Trust or distrust? AIGC trustworthiness and an extended analysis within nomology framework
Mingqian Li, Rong Du, Shizhong Ai, Richard A. Hunt, Jianing Xie · Data Science and Management · 2025
Given the dizzying advancements in artificial intelligence (AI) applications such as ChatGPT and DeepSeek, AI-generated content (AIGC) has attracted considerable attention from scholars, practitioners, and policymakers, each of whom is grappling with fundamental issues involving individual, organizational, and even societal impacts, which are exciting and daunting. Central to the process of identifying and assessing the benefits and detriments of AIGC are critical issues involving reliability, intelligibility, desirability, and, perhaps most of all, trustworthiness. Although humans have generally accepted the reality that AI is destined to play a prominent role in our lives, we are still in the very early stages of determining how we feel about the complex relationships that emerge between people and AI. Management and organizational scholars play a key role in developing theories that demarcate and predict the evolving structure and content of these relationships. Towards that end, this study adopts a mixed-methods design to systematically examine perceptions of AIGC trustworthiness. We begin by employing grounded theory to develop a nomology framework that integrates the extroverted and introverted dimensions of human cognition. We then subject the interview corpus to text mining and sentiment analysis to distill targeted, evidence-based strategies for strengthening AIGC trustworthiness.