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This research is the first to examine disruptive marketing, a subset perspective of entrepreneurial marketing, and unintended consequences of marketing in the context of the nonprofit arts sector.

This exploratory research has two components: development of a body of knowledge and conceptual model of disruptive marketing, its predictors, and its intended/unintended consequences based on pertinent literature and input from arts organization executive directors, artistic directors, and marketing managers; and preliminary assessment, with a qualitative study, of the operationalization of disruptive marketing and related factors in arts organizations.

The study categorizes and analyzes qualitative study structured interview responses to outline commonality/lack of commonality among them and provide insight into perceptions of arts organization executive directors.

This initial study inaugurates an academic research stream on the topic of disruptive marketing which has the potential to make a significant contribution to the body of marketing knowledge. Future opportunities include scale development and quantitative testing of the proposed theoretical model, broadening the research scope to include multiple input sources from a wide variety of arts organizations and longitudinal research to assess the model factors over time.

Analyses of both preliminary input from arts organization managers and qualitative study responses of arts organization executive directors indicate significant interest in application of creative and innovative approaches to arts sector marketing‐related situations.

This paper is the first to study perceived scope/characteristics of disruptive marketing and unintended consequences of marketing in the nonprofit arts sector, and it presents results of a qualitative assessment of those topics, including consequences.

This paper aims to provide an example of how to use data mining techniques to identify patient segments regarding preferences for healthcare attributes and their demographic characteristics.

Data were derived from a number of individuals who received in‐patient care at a health network in 2006. Data mining and conventional hierarchical clustering with average linkage and Pearson correlation procedures are employed and compared to show how each procedure best determines segmentation variables.

Data mining tools identified three differentiable segments by means of cluster analysis. These three clusters have significantly different demographic profiles.

The study reveals, when compared with traditional statistical methods, that data mining provides an efficient and effective tool for market segmentation. When there are numerous cluster variables involved, researchers and practitioners need to incorporate factor analysis for reducing variables to clearly and meaningfully understand clusters.

Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.