Using Q-methodology to study the metaphors of consumption for mobile phone users

Even before business has really come to grips with the intricacies of incorporating emarketing and e-commerce into their organisational strategies, they are now being encouraged to make significant investments in developing capabilities for mobile phone marketing. Fuelled once again by huge profit predictions reminiscent of the mid-1990s when speaking of e-commerce (Anckar and D’Incau 2002) marketers are tapping into this mobility. So, instead of having to drive consumers to web sites through the ‘sit and search’ context, marketers are now exploring ways to develop strategies to deliver relevant and timely information in a ‘roam and receive’ context directly to their potential customers anywhere, anytime, any place (Mort and Drennan 2002).

Efficiently computing weighted proximity relationships in spatial databases

Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate the problem of evaluating the top k distinguished “features” for a “cluster” based on weighted proximity relationships between the cluster and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. Combining a standard multi-step paradigm with new lower and upper proximity bounds, we presented an efficient algorithm to solve the problem. The algorithm is implemented in several different modes. Our experiment results not only give a comparison among them but also illustrate the efficiency of the algorithm.