The Silent Impact of AI: Unveiling Motivational Side Effects in the Digital Workplace

Abstract

As artificial intelligence (AI) becomes increasingly embedded in knowledge-intensive work, concerns are growing about its psychological impact on employees. While much of the existing literature emphasizes productivity gains and task automation, this study explores a less visible dimension: the potential motivational side effects of AI integration. Specifically, we investigate how the use of AI tools in professional contexts may affect perceived social presence, cognitive effort, motivation, and ultimately, task performance.Grounded in the High-Performance Cycle (Locke & Latham, 2002) and complemented by theories of social presence, perceptual fluency, and cognitive engagement, we develop a comprehensive mediation model. The model hypothesizes that AI usage may reduce motivation indirectly through decreased perceptions of social presence and lower cognitive investment in tasks. Motivation, in turn, is expected to predict self-reported performance outcomes. We further examine whether technological self-efficacy moderates these pathways.To empirically test our model, we conducted a cross-sectional survey with 297 professionals who regularly use AI systems such as generative language models, automation platforms, and AI-driven analytics tools. Participants were recruited via professional networks (e.g., LinkedIn, Prolific Academic) and selected based on their experience with AI in daily work tasks. All key variables - AI usage, perceived social presence, cognitive effort, motivation, and performance - were measured using validated Likert-scale instruments. Data were analyzed using structural equation modeling (SEM) and bootstrapped mediation tests.Results reveal that AI usage has a statistically significant negative effect on both social presence (β = -0.42) and cognitive effort (β = -0.38), which in turn positively influence motivation (β = 0.35 and β = 0.31, respectively). Motivation emerged as the strongest predictor of performance (β = 0.58). Mediation analyses confirmed that the effects of AI on performance are fully mediated by motivational and perceptual variables. Additionally, exploratory moderation analyses showed that technological self-efficacy buffers the negative impact of AI usage on motivation, suggesting individual resilience factors play a role.This study contributes to the literature by extending motivational theories into AI-mediated work contexts and identifying key psychological mechanisms behind technology-induced disengagement. It highlights that AI systems, while operationally efficient, may alter the subjective experience of work in ways that reduce intrinsic engagement. The findings have implications for organizational design, system architecture, and human-AI interaction strategies. They also call for the development of motivationally aware AI systems and for training programs that enhance technological self-efficacy.In sum, our results underscore that the impact of AI is not only functional but also psychological. The future of work will depend not just on how well machines perform, but on how well they preserve the human motivation to engage, create, and perform.

Keywords: Artificial Intelligence, Motivation, Social Presence

DOI: 10.54941/ahfe1007094