Conversational robots (chatbots) are currently an extended Insurtech that is widely used to enable policyholders’ communication with insurance firms. This paper analyses customers’ acceptance of chatbots in procedures with regard to in-force policies. We analysed a semistructured survey answered by workers of a public Spanish university. Structured questions have been grounded in the well-known technology acceptance model (TAM), which is based on two explanatory variables: perceived usefulness (PU) and perceived ease of use (PEOU). We have also considered two additional explanatory factors: social influence (SI), whose impact on behavioral intention (BI) is mediated by PU, and trust (TRUST), whose influence on BI is supposed to be made throughout PU and PEOU. Likewise, we asked two open questions about the advantages and disadvantages of chatbot use to make procedures linked with in-force insurance contracts. We performed our analysis by using quantitative and qualitative methods. The quantitative analysis tests the suitability of TAM on our data and has been performed by using structural equation modelling with partial least squares (PLS-SEM). Subsequently, to reach a deeper understanding of the reasons that explain the respondents’ behavioral intention, we provide a systematic overview of the answers to open questions with the help of the groundwork provided by TAM. We have checked that the basic TAM along with social influence and trust provide a satisfactory explanation for behavioral intention toward bots. Likewise, we have observed a general reluctance toward the use of chatbots. The qualitative analysis showed that arguments explaining resistance come from all explanatory factors considered in the paper. Therefore, mainstream responses have outlined that interaction with chatbots is difficult, and many times, the procedure must be finished with the assistance of a human operator. Likewise, many responses point out as a relevant drawback that they provide a dehumanized service without empathy. Consequently, interactions with chatbots are perceived as cumbersome, ineffective, and a loss of time. Although some people perceive that the faster service provided by chatbots in concrete circumstances is an advantage, other theoretical consequences that may add value, such as temporal flexibility and the possibility of proving better services with the same cost and/or reducing insurance prices because of the reduction of firms’ administrative costs, are generally not perceived. Our findings have theoretical and practical implications. We have shown that TAM provides a reliable theoretical model to understand policyhoders’ acceptance of chatbot technology in an insurance setting. Perceived usefulness, reliability, social opinion about bot adoption, and usability must be improved to avoid generalized policyholders’ reluctance. That resistance is because of issues such as conversational skills, the capability to provide an interaction closer to being human, and users’ perception that chatbots actually add value to policyholders.

This paper makes a systematic bibliographical analysis of the contributions of fuzzy set theory (FST) on option pricing to state principal mainstream focuses and exposes the basic questions of the analytical foundation of the reviewed approaches. It performs a bibliographical analysis of journal articles and book chapters by applying PRISMA guidelines to the SCOPUS and WoS databases. We subsequently present a structured report of principal findings about research fields, outlets and authors of this topic. Once we have identified the ways in which FST has contributed to option pricing, we outline basics about their mathematic and conceptual grounds. We have identified four main approaches to fuzzy option pricing (FOP). The mainstream of papers, based on the so-called fuzzy-random approach, consists of fuzzifying option pricing formulas under the hypothesis that the parameters governing the stochastic movement of prices are not crisp but fuzzy numbers. The second stream of the literature also superposes FST to conventional option pricing models, but this is made by means of the distortion of neutral to risk probabilities with fuzzy measures. The third approach, so called fuzzy pay-off, is devoted to evaluating real options and uses strictly fuzzy number concepts. The fourth approach embeds tools such as fuzzy controllers or fuzzy neural networks, taking advantage of their capability to obtain good numerical approximations to any function from empirical data. Principal outlets of FOP are journals devoted to fuzzy mathematics and soft computing, and the evolution of contributions throughout time reveals that it has become a well-stablished topic in fuzzy mathematics. The bibliographical research developed in this paper provides a wide panoramic perspective about what the principal mainstreams of FOP research are, what is done, and thus, future research lines are suggested.

This paper shows how Fuzzy Set Theory can be used in investment analysis when, as usual, these investments are developed under uncertainty, i.e. the investor has only subjective estimates based on his experience or knowledge about the future cash-flows of the investments, the discount rate, etc. In particular, we will develop basic concepts for investment analysis as the Net Present Value and the Internal Rate of Return by assuming that the initial data are fuzzy numbers. Later we will analyse how to rank investments and how to select the tangible investment portfolios when the magnitudes are estimated subjectively by comparing fuzzy numbers and with possibilistic mathematical programming.