A Relational Build-up Model of Consumer Intention to Self-disclose Personal Information in E-commerce B2C Relationships

For business-to-consumer (B2C) electronic-commerce (ecommerce) transactions to work, website users must disclose sensitive information (such as credit card information). To establish a long-term customer relationship, organizations desire further information about current and potential customers (e.g., their name, user preferences, product preferences, physical address, and email address). Both ecommerce literature and interpersonal relationship research indicate that self-disclosure is a key dependent variable in burgeoning long-term relationships. In this study, I use a survey methodology (N = 281) and tests key antecedents that the ecommerce B2C relationship stage theory proposes as they relate to self-disclosure. This research model identifies the following antecedents of self-disclosure: attraction, perceived rewards, switching cost, involvement, and trust. Survey results show that trust and perceived rewards explain significant amounts of variance in self-disclosure intention in an online B2C context. I discuss implications for both practice and theory with the results

Personalized Ranking in eCommerce Search

We address the problem of personalization in the context of eCommerce search. Specifically, we develop personalization ranking features that use in-session context to augment a generic ranker optimized for conversion and relevance. We use a combination of latent features learned from item co-clicks in historic sessions and content-based features that use item title and price. Personalization in search has been discussed extensively in the existing literature. The novelty of our work is combining and comparing content-based and content-agnostic features and showing that they complement each other to result in a significant improvement of the ranker. Moreover, our technique does not require an explicit re-ranking step, does not rely on learning user profiles from long term search behavior, and does not involve complex modeling of query-item-user features. Our approach captures item co-click propensity using lightweight item embeddings. We experimentally show that our technique significantly outperforms a generic ranker in terms of Mean Reciprocal Rank (MRR). We also provide anecdotal evidence for the semantic similarity captured by the item embeddings on the eBay search engine.Comment: Under Revie

Evaluating trust in electronic commerce : a study based on the information provided on merchants' websites

Lack of trust has been identified as a major problem hampering the growth of Electronic Commerce (EC). It is reported by many studies that a large number of online shoppers abandon their transactions because they do not trust the website when they are asked to provide personal information. To support trust, we developed an information framework model based on research on EC trust. The model is based on the information a consumer expects to find on an EC website and that is shown from the literature to increase his/her trust towards online merchants. An information extraction system is then developed to help the user find this information. In this paper, we present the development of the information extraction system and its evaluation. This is then followed by a study looking at the use of the identified variables on a sample of EC websites

Informed Consent to Address Trust, Control, and Privacy Concerns in User Profiling

More and more, services and products are being personalised or\ud tailored, based on user-related data stored in so called user profiles or user\ud models. Although user profiling offers great benefits for both organisations and\ud users, there are several psychological factors hindering the potential success of user profiling. The most important factors are trust, control and privacy\ud concerns. This paper presents informed consent as a means to address the\ud hurdles trust, control, and privacy concerns pose to user profiling

Business models to support content commons

The application of conventional, 'scarce resource' economics to content has been mistaken and harmful. More appropriate forms of economic analysis highlight the critical role that accessibility to information plays in the process of innovation. Meanwhile, down at the micro-economic level, there is an all-too-common perception that open content approaches are unsustainable and bad for business, and reflect naïve idealism on the part of their proponents. This paper identifies a range of suitable business models, and thereby demonstrates that the content commons is sustainable and appropriate for profit-oriented business enterprises