Exploring Digital Producers’Perception Types on AI Development Tools: A Q Methodology Approach

This study explores, from a Knowledge Management (KM) perspective, how software developers—the core 'knowledge workers' of the digital economy—perceive AI development tools using Q methodology. The advent of Generative AI is fundamentally transforming developers' job structures, with AI tools functioning beyond simple automation to become 'cognitive collaborators'. This transformation creates a complex tension involving expectations of 'productivity enhancement', threats to 'job identity', and the conflict between AI as an 'assistant' versus a ‘agent’. To investigate this subjective perceptual structure, 40 Q-statements were constructed based on global developer surveys and recent academic literature. Q-sorting was then conducted with 25 developers experienced in using AI development tools. Factor analysis revealed four distinct types of perception: (1) the ‘Expertise-Based Role Transformation Acceptor’; (2) the ‘Guideline-Reliant Passive Acceptor’; (3) the ‘Job Threat-Based Pragmatic Adopter’; and (4) the ‘Perceived Utility-Driven Optimizer’. All types shared a consensus that AI enhances productivity while simultaneously requiring the essential judgment and review of human developers. These results suggest that establishing effective Knowledge Management (KM) strategies in the AI era requires differentiated education and AI literacy enhancement tailored to the developers' diverse perception types. This study contributes to the KM discourse in the age of human-machine collaboration by empirically demonstrating the heterogeneous types of knowledge workers' psychological acceptance structures regarding technology.