Analysis of Factors Affecting Purchase of Self-Defense Tools among Women: A Machine Learning Ensemble Approach

Abstract

Street crime is one of the world’s top concerns and a surge in cases has alarmed people, particularly women. Related studies and recent news have provided proof that women are the target for crimes and violence at home, outdoors, and even in the workplace. To guarantee protection, self-defense tools have been developed and sales are on the rise in the market. The current study aimed to determine factors influencing women’s intention to purchase self-defense tools by utilizing the Protection Motivation Theory (PMT) and the Theory of Planned Behavior (TPB). The study applied multiple data analyses, Machine Learning Algorithms (MLAs): Decision Tree (DT), Random Forest Classifier (RFC), and Deep Learning Neural Network (DLNN), to predict purchasing and consumer behavior. A total of 553 Filipino female respondents voluntarily completed a 46-item questionnaire which was distributed online, yielding 22,120 data points. The MLAs output showed that attitude, perceived risk, subjective norm, and perceived behavioral control were the most significant factors influencing women’s intention to purchase self-defense tools. Environment, hazardous surroundings, relatives and peers, and thinking and control, all influenced the women’s intention to buy self-defense tools. The RFC and DLNN analyses proved effective, resulting in 96% and 97.70% accuracy rates, respectively. Finally, the MLA analysis in this research can be expanded and applied to predict and assess factors affecting human behavior in the context of safety.

Keywords

• deep learning neural network
• Protection Motivation Theory
• safety tools
• self-defense
• safety
• random forest classifier