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 Unveiling Online Algorithmic Purchase Decision Paths: Insights from Shopping Motivation and Presence
Tác giả hoặc Nhóm tác giả: Nguyen Thi Minh Tam; Ta Nguyet Phuong
Nơi đăng: Proceedings of 19th International Conference on Socio-economic and Environmental Issues in Development; Số: 19;Từ->đến trang: 1144-1159;Năm: 2024
Lĩnh vực: Kinh tế; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
The widespread adoption of artificial intelligence (AI) has brought about a significant revolution in marketing, reshaping consumer decision-making processes and transforming business-customer interactions (Mussa, 2020). However, the increasing use of AI also raises ethical concerns related to control, transparency, and biased data (Rodgers, et al., 2023). This study proposes a practical framework to address these issues and guide AI algorithm implementation throughout the customer journey. By integrating stages from the classical purchase behavior model and the Throughput Model (TPM), six distinct online algorithmic purchase decision paths are identified based on purchase motivation and presence. Interconnected and parallel processes are examined, highlighting the independent influence of motivation, presence, and judgment on decision choice. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and survey data from 557 respondents in Danang, Vietnam, this research focuses on the e-commerce market for beauty and personal care products. Valuable insights into online shoppers' decision-making processes are provided, benefiting consumers and businesses. The study enhances our understanding of customer behavior on e-commerce platforms and introduces a cognitive algorithmic framework for guiding machine learning algorithms. This framework offers practical guidance for addressing ethical concerns and improving customer experiences in the e-commerce domain.
ABSTRACT
The widespread adoption of artificial intelligence (AI) has brought about a significant revolution in marketing, reshaping consumer decision-making processes and transforming business-customer interactions (Mussa, 2020). However, the increasing use of AI also raises ethical concerns related to control, transparency, and biased data (Rodgers, et al., 2023). This study proposes a practical framework to address these issues and guide AI algorithm implementation throughout the customer journey. By integrating stages from the classical purchase behavior model and the Throughput Model (TPM), six distinct online algorithmic purchase decision paths are identified based on purchase motivation and presence. Interconnected and parallel processes are examined, highlighting the independent influence of motivation, presence, and judgment on decision choice. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) and survey data from 557 respondents in Danang, Vietnam, this research focuses on the e-commerce market for beauty and personal care products. Valuable insights into online shoppers' decision-making processes are provided, benefiting consumers and businesses. The study enhances our understanding of customer behavior on e-commerce platforms and introduces a cognitive algorithmic framework for guiding machine learning algorithms. This framework offers practical guidance for addressing ethical concerns and improving customer experiences in the e-commerce domain.
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