Medical devices are introduced in the market with an ever‐increasing rate, with important benefits for the whole society's health. In the greatest majority of cases, although representing the entry point for devices, healthcare local institutions/hospitals do not receive any macro‐level guidance from (inter)national authorities for their assessment. Hence, in the absence of a methodological support, local institutions/hospitals started to assess devices autonomously, building on Health Technology Assessment (HTA) tools based on Multi‐Criteria Decision Analysis (MCDA), in order to take into‐account the multifaceted aspects connected with devices. However, the strategies described so far in the scientific literature for implementing MCDA in local‐based HTA of medical devices suffer from a harsh methodological weaknesses – that is, the use of bespoke criteria for the specific device to be assessed ‐ that severely affect the evaluation of medical devices. Within this scenario, this work proposes a new tool based on peer‐to‐peer IF‐TOPSIS, intended for micro‐level assessment of medical devices with the main objective of overcoming the above‐mentioned critical issues and provide local institutions/hospitals with a general tool. An example of application of this tool in the choice between three neurological devices is shown. Contributions are both theoretical and practical. Theoretically, while proposing a general MCDA tool for micro‐level HTA, we answer the call for the identification of key methodological principles for the local assessment of medical devices. At the practical level, this tool is readily implementable and can be adapted to consider the local idiosyncratic characteristics of the context where decisions have to be made.

PurposeThe study aims to test the success of university-industry (U-I) collaboration in terms of innovation process efficiency. Then, this study explores the moderating role of a set of organizational routines in the U-I relationship, which can help in overcoming the issues undermining the collaboration success.Design/methodology/approachThe study is based on an international Open Innovation (OI) survey. The survey investigated the items to build the main variables of the conceptual framework, measured through seven-point Likert scales. Steps to ensure the reliability and validity of the variables were conducted. Then, hypotheses were tested with an ordinary least squares regression.FindingsResults show that the higher the collaboration intensity (depth) with universities, the higher the innovation process efficiency. Furthermore, organizational routines aimed at improving firms' assimilation absorptive capacity further strengthen the positive effects of intensive collaboration on innovation process efficiency.Practical implicationsFindings indicate that R&D managers should strive to build deep collaborations with universities to enhance process efficiency and invest in the quality of these relationships. Managers should create and maintain an internal environment that further enhances the positive effects of intensive collaboration on innovation process efficiency.Originality/valueThe OI literature has not reached a shared view on the positive contribution of universities toward industrial firms' innovation performance. The study adopts a process-efficiency view, rarely used by other OI studies usually focused on output indicators; this study unpacks, respectively, the role of the intensity of collaboration and the organizational routines, thus disclosing the benefit of U-I collaboration on innovation efficiency.

Purpose This paper aims to provide a helpful tool for those who plan to implement blockchain-based solutions for the governance of the electronic medical record (EMR) in health-care settings. The goals are to identify each type of stakeholders involved in these projects and to clarify the relevance, to achieve success, of their readiness, intended as availability and ability to adopt blockchain. Design/methodology/approach The chosen methodology is a multiple case study on three initiatives that used blockchain to manage EMRs. This study relied on multiple sources of evidence. The primary data consisted of two rounds of semi-structured interviews with different informants. This study followed a grounded theory approach and performed within- and cross-case analyses. Findings This study identified the types of stakeholders – nodes and not-nodes – of the network and how their readiness level affects the implementation of blockchain-based projects applied to EMR. The nodes (e.g. patients and doctors) are pivotal in making the network working once this has been constructed. Out of the four readiness dimensions suggested by literature, motivational readiness, has the higher impact. Not-nodes stakeholders play a pivotal role in the project’s pre-implementation phase. For them, structural readiness is the dimension with the higher relevance. Originality/value To the best of the authors’ knowledge, it is the first time that a paper analyses the differences between nodes and not-nodes stakeholders of the blockchain network, in terms not only of type but also of readiness. Identifying the readiness level to implement successful projects is a fundamental step that has never been analysed in the health field.