1e alinea column: Internet, social media, en al dan niet location based mobiele data hebben impact op de zichtbaarheid van de burger als consument, op business logica modellen en op hoe organisaties eruit gaan zien qua inrichting om in deze veranderende markt te overleven. In deze column de veranderingen in vogelvlucht en in onderling verband.
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User experience (UX) research on pervasive technologies faces considerable challenges regarding today's mobile context-sensitive applications: evaluative field studies lack control, whereas lab studies miss the interaction with a dynamic context. This dilemma has inspired researchers to use virtual environments (VEs) to acquire control while offering the user a rich contextual experience. Although promising, these studies are mainly concerned with usability and the technical realization of their setup. Furthermore, previous setups leave room for improvement regarding the user's immersive experience. This paper contributes to this line of research by presenting a UX case study on mobile advertising with a novel CAVE-smartphone interface. We conducted two experiments in which we evaluated the intrusiveness of a mobile locationbased advertising app in a virtual supermarket. The results confirm our hypothesis that context-congruent ads lessen the experienced intrusiveness thereby demonstrating that our setup is capable of generating preliminary meaningful results with regards to UX. Furthermore, we share insights in conducting these studies.
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This research investigates growth inhibitors for smart services driven by condition-based maintenance (CBM). Despite the fast rise of Industry 4.0 technologies, such as smart sensoring, internet of things, and machine learning (ML), smart services have failed to keep pace. Combined, these technologies enable CBM to achieve the lean goal of high reliability and low waste for industrial equipment. Equipment located at customers throughout the world can be monitored and maintained by manufacturers and service providers, but so far industry uptake has been slow. The contributions of this study are twofold. First, it uncovers industry settings that impede the use of equipment failure data needed to train ML algorithms to predict failures and use these predictions to trigger maintenance. These empirical settings, drawn from four global machine equipment manufacturers, include either under- or over-maintenance (i.e., either too much or too little periodic maintenance). Second, formal analysis of a system dynamics model based on these empirical settings reveals a sweet spot of industry settings in which such inhibitors are absent. Companies that fall outside this sweet spot need to follow specific transition paths to reach it. This research discusses these paths, from both a research and practice perspective.
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