A community leadership web site project: Sustainability and other issues

This paper reports on an ongoing partnership between Queensland University of Technology and Volunteering Queensland regarding the development and revision of a website for community leaders. The website, designed in late 2003, was established to provide a range of learning activities for community leaders including a problem based learning activity, case studies of community leaders and a range of resources deemed significant for leaders in the community. To date, anecdotal evidence as well as some more hard evidence (i.e. number of visits to the site), indicates that the site appears to be a valuable resource for community leaders. The purpose of this paper was firstly to investigate the utility of the site and secondly to consider some bigger issues concerning its sustainability. To achieve this, the paper explores the perceptions of (i) a group of community leaders regarding the strengths and weaknesses of the site; and (ii) key stakeholders (from QUT and Volunteering Queensland) who participated in a focus group discussion to consider important issues relating to its management and sustainability. Themes emerging from the two groups are provided and implications for small scale partnership projects such as this one are discussed.

Fuzzy logic in web data mining for website assessment

This paper proposes a novel application of fuzzy logic to web data mining for two basic problems of a website: popularity and satisfaction. Popularity means that people will visit the website while satisfaction refers to the usefulness of the site. We will illustrate that the popularity of a website is a fuzzy logic problem. It is an important characteristic of a website in order to survive in Internet commerce. The satisfaction of a website is also a fuzzy logic problem that represents the degree of success in the application of information technology to the business. We propose a framework of fuzzy logic for the representation of these two problems based on web data mining techniques to fuzzify the attributes of a website.