Purpose – The present study aims to examine how organisations adopt Generative AI and what are the key capabilities that influence the adoption of this technology. The study also investigates how Generative AI adoption influence market performance and sustainability performance. It additionally examines the moderating effect of regulatory support and organizational culture in influencing the association between Generative AI and firm performance. Design/methodology/approach – The study uses a mixed-methods design involving qualitative and quantitative data collection and analysis. The first stage begins with qualitative interviews followed by thematic analysis to establish leading capabilities behind Gen AI adoption, in the second stage, the data were collected from 385 respondents from different organizations which was then analysed using PLS-SEM structural equation modelling. Findings – The results observed that a firm’s digital transformation, innovation and marketing capabilities (MC) significantly enhance its Generative AI Adoption, which further influences firm performance. In addition, regulatory support emerges as a key moderator in driving DTC. Research limitations/implications – The findings emphasize that the firm should enhancing digital transformation capabilities, innovation capabilities and MC which can further strengthen the adoption of Generative AI and affect the firm market and sustainability performance. Whereas strengthening regulatory support can enhance the positive impact of DTC and Gen AI on firm sustainability performance. Originality/value – The study contributes to the literature on Generative AI by shaping an understanding of how the adoption of GenAI relates with the capability of firms in impacting sustainability performance. The research reports a critical gap in the literature by moving beyond the GenAI enthusiasm and assesses whether the advantages of adopting GenAI are durable as the technology diffuses across industries. The study contributes to literature by highlighting the role of regulatory support and determines that how environmental and firm-level factors jointly shape the effective and responsible capitalization of Generative AI for value creation over time.

This study examines the issues driving the non-adoption of AI-based hearing aids. The gap is critical since, despite increasingly improving technology, particularly with the integration of artificial intelligence (AI), the persistent non-adoption raises questions about the technology’s appeal and usefulness. The study uses a qualitative research methodology, collecting data from the study participants through written responses to interview questions. The data was collected from 56 people with hearing impairment and analysed using the grounded theory methodology to identify the barriers. The selective codes, representing the five themes (barriers) that broadly constitute resistance, are (i) technological performance barriers, (ii) financial and functional barriers, (iii) privacy and data security apprehensions, (iv) control and autonomy concerns, and (v) social and health concerns.