Matter over mind? How the acceptance of digital entities depends on their appearance, mental prowess, and the interaction between both

Stein, Jan‐Philipp; Appel, Markus; Jost, A.; Ohler, Peter

doi:10.1016/j.ijhcs.2020.102463

Cited by 42 publications

(20 citation statements)

References 36 publications

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“…Third, more attention should be paid to find more moderators that may influence the effect. For example, many studies have explored the moderating role of the anthropomorphism of AI recommenders such that the effect of AI aversion is likely to diminish when AI is anthropomorphized both in embodiment and mental capacity (Stein et al, 2020). Future research can continue to explore whether the effect of AI (vs. human) recommenders on consumers' preferences for experience versus search products will diminish when AI recommenders are anthropomorphized, which would thus broaden the realm for the application of AI recommendation.…”

Section: Discussionmentioning

confidence: 99%

The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

Xie

Zhang

et al. 2022

Psychology and Marketing

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Though artificial intelligence (AI) recommendation is a hot topic in recent marketing research, previous research has shown a convergent tendency for aversion to AI recommendation. It is imperative to find ways to promote AI usage and reduce consumers’ AI aversion. This study fills this void by exploring the effect of AI (vs. human) recommenders on consumers’ preferences for search versus experience products in the context of e‐commerce. Two studies provide convergent evidence that consumers show less avoidance of algorithms when recommending search products compared to experience products. A behavioral experiment (Study 1, N = 112) validates that consumers are less likely to purchase experience products recommended by AI, while there are no significant differences between AI versus human recommenders when recommending search products. Using event‐related potential (ERP), a further consumer neuroscience study (Study 2, N = 26) shows that consumers have a higher level of cognitive conflict (i.e., a larger magnitude of N2) when AI (vs. human) recommends experience products, while the effect disappears for search products. This paper shows that for search products, marketers can obtain similar evaluations using AI recommenders, which is relatively cheaper and more time‐saving compared with human recommenders. Therefore, our work provides important implications for theory and practice on e‐commerce and marketing communication.

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Section: Discussionmentioning

confidence: 99%

The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

Xie

Zhang

et al. 2022

Psychology and Marketing

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“…An exploration into the dynamic interplay among AI algorithmic features, as advocated by Stein et al. (2020), within the context of AI-enabled HRM practices could shed light on their combined effects on employees' experiences and provide a more holistic understanding of the mechanisms underlying negative outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…An exploration into the dynamic interplay among AI algorithmic features, as advocated by Stein et al (2020), within the context of AI-enabled HRM practices could shed light on their combined effects on employees' experiences and provide a more holistic understanding of the mechanisms underlying negative outcomes. From a methodological viewpoint, taking into account the nature of AI-enabled HRM and the limited empirical method, especially longitudinal and multilevel studies (Marler and Boudreau, 2017), there is a distinct need for more longitudinal multilevel research to track employee experiences and outcomes over extended periods in order to uncover evolving trends, potential adaptation mechanisms, and the persistence of negative effects.…”

Section: Future Directions and Limitationsmentioning

confidence: 99%

The dark side of AI-enabled HRM on employees based on AI algorithmic features

Zhou,

Wang,

Chen

2023

JOCM

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PurposeAI is an emerging tool in HRM practices that has drawn increasing attention from HRM researchers and HRM practitioners. While there is little doubt that AI-enabled HRM exerts positive effects, it also triggers negative influences. Gaining a better understanding of the dark side of AI-enabled HRM holds great significance for managerial implementation and for enriching related theoretical research.Design/methodology/approachIn this study, the authors conducted a systematic review of the published literature in the field of AI-enabled HRM. The systematic literature review enabled the authors to critically analyze, synthesize and profile existing research on the covered topics using transparent and easily reproducible procedures.FindingsIn this study, the authors used AI algorithmic features (comprehensiveness, instantaneity and opacity) as the main focus to elaborate on the negative effects of AI-enabled HRM. Drawing from inconsistent literature, the authors distinguished between two concepts of AI algorithmic comprehensiveness: comprehensive analysis and comprehensive data collection. The authors also differentiated instantaneity into instantaneous intervention and instantaneous interaction. Opacity was also delineated: hard-to-understand and hard-to-observe. For each algorithmic feature, this study connected organizational behavior theory to AI-enabled HRM research and elaborated on the potential theoretical mechanism of AI-enabled HRM's negative effects on employees.Originality/valueBuilding upon the identified secondary dimensions of AI algorithmic features, the authors elaborate on the potential theoretical mechanism behind the negative effects of AI-enabled HRM on employees. This elaboration establishes a robust theoretical foundation for advancing research in AI-enable HRM. Furthermore, the authors discuss future research directions.

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“…Finally, in the experimental design, we presumed that the persona's picture would be the major driver for the persona's attractiveness. However, perhaps the text content contributed to attractiveness as well, as other factors have been shown to affect the subjective evaluation of an image (Stein et al, 2020). We tested if this could be the case.…”

Section: Analysis Of Effect Sizesmentioning

confidence: 99%

How does an imaginary persona's attractiveness affect designers' perceptions and IT solutions? An experimental study on users' remote working needs

Salminen

Santos

Jung

et al. 2023

ITP

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PurposeThe “what is beautiful is good” (WIBIG) effect implies that observers tend to perceive physically attractive people in a positive light. The authors investigate how the WIBIG effect applies to user personas, measuring designers' perceptions and task performance when employing user personas for the design of information technology (IT) solutions.Design/methodology/approachIn a user experiment, the authors tested six different personas with 235 participants that were asked to develop remote work solutions based on their interaction with a fictitious user persona.FindingsThe findings showed that a user persona's perceived attractiveness was positively correlated with other perceptions of the persona. The personas' completeness, credibility, empathy, likability and usefulness increased with attractiveness. More attractive personas were also perceived as more agreeable, emotionally stable, extraverted and open, and the participants spent more time engaging with personas they perceived attractive. A linguistic analysis indicated that the IT solutions created for more attractive user personas demonstrated a higher degree of affect, but for the most part, task outputs did not vary by the personas' perceived attractiveness.Research limitations/implicationsThe WIBIG effect applies when designing IT solutions with user personas, but its effect on task outputs appears limited. The perceived attractiveness of a user persona can impact how designers interact with and engage with the persona, which can influence the quality or the type of the IT solutions created based on the persona. Also, the findings point to the need to incorporate hedonic qualities into the persona creation process. For example, there may be contexts where it is helpful that the personas be attractive; there may be contexts where the attractiveness of the personas is unimportant or even a distraction.Practical implicationsThe findings point to the need to incorporate hedonic qualities into the persona creation process. For example, there may be contexts where it is helpful that the personas be attractive; there may be contexts where the attractiveness of the personas is unimportant or even a distraction.Originality/valueBecause personas are created to closely resemble real people, the authors might expect the WIBIG effect to apply. The WIBIG effect might lead decision makers to favor more attractive personas when designing IT solutions. However, despite its potential relevance for decision making with personas, as far as the authors know, no prior study has investigated whether the WIBIG effect extends to the context of personas. Overall, it is important to understand how human factors apply to IT system design with personas, so that the personas can be created to minimize potentially detrimental effects as much as possible.

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Matter over mind? How the acceptance of digital entities depends on their appearance, mental prowess, and the interaction between both

Cited by 42 publications

References 36 publications

The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

The dark side of AI-enabled HRM on employees based on AI algorithmic features

How does an imaginary persona's attractiveness affect designers' perceptions and IT solutions? An experimental study on users' remote working needs

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