AI-Enhanced Marketing Management – Factors Influencing Adoption in SMEs

Abstract: Recent developments and hype around artificial intelligence (AI) have arisen as result of two main factors: increase in computational power and data. Although marketing is considered as one of the main business applications within AI today, there is a lack of literature combining the disciplines. Marketing management tools, which utilise AI in supporting decision making are referred to as knowledge-driven marketing management support systems (MMSS). These systems provide besides quantitative analysis, further qualitative facets into marketing management. Despite the willingness of many SMEs to engage with the technology that may foster competitive advantage, many adoption processes fail. The purpose of this thesis is to explore the factors influencing adoption of knowledge-driven MMSS in SMEs in Finland and Sweden. Qualitative primary data was collected from nine company representatives at top management level in Finnish and Swedish firms. Companies were classified in three categories, providers, adopters and non-adopters of knowledge-driven MMSS.   The findings show that there are several factors influencing adoption of knowledge-driven MMSS. The factors were grouped into technological, organizational and environmental factors, based on the TOE framework. Even though SMEs suffer from a lack of resources compared to large companies, this research suggests that they are at the forefront of adopting AI for marketing purposes. Additionally, it was found that the factors affecting adoption are dependent on whether the knowledge-driven MMSS is built in-house or outsourced.   This study has contributed to the identified gaps in literature by combining the disciplines of AI, marketing and SMEs, and by exploring the factors behind adoption of knowledge-driven MMSS. The authors of this thesis have the aspiration that the developed post-empirical framework will serve as a guiding tool for top management and marketing managers in SMEs looking to adopt knowledge-driven MMSS into their organizations.