Beyond the business issues, there are also the location factors to consider in particular the rural dimensions of SMEs may exacerbate some of the above. [...] The key principles of the framework are the influence of characteristics of the technology, the organisation context and the external environment (Tornatzky and Fleischer, 1990). [...] 3.1 Descriptive Analysis of AI in Rural SMEs The LSBS measure adoption of AI, and this has in the most recent survey change in the questioning around AI. [...] In 2018, 3.40% (n=126 of 3706) had taken on AI, in 2019, some 7.31% (n=131 of 1791) and then in 2020, the percentage decreases to 5.57% (n=78 of 1400) and then increases in 2021, to 6.62% (n=124 of 1873) and in 2022, to its highest level of 8.60% (n=819 of 9524). [...] This is a fundamental question that underpins AI research and whether it is dark? In terms of expected turnover in the coming financial year, 48.36% of the rural AI users were expecting and increased turnover, as opposed to the non-AI rural cohort, here only 33.96% expected an increase.
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Table of Contents
- CONTENTS 4
- Non-Technical Summary 5
- 1. Introduction 7
- 2. Relevant literature and past research 8
- 2.1 Artificial Intelligence Defined 8
- 2.2 Artificial Intelligence Controversy 9
- 2.3 Rural Small Medium Enterprises 10
- 2.4 Drivers of AI and Technology Adoption 10
- 2.4.1 Networks 10
- 2.4.2 Beyond SME (firm) Factors: Covid-19, Sustainability and Brexit 11
- 2.4.3 A Model to Explain Technology Adoption in Rural SMEs 12
- 2.4.4 Model for SMEs in Rural Areas 13
- 3. Methodology and results 14
- 3.1 Descriptive Analysis of AI in Rural SMEs 14
- 3.2 AI User Intentions 18
- 3.3 Predicting AI Uptake in Rural SMEs 19
- 4. Conclusions and policy implications 20
- 4.1 AI Can be Good 20
- 4.2 Networks and AI 20
- 4.3 The environment 21
- 4.4 Firm based Factors and AI 21
- Data Reference: 22
- References 23
- Appendix 29
