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The aim of this paper is to understand if service innovation (Helkkula et al., 2018), based on artificial intelligence (AI) systems, may guarantee healthcare service ecosystem (H-SES) well-being (Frow et al., 2019; Beirão et al., 2017), taking into account that many doubts relieved in terms of transparency may compromise the patients' perceived quality of health services provided through AI systems.

A literature review on service innovation, detected in terms of value co-creation, and service ecosystem, investigated in terms of well-being, is drawn. To analyze the implications of service innovation on a H-SES well-being, through the technology acceptance degree and predisposition to use by actors, a case study based on TAM-model 3 determinants as categories is carried out.

AI-based service innovation archetypes in healthcare may be considered as antecedents of the service ecosystem well-being conditions as long as they enable actors to co-create value. To make it possible, a patient-driven service innovation is necessary in order to mitigate the risks of its inactivity due to fears in terms of transparency.

Service innovation and service ecosystem well-being may be studied in an integrated way, with a multidisciplinary approach, and are linked by value co-creation, because only thanks a patient-driven service innovation is possible to foster service ecosystem well-being in healthcare.

This work tries to detect the factors that can impact service innovation in the retail sector according to a service ecosystem (SES) perspective. This paper aims to understand whether it is possible to study innovation focusing on the impact of technology on resource integration practices in SESs and to rank different patterns of innovation by evaluating their effects in terms of value co-creation.

To show up the perception of actors, a case study has been carried out through semi-structured interviews. The aggregates of practices and the service innovation archetypes, drawn from the theoretical background, have been used as categories of analysis.

Service innovation is reconceptualised as the result of the application of new technology to resource integration practices in the retail SES, and it is possible to rank its patterns and outcomes by deepening its effects on the emergence of value co-creation phenomena. Shared intentions have been identified as drivers of service innovation, but greater transparency in systems used to embolden a higher willingness to use could be necessary.

Service innovation has been studied by focusing on value co-creation; for this reason, the willingness to use technology emerged as a determinant of service innovation. This result implies the need for a multilevel reinterpretation of contemporary SES, both regarding the technical features of digital solutions and their adherence to users' skills and the effects of willingness or unwillingness to use on value co-creation.

This conceptual chapter aims to understand the role of artificial intelligence (AI) in value co-creation phenomena in a healthcare service ecosystem, through a literature review and the definition of a conceptual framework. AI, as an operant resource, can stimulate a completely patient-centered, adaptive and resilient healthcare system, and governance models in healthcare based on data-driven decision-making (DDDM), ensuring faster choices, more timely diagnosis and more personalized treatment paths. However, the full implementation of AI in healthcare is inhibited by some frictions, mainly related to the risk that the AI black box may generate an inadequate automatic decision, also due to the quality of data used, often partial and unstructured given the reluctance to share them by patients concerned by privacy threats. The co-design (multi-part and multi-level) of a predictive decision model based on the functional transparency of the AI algorithm would allow for augmented decision as result of an effective human–machine interaction. Healthcare actors could thus make decisions using the information detected by the software (based on clear cause-and-effect correlations and modifiable variables in case of mistakes), integrated with their professional knowledge. This would also help to strengthen the patient’s perception of the decision’s reliability and accuracy and the safety of the tool (factors that can affect his/her trust). AI may be considered as a driver for value co-creation in healthcare, thanks to transparency. It would allow the promotion of collaborative behaviors involving actors by generating new institutions and new resource integration practices among them.

Big data is a buzzword of our times, and yet the awareness of what big data is, how it permeates our daily lives, and how it is applied either in the policy-making process or in the business sector remains relatively low. From a different perspective, while specialists, that is, practitioners and researchers, dealing with the technical facets of big data successfully uncover new features, new domains, and new opportunities related to big data, there is a need of evaluating and examining these findings through the lens of social sciences and management. This chapter offers an insight into key issues and developments that shape the broad and multi-directional big data debate. To this end, the content of the book is elaborated and the key findings are highlighted. In this way, this chapter serves as a very useful guide into the question of how big data is applied across issues and domains and how it is valid and relevant to all of us today.