ORIGINAL RESEARCH
published: 22 November 2019
doi: 10.3389/fpsyg.2019.02628

Validation of the Japanese Version of
the Multidimensional Measure of
Family Supportive Supervisor
Behaviors (FSSB-J)
Hisashi Eguchi 1* , Yuko Kachi 1 , Hayami K. Koga 2 , Mariko Sakka 3 , Masahito Tokita 4 andAkihito Shimazu 5,61

Department of Public Health, Kitasato University School of Medicine, Sagamihara, Japan, 2 Department of Social and
Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States, 3 Division of Health Sciences
and Nursing, Department of Gerontological Home-Care and Long-Term Care Nursing, Graduate School of Medicine,
The University of Tokyo, Tokyo, Japan, 4 Keio Research Institute at SFC, Keio University, Fujisawa, Japan, 5 Faculty of Policy
Management, Keio University, Fujisawa, Japan, 6 Asia Pacific Centre for Work Health and Safety, University of South
Australia, Adelaide, SA, Australia

Edited by:
Mengcheng Wang,
Guangzhou University, China
Reviewed by:
Ying Ling,
Guangdong Industry TechnicalCollege, China
Lingyan Li,
Nanchang University, China*Correspondence:
Hisashi Eguchi
eguchi@med.kitasato-u.ac.jp
Specialty section:
This article was submitted to
Quantitative Psychology
and Measurement,a section of the journal
Frontiers in Psychology
Received: 22 July 2019
Accepted: 07 November 2019
Published: 22 November 2019
Citation:
Eguchi H, Kachi Y, Koga HK,
Sakka M, Tokita M and Shimazu A(2019) Validation of the Japanese
Version of the Multidimensional
Measure of Family Supportive
Supervisor Behaviors (FSSB-J).
Front. Psychol. 10:2628.
doi: 10.3389/fpsyg.2019.02628

Objective: The aim of the current study was to validate the Japanese version of
the family supportive supervisor behaviors (FSSB-J) measure. FSSB is conceptualized
as a multidimensional, superordinate construct constituted of four dimensions:emotional support, instrumental support, role modeling behaviors, and creative work–
family management.
Methods: The Japanese translated and back-translated FSSB-J questionnaire wasadministered to 1,670 men and women aged 20–59 years who were registered witha Japanese online survey company in November 2017. Confirmatory factor analyses
were performed to evaluate the factorial validity of the FSSB-J. Cross-time measurement
invariance was tested using multi-group confirmatory factor analyses. Construct validity
was assessed with the potential consequences of FSSB (e.g., work–family spillover,work engagement, intention to leave, job satisfaction, and psychological distress) andconvergent validity was assessed using similar concepts (e.g., organizational justice andsocial support). Internal consistency and test–retest reliability were examined to evaluate
the reliability of the four dimensions of the FSSB.
Results: A series of confirmatory factor analyses using the multiple-group method
revealed that the four-factor model fitted the data best. The latent factor structure
demonstrated configural, metric, and scalar invariance across time. Construct andconvergent validity were generally in line with expectations. Cronbach’s α coefficientand test–retest reliability were sufficient for each of the four dimensions of the FSSB.
Conclusion: This study suggests that FSSB-J is an adequate measure of FSSB in theJapanese context.
Keywords: workplace, psychosocial factor, Japan, supervisor behavior, work–life balance, scale validation

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Japanese Version of FSSB Measurespillover, job satisfaction, and turnover intentions over and abovemeasures of general supervisor support (Hammer et al., 2009).
Furthermore, FSSB has been linked to a number of employee
outcomes, including lower levels of turnover intentions andhigher levels of job satisfaction (Hammer et al., 2011; Crainet al., 2014; Odle-Dusseau et al., 2016; Yragui et al., 2017).
To investigate whether the application of FSSB in Japan isappropriate, the validation of a Japanese version of FSSB is anecessary first step.
Higher levels of supervisor support can benefit employees,
and is related to higher levels of work–family positive spillover(Thompson et al., 1999). In addition, studies have foundthat supervisor support enhances employee job satisfaction(Steinhardt et al., 2003; Odle-Dusseau et al., 2016), and lessens
turnover intentions (Nichols et al., 2016; Fukui et al., 2019)and psychological distress (Winnubst et al., 1982; Kawakami
et al., 2005). Therefore, it was hypothesized that FSSB ispositively associated with work–family positive spillover and job
satisfaction, and negatively associated with turnover intentions,which would confirm the construct validity of the scale.

INTRODUCTION
In most contemporary developed countries, many households arefinancially supported by dual-earner couples, with approximately
60% of couples supporting families together (Organisation for
Economic Co-operation and Development, 2011). With anincrease in the proportion of dual-earner families, work andnon-work demands are now more likely to conflict (Shimazu
et al., 2010; Cabinet Office, 2018; French et al., 2018). For
dual-earner families, it has become important to maintain an
appropriate balance between work and family roles. Japan isunique in terms of its strong corporate culture, in which
employees are judged by their willingness to spend extended
hours face-to-face with co-workers, being required to attend
endless successions of meetings, and expected to prioritize workover private life. These societal features make the implementationof work–family balance policies particularly challenging in Japan(Cabinet Office, 2018).
Work–family balance is particularly critical in Japan, where
fertility rates have plummeted over the last several years to thelowest level of any country, contributing to rapid population
decline (Japan Ministry of Health Labour and Welfare, 2017).
Since previous studies have found that the promotion of workand family–life balance is associated with improved fertility rates,
this may be an important point of intervention (Yamaguchi andYoum, 2012). As an attempt to combat this decline in birthrate,and, subsequently, the population, the Japanese government has
recently promoted a reform of working styles known as “Workstyle Reform.” This reform includes the introduction of legal
caps on overtime hours, rules to establish the “equal work,
equal pay” principle by improving working conditions for people
with irregular employment status, and a new system that allows
corporate employees to be paid based on their performance
rather than on the number of hours spent in the workplace. The
promotion of work and family–life balance is an important partof this proposed work-style reform (Japan Ministry of Health
Labour and Welfare, 2017).
Work–family research has identified social support fromsupervisors as an important resource that can reduce the negative
effects of work and family stressors (O’Driscoll et al., 2003).
Moreover, the Japanese government has started the “iku boss”(i.e., educating the boss) system to re-educate managers about theneed to support the work–family balance of their subordinates(Japan Ministry of Health Labour and Welfare, 2017). To help
promote this change, specific measures for assessing supervisors’
behaviors in encouraging work–family balance among theirsubordinates are needed in Japan.
Family supportive supervisor behaviors (FSSB) are defined
as behaviors exhibited by supervisors that are supportive of thefamily roles of employees (Hammer et al., 2009). FSSB was
originally conceptualized as a multidimensional superordinate
construct which is assessed with 14 items that construct thefour dimensions; emotional support, instrumental support, rolemodeling behaviors, and creative work–family management. Thismeasure has been shown to have sufficient construct, criterionrelated, and incremental validity. It has been significantly
associated with work–family conflict, work–family positive

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The Current Study
This study aimed to validate the Japanese version of the FSSB(FSSB-J) in a sample of Japanese employees. Specifically, thefactorial and construct validity of this measure were examined,
as well as its reliability (i.e., internal consistency and test–retest
reliability). The development of the Japanese version of the
FSSB scale was based on the COnsensus-based Standards forthe selection of health Measurement INstruments (COSMIN)(Mokkink et al., 2018).

MATERIALS AND METHODSTranslation of the FSSB
Professor Leslie Hammer, the author of the original version of
FSSB, approved the development of the FSSB-J. First, the English
version of the FSSB was translated into Japanese by two of theauthors of this study (HE and YK), then back-translated toEnglish by one author of this study (HK) who was blinded tothe original items and is proficient in both English and Japanese.
The back-translated version was confirmed by Professor Leslie
Hammer. The original English version was compared with the
back-translated version of the FSSB and created a preliminary
Japanese version after terminology corrections.

Study Design and Population
A series of online surveys was administered to men andwomen who were registered with a Japanese online survey
company in December 2017. Of all workers who were registeredwith this online survey company, 1,670 married workers
who were 20–59 years of age were selected and invited bye-mail to complete a web-based questionnaire in the orderof arrival. The online survey company had access to morethan 2,000,000 potential participants who showed interest in
participating in surveys that provided fiscal incentives. The fiscal
incentive for this survey was low, valued at several U.S. dollars.

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Japanese Version of FSSB Measure

A total of 249 participants were excluded from the first survey
and 135 were excluded from the second survey because theyreported being self-employed or freelance workers. A further
43 participants who had at least one missing response on thequestionnaire in the first survey were excluded, resulting ina final sample of 1,378 respondents for the first survey and842 respondents for the second survey. COSMIN requires theresearcher to evaluate the percentage of missing items because
a high number of missing items can introduce bias in the
study results. In the online survey, it was possible to restrict
the participants from answering all questions. To evaluate bias,
the restriction was removed only for FSSB items in the firstsurvey. Since the online survey required participants to answer

For financial reasons, recruitment stopped when the number ofparticipants reached 1,670. The survey was repeated with thesame respondents 1 month later because an interval between
surveys of approximately 1 month is typically consideredappropriate for the evaluation of instruments (Streiner et al.,2015). Respondents in the second survey were recruited fromparticipants who completed the first survey on a first-comefirst-served basis. The recruitment procedure in the second
survey was the same as the first survey. Recruitment ceased
when the number of participants reached 1,000. There wereno statistically significant differences in FSSB scores across theparticipant demographic characteristics between the first andsecond surveys (Table 1).

TABLE 1 | Participant demographics and means and deviations of total FSSB score.
Baseline survey (n = 1,378)
(%)

n

Mean

Follow-up survey (n = 842)
SD

n

(%)

Mean

SD

Sex
Men

965

(70.0)

41.2(12.9)

597(70.9)

39.3(11.6)

Women

413(30.0)

42.5(14.0)

245(29.1)

41.5(12.5)

Age (years)
20–29

27

(2.0)

43.1

(2.0)

14

(1.7)

42.6

(7.4)

30–39

279

(20.3)

44.6(20.1)

192(22.8)

42.4(11.5)

40–49

532(38.6)

41.2(38.6)

319(37.9)

39.8(11.8)

50–59

540(39.2)

40.4(39.2)

317(37.6)

38.5(12.1)

Educational status
7(0.51)

45.7(14.1)

2

(0.2)

46.0

(2.8)

High school

410(29.8)

39.6(13.3)

251(29.8)

38.6(11.9)

College164(11.9)

43.2(13.2)

90(10.7)

40.8(12.7)

University

672(48.8)

42.3(13.2)

421(50.0)

40.4(11.8)

Graduate school

124

(9.0)

42.2

(12.8)

77

(9.1)

41.0

(11.3)

1

(0.1)

44.0

(0.0)

1

(0.1)

37.0

(0)

Junior high school

OthersHaving children
Yes

1080

(78.4)

41.4(13.3)

657(78.0)

39.7(11.7)

No

298(21.6)

42.5(13.2)

185(22.0)

40.8(12.7)

Occupation
Professional and technical job

330(24.0)

40.7(13.1)

215(25.5)

39.5(11.5)

Managerial job

220(16.0)

43.0(13.3)

131(15.6)

40.1(11.7)

Clerical job

342(24.8)

43.1(13.6)

217(25.8)

41.5(11.7)

Sales job

159

(11.5)

41.2(13.1)

93(11.0)

39.8(12.6)

Service job

92

(6.7)

41.7

(13.1)

47

(5.6)

40.8

(12.4)

Manufacturing job

112

(8.1)

38.0

(13.1)

68

(8.1)

36.4

(13.2)

Security job

29

(2.1)

45.9

(12.2)

17

(2.0)

42.7

(9.3)

Agriculture, forestry, and fishery job

4

(0.3)

43.0

(13.4)

1

(0.1)

62.0

(0.0)

Transportation and communication job

42

(3.1)

39.3

(12.2)

28

(3.3)

35.2

(11.9)

Others

48

(3.5)

40.0

(12.8)

25

(3.0)

42.0

(9.4)

Employment status
Regular1081(78.5)

41.5(13.2)

678(80.5)

39.5(11.8)

Non-regular297(21.6)

42.2(13.4)

164(19.5)

41.7(12.3)

Weekly working hours
−19

223

(16.2)

41.5(14.6)

126(15.0)

39.0(12.4)

20–39

233(16.9)

42.5(13.1)

136(16.2)

42.4(12.1)

40–59

762(55.3)

41.8(12.9)

473(56.2)

40.1(11.6)

60–79

136

(9.9)

39.3

(13.2)

89(10.6)

37.8(11.7)

80–

24

(1.7)

40.2

(14.6)

18

(2.1)

37.2

(12.9)

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Japanese Version of FSSB Measureitems (e.g., “You manage your time at home more efficiently as aresult of the way you do your job”) (response range: 0–12). FWPS
was measured with four items (e.g., “After spending a pleasantweekend with your spouse/family/friends, you have more fun inyour job”) (response range: 0–12). Items are scored on a fourpoint Likert scale, ranging from 0 (“never”) to 3 (“always”).
The item scores are summed to calculate the total score, withhigher scores indicating greater levels of WFPS and FWPS. The
reliability and validity of the Japanese version of the scale has
previously been shown to be sufficient (Shimada et al., 2019). The
Cronbach’s α coefficients in the sample were 0.72 for WFPS and0.81 for FWPS.

all questions except for the FSSB items in the first survey, no
participants had missing data on the FSSB items in the second
survey, or for the non-FSSB items in both surveys.

MeasuresFamily Supportive Supervisor Behavior
Family Supportive Supervisor Behavior was assessed with apreliminary Japanese version of the FSSB measure. The items of
the FSSB measure are grouped into four subscales that reflect theunderlying dimensions of FSSB: emotional support (five items),role modeling behaviors (three items), instrumental support(four items), and creative work–family management (six items).
All items are scored on a five-point Likert scale ranging from 1(“strongly disagree”) to 5 (“strongly agree”). The total score of thesubscales was used in the analyses with higher scores representing
higher levels of the construct.

Work Engagement
Work engagement was assessed using the short form of the
Utrecht Work Engagement Scale (UWES) (Schaufeli et al., 2006).
The UWES includes three subscales that reflect the underlying
dimensions of engagement: vigor, dedication, and absorption(three items for each dimension). All items were scored on a
7-point Likert scale ranging from 0 (“never”) to 6 (“always”)(response range: 0–54). The item scores are summed to calculate
the total score. Higher scores indicate greater levels of work
engagement. A previous validation study of the Japanese versionof the UWES recommended that work engagement should betreated as a unitary construct due to the high correlations among
the three components (Shimazu et al., 2008). In the sample,
Cronbach’s α coefficient was 0.93 for the UWES.

Procedural and Interactional JusticeTwo aspects of organizational justice (i.e., procedural andinteractional) were assessed using the Japanese version of theOrganizational Justice Questionnaire (OJQ). This scale wasdeveloped by Moorman (1991) and modified by Elovainio
et al. (2002). The scale comprises of a seven-item scale thatmeasures procedural justice, and a six-item scale that measuresinteractional justice, both of which are rated on a five-point Likert
scale ranging from 1 (“strongly disagree”) to 5 (“strongly agree”).
The total score for each OJQ subscale was calculated by averaging
item scores (score range 1–5). The subscale scores are summed
to calculate the total score, where higher scores indicate higher
levels of organizational justice. This Japanese version has beenshown to have sufficient reliability and validity (Inoue et al.,2009). In the present study sample, Cronbach’s α coefficientsfor the procedural and interactional justice scales were 0.88 and
0.94, respectively.

Intention to Leave
Intention to leave was measured with a three-item scale usedby Geurts et al. (1998). Respondents used a five-point Likert
scale ranging from 1 (“I agree completely”) to 5 (“I disagreecompletely”) to rate the extent to which they have the intention toleave their current employment in the following month (response
range: 3–15). The item scores are summed to calculate the total
score. Higher scores indicate greater levels of intention to leave.
The internal consistency reliability of this measure was found to
be good in the sample (α = 0.85).

Source-Specific Workplace Social Support
To assess source-specific workplace social support, the New
Brief Job Stress Questionnaire (New BJSQ) (Shimomitsu, 2000)
was used. The New BJSQ includes a three-item supervisor andcoworker support scale (response range: 3–12), with items such as“How freely can you talk with the supervisor/coworker?,” “Howreliable is the supervisor/coworker when you are troubled?,” and“How well will the supervisor/coworker listen to you when you
ask for advice on personal matters?” Participants responded tothese statements on a four-point Likert scale ranging from 1(“Extremely”) to 4 (“not at all”). The item scores are summed to
calculate the total score. The higher scores indicate the greaterlevels of supervisor and co-worker support. The Cronbach’s
α coefficient was 0.91 for supervisor support and 0.80 for
co-worker support.

Job Satisfaction
Job satisfaction was measured using the subscales of the New
BJSQ (Inoue et al., 2014). Job satisfaction, which was a single
item measure, was classified into four categories; 1 = dissatisfied,
2 = somewhat dissatisfied, 3 = relatively satisfied, or 4 = satisfied.

Psychological Distress
The K6 scale was developed by Kessler et al. (2002). The scaleconsists of six items that measure the extent of psychologicaldistress using a five-point response option 0 (“none of the time”)
to 4 (“all of the time”). The Japanese version of the K6 scale has
been shown to have sufficient reliability and validity (Furukawa
et al., 2008). The item scores are summed to calculate thetotal score. Higher scores indicate greater levels of psychologicaldistress. In the current study, the Cronbach’s α coefficient for theK6 scale was 0.88.

Work–Family Positive Spillover
Work–family positive spillover effects [work-to-family positive
spillover (WFPS) and family-to-work positive spillover (FWPS)]
were measured with 22 items using the Survey Work–HomeInteraction–NijmeGen (SWING), which was developed in the
Netherlands (Geurts et al., 2005). WFPS was measured with four

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Japanese Version of FSSB Measure

COnsensus-based Standards for the selection of health
Measurement INstruments suggests that hypothesis testingshould be used when a gold standard of a construct is notavailable (Mokkink et al., 2018) and FSSB is theoreticallyrelated to other psychosocial factors in the workplace (Hammer
et al., 2009). Based on previous studies (Hammer et al., 2009),
it was hypothesized that FSSB is negatively correlated withintention to leave and psychological distress, and positively
correlated with procedural justice, interactional justice,supervisor support, coworker support, work–family positive
spillover, family–work positive spillover, work engagement, andjob satisfaction.

factor loadings to be equal for the two levels. Factor varianceswere set to 1.0 to identify the model. Third, scalar invariance wastested by constraining all subscale and item intercepts to be equal
for the two levels. Model fit was assessed using a combinationof fit indices including the Tucker–Lewis index (TLI), the CFI,
and the RMSEA. The overall scale reliability was quantified byboth an intraclass correlation coefficient (ICC) (2,1) based on asingle measurement two-way random effects model of absoluteagreement and the standard error of measurement (SEM). ICC(2,1) was red in compliance with published recommendations;an ICC (2,1) of 0.90 or higher was considered excellent, 0.75
or higher was considered good, 0.50 or higher was considered
moderate, and lower than 0.50 was considered poor (Shrout
and Fleiss, 1979). At least 301 participants were deemed to benecessary to detect an ICC (2,1) ≥ 0.50 (error α = 0.05 andβ = 0.20) between the first and second survey (Walter et al.,1998). Previous studies indicate that this is a modest sample size
(Terwee et al., 2012). Larger SEM represents lower test reliability
and less precision in the measures and scores obtained (de Vet
et al., 2006). Cronbach’s α coefficient was calculated to evaluate
internal consistency and Pearson’s correlation coefficients were
calculated to evaluate construct validity and convergent validity.
All analyses were conducted in Stata 15 (College Station, TX,
United States) and AMOS Version 25 for Windows (Chicago,
IL, United States).

Statistical Analysis

Ethics Statement

Classical test theory was used to measure FSSB. Meanvalues and standard deviations were calculated for each itemof the FSSB scale. Item-total Spearman’s correlations were
examined. CFA using maximum-likelihood estimation was
conducted with AMOS (Chicago, IL, United States). The
hypothesized four-factor model (Model 2) was used with a
one-factor model (Model 1), whereby all items were loaded
on one general FSSB factor. Moreover, to consider genderdifferences in the model, model testing was carried out inmale and female samples simultaneously using the multiplegroup method (Model 3 for the one-factor model and Model
4 for the four-factor model). Model fit was assessed using
a combination of fit indices including the goodness of fit
index (GFI), parsimony goodness of fit index (PGFI), nonnormed fit index (NNFI), the comparative fit index (CFI),
parsimony normed fit index (PNFI), and the root meansquare error of approximation (RMSEA). The acceptability ofmodel fit was judged by the following criteria; GFI, PGFI,NNFI, CFI, and PNFI of >0.90 and RMSEA of <0.08(Hooper et al., 2008).
The survey was repeated after 1 month using the same FSSB
scale on 1,000 of the 1,670 respondents who completed the firstsurvey to assess the replicability of the FSSB with the test–retest
method. To evaluate the cross-time measurement invariance,
configural, metric, and scalar invariance were evaluated in threesteps. First, configural invariance was tested using a two-level(i.e., first and second survey) CFA in which all parameters were
estimated freely, except for the highest loading items for eachfactor, which were set to 1.0, while the factor means were set
to 0. Second, metric invariance was tested by constraining all

This study was approved by the Ethics Committee of the
Graduate School of Medicine and Faculty of Medicine at The
University of Tokyo [11666-(1)].

Sociodemographic Factors
Sociodemographic factors included sex, age range (i.e., 20–
29, 30–39, 40–49, and 50–59 years), educational attainment(junior high school, high school, junior college/technical school,
university, graduate school, and others), having children (yes,no), occupation (professional and technical job, managerial job,clerical job, sales job, service job, manufacturing job, security
job, agriculture, forestry or fishery job, transportation andcommunication job, others), employment contract (regularor part-time), and weekly working hours (≤19, 20–39,
40–59, 60–79, ≥80).

Hypothesis Testing

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RESULTS
The majority of the sample at baseline was male (70.0%), hadmore than high school education (69.7%), had children (78.4%),
had regular employment status (78.5%), and worked >40 h per
week (66.9%) (Table 1).

Validity
A confirmatory factor analysis was conducted. As shown in
Table 2, the hypothesized four-factor model (Model 2: each
item loads on a hypothesized factor) showed a significantly
better fit to the data than the one-factor model (Model 1: allitems measuring the four constructs load on one general FSSB
factor). The fit indices of the four-factor model (Model 2)
were GFI = 0.923, PGFI = 0.624, NNFI = 0.961, CFI = 0.964,
PNFI = 0.750, and RMSEA = 0.083. The fit indices of the
four-factor model using a multiple-group model (Model 4)
were GFI = 0.918, PGFI = 0.621, NNFI = 0.957, CFI = 0.964,
PNFI = 0.747, and RMSEA = 0.059. A formal χ2 difference
test revealed that the difference between the two models was
not significant [1χ2 (71) = 73.265, p > 0.05]. These results
suggest that factor loadings were indeed invariant between
males and females.
Construct validity was tested by exploring the associations
between the FSSB score and theoretically related constructs,

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TABLE 2 | Results of confirmatory factor analyses: comparison of goodness-of-fit indices among one-factor models and four-factor models.
Model

GFI

PGFI

NNFI

CFI

PNFI

RMSEA

χ2

df

p

Model 1 (one-factor model)

0.659

0.483

0.816

0.819

0.690

0.179

3511.1977<0.01

Model 2 (four-factor model)

0.923

0.624

0.961

0.964

0.750

0.083

746.98771<0.01

Model 3 (one-factor model) (multiple-group method)

0.658

0.483

0.813

0.820

0.688

0.126

3567.96

154

<0.01

Model 4 (four-factor model) (multiple-group method)

0.918

0.621

0.957

0.964

0.747

0.059

820.252

142

<0.01

GFI, goodness of fit index; AGFI, adjusted goodness of fit index; NNFI, non-normed fit index; CFI, comparative fit index; PNFI, parsimony normed fit index; RMSEA, root
mean square error of approximation.
TABLE 3 | Association between family supportive supervisor behavior score and other constructs at baseline.
Correlation coefficientsa

Other constructs
Overall scale

Emotional support

Instrumental support

Role modeling

Creative work-family

Procedural justice

0.52

0.44

0.46

0.46

0.51

Interactional justice

0.73

0.66

0.68

0.61

0.69

Supervisor support

0.60

0.59

0.56

0.47

0.52

Coworker support

0.30

0.29

0.30

0.21

0.25

WFPS

0.21

0.18

0.20

0.19

0.18

FWPS

0.20

0.18

0.18

0.18

0.17

Work engagement

0.35

0.31

0.31

0.30

0.33

Intention to leave

−0.35

−0.30

−0.33

−0.30

−0.32

Job satisfaction
Psychological distress

0.39

0.35

0.36

0.32

0.36

−0.10

−0.09

−0.14

−0.05

−0.07

a Pearson’s correlations. WFPS, work-family positive spillover; FWPS, family-work positive spillover. Bold values denote statistical significance at the p < 0.05 level.

Cronbach’s alpha values were 0.94 for emotional factors, 0.90 for
instrument support, 0.92 for role model, and 0.91 for creativework–family management.

including procedural justice, interactional justice, supervisor
support, coworker support, WFPS, FWPS, work engagement,
intention to leave, and psychological distress. Table 3 showsthat FSSB scores were significantly positively correlated withprocedural justice, interactional justice, supervisor support,
coworker support, WFPS, FWPS, and work engagement, andnegatively correlated with intention to leave and psychologicaldistress (all p < 0.01).

DISCUSSION
The current results revealed that the Japanese version of the
FSSB has good reliability, construct validity, and convergent
validity, as well as adequate structural validity. In accordance with
the COSMIN model, acceptable levels of internal consistency
reliability, convergent validity, and construct validity were
found among Japanese men and women. However, the current
study did not investigate cross-cultural validity, interpretability,
or responsiveness.

Reliability
Table 4 shows the means, standard deviations, item-total
correlations, and Cronbach’s α coefficients of the scale when each
item was removed. The mean score for each item was between
2.5 and 3.1. Correlations between items varied from 0.52 to 0.85
(p < 0.01). The item-total correlations ranged from 0.75 to 0.87
(p < 0.01). The correlations between the FSSB scores of the firstand second surveys indicated high test–retest reliability (r = 0.72,
p < 0.01). Table 5 shows the results of configural, metric, scalar
invariance across time. In line with criteria defined by Bentler
and colleagues (CFI > 0.900, RMSEA < 0.080) (Bentler, 1990;
Hu and Bentler, 1999), the latent factor structure appeared todemonstrate configural, metric, and scalar invariance across time.
The ICC (2,1) and SEM for FSSB were 0.70 [95% confidence
interval (CI): 0.66–0.73] and 2.58 (95% CI: 2.52–2.64). The meantime variation for the FSSB was 0.03 ± 0.005. The internalconsistency of the whole scale with 14 items was excellent, witha Cronbach’s α coefficient of 0.93. Omission of any of the items
did not substantially alter the internal reliability of the scale.

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Validity
A series of confirmatory factor analyses using the multi-groupmethod revealed that the four-factor model (Model 4) fitted
the data better than the one-factor model (Model 3). In
addition, as some intervention studies have been implemented
using the FSSB with four factors (Hammer et al., 2011,2016; McHale et al., 2016), FSSB was treated as a fourfactor model, in line with the original study of FSSB
(Hammer et al., 2009). This feature of the present study
facilitates comparability between the current results and previous
research findings.
Family demands and work–life conflict differ according tosocial and cultural context (Annor and Burchell, 2018). In Asian

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Japanese Version of FSSB Measure

TABLE 4 | Means, standard deviations, item-total correlations, and Cronbach’s α if item deleted.
Means

SD

Item-total

Cronbach’s αcorrelation

if item deleted

0.96

Emotional support
Item 1

My supervisor is willing to listen to my problems in juggling work and non-work life.

2.88(1.16)

0.81

Item 2My supervisor takes the time to learn about my personal needs.

2.86(1.12)

0.82

0.96

Item 3My supervisor makes me feel comfortable talking to him or her about my conflicts between
work and non-work.

2.67(1.06)

0.87

0.96

My supervisor and I can talk effectively to solve conflicts between work and non-work issues.

2.73(1.08)

0.85

0.96

Item 4

Instrument support
Item 5

I can depend on my supervisor to help me with scheduling conflicts if I need it.

3.03(1.15)

0.79

0.96

Item 6

I can rely on my supervisor to make sure my work responsibilities are handled when I haveunanticipated non-work demands.

3.12(1.13)

0.76

0.96

Item 7

My supervisor works effectively with workers to creatively solve conflicts between work andnon-work.

2.90(1.05)

0.82

0.96

Role model
Item 8My supervisor is a good role model for work and non-work balance.

2.63(1.05)

0.75

0.96

Item 9My supervisor demonstrates effective behaviors in how to juggle work and non-work balance.

2.63(1.01)

0.78

0.96

Item 10My supervisor demonstrates how a person can jointly be successful on and off the job.

2.53

(0.98)

0.75

0.96

Creative work–family management
Item 11

My supervisor thinks about how the work in my department can be organized to jointly benefit
employees and the company.

2.83(1.09)

0.76

0.96

Item 12My supervisor asks for suggestions to make it easier for employees to balance work andnon-work demands.

2.56(1.04)

0.80

0.96

Item 13

My supervisor is creative in reallocating job duties to help my department work better as a team.

2.81(1.10)

0.78

0.96

Item 14

My supervisor is able to manage the department as a whole team to enable everyone’s needs
to be met.

2.75

(1.08)

0.80

0.96

SD, standard deviation; item-total correlations: all Spearman correlations; p < 0.01 in all cases.

significant construct and convergent validity with respect to allimportant variables.

cultures, females and males typically play substantially different
family roles, and exhibit differences in family- and workrelated values. In the Global Gender Gap Index in 2018, Asian
countries typically ranked lower than western countries (World
Economic Forum, 2018). Japanese women tend to bear the
majority of the responsibility for child care and housekeeping,even when employed outside the home. Female workers spend
an average of 208 min per day on child care and housekeeping,compared with 44 min per day for men (Statistics Bureau
Japan Ministry of Internal affairs and Communications, 2017).
Contrary to expectations, the four-factor structure was invariant
between men and women in the current study; that is, thefactor loading of the items on the underlying factors did not
differ systematically between men and women. Future researchshould examine gender differences in the impact of FSSB in aninternational context.
To provide evidence of construct validity, FSSB-J scores wereused as predictors of six important aspects of work–family
constructs (i.e., work–family positive spillover, family–
work positive spillover, work engagement, intention toleave, job satisfaction, and psychological distress). FSSB was
significantly and negatively correlated with intention to leave andpsychological distress, and positively correlated with procedural
justice, interactional justice, supervisor support, coworker
support, WFPS, FWPS, work engagement, and job satisfaction.
These results are consistent with previous studies (Hammer
et al., 2009, 2011; Crain et al., 2014). Thus, FSSB-J scores have

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Reliability
The internal consistency values of each of the four dimensions
were excellent (0.90 < α < 0.94). These values are comparable toor higher than those reported in the original study of the FSSB
(0.74 < α < 0.90) (Hammer et al., 2009). Moreover, test–retest
reliability was high and the latent factor structure of FSSB was
relatively stable over time. Thus, the Japanese version of FSSB
appears to have a level of reliability that is comparable to theoriginal version.
TABLE 5 | Evaluation of measurem ent invariance: comparison of goodness-of-fit
indices among configural invariance, metric invariance, and scalar invariance.
TLI

CFI

RMSEA

χ2

df

0.949

0.965

0.051

1168.29

142

Model 2

0.952

0.965

0.049

1179.37152

1 in fit from Model 1

0.003

0.000

−0.00211.08

10Model 3

0.956

0.965

0.047

1202.14166

1 in fit from Model 2

0.004

0.000

−0.002

22.77

14

Model
Configural invariance
Model 1
Metric invariance

Scalar invariance

TLI, Tucker–Lewis index; CFI, comparative fit index; RMSEA, root mean squareerror of approximation.

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Japanese Version of FSSB Measure

ETHICS STATEMENT

Limitations and Future Directions
The present study involved several limitations. First, all measuresused in this study were self-reported, which could be a source ofbias. Common method variance may have affected the results,suggesting that the true associations between variables may be
weaker than those observed in this study. Future studies could
use objective measures (e.g., objective company records and otherrecords that capture work–life balance) to further investigate
this construct. Second, this study used a convenience sample
of people registered with an online survey company, and so
may not be a representative sample of the Japanese working
population. This could be a concern, as the reliability and validityof scales have been reported to depend on the characteristics ofthe sample (Mokkink et al., 2018). Third, this study included only
married workers and did not take into account the possibility
that unmarried individuals may also have family needs andwork–family conflicts. The generalizability of the results is
unclear, because FSSB is highly dependent on the context ofthe organization. Finally, the sample was predominantly male(∼70% of the sample in both surveys), potentially limiting thegeneralizability of the current findings across genders.

The studies involving human participants were reviewed andapproved by the Ethics Committee of the Graduate School of
Medicine and Faculty of Medicine at The University of Tokyo[11666-(1)]. Written informed consent for participation was not
required for this study in accordance with the national legislationand the institutional requirements.

AUTHOR CONTRIBUTIONS
HE contributed to the conception and design of the study,
performed the statistical analysis, and wrote the first draft ofthe manuscript with support from YK, HK, MS, MT, and AS.
MS and MT organized the database. YK contributed to translate
into Japanese. HK contributed to the back translation. AS was
involved in planning and supervised the work. All authors
contributed to manuscript revision, and read and approved thesubmitted version of the manuscript.

FUNDING
CONCLUSION

This study was supported by the Grant-in-aid scientific research,
Grant-in-Aid for Scientific Research (A), “Work–life balanceand health among dual-earner couples with children: Program
development and evaluation” (Research ID: 15H01832), Japan
Society for the Promotion of Science.

The current study suggests that the FSSB-J adequately measures
FSSB, and can be used effectively in the Japanese context. The
introduction of this questionnaire in Japan should stimulate
further research on work–life balance in Japan and alsointernational research collaborations on work–life balance.

ACKNOWLEDGMENTSDATA AVAILABILITY STATEMENT
We thank Benjamin Knight, MSc., from Edanz Group(www.edanzediting.com/ac) for editing a draft ofthis manuscript.

The datasets generated for this study are available on request to
the corresponding author.

REFERENCES

Fukui, S., Wu, W., and Salyers, M. P. (2019). Mediational paths from supervisor
support to turnover intention and actual turnover among community mental
health providers. Psychiatr. Rehabil. J. doi: 10.1037/prj0000362 [Epub ahead ofprint].
Furukawa, T. A., Kawakami, N., Saitoh, M., Ono, Y., Nakane, Y., Nakamura, Y.,
et al. (2008). The performance of the Japanese version of the K6 and K10 in theworld mental health survey Japan. Int. J. Methods Psychiatr. Res. 17, 152–158.
doi: 10.1002/mpr.257
Geurts, S., Schaufeli, W., and De Jonge, J. (1998). Burnout and intention to leaveamong mental health-care professionals: a social psychological approach. J. Soc.
Clin. Psychol. 17, 341–362. doi: 10.1521/jscp.1998.17.3.341
Geurts, S. A. E., Taris, T. W., Kompier, M. A. J., Dikkers, J. S. E., Van Hooff,
M. L. M., and Kinnunen, U. M. (2005). Work-home interaction from a work
psychological perspective: development and validation of a new questionnaire,the SWING. Work Stress 19, 319–339. doi: 10.1080/02678370500410208
Hammer, L. B., Johnson, R. C., Crain, T. L., Bodner, T., Kossek, E. E., Davis,
K. D., et al. (2016). Intervention effects on safety compliance and citizenshipbehaviors: evidence from the work, family, and health study. J. Appl. Psychol.
101, 190–208. doi: 10.1037/apl0000047
Hammer, L. B., Kossek, E. E., Anger, W. K., Bodner, T., and Zimmerman, K. L.
(2011). Clarifying work-family intervention processes: the roles of work-family
conflict and family-supportive supervisor behaviors. J. Appl. Psycho. 96, 134–
150. doi: 10.1037/a0020927

Annor, F., and Burchell, B. (2018). A cross-national comparative study of work
demands/support, work-to-family conflict and job outcomes: Ghana versus
the United Kingdom. Int. J. Cross Cult. Manag. 18, 53–72. doi: 10.1177/
1470595817746195
Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychol. Bull.
107, 238–246. doi: 10.1037//0033-2909.107.2.238
Cabinet Office, (2018). White Paper on Children and Young People. Cabinet
Office; 2018. Available from: https://www8.cao.go.jp/youth/english/policy_
2018.html (accessed July 22, 2019).
Crain, T. L., Hammer, L. B., Bodner, T., Kossek, E. E., Moen, P., Lilienthal,R., et al. (2014). Work-family conflict, family-supportive supervisor behaviors(FSSB), and sleep outcomes. J. Occup. Health. Psychol. 19, 155–167. doi: 10.
1037/a0036010
de Vet, H. C., Beckerman, H., Terwee, C. B., Terluin, B., and Bouter, L. M. (2006).
Definition of clinical differences. J. Rheumatol. 33:434.
Elovainio, M., Kivimaki, M., and Vahtera, J. (2002). Organizational justice:evidence of a new psychosocial predictor of health. Am. J. Public Health. 92,
105–108. doi: 10.2105/ajph.92.1.105
French, K. A., Dumani, S., Allen, T. D., and Shockley, K. M. (2018). A metaanalysis of work-family conflict and social support. Psychol. Bull. 144, 284–314.
doi: 10.1037/bul0000120

Frontiers in Psychology | www.frontiersin.org

8

November 2019 | Volume 10 | Article 2628

Eguchi et al.

Japanese Version of FSSB Measure

Hammer, L. B., Kossek, E. E., Yragui, N. L., Bodner, T. E., and Hanson, G. C.
(2009). Development and validation of a multidimensional measure of family
supportive supervisor behaviors (FSSB). J. Manag. 35, 837–856. doi: 10.1177/
0149206308328510
Hooper, D., Coughlan, J., and Mullen, M. (2008). Structural equation modelling:
guidelines for determining model fit. Electron. J. Bus. Res. Methods 6, 53–60.
doi: 10.1016/j.acap.2015.07.001
Hu, L. T., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariancestructure analysis: conventional criteria versus new alternatives. Struct. Equ.
Modeling 6, 1–55. doi: 10.1080/10705519909540118
Inoue, A., Kawakami, N., Shimomitsu, T., Tsutsumi, A., Haratani, T., Yoshikawa,T., et al. (2014). Development of a short version of the new brief job stressquestionnaire. Ind. Health 52, 535–540. doi: 10.2486/indhealth.2014-0114
Inoue, A., Kawakami, N., Tsutsumi, A., Shimazu, A., Tsuchiya, M., Ishizaki, M.,
et al. (2009). Reliability and validity of the Japanese version of the organizational
justice questionnaire. J. Occup. Health. 51, 74–83. doi: 10.1539/joh.
l8042
Japan Ministry of Health, Labour and Welfare, (2017). Annual Health, Labour andWelfare Report 2017. Tokyo: Japan Ministry of Health, Labour and Welfare.
Kawakami, N., Kobayashi, Y., Takao, S., and Tsutsumi, A. (2005). Effects of webbased supervisor training on supervisor support and psychological distressamong workers: a randomized controlled trial. Prev. Med. 41, 471–478. doi:
10.1016/j.ypmed.2005.01.001
Kessler, R. C., Andrews, G., Colpe, L. J., Hiripi, E., Mroczek, D. K., Normand,
S. L., et al. (2002). Short screening scales to monitor population prevalences
and trends in non-specific psychological distress. Psychol. Med. 32, 959–976.
doi: 10.1017/s0033291702006074
McHale, S. M., Davis, K. D., Green, K., Casper, L., Kan, M. L., Kelly, E. L.,
et al. (2016). Effects of a workplace intervention on parent-child relationships.
J. Child Fam. Stud. 25, 553–561. doi: 10.1007/s10826-015-0254-z
Mokkink, L. B., de Vet, H. C. W., Prinsen, C. A. C., Patrick, D. L., Alonso,J., Bouter, L. M., et al. (2018). COSMIN risk of bias checklist for systematic
reviews of patient-reported outcome measures. Qual. Life Res. 27, 1171–1179.
doi: 10.1007/s11136-017-1765-4
Moorman, R. H. (1991). Relationship between organizational justice andorganizational citizenship behaviors: do fairness perceptions influence
employee citizenship? J. Appl. Psychol. 76, 845–855. doi: 10.1037/0021-9010.76.
6.845
Nichols, H. M., Swanberg, J. E., and Bright, C. L. (2016). How does supervisor
support influence turnover intent among frontline hospital workers? The
mediating role of affective commitment. Health Care Manag. 35, 266–279.
doi: 10.1097/hcm.0000000000000119
Odle-Dusseau, H. N., Hammer, L. B., Crain, T. L., and Bodner, T. E.
(2016). The influence of family-supportive supervisor training on employee
job performance and attitudes: an organizational work-family intervention.
J. Occup. Health Psychol. 21, 296–308. doi: 10.1037/a0039961
O’Driscoll, M. P., Poelmans, S., Spector, P. E., Kalliath, T., Allen, T. D., Cooper,
C. L., et al. (2003). Family-responsive interventions, perceived organizationaland supervisor support, work-family conflict, and psychological strain. Int. J.
Stress Manag. 10, 326–344. doi: 10.1037/1072-5245.10.4.326
Organisation for Economic Co-operation and Development, (2011). Doing Better
for Families: OECD Publishing; 2011. Available from: http://www.oecd.org/
social/soc/doingbetterforfamilies.htm (accessed July 22, 2019).
Schaufeli, W. B., Bakker, A. B., and Salanova, M. (2006). The measurement of work
engagement with a short questionnaire: a cross-national study. Educ. Psychol.
Meas. 66, 701–716. doi: 10.1177/0013164405282471
Shimada, K., Shimazu, A., Geurts, S. A. E., and Kawakami, N. (2019). Reliability
and validity of the Japanese version of the survey work–home interaction –Nijmegen, the SWING (SWING-J). Community Work Fam. 22, 267–283. doi:
10.1080/13668803.2018.1471588

Frontiers in Psychology | www.frontiersin.org

Shimazu, A., Bakker, A. B., Demerouti, E., and Peeters, M. C. (2010). Work-family
conflict in Japan: how job and home demands affect psychological distress. Ind.
Health. 48, 766–774. doi: 10.2486/indhealth.ms1131
Shimazu, A., Schaufeli, W. B., Kosugi, S., Suzuki, A., Nashiwa, H., Kato, A., et al.
(2008). Work engagement in Japan: validation of the Japanese version of theutrecht work engagement scale. Appl. Psychol. 57, 510–523. doi: 10.1111/j.14640597.2008.00333.x
Shimomitsu, T. (2000). “The final development of the brief job stress questionnaire
mainly used for assessment of the individuals,” in Ministry of Labour Sponsored
Grant for the Prevention of Work-Related Illness: The 1999 Report, ed. M. Kato,(Tokyo: Tokyo Medical College).
Shrout, P. E., and Fleiss, J. L. (1979). Intraclass correlations: uses in assessing
rater reliability. Psychol. Bull. 86, 420–428. doi: 10.1037//0033-2909.86.
2.420
Statistics Bureau Japan Ministry of Internal affairs and Communications, (2017).
Survey on Time Use and Leisure Activities 2016. Available from: http://www.stat.
go.jp/english/data/shakai/2016/gaiyo.html (accessed July 22, 2019).
Steinhardt, M. A., Dolbier, C. L., Gottlieb, N. H., and McCalister, K. T. (2003).
The relationship between hardiness, supervisor support, group cohesion, andjob stress as predictors of job satisfaction. Am. J. Health Promot. 17, 382–389.
doi: 10.4278/0890-1171-17.6.382
Streiner, D. L., Norman, G. R., and Cairney, J. (2015). Health Measurement Scales: a
Practical Guide to Their Development and Use. Oxford: Oxford University Press.
Terwee, C. B., Mokkink, L. B., Knol, D. L., Ostelo, R. W. J. G., Bouter, L. M., and deVet, H. C. W. (2012). Rating the methodological quality in systematic reviews ofstudies on measurement properties: a scoring system for the COSMIN checklist.
Qual. Life Res. 21, 651–657. doi: 10.1007/s11136-011-9960-1
Thompson, C. A., Beauvais, L. L., and Lyness, K. S. (1999). When work-family
benefits are not enough: the influence of work-family culture on benefit
utilization, organizational attachment, and work-family conflict. J. Vocat.
Behav. 54, 392–415. doi: 10.1006/jvbe.1998.1681
Walter, S. D., Eliasziw, M., and Donner, A. (1998). Sample size and optimal
designs for reliability studies. Stat. Med. 17, 101–110. doi: 10.1002/(sici)10970258(19980115)17:1<101::aid-sim727>3.0.co;2-eWinnubst, J. A. M., Marcelissen, F. H. G., and Kleber, R. J. (1982). Effects of social
support in the stressor-strain relationship: a dutch sample. Soc. Sci. Med. 16,
475–482. doi: 10.1016/0277-9536(82)90056-9
World Economic Forum, (2018). The Global Gender Gap Report 2018. Available
from: http://www3.weforum.org/docs/WEF_GGGR_2018.pdf (accessed July22, 2019).
Yamaguchi, K., and Youm, Y. (2012). The Determinants of Low Marital Fertility in
Korea: A Comparison With Japan. The Research Institute of Economy, Trade andIndustry, Discussion Paper Series. 2012:1-21. Available from: https://www.rieti.
go.jp/en/publications/summary/12030001.html (accessed July 22, 2019).
Yragui, N. L., Demsky, C. A., Hammer, L. B., Van Dyck, S., and Neradilek,
M. B. (2017). Linking workplace aggression to employee well-being and work:
the moderating role of family-supportive supervisor behaviors (FSSB). J. Bus.
Psychol. 32, 179–196. doi: 10.1007/s10869-016-9443-z
Conflict of Interest: The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could be construed as apotential conflict of interest.
Copyright © 2019 Eguchi, Kachi, Koga, Sakka, Tokita and Shimazu. This is an
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