Clustering Consumers Based on Trust, Confidence and Giving Behaviour: Data-Driven Model Building for Charitable Involvement in the Australian Not-For-Profit Sector

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{devries:2015:plosone,

• author = "Natalie Jane {de Vries} and Rodrigo Reis and Pablo Moscato",
• title = "Clustering Consumers Based on Trust, Confidence and Giving Behaviour: Data-Driven Model Building for Charitable Involvement in the Australian Not-For-Profit Sector",
• journal = "PLOS ONE",
• year = "2015",
• volume = "10",
• number = "4",
• month = apr # " 7",
• keywords = "genetic algorithms, genetic programming",
• publisher = "Public Library of Science",
• ISSN = "1932-6203",
• DOI = "doi:10.1371/journal.pone.0122133",
• size = "28 pages",
• abstract = "Organisations in the Not-for-Profit and charity sector face increasing competition to win time, money and efforts from a common donor base. Consequently, these organisations need to be more proactive than ever. The increased level of communications between individuals and organisations today, heightens the need for investigating the drivers of charitable giving and understanding the various consumer groups, or donor segments, within a population. It is contended that trust is the cornerstone of the not-for-profit sectors survival, making it an inevitable topic for research in this context. It has become imperative for charities and not-for-profit organisations to adopt for-profits research, marketing and targeting strategies. This study provides the not-for-profit sector with an easily-interpretable segmentation method based on a novel unsupervised clustering technique (MST-kNN) followed by a feature saliency method (the CM1 score). A sample of 1562 respondents from a survey conducted by the Australian Charities and Not-for-profits Commission is analysed to reveal donor segments. Each clusters most salient features are identified using the CM1 score. Furthermore, symbolic regression modeling is employed to find cluster-specific models to predict low or high involvement in clusters. The MST-kNN method found seven clusters. Based on their salient features they were labeled as: the non-institutionalist charities supporters, the resource allocation critics, the information-seeking financial sceptics, the non-questioning charity supporters, the non-trusting sceptics, the charity management believers and the institutionalist charity believers. Each cluster exhibits their own characteristics as well as different drivers of involvement. The method in this study provides the not-for-profit sector with a guideline for clustering, segmenting, understanding and potentially targeting their donor base better. If charities and not-for-profit organisations adopt these strategies, they will be more successful in todays competitive environment.",
• notes = "Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine, The University of Newcastle, Callaghan, New South Wales, Australia",

}

Genetic Programming entries for Natalie Jane de Vries Rodrigo Reis Pablo Moscato

Citations