操作化
心理学
社会心理学
维数(图论)
规则网络
工作-家庭冲突
实证研究
结构方程建模
认识论
工作(物理)
数学
统计
机械工程
工程类
哲学
纯数学
作者
Andrea L. Hetrick,Nicholas J. Haynes,Malissa A. Clark,Katelyn N. Sanders
摘要
Most work-family conflict (WFC) research does not theorize, hypothesize, or empirically test phenomena at the dimension level. Instead, researchers have predominantly used composite-level approaches based on the directions of WFC (work-to-family and family-to-work conflict). However, conceptualizing and operationalizing WFC at the composite level instead of at the dimension level has not been confirmed as a well-founded strategy. The goal of the current research is to explore whether there is theoretical and empirical evidence in the WFC literature to support the importance of dimension-level theorizing and operationalization when compared to composite-level approaches. To advance theory related to the dimensions of WFC, we begin by reviewing WFC theories and then demonstrate the relevance of resource allocation theory to the time-based dimension, spillover theory to the strain-based dimension, and boundary theory to the behavior-based dimension. From this theorizing, we highlight and meta-analytically test the relative importance of specific variables from the WFC nomological network that are theoretically connected to each dimension: time and family demands for the time-based dimension, work role ambiguity for the strain-based dimension, and family-supportive supervisor behaviors and nonwork support for the behavior-based dimension. Reviewing and drawing from bandwidth-fidelity theory, we also question whether composite-based WFC approaches are more appropriate for broad constructs (i.e., job satisfaction and life satisfaction). The results of our meta-analytic relative importance analyses generally support a dimension-based approach and overall follow the pattern of results expected from our dimension-level theorizing, even when broad constructs are considered. Theoretical, future research, and practical implications are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
科研通智能强力驱动
Strongly Powered by AbleSci AI