Goal attainment on long tail web sites: An information foraging approach

The long tail has attracted substantial theoretical as well as practical interest, yet there have been few empirical studies that have explicitly examined the factors that drive online conversions at these sites. This research tests several hypotheses derived from Information Foraging Theory (IFT) that pertain to goal achievement on long tail Web sites. IFT introduced concepts of information patches and information scent to model information seeking behavior of individuals, but has mostly been tested in production rule environments where the theory is used to simulate user behavior. Testing IFT‐driven hypotheses on real data required learning information patches and scents using an inductive approach and in this paper we adapt existing algorithms for these discovery tasks. Our results based on clickstream data from forty‐seven small business Web sites show both the existence of valuable information patches and information scent trails as well as their importance in explaining conversion on these sites. The majority of the hypotheses were supported and we discuss the implications of this for researchers and practitioners.