Human-AI Interaction and User Trust: The Mediating Role of Perceived Transparency

Dr. Zaheer Ahmed Babar — 2026-07-21

The current study explored the effect of the quality of HAI interaction on user trust and whether the user's perception of transparency would serve as a mediation effect. Using purposive sampling and taking into account their familiarity with digital technologies and AI-enabled applications, 180 participants were selected for the study, which was design based quantitative experimental in which they interacted with AI-based decision-support scenarios of varying interaction and explanation levels. Participants then engaged with the assigned scenario and filled out a structured questionnaire on a 5-point likert scale regarding perceived quality of human-AI interaction, perceived transparency, and user trust. All data were analyzed using regression based mediation analysis. The reliability and the validity tests were conducted for internal consistency and convergent validity, respectively, and all constructs were found to be reliable and valid. The interrelationship between the human-AI interaction, perceived transparency and user trust was found to be significant and positive with the use of correlation analysis. The regression results showed that human-AI interaction significantly positively influenced user trust as well as perceived transparency, which in turn significantly positively influenced user trust. The 5,000 resample bootstrapped confidence intervals validated the mediation of the perceived transparency on the connection between human-AI interaction and user trust, which accounted for over 50% of the total effect. The results indicate that the quality of the human-AI interaction is not the only factor affecting the user's trust, and that the comprehensibility and explainability of an AI system plays an important role in the conversion of interaction quality into trust. Theoretical and design implications and limitations are discussed as well as directions for future research.

AIoT-Driven Intelligent Elevator Maintenance Management: Machine Learning-Based Prediction of MTBF

Qirong Shen — 2026-07-23

Traditional elevator maintenance and operation practices commonly face industry-wide challenges, such as delayed fault early-warning systems, homogeneous maintenance and operation strategies, and low accuracy in Mean Time Between Failures (MTBF) prediction. To address these issues, this study proposes an intelligent elevator maintenance and management system empowered by artificial intelligence and the Internet of Things (IoT), establishing a MTBF prediction framework that integrates an improved noise reduction algorithm with a hybrid machine learning model. The research leverages a multi-source sensing network to achieve comprehensive collection of elevator operational status data; utilizes an edge-cloud collaborative architecture for hierarchical data processing and model inference; and employs digital twin technology to analyze fault evolution mechanisms and optimize maintenance strategies. This paper employs a cosine-based optimized variational modal decomposition algorithm for adaptive noise reduction of industrial time-series signals, constructs a CNN-BiLSTM prediction model incorporating an attention mechanism, uses the Particle Swarm Optimization (PSO) algorithm to optimize the model's hyperparameters, and applies the Weibull distribution to calibrate the reliability of the prediction results. To tackle the challenges associated with limited elevator datasets and cross-model adaptability, this study introduces transfer learning and an unsupervised health index pre-training method to enhance the model's generalization capability. Experimental results based on real-world measurement datasets from commercial complexes demonstrate that the proposed model achieves an average absolute percentage error of only 4.27%, representing a significant improvement in prediction accuracy compared to standalone time-series models and convolutional neural network models. Practical engineering applications confirm that this intelligent maintenance system can effectively reduce elevator fault frequency, shorten downtime, and substantially lower operational and maintenance costs, providing reliable technical support for the intelligent lifecycle maintenance of elevators.

Pakistan Journal of Multidisciplinary Innovation

Human-AI Interaction and User Trust: The Mediating Role of Perceived Transparency

AIoT-Driven Intelligent Elevator Maintenance Management: Machine Learning-Based Prediction of MTBF