Machine gaze in online behavioral targeting: The effects of algorithmic human likeness on social presence and social influence

Abstract

Digital platforms increasingly use online behavioral targeting (OBT) to enhance consumers' engagement, which involves using algorithms to “gaze” at consumers—tracking their online activities and inferring their preferences—so as to deliver relevant, personalized messages (e.g., advertisements, recommendations) to consumers. In light of the rising call for algorithmic transparency, this study investigates the effects of algorithmic transparency on consumers' experience of social presence and OBT effectiveness, when the OBT algorithm has low or high level of similarity to humans' conscious mental processes. A one-factor, three-level (no transparency, vs. “observer” algorithm, vs. “judge” algorithm) online experiment with 209 participants was conducted. Results show that for individuals with low anthropomorphism tendency, the “observer” algorithm that did not form meaningful representations of consumers (i.e., low cognitive similarity to humans) reduced social presence, thereby compromising OBT effectiveness. The algorithm that “judged” consumers on meaningful dimensions (i.e., high cognitive similarity to humans) had no such effects. Findings suggest that anthropomorphism, as an important psychological mechanism underlying consumers' interaction with OBT platforms, may be inhibited by algorithmic transparency. Theoretical implications for understanding individuals’ experience with OBT and human-machine communication in general and practical implications for designing algorithmic transparency in OBT practices are discussed.