Compensating for Innovation: Extreme Product Incongruity Encourages Consumers to Affirm Unrelated Consumption Schemas

Abstract

New products are often extremely incongruent with expectations. The inability to make sense of these products elevates anxiety and leads to negative evaluations. Although scholars have predominantly focused on combating the negative response to extreme incongruity, we propose that extreme incongruity may have implications that extend beyond the category. We base our predictions on the concept of fluid compensation, which suggests that when people struggle to make sense of something, they will nonconsciously reinforce highly accessible schemas in unrelated domains. Four studies confirm that extreme incongruity encourages fluid compensation, such that it elevates preference for dominant brands (study 1), green consumption (studies 2 and 4), and ethnocentric products (study 3). We isolate the causal role of anxiety using moderation tasks and biometric feedback. Furthermore, we demonstrate that compensation has an immediate dampening effect on arousal intensity. Thus, if consumers can compensate before explicitly evaluating an extremely incongruent product, their evaluations tend not to be negative. Taken together, we document that extreme innovations encourage compensation, and in compensating, consumers can become more receptive to extreme innovations.

Strong Anxiety Boosts New Product Adoption When Hope Is Also Strong

“…We are the first to document that when products are new, strong feelings of anxiety about potential outcomes from new product adoption can actually boost (vs. dampen) adoption decisions when hope is also strong. These findings augment prior studies that show that that anxiety can cause individuals to reject new products (e.g., Lee, Kim, and Vohs 2011;Meuter et al 2003), as well as studies that document methods to reduce anxiety when new products are incongruent with expectations (i.e., Taylor and Noseworthy 2020). Our findings suggest that, in some situations, anxiety can induce an approach (vs. avoidance) response, motivating information seeking and effortful information processing.…”

Your Fries are Less Fattening than Mine: How Food Sharing Biases Fattening Judgments Without Biasing Caloric Estimates

“…A related principle in categorization theory that fits the mental accounting lens could be how psychological budgets interact. If consumers see calorie budgets similar to financial budgets, sharing in one may lead people to compensate for a biased shortfall in the other-re: fluid compensation (Taylor & Noseworthy, 2020). Another possibility relates to egocentric categorization theory (Weiss & Johar, 2016), which shows that people assimilate to products they own, and contrast products they do not own.…”

News from Artificial Intelligence is Believed Less

“…To further probe aversion toward algorithmic reporting, we explored whether trust mediated the effect of reporter on perceived accuracy. To do so, we averaged all of the responses for each participant, and then conducted a mediation using linear models for the mediation and outcome equations (1,000 simulations; Mediation Package for R; Tingley, Yamamoto, Hirose, Keele, & Imai, 2014, Taylor & Noseworthy, 2020. Note that this analysis requires the assumption of sequential ignorability, which is quite stringent and implies that there are no unmodeled variables that affect both the trust and accuracy.…”

“…To account for the fact that participants were treated for some news items and not others (i.e., viewed some news items tagged as written by an AI reporter but not others), we used a multi-level mediation model to analyze Experiment 2. Specifically, we modeled both the mediation and outcome equations as mixed models with participant random effects (1,000 simulations; Mediation Package for R; Tingley, Yamamoto, Hirose, Keele, & Imai, 2014, Taylor & Noseworthy, 2020. The effect of reporter (human vs. AI) on trust was significant (b = -.64, p < .001, CI: [-0.66, -0.63]), and trust had a significant effect on perceived accuracy (b = .14, p <.001, CI: [.12, .16]).…”