AI Adoption, Digital Capability, and Sustainable Performance in Media and Communication Studies Departments of Sindh Province, Pakistan

Abstract

Artificial intelligence (AI) is reshaping how organizations generate, process, and disseminate knowledge, and higher education institutions (HEIs) in Pakistan are increasingly active participants in that shift. Departments of Media and Communication Studies across Sindh Province , including those at the University of Sindh, Shah Abdul Latif University, Sukkur IBA University, Iqra University, Shaheed Benazir Bhutto University, Sindh Madressatul Islam University (SMIU), and the University of Karachi ,are experimenting with AI-based tools for content analysis, newsroom simulation, audience research, media production, public relations, and administrative decision-making. Drawing on a substantial body of Sindh-based empirical research, most notably the published work of Dastar Ali Chandio and colleagues on AI-generated disinformation, social-media-driven climate literacy, online activism, and AI adoption in public relations at Sindh's public-sector universities, this paper synthesizes what is currently known about AI adoption in the province's media and communication field and develops a theory-grounded account of how such adoption is likely to translate  or fail to translate  into sustainable departmental performance. The paper argues that this translation is not automatic: it is conditioned by digital capability, understood as the technological infrastructure, digitally competent faculty and staff, data-governance practice, and organizational learning culture that a department builds around its AI tools. Integrating the resource-based view, digital transformation theory, and dynamic capability theory, the paper proposes a mediation model ,AI Adoption , Digital Capability , Sustainable Performance ,and specifies the research design, sampling strategy, instruments, and statistical analysis (descriptive statistics, reliability testing, correlation analysis, confirmatory factor analysis, and structural equation modeling) required to test it empirically across the seven institutions named above. The paper closes with a discussion of practical implications for department chairs and policymakers, the study's anticipated contribution to the literature, and its limitations.