Conversational robots powered by artificial intelligence (AI) are intensively implemented in the insurance industry. This paper aims to determine the current level of acceptance among consumers regarding the use of conversational robots for interacting with insurers and seeks to identify the factors that influence individuals’ behavioral intention to engage with chatbots. To explain behavioral intention, we tested a structural equation model based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. It was supposed that behavioral intention is influenced by performance expectancy, effort expectancy, social influence, and trust, and by the moderating effect of insurance literacy on performance expectancy and effort expectancy. The study reveals a significant overall rejection of robotic technology among respondents. The technology acceptance model tested demonstrates a strong ability to fit the data, explaining nearly 70% of the variance in behavioral intention. Social influence emerges as the most influential variable in explaining the intention to use conversational robots. Furthermore, effort expectancy and trust significantly impact behavioral intention in a positive manner. For chatbots to gain acceptance as a technology, it is crucial to enhance their usability, establish trust, and increase social acceptance among users.

Medical implantable technologies, such as cochlear implants or joint prostheses, have been commonly used since the late 20th century. By contrast, the market for this type of technology is expanding when the purpose is not medical, even though it is more marginal. This study tests a technology acceptance model for the latter type of insideable technology based on an extension of the technology acceptance models TAM and TAM2 proposed for hedonic technologies by van del Heijden. So, the behavioral intention of insertables is explained by the perceived usefulness and perceived ease of use, as well as social influence, as proposed in the TAM2 by Venkatesh and Davis. Additionally, the perceived enjoyment, included in the extension by Van der Heijden, is added as an explanatory factor. We applied structural equation modeling to the theoretical scheme provided by the modified TAM and performed a necessary condition analysis. Statistical analysis showed that all variables considered in the model have a significantly positive influence on behavioral intention. Likewise, the model has good properties both from the point of view of the fit obtained, since it predicts 70% of behavioral intention, and from the predictive point of view. The necessary condition analysis allows us to analyze whether the presence of some of the latent variables postulated to explain the attitude toward implantables is necessary to produce the said acceptance. Therefore, its absence is a critical aspect of expansion. We observed that perceived usefulness manifests itself as a necessary condition for behavioral intention with a medium size. Perceived ease of use and enjoyment also present a significant necessity effect size, but their strength is smaller. By contrast, the subjective norm does not have the status of a necessary variable.

(1) Background: Loyalty programmes are business strategies aimed at increasing customer fidelity to brands. One of the most promising technologies of the 21st century is blockchain, whose application to the management of loyalty programmes can increase transparency and reliability, enable interoperability, and facilitate centralised management. (2) Methods: This study evaluates the antecedents of the acceptance of blockchain-based loyalty programmes (BBLPs) using a model grounded in the technology acceptance model UTAUT2. The four basic constructs of UTAUT, which we call the UTAUT-baseline, are considered explanatory factors: performance expectancy (PER), effort expectancy (EFF), social influence (SOC), and facilitating conditions (FAC). Additionally, we consider the constructs introduced in UTAUT2: hedonic motivation (HED) and perceived price value (PRI), to which we add innovativeness (INN) and trust (TRU), and we refer to these as UTAUT-extended, along with the moderating variables of UTAUT and UTAUT2: gender, age, and experience. The analytical approach used is complexity theory, which aims to capture the configurations that lead to both acceptance and rejection positions regarding BBLPs. This analysis is performed via fuzzy set qualitative comparative analysis (fsQCA). (3) Results: Eight explanatory configurations of the acceptance of BBLPs have been obtained, in which the presence of all UTAUT-baseline and UTAUT-extended constructs is a condition in at least three prime implicates. The constructs with the greatest presence as core conditions are PER, EFF, and TRU. On the other hand, nine configurations leading to the rejection of BBLPs have been identified. The absence of all UTAUT-baseline and UTAUT-extended variables is a condition in at least three configurations. The core conditions with the greatest presence in the rejection of BBLPs are the lack of FAC, HED, and PRI. (4) Practical implications: The results obtained are of great interest to business owners, as they allow for the characterisation of different profiles of people potentially engaged with BBLPs, as well as the profiles of consumers reluctant to adopt them.

Companies are increasingly giving more attention to chatbots as an innovative solution to transform the customer service experience, redefining how they interact with users and optimizing their support processes. This study analyzes the acceptance of conversational robots in customer service within the insurance sector, using a conceptual model based on well-known new information systems adoption groundworks that are implemented with a combination of machine learning techniques based on decision trees and so-called importance–performance map analysis (IPMA). The intention to interact with a chatbot is explained by performance expectancy (PE), effort expectancy (EE), social influence (SI), and trust (TR). For the analysis, three machine learning methods are applied: decision tree regression (DTR), random forest (RF), and extreme gradient boosting (XGBoost). While the architecture of DTR provides a highly visual and intuitive explanation of the intention to use chatbots, its generalization through RF and XGBoost enhances the model’s explanatory power. The application of Shapley additive explanations (SHAP) to the best-performing model, RF, reveals a hierarchy of relevance among the explanatory variables. We find that TR is the most influential variable. In contrast, PE appears to be the least relevant factor in the acceptance of chatbots. IPMA suggests that SI, TR, and EE all deserve special attention. While the prioritization of TR and EE may be justified by their higher importance, SI stands out as the variable with the lowest performance, indicating the greatest room for improvement. In contrast, PE not only requires less attention, but it may even be reasonable to reallocate efforts away from improving PE in order to enhance the performance of the more critical variables.