Assistive technology supports maintenance or improvement of an individual’s functioning and independence, though for people in need the access to assistive products is not always guaranteed. This paper presents a generic quality framework for assistive technology service delivery that can be used independent of the setting, context, legislative framework, or type of technology. Based on available literature and a series of discussions among the authors, a framework was developed. It consists of 7 general quality criteria and four indicators for each of these criteria. The criteria are: accessibility; competence; coordination; efficiency; flexibility; user centeredness, and infrastructure. This framework can be used at a micro level (processes around individual users), meso level (the service delivery scheme or programme) or at a macro level (the whole country). It aims to help identify in an easy way the main strengths and weaknesses of a system or process, and thus guide possible improvements. As a next step in the development of this quality framework the authors propose to organise a global consultancy process to obtain responses from stakeholders across the world and to plan a number of case studies in which the framework is applied to different service delivery systems and processes in different countries.
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The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.
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Purpose: To facilitate the design of viable business models by proposing a novel business model design framework for viability. Design: A design science research method is adopted to develop a business model design framework for viability. The business model design framework for viability is demonstrated by using it to design a business model for an energy enterprise. The aforementioned framework is validated in theory by using expert opinion. Findings: It is difficult to design viable business models because of the changing market conditions, and competing interests of stakeholders in a business ecosystem setting. Although the literature on business models provides guidance on designing viable business models, the languages (business model ontologies) used to design business models largely ignore such guidelines. Therefore, we propose a business model design framework for viability to overcome the identified shortcomings. The theoretical validation of the business model design framework for viability indicates that it is able to successfully bridge the identified shortcomings, and it is able to facilitate the design of viable business models. Moreover, the validation of the framework in practice is currently underway. Originality / value: Several business model ontologies are used to conceptualise and evaluate business models. However, their rote application will not lead to viable business models, because they largely ignore vital design elements, such as design principles, configuration techniques, business rules, design choices, and assumptions. Therefore, we propose and validate a novel business model design framework for viability that overcomes the aforementioned shortcomings.
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