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PurposeThe article provides a conceptual replication and enrichment of the status quo bias theory in the specific context of understanding IT department user resistance and user adoption. The findings can assist technology renewals and associated change management professionals to assess and plan the adoption and active usage of human resource systems.Design/methodology/approachThe authors used survey method to gather data. All items were based on prior literature. They administrated the survey to employees of GOODTECH (name changed), information systems (IS) department members, situated in France. They obtained 103 valid responses along with usage data from the system to run their path model, in order to validate the proposed research model.FindingsThe study offers an enriched user resistance model (URM) to understand why IT-savvy employees would resist or adopt new human resource tools. Apart from providing partial validity to status quo bias theory in the French context, the enriched model uses behavioral intention to use as an intermediate variable to explain the influence of two key constructs of the original theory: switching cost and switching benefits. This research provides a better explanatory power to understand the cause of user resistance and new IT use.Research limitations/implicationsThe sample size used in the study can be considered as a limitation, although power analysis reveals that the results are significant and valid. The context of the study is also limited to one country and to a specific type of IS implementation scenario. Since the purpose of the paper was to offer contextual theory enhancement, the findings are valid for this purpose.Practical implicationsDigital project managers are offered a framework to increase technology adoption of new human resource tools and evaluate how to reduce user resistance at times of technology renewals. Self-efficacy for change and colleagues’ opinion can indirectly impact behavioral intention to use via switching cost and switching benefit perceptions and thus reducing resistance perceptions as well as increasing adoption of new IT tools in post-implementation phases.Originality/valueThe paper enriches the well-established user resistance theory in IS domain in a context of human resource post-implementation phase by studying IT-savvy end user's perceptions. The paper demonstrates the need to integrate user adoption and user resistance variables in one parsimonious framework and extends support to emerging research on dual focus perspective.

A evolução da plataforma de Internet, as novas tecnologias de comunicação e monitoramento, bem como o surgimento do fenômeno das mídias sociais, permitem a geração, a coleta e o intercâmbio de uma vasta quantidade de dados de várias fontes, como dispositivos móveis, sensores, baixados livros, jogos, sons e imagens. Diariamente, esses usuários compartilham suas preferências, opiniões, amigos e estilos de vida, fornecendo uma rica fonte de informações sobre seu comportamento e preferências, desafiando os RAs a lidar com essa quantidade de dados e continuar fornecendo boas recomendações aos usuários. Agora, os RAs devem incluir dados de fontes externas, como informações de conexão social do usuário (por exemplo, amigos próximos, colegas, colegas de escola, influenciadores e preferências de marca) para aumentar a captura e entendendimento das necessidades dos consumidores, a fim de sugerir produtos que melhor atendam aos interesses dos consumidores, evoluindo para agentes de recomendação de big data. Estudos existentes mostraram que os clientes precisam confiar no RA antes de usá-lo. No entanto, apesar do fato de haver muitas discussões sobre confiança na literatura de SI, apenas algumas abordam o problema da confiança no contexto de big data e analytics. Poucos trabalhos associados a big data e analytics têm uma preocupação secundária sobre os temas de confiança e desconfiança. Para preencher essa lacuna, foi realizado um experimento com quatrocentos alunos para avaliar o grau de confiança e desconfiança nos Agentes de Recomendação de Big Data - BDRA. No contexto da seleção de um programa de intercâmbio (por exemplo, estudar no exterior). Desenvolvemos três artigos que cobrem: (1) os antecedentes de confiança e desconfiança no BDRA, (2) a influência contextual das crenças de confiança na adoção de agentes de recomendação de big data e (3) o poder das crenças de desconfiança do BDRA no conseqüentes usando a lente teórica de aceitação e resistência do usuário. Através desses três resultados diferentes de estudos, foi possível estender as teorias de confiança, desconfiança, aceitação do usuário e resistência do usuário, adicionando novos constructos, validando a literatura anterior e incluindo desconfiança e big data na literatura de Agentes de Recomendação. O estudo também construiu e testou um modelo nomológico de confiança e desconfiança relacionada à agentes de recomendação de big data - BDRA. 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.