Gumasing Ma Janice J, Ong Ardvin Kester S, Sy Madeline Anne Patrice C, Prasetyo Yogi Tri, Persada Satria Fadil
School of Industrial Engineering and Engineering Management, Mapúa University, Philippines. 658 Muralla St., Intramuros, Manila, 1002, Philippines.
E.T. Yuchengo School of Business, Mapúa University. 1191 Pablo Ocampo Sr. Ext., Makati, Metro Manila 1205, Philippines.
Heliyon. 2023 Oct 4;9(10):e20644. doi: 10.1016/j.heliyon.2023.e20644. eCollection 2023 Oct.
The emergence of e-commerce platforms, especially online grocery shopping, is heightened by the COVID-19 pandemic. Filipino consumers started to adapt online due to the strict quarantine implementations in the country. This study intended to predict and evaluate factors influencing the intention and usage behavior towards online groceries incorporating the integrated Protection Motivation Theory and an extended Unified Theory of Acceptance and Use of Technology applying machine learning ensemble. A total of 373 Filipino consumers of online groceries responded to the survey and evaluated factors under the integrated framework. Artificial Neural Network that is 96.63 % accurate with aligned with the result of the Random Forest Classifier (96 % accuracy with 0.00 standard deviation) having Perceived Benefits as the most significant factor followed by Perceived Vulnerability, Behavioral Intention, Performance Expectancy, and Perceived. These factors will lead to very high usage of online grocery applications. It was established that machine learning algorithms can be used in predicting consumer behavior. These findings may be applied and extended to serve as a framework for government agencies and grocers to market convenient and safe grocery shopping globally.
电子商务平台的出现，尤其是在线食品杂货购物，因新冠疫情而更为凸显。由于菲律宾实施严格的隔离措施，菲律宾消费者开始适应线上购物。本研究旨在结合整合保护动机理论和扩展的技术接受与使用统一理论，运用机器学习集成方法，预测和评估影响在线食品杂货购买意愿及使用行为的因素。共有373名菲律宾在线食品杂货消费者参与了调查，并在整合框架下对相关因素进行了评估。人工神经网络的准确率为96.63%，与随机森林分类器的结果一致（准确率为96%，标准差为0.00），其中感知利益是最显著的因素，其次是感知脆弱性、行为意愿、绩效期望和感知。这些因素将导致在线食品杂货应用程序的高使用率。研究证实机器学习算法可用于预测消费者行为。这些研究结果可应用并扩展，为政府机构和食品杂货商在全球推广便捷安全的食品杂货购物提供框架。
