A rough cluster analysis of shopping orientation data

This paper describes the application of a new technique, rough clustering, to the problem of market segmentation. Rough clustering produces different solutions to k-means analysis because of the possibility of multiple cluster membership of objects. Traditional clustering methods generate extensional descriptions of groups, that show which objects are members of each cluster. Clustering techniques based on rough sets theory generate intensional descriptions, which outline the main characteristics of each cluster. In this study, a rough cluster analysis was conducted on a sample of 437 responses from a larger study of the relationship between shopping orientation (the general predisposition of consumers toward the act of shopping) and intention to purchase products via the Internet. The cluster analysis was based on five measures of shopping orientation: enjoyment, personalization, convenience, loyalty, and price. The rough clusters obtained provide interpretations of different shopping orientations present in the data without the restriction of attempting to fit each object into only one segment. Such descriptions can be an aid to marketers attempting to identify potential segments of consumers.

Learning orientation and market orientation: From an alliance perspective

The literature on market orientation has grown in significance during the past decade. Whilst there is more literature on market orientation, the literature on learning orientation is also starting to increase. Numerous researchers have studied and found support for the positive relationship between market orientation and learning orientation (eg Slater and Narver 1995). However, not many researchers have investigated the relationship that firms with a high learning orientation have a high market orientation (eg Sinkula, Baker and Noordewier 1997). This paper attempts to investigate that link in more detail. An alliance perspective is taken in understanding this relationship.