Dynamic adaptation of non standard service tasks through reinforcement learning driven task technology fit and service interaction

Non-standard service-oriented microenterprises face significant challenges in digital transformation due to heterogeneous tasks and the absence of standardized workflows. This study proposes a lightweight, embedded platform construction methodology-the Task-Service-HCI (TSH) method-which integrates Task-Technology Fit (TTF), Service Process Customization (SPC), and Human-Computer Interaction (HCI) into a closed-loop mechanism for task recognition, workflow configuration, and interactive feedback. Based on this method, a Reinforcement Learning-driven Task-Technology Fit Optimization (RL-TTFO) system architecture is designed to enable semantic task parsing and adaptive workflow generation.A prototype platform was deployed in the professional organizing industry, collecting behavioral logs and survey data from 300 users. Structural equation modeling results show that task-technology fit has a significant positive effect on user satisfaction (β = 0.62, p < 0.001), with configuration participation playing a mediating role in perceived control (p < 0.01). Regression analysis further indicates that the RL-TTFO mechanism increases usage frequency by 35.4% and improves perceived system intelligence by 27.1% compared with static workflow configurations (p < 0.001). These findings demonstrate that the proposed approach provides a sustainable, low-barrier digital transformation solution for resource-constrained microenterprises operating in highly customized service environments.