Thumbs Up or Down: Consumer Reactions to Decisions by Algorithms Versus Humans

Abstract

Although companies increasingly are adopting algorithms for consumer-facing tasks (e.g., application evaluations), little research has compared consumers’ reactions to favorable decisions (e.g., acceptances) versus unfavorable decisions (e.g., rejections) about themselves that are made by an algorithm versus a human. Ten studies reveal that, in contrast to managers’ predictions, consumers react less positively when a favorable decision is made by an algorithmic (vs. a human) decision maker, whereas this difference is mitigated for an unfavorable decision. The effect is driven by distinct attribution processes: It is easier for consumers to internalize a favorable decision outcome that is rendered by a human (vs. an algorithm), while it is easy to externalize an unfavorable decision outcome regardless of the decision maker type. The authors conclude by advising managers on how to limit the likelihood of less positive reactions toward algorithmic (vs. human) acceptances.

Exploring the role of AI algorithmic agents: The impact of algorithmic decision autonomy on consumer purchase decisions

“…The ability to predict consumer behavior and decisions is a must for business success ( Struhl, 2017 ), therefore, more and more companies are using algorithms to make business decisions that directly affect potential and existing customers ( Yalcin et al, 2022 ), using algorithms to collect and process information about data generated by consumers during shopping activities to make automated decisions about data analytics ( Helbing et al, 2018 ), driving a shift from descriptive to predictive models for algorithmic data analysis. However, various problems arising from the use of autonomous algorithm decision-making also make people face the risks and challenges it brings, and even cause humans to lose control of it ( Günther et al, 2017 ).…”

“…For companies using algorithms for decision-making, algorithms are not only marketing or sales tools, but also an important driving force to stimulate insight, innovation, and user participation. Therefore, with the popularity of algorithms in consumer-oriented decision-making, it is of great significance to understand how consumers react to algorithmic decisions ( Hoffman et al, 2022 ; Yalcin et al, 2022 ).…”

“…A review of the relevant literature reveals that most of the existing studies on algorithms have focused on technical improvements in human-computer interaction or investigated the influence of algorithm-related features on people’s motivation to accept ( Logg et al, 2019 ), or compared algorithmic decisions with human decisions to derive the reasons and factors influencing preferences ( Dietvorst et al, 2015 ; Dietvorst and Bharti, 2020 ; Yalcin et al, 2022 ), while studies exploring the interactive processes and psychological mechanisms of consumer-algorithmic decision-making are relatively limited, and the impact of algorithmic decision autonomy on consumer purchase decisions and its mechanisms of action from the perspective of algorithmic decision autonomy has not been studied yet ( Paschen et al, 2019 ). Existing research reviews on the impact of algorithmic decision-making are mixed and have yet to reach a consistent conclusion.…”

Users taking the blame? How service failure, recovery, and robot design affect user attributions and retention

“…Users attribute successful outcomes to themselves because of their unique skills and characteristics, which humans, not technologies, possess. Moreover, users do not think that technologies can fulfill their individual needs to a satisfactory degree because, for technology, every user is just a number (Yalcin et al, 2022 ). This is also reflected in failure outcomes, as users can easily blame technologies for failing to meet individual user needs (Longoni et al, 2019 ), so they are less likely to attribute undesirable outcomes to themselves.…”

“…Based on attribution theory, we assume that responsibility attribution is an important explanatory mechanism that indicates how users evaluate service outcomes and how this influences their behavior. Previous research indicates that internal responsibility attribution can positively affect users' perceptions and behavior toward service agents and firms (e.g., Leung et al, 2018 ; Yalcin et al, 2022 ). Therefore, firms need to understand how robots can facilitate this internal responsibility attributions if they act as service agents.…”