Reducing Industrial Water Consumption: The Impact of Organizational Learning

• 成果类型: Article
• 署名作者: Awaysheh, Amrou; Narayanan, Sriram; Jacobs, Brian W.
• 署名单位: Indiana University System; Indiana University Indianapolis; IU Kelley School of Business; Michigan State University; Michigan State University's Broad College of Business; Pepperdine University
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478231224929
• 发表日期: 2024
• 页码: 225-242
• 关键词: Water consumption; water scarcity; learning curves; experience curves; vicarious learning; sustainability

摘要

Using factory-level data from a large multinational manufacturer, we examine the effects of both organizational experience and knowledge transfer on an increasingly critical environmental performance measure, the consumption of water required for manufacturing. We estimate the direct effects on water consumption from in-factory cumulative production experience and the vicarious learning from peer factories in the same product category. We consider vicarious learning from three potential sources: observation of peer factories' cumulative production experience; and benchmarking of water consumption performance with the best and worst performing peer factories. For each learning channel, we test for the moderating effects of water scarcity and geographic proximity. We find that factories learn to reduce their water consumption from their own experience but at a greater rate in water-scarce locations. Although we find that factories learn significantly from observing the cumulative production experience of peer factories, this effect does not hold in water-scarce locations or across geographic regions. We document that learning effects from observing others' experience are quite distinct from learning effects by benchmarking others' performance. We find vicarious learning effects from benchmarking the best-performing peer factories result in significant reductions in water consumption, and this effect is greater when the factory is in a water-scarce location, and when benchmarking other regions rather than within the same region. Finally, we find less significant vicarious learning from observing the worst-performing factories.