J. Theor. Appl. Electron. Commer. Res., Volume 8, Issue 2 (August 2013) – 11 articles

Real-time and continuous monitoring of high value goods can considerably improve the reliability and effectiveness of supply chains. Wireless Sensor Networks (WSN) offer technical capabilities for continuous sense and respond capabilities. WSN offer complementary advantages over the use of Radio Frequency Identification (RFID) in supply chains. Still, WSN have not been massively adopted. While some success stories on the use of RFID in supply chains have appeared, little research is available that studies the slow adoption of WSN. This paper presents results of a qualitative case study of the ongoing adoption of WSN in a Pharmaceutical Cold Chain to prevent loss of high value shipments. Based on interviews with various actors in the supply chain, benefits and barriers that impact the adoption process are identified. Using a process view and inter-organizational adoption model, the complex relationships between adoption factors are analyzed. The results show that WSN can effectively improve process quality and reduce waste in the cold chain. However, careful attention needs to be paid to managing the various interconnected factors that may support or hinder adoption. The study demonstrates that a process view contributes to understanding the adoption process. Moreover, an inter-organizational view to the adoption process is needed to successfully introduce WSN in the Pharmaceutical Cold Chain. Full article

The growing interest in Radio Frequency Identification (RFID) technology in recent years has sparked an intensive debate on the benefits to be expected. With the growth of RFID implementations in size and scope comes a shift away from infrastructural aspects to the question of how to draw value from the large amounts of collected data. However, the necessary procedures for the handling of massive RFID data sets are still an under-researched issue. Against this background, the study presents results from a real-world trial conducted by a large apparel retailer. The objective of the trial was to explore the opportunities for generating novel performance indicators and reports on the reality of store processes and customer behavior on the sales floor. We give an overview of the algorithms used for RFID data processing and the interpretation of the resulting insights from a practitioner’s point of view. The case example thus provides an overview of the potential of RFID as a powerful tool for assortment optimization, customer research, store layout design, and other management tasks in retail. Full article

In recent years the print industry has been facing constant and increasing competition from the double onslaught of the Internet and the proliferation of television channels. In order to survive as a viable industry, print enterprises must streamline and exploit up-to-date economizing measures. A key cost element in the print industry is paper, representing between 20% and 30% of total printing expenditures. Thus, any improvement in the operational efficiency of paper reel management has the potential of yielding considerable cost savings. It is in this context that we analyze the effect of enhancing the available paper reel supply chain information through the use of semi passive Radio Frequency IDentification (RFID) technology. Focusing on the printing house, we develop both simulation models and formulas that forecast savings from having better information. Thereafter we implemented the system and compared the forecasts to actual savings. Using standard economic evaluation techniques we unequivocally show the economic imperative of introducing semi passive RFID technology. Even though this study was carried out in a specific printing house, we argue that its implications are far wider, affecting other industries. Full article

Enterprises choose Enterprise Architecture (EA) solution, in order to overcome dynamic business challenges and in order to coordinate various enterprise elements. In this article, a solution is suggested for the Enterprise Architecture based on a conceptual model of Enterprise Ontology (EO). Enterprise Ontology provides a common structure for data collection. First, conceptual model of Enterprise Ontology based on the Zachman Framework is presented. Then, the Enterprise Architecture development process based on Enterprise Ontology is proposed. Using this solution, Staffs, stakeholders, users, architects, and systems achieved a common understanding of enterprise concepts and relationships and therefore, architecture data are collected in a correct way. The primary focus is collecting accurate architecture data instead of developing architecture artifacts; also meet the decision maker's needs by fit for purpose modeling. Finally, we demonstrate our solution in a case study and show the appropriate results and conclusions. Full article

The paper is centered on the role of technology and specifically of IT in the recombination of the factors of production. Particularly, the objective is to see how this technology contributes to the formulation of business strategies, the outlining of organizational structures and the alignment with the business environment. A theoretical framework is proposed that combines three perspectives. The first perspective stresses the importance of digital architectures in the modalities through which firms organize for innovation. Second, organization as a platform outlines a scenario for generating multiple organizational forms based on recombination of existent resources, routines and transactions. Third, digital platforms are seen as determinant tools for a dematerialization process that consents to recombine the factors of production for achieving a competitive advantage. The Midblue project provides an illustrative example of how strategy, structure and information technology co-evolve in organizations and how digital platforms can trigger transformation at both strategic and organizational levels. Full article

During the Web era e-procurement has witnessed a steep rise in marketplace deployment; this has been followed by a substantial number of failures. A number of larger technology providers are now left to support both small and large businesses. Flexibility has been a key enabler in supporting network evolution across a varied number of domains. The aim of this study is to investigate flexibility around marketplace evolution, success and failure. In particular, explore the inter-relationships between architectural flexibility and the evolving Web and Internet. A systematic literature review (SLR) was carried out in order to uncover the changes that have taken place over the past fifteen years. A conceptual model is produced early in the research in order to provide contextual underpinning. We employed a manual search of 5 journals. Of the 22 relevant studies, one addressed research trends around e-procurement in pharmaceutical organizations. Three addressed eprocurement in financial organizations. A number of flexibility categories are uncovered by the SLR and then used as a means to support flexible e-procurement marketplace (EPM) design and adoption, recognising EPM evolution over the volatile study period. Flexibility categories are uncovered and comprise technical, organisational, environmental and strategic (TOES) concerns. Full article

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications. Full article

The customer relationship management (CRM) is a business methodology used to build long term profitable customers by analyzing customer needs and behaviors. The customer behavior is analyzed by choosing important attributes in the customer database. The customers are then segmented into groups according to their attribute values. The rules are generated using rule induction algorithms to describe the customers in each group. These rules can be used by the entrepreneur to predict the behavior of their new customers and to vary the attraction process for existing customers. In this paper a new rule algorithm has been proposed based on the concepts of rough set theory. Its performance has been compared with LEM2 (Learning from Examples Module, version 2) algorithm, an existing rough set based rule induction algorithm. Real data set of the customer transaction is used for analysis. Recency(R), Frequency (F), Monetary (M) and Payment (P) are the attributes chosen for analyzing customer data. The proposed algorithm on average achieves 0.439% increase in sensitivity, 0.007% increase in specificity, 0.151% increase in accuracy, 0.014% increase in positive predictive value, 0.218% increase in negative predictive value and 0.228% increase in F-measure when compared to LEM2 algorithm. Full article

A literature review of existing assessment frameworks for B2C websites over more than ten years reveals that they are all built bottom-up through an essentially empirical approach of recording functionalities/features without any theoretical construct to guide the design of the framework. In an attempt to develop a theoretically consistent framework, this paper first identifies all relevant assessment categories and sub-categories for B2C websites in the luxury fashion industry. In total, eight categories and 29 sub-categories (actual assessment criteria) are identified. This framework is then applied to 15 luxury fashion brands in a longitudinal study in the years of 2006, 2008, 2010 and 2012. These four surveys document the substantial changes in website functionalities/features over the period, especially relating to how Web 2.0 and social media are diffused and adopted in the luxury fashion industry. A careful analysis of the eight categories and sub-categories reveals that the B2C website design can be characterized (and indeed defined) by virtue of three strategic choices regarding website design. The three strategic choices together with the eight categories form the basis for a theory for website design, which we name the CUBE model. We believe this model to be relevant beyond the luxury/fashion industry. Full article

Next year, 2014, it will be 30 years since the first Business-to-Customer online shopping occurred (i.e., Michael Aldrich teleshopping; Gateshead SIS/Tesco as merchant and Mrs. Snowball as the first online home shopper; (Site 1), and 20 years since the first web-based online ordering service was launched. […] Full article