一种机器学习整体方法来预测影响菲律宾在线杂货应用的意图和使用行为因素
Ma Janice J Gumasing1, Ardvin Kester S Ong1,2, Madeline Anne Patrice C Sy1
1School of Industrial Engineering and Engineering Management, Mapúa University, Philippines. 658 Muralla St., Intramuros, Manila, 1002, Philippines.
Heliyon
|October 11, 2023
概括
在COVID-19大流行期间,菲律宾消费者迅速采用在线杂货购物. 感知到的好处和脆弱性显著影响了他们的意图和使用,正如机器学习模型所预测的那样.
科学领域:
- 消费者行为 消费者行为
- 电子商务是一个电子商务.
- 医疗信息学 医疗信息学
背景情况:
- 由于严格的隔离措施,COVID-19大流行加速了菲律宾消费者采用电子商务,特别是在线杂货购物,由于严格的隔离措施.
- 了解消费者行为转变对于企业和决策者在不断发展的数字市场中至关重要.
研究的目的:
- 预测和评估影响在线杂货的意图和使用行为因素.
- 整合保护动机理论和技术接受和使用的统一理论.
- 应用机器学习组合方法来分析消费者数据.
主要方法:
- 调查对373名菲律宾在线杂货消费者进行了调查.
- 采用了一个综合框架,结合了保护动机理论和技术接受和使用的统一理论.
- 机器学习组合算法,包括人工神经网络和随机森林分类器,用于预测和评估.
主要成果:
- 人工神经网络实现了96.63%的准确性,而随机森林分类器显示了96%的准确性.
- 感知好处成为影响在线杂货采用最重要的因素.
- 其他关键因素包括感知脆弱性,行为意图,绩效预期和感知有用性.
结论:
- 机器学习算法在预测在线杂货购物中的消费者行为方面非常有效.
- 鉴定的因素为营销方便和安全的在线杂货服务提供了一个框架.
- 调查结果可以为全球的政府机构和杂货商提供信息,以增强在线购物体验.
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