Dynamical trust and reputation computation model for B2C E-Commerce

Trust is one of the most important factors that influence the successful application of network service environments, such as e-commerce, wireless sensor networks, and online social networks. Computation models associated with trust and reputation have been paid special attention in both computer societies and service science in recent years. In this paper, a dynamical computation model of reputation for B2C e-commerce is proposed. Firstly, conceptions associated with trust and reputation are introduced, and the mathematical formula of trust for B2C e-commerce is given. Then a dynamical computation model of reputation is further proposed based on the conception of trust and the relationship between trust and reputation. In the proposed model, classical varying processes of reputation of B2C e-commerce are discussed. Furthermore, the iterative trust and reputation computation models are formulated via a set of difference equations based on the closed-loop feedback mechanism. Finally, a group of numerical simulation experiments are performed to illustrate the proposed model of trust and reputation. Experimental results show that the proposed model is effective in simulating the dynamical processes of trust and reputation for B2C e-commerce

Anonymous reputation based reservations in e-commerce (AMNESIC)

Online reservation systems have grown over the last recent years to facilitate the purchase of goods and services. Generally, reservation systems require that customers provide some personal data to make a reservation effective. With this data, service providers can check the consumer history and decide if the user is trustable enough to get the reserve. Although the reputation of a user is a good metric to implement the access control of the system, providing personal and sensitive data to the system presents high privacy risks, since the interests of a user are totally known and tracked by an external entity. In this paper we design an anonymous reservation protocol that uses reputations to profile the users and control their access to the offered services, but at the same time it preserves their privacy not only from the seller but the service provider

Closed-loop feedback computation model of dynamical reputation based on the local trust evaluation in business-to-consumer e-commerce

Trust and reputation are important factors that influence the success of both traditional transactions in physical social networks and modern e-commerce in virtual Internet environments. It is difficult to define the concept of trust and quantify it because trust has both subjective and objective characteristics at the same time. A well-reported issue with reputation management system in business-to-consumer (BtoC) e-commerce is the “all good reputation” problem. In order to deal with the confusion, a new computational model of reputation is proposed in this paper. The ratings of each customer are set as basic trust score events. In addition, the time series of massive ratings are aggregated to formulate the sellers’ local temporal trust scores by Beta distribution. A logical model of trust and reputation is established based on the analysis of the dynamical relationship between trust and reputation. As for single goods with repeat transactions, an iterative mathematical model of trust and reputation is established with a closed-loop feedback mechanism. Numerical experiments on repeated transactions recorded over a period of 24 months are performed. The experimental results show that the proposed method plays guiding roles for both theoretical research into trust and reputation and the practical design of reputation systems in BtoC e-commerce

BUSINESS REPUTATION SYSTEMS BASED ON BLOCKCHAIN TECHNOLOGY—A RISKY ADVANCE

Reputation is indispensable for online business since it supports customers in their buying decisions and allows sellers to justify premium prices. While IS research has investigated reputation systems mainly as review systems on online platforms for business-to-consumer (B2C) transactions, no proper solutions have been developed for business-to-business (B2B) transactions yet. We use blockchain technology to propose a new class of reputation systems that apply ratings as voluntary bonus payments: Before a transaction is performed, customers commit to pay a bonus that is granted if a service provider has performed a service properly. As opposed to rival reputation systems that build on cumulated ratings or reviews, our system enables monetized reputation mechanisms that are inextricably linked with online transactions. We expect this system class to provide more trustworthy ratings, which might reduce agency costs and serve quality providers to establish a reputation towards new customers, building on second-order trust

Exploring the effects of consumers’ trust : a predictive model for satisfying buyers’ expectations based on sellers’ behaviour in the marketplace

In recent years, Consumer-to-Consumer (C2C) marketplaces have become very popular among Internet users. However, compared to traditional Business-to-Consumer (B2C) stores, most modern C2C marketplaces are reported to be associated with stronger negative sentiments among consumers. These negative sentiments arise from the inability of sellers to meet certain buyers’ expectations and are linked to the low trust relationship among sellers and buyers in C2C marketplaces. The growth of these negative emotions might jeopardize buyers’ decisions to opt for C2C marketplaces in their future purchase intentions. In the present study, we extend the definition of trust as an emotion to cover the digital world and demonstrate the trust model currently used by most online stores. Based on the buyer’s behaviour in the C2C marketplace, we propose a conceptual framework to predict trust between the buyer and the seller. Given that C2C marketplaces are rich sources of data for trust mining and sentiment analysis, we perform text mining on Airbnb to predict the trust level in host descriptions of offered facilities. The data are acquired from the US city of Ashville, Alabama, and Manchester in the UK. The results of the analysis demonstrate that guest negative feedback in reviews are high when the description of the host’s property has the emotion of joy only. By contrast, guest negative sentiments in reviews are at a minimum when the host’s sentiment has mixed emotions (e.g., joy and fear)

Development of an intelligent e-commerce assurance model to promote trust in online shopping environment

Electronic commerce (e-commerce) markets provide benefits for both buyers and sellers; however, because of cyber security risks consumers are reluctant to transact online. Trust in e-commerce is paramount for adoption. Trust as a subject for research has been a term considered in depth by numerous researchers in various fields of study, including psychology and information technology. Various models have been developed in e-commerce to alleviate consumer fears, thus promoting trust in online environments. Third-party web seals and online scanning tools are some of the existing models used in e-commerce environments, but they have some deficiencies, e.g. failure to incorporate compliance, which need to be addressed. This research proposes an e-commerce assurance model for safe online shopping. The machine learning model is called the Page ranking analytical hierarchy process (PRAHP). PRAHP builds complementary strengths of the analytical hierarchy process (AHP) and Page ranking (PR) techniques to evaluate the trustworthiness of web attributes. The attributes that are assessed are Adaptive legislation, Adaptive International Organisation for Standardisation Standards, Availability, Policy and Advanced Security login. The attributes were selected based on the literature reviewed from accredited journals and some of the reputable e-commerce websites. PRAHP’s paradigms were evaluated extensively through detailed experiments on business-to-business, business-to-consumer, cloud-based and general e-commerce websites. The results of the assessments were validated by customer inputs regarding the website. The reliability and robustness of PRAHP was tested by varying the damping factor and the inbound links. In all the experiments, the results revealed that the model provides reliable results to guide customers in making informed purchasing decisions. The research also reveals hidden e-commerce topics that have not received attention, which generates knowledge and opens research questions for future researchers. These ultimately made significant contributions in e-commerce assurance, in areas such as security and compliance through the fusing of AHP and PR, integrated into a decision table for alleviating trustworthiness anxiety in various e-commerce transacting partners, e-commerce platforms and markets.College of Engineering, Science and TechnologyD. Phil. Information System

Automated Purchase Negotiations in a Dynamic Electronic Marketplace

Nowadays, there is a surge of B2C and B2B e-commerce operated\ud on the Internet. However, many of these systems are often nothing\ud more than electronic product or service catalogues. Against this background,\ud it is argued that new generation systems based on automatic\ud negotiation will emerge. This paper covers a particular kind of automatic\ud negotiation systems, where a number of participants in a mobile\ud dynamic electronic marketplace automatically negotiate the purchase of\ud products or services, by means of multiple automated one-to-one bargainings.\ud In a dynamic e-marketplace, the number of buyers and sellers\ud and their preferences may change over time. By mobile we mean that\ud buyers in a commercial area may initiate simultaneous negotiations with\ud several sellers using portable devices like cell phones, laptops or personal\ud digital assistants, so these negotiations do not require participants to be\ud colocated in space. We will show how an expressive approach to fuzzy\ud constraint based agent purchase negotiations in competitive trading environments,\ud is ideally suited to work on these kind of e-marketplaces. An\ud example of mobile e-marketplace, and a comparison between an expressive\ud and an inexpressive approach will be presented to show the efficiency\ud of the proposed solution