Innovation Diffusion in Family Firms : A quantitative analysis of the driving forces behind large language model adoption in family firms

Since the launch of OpenAI's ChatGPT 3.0 the potential of AI applications in natural language processing has become increasingly evident. The rapid advancements of artificial intelligence (AI) technologies, particularly large language models (LLMs), have captured the attention of both academia and industry, illustrating a demand for a deep investigation into the driving forces behind their adoption. Family firms, due to their importance to global economies represent an important subject of innovation diffusion research. The distinct characteristics of family firms have been shown to create unique innovation opportunities and challenges. By applying and expanding upon the diffusion of innovations framework, this research examines how the perceived attributes of LLMs, individual innovativeness and the family’s influence on the firm affect the intention to use a LLM. For these purposes, a quantitative cross-sectional design was employed, gathering data through an online survey from 77 respondents across 30 Tirolean family firms. The data was analysed using a mixed linear model to account for nested data structures. The findings indicate that the compatibility of LLMs with existing workflows and values, as well as the observability of results obtained with a LLM, are significant drivers of innovation adoption. Additionally, a person’s propensity to adopt new technologies earlier than others, represented by the innovativeness construct, is found to have a very strong effect on the intention to use a LLM. Lastly, the study is not able to find a statistically significant moderation effect driven by family firm influence. The findings contribute to the academic literature on innovation diffusion and offer practical insights for enhancing AI adoption strategies in the unique organizational context of family business. This research provides a robust framework for future studies exploring the intersection of family dynamics and innovation diffusion.
Masterarbeit Universität Innsbruck 2024