Understanding the key barriers to adoption of artificial intelligence by UK SMEs: a case of AI in marketing

Abstract

Small and medium sized enterprises (SMEs) are the backbone of UK economy. However, in this era where artificial intelligence (AI) is reshaping the business landscape, SMEs are still lagging in harnessing the significant benefits of AI. Despite the ever-growing importance of AI, little is known about how UK SMEs perceive AI and what the key barriers are to their successful deployment in the case of marketing activities. This thesis aims to address this knowledge gap by exploring UK SME adoption behaviours and factors influencing their AI adoption in marketing. More specifically, this study will develop comprehensiveunderstanding of the AI adoption through in-depth analysis and identification of the key barriers and evaluate AI providers' role in facilitating AI adoption in marketing by SMEs. This study uses qualitative exploration through case studies, focus group, and semi-structured interviews with 32 SME decision-makers from retail, wholesale, charities, and service sector, and six AI providers involved in developing and/or vending AI marketing applications, to gain in-depth and holistic insights into the perception, experiences, and challenges surrounding AI adoption by SMEs. Additionally, the interplay between SMEs and AI providers is explored, incorporating both perspectives to analyses the role of AI providers in facilitating AI adoption. The rich qualitative data were analysed using thematic analysis to reveal the nuanced findings, which were then reflected through a theoretical lens using the Fear of the Unknown Theory and the Uncertainty Reduction Theory. Findings reveal that, although SMEs acknowledge AI's potential, a lack of knowledge and understanding, along with distrust and apprehensions, lead to hesitation and avoidance behaviour. This study uncovers the role of AI providers to be central in SME AI adoption in helping them overcome the knowledge and trust barriers, through tailored knowledge transfer and training. However, distrust of AI providers was discovered in SMEs due to poor communication and understanding. The research identifies three key barriers to AI adoption in SMEs as: a lack of knowledge and understanding (regarding AI and between SMEs and AI providers); lack of trust in AI; and lack of trust in AI providers, that hinder successful AI deployment. By highlighting how the key barriers have placed the SMEs in a detrimental cycle restricting access to necessary knowledge and support to make informed AI adoption decisions, this research contributes to technology adoption theory, enriching theoretical understanding beyond traditional technology adoption barriers. The significance of this research extends beyond academic understanding of AI adoption in UK SMEs. It provides practical recommendations for both the SME decision-makers to seek out credible AI providers in efforts to learn about AI and its integration in marketing activities; and for the AI providers to be transparent and provide SME-friendly solutions and training. Inthis way they can build a trusting relationship that is mutually beneficial. In conclusion, this study advances the understanding of AI adoption in SMEs, particularly the trust related and cognitive barriers that impede implementation of AI in marketing. Itunderscores the importance of developing trust-building and knowledge sharing strategies, emphasizing the role of AI providers in facilitating AI adoption. These findings not only provide a foundation for future research on AI adoption by SMEs but also offer practical insights for businesses and policymakers aiming to drive digital transformation in SMEs. Addressing these barriers will allow SMEs to harness the power of AI for business growth and competitive success.Keywords: Artificial Intelligence (AI), Marketing, Small and Medium Sized Enterprises (SMEs),Adoption Barriers, Role of AI Providers, Lack of Knowledge and Understanding, Lack of trust.

Date of Award	14 Nov 2024
Original language	English
Awarding Institution	University of Bedfordshire
Supervisor	Yanqing Duan (Supervisor) & Annie Danbury (Second supervisor)