Trust and distrust in big data recommendation agents

• Autor(a) principal: Moraes, Heverton Roberto de Oliveira Cesar de
• Data de Publicação: 2020
• Tipo de documento: Tese
• Idioma: eng
• Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
• Texto Completo: https://hdl.handle.net/10438/29465
• topic: Trust; Distrust; Big data; Recommendation agents; Algorithm innovativeness; User acceptance; User resistance; Status quo bias; Tecnologia da informação; Sistemas de suporte de decisão; Comportamento do consumidor; Confiança do consumidor; Administração de empresas; Big data; Tecnologia da informação; Sistemas de suporte de decisão; Comportamento do consumidor; Confiança do consumidor

The internet platform evolution, the new technologies for communication and monitoring, as well as the emergence of the social media phenomenon enable the generation, collection and exchange of a vast amount of data from a variety of sources, e.g., mobile devices, sensors, downloaded books, games, sound, and images (Aggarwal 2016). Daily, these users share their preferences, opinions, friends, and lifestyles, providing a rich source of information about their behavior and preferences, challenging RAs to deal with this amount of data to keep giving good recommendations to users. Now, RAs must include data from external sources such as user’s social connection information (e.g., close friends, colleagues, schoolmates, influencers, and brand preferences) to boost the elicitation of consumers' needs in order to suggest products that best fit consumer interests, evolving to big data recommendation agents. Extant studies have shown that customers need to trust in the RA before using it. However, despite the fact that there are many discussions about trust in the IS literature, only a few addresses the problem of trust in the context of big data and analytics. Few papers associated with big data and analytics have a secondary concern about the themes of trust and distrust. An experiment with four hundred students was performed to fulfill this gap, assessing the degree of trust and distrust in Big Data Recommendation Agents – BDRA, in the selection of an exchange program (e.g., study abroad). We developed three papers to cover: (1) the antecedents of trust and distrust in BDRA, (2) the contextual influence of trust beliefs in the adoption of a big data recommendation agents, and (3) the power of resistance - status quo bias on BDRA distrust beliefs and its consequents on perceived enjoyment and perceived usefulness. Through these three different studies results, it was possible to extend trust, distrust, user acceptance, and user resistance theories by adding new constructs, validating prior literature and including distrust and big data into recommendation agent literature. The study also built and tested a nomological network-related trust and distrust in big data recommendation agents.