Witschel, H. F., Loo, S., & Riesen, K. (2015). How to Support Customer Segmentation with Useful Cluster Descriptions. In P. Perner (Eds.), Advances in Data Mining: Applications and Theoretical Aspects. ICDM 2015. Lecture Notes in Computer Science (Vol. 9165, pp. 17-31). Springer.
https://doi.org/10.1007/978-3-319-20910-4_2

has been cited by the following article:

• TITLE: Collaborative AI Enabled Place Branding: A Hybrid-Narrative Review and Prospects for Future Research

  AUTHORS: Liying Chen, Ziqi Wang, Hongli Pang

  KEYWORDS: Collaborative Artificial Intelligence (AI), Place Branding, Collaborative AI Enabled Place Branding (CAIEPB), Affordance, S-O-R-M Framework

  JOURNAL NAME: Open Journal of Social Sciences, Vol.12 No.4, April 28, 2024

  ABSTRACT: Place branding is undergoing substantial transformations fueled by Artificial Intelligence (AI) technology. Effective Collaborative AI could build up a synergy between men and machines to achieve goals. Collaborative AI enabled place branding (CAIEPB) has profound implications and draws attention from scholars, practitioners and consumers for various tourism destinations to delineate place complexity. Based on hybrid-narrative review, we identified 7 key research themes-the origin, definition, technology affordance, spatiotemporal changing patterns, antecedents and formation mechanism, outcomes and impact mechanism, and research method of CAIEPB. The review of 207 studies provided a holistic view of future research directions from seven dimensions. Building on the Stimulus-Organism-Response (S-O-R) framework, we proposed an integrated theoretical model of Stimulus-Organism-Response-Moderating (S-O-R-M) to synthesize CAIEPB literature, which would shed light on more fruitful theories and move diverse venues in research and application forward.