Shared Benefits and Information Privacy: What Determines Smart Meter Technology Adoption?

An unexplored gap in IT adoption research concerns the positive role of shared benefits even when personal information is exposed. To explore the evaluation paradigm of shared benefits versus the forfeiture of personal information, we analyze how utility consumers use smart metering technology (SMT). In this context, utility companies can monitor electricity usage and directly control consumers’ appliances to disable them during peak load conditions. Such information could reveal consumers’ habits and lifestyles and, thus, stimulating concerns about their privacy and the loss of control over their appliances. Responding to calls for theory contextualization, we assess the efficacy of applying extant adoption theories in this emergent context while adding the perspective of the psychological ownership of information. We use the factorial survey method to assess consumers’ intentions to adopt SMT in the presence of specific conditions that could reduce the degree of their privacy or their control over their appliances and electricity usage data. Our findings suggest that, although the shared benefit of avoiding disruptions in electricity supply (brownouts) is a significant factor in electricity consumers’ decisions to adopt SMT, concerns about control and information privacy are also factors. Our findings extend the previous adoption research by exploring the role of shared benefits and could provide utility companies with insights into the best ways to present SMT to alleviate consumers’ concerns and maximize its adoption

Consumers’ Privacy Concerns about Smart Meters

Smart meters are digital electrical meters that offer “two-way, near real time communication” between homes and utilities. While smart meters have the potential to solve our current energy problems, they have also brought new problems regarding the consumers’ privacy. We first develop a measurement model to measure the consumers’ concerns about information privacy in adopting smart meters. Then, we propose a conceptual model to examine the relationship between privacy concerns, trusting beliefs, risk beliefs, and intentions to adopt smart meters. Empirical data were collected from 103 survey respondents and analyzed using CFA and PLS regression techniques. Results show that consumers’ information privacy concerns about adopting smart meters can be measured in three dimensions: collection, secondary use, and improper access. In addition, the effect of information privacy concerns about intentions is fully mediated by risk beliefs. Among the control variables, privacy experiences have a significantly negative effect on intentions