Journal of Occupational Health · 2020 · 18 citations · 40 references
Social SciencesPsychologyPsychometricsEmployee Well-beingWorkplace Well-beingCross-cultural Measurement EquivalenceCross-cultural Scale ValidationEmployee Well-being MeasurementWell-being
Abstract Objectives Workplace gratitude is important for improving work-related outcomes and individual well-being. Although the gratitude at work scale (GAWS) was developed in the United States, it has not been corroborated in Asian countries with interdependent cultures. This study aimed to develop and validate the GAWS among Japanese workers. Method Japanese workers completed online surveys at baseline (N = 206) and 2 weeks later (N = 103). The Japanese GAWS was developed according to the international guidelines. We measured (a) trait gratitude as comparison for the criterion-related validity, (b) work-related outcomes/factors (eg, work engagement), and (c) well-being (eg, eudemonic well-being at work) as comparisons for convergent validity. Cronbach’s alpha, intra-class correlation coefficients (ICCs), and measurement errors were calculated to assess reliability; measurement validity was evaluated by correlational analyses and confirmatory factor analysis (CFA). Results A total of 206 and 93 workers were included for baseline and follow-up analyses, respectively. Cronbach’s alpha and ICCs of the Japanese GAWS ranged from 0.81 to 0.91. CFA showed that the 2-factor model (ie, gratitude for (a) a supportive work environment and (b) meaningful work) demonstrated a good fit (χ2 (34) = 67.58, CFI = 0.967, TLI = 0.956, RMSEA = 0.069, SRMR = 0.037), similar to the original version. As we had hypothesized, overall GAWS and the two domains were significantly correlated with trait gratitude, work-related outcomes/factors, and well-being. Conclusions The Japanese GAWS demonstrated good reliability and validity. Future research should explore mechanisms related to workplace gratitude and further intervention studies among workers.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Jacob Cohen · Psychological Bulletin · 1992 · 42.2K citations