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Determinants of consumer intention to adopt a self-service technology strategy for last-mile delivery in Guangzhou, China.
Liu, Song; Luo, Gusong; Cai, Yonglong; Wu, Wenjie; Liu, Weitao; Zou, Rong; Tan, Wenxuan.
Afiliação
  • Liu S; School of Economics, Jinan University, Guangzhou 510630, China.
  • Luo G; Guangzhou Academy of Social Sciences, Guangzhou 510410, China.
  • Cai Y; Guangzhou Academy of Social Sciences, Guangzhou 510410, China.
  • Wu W; School of Geography & Environmental Science, Guizhou Normal University, Huaxi Universities Town, Guian New Area 550003, China.
  • Liu W; School of urban design, Wuhan University, Wuhan 430072, China.
  • Zou R; Guangzhou Academy of Social Sciences, Guangzhou 510410, China.
  • Tan W; School of Economics, Jinan University, Guangzhou 510630, China.
Math Biosci Eng ; 21(2): 3262-3280, 2024 Feb 02.
Article em En | MEDLINE | ID: mdl-38454727
ABSTRACT
Self-service technology (SST) is a logistic innovation in e-commerce that enhances last-mile delivery efficiency in supply chain management. By combining Innovation Diffusion Theory with Resource Matching Theory, we proposed a comprehensive framework to explain the relationships between beliefs, attitude, and intention in Guanzhou, China. The findings revealed that attitude played a crucial role in influencing consumer intention to adopt SST and that attitude has direct and indirect effects. Additionally, consumer perceptions of compatibility, relative advantage, reliability, and complexity indirectly affected their adoption intention through attitude. These factors had positive and negative effects. The results highlighted the importance of attitudes as immediate predictors of intention, as consumer attitudes (favorable and unfavorable) were shaped by their perceptions. We conclude by recommending strategies to promote positive attitudes toward SST and enhance safety, efficiency, and the overall user experience.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Atitude / Intenção Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Atitude / Intenção Idioma: En Ano de publicação: 2024 Tipo de documento: Article