Risk assessment of E-Commerce projects using evidential reasoning

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.

Research intensity and knowledge transfer activity in UK universities

As the innovation process has become more open and networked, Government policy in the UK has sought to promote both research excellence in the university sector and the translation of this into economic benefit through university–business engagement. However, this policy approach has tended to be applied uniformly with little account for organisational differences within the sector. In this paper we consider if differences between universities in their research performance is reflected in their knowledge transfer activity. Specifically, as universities develop a commercialization agenda are the strategic priorities for knowledge transfer, the organisational supports in place to facilitate knowledge transfer and the scale and scope of knowledge transfer activity different for high research intensive (HRI) and low research intensive (LRI) universities? The findings demonstrate that universities’ approach to knowledge transfer is shaped by institutional and organisational resources, in particular their ethos and research quality, rather than the capability to undertake knowledge transfer through a Technology Transfer Office (TTO). Strategic priorities for knowledge transfer are reflected in activity, in terms of the dominance of specific knowledge transfer channels, the partners with which universities engage and the geography of business engagement.