Explore success factors that impact artificial intelligence adoption on telecom industry in China
Abstract
As the core driving force of the new round of informatization development and industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, this paper proposes a framework to explore the impacts of success factors on AI adoption in telecom industry by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. Particularly, this framework consists of factors regarding external environment, organizational capabilities, and innovation attributes of AI. The framework is empirically tested with data collected by surveying telecom companies in China. Structural equation modeling is applied to analyze the data. The study provides support for firms’ decision-making and resource allocation regarding AI adoption.
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Several studies in public administration have used the TOE framework to study AI adoption.
“Several studies in public administration have used the TOE framework to study AI adoption (Chen, Ling, and Chen 2021; Desouza, Dawson, and Chenok 2020; Mikalef et al. 2021).”
From Exploring artificial intelligence adoption in public organizations: a comparative case study (2024) · cites this paper as Chen, Ling, and Chen 2021
Numerous studies are conducted on TOE, assessing the factors in successful AI implementation at the individual level in various domains.
“Furthermore, numerous studies are conducted on TOE, assessing the factors in successful AI implementation at the individual level in various domains (Alsheibani et al., 2020; Chen et al., 2021; Oyetunde et al., 2022).”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Chen et al., 2021
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- Authors
- Chen, H., Li, L., & Chen, Y.
- Affiliations
- Indiana University East (US)
- Old Dominion University (US)
- Texas A&M International University (US)
- Corresponding author
- Chen, Hong
- Venue
- Journal of Management Analytics
- Publisher
- Informa UK Limited
- Published
- 2020-12-22
- Volume
- 8
- Issue
- 1
- Pages
- 36-68
- ISSN
- 2327-0012 (ISSN-L)
- 2327-0039
- OpenAlex
- W3117699358
- Cited by
- 300
- References
- 114
- Citations by year
- 2026: 38 · 2025: 83 · 2024: 61 · 2023: 47 · 2022: 53 · 2021: 18
- FWCI
- 29.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Management Analytics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Decision Sciences › Management Science and Operations Research › Innovation Diffusion and Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence adoption, telecommunications industry, success factors, technology-organization-environment framework, diffusion of innovation theory, organizational capabilities, external environment, telecommunications companies, China
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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