Repairing a Cracked Mirror: The Heterogeneous Effect of Personalized Digital Nudges Driven by Misperception

Abstract

Our study aims to deepen the understanding of personalized digital nudges by evaluating their effects on energy‐saving behavior. We conducted a field experiment with a leading smart metering company in South Korea to investigate whether customers save more energy when a personalized goal and feedback are provided, and how the impacts of nudges vary according to the types of misperception. Specifically, we focused on the behavior of customers who underestimate or overestimate their past electricity usage compared to their actual consumption. We merged daily energy consumption with a pre‐experiment survey for the customers. We found that goal‐setting and feedback mechanisms have a markedly different impact on each type of misperception. Underestimating customers reduced energy consumption only under the “goal‐setting with feedback" treatment. Conversely, overestimating customers reduced energy consumption even under the “goal‐setting without feedback" condition. The underlying mechanism is suggested as updating biased beliefs toward goal achievement. Overall, the results demonstrate that personalized nudges lead to heterogeneous behavioral responses and that service providers and policymakers can use these signals to enrich their planning of behavioral nudges.

Performance feedback and productivity: Evidence from a field experiment

“…How much employees learn from the feedback may vary across individuals if their prefeedback beliefs about their performance are to some extent biased and these biases vary across individuals: Those with more biased beliefs should react more to feedback. In particular, individuals may react differently depending on whether they learn that they were overestimating or underestimating their performance (Azmat et al., 2019; Chen et al., 2010; Jung et al., 2021; Kuhnen & Tymula, 2012; Lourenço et al., 2018).…”

Field experiments in operations management

“…Students who followed through and actually conducted one-on-one discussions improved their quiz completion rates and quiz scores by 10% in the subsequent week. Jung et al (2021) examine how consumers' misconception of their past energy consumption determines their effort provision and daily energy consumption behaviors when people's goal-setting and feedback are provided. Based on a field experiment that exploits the widespread deployment of smart metering services, they find that (1) goal-setting intervention reduces overestimating users' energy consumption while having no impact on underestimating users, and (2) performance feedback makes underestimating users consume less energy.…”

“…Field experiments have become a popular tool for studies focusing on service operations to causally evaluate the effect of a policy on performance outcomes, such as consumer outcomes (Buell et al, 2017;Jung et al, 2021;Retana et al, 2016;Zhang et al, 2017), efficiency (Bichler & Merting, 2021;Bray et al, 2016), and fairness (Cui et al, 2020), and so forth.…”

“…In these cases, researchers will need to determine if participants notice the manipulation and how they interpret the change. For instance, with studies that rely on randomized text message interventions with different framing, researchers shall refer to a manipulation check and examine whether participants interpret the message as designed (Cohen et al, 2021; Jung et al, 2021; Lu et al, 2021).…”

“…The remaining 10 studies investigate how various factors affect performance outcome (sales and revenue) in the context of offline retailers (Abbey, Meloy, et al, 2015; Buell & Kalkanci, 2021; Chuang et al, 2016; Ding et al, 2021; Gallien et al, 2015; Gaur & Fisher, 2005) and online retail management (Cui, Zhang, & Bassamboo, 2019; Feldman et al, 2022; Gallino & Moreno, 2018; Zhang et al, 2019). In service operations, researchers have utilized field experiments to causally evaluate the effect of policy changes on performance outcomes, such as consumer outcomes (Buell et al, 2017; Jung et al, 2021; Lu et al, 2021; Retana et al, 2016; Zhang et al, 2017), auction outcomes (Abhishek & Hosanagar, 2013; Haruvy et al, 2014), scheduling efficiency (Bichler & Merting, 2021; Bray et al, 2016), ride‐sharing (Cohen et al, 2021; Mejia & Parker, 2021), and fairness (Cui et al, 2020). OM researchers have also focused on ways to improve various health care outcomes, such as physician performance (Tucker & Singer, 2015), quality and efficiency of care (Kistler et al, 2021; Queenan et al, 2019).…”