Impact of the expected B2B internal customer experience on intention to adopt artificial intelligence (AI)

Dongmin Park et al.

Journal of Global Scholars of Marketing Science2025https://doi.org/10.1080/21639159.2025.2465299article
ABDC B
Weight
0.46

Abstract

This study analyzes the impact of AI transformation (Applied AI, AI readiness, AI technology, AI data-driven culture) on AI adoption intention through expected customer experience (service quality, technology support, value created). It verifies whether entrepreneurial orientation is a moderating variable in this relationship. To this end, a survey was conducted targeting 141 B2B companies, and analysis was performed using a structural equation model (PLS-SEM). The study results showed that Applied AI and AI data-driven culture were key factors in improving the expected customer experience and increasing the intention to adopt AI. Additionally, technology support and value created were found to have a positive impact on AI adoption intention. Furthermore, entrepreneurial orientation was found to be an important moderating variable in the AI adoption process, and companies with high entrepreneurial orientation tended to focus on AI data utilization and value created, while companies with low entrepreneurial orientation tended to focus on improving service quality. Therefore, this suggests that AI adoption strategies require customized strategies that consider the level of AI utilization and entrepreneurial orientation of companies, rather than a simple technical approach. This study provides practical insights to practitioners and managers of companies considering AI adoption and makes theoretical contributions to the establishment of AI-based management strategies.

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https://doi.org/https://doi.org/10.1080/21639159.2025.2465299

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@article{dongmin2025,
  title        = {{Impact of the expected B2B internal customer experience on intention to adopt artificial intelligence (AI)}},
  author       = {Dongmin Park et al.},
  journal      = {Journal of Global Scholars of Marketing Science},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/21639159.2025.2465299},
}

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Evidence weight

0.46

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

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.60 × 0.15 = 0.09
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.