Integrating Work and Study Among Young Adults

Abstract

The current study examined the applicability of Frone’s model of work–family relations to work–study relations. The contribution of internal and external antecedents to conflict and facilitation relations between work and study was tested. The model also includes the effects of these relations on academic and psychological health outcomes. The participants were 661 Israeli working students (M _age = 26.08, SD = 3.18). Structural equation modeling (SEM) analysis indicated an adequate index fit, suggesting that aspects of conflict and facilitation relations coexist when blending work and study. Number of working hours and financial support predicted conflict relations that, in turn, lowered grades, negatively affected further academic plans, and increased depression. Work salience, social, and academic support predicted facilitation relations, encouraged further academic study, and boosted grades. Results emphasize the advantage in examining conflict and facilitation relations simultaneously when investigating career development and psychological health of working students.

Keywords

working students work–study conflict work–study enrichment academic achievement support depression

Blending work and study during an individual’s college years has emerged as a common lifestyle among young adults in Western countries. This growing phenomenon has received relatively little empirical attention, thus limiting researchers’ and counselors’ ability to fully understand the effects of blending work and study on young adults’ academic and career development (Park & Sprung, 2013; Riggert, Boyle, Petrosko, Ash, & Rude-Parkins, 2006). In order to narrow this gap, the current study tested the applicability of Frone’s model of work–family relations (2003) to the investigation of work–study relations.

The Rise in Student Employment

The participation of colleges and university students in the labor market has been identified as a global trend among students from different socioeconomic status and cultural backgrounds (McNall & Michel, 2011). In the United States, for example, nearly 80% of college students are employed in the course of their undergraduate education, for approximately 30 hr per week (Park & Sprung, 2013). Similar rates are reported in the United Kingdom and Australia (Curtis & Shani, 2002; Robbins, 2010). Also in Israel, almost 50% of university students work for approximately 30 hr per week (Romanov, Tur Sinai, & Zussman, 2007).

These statistics suggest that many higher education students juggle these two demanding roles: worker and student. Unfortunately, our knowledge about the various outcomes of blending work and study among higher education students and the antecedents of these results is very limited (Butler, 2007; Park & Sprung, 2013; Riggert et al., 2006). As a result, we have little guidance to offer students, career counselors, and educational institutions concerning the successful management of work and school. Furthermore, since initial work experience and higher education are important components of the individual’s career development, understanding the costs and benefits of managing work and study may contribute to the theories of young adults’ career development.

Integrating Work and College Study

Research on the integration of work and study roles has focused primarily on the high school student population, yielding inconsistent and equivocal findings. Most of this research has highlighted the negative ramifications of this phenomenon, such as lower school achievement levels and the presence of high-risk behaviors (for a review, see Bachman, Staff, O’Malley, Schulenberg, & Freedman-Doan, 2011). However, other studies, though few in number, have indicated a positive impact of work–study integration, such as acquiring new skills, and having a greater career orientation (Paschall, Flewelling, & Russell, 2004; Staff, Mortimer, & Uggen, 2003; Warren, 2002).

The specific and unique characteristics of college students (e.g., the desire to be closer to the world of work) prevent us from embracing current knowledge on the work–study interface among adolescents in the absence of further investigation. Relatively few studies have investigated work–study integration among students in higher education. Broadbridge and Swanson (2006), who explored the authentic experience of blending work and study among Scottish undergraduates, demonstrated the complexity of the experience as well as the co-occurrence of positive and negative outcomes. Other studies reported negative outcomes of working during postsecondary study: decrease in academic achievement (Barke et al., 2000; Butler, 2007; Derous & Ryan, 2008; Markel & Frone, 1998; Trockel, Barnes, & Egget, 2000), poor study skills (Lammers, Onweugbuzie, & State, 2001), low investment in study (Hall, 2010; King & Bannon, 2002), stress and fatigue (Broadbridge & Swanson, 2006), lower school satisfaction (Butler, 2007), and unhealthy alcohol drinking (Butler, Dodge, & Faurote, 2010).

It should be noted that some studies did not find negative correlations between work hours and grade point average (e.g., Furr & Elling, 2000; Volkwein, Schmonksy, & Im, 1989), even demonstrating positive outcomes. Working college students have reported increased confidence in dealing with people and money as a result of their work, an improvement in their social life (Curtis & Shani, 2002; Hodgson & Spours, 2001), increased school satisfaction (Butler, 2007; Hall, 2010), and an increase in time management efficacy (Hecht & McCarthy, 2010).

These contradictory results accentuate the need for an integrative theoretical model to guide researchers in their efforts to more fully understand the phenomenon of working students and the mechanism through which both positive and negative outcomes coexist (Butler, 2007; Park & Sprung, 2013).

An Integrative Model to Investigate Work–Study Interface

Frone’s (2003) taxonomy of the work–family interface, claimed by many (e.g., Whiston, Campbell, & Maffini, 2012) to be the most influential model in terms of generating work–family research, emanates from role theory (Katz & Kahn, 1978) and Bronfenbrenner’s ecological system theory (1989). Both emphasize the interrelations between diverse domains and levels of human functioning. Based on the assumptions that individuals’ resources are scarce, and that multiple roles are in competition with each other as well as the view that resource expansion can engender benefits of engaging in multiple roles (Sieber, 1974), Frone’s (2003) extended model incorporates the co-occurrence of both the conflict and the facilitation aspects in the interaction of work and family. The construct of conflict relations relates to the mutual interference of the two roles, due to an individual’s limited resources, while the construct of facilitation relations refers to the mutual enhancement of these roles. Each of these relations has specific internal and environmental antecedents (such as role salience, number of working hours, and social support) and specific outcomes in three domains: the work domain, the family domain, and the health domain (for a review on the model and relevant research, see Whiston & Cinamon, 2015).

The applicability of this model to the investigation of the work–study interface will be tested in the current study, based on the assumption that work and study are two central demanding social roles in the life of working students, just as work and family are central demanding social roles in the life of working adults. The tested integrative model incorporates conflict and facilitation relations between work and study. Work–study conflict (WSC) is defined as the extent to which work interferes with the students’ related responsibilities (Markel & Frone, 1998). Work–study facilitation (WSF) is defined as the extent to which students’ work experience enhances the quality of the student role (Butler, 2007). I will examine whether conflict and facilitation between work and study co-occur, as they do in the work–family interface as well as whether each of these relations has specific antecedents and unique outcomes, as suggested by Frone (2003).

Several environmental and internal antecedents will be examined: number of working hours, social support, financial support, and role salience. Three different outcomes will be examined as well: academic performance, psychological health, and future academic planning (see Figure 1). The first goal of the current study was to test the suggested model and to examine its index indicators through structural equation modeling (SEM) analysis.

Figure 1.

Structural model (for reading convenience, error measurement is not specified).

Antecedents of Work–Study Relations

Number of working hours

Number of working hours has been documented as an important source of work–family conflict relations among employed adults. Longer working hours were positively correlated with higher levels of work–family conflict (see Bellavia & Frone, 2005; Whiston & Cinamon, 2015) and negatively correlated with work–family facilitation (Wayne, Randel, & Stevens, 2006). Therefore, the first hypothesis is:

Hypothesis 1a: Number of working hours will be positively correlated with conflict relations between work and study.

Hypothesis 1b: Number of working hours will be negatively correlated with facilitation relations between work and study.

Social support

Social support is considered an important resource for managing stress in general and in managing multiple social roles in particular. Two meta-analyses (Ford, Heinen, & Langkamer, 2007; Michel, Mitchelson, Pichler, & Cullen, 2010) found that social support plays a critical role in attenuating conflict relations from work and family. Other studies demonstrated the important role of social support as a factor in promoting facilitation relations between work and family (Hill, 2005; Wayne et al., 2006). These studies lead to the second hypothesis:

Hypothesis 2a: Social support will be negatively correlated with conflict relations between work and study.

Hypothesis 2b: Social support will be positively correlated with facilitation relations between work and study.

Financial support

A particular category of support is examined in the current study: financial support. Due to the nature of emerging adulthood, comprising an individual’s transition to adulthood, some students enjoy financial support from those parents who can afford to grant it. Others are provided with financial assistance in the form of scholarships. Hoobler, Hu, and Wilson’s (2010) meta-analysis found that salary was positively related to conflict relations between work and family. This finding may be related to time spent at work, and those who spend more time at work may also, consequently, have higher salaries and more conflict relations. However, students typically work only part time and not necessarily in jobs related to their interests. Therefore, I hypothesize that financial aid as an income supplement for working students will negatively correlate with conflict relations, as it will enable the students more freedom to regulate the time they invest in work. Based on studies demonstrating the contribution of income to facilitation relations between work and family (e.g., Greenhaus & Powell, 2006), it can be hypothesized that financial support will be positively correlated with facilitation relations between work and study.

Hypothesis 3a: Financial support will be negatively correlated with conflict relations between work and study.

Hypothesis 3b: Financial support will be positively correlated with facilitation relations between work and study.

Role salience

The more importance attributed to work or to family roles, the more time and energy will be invested in it, allowing less time and energy for the individual’s other roles (Greenhaus & Beutell, 1985). Indeed, several studies documented the contribution of work salience to work–family conflict (e.g., Cinamon, 2009; Cinamon & Rich, 2002) and to work–family facilitation (Cinamon & Rich, 2010; Wayne et al., 2006). Based on these studies, the fourth hypothesis is:

Hypothesis 4a: Work salience will be positive correlated with conflict relations between work and study.

Hypothesis 4b: Work salience will be positive correlated with facilitation relations.

Outcomes of Work-Study Relations Between Work and Study

Academic performance

Based on the view that working consumes scarce resources at the expense of resources needed for performing the student role, studies on working college students have found that longer working hours are associated with poorer study skills (Lammers et al., 2001) and poorer academic performance (Barke et al., 2000; Butler, 2007; Derous & Ryan, 2008; Markel & Frone, 1998; Trockel et al., 2000). These results are consistent with many studies on working adolescents (for a review, see Bachman et al., 2011).

Research on the outcomes of conflict relations between work and family has demonstrated lower work performance as well as lower family performance (for a review, see Whiston & Cinamon, 2015). Frone’s taxonomy (2003) also includes the principle of resource expansion. According to this principle, performing multiple roles can also be beneficial to individuals (Sieber, 1974). Different types of resources may be accumulated through role occupancy, such as skills and perspectives, material resources, psychological resources, and social capital (Greenhaus & Powell, 2006). Similar mechanisms may promote facilitation between work and study. Indeed, some studies have indicated that work may also be related to better grades among working adolescents (see Bachman et al., 2011) and among working college students (Butler, 2007). Therefore, I hypothesize:

Hypothesis 5a: Conflict relations between work and study will be negatively correlated with academic grades.

Hypothesis 5b: Facilitation relations between work and study will be positively correlated with grades.

Academic planning

Due to the importance of higher education as a source of both economic and social benefits (e.g., Rowley & Hurtado, 2002), it is important to understand how this growing phenomenon of blending work and study affects young adults’ future academic plans. Based on the limited-resource perspective, it can be assumed that the pressure and strain accompanying a working student will result in a decline of future academic plans in order to avert such stress and tension in the future. Empirical studies of working among higher education students have indeed indicated a decline in academic achievements (Barke et al., 2000; Butler, 2007; Derous & Ryan, 2008; Markel & Frone, 1998; Trockel et al., 2000), poorer study skills (Lammers et al., 2001), lower investment in study time (Broadbridge & Swanson, 2005; Hall, 2010; King & Bannon, 2002), and a decline in school satisfaction (Butler, 2007). These negative academic outcomes can lead to an eschewal of future academic plans.

On the other hand, the resource-expansion perspective also asserts positive aspects of role blending, such as self-confidence. Indeed, studies have indicated benefits enjoyed by working students, such as greater school satisfaction (Butler, 2007; Hall, 2010) and increased time management efficacy (Cinamon, 2014; Hecht & McCarthy, 2010). These findings can lead to the expectation that facilitation relations will enhance academic planning due to the benefits derived from blending these roles. Therefore, the sixth hypothesis is:

Hypothesis 6a: Conflict relations will be negatively correlated with academic planning.

Hypothesis 6b: Facilitation relations will be positively correlated with academic planning.

Psychological health

Many studies have considered the relationships between work–family interface variables and psychological functioning (for a review, see Whiston & Cinamon, 2015), with the assumption that mental health is optimized when work–family conflict is low and family–work enrichment is high (Grzywacz & Bass, 2003). Conflict relations between work and family have been shown to relate negatively to psychological functioning (Frone, 2003) and to life satisfaction (Sumer & Knight, 2001) and positively correlated with depression (Allen, Herst, Bruck, & Sutton, 2000). Furthermore, WSC was a significant predictor of psychological health among working college students (Park & Sprung, 2013).

Facilitation relations between work and family was correlated positively with enhanced mental and physical well-being (Grzywacz & Bass, 2003) as well as with life satisfaction and mental health (McNall, Nicklin, & Masuda, 2010). Other studies also indicate that work–family facilitation positively correlates with enhanced mental and physical well-being (Hill, 2005) and decreased depression (Hammer, Cullen, Neal, Sinclair, & Shafiro, 2005). Two psychological variables were assessed in the current study: depression (feelings of helplessness, hopelessness, guilt, and worthlessness) and life satisfaction. Global life satisfaction is a subjective judgment of one’s life which has been shown to relate positively to well-being and negatively to psychopathology (Pavot, Diener, Colvin, & Sandvik, 1991). Pavot and colleagues speculated that satisfaction with life is a relatively stable and global phenomenon and a component of subjective well-being. Following the cited empirical evidence, I hypothesize:

Hypothesis 7a: Conflict relations will be positively correlated with depression and negatively correlated with life satisfaction.

Hypothesis 7b: Facilitation relations will be negatively correlated with depression and positively correlated with life satisfaction.

Method

Participants

The participants were 661 Israeli university students between the age of 19 and 35 (M _age = 26.08, standard deviation [SD] = 3.18). It should be noted that in Israel, 2–3 years of post-high school military service is mandatory, making Israeli students typically older than their American or European peers. Three hundred fifty-seven (54%) of the participants were females. Religious distribution comprised 82% Jews, 9.5% Muslims, 2.6% Christians, and 2.4% Druze (the remaining 3.5% did not indicate their religion). Most participants (85%) were born in Israel and the rest were foreign born. The majority of the participants (60.4%) were single, 142 (21.5%) were in a romantic relationship, and 119 (18%) were married.

Five hundred twenty-two (79%) were enrolled in a bachelor’s degree program, 133 (20%) in a master’s program, and 6 (0.6%) participants were doctoral students. Six participants were medical students, 75 (11.3%) in life sciences, 177 (26.8%) in social sciences, 116 (17.5%) in humanities, 70 (10.6%) in exact sciences, and 118 (17.9%) were studying engineering (the remaining participants were in combined programs). The participants reported studying for an average of 21.93 weekly hours (SD = 10.59) on campus and an average of 13.95 weekly hours (SD = 13.36) at home. All participants worked, in conjunction with their studies, between 8 and 200 monthly hours (M = 68.81, SD = 49.17). The majority (73%) described their work as “temporary student work,” while the remainder described it as a “permanent” job.

Measures

In all cases, items were scored so that higher values indicated greater levels of the named construct. Prior to conducting the analyses, I examined the validity of each scale by inspecting the correlation of item-total scores, means, and SDs. Also, I inspected skewness and kurtosis for each variable in order to meet the assumption of normality, of particular importance in SEM (Hong, Malik, & Lee, 2003). Missing data at the item level were treated using the full-information maximum likelihood method with Mplus (Muthén & Muthén, 2011).

WSC

An 8-item questionnaire measured the degree to which work and study negatively interfere with each other. I used Markel and Frone’s (1998) 4-item measure of work interfering school (e.g., My job takes up time that I’d rather spend at school or on schoolwork). In order to capture the concept of mutual interference between work and study, an additional 4 items were taken from Cinamon and Rich’s (2002) Work–Family Conflict Scale, in order to measure the school interfering work by modifying the items to fit the study–work context, replacing family with study (e.g., My study demands interfere with my work). Items were on a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). Confirmatory factor analysis supported the construct of two subscales with the relevant items: absolute fit measures: χ² = 95.35, p < .001, χ²/df = 5.0, goodness-of-fit index (GFI) = .96, root mean square error of approximation (RMSEA) = .079; incremental fit measures: incremental fit index (IFI) = .98, Ticker–Lewis index (TLI) = .97, comparative fit index (CFI) = .98; parsimonious fit measure: Parsimony-adjusted GFI (PGFI) = .51, Parsimony normed fit index (PNFI) = .66, Parsimony-adjusted CFI (PCFI) = .66.

WSF

This 9-item questionnaire measured the degree to which work and study experiences were mutually enhancing. The questions were presented on a 5-point Likert-type scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The degree to which work enhanced study experiences at school was measured by 5 items taken from McNall and Michel’s (2011) Work–School Enrichment Scale. An additional 4 items were taken from Wayne, Randel, and Stevens’s (2006) Work–Family Facilitation Scale to assess the degree to which study enhanced work experiences by adapting the items to fit the study–work context (e.g., Conversation with a friend from the university helps me deal with problems at work). Five items assessed WSF (e.g., My development as a worker makes me a better student). Confirmatory factor analysis supported the construct of two subscales with the relevant items: absolute fit measures: χ² = 57.60, p < .001, χ²/df = 2.30, GFI = .98, RMSEA = .045, incremental fit measures: IFI = .98, TLI = .98, CFI = .98; parsimonious fit measures: PGFI = .54, PNFI = .68, PCFI = .69.

Work salience

Attribution of importance to the work role was measured with the relevant items of the Life Role Salience Scale (Amatea, Cross, Clark, & Bobby, 1986). We used only the 10-item subscale that taps the work role and comprises 5 items reflecting commitment to the role (e.g., I intend to invest much time and energy in improving my work) and 5 items reflecting the value attributed to the role (e.g., My life’s aim is to have an interesting career). Confirmatory factor analysis supported the construct of two subscales with the relevant items, after excluding Items 4 and 6: absolute fit measures: χ² = 56.40, p < .001, χ²/df = 4.02, GFI = .98, RMSEA = .068; incremental fit measures: IFI = .98, TLI = .98, CFI = .96; parsimonious fit measure: PGFI = .40, PNFI = .50, PCFI = .50. Cronbach’s α for this scale was α = .76.

Depression

The Center for Epidemiologic Studies–Depression Scale (CES-D) is a 20-item scale used to measure depressive symptomatology (Radloff, 1977) on a 4-point Likert-type scale, ranging from 1 (rarely or none of the time [less than 1 day per week]) to 4 (most or all of the time [5-7 days per week]). A sample item included, I felt sad. Items were reverse scored, and item scores were summed with higher values indicating a larger number of symptoms of depression. An internal consistency estimate of α = .92 was found, and scores on the CES-D Scale have been shown to distinguish depressed from nondepressed individuals (Nishiyama, Ozaki, & Iwata, 2009). Additional support for construct validity was noted when high scores on the CES-D were related to low levels of social support, poor general health, and stressful life events (Li & Hicks, 2010). Confirmatory factor analysis supported the construct of one factor after excluding Items 1, 4, and 8: absolute fit measures: χ² = 338.99, p < .001, χ²/df = 2.92, GFI = .94, RMSEA = .05; incremental fit measures: IFI = .94, TLI = .93, CFI = .94; parsimonious fit measure: PGFI = .71, PNFI = .78, PCFI = .80.

Life satisfaction

The Satisfaction with Life Scale is a 5-item scale measuring global life satisfaction (Diener, Emmons, Larson, & Griffin, 1985), presented on a 7-point Likert-type scale, ranging from 1 (strongly disagree) to 7 (strongly agree). Example items included, In most ways, my life is close to my ideal, and I am satisfied with my life. The Satisfaction with Life Scale has correlated with other measures of life satisfaction (Diener et al., 1985). Cronbach’s α of .87 was found in the original scale (Diener et al., 1985). Confirmatory factor analysis supported the construct of one factor. Absolute fit measures: χ² = 15.64, p < .001, χ²/df = 3.91, GFI = .94, RMSEA = .06; incremental fit measures: IFI = .99, TLI = .97, CFI = .99; parsimonious fit measure: PGFI = .26, PNFI = .39, PCFI = .39.

Social support scales

The Multidimensional Scales of Perceived Social Support was used to measure three different sources of social support: family, friends, and significant others. Each of these three subscales was measured by 4 items (totaling 12 items) on a 7-point Likert-type scale, ranging from 1 (strongly disagree) to 7 (strongly agree). A sample item is I can trust my friends when I have problems to solve. The score for each subscale comprised the mean of its 4 items. Validity and reliability were measured by Zimet, Dahlem, Zimet, and Farley (1988) with five different samples in the United States, with the α coefficients of the three subscales ranging from .85 to .91, and .88 for the total score. α Coefficients in the current study were .91 for family support, .92 for friends’ support, .94 for significant others’ support, and .93 for total support. Confirmatory factor analysis supported the construct of three subscales with the relevant items. Absolute fit measures: χ² = 187.09, p <.001, χ²/df = 3.89, GFI = .95, RMSEA = .068; incremental fit measures: IFI = .98, TLI = .97, CFI = .98; parsimonious fit measure: PGFI = .58, PNFI = .70, PCFI = .71. Coefficient αs of the above scales are presented in Table 1.

Table 1.

Means, Standard Deviations, Correlations, and α Coefficients (In Parentheses).

	Mean	SD	1	2	3	4	5	6	7
1. Work–study conflict	2.96	0.90	(.90)
2. Work–study facilitation	2.85	0.85	.12**	(.87)
3. Role salience	3.68	0.50	.05	.30**	(.76)
4. Social support	5.72	1.17	−.05	.16**	.02	(.88)
5. Depression	1.85	0.47	.16**	−.01	.04	−.38**	(.88)
6. Life satisfaction	4.73	1.22	−.09	.18**	.07	.48**	−.45**	(.85)
7. Number of working hours	67.99	46.19	.21*	−.01	.05	−.04	−.09	.00
8. Grades	84.22	7.99	−.06	.12**	−.05	.22**	−.12**	.18**	.02

*p < .05. **p < .01.

Academic planning

This variable was assessed with a single question: What are your plans regarding your education/studies? offering four response options: (1) planning to quit my current studies, (2) considering quitting my current studies, (3) planning to finish my current degree, and (4) Planning to continue to a higher degree after finishing my current degree.

Financial support

This variable was measured with a single question: Do you receive financial support? offering five response options: (1) not at all, (2) have minimum support, (3) have support, (4) enjoy good support, and (5) have very good/generous support.

Demographic questionnaire

The demographic questionnaire requested participants to indicate their gender, age, religion, place of birth, relationship status, employment status, number of working hours, and their grade average for the previous semester.

Procedure

The data were collected online using Google Survey. An invitation to participate in the study was disseminated on the Internet via social networks, such as Facebook and Twitter by four research assistants, three master’s students, and one doctoral student. Upon accessing the survey, participants were asked their academic status, employment status, and parental status, in order to determine their fit for participation criteria. Those meeting the criteria (active students, nonparent, and working) were then provided with the measures and were given the opportunity to enter a lottery to win an IPod Mini.

Strategy of data analysis

The proposed theoretical model (Figure 1) was tested using an SEM with AMOS 20 (Arbuckle, 2011), a preferred procedure for analyzing models with latent constructs (Baron & Kenny, 1986). The SEM consists of two main parts: the measurement model, which specifies the posited relationship of the observed items to the underlying constructs, and the structural model, which examines the potential causal dependencies between endogenous and exogenous variables.

The substantive model includes 10 latent constructs, 4 of which had single-item indicators (number of working hours, financial support, academic plans, and grades). Given that these constructs were relatively concrete, I fixed the factor loading at 1.0 and measurement error to 0.0. Descriptive statistics, correlations, and reliability coefficients are presented in Table 1.

To examine the data fit, I used the CFI, the TLI, and the RMSEA. CFI and TLI values of >.90 and RMSEA value of <.08 represent acceptable fit, whereas values of >.95 and <.06 represent a good fit (Hu & Bentler, 1999).

I performed a latent-variable structural path analysis with maximum estimation (e.g., Joreskog & Sorbom, 1995). To test the model, I included the latent variables: work salience (the 10 items of the Work Salience Scale), social support (the 12 items of the Social Support Scale), conflict relations (the two constructs of WSC and study–work conflict) and facilitation relations (the two constructs of work–study enrichment and study–work enrichment), depression (the items of the Depression Scale), and life satisfaction (the items of the Life Satisfaction Scale).

Results

Table 1 shows the means, SDs, intercorrelations, and coefficient α of the study variables. However, given the strengths of SEM, the study’s findings are presented with a focus on model fit and pathway estimates. Likewise, nonsignificant pathway estimates are not reported, as they are assumed to be nondifferent from zero.

Test of the Measurement Model

The absolute fit measures, incremental fit measures, and parsimonious fit measures were utilized to assess the overall fit of the measurement model. The model fit indices were satisfied: absolute fit measures: χ² = 599.98, p < .001, χ²/df = 6, GFI = .91, RMSEA = .09; incremental fit measures: IFI = .96, TLI = .94, CFI = .96; parsimonious fit measure: PGFI = .59, PNFI = .68, PCFI = .69. These results demonstrate a good fit of the proposed measurement model.

Test of the Structural Model

The absolute fit measures, incremental fit measures, and parsimonious fit measures were also used to analyze the fit of the proposed structural model. The model fit indices were satisfied: absolute fit measures: χ² = 505.99, p <.001, χ²/df = 4.05, GFI = .93, RMSEA = .06; incremental fit measures: IFI = .94, TLI = .92, CFI = .94; parsimonious fit measure: PGFI = .61, PNFI = .69, PCFI = .70. These results demonstrate a good fit of the proposed structural model.

In summary, the results indicated that the overall goodness of fit for the proposed research model is satisfactory. Therefore, the hypothesis that the proposed theoretical model fits with the sample data was supported by the analysis.

The test of the structural model included examining the statistical significance of the path coefficients from one latent variable to another. Figure 1 shows the resulting path coefficients of the proposed theoretical model. The following hypotheses were supported with the significant relations between the variables at the .01 significance level:

Antecedents: Hypothesis 1a: number of working hours predict conflict relations (β = .30, t value = 7.82). Hypothesis 2b: social support predicted facilitation relations (β = .16, t value = 4.03); Hypothesis 3a: financial support reduced conflict relations (β = −.13, t value = −3.45). Hypothesis 4b: work salience predicted facilitation relations (β = .33, t value = 8.82).

Hypothesis 3b was not confirmed: financial support reduced facilitation relations (β = −.13, t value = −3.45) rather than confirming our expectation that they would be enhanced by financial support.

Outcomes: Hypothesis 5a: conflict relations negatively correlated with grades (β = −.10. t value = −2.36); Hypothesis 5b: facilitation relations positively correlated with grades (β = .12, t value = 2.98). Hypothesis 6a: conflict relations diminished academic planning (β = −.16, t value = −3.14); Hypothesis 6b: facilitation relations enhanced academic planning (β = .14, t value = 2.63). Hypothesis 7a: conflict relations positively correlated with depression (β = .10, t value = 2.63) but did not negatively correlate with life satisfaction. Hypothesis 7b: facilitation relations positively correlated with life satisfaction (β = .11, t value = 2.71) but did not negatively correlate with depression.

Hypotheses 1b, 2a, and 4a were not supported. Hypotheses 7a and 7b were partially supported.

Discussion

While the participation rates of college students in the labor market is increasing in many Western countries, our understanding of the consequences of this role blending, as well as the antecedents of these outcomes, is very limited. Some studies have begun to explore the process by which work and study impact each other (e.g., Butler, 2007; Park & Sprung, 2013), but an integrative model relating to both negative and positive outcomes of role blending in multiple domains is lacking.

The current study sought to narrow this gap and examined the applicability of a recognized model in the area of work–family interface research, that of Frone (2003), to the investigation of working students. Results indicated that Frone’s (2003) taxonomy of the coexistence of conflict and facilitation relations between work and family is suitable for understanding the work–study interface. Each of these distinct and unique relations has unique antecedents and explicit health and performance outcomes. This finding suggests that perspectives of both scarcity and expansion resources coexist and should be considered simultaneously in future research on inter-role processes while examining the interface of any life roles. This integrative concept of assessing aspects of conflict and facilitation relations between any other role interfaces may enable a more precise understanding of this phenomenon, compared to an approach focusing on only one aspect at a time. This recommendation was recently applied in an investigation of the family–study interface, demonstrating the coexistence of both conflict and facilitation aspects in blending study and family (Meeuwisse, Born, & Severiens, 2011).

The coexistence of conflict and facilitation relations can explain the mixed results in the literature regarding the positive and negative influences of employment on students’ grades. The nature of the influence depends on the type of relations. It can be assumed that when positive influence on grades was found, it was driven by higher levels of facilitation between the roles, while when a negative influence on grades was reported, it was due to higher levels of facilitation between the roles.

Conflict Relations Between Work and Study

Conflict relations between work and study were found to be a significant predictor of most of the expected outcomes. These conflict relations reduced grades, diminished future academic planning, and increased depression. Just as in conflict relations between work and family, also conflict relations between work and study affected the performance and the psychological health of the individual. These negative outcomes emphasize the importance of reducing these outcomes by identifying the specific antecedents of conflict relation of any role interface (e.g., work–family, work–study, and study–family).

Results of the current study point to two significant predictors of conflict relations between work and study: number of working hours and financial support. These results suggest the importance of supplementary financial support, even for the working student, and the recommendation to regulate the number of working hours in order to reduce the negative outcomes. Number of working hours is also a significant predictor of the work–family interface, suggesting this variable is one of the key elements in managing multiple roles.

Role salience was not found to be a significant predictor as expected. This result can be explained by the fact that the majority of the participants described their work as only temporary student work. Therefore, role salience had no significant impact on conflict relations among the student participants as would be expected among working adults. Even if they attribute high importance to the worker role, they would be unlikely to invest in the job as much as they would like (due to the student role), and even if they work many hours, they are likely to perceive this situation as temporal. Furthermore, they do not necessarily perceive their current job as one they value and anticipate retaining after completing their studies. The nature of student work as temporal leads to the nonsignificant contribution of role salience to conflict relations. At the same time, role salience was found to be a significant antecedent of facilitation relations. This finding suggests that for those who attribute high importance to the work role, even temporary jobs can generate aspects of enrichment between the roles.

Facilitation Relations Between Work and Study

Blending work and study also comprise aspects of mutual enrichment between the roles. Results indicated that this type of relation increased grades and future academic planning and predicted life satisfaction. As in facilitation between work and family, also the facilitation of the work–study interface affected performance and psychological health. Facilitation relations between work and study have specific antecedents, such as financial support, and also share some antecedents with the work–family interface, such as social support and role salience. Current findings suggest that high levels of social and financial support granted to working students are predictive of high levels of facilitation between work and study that will, in turn, predict higher grades, future academic planning, and life satisfaction.

Number of working hours did not predict facilitation relations as was expected. It can be assumed that facilitation relations are more affected by one’s internal positive disposition, seeing the glass as “half full.” As a result, facilitation relations are less sensitive to external parameters, such as number of working hours. Indeed, some studies have demonstrated that personality traits, such as positive affect (Michel & Clark, 2009) and extroversion (Eby, Maher, & Butts, 2010), tend to be associated with facilitation relations between work and family.

These results imply several recommendations for students, institutions of higher education, and career counselors. The cumulative findings of the current study, combined with the results of previous studies, support the idea of blending work and study under several conditions. Work experience is essential and important for the transition from school to work (Riggert et al., 2006), for career exploration (Creed, Patton, & Prideaux, 2007), and for career adaptability (Darolia, 2014). Furthermore, since working students can anticipate several positive outcomes, this status can be recommended, especially if the student has the ability to control the number of working hours and to obtain financial and social support. Current findings also highlight the importance of social support. Teachers can serve as an important source of social support in higher education settings, especially for minority students (e.g., Cinamon, Habayeb, & Ziv, in press). Conversations with students regarding their work experiences and regarding possible contributions of their work to their studies can be helpful for them.

College career counselors can recommend to working students that they regulate their working hours, especially during exam periods, in order to ease the level of inter-role conflict. Findings highlight the need to support students, especially working students with scholarships, in order to allow them to reduce their working hours, so that the facilitating aspects of being working students can be enhanced.

Current results highlight the need to train college students how to blend roles effectively, how to manage conflict, and how to generate facilitation between work and study. Cinamon (2014) has described an intervention model aimed at enhancing young adults’ self-efficacy to manage work and family roles. The principles of her model can also be applied to training and guiding students how to manage work and study. Following her model, three components can be included in the guidance of working students: (a) clarification of work values and the importance of lifelong learning; (b) information about the work–study interface; and (c) practicing self-management skills, such as time management and generating social support.

Limitation and Conclusions

This study’s limitations stem from the sample, the design, and the method. The sample focused on Israeli college students, mostly enrolled in universities. While there is no compelling reason why the suggested model would not hold across other samples of young adults blending work and study, generalizability of the findings would be more robust with other samples from different countries.

Another potential limitation is that the results were based on cross-sectional self-report data, which increase concerns for common method bias and for conclusions regarding causality. Future research should assess the outcomes variables as well as the work–study interface from more than one source (e.g., teachers, roommates, and supervisors) and from longitudinal data (e.g., Butler & Mathews, 2009). These findings would also assist in determining the direction of causality.

In the current study, I focused on working students rather than on working adults who go to school. Future research can measure the attribution of importance to work and study in order to distinguish between students who work and working adults who go to school. Finally, more information is needed in order to expand our understanding of the broader context of students who work, such as understanding students’ motivation to work (to support their needs, to help their families, etc.), as well as the nature of their work, aspects that may affect the work–study interface.

Footnotes

Acknowledgments

The author acknowledges, with gratitude, funding received for this study from the Caesarea Foundation.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

References

Allen

T. D.

Herst

D. E. L.

Bruck

C. S.

Sutton

(2000). Consequences associated with work to-family conflict: A review and agenda for future research. Journal of Occupational Health Psychology, 5, 278–308.

Arbuckle

J. L.

(2011). Amos (Version 20.0) [Computer Program]. Chicago, IL: SPSS.

Amatea

E. S.

Cross

E. G.

Clark

J. E.

Bobby

C. L.

(1986). Assessing the work and family role expectation of career-oriented men and women: The Life-Role Salience Scale. Journal of Marriage and Family, 18, 831–838.

Bachman

J. G.

Staff

O’Malley

P. M.

Schulenberg

J. E.

Freedman-Doan

(2011). Twelfth-grade student work intensity linked to later educational attainment and substance use: New longitudinal evidence. Developmental Psychology, 47, 344–363.

Baron

R. M.

Kenny

D. A.

(1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

Barke

Braidford

Houston

Hunt

Lincoln

Morphet

Walker

(2000). Students in the labour market: Nature, extent and implications of term-time employment among University of Northumbria undergraduates (Vol. 215). Great Britain, England: Department for Education and Employment.

Bellavia

G. M.

Frone

M. R.

(2005). Work-family conflict. In Barling

Kelloway

E. K.

Frone

M. F.

(Eds.), Handbook of work stress (pp. 113–148). Thousand Oaks, CA: Sage.

Broadbridge

Swanson

(2005). Earning and learning: How term time employment impacts on students’ adjustment to university life. Journal of Education and Work, 18, 235–249.

Broadbridge

Swanson

(2006). Managing two roles: A theoretical study of students’ employment whilst at university. Community, Work and Family, 9, 159–179.

10.

Bronfenbrenner

(1989). Ecological systems theory. Annals of Child Development, 6, 187–249.

11.

Butler

A. B.

(2007). Job characteristics and college performance and attitudes: A model of work-school conflict and facilitation. Journal of Applied Psychology, 92, 500–510.

12.

Butler

A. B.

Dodge

K. D.

Faurote

E. J.

(2010). College student employment and drinking. Journal of Occupational Health Psychology, 15, 291–303.

13.

Butler

A. B.

Matthews

(2009, 4). A daily study of work-school conflict and enrichment. Poster presented at the Society for Industrial-Organizational Psychology Conference, New Orleans, LA.

14.

Cinamon

R. G.

(2009). Role salience, social support, and work–family conflict among Jewish and Arab female teachers in Israel. Journal of Career Development, 36, 139–158.

15.

Cinamon

R. G.

(2014). “The synergy project”: A group career counseling intervention to enhance work-family management. In Hartung

P. J.

Savickas

M. L.

Walsh

W. B.

(Eds.), APA handbook of career intervention, Vol. 2: Applications (pp. 413–425). Washington, DC: American Psychological Association.

16.

Cinamon

R. G.

Habayeb

Ziv

(in press). The meaning of work and studies among Israeli Arab women studying early childhood education. Journal of Higher Education.

17.

Cinamon

R. G.

Rich

(2002). Profiles of attribution of importance to life roles and their implications for the work-family conflict. Journal of Counseling Psychology, 49, 212–220.

18.

Cinamon

R. G.

Rich

(2010). Work family relations: Antecedents and outcomes. Journal of Career Assessment, 18, 59–70.

19.

Creed

P. A.

Patton

Prideaux

L. A.

(2007). Predicting change over time in career planning and career exploration for high school students. Journal of Adolescence, 30, 377–392.

20.

Curtis

Shani

(2002). The effect of taking paid employment during term-time on students’ academic studies. Journal of Further and Higher Education, 26, 37–41.

21.

Darolia

(2014). Working (and studying) day and night: Heterogeneous effects of working on the academic/performance of full-time and part-time students. Economics of Education Review, 38, 38–50.

22.

Derous

Ryan

A. M.

(2008). When earning is beneficial for learning: The relation of employment and leisure activities to academic outcomes. Journal of Vocational Behavior, 73, 118–131.

23.

Diener

E. D.

Emmons

R. A.

Larsen

R. J.

Griffin

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

24.

Eby

L. T.

Maher

C. P.

Butts

M. M.

(2010). The intersection of work and family life: The role of affect. Annual Review of Psychology, 61, 599–622.

25.

Ford

Heinen

Langkamer

(2007). Work and family satisfaction and conflict: A meta-analysis of cross-domain relations. Journal of Applied Psychology, 92, 57–80.

26.

Frone

M. R.

(2003). Work-family balance. In Quick

J. C.

Tetrick

L. E.

(Eds.), Handbook of occupational health psychology (pp. 143–162). Washington, DC: American Psychological Association.

27.

Furr

S. R.

Elling

T. W.

(2000). The influence of work on college student development. NASPA Journal, 37, 454–470.

28.

Greenhaus

J. H.

Beutell

N. J.

(1985). Source of conflict between work and family roles. Academy of Management Review, 10, 76–88.

29.

Greenhaus

J. H.

Powell

G. N.

(2006). When work and family are allies: A theory of work-family enrichment. Academy of Management Review, 31, 72–92.

30.

Grzywacz

J. G.

Bass

B. B.

(2003). Work, family, and mental health: Testing different models of work–family fit. Journal of Marriage and the Family, 65, 248–262.

31.

Hall

(2010). The work–study relationship: Experiences of full‐time university students undertaking part‐time employment. Journal of Education and Work, 23, 439–449.

32.

Hammer

L. B.

Cullen

J. C.

Neal

M. B.

Sinclair

R. R.

Shafiro

M. V.

(2005). The longitudinal effects of work-family conflict and positive spillover on depressive symptoms among dual-earner couples. Journal of Occupational Health Psychology, 10, 138–154.

33.

Hecht

T. D.

McCarthy

J. M.

(2010). Coping with employee, family, and student roles: Evidence of dispositional conflict and facilitation tendencies. Journal of Applied Psychology, 95, 631.

34.

Hill

E. J.

(2005). Work-family facilitation and conflict, working fathers and mothers, work-family stressors and support. Journal of Family issues, 26, 793–819.

35.

Hodgson

Spours

(2001). Part-time work and full-time education in the UK: The emergence of a curriculum and policy issue. Journal of Education and Work, 14, 373–388.

36.

Hong

Malik

M. L.

Lee

M. K.

(2003). Testing configural, metric, scalar, and latent mean invariance across genders in sociotropy and autonomy using a non-Western sample. Educational and Psychological Measurement, 63, 636–654.

37.

Hoobler

J. M.

Wilson

(2010). Do workers who experience conflict between the work and family domains hit a “glass ceiling?”: A meta-analytic examination. Journal of Vocational Behavior, 77, 481–494.

38.

L. T.

Bentler

P. M.

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

39.

Joreskog

K. G.

Sorbom

(1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Hillsdale, England: Lawrence Erlbaum Associates.

40.

Katz

Kahn

R. L.

(1978). The social psychology of organizations (2nd ed.). New York, NY: Wiley.

41.

King

Bannon

(2002). At what cost? The price that working students pay for a college education. Washington, DC: The State PIRGs’ Higher Education Project. Retrieved from http://www.pirg.org/highered/atwhatcost4_16_02.pdf

42.

Lammers

W. J.

Onwuegbuzie

A. J.

Slate

J. R.

(2001). Academic success as a function of the sex, class, age, study habits, and employment of college students. Research in the Schools, 8, 71–81.

43.

Hicks

M. H. R.

(2010). The CES-D in Chinese-American women: Construct validity, diagnostic validity for major depression, and cultural response bias. Psychiatry Research, 175, 227–232.

44.

Markel

K. S.

Frone

M. R.

(1998). Job characteristics, work–school conflict, and school outcomes among adolescents: Testing a structural model. Journal of Applied Psychology, 83, 277–287.

45.

Michel

J. S.

Clark

M. A.

(2009). Has it been affect all along? A test of work-to-family and family-to-work models of conflict, enrichment, and satisfaction. Personality and Individual Differences, 47, 163–168.

46.

Michel

J. S.

Mitchelson

J. K.

Pichler

Cullen

K. L.

(2010). Clarifying relationships among work and family social support, stressors, and work–family conflict. Journal of Vocational Behavior, 76, 91–104.

47.

McNall

L. A.

Michel

J. S.

(2011). A dispositional approach to work-school conflict and enrichment. Journal of Business and Psychology, 26, 397–411.

48.

McNall

L. A.

Nicklin

J. M.

Masuda

A. D.

(2010). A meta-analytic review of the consequences associated with work-family enrichment. Journal of Business Psychology, 25, 381–396.

49.

Meeuwisse

Born

Severiens

S. E.

(2011). The family-study interface and academic outcomes: Testing a structural model. Journal of Educational Psychology, 10 3, 982–990.

50.

Muthén

L. K.

Muthén

B. O.

(2011). Mplus (Version 6.11) [Computer software and manual]. Los Angeles, CA: Author.

51.

Nishiyama

Ozaki

Iwata

(2009). Practice‐based depression screening for psychiatry outpatients: Feasibility comparison of two‐types of center for epidemiologic studies depression scales. Psychiatry and Clinical Neurosciences, 63, 632–638.

52.

Paschall

Flewelling

Russell

(2004). Why is work intensity associated with heavy alcohol use among adolescents? Journal of Adolescent Health, 34, 79–87.

53.

Pavot

Diener

Colvin

C. R.

Sandvik

(1991). Further validation of the satisfaction with life scale: Evidence for the cross-method convergence of well-being measures. Journal of Personality Assessment, 57, 149–161.

54.

Park

Sprung

(2013). Work-school conflict and health outcomes: Beneficial resources for working college students. Journal of Occupational Health Psychology, 18, 384–394.

55.

Radloff

L. S.

(1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.

56.

Riggert

Boyle

Petrosko

Ash

Rude-Parkins

(2006). Student employment and higher education: Empiricism and contradiction. Review of Educational Research, 76, 63–92.

57.

Robbins

W. M.

(2010). Learning with hard labor: University students as workers. Australian Bulletin of Labor, 36, 103–120.

58.

Romanov

Tur-Sinai

Zussman

(2007). Employment of undergraduate students and its implications on their course of studies (Working paper series no 31). Jerusalem, Israel: Israel Central Bureau of Statistics.

59.

Rowley

L. L.

Hurtado

(2002). The non-monetary benefits of an undergraduate education. Ann Arbor: University of Michigan Center for Study of Higher and Post-secondary Education.

60.

Staff

Mortimer

J. T.

Uggen

(2003). Work and leisure in adolescence. In Mortimer

J. T.

Shanahan

(Eds.), Handbook of the life course (pp. 429–450). New York, NY: Kluwer Academic/Plenum Press.

61.

Sieber

S. D.

(1974). Toward a theory of role accumulation. American Sociological Review, 39, 567–578.

62.

Sumer

H. C.

Knight

P. A.

(2001). How do people with different attachment styles balance work and family? A personality perspective on work-family linkage. Journal of Applied Psychology, 86, 653–663.

63.

Trockel

M. T.

Barnes

M. D.

Egget

D. L.

(2000). Health-related variables and academic performance among first-year college students: Implications for sleep and other behaviors. Journal of American College Health, 4 9, 125–131

64.

Volkwein

J. F.

Schmonksy

R. J.

Y. S.

(1989). The impact of employment on the academic achievement of full-time community college students. Paper presented at the Association of Institutional Research Annual Forum, Baltimore, MA.

65.

Warren

S. R.

(2002). Stories from the classrooms: How expectations and efficacy of diverse teachers affect the academic performance of children in poor urban schools. Educational Horizons, 80, 109–116.

66.

Wayne

J. H.

Randel

A. E.

Stevens

(2006). The role of identity and work-family support, work-family enrichment and its work-related consequences. Journal of Vocational Behavior, 69, 445–461.

67.

Whiston

S. C.

Campbell

W. L.

Maffini

C. S.

(2012). Work-family balance: A counseling psychology perspective. In Fouad

N. A.

Carter

J. A.

Subich

L. M.

(Eds.), APA handbook of counseling psychology, Vol. 2: Practice, interventions, and applications (pp. 75–102). Washington, DC: American Psychological Association. doi:https://dx-doi-org.web.bisu.edu.cn/10.1037/13755-004

68.

Whiston

S. C.

Cinamon

R. G.

(2015). The work–family interface: Integrating research and career counseling practice. The Career Development Quarterly, 63, 44–56.

69.

Zimet

G. D.

Dahlem

N. W.

Zimet

S. G.

Farley

G. K.

(1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 51, 30–41.