Carlos J. Costa

Carlos J. Costa, Joao Tiago Aparício, Manuela Aparício

As generative artificial intelligence (GenAI) diffuses across industries and becomes broadly accessible, the locus of sustainable competitive advantage shifts from technology ownership toward the quality of employee-level adoption and use. This paper develops a cross-level conceptual framework linking firm-level GenAI investment and governance to individual-level AI democratization, defined as the extent to which employees meaningfully, responsibly, and effectively use GenAI in their daily work. We argue that individual-level AI democratization, grounded in three micro foundations (AI usefulness, ease of use, and AI literacy), mediates the relationship between organizational GenAI investments and sustainable competitive advantage. Drawing on the technology acceptance model, resource-based theory, and emerging empirical evidence on AI productivity effects, we advance six propositions linking perceived usefulness, ease of use, AI literacy, responsible use, and innovation outcomes to organizational transformation and sustained relative performance. The framework provides a measurement scaffold for empirical research and offers managerial guidance on treating GenAI as augmentation infrastructure rather than solely as automation. We conclude by outlining future research directions, including longitudinal and cross-cultural investigations of literacy, governance, and transformation dynamics.

Carlos J. Costa, Joao Tiago Aparicio

This paper explores artificial intelligence's potential societal and economic impacts (AI) through generating scenarios that assess how AI may influence various sectors. We categorize and analyze key factors affecting AI's integration and adoption by applying an Impact-Uncertainty Matrix. A proposed methodology involves querying academic databases, identifying emerging trends and topics, and categorizing these into an impact uncertainty framework. The paper identifies critical areas where AI may bring significant change and outlines potential future scenarios based on these insights. This research aims to inform policymakers, industry leaders, and researchers on the strategic planning required to address the challenges and opportunities AI presents

Carlos J. Costa

In this work, a thorough mathematical framework for incorporating Large Language Models (LLMs) into gamified systems is presented with an emphasis on improving task dynamics, user engagement, and reward systems. Personalized feedback, adaptive learning, and dynamic content creation are all made possible by integrating LLMs and are crucial for improving user engagement and system performance. A simulated environment tests the framework's adaptability and demonstrates its potential for real-world applications in various industries, including business, healthcare, and education. The findings demonstrate how LLMs can offer customized experiences that raise system effectiveness and user retention. This study also examines the difficulties this framework aims to solve, highlighting its importance in maximizing involvement and encouraging sustained behavioral change in a range of sectors.