Psychological elements explaining the consumer's adoption and use of a website recommendation system: A theoretical framework proposal

The purpose of this paper is to understand, with an emphasis on the psychological perspective of the research problem, the consumer's adoption and use of a certain web site recommendation system as well as the main psychological outcomes involved. The approach takes the form of theoretical modelling. Findings: A conceptual model is proposed and discussed. A total of 20 research propositions are theoretically analyzed and justified. Research limitations/implications: The theoretical discussion developed here is not empirically validated. This represents an opportunity for future research. Practical implications: The ideas extracted from the discussion of the conceptual model should be a help for recommendation systems designers and web site managers, so that they may be more aware, when working with such systems, of the psychological process consumers undergo when interacting with them. In this regard, numerous practical reflections and suggestions are presented

Safeguarding E-Commerce against Advisor Cheating Behaviors: Towards More Robust Trust Models for Handling Unfair Ratings

In electronic marketplaces, after each transaction buyers will rate the products provided by the sellers. To decide the most trustworthy sellers to transact with, buyers rely on trust models to leverage these ratings to evaluate the reputation of sellers. Although the high effectiveness of different trust models for handling unfair ratings have been claimed by their designers, recently it is argued that these models are vulnerable to more intelligent attacks, and there is an urgent demand that the robustness of the existing trust models has to be evaluated in a more comprehensive way. In this work, we classify the existing trust models into two broad categories and propose an extendable e-marketplace testbed to evaluate their robustness against different unfair rating attacks comprehensively. On top of highlighting the robustness of the existing trust models for handling unfair ratings is far from what they were claimed to be, we further propose and validate a novel combination mechanism for the existing trust models, Discount-then-Filter, to notably enhance their robustness against the investigated attacks

Reputation based Buyer Strategies for Seller Selection in Electronic Markets

Reputation based adaptive buying agents that reason about sellers for purchase decisions have been designed for B2C ecommerce markets. Previous research in the area of buyer agent strategies for choosing seller agents in ecommerce markets has focused on frequent purchases. In this thesis, we present reputation based strategies for buyer agents to choose seller agents in a decentralized multi agent based ecommerce markets for frequent as well as infrequent purchases. We consider a marketplace where the behavior of seller agents and buyer agents can vary, they can enter and leave the market any time, they may be dishonest, and quality of the product can be gauged after actually receiving the product. Buyer agents exchange seller agents' information, which is based on their own experiences, with other buyer agents in the market. However, there is no guarantee that when other buyer agents provide information, they are truthful or share similar opinions. First we present a method for buyer agent to model a seller agent's reputation. The buyer agent computes a seller agent's reputation based on its ability to meet its expectations of product quality and price as compared to its competitors. We show that a buying agent acting alone, utilizing our model of maintaining seller agents' reputation and buying strategy does better than buying agents acting alone employing strategies proposed previously by other researchers for frequent as well as for infrequent purchases. Next we present two methods for buyer agents to identify other trustworthy buyer agent friends who are honest and have similar opinions regarding seller agents, based on sharing of seller agents' information with each other. In the first method, buyer agent utilizes other buyer agents' opinions and ratings of seller agents to identify trustworthy buyer agent friends. Reputation of seller agents provided by trustworthy buyer agent friends is adjusted to account for the differences in the rating systems and combined with its own information on seller agents to choose high quality, low priced seller agent. In the second method, buyer agent only utilizes other buyer agents' opinions of seller agents to identify trustworthy buyer agent friends. Ratings are assigned to seller agents by the buyer agent based on trustworthy friend buyer agents' opinions and combined with its own rating on seller agents to choose a high quality, low priced seller agent to purchase from. We conducted experiments to show that both methods are successful in distinguishing between trustworthy buyer agent friends, whose opinions should be utilized in decision making, and untrustworthy buyer agent friends who are either dishonest, or have different opinions. We also show that buyer agents using our models of identifying trustworthy buyer agent friends have higher performance than a buyer agent acting alone for infrequent purchases and for increasing numbers of sellers in the market. Finally we analyze the performances of buyer agents with risk taking and conservative attitudes. A buyer agent with risk taking attitude considers a new seller agent as reputable initially and tends to purchase from a new seller agent if they are offering the lowest price among reputable seller agents. A buyer agent with conservative attitude is cautious in its approach and explores new seller agents at a rate proportional to the ratio of unexplored seller agents to the all the seller agents who have sent bids. Our results show that, when buyer agents are making decisions based on their own information, a buyer agent with conservative attitude has the best performance. When buyer agents are utilizing information provided by their trusted friends, a buyer agent with risk taking attitude and using only trusted friend buyer agents' opinions of seller agents has the best performance. In summary, the main contributions of this dissertation are: 1.A new reputation based way to model seller agents by buyer agents based on direct interactions. 2.A protocol to exchange reputation information about seller agents with other buyer agent friends based on the friends' direct interaction with seller agents. 3.Two methods of identifying trustworthy buyer agent friends who are honest and share similar opinions, and utilizing the information provided by them to maximize a buyer agent's chances of choosing a high quality, low priced seller agent to purchase from

Trust and reputation management in decentralized systems

In large, open and distributed systems, agents are often used to represent users and act on their behalves. Agents can provide good or bad services or act honestly or dishonestly. Trust and reputation mechanisms are used to distinguish good services from bad ones or honest agents from dishonest ones. My research is focused on trust and reputation management in decentralized systems. Compared with centralized systems, decentralized systems are more difficult and inefficient for agents to find and collect information to build trust and reputation. In this thesis, I propose a Bayesian network-based trust model. It provides a flexible way to present differentiated trust and combine different aspects of trust that can meet agents’ different needs. As a complementary element, I propose a super-agent based approach that facilitates reputation management in decentralized networks. The idea of allowing super-agents to form interest-based communities further enables flexible reputation management among groups of agents. A reward mechanism creates incentives for super-agents to contribute their resources and to be honest. As a single package, my work is able to promote effective, efficient and flexible trust and reputation management in decentralized systems

Online buying behavior in technological and office products

The evolution and growth of E-commerce nowadays is irrefutable. The revolution and introduction of new technologies have profound implications in business management, offering tools for the globalization of the market. This evolution and change in the market also leads to different realities towards not just the business itself but also the consumer. It is important to understand what are the main features of information search and purchase behavior online and understand online consumer behavior (what are the characteristics, what motivates somebody to buy online, what are his fears, etc). This study aims, from existing knowledge about information search and purchase behavior online, to deepen the knowledge of Portuguese behavior to give information to companies to better develop their business. In this case, this study will be made for Technological and Office products.A evolução e o crescimento do E-Commerce hoje em dia é irrefutável. A revolução e a introdução de novas tecnologias têm implicações profundas na gestão empresarial, oferecendo ferramentas and a globalização do mercado. Este evolução e mudança no mercado leva a uma diferente realidade não só no negócio em si mas também no consumidor. É importante perceber quais são as principais caracteristícas na procura de informação online e na compra e perceber o comportamento do consumidor online (as suas características, as motivações, os medos, etc). Este estudo procura partir do conhecimento existente sobre o comportamento da procura de informação e compra online, aprofundar o conhecimento do comportamento dos Portugueses de forma a dar informação às compresas de como desenvolver melhor o seu negócio. Neste caso, o estudo será feito sobre produtos de Tecnologia e Escritório

Personalization & Trust-Enhancing Signals in E-Commerce

Despite worldwide growing revenue rates in e-Commerce, a lot of economic potential remains unused, which is manifesting in low conversion rates. Only a fraction of website visitors can be transformed to website buyers, which may be explained by a lack of trust in the retailer. In e-Commerce, trustworthiness can be signaled through special stimuli presented on the website as interaction platform between customer and retailer. By personalization of these signals, consumers can conveniently collect information needed to reduce their individual risk concerns. The objective of this study is to understand whether and how the personalization of trust-enhancing signals has an effect on trusting attitudes, buying intentions and buying behaviors. First promising preliminary results refer to the central importance of trust-enhancing signals for both a trustworthy impression and trust-related buying behavior. These insights will hold practical and managerial implications for web designers, online retailers and the integration of personalization into the business model

Dynamic Credibility Threshold Assignment in Trust and Reputation Mechanisms Using PID Controller

In online shopping buyers do not have enough information about sellers and cannot inspect the products before purchasing them. To help buyers find reliable sellers, online marketplaces deploy Trust and Reputation Management (TRM) systems. These systems aggregate buyers’ feedback about the sellers they have interacted with and about the products they have purchased, to inform users within the marketplace about the sellers and products before making purchases. Thus positive customer feedback has become a valuable asset for each seller in order to attract more business. This naturally creates incentives for cheating, in terms of introducing fake positive feedback. Therefore, an important responsibility of TRM systems is to aid buyers find genuine feedback (reviews) about different sellers. Recent TRM systems achieve this goal by selecting and assigning credible advisers to any new customer/buyer. These advisers are selected among the buyers who have had experience with a number of sellers and have provided feedback for their services and goods. As people differ in their tastes, the buyer feedback that would be most useful should come from advisers with similar tastes and values. In addition, the advisers should be honest, i.e. provide truthful reviews and ratings, and not malicious, i.e. not collude with sellers to favour them or with other buyers to badmouth some sellers. Defining the boundary between dishonest and honest advisers is very important. However, currently, there is no systematic approach for setting the honesty threshold which divides benevolent advisers from the malicious ones. The thesis addresses this problem and proposes a market-adaptive honesty threshold management mechanism. In this mechanism the TRM system forms a feedback system which monitors the current status of the e-marketplace. According to the status of the e-marketplace the feedback system improves the performance utilizing PID controller from the field of control systems. The responsibility of this controller is to set the the suitable value of honesty threshold. The results of experiments, using simulation and real-world dataset show that the market-adaptive honesty threshold allows to optimize the performance of the marketplace with respect to throughput and buyer satisfaction

IMG-GUARD: Watermark Based Approach for Image Privacy in OSN Framework

A social networking service (also social networking site, SNS or social media) is an online platform that is used by people to build social networks or social relations with another persons who are share their own details or career interests, activities, backgrounds or real-life connections. Social networking sites are varied and they incorporate a range of new information and various tools such as availability personal computers, mobile devices such as tablet computers and smart phones, digital photo/video/sharing and "web logging" diary entries online (blogging). While Online Social Networks (OSNs) enable users to share photos easily, they also expose users to several privacy threats from both the OSNs and external entities. The current privacy controls on social networks are far from adequate, resulting in inappropriate flows of information when users fail to understand their privacy settings or OSNs fail to implement policies correctly. Social networks may be complicated because of privacy expectations when they reserve the right to analyze uploaded photos using automated watermarking technique. A user who uploads digital data such as image to their home page may wish to share it with only mutual friends, which OSNs partially satisfy with privacy settings. In this paper, we concentrate to solve the privacy violation problem occurred when images are published on the online social networks without the permission. According to such images are always shared after uploading process. Therefore, the digital image watermarking based on DWT co-efficient. Watermark bits are embedded in uploaded images. Watermarked images are shared in user homages can be difficult to misuse by other persons