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Can precise numbers boost energy efficiency?

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  • Saurabh Bansal
  • Suresh Muthulingam

Abstract

The United States has long recognized the potential of improved energy efficiency to profitably reduce energy consumption in manufacturing. The Department of Energy (DOE) has also recognized the potential and initiated several programs to enhance energy efficiency in manufacturing. Despite the attention, studies point out that many energy‐efficiency opportunities remain unexploited. Increasingly, scholars have emphasized that behavioral factors could facilitate the uptake of energy‐efficiency practices. Accordingly, we adopt a behavioral perspective and investigate whether precision (a hitherto unexplored behavioral factor) affects the adoption of energy‐efficiency practices. Specifically, we examine whether the implementation of energy‐efficiency practices depends on whether the costs and savings associated with such practices are presented as precise (e.g., $20,431) or round (e.g., $20,400) numbers. First, we explore the impact of precision with econometric analysis using data on energy‐efficiency recommendations made by the DOE to small‐ and medium‐sized manufacturing firms. Our econometric analysis indicates that energy‐efficiency recommendations with precise cost and precise saving exhibit higher adoption rates as compared to other recommendations. Next, we use individual interactions with managers and laboratory experiments with managers and students to replicate and isolate the precision effects. Then, we elicit the mechanism by which precision affects the adoption of energy‐efficiency initiatives. We find that credibility serves as a key mechanism that drives the precision effect. Our findings suggest that credibility‐enhancing cues such as precision can be leveraged to increase the uptake of energy efficiency and possibly other process improvement practices.

Suggested Citation

  • Saurabh Bansal & Suresh Muthulingam, 2022. "Can precise numbers boost energy efficiency?," Production and Operations Management, Production and Operations Management Society, vol. 31(8), pages 3264-3287, August.
    • Handle: RePEc:bla:popmgt:v:31:y:2022:i:8:p:3264-3287
    DOI: 10.1111/poms.13749
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    2. Ting Hou & Meng Li & Yinliang (Ricky) Tan & Huazhong Zhao, 2024. "Physician Adoption of AI Assistant," Manufacturing & Service Operations Management, INFORMS, vol. 26(5), pages 1639-1655, September.

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