Harnessing the potential of generative AI in digital marketing using the Behavioral Reasoning Theory approach

Abstract

• This is a thematic analysis of interviews of digital marketing experts from India. • Behavioral Reasoning Theory is used to conceptualize the proposed model. • Article highlights the drivers and challenges of GAI adoption in DM. • Drivers: creative content, speed, efficiency, customization, predictive analytics. • Challenges: IP infringement, data quality, security, data quality, reduced manpower. Generative AI (GAI) is an upcoming field and its impact on marketing is indisputable. Very little evidence in academic literature is present regarding the factors affecting the usage of GAI in Digital Marketing (DM). This study addresses this gap by exploring the key drivers and barriers associated with using GAI in DM. Leveraging Behavioral Reasoning Theory (BRT), the research validates prior findings and introduces a conceptual model outlining factors that shape attitudes toward adopting GAI in DM to enhance customer experiences. A qualitative inductive approach was undertaken by conducting expert interviews to investigate the “reasons for” and ‘reasons against’ using GAI in DM and its impact on customer experience. The transcripts generated were manually coded and a deductive thematic analysis was done using the BRT as the theoretical framework. The findings indicate four significant themes for adopting GAI in digital marketing viz: innovation, creative communication and content creation, speed, efficiency and timesaving, enhanced customization and personalization; predictive analytics and simulation. It also indicates five significant themes related to the key barriers were also identified viz: ethics and infringement of Intellectual Property; security and deepfake; learning ecosystem for the adoption of new technology; quality of data; reduced manpower requirement. The study further highlights how GAI influences customer experience in DM. This study contributes to the field by (a) proposing a conceptual framework for applying GAI in DM to improve customer experiences, (b) examining the drivers and challenges of GAI adoption in DM, and (c) presenting a research agenda to guide future studies. These insights offer value to researchers, marketing practitioners, and academics navigating the dynamic intersection of GAI and Digital Marketing