A field experiment on workplace norms and electric vehicle charging etiquette

Abstract

The increase in electric vehicles as a low‐carbon mobility option has driven interest from many workplaces and local governments to offer charging services for employees, customers and visitors. However, the lack of incentives to limit over‐consumption in shared charging resources has led to congestion issues. In this paper, we use high‐frequency data to study two deterrence mechanisms implemented at one of the largest workplace charging programs in the United States. We study both price and nonprice interventions that encourage adoption of workplace norms and charging etiquette for resource sharing in charging stations. To study these mechanisms, we use a dynamic regression discontinuity design to separately identify treatment effects with digital platform data. Our findings provide new evidence that group norms can play an important role in driving behavioral compliance when setting EV access policies. We also find that workplace norms are complements to dynamic pricing policies. We discuss the implications of this data discovery for the effective management of common pool resources in the context of workplace charging and space‐constrained environments. This article met the requirements for a Gold‐Gold JIE data openness badge described at http://jie.click/badges.

Data innovation in industrial ecology

“…To this end, the transparency and openness of the research are key. It is therefore particularly encouraging that many studies of this special issue have published their entire datasets in an open manner (Xie et al., 2021), or even earned JIE ’s Data Openness Badge (Agez et al., 2021; Asensio et al., 2021; Huang & Eckelman, 2020; Kerdlap et al., 2021; Sprecher et al., 2021; Vilaysouk et al., 2021). Similarly, studies in this special issue contributed new open‐source code (Agez et al., 2021; Joyce & Björklund, 2021) or extended open‐source software from this community (Vunnava et al., 2021).…”

“…Such high‐frequency data can also allow for a quantitative evaluation of citizen‐consumer behavior, as illustrated by the field experiment of Asensio et al. (2021) on workplace electric vehicle charging norms, etiquette, and incentive mechanisms. Even in broad urban areas, where automatic data collection by sensors is of limited avail, citizen‐science and crowdsourcing data collections open new opportunities.…”

Electric vehicle charging stations in the workplace with high-resolution data from casual and habitual users

“…This has made it difficult to investigate patterns of charging demand at the high resolution needed to understand individual-level behaviour. For example, high temporal resolution data is needed to evaluate the effectiveness of real-time or dynamic pricing policies 3 , which are critical for large-scale system optimization and demand and capacity planning. This situation has created Digital data collection.…”

“…Employees at high-demand facilities were also sent reminders about resource sharing. This behavior by program hosts to include behavioral messaging can be considered a leading indicator of strategies for managed infrastructure in high growth and space-constrained environments 3 . In the next section, we share our protocols for pre-processing and anonymizing the data.…”

“…It is also possible to benchmark the data with charging behaviour at other points of interest such as retailers, restaurants, or transit centres, see refs. 3,20 .…”