An Extended Framework for Recovering From Trust Breakdowns in Online Community Settings

Abstract

Abstract:The violation of trust as a result of interactions that do not proceed as expected gives rise to the question as to whether broken trust can possibly be recovered. Clearly, trust recovery is more complex than trust initialization and maintenance. Trust recovery requires a more complex mechanism to explore different factors that cause the decline of trust and identify the affected individuals of trust violation both directly and indirectly. In this study, an extended framework for recovering trust is presented. Aside from evaluating whether there is potential for recovery based on the outcome of a forgiveness mechanism after a trust violation, encouraging cooperation between interacting parties after a trust violation through incentive mechanisms is also important. Furthermore, a number of experiments are conducted to validate the applicability of the framework and the findings show that the e-marketplace incorporating our proposed framework results in improved efficiency of trading, especially in long-term interactions.

A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective

“…This was because the development of AI-enabled systems was often faster than the readiness of their potential users, and a mismatch of readiness levels might lead to low user trust. It was suggested to set up mechanisms in place to foster, maintain, and recover user trust (Binmad et al, 2017), by, for example, ensuring user data protection (Foehr & Germelmann, 2020), encouraging high-quality user interactions (Lin et al, 2019), and solution-oriented technical support (Thielsch et al, 2018). It was also suggested that user trust was likely to increase over time (Elkins & Derrick, 2013).…”

“…A comparison of consumer perception of trust-triggering appearance features on Indian group buying websites (Sharma, 2015) A qualitative research framework for the design of usercentered displays of explanations for machine learning model predictions in healthcare (Barda et al, 2020) A review of theory-driven models of trust in the online health context (Khosrowjerdi, 2016) Alexa, can I trust you? Exploring consumer paths to trust in smart voice-interaction technologies (Foehr & Germelmann, 2020) An extended framework for recovering from trust breakdowns in online community settings (Binmad et al, 2017) Brokerbot: a cryptocurrency chatbot in the social-technical gap of trust (Lee et al, 2021) Building e-commerce satisfaction and boosting sales: the role of social commerce trust and its antecedents (Lin et al, 2019) Effects of personality traits on user trust in human-machine collaborations (Zhou et al, 2020) Exploring trust of mobile applications based on user behaviors: an empirical study (Yan et al, 2013) Human analyzing artificial divide elements (e.g., rejection of an AIenabled system) and addressing challenges properly (e.g., early stage user involvement, training, enhanced user experience and empowerment) to foster user trust and prevent mistrust (Klumpp & Zijm, 2019). The second study suggested that user uncertainties had to be addressed by identifying and prioritizing the uncertainties and their antecedents in relation to a specific AI-enabled system, improving user understandability, sense of control, and information accuracy (Hoffmann & S€ ollner, 2014).…”