The effects of personality and locus of control on trust in humans versus artificial intelligence

Sharan, Navya N.; Romano, Daniela M.

doi:10.1016/j.heliyon.2020.e04572

Cited by 61 publications

(34 citation statements)

References 43 publications

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“…Furthermore, a person’s locus of control (LOC; Rotter, 1966) should also be important for perceived control over an AI decision aid in this context. Research indicates that people having an internal LOC trust an AI decision aid more than individuals who have an external LOC (Sharan & Romano, 2020). This is likely because people having an internal LOC feel capable of influencing their environment.…”

Section: A Conceptual Model Of Trust Perceived Risk and Reliance On A...mentioning

confidence: 99%

A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids

Solberg

Kaarstad

Eitrheim

et al. 2022

Group & Organization Management

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There is increasing interest in the use of artificial intelligence (AI) to improve organizational decision-making. However, research indicates that people’s trust in and choice to rely on “AI decision aids” can be tenuous. In the present paper, we connect research on trust in AI with Mayer, Davis, and Schoorman’s (1995) model of organizational trust to elaborate a conceptual model of trust, perceived risk, and reliance on AI decision aids at work. Drawing from the trust in technology, trust in automation, and decision support systems literatures, we redefine central concepts in Mayer et al.’s (1995) model, expand the model to include new, relevant constructs (like perceived control over an AI decision aid), and refine propositions about the relationships expected in this context. The conceptual model put forward presents a framework that can help researchers studying trust in and reliance on AI decision aids develop their research models, build systematically on each other’s research, and contribute to a more cohesive understanding of the phenomenon. Our paper concludes with five next steps to take research on the topic forward.

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Section: A Conceptual Model Of Trust Perceived Risk and Reliance On A...mentioning

confidence: 99%

A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids

Solberg

Kaarstad

Eitrheim

et al. 2022

Group & Organization Management

View full text Add to dashboard Cite

show abstract

“…Control over vehicles is essential, especially in intelligent driving, as previous accidents attributed to the loss of control. Thus, SoC also helps ensure security and fosters users’ trust in intelligent vehicles ( Sharan and Romano, 2020 ). We can conclude that SoC facilitates users’ perceived empowerment and behavioral intentions than its interaction effects with empowerment or intelligence.…”

Section: Discussionmentioning

confidence: 99%

An Empirical Study on Drivers’ Willingness to Use Automatic Features of Intelligent Vehicles: A Psychological Empowerment Perspective

Gupta

Zhou

2021

Front. Psychol.

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With the advancement in AI and related technologies, we are witnessing more remarkable use of intelligent vehicles. Intelligent vehicles use smart automatic features that make travel happier, safer, and efficient. However, not many studies examine their adoption or the influence of intelligent vehicles on user behavior. In this study, we specifically examine how intelligent vehicles’ sensing and acting abilities drive their adoption from the lens of psychological empowerment theory. We identify three dimensions of users’ perceived empowerment (perceived cognitive empowerment, perceived emotional empowerment, and perceived behavioral empowerment). Based on this theory, we argue that product features (sensing and acting in intelligent vehicles) empower users to use the product. Our proposed model is validated by an online survey of 312 car owners who are familiar with driving conditions, the results of this study reveal that driver’s perceived empowerment is vital for using automatic features of intelligent vehicles. Theoretically, this study combines the concept of empowerment with the intelligent-driving scenario and reasonably explains the mechanism of the intelligence of vehicles on users’ behavior intention.

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“…These findings suggest that there is a common factor underlying both trust in humans and trust in automated systems (Kraus et al, 2021 ). Besides automated driving, Sharan and Romano ( 2020 ) investigated trust in AI by providing participants with suggestions from an AI-algorithm when making decisions in a card game and found none of the Big 5 traits correlated to any trust indicators.…”

Section: Outlook On Distributed Cognition In An Ai-enhanced Futurementioning

confidence: 99%

Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future

Grinschgl

Neubauer

2022

Front. Artif. Intell.

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In the present article, we explore prospects for using artificial intelligence (AI) to distribute cognition via cognitive offloading (i.e., to delegate thinking tasks to AI-technologies). Modern technologies for cognitive support are rapidly developing and increasingly popular. Today, many individuals heavily rely on their smartphones or other technical gadgets to support their daily life but also their learning and work. For instance, smartphones are used to track and analyze changes in the environment, and to store and continually update relevant information. Thus, individuals can offload (i.e., externalize) information to their smartphones and refresh their knowledge by accessing it. This implies that using modern technologies such as AI empowers users via offloading and enables them to function as always-updated knowledge professionals, so that they can deploy their insights strategically instead of relying on outdated and memorized facts. This AI-supported offloading of cognitive processes also saves individuals' internal cognitive resources by distributing the task demands into their environment. In this article, we provide (1) an overview of empirical findings on cognitive offloading and (2) an outlook on how individuals' offloading behavior might change in an AI-enhanced future. More specifically, we first discuss determinants of offloading such as the design of technical tools and links to metacognition. Furthermore, we discuss benefits and risks of cognitive offloading. While offloading improves immediate task performance, it might also be a threat for users' cognitive abilities. Following this, we provide a perspective on whether individuals will make heavier use of AI-technologies for offloading in the future and how this might affect their cognition. On one hand, individuals might heavily rely on easily accessible AI-technologies which in return might diminish their internal cognition/learning. On the other hand, individuals might aim at enhancing their cognition so that they can keep up with AI-technologies and will not be replaced by them. Finally, we present own data and findings from the literature on the assumption that individuals' personality is a predictor of trust in AI. Trust in modern AI-technologies might be a strong determinant for wider appropriation and dependence on these technologies to distribute cognition and should thus be considered in an AI-enhanced future.

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The effects of personality and locus of control on trust in humans versus artificial intelligence

Cited by 61 publications

References 43 publications

A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids

A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids

An Empirical Study on Drivers’ Willingness to Use Automatic Features of Intelligent Vehicles: A Psychological Empowerment Perspective

Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future

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