The Effect of past Algorithmic Performance and Decision Significance on Algorithmic Advice Acceptance

The Effect of Interpretable Artificial Intelligence on Repeated Managerial Decision-Making Under Uncertainty

“…Clearly, its objective is not to predict the next period's demand but rather to maximize the long-run cumulative profit. Thus, our results support that participants evaluate the observed accuracy as the main performance measure of AI, and they decreased their usage over time due to perceived low-performance (Saragih & Morrison, 2022;Yu et al, 2016).…”

Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings

“…For instance, issues of system capability were prominent. Indeed, users’ perceptions of the performance limitations of DSSs appear central to their acceptance, as is regularly cited in the algorithm aversion literature (Dievorst, et al, 2015; Saragih & Morrison, 2022). As such, developers should consider the ways in which users may be better informed of the capacities and limitations of DSSs, to ensure that they are not incorrectly or over-weighting factors in the determination of system utility.…”

The public perceptions of algorithmic decision-making systems: Results from a large-scale survey

“…There is a growing body of empirical literature on the perceptions and attitudes of individuals toward ADM systems. First, a stream of research has investigated the algorithmic aversion attitudes of decision-makers (Burton et al, 2020) and the preference for human vs ADM system decision-makers due to system performance (Saragih and Morrison, 2022). Second, attitudes towards ADM systems have been analyzed by measuring user acceptance (Gursoy et al, 2019;Park and Mo Jones-Jang, 2022;Sohn and Kwon, 2020) and other attitudes and behaviors such as perceived usefulness, risk, emotional responses, satisfaction, and information seeking (Araujo et al, 2020;Shin 2020bShin , 2022bShin , 2022a.…”