PurposeThis study aims to develop an understanding of how customers of a physical retail store valuate receiving location-based mobile phone messages when they are in proximity of the store. It proposes and tests a model relating two benefits (personalization and location congruency) and two sacrifices (privacy concern and intrusiveness) to message value perceptions and store visit attitudes.Design/methodology/approachThe study uses a vignette-based survey to collect data from a sample of 1,225 customers of a fashion retailer. The postulated research model is estimated using SmartPLS 3.0 with the consistent-PLS algorithm and further validated via a post-hoc test.FindingsThe empirical testing confirms the predictive validity and robustness of the model and reveals that location congruency and intrusiveness are the location-based message characteristics with the strongest effects on message value and store visit attitude.Originality/valueThe paper adds to the underexplored field of store entry research and extends previous location-based messaging studies by integrating personalization, location congruency, privacy concern and intrusiveness into one validated model.
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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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Although it appears increasingly important yet potentially challenging to attract customers to physical stores, location-based messaging, i.e., delivering mobile phone messages using data about the recipient's location when that recipient is near the sender, has been said to enable such attraction. Still, existing studies offer very limited insight into which particular location-based persuasion approach retailers should use. Drawing on persuasion theory, this exploratory study aims to investigate and compare the potential of two discrepant persuasion techniques (scarcity and social proof) to influence customers' experiences and thereby stimulate them to visit the retailer's physical store. A factorial survey design was applied to test the research model. Data were collected from a sample of actual customers of a Dutch fashion retailer (n = 579). The results suggest that scarcity is a more effective persuasion technique in the studied context than social proof; scarcity-focused messages appear to be experienced as more informative, more entertaining and less irritating, seem to be valued more because of this, and are thus more likely to incline customers to visit the store. We discuss these findings and their implications for theory as well as for practice.
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Although it appears increasingly important yet potentially challenging to attract consumers to physical stores, location‐based messaging has been said to enable such attraction. Still, existing studies offer very limited insight into which particular location‐based persuasion approach retailers should use. This study aimed to establish and compare the potential of two discrepant persuasion strategies to influence consumers’ experiences and thereby stimulate them to visit the retailer's physical store. Drawing on persuasion theory and construal level theory, and using a vignette‐based online survey method, we determined that scarcity is a more effective persuasion strategy in the studied context than social proof; scarcity‐focused messages are experienced as more informative, more entertaining and less irritating, are therefore valued more, and are thus more likely to induce store visits. We discuss these findings and their implications for theory as well as for practice.
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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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Information and communications technologies (ICTs) in human services are on the rise and raise concerns about their place and impact on the daily activities of professionals and clients. This article describes a study in which a social mobile application was developed for job coaches and employees and implemented in a pilot phase. The aim of the mobile application was to provide a better communication between employees and their job coaches and to provide more up-to-date information about the organization. The application consisted of a personal web environment and app with vacancies, personal news, events, tips, and promotions. A qualitative methodology was used in the form of focus groups and in-depth interviews. The results of this study show that the participants are partly positive about the social mobile application. It can be concluded that the use of mobile technologies can be beneficial in a range of human services practice settings for both professionals and clients and, therefore, requires more attention from the academic field to focus on this relatively new but promising theme.
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Buildings need to be carefully operated and maintained for optimum health, comfort, energy performance, and utility costs. The increasing use of Machine Learning combined with Big Data in the building services sector has shown the potential to bring energy efficiency and cost-effectiveness. Therefore, upskilling and reskilling the current workforce is required to realize new possibilities. In addition, sharing and preserving knowledge are also required for the sustainable growth of professionals and companies. This formed the basis for the Dutch Research Council funded TransAct project. To increase access to education on the job, online learning is experiencing phenomenal growth. A study was conducted with two focus groups - professionals of a building service company and university researchers - to understand the existing challenges and the ways to improve knowledge sharing and upskilling through learning on the job. This study introduced an Enterprise Social Network platform that connects members and may facilitate knowledge sharing. As a community forum, Yammer from office 365 was used. For hosting project files, a SharePoint page was created. For online courses, the company’s online learning site was utilized. The log data from the online tools were analysed, semi-structured interviews and webinars were conducted and feedback was collected with google forms. Incentive models like social recognition and innovative project results were used to motivate the professionals for online activities. This paper distinguishes the impacts of initiatives on the behaviour of university researchers vs company employees.
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Recent years have shown the emergence of numerous local energy initiatives (prosumer communities) in the Netherlands. Many of them have set the goal to establish a local and sustainable energy provision on a not-for-profit basis. In this study we carried out exploratory case studies on a number of Dutch prosumer communities. The objective is to analyse their development process, to examine the barriers they encounter while organising their initiative, and to find how ICT could be applied to counteract these barriers and support communities in reaching their goals. The study shows that prosumer communities develop along a stepwise, evolutionary growth path, while they are struggling with organising their initiative, because the right expertise is lacking on various issues (such as energy technology, finance and legislation). Participants stated that, depending on the development phase of their initiative, there is a strong need for information and specific expertise. With a foreseeable growing technical complexity they indicated that they wanted to be relieved with the right tools and services at the right moment. Based on these findings we developed a generic solution through the concept of a prosumer community shopping mall. The concept provides an integrated and scalable ICT environment, offering a wide spectrum of energy services that supports prosumer communities in every phase of their evolutionary growth path. As such the mall operates as a broker and clearing house between 2 prosumer communities and service providers, where the service offerings grow and fit with the needs and demands of the communities along their growth path. The shopping mall operates for many prosumer communities, thus providing economies of scale. Each prosumer community is presented its own virtual mall, with specific content and a personalised look-and-feel.
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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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