The impact of Artificial Intelligence focus on firm performance: the role of R&D and top manager education level

This study examines the links between the dimensions of companies' focus on artificial intelligence (AI) and firm performance (financial and market performance). AI focus refers to the practice of focusing on specific domains that use cutting-edge technologies enabled by AI to support their operations, realising cooperative development and efficiency improvement between employees and organisations. In addition, the potential mediating role in this relationship between research and development (R&D) staff ratio and R&D investment is examined, and a series of research hypotheses are proposed. Furthermore, the study focuses on evaluating top manager education level as a moderating variable to study the mechanism of AI focus and firm performance.

A panel data estimation approach was employed to empirically test the relationship between AI focus and firm performance to achieve this goal. A rigorous two-stage methodology was implemented. Initial foundations were laid with a careful qualitative synthesis of the existing literature, which helped understand AI focus and how it might affect firm performance. The research used an exploratory approach by analysing the annual reports of Chinese-listed companies for the period 2014 to 2022. This innovative approach offered valid initial data correlating AI focus and firm performance. Finally, the study rigorously validated the proposed model by integrating appropriate financial information and using the sophisticated functionalities of STATA statistical software. This in-depth empirical study enables scholars to confirm their theoretical framework and finds a negative relationship between AI focus and firm performance.

The main contribution of this thesis is to advance knowledge about how companies can capitalise on AI. Firstly, it pioneers the development of AI focus. This multifaceted approach considers how organisations choose to influence and incorporate AI not only into their operations and decision-making but also into their customer interactions. This novel concept is further enriched by categorising AI focus into three distinct dimensions: mechanical, thinking, and feeling AI. This nuanced view reveals the full range of possible organisational AI deployments. Secondly, the study transcends mere definition and discusses the underlying theories of AI focus and the effect on firm performance. It suggests a novel model grounded on the proven economic, dynamic capabilities and talent management theories. The hypothesised negative impact of AI focus on performance is confirmed, which the study argues is based in part on the additional supporting resources devoted to AI, which increase costs, and on the need for organisational change when AI is introduced. Lastly, the research gives practical advice to businesses that want to develop a strong orientation towards AI. It identifies essential enablers, including employee education and R&D investment, as well as well-focused marketing efforts. This actionable knowledge allows companies to strategically position and optimise their AI focus, assisting them to gain an edge in the competitive reach of AI. Overall, this research contributes significantly to the field of AI and business management. It broadens understanding of AI's focus, reveals its theoretical basis, and offers practical advice to companies trying to harness AI's transformative power.