefore actively seek feedback themselves. Lohman (2005) found that a lack of proximity to colleagues was detrimental for workplace learning. Hence, adjusting social interactions in the work context might also be an important strategy in increasing opportunities for informal learning. Through the formation of a web of relationships and networks for learning ( Schürmann and Beausaert, 2016 ), the exchange of information, knowledge and experiences is facilitated, which in turn triggers learning, resulting in professional and personal growth.

Scale development

With the literature on work adjustment, job design and workplace learning as a starting point, items were generated for the two dimensions of SIWAL, resulting in an initial set of 16 items. After extensive discussions, four items were deleted and others were rewritten and edited until the authors agreed upon their fit with the SIWAL construct and its two dimensions. Deleted items were found to measure either different behavior, more than one behavior or behavior that was already covered by another item.

A pilot study was then conducted to examine the quality of the items in a sample of 37 participants, with 18 employees in different professions, ten psychology students and nine academic researchers. As lengthy scales can cause practical problems in research and field settings, another objective was to reduce the number of items. After receiving an explanation of the SIWAL construct and its two dimensions, participants provided feedback on the content validity, comprehensibility and wording of the items by filling out a questionnaire. Their responses led to several, small textual adjustments and the deletion of four more items.

The resulting eight items captured the two dimensions and were included in Study 1. Items followed the initial question “What do you do in your work to learn new things?” Responses were made on a five-point Likert-type scale ranging from 1 ( never ) to 5 ( very often ). In Study 1, the eight items (four items for each dimension) were presented to a new and larger sample, with the objective to investigate the factor structure of the SIWAL measure.

Study 1: scale development and initial validation of the factor structure

The first study aimed to establish the factor structure and reliability of the new measure for SIWAL. The measure includes two dimensions, adjusting job responsibilities and adjusting social interactions, and should be applicable regardless of the professional context.

Participants and procedure

Participants were employees of a Dutch municipality in the middle of The Netherlands. Prior to data collection, the aim and conditions of the study were discussed with a senior HR advisor and the manager of one of the largest departments of the municipality, who approved of the study. Approval was also obtained from the Ethical Committee of the research institute that conducted the study (registration number: U2017/09062/HVM), implying that research participants were treated in accordance with the ethical guidelines set out by the American Psychological Association (2017) .

After the organization had decided to participate in the study, an email with a link to the online survey was sent to 780 staff members explaining the purpose of the study, and emphasizing that participation was voluntary and anonymous, and that participants could withdraw at any time. The questionnaire was fully completed by 208 employees (27% response). Mean age was 45.2 years ( SD  = 11.3); mean organizational tenure was 11.5 years ( SD  = 9.6); mean job tenure was 4.8 years ( SD  = 5.8); 65.9% were female. With 43.3% higher vocational education and 39.9% university, the educational level of the participants was relatively high.

Results and conclusion

The eight items resulting from the pilot study were subjected to an exploratory factor analysis with principal axis factoring and promax rotation, which showed a clear two-factor structure with all items loading on their intended factor, explaining 66.8% of the variance in the items. However, one item intended to load on the “adjusting job responsibilities” factor showed a relatively low factor loading (0.46), suggesting low correspondence with or a lack of close fit with this factor. As moderate to strong relationships should show coefficients of at least 0.50 ( Bagozzi and Yi, 1988 ), this item was removed.

A new exploratory factor analysis (principal axis factoring and promax rotation) was conducted with the remaining seven items (three items for adjusting job responsibilities and four for adjusting social interactions). The items, means, standard deviations, Cronbach's α and factor loadings are presented in Table 1 . Together, these findings indicate that the SIWAL scale has a clear two-dimensional structure, and that the sub-scales possess sufficient internal consistency. Cronbach's alpha was 0.86 for adjusting job responsibilities and 0.75 for adjusting social interactions, which is well above the required 0.70 ( Nunnally and Bernstein, 1994 ).

Study 2: confirmation of the factor structure and establishing convergent, divergent and concurrent validity

The second study aimed to reconfirm the measure's factor structure in a new sample, and to establish convergent, divergent and concurrent validity by investigating the scale scores' relationships with conceptually related and unrelated variables in the nomological network.

Convergent validity is demonstrated when an instrument shows positive and rather high associations with instruments that are intended to study theoretically similar concepts. Since SIWAL entails that employees take the initiative to change their work to learn, it is assumed that the two dimensions of SIWAL are conceptually related to employee engagement in learning activities, which refers to continuous activities initiated and carried out by employees to learn new knowledge, skills and abilities ( Bezuijen et al. , 2009 ; Cerasoli et al. , 2018 ). The concept of engagement in learning activities is somewhat broader than the SIWAL concept, as it also refers to learning outside one's formal task responsibilities and includes training assignments on and off the job, challenging and novel tasks, special projects and job transitions ( Bezuijen et al. , 2009 ; Birdi et al. , 1997 ). However, since the emphasis is on employees actively engaging in workplace learning, we expected a positive association with employees' SIWAL behaviors.

Divergent validity is established when a construct shows non-significant or low associations with a theoretically unrelated (or weakly related) construct ( Nunnally and Bernstein, 1994 ). For this purpose, we used emotion-focused coping, which pertains to behaviors that aim to reduce and manage the intensity of the negative and distressing emotions that occur in a stressful situation ( Cohan et al. , 2006 ). As emotional coping is theoretically different from SIWAL, we expected that the SIWAL dimensions would be unrelated or only very weakly related to emotion-focused coping.

Concurrent validity is established when the new scale is significantly related to measures that are likely to be associated with the new construct. We used dispositional learning goal orientation to investigate the concurrent validity of the SIWAL scale. Dispositional learning goal orientation refers to individuals' tendency to set learning goals in achievement situations, with the aim of developing their competence by acquiring new skills and mastering new situations ( Vande Walle, 1997 ). Both SIWAL and dispositional learning goal orientation refer to learning and the deployment of learning strategies ( Holman et al. , 2012 ). Moreover, it is likely that employees with a strong learning goal orientation will show SIWAL behaviors more readily. Therefore, we expected a positive association between these two variables.

Participants were 178 employees (18% response) of a Dutch bank organization. Prior to data collection, the purpose and conditions of the study were discussed with the chief human resources officer of the bank and the sustainable employability policy advisor, who approved of the study. Respondents were invited via Yammer, a social network of approximately 950 staff members within the organization. The message explained the aim of the study and emphasized that participation was voluntary and anonymous, and that participants could withdraw at any time. A link to the online survey was included. Mean age was 45 years ( SD  = 13.5); mean organizational tenure was 16.7 years ( SD  = 12.8); mean job tenure was 4.4 years ( SD  = 5.1); 57.3% was female. With 59.6% higher vocational education and 29.8% university, the education level of the participants was relatively high.

SIWAL. The seven-item SIWAL measure, established in Study 1, was used to measure SIWAL. A five-point response scale was used, ranging from 1 ( never ) to 5 ( very often ). Cronbach's α was 0.88 for adjusting job responsibilities and 0.79 for adjusting social interactions.

Employee engagement in learning activities . Bezuijen et al. 's (2009) eight-item scale ( α  = 0.86) was used to measure employee engagement in learning activities. Participants could respond on a five-point scale ranging from 1 ( never ) to 5 ( very often ). An example item was “I invest time in participating in training or courses.”

Emotion-focused coping. Emotion focused coping was measured with Cohan et al. 's (2006) seven-item scale ( α  = 0.90). A five-point response scale was used, ranging from 1 ( not at all ) to 5 ( very much ). An example item was “I blame myself for being too emotional about the situation.”

Dispositional learning goal orientation. Vande Walle's (1997) five-item scale ( α  = 0.77) was used to assess dispositional goal orientation. Respondents could respond on a five-point scale, ranging from 1 ( fully disagree ) to 5 ( fully agree ). An example item was “I often look for opportunities to develop new skills and knowledge.”

Confirmatory factor analysis

Confirmatory factor analysis (CFA) was conducted to examine the factor structure of the SIWAL measure. Two nested models were compared. Model 1 comprised a one-factor model, with all items loading on one general SIWAL factor; Model 2 reflected the two-dimensional theoretical model, allowing each sub-scale to load on its own factor. To assess model fit, a number of fit indices were used ( Byrne, 2010 ), chi-square test ( χ 2 ), root-mean-square errors of approximation (RMSEA ≤ 0.08), the normed fit index (NFI ≥ 0.90), normed comparative fit index (CFI ≥ 0.90) and the Tucker–Lewis index (TLI ≥ 0.90). The analyses were conducted using structural equation modeling (AMOS 24).

The fit indices of Model 2 ( χ 2 (13) = 18.70, NFI = 0.97, CFI = 0.99, TLI = 0.98, RMSEA = 0.05), representing the two-factor model, indicated a good fit. Model 1 ( χ 2 (14) = 171.53 , NFI = 0.70, CFI = 0.72, TLI = 0.57, RMSEA = 0.25) showed poor fit. Differences between Models 1 and 2 were significant (Δ χ 2  = 152.83, p  < 0.001). These findings indicate that the two-factor model of the SIWAL scale is empirically supported. Factor loadings ranged from 0.73 to 0.94 for adjusting job responsibilities, and 0.54 to 0.79 for adjusting social interactions.

Convergent, divergent and concurrent validity

Table 2 presents the means, standard deviations and inter-correlations of the study variables. As expected, the two SIWAL sub-scales were positively and significantly related to employee engagement in learning activities ( r  = 0.62–0.64; p  < 0.01), suggesting convergent validity. Additionally, the SIWAL sub-scales were very weakly related (adjusting job responsibilities, r  = −0.15, p  < 0.05) or unrelated (adjusting social interactions; r  = −0.01, ns ) to emotion-focused coping, suggesting divergent validity. Both SIWAL sub-scales showed significant positive correlations with dispositional learning goal orientation ( r  = 0.39–0.67, p  < 0.01), suggesting concurrent validity. Moreover, both SIWAL sub-scales were moderately and negatively associated with age ( r  = −0.21 to −0.30, p  < 0.01) and job tenure ( r  = −0.28 to −0.27, p  < 0.01). The SIWAL sub-scale adjusting job responsibilities was weakly related ( r  = −0.15, p  < 0.05), and the SIWAL sub-scale adjusting social interactions was unrelated ( r  = −0.01, ns ) to education.

The findings confirm that the SIWAL scale consist of two factors, adjusting job responsibilities and adjusting social interactions, that are internally consistent. Moreover, the pattern of associations suggests that the scales show good convergent, divergent and concurrent validity.

Based on the workplace learning, work adjustment and job design literature (e.g. Cerasoli et al. , 2018 ; Wielenga-Meijer et al. , 2010 ), this study developed and validated a parsimonious measure for self-initiated work adjustment for learning that includes two sub-scales. Adjusting job responsibilities deals with the aspects of the task environment that promote task-related learning; adjusting social interactions concerns aspects of the social work environment that stimulate interactional learning. The results showed that the SIWAL scale has good psychometric characteristics. The two-factor structure was supported, indicating that adjusting job responsibilities and adjusting social interactions can be conceived as separate SIWAL behaviors. Sub-scales showed internal consistency, as well as convergent, divergent and concurrent validity.

Before interpreting the results of this study, some limitations have to be considered. The research was conducted by means of self-reports, which can cause method bias ( Podsakoff et al. , 2003 ). Future research could apply supervisor or peer ratings of the SIWAL behaviors and its antecedents and outcomes. Highly qualified service workers were overrepresented in our samples, which may raise questions about the broader generalization of the findings. Future research should investigate SIWAL in other settings such as industry or among lower-skilled employees. Despite these limitations, this study has important theoretical and practical implications.

Theoretical implications and future research

As the SIWAL concept is situated at the intersection of different fields, i.e. workplace learning, work adjustment and job design, it extends to developments in these domains. For instance, in job design theory, changing aspects of work is viewed as a management responsibility ( Parker, 2014 ). Similarly, much research on workplace learning has applied a top-down approach, advancing knowledge about how organizations can develop and apply effective policies and practices to promote learning (e.g. Battistelli et al. , 2019 ; Doornbos et al. , 2008 ; Lohman, 2005 ). The idea that the worker is an active participant in job design and learning, who can influence learning conditions in the workplace, is less prominent in such research. This study, therefore, addresses a gap in job design and workplace learning literature. This does not imply that continuous professional development should only depend on employee engagement in self-directed learning initiatives. Whether or not employees adjust their work to increase its learning potential will also depend on contextual factors, such as autonomy ( Holman et al. , 2012 ) or the proximity of others ( Lohman, 2005 ). These factors may fall outside the sphere of influence of employees and can serve as a lever or obstacle for effective work adjustments. Importantly, the one-sided optimistic view on the benefits of informal learning should be nuanced. Employees may consider themselves as owner of the knowledge and competencies acquired through informal learning, because these result from self-directed learning efforts. Hence, these efforts could be more strongly oriented toward personal goals instead of organizational success ( Bolino et al. , 2010 ). Organizational arrangements such as a strong learning climate or the installation of learning communities may be needed to safeguard the capacity of an organization to acquire, share and retain organization-specific knowledge ( Bolino et al. , 2010 ). In this respect, SIWAL and informal learning should not be considered as plain substitutes for formal training and learning in organizations. Future research could investigate both the conditions that promote or hinder employees' SIWAL behaviors and the conditions that increase or decrease the organizational benefits of SIWAL and informal learning, e.g. successful organizational learning and knowledge sharing ( Bolino et al. , 2010 ).

In general, more research is needed to better understand the antecedents of SIWAL. These may include organizational, work-related and personal factors. At the organizational level, learning climate ( Nikolova et al. , 2016 ), management and supervisor support for learning ( Bezuijen et al. , 2009 ; Lohman, 2005 ) and HRD policies ( Jeon and Kim, 2012 ) have been shown to promote workplace learning and thus may play a role in stimulating SIWAL. Additionally, these organizational factors may counterbalance undesirable consequences of SIWAL and informal learning, such as a sub-optimal knowledge-sharing or a one-sided focus on personal, instead of organizational goals ( Bolino et al. , 2010 ).

Regarding workplace characteristics, empirical evidence indicates that autonomy is an important condition for both adaptive behavior and learning ( Cerasoli et al. , 2018 ; Wielenga-Meijer et al. , 2010 ). Similarly, the level of work demands may affect SIWAL behaviors: a certain level of work pressure may be challenging and advance learning ( Doornbos et al. , 2008 ; LePine et al. , 2004 ), while too much work pressure may interfere with learning processes, as it puts a limit on the time needed for work adjustment and learning ( Ellström, 2001 ; Van Ruysseveldt and Van Dijke, 2011 ). Future research is needed to look into the role of autonomy, work pressure and other job demands and resources as antecedents of SIWAL. With respect to personal characteristics, future research could investigate the role of other personal characteristics for SIWAL, such as need for (informal) learning ( Cerasoli et al. , 2018 ) and self-efficacy ( Lohman, 2005 ).

Research is also needed on the outcomes of SIWAL. The SIWAL measure builds on a set of work characteristics that have been shown to promote engagement in informal learning behaviors ( Cerasoli et al. , 2018 ; Schürmann and Beausart, 2016) and learning outcomes ( Doornbos et al. , 2008 ; Wielenga-Meijer et al. , 2010 ). New research is needed to substantiate the claim that SIWAL contributes to workplace learning and professional and personal growth, and subsequently advances P-E fit ( Bayl-Smith and Griffin, 2018 ).

The work context may impact the use of specific SIWAL behaviors. For instance, workers operating in socially isolated work contexts might more heavily depend on task-related learning opportunities ( Janssens et al. , 2017 ), and therefore will invest in adjusting job responsibilities. For workers in routine work contexts with limited job control, learning may be more dependent on opportunities to interact with others. The impact of contextual factors on the prevalence of specific SIWAL behaviors requires deeper investigation.

Finally, future studies may also include Tims et al. 's (2012) job-crafting scale along with the SIWAL scale. Inclusion of both scales in studies may contribute to the incremental validity of SIWAL above and beyond job crafting to better understand the nature of the differences.

SIWAL can be considered an important aspect of employees' performance at work as employee learning contributes to organizational performance and innovation ( Noe et al. , 2017 ) and to employees' health, well-being and employability ( Parker, 2014 ). As organizations benefit from the maintenance and replication of the D-A fit ( Dawis, 2005 ), many organizations use job (re)design and HRD practices to facilitate workplace learning. This study draws attention to the fact that learning opportunities can also be created through self-initiated and self-directed interventions by the worker. Workers themselves can take responsibility for their development and growth by initiating work-adjustment activities to learn. Potentially, these SIWAL behaviors may supplement and/or strengthen existing job (re)design and HRD policies and practices in organizations, and, as a consequence, contribute to their effectiveness.

To stimulate these individual initiatives, it is of crucial importance that organizations facilitate SIWAL through supportive actions, such as providing time to adjust and learn, increasing room to maneuver on the job and promoting collaborations between colleagues within and beyond work units ( Cerasoli et al. , 2018 ; Jeon and Kim, 2012 ). Additionally, the workplace learning literature suggests that organizations can enhance SIWAL through organizational policies supportive of workplace learning, such as a strong learning climate ( Nikolova et al. , 2016 ) and development-oriented leadership ( Bezuijen et al. , 2009 ).

SIWAL may benefit individuals, as well as organizations that have continuous development and learning, high on their agenda. This is especially useful for organizations and work environments with few opportunities for formal training and development or without elaborated HRD policies, such as small and medium-sized enterprises ( Coetzer et al. , 2020 ). In these contexts, SIWAL can serve as a tool to promote learning opportunities at work and reduce employees' dependence on organizational workplace learning initiatives.

Conclusions

Individual employees can increase the learning potential of their workplace by adjusting job responsibilities and social interactions. Until now, these employees' initiatives have received limited research attention. The short SIWAL measure that was developed in this study has good psychometric properties and can be used by researchers and practitioners alike. Organizations can use the SIWAL scale to assess the occurrence and diffusion of SIWAL in the workplace. Modern economies and organizations need to foster a lasting capacity to adapt to technological, economic and social developments, and in this process, the continuous acquisition of new competencies through workplace learning plays a crucial role.

Descriptives, Cronbach's alpha and item factor loadings of the SIWAL scale (Study 1)