GERTIS: a Dempster-Shafer approach to diagnosing hierarchical hypotheses
Communications of the ACM
Frontiers of electronic commerce
Frontiers of electronic commerce
Designing systems for Internet commerce
Designing systems for Internet commerce
Evidence Theory and Its Applications
Evidence Theory and Its Applications
Evidence Theory and Its Applications
Evidence Theory and Its Applications
Model-Based Performance Risk Analysis
IEEE Transactions on Software Engineering
Fuzzy decision support system for risk analysis in e-commerce development
Decision Support Systems
International Journal of Information Management: The Journal for Information Professionals
On the security of e - commerce
MCBE'10/MCBC'10 Proceedings of the 11th WSEAS international conference on mathematics and computers in business and economics and 11th WSEAS international conference on Biology and chemistry
Assessment of E-Commerce security using AHP and evidential reasoning
Expert Systems with Applications: An International Journal
Knowledge-Based risk assessment under uncertainty in engineering projects
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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The purpose of this study is to develop a decision making system to evaluate the risks in E-Commerce (EC) projects. Competitive software businesses have the critical task of assessing the risk in the software system development life cycle. This can be conducted on the basis of conventional probabilities, but limited appropriate information is available and so a complete set of probabilities is not available. In such problems, where the analysis is highly subjective and related to vague, incomplete, uncertain or inexact information, the Dempster-Shafer (DS) theory of evidence offers a potential advantage. We use a direct way of reasoning in a single step (i.e., extended DS theory) to develop a decision making system to evaluate the risk in EC projects. This consists of five stages 1) establishing knowledge base and setting rule strengths, 2) collecting evidence and data, 3) determining evidence and rule strength to a mass distribution for each rule; i.e., the first half of a single step reasoning process, 4) combining prior mass and different rules; i.e., the second half of the single step reasoning process, 5) finally, evaluating the belief interval for the best support decision of EC project. We test the system by using potential risk factors associated with EC development and the results indicate that the system is promising way of assisting an EC project manager in identifying potential risk factors and the corresponding project risks.