The Impact of Chain Organization Size on Efficiency and Quality of Affiliated Facilities-Implications for Multi-Unit Organizational Forms

• 成果类型: Article
• 署名作者: Dreyfus, David; Nair, Anand; Talluri, Srinivas
• 署名单位: Rutgers University System; Rutgers University New Brunswick; Rutgers University Newark; Michigan State University; Michigan State University's Broad College of Business; Michigan State University; Michigan State University's Broad College of Business
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13177
• 发表日期: 2020
• 页码: 1605-1623
• 关键词: chain size; QUALITY; EFFICIENCY; Organizational learning; Dynamic panel data analysis

摘要

This study investigates how the affiliation of dialysis facilities within chain organizations of varying sizes impacts efficiency and quality outcomes of these facilities. We develop our hypotheses by building on the literature base that relates to organizational learning. The paper uses a panel dataset spanning the years 2008-2013. The efficiency of the dialysis facilities is operationalized by means of a data envelopment analysis model. This model considers the number of treatments in the dialysis facility as the output variable. The model uses four inputs-number of dialysis stations, number of full-time equivalents of physicians, number of full-time equivalents of clinical staff, and the number of employee hours per week at a dialysis facility. Quality is operationalized by considering facilities' performance scores on three measures developed by the Center for Medicaid and Medicare Services. The findings point to distinct effects of chain organization size on quality and efficiency performance of dialysis facilities. Specifically, the results obtained by analyzing a dynamic panel data model show that chain organization size has an inverse U-shaped curvilinear effect on quality outcomes and a U-shaped curvilinear effect on efficiency. The findings suggest that dialysis facilities affiliated with chain organizations of varying sizes experience learning effects and causal ambiguities depending on the specific performance measure under consideration. We discuss the implications of our findings for theory and practice and present directions for future research.