Reducing Industrial Water Consumption: The Impact of Organizational Learning

标杆管理 工厂(面向对象编程) 消费(社会学) 生产(经济) 业务 跨国公司 环境经济学 缺水 用水 营销 运营管理 计算机科学 经济 水资源 微观经济学 生态学 社会科学 财务 社会学 生物 程序设计语言
作者
Amrou Awaysheh,Sriram Narayanan,Brian W. Jacobs
出处
期刊:Production and Operations Management [Wiley]
卷期号:33 (1): 225-242
标识
DOI:10.1177/10591478231224929
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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
AA完成签到,获得积分10
1秒前
cocolu应助科研通管家采纳,获得10
1秒前
科研通AI2S应助科研通管家采纳,获得10
1秒前
1秒前
维尼发布了新的文献求助10
1秒前
大个应助科研通管家采纳,获得10
1秒前
cocolu应助科研通管家采纳,获得10
2秒前
充电宝应助科研通管家采纳,获得10
2秒前
2秒前
CipherSage应助科研通管家采纳,获得10
2秒前
科目三应助科研通管家采纳,获得10
2秒前
上官若男应助科研通管家采纳,获得30
2秒前
keker发布了新的文献求助10
2秒前
充电宝应助科研通管家采纳,获得10
2秒前
传奇3应助科研通管家采纳,获得10
2秒前
2秒前
2秒前
慕青应助科研通管家采纳,获得10
2秒前
3秒前
大米发布了新的文献求助10
3秒前
3秒前
彩色伯云发布了新的文献求助30
3秒前
buder完成签到,获得积分10
3秒前
3秒前
4秒前
4秒前
火炎焱燚发布了新的文献求助30
4秒前
5秒前
5秒前
5秒前
5秒前
火星上的醉山完成签到,获得积分10
5秒前
6秒前
顾矜应助哈雷彗星采纳,获得10
6秒前
7秒前
7秒前
悦耳伊完成签到,获得积分10
7秒前
欢喜的代容完成签到,获得积分10
7秒前
sara发布了新的文献求助10
7秒前
8秒前
高分求助中
Licensing Deals in Pharmaceuticals 2019-2024 3000
Effect of reactor temperature on FCC yield 2000
Very-high-order BVD Schemes Using β-variable THINC Method 1020
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 800
Near Infrared Spectra of Origin-defined and Real-world Textiles (NIR-SORT): A spectroscopic and materials characterization dataset for known provenance and post-consumer fabrics 610
Mission to Mao: Us Intelligence and the Chinese Communists in World War II 600
Promoting women's entrepreneurship in developing countries: the case of the world's largest women-owned community-based enterprise 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3305153
求助须知:如何正确求助?哪些是违规求助? 2939026
关于积分的说明 8491012
捐赠科研通 2613498
什么是DOI,文献DOI怎么找? 1427461
科研通“疑难数据库(出版商)”最低求助积分说明 663007
邀请新用户注册赠送积分活动 647648