Purpose This study aims to determine how Internet of Things (IoT) risks and benefits affect both the intention to use and actual use of a smartwatch. Methodology The stimulus–organism–behavior–consequence (SOBC) hypothesis is used to explain the mechanisms underpinning the discontinuity between intention and technology usage. A total of 394 questionnaires distributed to smartwatch users were analyzed, using convergent analysis, discriminant analysis and structural modeling. Findings The IoT’s technical features, such as continuous connectivity and real-time value, serve as effective stimuli for smartwatches, positively influencing individuals’ responses and behavioral consequences associated with smartwatch usage. While IoT risks such as data, performance and financial have no negative relationship with the usefulness of smartwatches, data and financial risks have a negative influence on their ease of use. Additionally, as ease of use and usefulness have a positive impact on intention to use, users’ behavior is positively influenced by their intentions to use a smartwatch. Value The study applies technology acceptance theory and the SOBC paradigm to smartwatches to determine if users’ intentions to use them impact their behavior. Furthermore, the research analyzed the technical elements of smartwatches in terms of IoT advantages and risks.

In the last decade financial markets haves shown a great transformation that has failed to reduce the high rate of existing banking even the current financial crisis. This high level of competition means that financial institutions are concerned about the loyalty of their customers to maintain or increase market share and profitability. In this paper we propose a statistical model that measures the risk of customer dropping out of a Spanish financial institution, and this is a widely method for the financial sector in general. The risk depend son socio-demographic, economic and, most importantly, the levels of satisfaction and customer confidence with the bank. Research shows that the proposed model can help institutions to know which customers have a greater risk of dropping out to establish, and some recommendations for their loyalty.

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