The effect of consumer ratings and attentional allocation on product valuations

Abstract

Online marketplaces allow consumers to leave reviews about the products they purchase, which are visible to potential customers and competitors. While the impact of reviews on valuations of worth and purchasing decisions has been intensively studied, little is known about how the reviews themselves are attended to, and the relation between attention and valuations. In three studies we use eye-tracking methodologies to investigate attention in subjective monetary valuations of consumer goods. We find that, when evaluating consumer goods, individuals’ attention to ratings are related to their frequencies, attention to positive or negative information is related to subjective valuations, and that perspective (owner vs. non-owner) influences the type of information attended to. These findings extend previous research regarding the valuations of risky prospects as implemented in abstract monetary gambles and suggest that similar cognitive processes might underlie both types of tasks.

Webcam-based online eye-tracking for behavioral research

“…To illustrate these issues and our solutions, we conducted a simple online value-based experiment on Amazon Mechanical Turk (MTurk). We aimed to replicate the robust links between gaze and choice that have been documented in the literature (e.g., Amasino et al, 2019;Ashby et al, 2015;Fisher, 2017;Ghaffari & Fiedler, 2018;Gluth et al, 2020;Krajbich et al, 2010;Pärnamets et al, 2015;Sepulveda et al, 2020;Sheng et al, 2020;Shimojo et al, 2003;Teoh et al, 2020). In particular, we used the same experimental paradigm as Krajbich et al (2010), and replicated empirical findings about the role of gaze in value-based decisions.…”

Visual attention and time preference reversals

“…First, establishing the differences in attention allocation during choice and bid tasks is useful to examine the contingent weighting hypothesis. To illustrate, individual attention allocation is related to subjective valuations when evaluating consumer goods (Ashby, Dickert & Glöckner, 2012;Ashby, Walasek & Glöckner, 2015) or risky options (Ashby et al, 2018). The relationship between attention and choice is also established in risky choice (Brandstätter & Körner, 2014;Glöckner & Herbold, 2011;Stewart, Hermens & Matthews, 2015) and intertemporal choice (Franco-Watkins, Mattson & Jackson, 2016;Liu et al, 2020).…”

The effect of autism on information sampling during decision-making: An eye-tracking study

“…Whether these patterns of sampling led to "better" or "worse" decision-making is an open question, but they run contrary to the idea that autism is associated with a more "analytical" or "deliberative" decision style (Brosnan et al, 2016;De Martino et al, 2008;Vella et al, 2018). More broadly, eye-tracking has helped to illuminate how neurotypical adults sample information in a range of decision paradigms, including risky choice (e.g., Fiedler & Glöckner, 2009;Glöckner & Herbold, 2011;Stewart, Hermens, et al, 2016), intertemporal choice (e.g., Reeck et al, 2017), on-line shopping (e.g., Ashby et al, 2015), and economic games (Stewart, Gächter, et al, 2016). Such methodologies, coupled with increasingly sophisticated mathematical models of eye movement data (e.g., Shi et al, 2013) could be used to compare autistic and neurotypical decision-makers, and may uncover important differences in their information-processing style -differences which might not be apparent in their overt choices (Gharib et al, 2015).…”