Examine the factors influencing the behavioral intention to use social commerce adoption and the role of AI in SC adoption

Shahzad Sadiq et al.

European Research on Management and Business Economics2025https://doi.org/10.1016/j.iedeen.2024.100268article
ABDC A
Weight
0.61

Abstract

This study examines both technological and human perspectives on social commerce platform adoption. It analyzes user interactions, social networks, and peer recommendations to investigate the influence of social factors on adoption decisions. Furthermore, it examines how artificial intelligence is used to adapt social commerce. This study uses UTAUT2 and Social Cognitive theory to develop a consumer adoption model. Data were collected primarily through survey questionnaires. Structural equation modeling and confirmatory factor analysis were used to examine data from 460 respondents from different countries. According to the findings, consumers adopt social commerce platforms if they believe they are convenient and easy to use, supporting the theories of the Unified Theory of Acceptance and Use of Technology 2 and Social Cognitive theory. Behavioural intentions are influenced by social influence, habit, hedonic motivation, and AI-related experience, and trust moderates the effects. AI tools, such as chatbots and recommendation engines, can target certain consumer categories better by leveraging insights on behavioural factors influencing SC adoption. Several AI-enabled tools, such as chatbots, AI user experiences, personalized suggestions, and intelligent algorithms, are changing how players engage with SC platforms. This study will offer a novel perspective on how AI affects users' behaviour by examining the relationship between AI technology and SC adoption.

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https://doi.org/https://doi.org/10.1016/j.iedeen.2024.100268

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@article{shahzad2025,
  title        = {{Examine the factors influencing the behavioral intention to use social commerce adoption and the role of AI in SC adoption}},
  author       = {Shahzad Sadiq et al.},
  journal      = {European Research on Management and Business Economics},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.iedeen.2024.100268},
}

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Evidence weight

0.61

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.63 × 0.4 = 0.25
M · momentum0.88 × 0.15 = 0.13
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.