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Research methods in social relations

https://scholarscompass.vcu.edu/cgi/viewcontent.cgi?article=1002&context=info_pubs

Modelling and testing consumer trust dimensions in e-commerce

The exponential advancement in artificial intelligence (AI), machine learning, robotics, and automation are rapidly transforming industries and societies across the world. The way we work, the way we live, and the way we interact with others are expected to be transformed at a speed and scale beyond anything we have observed in human history. This new industrial revolution is expected, on one hand, to enhance and improve our lives and societies. On the other hand, it has the potential to cause major upheavals in our way of life and our societal norms. The window of opportunity to understand the impact of these technologies and to preempt their negative effects is closing rapidly. Humanity needs to be proactive, rather than reactive, in managing this new industrial revolution. This article looks at the promises, challenges, and future research directions of these transformative technologies. Not only are the technological aspects investigated, but behavioral, societal, policy, and governance issues are reviewed as well. This research contributes to the ongoing discussions and debates about AI, automation, machine learning, and robotics. It is hoped that this article will heighten awareness of the importance of understanding these disruptive technologies as a basis for formulating policies and regulations that can maximize the benefits of these advancements for humanity and, at the same time, curtail potential dangers and negative impacts.

Artificial Intelligence, Machine Learning, Automation, Robotics, Future of Work and Future of Humanity: A Review and Research Agenda

Extension of the Technology Acceptance Model: Four Longitudinal Field : .

Forecasting advice from human advisors is often utilized more than advice from automation. There is little understanding of why “algorithm aversion” occurs, or specific conditions that may exaggerate it. This paper first reviews literature from two fields—interpersonal advice and human–automation trust—that can inform our understanding of the underlying causes of the phenomenon. Then, an experiment is conducted to search for these underlying causes. We do not replicate the finding that human advice is generally utilized more than automated advice. However, after receiving bad advice, utilization of automated advice decreased significantly more than advice from humans. We also find that decision makers describe themselves as having much more in common with human than automated advisors despite there being no interpersonal relationship in our study. Results are discussed in relation to other findings from the forecasting and human–automation trust fields and provide a new perspective on what causes and exaggerates algorithm aversion.

Understanding algorithm aversion: When is advice from automation discounted?

This paper studies the role of trust and trustworthiness as it applies to inanimate software artifacts. Extrapolating from the meaning and theory of interpersonal trust and supporting theories, this study develops software artifact trustworthiness and trust scales for the context of antiviral software. The proposed constructs deal with trustworthiness beliefs in software predictability and performance, corresponding respectively to a partial conceptual overlap with the interpersonal trustworthiness beliefs of predictability and ability. Data collected from 232 users support the psychometric properties of the new scales and demonstrate their nomological validity. The analysis shows that in the case of this software artifact, trustworthiness but not trust, predicts user satisfaction. This is in apparent contrast to interpersonal trust in online contexts where trust and trustworthiness are mostly indistinguishable, and where in the few cases that trust was studied separately from trustworthiness, trust had always been a significant predictor. The importance of trustworthiness, but apparently not of trust, in a software artifact is discussed.

Understanding Trust in IT Artifacts: An Evaluation of the impact of Trustworthiness and Trust on Satisfaction with Antiviral Software

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Understanding Trust in IT Artifacts: An Evaluation of the impact of Trustworthiness and Trust on Satisfaction with Antiviral Software