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Über dieses Buch

This book constitutes the refereed proceedings of the Workshop on E-Business (WEB 2011), held in Shanghai, China, on December 4, 2011.

The 40 papers, which were selected from 88 submissions to the workshop, touch on topics that are diverse yet highly relevant to the challenges faced by today's e-business researchers and practitioners. They are organized in topical sections on social networks, business intelligence, and social computing; economics and organizational implications of electronic markets; and e-business systems and applications.



Social Networks, Business Intelligence and Social Computing


Social Networks and Social Computing

Mining Implicit Social Network with Context-Aware Technologies

Given their growing importance with the fast advance of today’s information technologies, social networks have been extensively studied. However, a majority of existing published literature in this area consider only the explicit form of social networks. We consider its complement - implicit social networks. We adapt the social distance model and influence model to a basic implicit social network scenario. We then extend the basic model by incorporating the concept of multiple network paradigms.

Eun Jung Yoon, Wei Zhou

Using Social Network Classifiers for Predicting E-Commerce Adoption

This paper indicates that knowledge about a person’s social network is valuable to predict the intent to purchase books and computers online. Data was gathered about a network of 681 persons and their intent to buy products online. Results of a range of networked classification techniques are compared with the predictive power of logistic regression. This comparison indicates that information about a person’s social network is more valuable to predict a person’s intent to buy online than the person’s characteristics such as age, gender, his intensity of computer use and his enjoyment when working with the computer.

Thomas Verbraken, Frank Goethals, Wouter Verbeke, Bart Baesens

Exploring Innovation in the Context of Employee Relationship and IT-Enabled Knowledge Sharing

Innovation is one of the critical success factors for organizations. It is essential for business to understand the driving factors of innovation. This study investigates impacts of the following two aspects on innovation: employee relationship and knowledge sharing. 167 samples of firm level data were collected to construct the measurements of innovation, intensity of employee relationship, employee diversity, quality of knowledge sharing, and IT application maturity. It is found that all of these factors have significant impacts on innovation. Furthermore, IT application maturity has a significant moderating effect of enabling knowledge sharing to improve innovation. In order to further refine the characteristics of employee relationship, an individual level study was conducted to construct three consolidated employee social networks. It is shown that centrality of the information social network, which can be perceived as relationship characteristics of an individual employee in the network, is positively related to performance. With analysis at both organizational and individual level, this study empirically illustrates the importance of exploring innovation in the context of employee relationship and IT-enabled knowledge sharing.

Jianping Peng, Guoying Zhang, Zhengping Fu, Yong Tan

The Study of Construction and Analysis Method of Social Network Model Based on Cooperator Relationship

As the new work mode develops, virtual collaborative environments play an increasingly important role in future business and personal activities. Our research proposes a new method of constructing collaborators social network model which considers both communication relations and cooperative relations between cooperators. This method uses public information on the work platform to discover the cooperator’s behaviors, discovers community relationship intensity between collaborators and uses these to construct the coordinators social network model. Finally, we use the test data from the open source community website

to prove the validity of the model and the method. As a result, this method does not only improve the connectivity of social networks, but also reflects each collaborator’s role of team better.

Xiang Chen, Ning Gao

Are Online Review Helpfulness Ratings Biased or Not?

The helpfulness rating of Amazon product review, a popular vote feature used by Amazon to rank product reviews and display them to online shoppers, has important implications for online shopping decisions. This research investigates how objective those helpfulness ratings are. The general assumption is that the ratings are "representative" views of the shoppers. However, previous studies on product reviews indicate bias may also exist among helpfulness ratings. Using the survey questionnaire, the study found that there were indeed significant differences between the helpfulness ratings displayed at and those from a simulated online shopper population. The survey results also show that there are evidences of rating differences by gender, age, ethnicity, income and mobile device use for shopping. Thus the "true" ratings on online user reviews may well be quite different from what we see at Implications and limitations of this research are discussed.

Yun Wan, Makoto Nakayama

A Social Network Based Analysis of Deceptive Communication in Online Chat

The digital information era has allowed increasing online deception opportunities. In addition, the performance of detecting deception has been no better than chance in face-to-face communication, and is reported to be even worse in computer-mediated communication (CMC). Thus, there is a great need to uncover effective cues to deception in CMC. Online interaction weaves an implicit social network. However, Deception in CMC has not been examined from the social network perspective. To fill this gap, this research explores interaction patterns of deceivers in online chat. Based on an analysis of social networks created from online chat messages, this study provides preliminary evidence for the efficacy of social network based metrics in discriminating deceivers from truth-tellers. The findings of this study have significant implications for deception research in social media.

Jinie Pak, Lina Zhou

Research on Financial Super-Network Model Based on Variational Inequalities

How social networks and financial transaction networks interact on each other has drawn more and more interests for supervision agencies and financial institutions in their efforts to combat money laundering. By introducing super-network theory, we proposed a super-network model integrating social network and financial transaction network. Based on this super-network, we presented a multiple objective decision model, and after analyzing the optimal functions of the agents, the equilibrium flows for both social network and financial transition network are found so as for the super-network achieves an equilibrium state. Then we discussed how to analyze the suspicious transaction flows or suspicious financial agents using those equilibrium flows.

Xuan Liu, Jia Li, Zhigao Chen, Pengzhu Zhang

On the Volatility of Online Ratings: An Empirical Study

Many online rating systems represent product quality using metrics such as the mean and the distribution of ratings. However, the mean usually becomes stable as reviews accumulate, and consequently, it does not reflect the trend emerging from the latest user ratings. Additionally, understanding whether any variation in the trend is truly significant requires accounting for the volatility of the product’s rating history. Developing better rating aggregation techniques should focus on quantifying the volatility in ratings to appropriately weight or discount older ratings. We present a theoretical model based on stock market metrics, known as the Average Rating Volatility (ARV), which captures the fluctuation present in these ratings. Next, ARV is mapped to the discounting factor for weighting (aging) past ratings and used as the coefficient in Brown’s Simple Exponential Smoothing to produce an aggregate mean rating. This proposed method represents the “true” quality of a product more accurately because it accounts for both volatility and trend in the product’s rating history. Empirical findings on rating volatility for several product categories using data from Amazon further motivate the need and applicability of the proposed methodology.

Christopher S. Leberknight, Soumya Sen, Mung Chiang

EnterpriseWeb Mining, Web Analytics, and Business Intelligence

Supporting Patent Maintenance Decision: A Data Mining Approach

Nowadays, patents become much more important for companies to protect their rights and intellectual assets under the keen competitive business environments. However, it is not free for a granted patent. In the patent systems of many countries, a patent holder is required to pay a maintenance fee after the initial application to retain patent protection on his/her invention until the expiration of the protection period. Because not all the patents are worth maintaining by patent holders, firms and organizations need to identify “important patents” for maintenance and abandon “unimportant patents” to avoid unnecessary patent maintenance costs. In this paper, we employ the variables suggested by prior studies that would discriminate renewed patents from those abandoned ones and then take the data mining approach to construct a prediction model(s) on the basis of these variables for supporting patent maintenance decisions. Such a data-mining-based patent maintenance decision support system can help firms and organizations improve the effectiveness of their patent maintenance decisions and, at the same time, decrease the cost of their patent maintenance decisions. Our empirical results indicate that the effectiveness of our proposed system is satisfactory and practical for supporting patent maintenance decisions.

Chih-Ping Wei, Hung-Chen Chen, Ching-Tun Chang, Yen-Ming Chu

Neural Network Analysis of Right-Censored Observations for Occurrence Time Prediction

Introduced is a neural network method to build survival time prediction models with censored and completed observations. The proposed method modifies the standard back-propagation neural network process so that the censored data can be used without alteration. On the other hand, existing neural network methods require alteration of censored data and suffer from the problem of scalability on the prediction output domain. Further, the modification of the censored observations distorts the data so that the final prediction outcomes may not be accurate. Preliminary validations show that the proposed neural network method is a viable method.

Young U. Ryu, Jae Kyeong Kim, Kwang Hyuk Im, Hankuk Hong

Impact of Recommendations on Advertising-Based Revenue Models

Online content providers need a loyal user base for achieving a profitable revenue stream. Large number of visits and long clickstreams are essential for business models based on online advertising. In e-commerce settings, personalized recommendations have already been extensively researched on their effect on both user behavior and related economic performance indicators. We transfer this evaluation into the online content realm and show that recommender systems exhibit a positive impact for online content provider as well. Our research hypotheses emphasize on those components of an advertising-based revenue stream, which are manipulable by personalized recommendations. Based on a rich data set from a regional German newspaper the hypotheses are tested and conclusions are derived.

Philipp Bodenbenner, Markus Hedwig, Dirk Neumann

Exploration of a Multi-dimensional Evaluation of Books Based on Online Reviews: A Text Mining Approach

With advancements made to the Internet, a considerable increase in the number and types of products available online has come. Yet, the large amount of online consumer reviews may present an obstacle to potential buyers. This study proposes a four-dimensional book evaluation system for use by leading online booksellers, thereby enabling potential buyers to form decisions based on differentiated criteria. This book evaluation system was empirically examined by employing a text mining approach and multivariate regression model. The findings here-in may aid in improving the understanding of the construction of online product evaluation systems.

Tianxi Dong, Matti Hamalainen, Zhangxi Lin, Binjie Luo

The Investigation of Online Reviews of Mobile Games

The increasing computing capacity and popularity of smartphones has stimulated great demand for mobile applications, especially for mobile games. When consumers have a variety of choices of mobile games, online reviews become critical information for consumers in making decisions. Online reviews show the important characteristics of mobile games that consumers care most about. Therefore, the objective of this study is to identify the critical characteristics of mobile games from the online reviews. We conducted content analysis and analyzed 1,485 online reviews of 38 mobile games and identified and classified 2,145 terms into 20 characteristics. We found that the important characteristics of mobile games include fun, information richness, perceived value, after sales services, stableness, and challenge. Our findings provide empirical evidence of the critical determinants of purchasing mobile games which can serve as a precursor of future text mining research. In addition, this study provides practical insights for software developers and for smartphone manufacturers.

Shu-Chun Ho, Yu-Chung Tu

Economics and Organizational Implications of Electronic Markets


Online Security Informatics and Privacy Issues

Do Hacker Forums Contribute to Security Attacks?

There has been an increased amount of discussion about firms needing to account for the hacker’s perspective in protecting their information assets. However, we still have very little idea about how attack information is disseminated within the hacker community. In this paper, we study the role of hacker forums in disseminating vulnerability information that leads to attacks. We found that the discussions in online hacker forums correlate significantly with the number of cyber-attacks observed in the real world. Furthermore, hacker forums also play a moderating role in disseminating vulnerability and threat information. As cyber security becomes an increasingly prominent issue for firms, our study indicates that there is a need to study the behaviors of the participants in the hacker forum further in order to better understand the risks that they pose.

Qiu-Hong Wang, Wei T. Yue, Kai-Lung Hui

A Trust Perspective to Study the Intentions of Consumers to the Group Buying

With the rapid development of Internet and electronic commerce, more and more innovative online business models have been proposed. Internet group buying, one of the most interesting model, has attracted much attentions. Due to the properties of Internet and group behavior, trust on the Internet and among the group are both important factors that would affect the success of Internet group buying. In this research, we constructed a research model based on trust theory and conducted an experiment to investigate the impacts of trust on Internet group buying. Under different trust scenarios in our experiment, the subjects are invited to participate in a group buying activity. The results show that, the consumers have different intention to attend a group buying activity in different trust scenarios. It also means that different trust perspectives have different impacts on the consumers, and trust is a critical factor for Internet group buying.

Deng-Neng Chen, Yi-Shan Yang, Yi-Cheng Ku

Effects of Borrower-Defined Conditions in the Online Peer-to-Peer Lending Market

In online Peer-to-Peer lending market, the borrower-defined conditions of loan requests predetermine the successfulness to receive loans. We analyze the transaction data of PPDai, a leading Peer-to-Peer lending market provider in China. By using the multinomial logit model to investigate the importance of borrowers’ decisions and their effects on funding results, we reveal that loan amount, acceptable maximum interest rate, and loan period decided by borrowers significantly influence the loan outcomes. For the unsuccessful listings, the requested loan amount has much more importance than other factors, while for the listings attracting more supply than the requested amount, the borrower’s acceptable maximum interest rate are more dominant than other factors to the outcomes. Besides, consistent to prior literature’s findings, PPDai borrower’s personal information and social capital also play major role in the transactions.

Jiaxian Qiu, Zhangxi Lin, Binjie Luo

Cognitive Elaboration on Potential Outcomes and Its Effects on Employees’ Information Security Policy Compliance Intention–Exploring the Key Antecedents

IS security policy is one of the essential tools to ensure the secure use of information systems and technological assets. To enhance the effectiveness of policy implementation, organizations rely on security training, education and awareness (STEA) programs to help employees understand the IS security issues of the organization. However, different levels of STEA informativeness may have conflicting effects on employees’ compliance decisions. In addition, the urgency of a task may also lead employees to abandon the compliance decision occasionally. The existing corporate information security policy (ISP) could also serve as a deterrence message that would influence compliance decisions. An experimental survey was conducted to examine this phenomenon and test the related hypotheses. The results of this study can be used to inform and guide researchers and practitioners as to how to better enforce an IS security policy through better implementation of STEA programs and improved design of ISP in different task scenarios.

Xue Yang, Wei T. Yue, Choon Lin Sia

Economics of E-Commerce

Analyzing Monetization Models for the Digital Content Services: Channel Ownership and Royalty Contracts

The formats and access models of digital content are increasingly rich and diverse as the advancement of internet and mobile technologies. In this paper, utilizing a game theoretic model, we analyze the adoption and monetization models of heterogeneous content channels (mobile and website content). The pricing and advertising strategies of pay and advertisement-supported content under different channel and content ownership structures are examined. Furthermore, under asymmetric content ownership (only one of two channels own the content), popular monetary transfer contracts are realized to license the opponent channel. The impacts of market parameters (such as quality differentiation and online advertisement factors) on the development of business strategies are presented.

Yung-Ming Li, Yuan Fang, Bih-Huang Jin

Pricing Centralized and Decentralized Wireless Service: A Mechanism Design Approach

This study concentrates on how to price wireless access service and compare the two different operation models: centralized and decentralized service architecture. With the classical model of the mechanism design, some interesting results are discovered. In the mechanism design, a tele- communication service provider offers two service plans, and assumes that consumers select their service plans according to individual type. By involving queuing delay and service availability in our study, we find that the results in centralized service architecture are consistent with those in prior studies in the mechanism design; however, the results in decentralized service architecture are contrary to those. The phenomenon is caused by the factor that the level of service benefit in decentralized service architecture is positively associated with the number of contributors. Also, we examine social welfare in centralized service architecture and indicate that the government has to understand the real benefits received by different group of consumers if a subsidy is provided.

Jhih-Hua Jhang-Li

Parallel Importation: An Empirical Investigation of Online Unauthorized Distribution Channels for Luxury Fashion Goods

Parallel importation is prevalent as billions of dollars worth of genuine products are sold by unauthorized distributors across countries. In this research, we offer one of the first empirical investigations of online parallel importation. We find that for luxury handbags, less expensive products, products with greater market interests and products available via the authorized channel have more parallel importation activities. In addition, there are fewer parallel importation activities for the more prestigious brand.

Kexin Zhao, Xia Zhao, Jing Deng

Consumer Segmentation and the Information Rule of Online Reviews in Horizontally Differentiated Product Markets

Previous studies have explored the impact of online reviews on product sales at the aggregate level. This study contributes to the literature by investigating how online reviews take effect at the individual consumer level in a horizontally differentiated product market. We empirically test our hypotheses using data from a popular review website in China and consumers’ actual dining records. We find evidence that the information role of online reviews is moderated by consumers’ geographical locations. Our results have implications for consumer segmentation and targeting of consumers through local market advertising.

Qingliang Wang, Khim Yong Goh

Organizational Implications of Electronic Markets

Comparing the Quality of Customer Service in 3D Virtual Worlds to Web-Based Service

In the Internet era, web-based services have become a convenient alternative to physical customer service interactions. However, lack of face-to-face interaction makes web service communication inefficient. The 3D virtual worlds provide a new platform that offers customer service, where users can communicate “face to face” via their representative avatars. We propose a conceptual model to compare the quality of customer service and users’ satisfaction in 3D virtual worlds to that of web-based services. Theories of computer display technology, communication, and psychology are applied to address how a 3D virtual world impacts users’ sense of presence, and their perception of customer service quality. We design an experiment in Second Life and set up a mock-up website to collect data in a post-study questionnaire. Structural equation model is adopted as the main methodology to conduct the multiple group analysis.

Sulin Ba, Dan Ke, Jan Stallaert, Zhongju Zhang

The Impact of Query Suggestion in E-Commerce Websites

In this paper we propose a research agenda for studying the impact of query suggestion features on cognitive load and customer satisfaction during online shopping in e-commerce websites. Despite the popular use of query suggestion features in search engines and large e-commerce websites such as and eBay, there is little research in this area. Based on a review on prior literature in query suggestion and online shopping, a research model and five hypotheses are posed. A lab experiment is proposed to test the hypotheses and potential implications of the research are discussed.

Alice Lee, Michael Chau

Is Localization Advisable for E-Commerce Websites?

To address the possible impacts of cultural differences, companies tend to set up localized websites for different countries. These localized websites usually feature local content, local language, and local cultural elements. However, in an increasingly globalized economy, individuals around the world are now more exposed to multiple cultures and may even have internalized multiple cultures. Psychology theories suggest that these individuals’ perceptions and decision making may vary in response to different cultural cues. As such, localized websites could serve as cultural cues that impact online consumers’ building of trust and subsequent decision making. In this study, we focus on one basic consideration of localization (i.e., incorporating cultural elements into websites) and investigate whether the effects of social presence on trust building will be contingent upon it. This study intends to contribute to understanding the effectiveness of these localization strategies in building online consumers’ trust in e-commerce websites.

Muller Y. M. Cheung, James Y. L. Thong

The Impact of E-Commerce on Organizational Performance: The Role of Absorptive Capacity and Integrative Capability

This study examines how e-commerce creates value for firms from the perspective of dynamic capability theory. A theoretical model is proposed and tested using structural equation modeling techniques based on survey data collected from firms that have been using e-commerce in their operations for an average of 4 years and have more than 25% of sales or procurement via e-commerce channels. We find that top management participation is a key contributor to the development of a firm’s potential and realized absorptive capacities. These two forms of absorptive capacity in turn contribute to the firm’s integrative capability, theorized as a form of dynamic capability, which then impacts the firm performance indicators. Different contributions of the two absorptive capacities are delineated, so are the effects of top management on the absorptive capacities. Theoretical and practical contributions of these findings are discussed.

Qing Hu, Jianzheng Yang, Lifan Yang

IT Governance: The Key Factor of E-Government Implementation in China

According to E-government maturity model, Leavitt’s Diamond model, Fountain’s technology enactment framework and IT governance theory, this paper discusses the important impact that IT governance bring in the effectiveness of e-government implementation. Furthermore, the critical influence factors model of e-government implementation effectiveness is proposed. Data are collected from a survey and analyzed by following a psychometric procedure. All data from government official of China’s public sector across 29 provinces. The research result shows that the E-government governance capability is positively related with e-government implementation effectiveness, Moreover, the environmental readiness and organizational support are also positively related with e-government implementation effectiveness.

Tianmei Wang, Baowen Sun, Zhijun Yan

E-Business Systems and Applications


Cloud Computing and Applications

The Impact of Cloud Services on Independent Software Vendors: Should We Step into Cloud?

Cloud services have become one of the most popular industry terms since Google and IBM invested to build large data centers that users can program and research over the Internet. In practice, IT corporations often annually pay independent software vendors considerable license fees to save the cost of software upgrade and technology support. Although cloud services are considered a cost-down solution for small or medium IT corporations, there are some limitations making IT corporations hesitate to adopt it. In this research, we examine the investment strategy and profit for cloud service providers to better understand the business model of cloud services. We find that a cloud service provider with R&D capability will prefer vertical competition rather than partnering with an independent software vendor. In addition, for independent software vendors, maintaining a loose partnership is better than a tight one. Finally, we suggest that an independent software vendor shouldn’t support its cloud partner to enhance service security and compatibility, both of which may reduce its overall profit.

Jhih-Hua Jhang-Li, Chih-Yao Lee

SLA Based Dynamic Provisioning of Cloud Resource in OLTP Systems

In the era of cloud computing, an increasing amount of services are moving online. Also increasing is the amount of cloud resource to power these services. Among these modern online transactions, many belong to the category of Online Transaction Processing (OLTP), which can be processed with predictable time and resource. However, with a large user base and fluctuated usage patterns, providing OLTP services efficiently remains a major challenge. In this paper we present an online algorithm that solves for a cost-minimizing provision scheme under fluctuated user requests, constrained by a tail-distribution-based Service Level Agreement (SLA), and incorporated with Neural Network prediction. Experiment shows that the algorithm delivers significant savings in provision, and outperforms a simple look-forward provision plan with the same SLA compliance.

Xiaoqiu Qiu, Markus Hedwig, Dirk Neumann

Integrating Heterogeneous Prediction Models in the Cloud

As the emergence and rapid growth of cloud computing, business intelligence service providers will host platforms for model providers to share prediction models for other users to employ. Because there might be more than one prediction models built for the same prediction task, one important issue is to integrate decisions made by all relevant models rather than adopting the decision from a single model. Unfortunately, the model integration methods proposed by prior studies are developed based on one single complete training dataset. Such restriction is not tenable in the cloud environment because most of model providers may be unwilling to share their valuable and private datasets. Even if all the datasets are available, the datasets from different sources may consist of different attributes and hard to train a single model. Moreover, a user is usually unable to provide all required attributes for a testing instance due to the lack of resources or capabilities. To address this challenge, a novel model integration method is therefore necessary. In this work, we aim to provide the integrated prediction result by consulting the opinions of prediction models involving heterogeneous sets of attributes, i.e., heterogeneous models. Specifically, we propose a model integration method to deal with the models under a given level of information disclosure by adopting a corresponding measure for determining the weight of each involved model. A series of experiments are performed to demonstrate that our proposed model integration method can outperform the benchmark, i.e., the model selection method. Our experimental results suggest that the accuracy of the integrated predictions can be improved when model providers release more information about their prediction models. The generalizability and applicability of our proposed method is also demonstrated.

Hung-Chen Chen, Chih-Ping Wei, Yu-Cheng Chen, Ci-Wei Lan

Software Licensing in the Case of No Network Effects

Traditionally, consumers purchase software by paying the price upfront and install the software on their computers. However, allowing consumers “pay as you go” by subscribing to the software has become increasingly popular. The two licensing models are referred to as “on-premises model” and “Software as a Service (SaaS) model” respectively. This paper studies the software vendor’s choice of the two models considering consumers’ uncertainty on the software quality and the software upgrading issue.

Shengli Li, Hsing Kenneth Cheng

Collaborative Systems

Human Capital and Information Technology Capital Investments for Firm Innovation: Curvilinear Explanations

We investigate the relationship between a firm’s investments in knowledge-related assets and innovation. We propose curvilinear (inverted-U shaped) effects of human capital investments and IT capital investments on firm innovation respectively. Based on the analysis of 349 German firms across industries, corroborating support for the proposed relationships is presented. We contribute to the existing work on the resource-based view of the firm (RBV) by identifying and testing the possible downsides associated with the excessive accumulation of strategic resources.

John Qi Dong, Jinyu He, Prasanna Karhade

Attention-Aware Collaboration Modeling

Recently, a great variety of web-based collaboration support technologies (CSTs) have become available for people to collaborate for various purposes. On the other hand, CSTs are leading to more attention stress — more and more people are becoming overwhelmed by many simultaneous projects and the associated tasks. However, little research has been done on how to design collaboration management mechanisms that can help managers control collaboration activities for better collective efficiency. We lay the foundation of research in this regard by developing a model of team collaboration while emphasizing the attention aspects of collaboration, which we refer to as Attention-Aware Collaboration Modeling (AACM). In this paper, we present core concepts and basic principles of attention-aware collaboration management based on Attention Economy Theory.

Shaokun Fan, J. Leon Zhao

Human–Software Agent Negotiations: An Experimental Study

Negotiation is a powerful mechanism for facilitating effective economic exchanges. Electronic negotiations allow participants to negotiate online and use analytical support tools in making their decisions. Software agents offer the possibility of automating negotiation process using these tools. This paper aims at investigating the prospects of agent-to-human negotiations in B2C contexts using experiments with human subjects. Various types of agents have been configured and paired up with human counterparts for negotiating product sale. The paper discusses the results obtained both in terms of objective, as well as subjective measures.

Rustam Vahidov, Gregory E. Kersten, Raafat Saade

An Approach for Multiple Attribute Group Decision Making Based on Information Axiom

In this paper, a new method was presented for multiple attribute group decision making. In the proposed method, the comprehensive evaluation value of each attribute was obtained by set-valued statistics model, and the ranking of all alternatives was judged by the information axiom. Finally, a software product evaluation case was given, and the case was calculated by the proposed method and traditional method. The result illustrates the feasibility and applicability of the proposed method.

Jie Lin, Houxing You

Banking Event Modeling and Simulation in Scenario-Oriented Stress Testing

The recent 2008 financial tsunami has made the financial regulators realize the importance of stress testing in banking systems. One of the major challenges in stress testing is to model and calibrate “exceptional but plausible” scenarios in which macroeconomic shocks may cause contagious bank failures that may lead to the breakdown of a banking system. Presently, existing stress testing methods mainly focus on modeling single or multiple risk factors through a “static snapshot” of the banking systems. However, real-world bank crisis scenarios are much more dynamic such that different event occurrence sequences may have different impacts on individual banks and banking systems. For purposes of predicting contagious bank failures in stress testing, we propose the use of event-driven process chains in modeling bank failure scenarios. We refer to this approach as Banking Event-driven Scenario-oriented Stress Testing (or simply the BESST approach). We compare the pros and cons of the BESST approach with two existing approaches in an example scenario. In addition, we conducted a financial simulation based on this example scenario to demonstrate the validity of the BESST approach.

Daning Hu, J. Leon Zhao, Zhimin Hua

Supply Chain and Distributed Systems

Hierarchical RFID Tag Ownership and Transfer in Supply Chains

A majority of RFID tag applications are related to tracking and tracing of the RFID-tagged items throughout supply chains. Tracking and tracing require communication with the tag to identify and authenticate the tag. While identification can be readily accomplished in the absence of adversaries, identification and authentication in the presence of adversaries is not a trivial task. This process is exacerbated when the tagged item transfers ownership as it passes through a supply chain. Recent developments in cryptography facilitates accomplishing these in a seamless manner. We consider a specific scenario in supply chains where ownership of RFID-tagged items follow a hierarchical relationship. We present an ownership transfer protocol for this scenario and briefly consider its security properties. The proposed protocol can be used to seamlessly manage hierarchical ownership transfers in supply chains.

Wei Zhou, Eun Jung Yoon, Selwyn Piramuthu

Negotiation and Auction Mechanisms: Two Systems and Two Experiments

Auction and negotiation are mechanisms used in market exchanges. Behavioral economics experiments focused on the mechanism efficiency which required highly simplified problems and contexts. This paper discusses an on-going project involving an experimental comparison of auction and negotiation mechanisms embedded in software which we have developed. Both reverse multi-attribute auctions and multi-bilateral negotiations are used in a transportation service procurement scenario. The potential contribution includes the verification of theoretical claims that auctions are more profitable for auction givers than negotiations. It also includes formulation of guidelines for appropriate design of multiattribute market mechanisms and their selection.

Gregory E. Kersten, Pierpaolo Pontrandolfo, Rustam Vahidov, Dmitry Gimon

Research on the Hybrid Push/Pull Production System for Mass Customization Production

To start with, literatures of the CODP theory and the researches on the hybrid push/pull production system are reviewed. CODP is the point where push production integrate with the push production mode, therefore based on the research of manufacturing strategy of the CODP, the production planning model of the push/pull production of the single-CODP mass customization system is put forward. Afterwards the model is extended to the multi-CODP mass customization production system and the production planning of the working points where the push and pull production coexist is emphatically discussed.

Jie Lin, Xing Shi, Yu Wang

A Study of Users’ Intention to Voluntarily Contribute Real-Time Traffic Information through Mobile Devices

Advanced traffic information systems are increasingly available to provide users with real-time traffic information services. Nowadays, this kind of public information collection relies extensively on contributions made by end users on a voluntary basis. Although the literature has looked into the motivations that may lead to free online contributions, it has largely overlooked the actual use of the same systems based on mobile devices. Considering the issue from the perspectives of complexity, social effects and practical considerations, this paper explores and examines factors influencing mobile device users to freely contribute to a traffic information system. The methodology is planned to be a combination of qualitative and quantitative research methods. From the first stage of this research, which involves three face-to-face interviews and a focus group discussion among mobile application developers and users, we gleaned interesting results which are instructive for future empirical work.

Chen Zhu, Kai Kwong Wat, Chao Ren, Stephen Shaoyi Liao

The Effects of Application Discoverability on User Benefits in Mobile Application Stores

This document is in the required format. Mobile applications and mobile application stores are becoming people’s commodities in everyday life, offering unprecedented mobile services. In mobile application stores with numerous applications finding the right applications is painstaking for users. Therefore, this study aims to explicate the effect of application discoverability on user benefits in mobile application stores by identifying the relationships of need specificity, application discoverability, and application quantity. Using a survey methodology, we found that app users’ need specificity has an impact on application discoverability and quantity-sufficiency of applications, but not quantity-overload of applications. Our findings also show that application discoverability plays a substantial role in enriching users’ utilitarian and hedonic benefits in mobile application stores.

Jaeki Song, Junghwan Kim, Donald R. Jones


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