Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study

Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction

A survey on context awareness in big data analytics for business applications

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature

A new approach to retailing for successful competition in the new smart scenario

Purpose- This study develops the idea of smart retailing, exemplified in innovative, technology-enriched retail services as part of service-oriented strategies. In particular, the aim is to provide a new integrated framework to understand the emerging retail scenario based on the smart usage of technologies to improve retail service and develop innovation management strategies. This framework will provide a comprehensive understanding the basic forms of smart retailing as the current competitive scenario. Design/ Methodology/approach- As a viewpoint, this paper employs an interdisciplinary approach, drawing upon the actual challenges in retailing, to propose a new perspective, the smart retailing one, to describe the new competitive scenario and formulates an emerging research agenda. Findings- The present paper contributes to research on innovation and technology management for retailing by examining the key dimensions of smart retailing, which aims to enhancing retail service quality and retailers’ performance. Originality- The paper explains how current retailing is moving to a smart perspective, and how retail management should be adapted to successfully perform in the current service-dominant logic scenario, as a consequence of increasing consumer involvement in service co-production and the rapid growth of digital technologies

A new approach to retailing for successful competition in the new smart scenario

Purpose- This study develops the idea of smart retailing, exemplified in innovative, technology-enriched retail services as part of service-oriented strategies. In particular, the aim is to provide a new integrated framework to understand the emerging retail scenario based on the smart usage of technologies to improve retail service and develop innovation management strategies. This framework will provide a comprehensive understanding the basic forms of smart retailing as the current competitive scenario. Design/ Methodology/approach- As a viewpoint, this paper employs an interdisciplinary approach, drawing upon the actual challenges in retailing, to propose a new perspective, the smart retailing one, to describe the new competitive scenario and formulates an emerging research agenda. Findings- The present paper contributes to research on innovation and technology management for retailing by examining the key dimensions of smart retailing, which aims to enhancing retail service quality and retailers’ performance. Originality- The paper explains how current retailing is moving to a smart perspective, and how retail management should be adapted to successfully perform in the current service-dominant logic scenario, as a consequence of increasing consumer involvement in service co-production and the rapid growth of digital 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

Determinants of technology adoption in the retail trade industry - the case of SMEs in Spain

This paper analyzes the determinants of small and medium-sized enterprises’ technology adoption in the retail trade industry. From the theoretical perspective, two types of influential factors are differentiated in this respect: the personal characteristics of the manager/business owner and the business’s organizational characteristics. The empirical analysis is based on a survey of 268 small and medium-sized enterprises in the Spanish retail trade sector. A logistic regression specification is used as an econometric method. The results indicate that both the acquisition of new technical and electronic equipment and the obtaining of new software are affected by the two types of determinants previously pointed out. The manager/business owner’s entrepreneurial motivation and educational background have significant influences on technology adoption in this type of companies. Furthermore, being part of a business group, carrying out training activities for the employees and inter-firm cooperation also positively influence technology adoption in the retail trade industry

Critical review of the e-loyalty literature: a purchase-centred framework

Over the last few years, the concept of online loyalty has been examined extensively in the literature, and it remains a topic of constant inquiry for both academics and marketing managers. The tremendous development of the Internet for both marketing and e-commerce settings, in conjunction with the growing desire of consumers to purchase online, has promoted two main outcomes: (a) increasing numbers of Business-to-Customer companies running businesses online and (b) the development of a variety of different e-loyalty research models. However, current research lacks a systematic review of the literature that provides a general conceptual framework on e-loyalty, which would help managers to understand their customers better, to take advantage of industry-related factors, and to improve their service quality. The present study is an attempt to critically synthesize results from multiple empirical studies on e-loyalty. Our findings illustrate that 62 instruments for measuring e-loyalty are currently in use, influenced predominantly by Zeithaml et al. (J Marketing. 1996;60(2):31-46) and Oliver (1997; Satisfaction: a behavioral perspective on the consumer. New York: McGraw Hill). Additionally, we propose a new general conceptual framework, which leads to antecedents dividing e-loyalty on the basis of the action of purchase into pre-purchase, during-purchase and after-purchase factors. To conclude, a number of managerial implementations are suggested in order to help marketing managers increase their customers’ e-loyalty by making crucial changes in each purchase stage