Examining the constructs of work-to-family enrichment andpositive spillover

Aline D. Masuda a,⁎, Laurel A. McNall b, Tammy D. Allen c, Jessica M. Nicklin d

a EADA Business School, Barcelona, Spainb The College at Brockport, State University of New York, Brockport, NY, USAc University of South Florida, Tampa, FL, USAd The University of Hartford, West Hartford, CT, USA

a r t i c l e i n f o a b s t r a c t

Article history:Received 28 March 2011Available online 12 June 2011

This paper reports three studies examining construct validity evidence for two recentlydeveloped measures of the positive side of the work–family interface: work-to-family positivespillover (WFPS; Hanson, Hammer, & Colton, 2006) and work-to-family enrichment (WFE;Carlson, Kacmar, Wayne, & Grzywacz, 2006). Using confirmatory factor analysis, the resultsfrom the first two studies indicate that the best fitting model distinguishes between WFPS andWFE, each with three sub-dimensions. However, these studies also showed that several itemsmeasuring WFE cross-loaded onto factors measuring WFPS. Results from the discriminantanalyses showed that the sub-dimensions ofWFPS andWFE uniquely predicted job satisfactionand life satisfaction. Yet, when WFPS and WFE were examined as one dimension, the measureof WFE predicted life satisfaction, but the measure of WFPS did not add to the prediction aboveWFE. Across both studies, WFE mediated the relationship betweenWFPS with both job and lifesatisfaction. Lastly, Study 3 provides some evidence of the content adequacy of these items;however, several items overlapped in content. These results suggest that enrichment andpositive spillover are distinct but related constructs, each with three sub-dimensions. Further,more work is needed to refine the measurement of WFE and WFPS; however, this researchhelps advance our understanding of the positive side of the work–family interface.

© 2011 Elsevier Inc. All rights reserved.

Keywords:Work–family interfaceWork–family enrichmentWork–family positive spilloverJob satisfactionLife satisfaction

Constructs defining work–family positive synergies, such as work–family enhancement (Ruderman, Ohlott, Panzer, & King,2002)work–family positive spillover (Hanson, Hammer, & Colton, 2006),work–family enrichment (Greenhaus & Powell, 2006), andwork–family facilitation (Frone, 2003) have received increased attention in the work–family literature over the past several years.The proliferation of constructs used to describe the positive aspects of combining work and family led to confusion about themeaning of the positive side of the work–family interface (Wayne, 2009). Although researchers contend that these constructsconceptually and operationally differ from each other (Greenhaus & Powell, 2006; Wayne, 2009), to our knowledge, there havebeen no empirical studies based on validated measures that support this claim.

To advance research and theory with regard to the positive side of the work–family interface, it is important to establish widelyaccepted definitions and validated measures of relevant constructs. Recently, Carlson, Kacmar, Wayne, and Grzywacz (2006) andHanson et al. (2006) published and validated comprehensive measures intended to operationalize two constructs associated withthe positive side ofwork and family; namely,work–family enrichment andwork–family positive spillover, respectively. Using thesetwo validated measures we can test whether these constructs are related, different, or similar from each other. This clarification isimportant because the use of arbitrary terms to describe similar constructs can impede theoretical development (Locke, 2003).

Journal of Vocational Behavior 80 (2012) 197–210

⁎ Corresponding author at: EADA—Escuela de Alta Dirección y Administración, C/Aragó, 204-08011 Barcelona, Spain. Fax: +34 933 237 317.E-mail addresses: amasuda@eada.edu, alinemasuda@eada.edu (A.D. Masuda), lmcnall@brockport.edu (L.A. McNall), tallen@mail.usf.edu (T.D. Allen),

nicklin@hartford.edu (J.M. Nicklin).

0001-8791/$ – see front matter © 2011 Elsevier Inc. All rights reserved.doi:10.1016/j.jvb.2011.06.002

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To this end we conducted three studies with two overall objectives. The first objective was to investigate whether Carlson etal.'s (2006) work-to-family enrichment (WFE) measure can be distinguished from Hanson et al.'s (2006) work-to-family positivespillover (WFPS) measure. Specifically, we examined construct dimensionality, content adequacy, and incremental validity ofWFPS and WFE in predicting job satisfaction and life satisfaction. We opted to focus on the work-to-family direction given thatprevious research has found that this direction is most strongly linked to important work outcomes (McNall, Nicklin, & Masuda,2010; Wayne, Musisca, & Fleeson, 2004; Wayne, Randel, & Stevens, 2006). We chose to focus on job and life satisfaction becausethese variables are among the most commonly studied in the organizational behavior literature (Spector, 1997) and they arerelated to relevant workplace variables (Schleier, Hansen, & Fox, 2010). By empirically examining if these constructs are differentwe can advance our theoretical understanding of the positive side of the work–family interface.

The second objective was to examine the relationship between WFE and WFPS. We explored two competing mediationquestions: whether WFE (WFPS) is the intervening variable between WFPS (WFE) and job satisfaction. In Study 1 we examinedjob satisfaction as the dependent variable. We replicated and expanded our results using a second sample (Study 2) by examiningjob satisfaction and life satisfaction. Lastly, we examined the content adequacy of the items (Study 3). Demonstrating reasons forwhy these constructs are different from each other may allow us to move toward the development of a nomological network thatcaptures the positive side of work–family interface. This can help reduce ambiguity and move the theory of positive work familyinteractions forward.

Defining the positive side of the work–family interface

As previously mentioned, researchers have used a variety of constructs and definitions to describe positive work–familyinteractions. In this study we focus on enrichment and positive spillover. Drawing on work by Edwards and Rothbard (2000),positive spillover is defined as “the transfer of positively valenced affect, skills, behaviors, and values from the originating domain tothe receiving domain, thus having beneficial effects on the receiving domain” (1, p. 251). Work–family enrichment describes “theextent to which experiences in one role improves the quality of life in the other role (Greenhaus & Powell, 2006 p. 73).

Wayne (2009) developed a conceptual framework intended to explain the differences between enrichment and positivespillover. Specifically, she argues that positive spillover occurs when an individual transfers the gains from one domain to a seconddomain. For example, the multitasking skills a person gains at work may be transferred and applied to the home domain. In orderforwork–family enrichment to occur, Wayne says that the individual must successfully apply the gains to the other domain. Thus, ifthe multitasking skills developed at work result in a higher quality of life at home, then work-to-family enrichment has occurred.According to Wayne's model, for enrichment to occur, the individual will not only have to experience resource gains transferredfrom one domain to another (positive spillover), but will also have to perceive that the resource transfer improved performance orquality of life (enrichment). Thus, Wayne conceptualizes spillover and enrichment as “overlapping but distinct constructs” (p. 16)and other researchers agree (Carlson et al., 2006; Hanson et al., 2006).

Despite this supposition, to date there has been no empirical research examining the relationship between positive spilloverand enrichment using Carlson et al.'s (2006) and Hanson et al.'s (2006) measures. Because these measures were developed basedon the distinct definitions of enrichment and positive spillover discussed above, we predict that differentiating these twoconstructs (WFPS and WFE) will lead to a better fitting model than will treating enrichment and positive spillover asinterchangeable constructs.

Hypothesis 1. A two factor model that differentiates WFPS from WFE will be a better fit than a one factor model.

Theoretical development and sub-dimensions of WFE and WFPS

Both Hanson et al. and Carlson et al. cite Greenhaus and Powell (2006) theory of work–family enrichment to describe theirconstructs and create their measures. Greenhaus and Powell's conceptual framework was based in part on role enhancement theory,which is the dominant theoretical perspective used to explain why individuals perceive benefits from multiple role memberships(Marks, 1977; Sieber, 1974). According to Sieber (1974), people earn various rewards by partaking in multiple domains, such as: (1)greater role privileges, (2) lower strain in one role due to a buffering effect of other roles, (3) greater status enhancement, and (4)personality enrichment (e.g., greater flexibility). Based on this logic, Greenhaus and Powell (2006) offeredfive categories of resourcesthatmay be acquired in one role to improve performance in the other role, either directly (instrumental path) or indirectly (affectivepath). These resources include skills and perspectives (e.g., interpersonal skills, coping skills, respecting individual differences),psychological and physical resources (e.g., self-efficacy, hardiness, optimism), social-capital resources (e.g., networking, information),flexibility (e.g., flexible work arrangements), andmaterial resources (e.g., money, gifts). For example, the resources employees gain intheir work role (e.g., flexibility) may directly improve their performance in their family role.

Both Hanson et al.'s and Carlson et al.'s scales include items that reflect instrumental and affective paths. However, notonly do these scales purportedly capture different constructs, there are also variations in the types of instrumental andaffective resources that are transferred from one domain to another in the respective constructs. For example, Carlson et al.'sWFE scale captures development (e.g., skills, knowledge, behaviors), affect (e.g., positive emotional state or attitude), andcapital (e.g., security, confidence) resources, which can be transferred fromwork to family and result in improved functioningas a family member. Hanson et al.'s WFPS scale includes an instrumental path that encompasses behavior-based resources(e.g., habits) and value-based resources (e.g., money), in addition to an affective path (e.g., mood). However, the Hanson et al.

198 A.D. Masuda et al. / Journal of Vocational Behavior 80 (2012) 197–210

measure was developed with the intention of capturing only transfer of resources and not the positive impact the resourceshave on the other domain.

Based on Greenhaus and Powell's (2006) work–family enrichment model, a model distinguishing between instrumental andaffective path should provide better fit than a model that does not distinguish between these two paths. Moreover, based on thedifferent resource dimensions proposed by Carlson et al. and Hanson et al. we hypothesize that the best fitting model will furtherdifferentiate the instrumental dimensions into sub-dimensions of resource transfer. That is, for Hanson et al.'s scale, there will betwo sub-dimensions that capture instrumental resources (i.e., value and behavior) and for Carson et al.'s scale, there will be twosub-dimensions that capture instrumental resources (i.e., capital, development).

Hypothesis 2. A four-factor model (discriminating instrumental and affect for each scale) fits the data better than a two-factormodel.

Hypothesis 3. A six- factor model (discriminating each resource transferred for each construct) fits the data better than a four-factor model (see Fig. 1).

Incremental validity

The usefulness of maintaining WFPS and WFE as two separate constructs in the work–family literature can be supported bydemonstrating that they both provide incremental validity over the other in the prediction of outcomes. To test for incrementalvalidity, we examined ifWFPS andWFE each uniquely relate to job satisfaction (Study 1 and Study 2) and to life satisfaction (Study2). A recent meta-analysis by McNall et al. (2010) demonstrated that positive work-to-family interactions relate positively to jobsatisfaction (ρ=.34) and life satisfaction (ρ=.32). Further, McNall et al. found that bothWFE andWFPSwere significantly relatedto job and life satisfaction.

Despite these findings the incremental validity of the two constructs remains untested. Given that the WFE and WFPS scaleswere constructed based on the assumption that enrichment and positive spillover are unique but related constructs (Hanson et al.,2006; Wayne, 2009), and assuming that both scales measure distinct types of resources being transferred from one domain toanother, we predict that bothWFPS andWFEwill contribute uniquely to the variance associated with job and life satisfaction. Thisis because each concept serves a different and important function to improve life and job satisfaction. That is, satisfaction canimprove if transfer of resources occurs (i.e. spillover) and if individuals perceive this transfer as something that improves thefamily domain (i.e. enrichment).

Hypothesis 4. WFPS and WFE each contribute unique variance associated with job satisfaction.

Hypothesis 5. WFPS and WFE each contribute unique variance associated with life satisfaction (Study 2 only).

Relationship between WFPS and WFE

Although most researchers seem to agree thatWFPS andWFE are distinct constructs (Greenhaus & Powell, 2006; Wayne, 2009),there is disagreement as to why they are different from each other. Carlson et al. (2006) argued that the distinction between work–family enrichmentand spillover is, “that experiences inonedomain canbe transferred (i.e., spillover) yet not improve thequalityof lifeor individual performance in the other role” (p. 133). Hence, Carlson et al. argued that spillover can occurwithout enrichment and thisfits with Wayne's (2009) aforementioned clarification. However, Powell and Greenhaus (2010) argued that positive spillover isdifferent than work–family enrichment because the focus is “on the specific transfer of positive affect, values, skills and behaviors(Hanson et al., 2006) rather than transfer of a broad set of resources including psychological, social capital, and material resources”(p. 15). For Powell andGreenhaus, the application of resources fromonedomain to another is not a sufficient condition for spillover tooccur. Instead, positive spillover occurs when the resources generate positive effects in the other domain. Therefore, Powell andGreenhaus argue that thekeydistinctionbetween thesedomains is the specificity level of resources being transferred. Given these twoopposing views, we test competing research questions to examinewhether enrichment (positive spillover) is a proximal predictor ofjob and life satisfaction and a mediator between spillover (enrichment) and job and life satisfaction.

Research question 1a. Does WFE mediate the relationship of WFPS with job satisfaction and life satisfaction?

Research question 1b. Does WFPS mediate the relationship of WFE with job satisfaction and life satisfaction?

Study 1

Methodology

Participants and proceduresParticipants were recruited from the Study Response internet database Stanton and Weiss (2002). In exchange for

participation, respondents were entered into a random drawing for gift certificates. E-mail invitations were sent to 1,700 database

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E20

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Fig. 1. Six factor model of work-to-family enrichment andwork-to-family positive spillover. Factor 1=work-to-family enrichment development, Factor 2=work-to-family enrichment affect , Factor 3=work-to-family enrichment capital , Factor 4=work-to-family positive spillover affect , Factor 5=work-to-family positivespillover behavior, Factor 6=Work-to-family positive spillover values.

200 A.D. Masuda et al. / Journal of Vocational Behavior 80 (2012) 197–210

members who indicated that they were at least 18 years old and employed. The final sample included 220 working adults (107women, 96 men, 17 unknown), resulting in a response rate of approximately 12.9%. The sample had a mean age of 37.39 years,SD=11.32. Seventy percent reported living with a spouse/partner, and 53.6% of participants did not have children, 21.4% had onechild, and 24.7% had two or more children. Fourteen and a half percent of the sample worked less than 25 hours per week, 49.5%worked between 25 to 40 hours a week, and 35.9% worked more than 40 hours per week.

MeasuresThe survey questions weremixed across different constructs and dimensions to avoid response bias. Participants were asked to

indicate agreement with each item using a 5-point Likert scale (1=strongly disagree to 5=strongly agree). Table 1 provides theitems for WFPS and WFE. Reliabilities are reported in Table 2.

Work-to-family positive spillover. The 11-item Hanson et al. (2006) WFPS scale was used.

Work-to-family enrichment. The 9-item (Carlson et al., 2006) WFE scale was used.

Job satisfaction. The 3-item scale (Spector et al., 2004) was used to assess job satisfaction. A sample item was “In general, I like mywork.”

Control variables. We examined gender, age, education, marital status, number of children, and number of hours per week workedas controls.

Table 1Items included in the analyses. a,b,c

Types of scales Type of items Study1 Study2 Study3correct

Study 3incorrect

Work-to-family enrichment (Carlson et al., 2006)1. Helps me to understand different viewpoints and this helps me

be a better family member.V1 (development) X 63% 37%

2. Helps me to gain knowledge and this helps me be a betterfamily member.

V2 (development) X 46% 54%

3. Helps me acquire skills and this helps me be a better family member. V3 (development) X X 46% 54%4. Puts me in a good mood and this helps me be a better family member. V4 (affect) X 48% 52%5. Makes me feel happy and this helps me be a better family member. V5 (affect) X 66% 34%6. Makes me cheerful and this helps me be a better family member. V6 (affect) X 44% 56%7. Helps me feel personally fulfilled and this helps me be

a better family member.V7 (capital) X X 57% 43%

8. Provides me with a sense of accomplishment and this helps me bea better family member.

V8 (capital) X X 68% 32%

9. Provides me with a sense of success and this helps me be a betterfamily member.

V9 (capital) 77% 23%

Work-to-Family Positive Spillover (Hanson et al., 2006)1. When things are going well at work, my outlook regarding family

life is improved.V10 (Affect ) X X 36% 64%

2. Being in a positive mood at work helps me to be in a positive moodat home.

V11 (Affect) X 72% 28%

3. Being happy at work improves my spirits at home. V12 (Affect) X X 66% 34%4. Having a good day at work allows me to be optimistic with my family. V13 (Affect ) 53% 47%5. Skills developed at work help me in my family life. V14 (Behavioral) X 52% 48%6. Successfully performing tasks at work helps me to more effectively

accomplish family tasks.V15 (Behavioral) X X 35% 65%

7. Behaviors required by my job lead to behaviors that assist me in myfamily life.

V16 (Behavioral) 64% 36%

8. Carrying out my family responsibilities is made easier by usingbehaviors performed at work.

V17 (Behavioral) X X 45% 55%

9. Values developed at work make me a better family member. V18 (Value) 62% 38%10. I apply the principles of my workplace values in family situations. V19 (Value) X 66% 34%11. Values that I learn through my work experiences assist me in fulfilling

my family responsibilities.V20 (Value) 51% 49%

a “X” indicates that the item cross-loaded onto other factors.b Bolded items are problematic across all three studies.c Correct means number of times items were mapped correctly. Incorrect means number of times items were mapped incorrectly.

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Results

See Table 2 for correlations and descriptive statistics for Study 1. Prior to the analysis, we examined the variables for accuracy ofdata entry, missing values, and the fit between their distributions and the assumptions of multivariate analysis. The data were freefrom any multivariate outliers.

To test Hypotheses 1–3 we conducted Confirmatory Factor Analyses (CFA) using EQS and maximum likelihood estimationwith raw data as input to compare the fit of four a priori models. We chose CFA instead of Exploratory Factor Analyses (EFA)because CFA is more appropriate to test a priori hypotheses than is EFA (Raykov & Marcoulides, 2006). To evaluate the fit ofthe models the incremental fit index (IFI), comparative fit index (CFI), Standardized rood mean square residual (SRMR), androot mean square error of approximation (RMSEA) (Bentler, 1995; Hu & Bentler, 1999) were examined, (see Table 3). A valueof .90 for the IFI and the CFI and .06 for the RMSEA indicates a good fit between the hypothesizedmodel and the observed data(Bentler, 1995).

First, we tested a one-factor model, in which all items in Table 1 were indicative of a single factor. Second, we tested a two-factormodel, withWFE items as indicative of aWFE factor, and theWFPS items as indicative of aWFPS factor (see Fig. 1). Third, wetested a four-factor model in which items measuring work-to-family development and work-to-family capital were loaded ontoan “instrumental WFE” latent factor and items measuring work-to-family affect loaded onto an “affect WFE” latent factor.Similarly, two other factors were created in which itemsmeasuring behavior-based and value-based positive spillover loaded ontoan “instrumental WFPS” latent factor and items measuring affect-based positive spillover loaded onto an “affect WFPS” latentfactor. The final model tested Hanson et al.'s (2006) three factor model and Carlson et al.'s (2006) three factor model. In all cases,the latent variables were correlated and errors were uncorrelated.

Table 3 shows that the six factor model fit the data significantly better than the one-factor, two-factor, and four-factor models,supporting Hypotheses 1–3. However, the RSMEA score of .07 from the six-factor model showed that the best model did not resultin optimal fit (Hu & Bentler, 1999). For this reason, we also looked at the modification indexes based on the LM test. Resultsshowed that 10 items loaded across different factors (see Table 4 for items that cross-loaded). Subsequently, we tested the six-factor model without the cross-loading items and results showed a significantly better fitting model (see Table 3).

Table 2Descriptive statistics and correlations among study variables (Study 1 data, N=220).a

M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1. Gender 1.53 .50 –

2. Education 4.40 1.63 − .08 –

3. Working hours 5.82 1.92 − .24** .13 –

4. Age 37.39 11.32 − .07 .03 − .12 –

5. Children .82 1.08 .15* − .01 .11 − .13 –

6. Marital status .70 .46 .20** .08 .05 .07 .29** –

7. WFPS 3.57 .70 .05 .04 − .04 .17* .11 0.09 (.92)8. WFE 3.58 .80 .07 .05 − .04 .18** .09 0.11 .84** (.94)9. WFE development 3.59 .85 .04 .01 .03 .12 .10 0.12 .78** .90** (.85)10. WFE affect 3.54 .90 .07 .07 − .09 .20** .07 0.10 .74** .93** .73** (.87)11. WFE capital 3.62 .83 .08 .05 − .03 .21** .09 0.08 .82** .92** .78** .82** (.88)12. WFPS affect 3.90 .66 .07 .04 .02 .14* .11 0.12 .82** .62** .56** .51** .65** (.82)13. WFPS behavior 3.39 .84 .02 .07 − .05 .14 .10 0.07 .93** .81** .76** .72** .77** .62** (.88)14. WFPS value 3.37 .87 .04 .01 − .09 .16* .10 0.06 .91** .82** .76** .75** .77** .61** .83** (.85)15. Job satisfaction 3.78 .83 .15* − .03 − .02 .18* .06 0.06 .53** .60** .48** .58** .58** .42** .45** .56** 1

*pb .05, **pb .01.a Alpha coefficients are presented in parentheses. All positive spillover and enrichment items range from 1–5. Higher scores indicate more spillover/enrichment.

WFE=work-to-family enrichment; WFPS=work-to-family positive spillover.

Table 3Comparison of Work-to-Family Enrichment and Positive Spillover Factor Structures (Study 1 data). a,b,c,d,e

Structure χ2 Δχ2 df Δdf IFI CFI SRMR RMSEA RMSEA confidence interval

One factor 600** 170 .86 .86 .06 .11 .10–.22Two factor 519** 81** 169 1 .89 .89 .06 .10 .09–.11Four factor 378** 140** 164 5 .93 .93 .05 .08 .07–.09Six factor 323** 55** 155 9 .95 .95 .05 .07 .06–.09Six factorsc 31** 292** 20 135 .99 .99 .02 .05 .00–.08Six factorsd 102** 71** 50 30 .97 .97 .07 .07 .05–.09

a All x2 and Δχ2values are significant at pb .001.b IFI=incremental fit index; CFI=comparative fit index; RMSEA=root-mean-square error of approximation.c Analyses with cross items deleted.d Analyses done with items that cross loaded in all studies deleted.

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To test Hypothesis 4, which stated that WFE would predict unique variance associated with job satisfaction above and beyondthat of WFPS, we ran hierarchical regression using the overall factors of WFE and WFPS (including the cross-loading items), andeach of the sub-dimensions of WFE andWFPS. The control variables were entered into the first step, WFPS in the second step, andWFE in the third step. Results showed that after introducing the control variables in the first step, WFPS contributed uniquely tothe variance associated with job satisfaction, ΔR²=.29, F (1,195)=86.56, pb .01. Further, WFE contributed uniquely to thevariance associated with job satisfaction above that of WFPS, ΔR²=.07, F (1,194)=23.42 pb .01. We conducted a similar analysis,except we reversed the order (i.e.,WFE in the second step after the controls andWFPS in the third step).WFE contributed uniquelyto the variance of job satisfaction, ΔR²=.35, F (1,195)=117.73, pb .01. However, WFPS did not add to the prediction of jobsatisfaction above WFE, ΔR²=.01, F (1, 194)=1.74, pb .01. Thus, using the aggregated variable, Hypothesis 4 was partiallysupported. Specifically, WFE contributed above and beyond WFPS, but WFPS did not add to prediction above WFE.

We conducted similar regression analyses using the WFE sub-dimensions and the WFPS sub-dimensions. Results showed thatWFE had an incremental relationship above that of WFPS, ΔR²=.07, F (3,194)=8.72, pb .01. However, WFPS sub-dimensions alsocontributed uniquely to the prediction of job satisfaction above that of WFE, ΔR²=.04, F (3,194)=4.70, pb .01. Based on the sub-factors, both WFE and WFPS related uniquely to job satisfaction.

To address the research questions, we used the causal step approach to test mediation proposed by Baron and Kenny (1986).Specifically mediation occurs when (1) the IV is significantly related to the mediator, (2) the IV is significantly related to the DV inthe absence of the mediator, (3) the mediator is significantly related to the DV, and (4) relationship between the IV on the DVdecreases upon the addition of the mediator to the model. Table 5 reports that WFE mediated the WFPS - job satisfactionrelationship. On the other hand, WFPS was not a mediator of WFE-job satisfaction relationship.

Discussion of Study 1

The Study 1 results support the contention that WFE andWFPS are distinct yet related constructs. Specifically, results from theCFA showed that the 6 factor model was the best fitting model. This model differentiates WFE and WFPS and the original sub-dimensions proposed by Hanson et al. (2006) and Carlson et al. (2006). The results also showed that both the sub-dimensions ofWFE and the sub-dimensions of WFPS uniquely predicted job satisfaction. However, the aggregated measure of WFPS did notpredict job satisfaction aboveWFE. Instead,WFEwas amediator between theWFPS and job satisfaction relationship. Furthermore,there were a significant number of cross-loadings. These results should be interpreted with caution given that the response ratewas 13%. To help determine if these findings generalize, we conducted a second study using life satisfaction and job satisfaction asdependent variables.

Table 4Cross Loadings into the 6 Factors (Study1) a,b.

Items Factors

Work-to-familyenrichment

Work-to-family spillover

F1WFED

F2WFEA

F3WFEC

F4WFPSA

F5WFPSB

F6WFPSV

V1. Helps me to understand different viewpoints and this helps me be a better family member. 1 .20V2. Helps me to gain knowledge and this helps me be a better family member. .81V3. Helps me acquire skills and this helps me be a better family member. .55 .66 .34V4. Puts me in a good mood and this helps me be a better family member. .29 .61 .02 .02V5. Makes me feel happy and this helps me be a better family member. .41 .80 − .24 − .04 − .11V6. Makes me cheerful and this helps me be a better family member. .94V7. Helps me feel personally fulfilled and this helps me be a better family member. − .24 1.10V8. Provides me with a sense of accomplishment and this helps me be a better family member. .23 .64V9. Provides me with a sense of success and this helps me be a better family member. .81V10.When things are going well at work, my outlook regarding family life is improved. .12 .27 .11 .34 .15 .56V11. Being in a positive mood at work helps me to be in a positive mood at home. .76V12. Being happy at work improves my spirits at home. .02 .26 .97 .74 −1.19V13. Having a good day at work allows me to be optimistic with my family. .81V14. Skills developed at work help me in my family life. .87V15. Successfully performing tasks at work helps me to more effectively accomplish family tasks. .20 .72V16. Behaviors required by my job lead to behaviors that assist me in my family life. .77V17. Carrying out my family responsibilities is made easier by using behaviors performed at work. − .10 .84V18. Values developed at work make me a better family member. .89V19. I apply the principles of my workplace values in family situations. .68V20. Values that I learn through my work experiences assist me in fulfilling my family responsibilities. .85

The numbers in bold are standarized loadings that are cross loadings.a An X indicates that the items cross loaded into that factor and that if deleted chi-square improves significantly at pb .05 (based on Lmtest).b WFED=work-to-family enrichment developmental based, WFEA=work-to-family enrichment affective based, WFEC=work-to-family enrichment capital

based, WFPSA=work-to-family positive spillover affect based, WFPSB=work-to-family positive spillover behavioral based, WFPSV=work-to-family positivespillover value based.

203A.D. Masuda et al. / Journal of Vocational Behavior 80 (2012) 197–210

Study 2

Methodology

Participants and proceduresParticipants were recruited by undergraduate college students enrolled in a psychology course at a University in the Northeast

United States. Students were asked to recruit up to four working adults in exchange for extra credit. The final sample included 222working adults (159 women, 62 men, 1 unknown). The sample had a mean age of 37.39 years, SD=11.32. Sixty-two percentreported living with a spouse/partner, and 63% of participants did not have children, 14.4% had one child, and 22.5% had two ormore children. Fourteen percent of the sample worked less than 25 hours per week, 39.5%worked between 25 to 40 hours a week,and 46.4% worked more than 40 hours per week. Twenty eight percent of the sample held high school degrees, 27.5% hadbachelor's degrees, 22.5% hadmasters degrees or higher, 20.3% had associates degrees, and 1.8% did not have a high school degree.

Measures

Work-to-family positive spillover, work-to family enrichment, and job satisfaction were measured using the same itemsdescribed in Study 1. The construction of the survey was done in a similar matter as in study 1. Reliabilities are in Table 6.

Life satisfactionThe 5-item (Diener, Emmons, Larsen, & Griffin, 1985) life satisfaction scale was used based on a 5-point Likert scale

(1=strongly disagree to 5=strongly agree). A sample item is “In most ways my life is close to ideal.”

Results

As in Study 1, the data were first screened and found to be free of any multivariate outliers. Correlations among study variablesare reported in Table 6. The main results are shown in Table 7. The results of Study 2 were similar to Study 1, in support ofHypotheses 1–3. That is, the best fitting model was a six-factor model. However, because the best model did not result in optimal fitbased on the RMSEA, we also looked at the modification indexes. Results showed that 12 items cross-loaded across different factors(see Table 8 for items that cross loaded in Study 2). After deleting these items the model significantly improved (see Table 7).

To test Hypothesis 4, we used similar analyses as in Study 1 and found results consistent with Study 1. Specifically, resultsshowed that after introducing the control variables in the first step, WFE contributed uniquely to the variance of job satisfaction,ΔR²=.39, F (1, 211)=156.65, pb .01. However, WFPS did not contribute above and beyond WFE, ΔR²=.00, n.s. We conductedsimilar analyses in the reverse order. In this case, WFPS contributed uniquely to job satisfaction above the control variables,

Table 5WFE as a Mediator between WFPS and Job Satisfaction Relationship (Study 1) a,b.

Variable β SEβ β t R² Δ F df p Total R²

Regression 1 : WFE predicting Job SatisfactionStep 1 .06* 6.22 2,200 b .01 .06Age .00 .00 .08 1.37Gender .19 .09 .12 2.10**

Step 2 .35** 118.05 1, 199 b .01 .41WFE .63 .06 .60 10.86**

Regression 2: WFPS predicts WFEStep 1 .04* 3.17 2, 200 b .05 .03Age .00 .05 .01 − .25Gender .00 .02 − .01 − .24

Step 2 .68** 475.42 1, 199 b .001 .71WFPS .74 .03 .84 21.80**

Regression 3: WFPS predicts Job SatisfactionStep 1 .06** 6.22 2, 200 b .05 .06Age .01 .00 .09 1.68Gender .21 .09 .13 2.29*

Step 2 .29** 88.58 1, 199 b .001 .35WFPS .65 .07 .54 9.41**

Regression 4: WFE and WFPS predicting Job SatisfactionStep 1 .06** 6.22 2, 200 b .05 .06Age .01 .00 .07 1.34Gender .19 .09 .11 2.13

Step 2 .36** 69.29 2, 198 b .001 .42WFPS .17 .12 .14 1.40WFE .51 .11 .48 4.74**

a WFE=work-to-family enrichment; WFPS=work-to-family positive spillover.b * pb .05, **pb .01.

204 A.D. Masuda et al. / Journal of Vocational Behavior 80 (2012) 197–210

ΔR²=.15, F (1, 211)=42.23, pb .01. When WFE was entered in the third step, it added uniquely to the prediction of jobsatisfaction, ΔR²=.23, F (1, 210)=95.18, pb .01. Hence, Hypothesis 4 was partially supported. Consistent with Study 1, WFEcontributed above and beyond WFPS. However, WFPS did not add to the prediction above WFE. We also conducted similaranalyses using the sub-dimensions of both WFE andWFPS. Results were similar using the sub-dimensions compared with resultsusing the aggregated variables. Thus, unlike Study 1 we report results with aggregated variables.

To test Hypothesis 5, we also conducted regression analyses. Using aggregatedmeasures ofWFE andWFPS results showed thatafter introducing the control variables in the first step,WFE contributed uniquely to the variance of life satisfaction,ΔR²=.06, F (1,211)=15.40, pb .01. However, WFPS did not contribute above and beyond WFE, ΔR²=.00, n.s. We conducted similar analyses inthe reverse order. In this case, WFPS contributed to variance in life satisfaction, ΔR²=.02, F (1,211)=3.65, pb .05. WhenWFE wasentered in the third step, it added uniquely to the prediction of life satisfaction, ΔR²=.05, F (1,210)=12.16, pb .01. Hence, usingaggregated variables, WFE predicted life satisfaction above WFPS, but WFPS did not predict life satisfaction above WFE.

We also tested Hypothesis 5 using the three WFE sub-factors and the three WFPS sub-dimensions. This time, results showedWFE predicted life satisfaction above and beyond the control variables WFPS, ΔR²=.06, F (1, 211)=15.4, pb .01. However, thesub-factors of WFPS did not add to the prediction of life satisfaction above the sub-dimensions of enrichment. We conductedsimilar analyses in the reverse order. This time, the sub-dimensions of WFPS predicted life satisfaction above and beyond that ofthe control variables, ΔR²=.05, F (3, 209)=4.19, pb .01 and the WFE sub-dimensions added significantly to the prediction abovethe sub-dimensions of WFPS, ΔR²=.05, F (3,209)=4.19, pb .01. Hence, Hypothesis 5 was partially supported; WFE and WFPShave unique relationships with life satisfaction when the sub-dimensions are examined. However, using the aggregated measureWFE predicted life satisfaction above WFPS, but WFPS did not predict life satisfaction above WFE.

As in Study 1, mediation was tested using the Baron and Kenny (1986) approach. Similar to Study 1, WFE was a mediatorbetweenWFP and job satisfaction (see Table 8) andWFEwas a mediator betweenWFPS and life satisfaction (see Tables 9 and 10).

Discussion of Study 2

Consistent with Study 1, the results of Study 2 showed that the best fitting model was the six factor model that differentiatedbetween the constructs of WFPS and WFE and also each of their sub-dimensions. The results of Study 2 also showed that WFE

Table 7Comparison of work family enrichment and positive spillover factor structures (Study 2 data). a,b

Structure χ2 Δχ2 df Δdf IFI CFI SRMR RMSEA RMSEA confidence interval

One factor 1397** 170 .59 .59 .12 .19 .18–.19Two factor 1121** 185** 169 1 .69 .69 .11 .17 .16–.17Four factor 631** 490** 164 5 .85 .85 .07 .12 .11–.13Six factor 369** 262** 155 9 .93 .93 .06 .08 .07–.09Six factorc 5.22** 364** 5 150 .1 1 .01 .01 .00–.09Six Factord 85 80** 50 45 .98 .98 .04 .06 .04–.08

a All x2 and Δχ2values are significant at pb .001.b IFI=incremental fit index; CFI=comparative fit index; RMSEA=root-mean-square error of approximation.c Analyses done with cross items deleted.d Analyses done with items that cross loaded in all studies deleted.

Table 6Correlations among study variables (Study 2 data, N=222)a.

M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1. Gender 1.72 .45 –

2. Education 3.45 1.25 − .08 –

3. Working Hours 5.17 1.91 − .08 .25** –

4. Age 4.42 2.63 − .05 .13* .31** –

5. Children .67 1.02 − .01 − .03 .01 .10 –

6. Marital Status 1.62 .49 − .14* .24** .19** .31** .23** –

7. WFPS 3.75 .58 .18** .11 .02 .07 .03 .00 (.91)8. WFE 3.82 .68 .23** .12 − .01 .10 .08 .01 .67** (.91)9. WFE development 3.95 .71 .27** .14* .01 .05 .04 − .03 .66** .79** (.84)10. WFE affect 3.52 .91 .14* .10 − .02 .13 .14* .02 .50** .89** .51** (.91)11. WFE capital 4.00 .77 .20** .07 − .01 .08 .00 .03 .58** .88** .55** .69** (.88)12. WFPS affect 4.04 .63 .20** .11 .05 .05 − .03 .09 .80** .48** .46** .34** .43** (.89)13. WFPS behavior 3.63 .68 .13 .14* .02 .04 .02 − .03 .88** .64** .68** .44** .53** .54** (.86)14. WFPS value 3.46 .73 .12 .02 − .02 .08 .09 − .04 .86** .61** .54** .52** .50** .47** .74** (.81)15. Job satisfaction 3.95 .92 .08 .19** .19** .21** .07 .149* .43** .65** .38** .62** .63** .32** .37** .42** (.93)16. Life satisfaction 3.64 .79 .03 .08 − .17* − .03 .10 .17* .15* .26** .20** .22** .24** .03 .19** .19** .26** (.89)

a Alpha coefficients are presented in parentheses. All positive spillover and enrichment items range from 1 to 5. Higher scores indicate more spillover/enrichment. WFE=work-to-family enrichment. WFPS=work-to-family positive spillover * pb .05, **pb .01.

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contributed uniquely to job satisfaction above and beyondWFPS. However, WFPS did not contribute to job satisfaction above andbeyond WFE. Further, WFE mediated the WFPS and job satisfaction relationship.

Results showed a similar pattern when predicting life satisfaction. Specifically, WFE predicted life satisfaction aboveWFPS, butnot the other way around. However, both the sub-dimensions of WFE and WFPS uniquely contributed to life satisfaction. Further,WFE mediated the WFPS and life satisfaction relationship. This supports Wayne (2009); WFE is a more proximal predictor ofoutcome variables andWFPS is an antecedent of enrichment. That is, in order for enrichment to occur, spillover needs to occur first.

Of note, consistent with Study 1, in Study 2 multiple items cross loaded and the measurement model improved significantly

Table 9Mediating role of WFE on the WFPS and Job Satisfaction Relationship (Study 2). a,b

Variable Β SEβ Β t R² Δ F df p Total R²

Regression 1 : WFE predicting Job SatisfactionStep 1 .04* 9,76 1,220 b .01 .05Age .05 .02 .14 2.75**

Step 2 .44** 153,42 1,219 b .001 .44WFE .85 .07 .63 12.38**

Regression 2: WFPS predicting WFEStep 1 .05* 12,39 1,219 b .01 .05Gender .17 .08 .11 2.30**

Step 2 .42** 117,04 1,218 b .001 .47WFPS .77 .05 .66 13.07**

Regression 3: WFPS predicting Job SatisfactionStep 1 .04* 9,73 1,220 b .01 .04Age .07 .02 .17 2.98**

Step 2 .17* 48,53 1,219 b .01 .22WFPS .66 .09 .41 6.96**

Regression 4: WFE and WFPS predicting Job SatisfactionStep 1 .04* 9,73 1,220 b .01 .04Age .04 .01 .14 2.74*

Step 2 .40* 76,33 2,218 b .01 .44*WFPS .01 .11 − .00 − .07

WFE .86 .09 .63 9.25**

a WFE=work-to-family enrichment; WFPS=work-to-family positive spillover.b * pb .05, **pb .01.

Table 8Cross loadings into the 6 factors (Study2). a,b

Items Factors

Work-to-familyenrichment

Work-to-family positivespillover

F1WFED

F2WFEA

F3WFEC

F4WFPSA

F5WFPSB

F6WFPSV

V1. Helps me to understand different viewpoints and this helps me be a better family member. .81V2. Helps me to gain knowledge and this helps me be a better family member. 1 .01 .18V3. Helps me acquire skills and this helps me be a better family member. .72 .12 .02V4. Puts me in a good mood and this helps me be a better family member. .83V5. Makes me feel happy and this helps me be a better family member. .87V6. Makes me cheerful and this helps me be a better family member. .94 .01V7. Helps me feel personally fulfilled and this helps me be a better family member. .16 .41 .40 .06V8. Provides me with a sense of accomplishment and this helps me be a better family member. .05 .12 .80V9. Provides me with a sense of success and this helps me be a better family member. .96V10.When things are going well at work, my outlook regarding family life is improved. .54 .59 .39V11. Being in a positive mood at work helps me to be in a positive mood at home. .16 .84V12. Being happy at work improves my spirits at home. .97 .04V13. Having a good day at work allows me to be optimistic with my family. .85V14. Skills developed at work help me in my family life. .61 1.21 .89V15. Successfully performing tasks at work helps me to more effectively accomplish family tasks. − .09 .85V16. Behaviors required by my job lead to behaviors that assist me in my family life. .79V17. Carrying out my family responsibilities is made easier by using behaviors performed at work. − .10 .37 .44V18. Values developed at work make me a better family member. .74V19. I apply the principles of my workplace values in family situations. .25 .09 .30 .26V20. Values that I learn through my work experiences assist me in fulfilling my family responsibilities. .78

The numbers in bold are standarized loadings that are cross loadings.a An X indicates that the items cross loaded into that factor and that if deleted chi-square improves significantly at pb .05 (Based on Lmtest).b WFED=work-to-family enrichment developmental based, WFEA=work-to-family enrichment affective based, WFEC=work-to-family enrichment capital

based, WFPSA=work-to-family positive spillover affect based, WFPSB=work-to-family positive spillover behavioral based, WFPSV=work-to-family positivespillover value based.

206 A.D. Masuda et al. / Journal of Vocational Behavior 80 (2012) 197–210

when these items were deleted. To try and better understand the basis for the cross-loadings, we conducted a content adequacystudy.

Study 3

Content adequacy

Content adequacy is a way to estimate content validity and it is defined as “the degree to which a measure's items are a propersample of the theoretical content domain of a construct” (Schriesheim, Powers, Scandura, Gardiner, & Lankau, 1993, p. 386). Toestimate the content adequacy of the WFE and WFPS items, we recruited 85 undergraduates students from a medium sizedUniversity in the Northeast to participate in exchange for extra credit. Although college students have limited full time workexperience, based on Schriesheim et al. (1993) we deemed college students as content adequacy raters appropriate on the basisthat they possess the intellectual capacity to read task statements and categorize them into a priori categories. The respondentswere 31%male and 53%were female, and had an average age of 19 years.We asked participants to first familiarize themselves withthe definitions of work–family enrichment andwork–family positive spillover, and then examine each of theWFE andWFPS itemsand indicate which definition the item most appropriately reflected. The order of the items presented to participants wasrandomized. The number of times an item was mapped to a definition was calculated. Some researchers argue that an item mustbe placed 80% of the time in the correct category in order to be retained (Carlson et al., 2006) whereas others have argued that anitem can have 70% cut-off (Schriesheim & Hinkin, 1990). Table 1 shows that only two items met the 70% cut off score.

Post-hoc analyses

Based on results from all three studies, we deleted the items that were found to be consistently problematic (see Table 1). Afterdeleting these items we conducted similar analyses using the data from Study 1 and Study 2. Results from Study 1 did not change.For Study 2, results pertaining to job satisfaction also did not change. However, when testing Hypothesis 5 with the newly refinedmeasure aggregated, WFPS predicted life satisfaction and WFE added to the prediction above and beyond. The results whenlooking at sub-dimensions remained the same. Further, the mediation results also remained the same.

Discussion of Study 3

The results from Study 3 provide additional evidence that a number of items used to represent the two constructs overlap.These results indicate that muchmorework needs to be done to improve the scalesmeasuringWFE andWFPS. Note that in Studies1 and 2 the items that cross loaded across constructs were deleted, resulting in significant differences. However, it is important tonote that by deleting these itemswe are in essence creating a new scale measuring unique sub-dimensions, whichmay be tappinginto more specific resources. More studies need to be done to further understand what these sub-constructs are capturing.

Table 10Mediating role of WFE on the WFPS and Life Satisfaction Relationship (Study 2). a,b

Variable Β SEβ β t R² Δ F df p Total R²

Regression 1 : WFE predicting Life SatisfactionStep 1 .06** 8.34 1,218 b .001 .07Marital Status .34 .10 .21 3.24Working Hours − .09 .03 − .20 −3.17

Step 2 .07** 16.54 2,219 b .001 .14WFE .30 .07 .26 4.06

Regression 2: WFPS predicting WFEStep 1 .45** 178.57 1,220 b .001 .45

WFPS .78 .06 .67 13.36Regression 3: WFPS predicting Life SatisfactionStep 1 .06** 8.34 2,219 b .001 .07

Marital Status .34 .10 .21 3.21Working Hours − .09 .03 − .21 −3.19

Step 2 .02** 5.39 1,218 b .01 .09WFPS .21 .09 .15 2.32

Regression 4: WFE and WFPS predicting Life SatisfactionStep 1 .07** 8.34 2,219 b .001 .07Marital Status .34 .10 .21 3.23**Working Hours − .08 .03 − .20 −3.15**

Step 2 .07** 8.34 2,217 b .001 .14WFPS − .05 .12 − .04 − .46WFE .33 .10 .28 3.32**

a WFE=work-to-family enrichment; WFPS=work-to-family positive spillover.b *pb .05, **pb .01.

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General discussion summary of findings

The present research represents an important step toward a better understanding of the operationalization of WFE andWFPS.First, this study lends empirical support for researchers' claims that WFPS andWFE are related, but distinct constructs (Carlson etal., 2006; Powell & Greenhaus, 2010; Wayne, 2009). Consistent with Carlson et al.'s (2006) and Hanson et al.'s (2006)comprehensive models of enrichment and spillover, our CFA shows that the best fitting model differentiates between enrichmentand spillover and captures the different instrumental and affective resources. Further, both Study 1 and Study 2 showed that thesub-dimensions of WFPS andWFE had unique contributions when predicting job satisfaction and life satisfaction. However, whenlooking at the aggregated variables, WFPS did not add significantly to the prediction of job satisfaction above WFE. Instead, onlyWFE contributed above and beyond WFPS. These results also emphasize the importance of using the sub-scales that capture thedifferent resources being transferred from one domain to another.

Second, this study provides empirical evidence as towhyWFE andWFPS are different from each other. Carlson et al. (2006) andWayne (2009) argued that spillover is an antecedent of enrichment while Powell and Greenhaus (2010) argued that spillover isdifferent than work–family enrichment because the resources being transferred in positive spillover are more specific. In Study 1and Study 2WFE was a mediator betweenWFPS and job satisfaction and not the other way around. In study 2, WFE also mediatedthe relationship betweenWFPS and life satisfaction. These results show that theoreticallyWFPS can be an antecedent of enrichmentinstead of an outcome. Specifically, it lends empirical support to Wayne's (2009) conceptual framework differentiating WFE fromWFPS.

Across all three studies, our results showed that several items cross-load onto both constructs. When these itemswere deleted,the measurement model significantly improved. Further, the results from the content analyses showed that only two items weremapped correctly onto their definitions at least 70% of the time. These results suggest that other items need to be developed tobetter differentiate between these two constructs (see Table 1).

There could be at least three reasons for the observed cross loadings. First, although these items were developed to measureone construct (e.g., WFPS) they could be measuring the other construct (e.g., WFE) and vice versa. For example, WFPS items thatimply gaining a resource from work improves ability to accomplish family tasks could be capturing WFE as well. Based on Wayne(2009) framework, the items measuring WFPS should only describe transfer of positive resources and not the transfer of positiveresources that would lead to overall improvement in the family domain (Wayne, 2009). For example, the item “Having a good dayat work allows me to be optimistic with my family” could be a good sample of the theoretical domain of WFSP. As noted, this itemdescribes improvements in a specific area of the family domain (i.e. being optimistic with family) instead of describing overallimprovement in the family domain (e.g. family life is improved). People may agree that having a good day at work makes themmore optimistic with their family. However, they may not agree that having a good day at work makes them a better familymember.

The second reason for the observed cross loadings could be that the construct of WFE actually includes the construct of WFPS.As some authors argued, one condition to experience enrichment is to first experience spillover (Wayne, 2009). If this is the case, itis expected that items capturingWFEmay be also capturingWFPS. As observed, the items from theWFE scale are double-barreled.That is, the first idea captured is whether transfer of positive resources occurred while the second idea is whether they believe theresource being transferred leads to increased performance in that particular life domain. Hence, it is possible that the WFE itemsalso capture WFPS. This may have caused some confusion when participants were trying to match the items with the definitions.We encourage the development of different ways to capture enrichment. One possibility would be to measure positive spilloverand correlate this with performance measures as a way to tap enrichment.

The third reason that may explain why the items above cross loaded in all samples was because some of these items wereactually measuring more specific resources such as behavior and skills instead of social capital and material resources intended tobe measured by enrichment (Powell & Greenhaus, 2010). For example, the items from theWFE scale that were initially developedto measure the transfer of broader developmental resources could be in fact measuring the transfer of more behavioral resources.For example, the item “Helps me acquire skills and this helps me be a better family member” cross loaded into WFPS values andbehavior sub-dimension, and the item “Helps me feel personally fulfilled and this helps me be a better family member” also crossloaded inWFPS behavior. Hence, there is also a need to revise these items to ensure that they are capturing the resources that theywere intended to capture.

Limitations and future research

Like any study, there are limitations that must be acknowledged. First, these studies were cross-sectional and based on self-report data, which has the potential to inflate correlations and also limits the ability to make causal inferences. Other studiesmeasuring WFPS and WFE from different sources is desirable and may help the refinement of these measures. Additionally,researchers should examine positive work–family interaction by employing longitudinal designs, following the lead of Hammer,Cullen, Neal, Sinclair, and Shafiro (2005).

Although our results suggest that WFE was a mediator between WFPS and job satisfaction, follow-up studies are needed tounderstand how different sub-dimensions relate to outcomes (e.g., job performance, organizational citizenship behaviors). Futurestudies should also examine whether WFE mediates the relationship between positive spillover and other outcome variables.Lastly, the goal of our study was to examine only two constructs in the work-to-family direction. Future studies should examineother constructs that measure the positive side of work and family (e.g., facilitation) and also include the family-to-work direction.

208 A.D. Masuda et al. / Journal of Vocational Behavior 80 (2012) 197–210

Practical and theoretical implications

The present research contributes to theory in several ways. First, it provides clarification between the twomost frequently usedconstructs in the literature that examine the positive side of work and family interface. As Locke (2003) suggested, strongdefinitions are the epistemological foundation of social sciences. This study clarifies these definitions by not only showing thatWFE andWFPS are different from each other, but by also showing that it is important to distinguish the different resources that arebeing transferred when one is experiencing enrichment and positive spillover. In fact, in Study 1 the unique effect of enrichmentand positive spillover on job satisfaction was observed only when we used the sub-dimensions in the regression analyses insteadof the aggregate variables.

Second, this study clarifies some of the confusion with regard to the relationship betweenWFE andWFPS. While some authorshave argued that the difference lies in the level of specificity of resources being transferred (Powell & Greenhaus, 2010) othershave suggested that spillover is an antecedent of enrichment (Wayne, 2009). This study provides support for the idea thatspillover is an antecedent of enrichment and not the reverse. This is an important step toward the development of a nomologicalnetwork that explains the positive side of the work and family interface.

Third, this study encourages the development of measures that not only capture the differences between enrichment andpositive spillover but also the differences between the resources being transferred. With more refined measures we can begintesting whether different resources relate with different outcome variables (i.e. health-related variables, work-related variables orfamily- related variables), or how these resources relate with each other to predict outcome variables. For example, transfer ofaffect resources (e.g. mood) could be more closely related with affect related outcome variables such as job satisfaction, whiletransfer of instrumental resources (e.g. skill) could be more closely related with other variables such as job performance. Further,the concept of enrichment can be applied and measured not only at the individual and organizational level but also at thecommunity level.

Our research also encourages the development of measures that capture enrichment and spillover at multiple levels ofanalyses. This is because resources gained from other forms of social interaction could also be transferred to the workplace.Coleman's (1988) concept of social capital may be used to expand the nomological network of enrichment. Coleman explains thatmultiple entities comprised of social structures facilitate individual or organizational actions. He argues that the resourcesobtained from these social interactions can be transformed into human capital. Hence, based on Coleman´s conceptualization ofsocial capital, enrichment can occur not only between work and family but also between communities, social networks,friendships and work. For example, Coleman mentioned that in some communities adults are expected to watch over childrenwho play alone. Living in communities that provide this type of support can improve one's mood, which in turn can be transferredto the workplace. Hence, we encourage expanding the nomological network that defines positive synergies between work andnon-work domains considering multiple levels of analyses and including resources that can be gained from other types of socialinteraction.

Finally, this study provides additional evidence showing that employees who are able to perceive positive synergies betweenwork and life domains are generally more satisfied with their jobs and life in general. These findings should encourageorganizations to educate employees on the benefits they can obtain from participating and multiple roles, and developinterventions to facilitate resource generation across work and family domains.

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