"Producers and Consumers in EU E-Commerce Law by John Dickie, Oxford: Hart Publishing, 2005 (ISBN 1-841-13454-6)"

Book Review

Trust, Satisfaction and Online Repurchase Intention: The Moderating Role of Perceived Effectiveness of E-commerce Institutional Mechanisms

The effects of e-commerce institutional mechanisms on trust and online purchase have traditionally been understood in the initial online purchase context. This study extends this literature by exploring the role of e-commerce institutional mechanisms in the online repurchase context. In doing so, it responds to the emerging call for understanding the institutional context under which customer trust operates in an e-commerce environment. Specifically, this study introduces a key moderator, perceived effectiveness of e-commerce institutional mechanisms (PEEIM), to the relationships between trust, satisfaction, and repurchase intention. Drawing on the theory of organizational trust, and based on a survey of 362 returning online customers, we find that PEEIM negatively moderates the relationship between trust in an online vendor and online customer repurchase intention, as it decreases the importance of trust to promoting repurchase behavior. We also find that PEEIM positively moderates the relationship between customer satisfaction and trust as it enhances the customer’s reliance on past transaction experience with the vendor to reevaluate trust in the vendor. Consistent with the predictions made in the literature, PEEIM does not directly affect trust or repurchase intention. Academic and practical implications and future research directions are discussed.

Evaluation of information extraction techniques to label extracted data from e-commerce web page

Automatically determining and assigning shared and meaningful text labels to data extracted from an e-Commerce web page is a challenging problem. An e-Commerce web page can display a list of data records, each of which can contain a combination of data items (e.g. product name and price) and explicit labels, which describe some of these data items. Recent advances in extraction techniques have made it much easier to precisely extract individual data items and labels from a web page, however, there are two open problems: 1. assigning an explicit label to a data item, and 2. determining labels for the remaining data items. Furthermore, improvements in the availability and coverage of vocabularies, especially in the context of e-Commerce web sites, means that we now have access to a bank of relevant, meaningful and shared labels which can be assigned to extracted data items. However, there is a need for a technique which will take as input a set of extracted data items and assign automatically to them the most relevant and meaningful labels from a shared vocabulary. We observe that the Information Extraction (IE) community has developed a great number of techniques which solve problems similar to our own. In this work-in-progress paper we propose our intention to theoretically and experimentally evaluate different IE techniques to ascertain which is most suitable to solve this problem.