Is Explanation a Marketing Problem? The Quest for Trust in Artificial Intelligence and Two Conflicting Solutions

Triberti, Stefano; Durosini, Ilaria; Curigliano, Giuseppe; Pravettoni, Gabriella

doi:10.1159/000506014

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…Thus, when an information system is easy to access and contains no errors (e.g. miscalculations, inaccuracies, misinterpretations, overestimations or underestimations), it indicates its trustworthiness (Kim & Peterson, 2017;Triberti et al, 2020). The perceived system quality often concerns security issues and whether the AI-supported system has an established structural assurance, e.g.…”

Section: Technology-related Factorsmentioning

confidence: 99%

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User trust in artificial intelligence: A comprehensive conceptual framework

Yang¹,

Wibowo

2022

Electron Markets

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This paper provides a systematic literature review of current studies between January 2015 and January 2022 on user trust in artificial intelligence (AI) that has been conducted from different perspectives. Such a review and analysis leads to the identification of the various components, influencing factors, and outcomes of users' trust in AI. Based on the findings, a comprehensive conceptual framework is proposed for a better understanding of users' trust in AI. This framework can further be tested and validated in various contexts for enhancing our knowledge of users' trust in AI.This study also provides potential future research avenues. From a practical perspective, it helps AIsupported service providers comprehend the concept of user trust from different perspectives. The findings highlight the importance of building trust based on different facets to facilitate positive cognitive, affective, and behavioral changes among the users.

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Section: Technology-related Factorsmentioning

confidence: 99%

“…In other words, people's decisions are usually dependent on others' opinions regarding the trustworthiness of the object. Therefore, gaining testimonials from a recognized expert or authority in the field can help an AI-enabled organization to be trusted and accepted (Triberti et al, 2020).…”

Section: Organizational Factorsmentioning

confidence: 99%

User trust in artificial intelligence: A comprehensive conceptual framework

Yang¹,

Wibowo

2022

Electron Markets

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“…Work psychologists use engagement to understand an employee’s work experience (De Bruyn et al, 2020; Suh & Li, 2022; Triberti et al, 2020), which concerns a person’s level of mental, emotional, and behavioral involvement in their work (Serrano et al, 2021; Stanley et al, 2021; Zhou et al, 2019), and has been identified as a critical factor in the literature around technology because of its impact on widespread adoption (De Bruyn et al, 2020). At hospital management, engagement is typically used to create policy to be implemented to create a novel care model with the ultimate goal of improving patient service and health outcomes (Triberti et al, 2020). Omuudu et al (2022) viewed engagement as an intervening variable in hotel information management system adoption by its users.…”

Section: Literature Reviewmentioning

confidence: 99%

What Drives People’s Behavioral Intention Toward Telemedicine? An Emerging Economy Perspective

Hossain

Amin²,

Masud

et al. 2023

Sage Open

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Because healthcare systems in developing nations are underfunded and unequal, practitioners must understand how telemedicine may be widely introduced and sustained. Unfortunately, telemedicine adoption receives less attention and there is limited knowledge regarding the interfering influence of patient engagement, satisfaction, and individual innovativeness. Consequentially, the current study seeks to investigate the factors that influence patients’ behavioral intentions toward telemedicine, while acknowledging engagement and satisfaction as mediators and personal innovativeness as a moderator. A questionnaire instrument was used to reach telemedicine users in Bangladesh, and finally, 305 samples were curated and explored applying structural equation modeling in AMOS software. The empirical results show that information quality, performance expectancy, engagement, and satisfaction are strong stimulators of behavioral intention toward telemedicine, whereas functionality, information quality, and performance expectancy drive satisfaction and engagement. One of the worthy findings of this study is that engagement and satisfaction play a significant mediating function, while personal innovativeness plays a moderating role, in influencing behavioral intention toward telemedicine. Confirming engagement and satisfaction as mediators and innovativeness as a moderator adds originality to this research, and overall will help to reduce healthcare bias prejudice among marginalized people in developing countries like Bangladesh.

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“…Involved in identification of treatment, often patient-specific solutions (genomics, precision medicine); it could participate in providing early interventions to delay the onset of chronic conditions (pre-emptive medicine) (22)(23)(24)(25) Health management/patient engagement Featured in devices that collect data on patient health status and provide recommendations for everyday care (eHealth, Digital Therapeutics, Ambient Intelligence) (26)(27)(28) Health Systems organization support/simulation Used in agent-based simulations that model care coordination capabilities, providing insights for organizational improvements (29,30) contrary, AI promises to become an active collaborator with human operators in a number of tasks and activities. AIs are able to analyze enormous quantities of data of various contents and formats, even where it is dynamically changing (Big Data).…”

Section: Diagnosismentioning

confidence: 99%

A “Third Wheel” Effect in Health Decision Making Involving Artificial Entities: A Psychological Perspective

Triberti

Durosini

Pravettoni

2020

Front. Public Health

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In the near future, Artificial Intelligence (AI) is expected to participate more and more in decision making processes, in contexts ranging from healthcare to politics. For example, in the healthcare context, doctors will increasingly use AI and machine learning devices to improve precision in diagnosis and to identify therapy regimens. One hot topic regards the necessity for health professionals to adapt shared decision making with patients to include the contribution of AI into clinical practice, such as acting as mediators between the patient with his or her healthcare needs and the recommendations coming from artificial entities. In this scenario, a "third wheel" effect may intervene, potentially affecting the effectiveness of shared decision making in three different ways: first, clinical decisions could be delayed or paralyzed when AI recommendations are difficult to understand or to explain to patients; second, patients' symptomatology and medical diagnosis could be misinterpreted when adapting them to AI classifications; third, there may be confusion about the roles and responsibilities of the protagonists in the healthcare process (e.g., Who really has authority?). This contribution delineates such effects and tries to identify the impact of AI technology on the healthcare process, with a focus on future medical practice.

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Is Explanation a Marketing Problem? The Quest for Trust in Artificial Intelligence and Two Conflicting Solutions

Cited by 14 publications

References 23 publications

User trust in artificial intelligence: A comprehensive conceptual framework

User trust in artificial intelligence: A comprehensive conceptual framework

What Drives People’s Behavioral Intention Toward Telemedicine? An Emerging Economy Perspective

A “Third Wheel” Effect in Health Decision Making Involving Artificial Entities: A Psychological Perspective

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