Evaluation of intelligent agents in consumer-to-business e-Commerce

Electronic commerce has changed traditional business trading behaviors, since consumers can easily consume through the Internet e-commerce platform. The Internet provides numerous products and services, but consumers find it is hard to choose their favorite ones. The consumer-to-business (C2B) is popular in recent years and it has become one of the best choices for customers forming a group to buy products. Thus, more and more consumers participate in online group buying. However, consumers anticipate to different prices when buying products and demand service. Thus, applying an intelligent agent into negotiation can effectively decrease efforts spent on collecting buyer information, transaction costs, and negotiation with sellers.This study proposed a system that applies an intelligent agent into the C2B e-commerce process, and evaluates the system through an experiment. Additionally, a questionnaire is used to investigate the benefits of the proposed intelligent agent systems. Analytical results show that the proposed intelligent system can increase user satisfaction, reduce performance risk, and raise perceived fairness, but nothing help on perceived value. It implied that the system still needs efforts and time to promote in nowadays commerce. If people can understand its value from finding the information they need, it must grant the more perceived value. Additionally, this system is not only applicable to C2B, but it can extend to other e-commerce models, because the agent can help the negotiation between the sellers and buyers.     

An integrated online pick-to-sort order batching approach for managing frequent arrivals of B2B e-commerce orders under both fixed and variable time-window batching

The emergence of e-commerce marketplace, especially the business-to-business (B2B) e-commerce sector, has created a vast market opportunity for retailers and logistics service providers (LSPs). However, facing the structural changes in B2B logistics orders, without any transformation of logistics operating process in distribution centres, LSPs are experiencing serious challenges in handling B2B e-commerce orders. In order for LSPs to grasp the market pie of the logistics and distribution sector of the e-commerce business through improving the core capability of handling B2B e-commerce order in their distribution centres, this paper proposes a novel approach for LSPs to systematically execute B2B order pre-processing so that the B2B e-commerce orders are better managed. An intelligent B2B order handling system (IOHS) is developed for efficient management of discrete, frequently arrived B2B e-commerce orders. By applying the “warehouse postponement” concept through the development of IOHS integrating cloud database management, fuzzy logic, and genetic algorithm approach, B2B order grouping solutions are effectively generated. A case study is conducted in a Hong Kong-based logistics service provider who handles outsourced B2B e-commerce orders, indicating the feasibility of the proposed solution to facilitate e-order fulfilment under both fixed and variable time-window batching settings in distribution centres. A significant improvement in terms of the throughput rate of handling B2B e-commerce order in the distribution centre is found, thereby increasing the satisfaction of buyers and sellers throughout the order handling and delivery process, as well as facilitating LSPs’ capability to manage a larger number of orders received from B2B e-commerce platform, given the greater product exposure of sellers in today’s B2B e-business.     

基于群组的C2C电子商务信任模型研究

国内的电子商务平台大多采用eBay信任模型,对买家反馈评分进行简单累加得到卖家信誉值,未区分买家反馈评分的合理性及其参考价值的重要程度。为此,提出一种基于群组的C2C电子商务信任模型,通过计算买家与卖家的熟悉度,确定买家的可信度,综合考虑反馈评分、交易次数、交易价格、交易时间、以往买家的可信度对信誉的影响,构建电子商务信任模型。模拟实验结果证明,相比Sporas模型,该模型能为买家提供更全面、更准确的卖家信誉信息。     

基于JADE的智能电子商务系统设计

传统电子商务系统无法让买卖双方就价格、性能、折扣等问题进行即时协商.利用智能体技术实现交易过程智能化是电子商务系统的发展趋势.JADE是基于Java语言的多智能体开发和运行环境,提供了对智能体的管理及通信控制.将JADE与Web访问技术结合起来,可以方便地构建智能电子商务系统,实现买方和卖方的交易协商.描述了JADE的工作原理,介绍了基于JADE平台设计并实现智能电子商务系统的基本方法,为智能电子商务的实现提供了一种技术途径.     

Analyzing strategies of mobile agents on malicious cloud platform with Agent-Based Computational Economic Approach

Cloud computing is a service model that enables resource-limited mobile devices to remotely execute tasks from a server. The mobile agent is a software program installed in the mobile device to negotiate a diversity of commerce transactions with other mobile agents in the cloud. However, the negotiation plans carried by mobile agents are easily be eavesdropped on by the malicious cloud platforms, since the codes of mobile agents are read and executed by the cloud platform. Thus, sellers can tailor the negotiation plans to cheat buyers for seizing buyers’ profits in negotiations after eavesdropping on buyers’ negotiation plans. In this paper, we consider the buyers can take actions to resist the sellers’ cheatings, which is that the buyers can tailor their plans with extremely low demand to decrease sellers’ profits before migrate to the hosts. In this paper, we consider actions that buyers can take to resist sellers’ cheatings, that is the buyers can tailor their plans with extremely low demands before migrate to the cloud platform. Above situations between buyers and sellers are modeled as a mathematical model called Eavesdropping and Resistance of Negotiation (ERN) Game. The strategies of the buyers and sellers playing the ERN Game are analyzed by the Agent-Based Computational Economic approach. The simulation results show the cooperative strategies will be emerged between buyers and sellers in the ERN Game.     

Marketplace and technology standards for B2B e-commerce: progress,challenges, and the state of the art

We have examined standards required for successful e-commerce (EC) architectures and evaluated the strengths and limitations of current systems that have been developed to support EC. We find that there is an unfilled need for systems that can reliably locate buyers and sellers in electronic marketplaces and also facilitate automated transactions. The notion of a ubiquitous network where loosely coupled buyers and sellers can reliably find each other in real time, evaluate products, negotiate prices, and conduct transactions is not adequately supported by current systems. These findings were based on an analysis of mainline EC architectures: EDI, company Websites, B2B hubs, e-Procurement systems, and Web Services. Limitations of each architecture were identified. Particular attention was given to the strengths and weaknesses of the Web Services architecture, since it may overcome some limitations of the other approaches.     

E-Commerce in the Indian Insurance Industry: Prospects and Future

With the advent of the Internet, online processes are replacing conventional models in our society. The greatest impact in online technology has been achieved by e-commerce. E-commerce is attractive both to buyers and sellers as it reduces search costs for buyers and inventory costs for sellers. In this paper we investigate the impact of e-commerce on the insurance industry in India. The recent growth of Internet infrastructure and introduction of economic reforms in the insurance sector have opened up the monopolistic Indian insurance market to competition from foreign alliances. We study the evolving scenario in the insurance industry in India and identify the features of online insurance that improve the conventional insurance model and thus, makes it more attractive for the Indian insurance industry to go online.     

Trust-oriented buyer strategies for seller reporting and selection in competitive electronic marketplaces

In competitive electronic marketplaces where some selling agents may be dishonest and quality products offered by good sellers are limited, selecting the most profitable sellers as transaction partners is challenging, especially when buying agents lack personal experience with sellers. Reputation systems help buyers to select sellers by aggregating seller information reported by other buyers (called advisers). However, in such competitive marketplaces, buyers may also be concerned about the possibility of losing business opportunities with good sellers if they report truthful seller information. In this paper, we propose a trust-oriented mechanism built on a game theoretic basis for buyers to: (1) determine an optimal seller reporting strategy, by modeling the trustworthiness (competency and willingness) of advisers in reporting seller information; (2) discover sellers who maximize their profit by modeling the trustworthiness of sellers and considering the buyers’ preferences on product quality. Experimental results confirm that competitive marketplaces operating with our mechanism lead to better profit for buyers and create incentives for seller honesty.     

Inter-agent Dialogues in Electronic Marketplaces

This paper presents an overall framework for carrying out different types of dialogues between intelligent and autonomous agents acting in an electronic marketplace. Such dialogues take place during various commercial transactions concerning requests and offers of products and services. The proposed dialogue framework has been adopted in the communication and collaboration protocols of an already implemented system, which enables buyers and sellers delegate a variety of tasks to their personal agents. Much attention has been paid to the personalization of collaborative agents, which may permanently live and interact in the market representing their owners' interests. Our overall approach builds on a modular decomposition of the agents involved, and a formal and operational modeling of the associated dialogues. Features of our framework are demonstrated through an illustrative example of dialogues deployed during interagent transactions on the establishment of a combined reservation for dinner and a movie. The main contribution of this work is that the proposed framework is capable to represent disparate dialogues taking place among agents having adopted diverse strategies for carrying out e-commerce transactions.     

Efficient communication architecture for the C2C agent

In C2C communication, all necessary information must be collected promptly when a buyer and a seller communicate. That is, an intelligent C2C agent is needed to provide information to buyers and sellers. Along with the evolution of computing technology, C2C agents can exploit the efficient delivery capabilities of peer-to-peer (P2P) technology. However, P2P also increases traffic between agents, but communication faults are a fatal problem for C2C business. This study proposes a robust communication architecture based on current P2P content-delivery standards and its efficiency and robustness have been verified from an experiment.     

MoRVAM: A reverse Vickrey auction system for mobile commerce

Internet auctions bring buyers and sellers together for the purpose of trading goods and services online. In order to get the goods, a buyer must search for items through several auction sites. When the auction starts, the buyer needs to connect to these auction sites frequently so that he/she can monitor the bid states and re-bid. In this paper, we propose an automated negotiation model between two participants, for mobile commerce, using collaborative mobile agents called MoRVAM, which mediates between the buyer and the sellers, and executes bidding asynchronously and autonomously. A new RVT protocol is also implemented to achieve unconditional bid privacy. Advantages of the RVT protocol are addressed as well. All the bidding process can be implemented without revealing losing bid and unnecessary information.     

Development of a DRAM spots negotiation system using core hierarchy negotiation mechanism

The major factors of the DRAM negotiable transactions system in this research are the concession strategy and the negotiation mechanism of the sellers and buyers. In terms of negotiation mechanism, the core hierarchy model negotiation mechanism makes use of multistage sub-negotiates procedure to do multi-attribute negotiation for the sellers and buyers. The negotiable mechanism can further increase the successful rate of negotiation and the acceptable degree. In terms of concession strategy, research in the Liang and Doong, the concession strategy is constituted by times of negotiation, method of concession and times of persistence. There are less interactive concession for the sellers and buyers in the concession method of the negotiation in the related research. This research is to study the negotiable results that using various combinations of interactive concession strategy and the core hierarchy model negotiation mechanism. Be used as the reference of negotiation for the DRAM to trade on network.     

A neural network approach to predicting price negotiation outcomes in business-to-business contexts

Price premiums are a key profit driver for long-term business relationships. For sellers in business-to-business (B2B) relationships, it is important to have appropriate strategies to negotiate price increases without trading off the relationships with their buyers. This paper aims to understand the annual price negotiation processes of companies by predicting whether a seller’s reservation price, target price, and initial offer positively affect the price negotiation outcome between the sellers and buyers. Data from 284 B2B relationships of a chemicals supplier based in Germany was used to examine our research model. In order to capture the non-linear decisions that are involved in price negotiations and to address collinearity among negotiations’ determinants, neural network analysis was used to predict the factors that influence price negotiation outcome. The neural network model was then compared with the results from regression analysis. Compared to regression analysis, the neural network has a lower standard error, and it showed that target price played a more important role in B2B price negotiations. The neural network was also able measure non-linear, non-compensatory decisions that are involved in price negotiations. The results imply that neural networks should be more widely used by researchers to address the threats that multi-collinearity poses. For companies, the results imply that price targets should be actively managed, e.g. through clear financial aims or through seminars aiming to help sales personnel to establish more challenging negotiation aims.     

Agents in E-Commerce: State of the Art

This paper surveys the state of the art of agent-mediated electronic commerce (e-commerce), especially in business-to-consumer (B2C) e-commerce and business-to-business (B2B) e-commerce. From the consumer buying behaviour perspective, the roles of agents in B2C e-commerce are: product brokering, merchant brokering, and negotiation. The applications of agents in B2B e-commerce are mainly in supply chain management. Mobile agents, evolutionary agents, and data-mining agents are some special techniques which can be applied in agent-mediated e-commerce. In addition, some technologies for implementation are briefly reviewed. Finally, we conclude this paper by discussions on the future directions of agent-mediated e-commerce. Received 14 September 2000 / Revised 13 January 2001 / Accepted in revised form 27 February 2001     

On agent-mediated electronic commerce

This paper surveys and analyzes the state of the art of agent-mediated electronic commerce (e-commerce), concentrating particularly on the business-to-consumer (B2C) and business-to-business (B2B) aspects. From the consumer buying behavior perspective, agents are being used in the following activities: need identification, product brokering, buyer coalition formation, merchant brokering, and negotiation. The roles of agents in B2B e-commerce are discussed through the business-to-business transaction model that identifies agents as being employed in partnership formation, brokering, and negotiation. Having identified the roles for agents in B2C and B2B e-commerce, some of the key underpinning technologies of this vision are highlighted. Finally, we conclude by discussing the future directions and potential impediments to the wide-scale adoption of agent-mediated e-commerce.     

Trust in consumer-to-consumer electronic commerce

We developed a model of consumer-to-consumer (C2C) e-commerce trust and tested it. We expected that two influences: internal (natural propensity to trust [NPT] and perception of web site quality [PWSQ]) and external (other's trust of buyers/sellers [OTBS] and third party recognition [TPR]) would affect an individual's trust in C2C e-commerce. However contrary to studies of other types of e-commerce, support was only found for PWSQ and TPR; we therefore discussed possible reasons for this contradiction. Suggestions are made of ways to help e-commerce site developers provide a trustworthy atmosphere and identify trustworthy consumers.     

Rainbow Product Ranking for Upgrading E-Commerce

Online product ranking is complicated by conflicting attributes. The authors propose a rainbow ranking system to solve this problem in e-commerce. This approach extends eBay's one-to-one shopping paradigm to a flexible one with many-to-many transactions. The system demonstrates a 50 to 70 percent transaction increase over skyline and eBay ranking methods. Buyers are allowed personalized selections of commodity products, whereas sellers benefit from both wholesale and retailing online. The rainbow scheme thus creates a win-win situation for both buyers and sellers in widened e-commerce marketplaces.     

一种乐观电子商务协议的公平性分析

乐观电子商务协议通常具有复杂结构,由多个子协议组合而成,与传统认证协议具有显著不同。电子商务协议最主要目的是实现买卖双方的公平交换,同时假定交换双方都可能是不诚实的,需要考虑来自协议合法实体的内部攻击。文章在深入分析公平交换协议各项属性的基础上,定义了电子项的认证属性,对SVO逻辑进行了扩展,使用SVO逻辑语法定义了电子商务协议应满足的公平性。文章以一种真实的电子商务协议为对象,演示了基于SVO逻辑的电子商务协议公平性分析方法,并指出该协议存在安全缺陷,提出改进意见。     

基于模糊约束规划的自动协商

给出了一个基于模糊约束规划模型的自动协商系统。建立了模糊约束规划模型并利用模糊模拟、神经网络和遗传算法给出了求解Pareto最优解的混合智能算法;协商过程中卖方智能体根据神经网络拟合的效用函数并运行混合智能体算法得到当前协商步的Pareto最优解,避免了对大型商品数据库的反复搜索,为系统推向实际应用奠定了基础;协商模型仿真实验表明了协商系统返回的解与实际调查得到的用户偏好相一致。     

An intelligent negotiator agent design for bilateral contracts of electrical energy

In this paper, an intelligent agent (using the Fuzzy SARSA learning approach) is proposed to negotiate for bilateral contracts (BC) of electrical energy in Block Forward Markets (BFM or similar market environments). In the BFM energy markets, the buyers (or loads) and the sellers (or generators) submit their bids and offers on a daily basis. The loads and generators could employ intelligent software agents to trade energy in BC markets on their behalves. Since each agent attempts to choose the best bid/offer in the market, conflict of interests might happen. In this work, the trading of energy in BC markets is modeled and solved using Game Theory and Reinforcement Learning (RL) approaches. The Stackelberg equation concept is used for the match making among load and generator agents. Then to overcome the negotiation limited time problems (it is assumed that a limited time is given to each generator–load pairs to negotiate and make an agreement), a Fuzzy SARSA Learning (FSL) method is used. The fuzzy feature of FSL helps the agent cope with continuous characteristics of the environment and also prevents it from the curse of dimensionality. The performance of the FSL (compared to other well-known traditional negotiation techniques, such as time-dependent and imitative techniques) is illustrated through simulation studies. The case study simulation results show that the FSL based agent could achieve more profits compared to the agents using other reviewed techniques in the BC energy market.