Artificial Intelligence (AI) in rural business
The drivers and effects on AI adoption in rural SMEs
Bibliographic Data
| ID | 7515024 |
|---|---|
| Authors | David Dowell (0000-0003-4788-5240, University of St Andrews), Robert Bowen (0000-0002-8492-2701, Cardiff University, corresponding author), Wyn Morris (Aberystwyth University), David R Morris (0000-0003-4355-2211) |
| Year | 2026 |
| Volume | 123 |
| Pages | 104104 |
| Publication date | 2026-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Rural Studies (JOURNAL) |
| Journal identifiers | ISSN: 0743-0167 • E-ISSN: 1873-1392 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.jrurstud.2026.104104 |
| OpenAlex | W7133324167 |
| Language | IT |
| References cited | 51 |
This paper investigates drivers of Artificial Intelligence (AI) adoption, specifically in rural small and medium-sized enterprises (SMEs). Research on AI has gained traction in recent times, however, remains an area in need of further investigation, notably the adoption of AI by SMEs, and particularly among rural SMEs. The focus on SMEs is important as they account for the majority of businesses worldwide, playing an important role in job creation and economic development. The research uses secondary data from the Longitudinal Survey for Small Business (LSBS), a large UK panel survey of SMEs, which provides a broad range of variables on a range of SMEs. Probit regression, using a series of environment, firm and network engagement factors as predictive variables, identifies drivers of AI adoption for rural SMEs. Among the numerous drivers of AI adoption in rural SMEs, networking, is identified as a key variable associated with adoption. This research contributes to the limited knowledge on this subject and more broadly to technology adoption in organisations. This leads to policy recommendations in promoting AI adoption among rural SMEs through better communication of the advantages of adoption among SMEs and network development. • A rural location is not a barrier to AI adoption for SMEs, with numerous rural SMEs adopting AI technology in their operations. • There are numerous antecedents to AI adoption for rural SMEs, including networking, prior innovative activity influence, and strategic planning. • Rural SMEs that adopt AI technology are more likely to have employees, tend to be larger, have more sites, and have a greater turnover than non-adopters. • Engagement in networks can facilitate the adoption of AI technology among rural SMEs, as well as enhance understanding of the benefits of AI technology to SMEs' operations. • Policy should seek to encourage increased levels of AI adoption among rural SMEs to support growth, and ensure that appropriate infrastructure exists to support the use of AI technology
Information and Communications Technology · Panel data · Probit model · Rural area · Rural management · Small and medium-sized enterprises · Survey data collection · AI in Service Interactions · Digital Transformation in Industry · Technology Adoption and User Behaviour
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| Citation velocity | historical |
|---|---|
| Highly cited | No |