Decision making strategies differ in the presence of collaborative explanations

Choosing between hotels: impact of bimodal rating summary statistics and maximizing behavioral tendency

“…Third, participants voluntarily took part in the study, and might (or might not) have been conveniently available to the experimenter. It should be emphasized, however, that our eye-tracking results correspond to the pattern of results reported in a previous choice-based conjoint experiment, derived from a larger randomized sample of 200 respondents with diverse international backgrounds and demographic characteristics (Coba et al 2019). This, in our opinion, offers support to the notion that the eye-tracked results for maximizers and satisficers in this study are not, in fact, confounded by convenience or quota sampling.…”

“…This observation resonates with the growing insight that individual differences in overall maximizing behavioral tendency are better understood in terms of separate goals, strategies, and causes (Nenkov et al 2008;Schwartz et al 2016). Focusing on the latter dimension (i.e., the trait-based causes underlying maximization), recent studies by Coba et al (2019) showed that maximizers respond differently to collaborative explanations of choice alternatives than satisficers, and that this effect especially holds for the individual difference items that tap into causes of maximization (labelled 'decision difficulty' in the literature, cf. Cheek and Schwartz 2016;Nenkov et al 2008;Schwartz et al 2002).…”

“…People, apparently, prefer a slightly lower mean rating value over a higher number of overall ratings-making the mean rating value appear more reliable. Marginal and null effects were observed, when additional characteristics of rating distributions, such as the variance or skewness were also taken into consideration (Coba et al 2019). However, rating distributions might actually exhibit an asymmetric bimodal (J-shaped) distribution (Hu et al 2009).…”

A historical perspective of explainable Artificial Intelligence

“…There is no clear consensus in the recommender systems literature on what makes for a good explanation (Nilashi, Jannach, & bin Ibrahim, O., Esfahani, M. D.,, & Ahmadi, H., 2016; Nunes & Jannach, 2017; Tintarev & Masthof, 2015). In fact, an explanation on the recommendation can have different goals, and impact decision-makers differently (Coba, Rook, et al, 2019). For example, a tailored explanation can persuade or help a user in finding an item more efficiently (Tintarev & Masthof, 2015).…”

“…They found that User Style explanations were the most preferred. In later studies, they also provided evidence that perception of explanations relates to personality characteristics, and they proposed model-based approaches to further personalize explanations (Coba, Rook, et al, 2019;.…”