Full tekst beschikbaar voor gebruikers van Linkedin. Driven by technological innovations such as cloud and mobile computing, big data, artificial intelligence, sensors, intelligent manufacturing, robots and drones, the foundations of organizations and sectors are changing rapidly. Many organizations do not yet have the skills needed to generate insights from data and to use data effectively. The success of analytics in an organization is not only determined by data scientists, but by cross-functional teams consisting of data engineers, data architects, data visualization experts, and ("perhaps most important"), Analytics Translators.
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Background:It is unclear why some physical activity (PA) mobile health (mHealth) interventions successfully promote PA whereas others do not. One possible explanation is the variety in PA mHealth interventions—not only do interventions differ in the selection of persuasive strategies but also the design and implementation of persuasive strategies can vary. However, limited studies have examined the different designs and technical implementations of strategies or explored if they indeed influenced the effectiveness of the intervention.Objective:This scoping review sets out to explore the different technical implementations and design characteristics of common and likely most effective persuasive strategies, namely, goal setting, monitoring, reminders, rewards, sharing, and social comparison. Furthermore, this review aims to explore whether previous mHealth studies examined the influence of the different design characteristics and technical operationalizations of common persuasive strategies on the effectiveness of the intervention to persuade the user to engage in PA.Methods:An unsystematic snowball and gray literature search was performed to identify the literature that evaluated the persuasive strategies in experimental trials (eg, randomized controlled trial, pre-post test). Studies were included if they targeted adults, if they were (partly) delivered by a mobile system, if they reported PA outcomes, if they used an experimental trial, and when they specifically compared the effect of different designs or implementations of persuasive strategies. The study methods, implementations, and designs of persuasive strategies, and the study results were systematically extracted from the literature by the reviewers.Results:A total of 29 experimental trials were identified. We found a heterogeneity in how the strategies are being implemented and designed. Moreover, the findings indicated that the implementation and design of the strategy has an influence on the effectiveness of the PA intervention. For instance, the effectiveness of rewarding was shown to vary between types of rewards; rewarding goal achievement seems to be more effective than rewarding each step taken. Furthermore, studies comparing different ways of goal setting suggested that assigning a goal to users might appear to be more effective than letting the user set their own goal, similar to using adaptively tailored goals as opposed to static generic goals. This study further demonstrates that only a few studies have examined the influence of different technical implementations on PA behavior.Conclusions:The different implementations and designs of persuasive strategies in mHealth interventions should be critically considered when developing such interventions and before drawing conclusions on the effectiveness of the strategy as a whole. Future efforts are needed to examine which implementations and designs are most effective to improve the translation of theory-based persuasive strategies into practical delivery forms.
This review is the first step in a long-term research project exploring how social robotics and AI-generated content can contribute to the creative experiences of older adults, with a focus on collaborative drawing and painting. We systematically searched and selected literature on human-robot co-creativity, and analyzed articles to identify methods and strategies for researching co-creative robotics. We found that none of the studies involved older adults, which shows the gap in the literature for this often involved participant group in robotics research. The analyzed literature provides valuable insights into the design of human-robot co-creativity and informs a research agenda to further investigate the topic with older adults. We argue that future research should focus on ecological and developmental perspectives on creativity, on how system behavior can be aligned with the values of older adults, and on the system structures that support this best.
Even though considerable amounts of valuable wood are collected at waste collection sites, most of it remains unused and is burned: it is too labor-intensive to sort, process and upcycle useable parts. Valuable wood thus becomes worthless waste, against circular economy principles. In MoBot-Wood, waste collection organizations HVC and the municipality of Amsterdam, together with Rolan Robotics, Metabolic and AUAS investigate how waste wood can be sorted and processed at waste collection sites, using an easy-to-deploy robotic solution. In various preceding and on-going projects, AUAS and partners are exploring circular wood intake, sorting and processing using industrial robots, including processes like machine vision, 3D scanning, sawing, and milling. These projects show that harvesting waste wood is a challenging matter. Generally, the wood is only partially useable due to the presence of metal, excessive paint, deterioration by fungi and water, or other contamination and damages. To harvest useable wood thus requires intensive sorting and processing. The solution of transporting all the waste wood from collection sites to a central processing station might be too expensive and have a negative environmental impact. Considering that much of collected wood will need to be discarded, often no wood is harvested at all, due to the costs for collection and shipping. Speaking with several partners in related projects, the idea emerged to develop a mobile robotic station, which can be (temporarily) deployed at waste collection sites, to intake, sort and process wood for upcycling. In MoBot-Wood, research entails the design of such station, its deployment conditions, and a general assessment of its potential impact. The project investigates robotic sorting and processing on location as a new approach to increase the amount of valuable, useable wood harvested at waste collection sites, by avoiding material transport and reducing the volume of remaining waste.
