Psychographic clustering of blood donors in Egypt using Kohonen's self‐organizing maps

Abstract

Blood donation has historically been marketed as one of the purest examples of altruistic or pro-social behavior. The recruitment of blood donors is, however, a challenge as transfusion centers worldwide strive to attract and retain blood donors. This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on blood donation in Egypt. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and nonlinear patterns. The results show that major variables affecting blood donation are related to altruistic values, perceived risks of blood donation, blood donation knowledge, attitudes toward blood donation, and intention to donate blood. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data.

Segmenting nonprofit stakeholders to enable successful relationship marketing: a review

“…Within the conducted review, only one study segments nonprofit stakeholders in a way that cannot be assigned to established criteria (Randle & Dolnicar, ). Marketing literature has proposed other innovative approaches; neuro‐linguistic programming and sensory representation systems (Mainwaring & Skinner, ) or clustering with neural network models and self‐organizing maps (Chan, ; Mostafa, ) might point towards new segmentation methods. The applicability and practical relevance of those approaches, however, have yet to be proven.…”