Designing appliances for mobile commerce and retailtainment

In the emerging world of the new consumer and the `anytime, anywhere' mobile commerce, appliances are located at the collision point of the retailer and consumer agendas. The consequence of this is twofold: on the one hand appliances that were previously considered plain and utilitarian become entertainment devices and on the other, for the effective design of consumer appliances it becomes paramount to employ multidisciplinary expertise. In this paper, we discuss consumer perceptions of a retailtainment commerce system developed in collaboration between interactivity designers, information systems engineers, hardware and application developers, marketing strategists, product development teams, social scientists and retail professionals. We discuss the approached employed for the design of the consumer experience and its implications for appliance design

Shopping center tracking and recommendation systems

Anacleto R., Luz N., Almeida A., Figueiredo L., Novais P., Shopping Center Tracking and Recommendation Systems, in Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011, Corchado E.; Snasel V., Sedano J., Hassanien A.E.; Calvo J.L., Slezak D. (Eds.), Springer - Series Advances in Intelligent and Soft Computing, vol. 87, ISBN 978-3-642-19643-0, pp 299-308, 2011.Shopping centers present a rich and heterogeneous environment, where IT systems can be implemented in order to support the needs of its actors. However, due to the environment complexity, several feasibility issues emerge when designing both the logical and physical architecture of such systems. Additionally, the system must be able to cope with the individual needs of each actor, and provide services that are easily adopted by them, taking into account several sociological and economical aspects. In this sense, we present an overview of current support systems for shopping center environments. From this overview, a high-level model of the domain (involving actors and services) is described along with challenges and possible features in the context of current Semantic Web, mobile device and sensor technologies

CONCEPTUALIZING CONTEXT FOR ADAPTIVE PERVASIVE COMMERCE

In retail, demographics are currently regarded as the most convenient base for successful personalized marketing. However, signs point to the dormant power of context recognition. While technologies that can sense the environment are advanced, questions such as what to sense and how to adapt context are largely unanswered. In this paper, we analyze the purchase context of a retail outlet and suggest a context model for adaptive pervasive commerce. Furthermore, we introduce one approach how to conceptualize context that may be applied to conceptualize context for adaptive pervasive advertising applications so that they really deliver on their potential: showing the right message to the right recipient at the right time

Data Mining for Shopping Centres – Customer Knowledge-Management Framework

Shopping centers are an important part of the UK economy and have been the subject of considerable research. Relying on complex interdependencies between shoppers, retailers and owners, shopping centers are ideal for knowledge management study. Nevertheless, although retailers have been in the forefront of data mining, little has been written on Customer Knowledge Management for shopping centers. In this chapter, the authors aim to demonstrate the possibilities and draw attention to the possible implications of improving customer satisfaction. Aspects of customer knowledge management for shopping centers are considered using analogies drawn from an exploratory questionnaire survey. The objectives of a Customer Knowledge Management system could include increasing rental incomes and bringing new life back into shopping centers and towns

Enhancing shopping experiences in smart retailing

The retailing market has undergone a paradigm-shift in the last decades, departing from its traditional form of shopping in brick-and-mortar stores towards online shopping and the establishment of shopping malls. As a result, “small” independent retailers operating in urban environments have suffered a substantial reduction of their turnover. This situation could be presumably reversed if retailers were to establish business “alliances” targeting economies of scale and engage themselves in providing innovative digital services. The SMARTBUY ecosystem realizes the concept of a “distributed shopping mall”, which allows retailers to join forces and unite in a large commercial coalition that generates added value for both retailers and customers. Along this line, the SMARTBUY ecosystem offers several novel features: (i) inventory management of centralized products and services, (ii) geo-located marketing of products and services, (iii) location-based search for products offered by neighboring retailers, and (iv) personalized recommendations for purchasing products derived by an innovative recommendation system. SMARTBUY materializes a blended retailing paradigm which combines the benefits of online shopping with the attractiveness of traditional shopping in brick-and-mortar stores. This article provides an overview of the main architectural components and functional aspects of the SMARTBUY ecosystem. Then, it reports the main findings derived from a 12 months-long pilot execution of SMARTBUY across four European cities and discusses the key technology acceptance factors when deploying alike business alliances

Exploration of location-based services adoption

As mobile technologies become more ubiquitous in the general population, it is reasonable to assume that individuals will consume services and software to enhance their aspirations and entertainment desires. This paper discusses a controlled experiment to explore aspects of user perceptions of their use of location-based services. This study examines a location-based service prototype experiment and analysis based on the UTAUT model. The results show significant indicators that suggest behavior patterns of early adopters of location-based services are being observed. We discuss these influences and attempt to explain their significance. Moreover, more curiously we discuss why some of our model was unsupported and postulate why

Towards personalized services in the healthcare domain

Healthcare services are designed for enabling the provision of medical care to the patient. The traditional healthcare services are based on the doctor-centric paradigm. Essentially, they enable healthcare providers to assess patients’ health status based on information derived from medical examination and information stored in patient’s electronic Medical Health Records (eMHRs) [1]. Hence, it is crucial for patient’s health data to be digitalized and organized in such a way allowing their exploitation by the healthcare provider at a later point of time [2]. The doctor-centric healthcare services enhance healthcare providers’ diagnosing skills and enable them to give patients accurate treatment directions aiming to their earlier and safer de-hospitalization