The outcomes of generative AI in the future of work: The role of technostress creators

Abstract

Generative artificial intelligence (Gen-AI) is reshaping the future of work, bringing both opportunities and challenges in an increasingly competitive global economy. Unlike traditional AI models that primarily analyze existing data, Gen-AI leverages advanced machine learning (ML) techniques, such as deep neural networks and transformer architectures, to model probabilistic distributions, generate contextually relevant content, and iteratively refine outputs based on training data and user interactions. While prior studies have explored its applications in specific domains, research on its broader implications for the future of work remains scarce. To address this lack, this study builds on the DeLone & McLean IS success model and aims to explore the outcomes of Gen-AI in the future of work focusing on the role of technostress creators—stressors induced by AI adoption—and their influence on shaping the adoption and integration of Gen-AI in organizational contexts. This study employs the belief-action-outcome (BAO) framework to structure the proposed conceptual model, which examines how user beliefs—such as technostress creators and quality perceptions—influence actions, including system use and satisfaction, ultimately shaping instrumental and humanistic outcomes. The research draws on both theoretical insights from the existing literature and empirical data collected through questionnaires targeting professionals who have some level of contact with this technology, where we gathered 305 valid responses. Our results highlight the strong impact of Gen-AI in the future of work and the role of technostress creators in user satisfaction and workplace outcomes. Moreover, technostress creators, besides explaining the outcomes, act as moderators in the relationship between AI usage and user satisfaction.