1,721,033 research outputs found
Why should I trust you ? The role of Explainable AI on AI-based recommendation systems trust and acceptance
No full textWhy should I trust you ? The role of Explainable AI on AI-based recommendation systems trust and acceptance
No full textHealth literacy and its effects on well-being:how vulnerable healthcare service users integrate online resources
No full textPurpose: This study aims to develop a better understanding of how online health community (OHC) members with different health literacy (HL) levels benefit from their participation, through the analysis and comparison of their resource integration (RI) processes. It investigates through a RI lens how the vulnerability of community members – captured as their level of HL – affects the benefits they derive from participation. Design/methodology/approach: Quantitative and qualitative methods were used to investigate the effects of healthcare service users’ vulnerability. Data were collected about their profiles and levels of HL. Furthermore, 15 in-depth interviews were conducted. Findings: The study demonstrates how low levels of HL act as a barrier to the integration of available online health resources. Participation in OHCs appears less beneficial for vulnerable users. Three types of benefits were identified at the individual level, namely, psychological quality-of-life, physical quality-of-life and learning. Benefits identified at the community level were: content generation and participation in the development of the community. Originality/value: This study has implications for the understanding of how service users’ activities affect their own outcomes and how the vulnerability of users could be anticipated and considered in the design of the community.</p
Healthcare service users as resource integrators:investigating factors influencing the co-creation of value at individual, dyadic and systemic levels
No full textPurpose: Patients, when using healthcare services, (co)create value by integrating their own resources with those of a range of stakeholders. These resource integration activities, however, require different types of skills and effort from the patients, and different types of interactions with stakeholders, while also having different effects on patients' well-being. The purpose of the present study is to develop a better understanding of why some patients are better able or willing to perform resource integration activities that impact their well-being. To reach this objective, barriers and facilitators of these activities in their interactions with various stakeholders were identified. Design/methodology/approach: The study uses a multiple case study design. Individual patients having received a lung transplant, together with their entourage (family, medical professionals, other patients) each represent a case. In-depth interviews were conducted with the patients and with various categories of stakeholders in their service delivery network who were relevant to their experience and with whom they integrated their resources. Findings: The study identifies three levels on which barriers and facilitators of the resource integration process occur: the individual, relational and systemic level. Factors on these levels affect different aspects of the process. Originality/value: This study takes a systems perspective and investigates how various systemic factors and stakeholders conduce or inhibit healthcare service users to perform resource integration activities, especially focusing on those activities that strongly affect their well-being.</p
„Why should I trust you? Influence of explanation design on consumer behavior in AI-based services“
No full textpeer reviewedPurpose: This study explores how the format of explanations used in AI-based services affects consumer behavior, specifically the effects of explanation detail (low vs. high) and consumer control (automatic vs. on demand) on trust and acceptance. The aim is to provide service providers with insights into how to optimize the format of explanations to enhance consumer evaluations of AI-based services.
Design/methodology/approach: Drawing on the literature on explainable AI (XAI) and
information overload theory, a conceptual model is developed. To empirically test the conceptual model, two between-subjects experiments were conducted wherein the level of detail and level of control were manipulated, taking AI-based recommendations as a use case. The data were analyzed via partial least squares (PLS) regressions. Findings: The results reveal significant positive correlations between level of detail and perceived
understanding and between level of detail and perceived assurance. The level of control negatively moderates the relationship between the level of detail and perceived understanding. Further analyses revealed that the perceived competence and perceived integrity of AI systems positively and significantly influence the acceptance and purchase intentions of AI-based services.
Originality/value: This article elucidates the nuanced interplay between the level of detail and control over explanations for nonexpert consumers in high-credence service sectors. The findingsoffer insights for the design of more consumer-centric explanations to increase the acceptance of AI-based services.
Practical implications: This research offers service providers key insights into how tailored
explanations and maintaining a balance between detail and control build consumer trust and enhance AI-based service outcomes
New services screening decision-making performance: A screening committee cognitive capabilities perspective, Ph.D dissertation
No full textIncreasing innovation success rates: Does screening matter? in The Newsletter of the Relational Enterprise Research Group, Volume 25, Fall 2010 (October)
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