Authors: Christopher Amaral (University of Bath, ca786@bath.ac.uk); Ceren Kolsarici (Queen’s University, ceren.kolsarici@queensu.ca); Iina Ikonen (University of Bath, imhi21@bath.ac.uk); Nicole Robitaille (Queen’s University, nicole.robitaille@queensu.ca)
Abstract: Despite a breadth of research showing how to reduce individuals’ energy consumption with techniques such as behavioral insights and dynamic energy pricing, we still know relatively little about their effectiveness in organizations and over time. We partnered with an energy consulting company in Ontario to develop and test a multidisciplinary approach to reduce organizations’ energy consumption in a demand pricing program. Using a multi-phase longitudinal randomized field experiment, we tested the effectiveness of improved demand forecast models using artificial intelligence and behaviorally informed emails (i.e., leveraging planning prompts) in large organizations. Our data indicate that enhancing the pricing program with both improved models and behaviorally informed emails each significantly contributed to organizations’ reduced energy consumption. In addition, we demonstrate that our interventions remained effective, even after repeated exposures. This article provides evidence for multidisciplinary solutions that reduce organizations’ energy consumption and has important implications for both behavioral theory and practice.
Presented at the 2023-2024 Gary L. Lilien ISMS Practice Prize Competition