Abstract

Many employees are often faced with an inter-role conflict between work and that of a family in the U.S. However, business leaders and Human Resource Management (HRM) may not recognize the problem affecting white-collar employees. The purpose of this non-experimental quantitative study was to determine whether or not a relationship existed between employees’ use of flexible working arrangements as predictor variables (such as flexible work schedules and telecommuting) and work-family conflict and family-work conflict as covariate variables, and organizational outcomes (such as organizational commitment and job performance outcome variables). The self-reported survey data included 237 employees who have utilized flexible work arrangements in service organizations in the state of Texas. The inconclusive ANCOVA parametric data assumption resulted in further employ Kruskal-Wallis statistical analysis with less restrictive normality assumption The ANCOVA and Kruskal-Wallis analyses tests revealed a statistically significant result for employees’ use of flexible work options (a combination of flexible work schedules and telecommuting) to alleviate family-work conflict. The use of a single option (flexible work schedules or telecommuting) was statistically insignificant to employees. Despite the rigorous study, limitations are inevitable particularly for self-reported data and non-experimental study. The difficulty to determine the participants’ honesty unintentional misrepresentations reflected in the validity of the study (Hunter, 2012; Matsui et al., 2005). Nevertheless, the study provided insight information to organizational management not to overlook the use of flexible work arrangement practices to mitigate employees’ family-work conflict (<xref ref-type="bibr" rid="j_manment-2019-0025_ref_024">Gözükara & Çolakoğlu, 2015</xref>) to achieve organizational outcomes. Future researchers should replicate this study to include flexible work arrangement users vs. non-flexible work arrangement employees in other states, regions, and industries.

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