Empowering Women Entrepreneurs: Navigating the Adoption of Generative AI Tools Through Innovation Diffusion Theory

Abstract

The role of women entrepreneurs in driving economic growth and instigating social change is crucial, yet they encounter distinctive obstacles when embracing innovative technologies, particularly Generative Artificial Intelligence (GenAI) tools. These tools have the potential to revolutionize business operations through task automation and data-driven insights. The present study aims to investigate the factors that influence the adoption of GenAI tools among women entrepreneurs, addressing gaps in existing literature concerning the specific challenges and barriers they face. Drawing on Rogers' innovation diffusion theory, the research utilizes Structural Equation Modeling (SEM) to examine the motivating factors (observability and trialability) and barriers (privacy concerns and biases in GenAI algorithms) that affect the adoption of GenAI tools by women entrepreneurs. The findings indicate that trialability and observability have a positive influence on GenAI adoption, while privacy concerns and algorithmic bias represent significant barriers. When taken together, these factors account for 53% of the variance in adoption rates, underscoring the pivotal role of privacy and security in the technology adoption process. The study recommends an expansion of the Diffusion of Innovations (DOI) model to encompass privacy concerns and algorithmic bias as significant barriers, particularly in gendered contexts. It underscores the necessity for robust data protection policies and strategies to mitigate bias in AI outputs, advocating for increased trialability and observability to empower women entrepreneurs in utilizing GenAI technologies.