Biblio
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Toward Interactional Trust for Humans and Automation: Extending Interdependence. 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :1348–1355.
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2019. Trust in human-automation interaction is increasingly imperative as AI and robots become ubiquitous at home, school, and work. Interdependence theory allows for the identification of one-on-one interactions that require trust by analyzing the structure of the potential outcomes. This paper synthesizes multiple, formerly disparate research approaches by extending Interdependence theory to create a unified framework for outcome-based trust in human-automation interaction. This framework quantitatively contextualizes validated empirical results from social psychology on relationship formation, stability, and betrayal. It also contributes insights into trust-related concepts, such as power and commitment, which help further our understanding of trustworthy system design. This new integrated interactional approach reveals how trust and trustworthiness machines from merely reliable tools to trusted teammates working hand-in-actuator toward an automated future.
Self-Disclosure and Perceived Trustworthiness of Airbnb Host Profiles. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing. :2397–2409.
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2017. Online peer-to-peer platforms like Airbnb allow hosts to list a property (e.g. a house, or a room) for short-term rentals. In this work, we examine how hosts describe themselves on their Airbnb profile pages. We use a mixed-methods study to develop a categorization of the topics that hosts self-disclose in their profile descriptions, and show that these topics differ depending on the type of guest engagement expected. We also examine the perceived trustworthiness of profiles using topic-coded profiles from 1,200 hosts, showing that longer self-descriptions are perceived to be more trustworthy. Further, we show that there are common strategies (a mix of topics) hosts use in self-disclosure, and that these strategies cause differences in perceived trustworthiness scores. Finally, we show that the perceived trustworthiness score is a significant predictor of host choice–especially for shorter profiles that show more variation. The results are consistent with uncertainty reduction theory, reflect on the assertions of signaling theory, and have important design implications for sharing economy platforms, especially those facilitating online-to-offline social exchange.