This article applies ‘outcome-driven innovation’ methodology, developed by Anthony Ulwick and popularised by Clayton Christensen, to the domain of clinical trials. Data were collected through in-depth, open interviews with doctors, nurses and researchers in the UK, France and Italy. Pain points for key players of the value chain were identified. The findings supported a multi-stakeholder approach to fully exploit the transformative potential of blockchain technology to resolve issues. The research identified a set of opportunities for innovators to improve the way hospitals conduct clinical trials using smart contracts and blockchain technology generally.
This paper investigated GP perception of artificial intelligence in relation to symptom checking, and specifically whether GPs view artificial intelligence as an opportunity or a threat. The authors advocate for a sustained collaboration between GPs and artificial intelligence as the way forward for patient and societal benefit. A collaborative approach requires broad-level adoption of artificial intelligence-enabled applications to complement rather than replace a GP's own expertise. Drawing on extant literature, this study investigated how measures of self-efficacy, being an individual's ability to believe in their own ability to organise and implement courses of action, influence the perception of artificial intelligence as an opportunity rather than as a threat. Prior work suggested that higher measures of self-efficacy would correlate with the view that artificial intelligence is an opportunity. In this work, 110 GPs from the UK were invited to be surveyed via a structured questionnaire about perceived self-efficacy and view of artificial intelligence. Of these, 26 GPs agreed to participate, giving a response rate of 24%. The results gave preliminary evidence that higher levels of perceived self-efficacy were associated with greater perceptions of artificial intelligence either as an opportunity or as a threat. This finding offers a new perspective for policy makers, leaders and academics, who are looking for predictors of artificial intelligence engagement. This work may form the basis for further research on the potential causal relationship between self-efficacy and AI adoption, which could ultimately help facilitate artificial intelligence adoption.
Purpose of the paper:The aim of this research is to investigate the role of telecommunication firms as technology partners in smart factory deployment and the potential impact on their own business model.Methodology: We adopted a qualitative methodology based on multiple-case study analysis with the purpose of theory-building.Results: i) Identification of three business model trajectories that are triggered by smart factory deployment both for recipient firms and for technology enabling firms; (ii) proposition of a directional framework that enables both smart factory recipient firms and technology partners to understand their options both in terms of Smart factory deployment and business model options and of the interconnections between them.Research limitations: Selected cases have been used according to the state of development of the project. A larger number of cases, as well as a more in depth analysis, would increase confidence in the findings.Practical implications: Identification of unexplored opportunities in the new, smart factory deployment paradigm that could transform the business model of the recipient firm as well as the role of the technology partners.Originality of the paper: We identify the central role of the technology partners focusing on Telecommunication companies, who can enable the evolution of the business model transformation of recipient firms as well as its own. Moreover, the identification of the ownership and accessibility of data produced by the Smart factory is considered the main barrier for technology partners to make their business model evolve beyond business model trajectories.
The process of developing new products always contains an element of uncertainty. This uncertainty translates into a significant risk for companies investing in the development of new products or services.The risk in new product development (NPD) can be based on 'disciplined experimentation': a structured process designed to rapidly identify the 'vivid' needs of the customer, test whether the main features of the new product or service will satisfy these needs (fast prototyping).'Disciplined experimentation', in particular, addresses assumptions about value (how the initiative will produce outcomes that outweigh the effort involved), growth (how the initiative can be scaled up beyond the first group of customers) and sustainability (how quickly the organisation can adapt to the new initiative and how easily competitors will be able to replicate it).
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