Selected work

Representative Papers

Context-Aware Hybrid Routing in Bluetooth Mesh Networks Using Multi-Model Machine Learning and AODV Fallback

Sep 25, 2025

To address the performance degradation of traditional AODV routing in Bluetooth Mesh networks under congestion and dynamic topologies, this paper proposes a lightweight hybrid intelligent routing framework. The method innovatively integrates four interpretable, lightweight machine learning models—packet delivery success classification, TTL/delay regression, and forwarding suitability classification—into a unified, context-aware scoring mechanism that enables dynamic neighbor ranking and adaptive next-hop selection. Crucially, the framework retains AODV’s fallback capability to ensure robustness. Evaluated across ten representative scenarios, the approach achieves a 99.97% packet delivery ratio—significantly outperforming standard AODV and existing hybrid schemes. Results demonstrate its effectiveness in infrastructure-less environments, offering high reliability, low overhead, and strong adaptability for multi-hop routing.

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Digital-GenAI-Enhanced HCI in DevOps as a Driver of Sustainable Innovation: An Empirical Framework

Aug 14, 2025

This study investigates the impact of AI-enhanced human–computer interaction (AI-HCI) in DevOps on sustainable innovation performance among A-share internet and technology firms in China. Using a panel dataset of 5,560 firm-year observations from 2018 to 2024, compiled from CNRDS and CSMAR databases, we employ fixed-effects regression models for causal identification. We theoretically articulate and empirically validate— for the first time—three mediating mechanisms through which AI-HCI drives sustainable innovation: (1) operational efficiency gains, (2) enhanced cross-functional knowledge integration, and (3) deepened stakeholder co-participation—thereby establishing a novel digital transformation framework tailored to emerging markets. Results indicate that AI-HCI implementation in DevOps improves firms’ innovation efficiency by 23.7%, providing robust causal evidence and mechanistic insights into how generative AI augments organizational innovation capacity.

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Human-AI Technology Integration and Green ESG Performance: Evidence from Chinese Retail Enterprises

Jul 03, 2025

This study investigates the impact mechanism of human–AI collaborative intelligence integration on retail firms’ green ESG performance. Using annual observations from 5,400 Chinese listed firms (2019–2023) drawn from CNRDS and CSMAR databases, we employ fixed-effects regression and mediation analysis. Results show that AI integration significantly enhances green ESG performance, with approximately 35% of this effect mediated by green technological innovation. Heterogeneity analyses reveal stronger effects among large firms, state-owned enterprises, and firms located in eastern, more developed regions. This research is the first to systematically identify a dual-path mechanism—“technology-driven” and “governance-enhanced”—through which AI enables sustainable development. By bridging AI adoption with ESG outcomes, it extends theoretical understanding of ESG drivers and provides empirical evidence and policy implications for aligning digital transformation with green performance in emerging markets.

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Nexus of Team Collaboration Stability on Mega Construction Project Success in Electric Vehicle Manufacturing Enterprises: The Moderating Role of Human-AI Integration

Jun 04, 2025

This study investigates how team collaboration stability influences project success in megaprojects undertaken by electric vehicle (EV) manufacturing firms, and examines the moderating role of human–machine collaborative integration. Drawing on empirical data from 187 EV project teams in China, we employ structural equation modeling (SEM) to test our hypotheses. Results indicate that team collaboration stability exerts a significant positive effect on project success; moreover, human–machine collaborative integration strengthens this relationship—higher levels of integration amplify the positive impact of stability on success. To our knowledge, this is the first empirical study to identify and validate the moderating mechanism of human–machine collaborative integration between team processes and project performance. By bridging theoretical gaps between traditional team dynamics research and AI-augmented project management scholarship, our findings offer both theoretical grounding and practical guidance for organizational design and technology integration in intelligent construction contexts involving highly complex engineering projects.

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AI-Driven Digital Transformation and Firm Performance in Chinese Industrial Enterprises: Mediating Role of Green Digital Innovation and Moderating Effects of Human-AI Collaboration

May 16, 2025

This study investigates how AI-driven digital transformation affects the performance of Chinese industrial enterprises, focusing on the mediating role of green digital innovation and the moderating effect of human–AI collaboration. Drawing on 6,300 firm-year observations from the CNRDS and CSMAR databases (2015–2022), we employ panel regression and structural equation modeling (SEM) for empirical analysis. Our contribution is threefold: first, we provide robust evidence that AI-driven digital transformation significantly enhances enterprise performance; second, we identify green digital innovation as a critical mediator in this relationship; third, we demonstrate that human–AI collaboration not only positively moderates the direct effect but also strengthens the mediated pathway. Notably, this research pioneers an integrative theoretical framework bridging technology management, environmental sustainability, and organizational theory. The findings offer both conceptual insights and actionable policy implications for synergistic digital–green transformation in industrial contexts.

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Recent publications

Latest Papers

Context-Aware Hybrid Routing in Bluetooth Mesh Networks Using Multi-Model Machine Learning and AODV Fallback

Sep 25, 2025

To address the performance degradation of traditional AODV routing in Bluetooth Mesh networks under congestion and dynamic topologies, this paper proposes a lightweight hybrid intelligent routing framework. The method innovatively integrates four interpretable, lightweight machine learning models—packet delivery success classification, TTL/delay regression, and forwarding suitability classification—into a unified, context-aware scoring mechanism that enables dynamic neighbor ranking and adaptive next-hop selection. Crucially, the framework retains AODV’s fallback capability to ensure robustness. Evaluated across ten representative scenarios, the approach achieves a 99.97% packet delivery ratio—significantly outperforming standard AODV and existing hybrid schemes. Results demonstrate its effectiveness in infrastructure-less environments, offering high reliability, low overhead, and strong adaptability for multi-hop routing.

0 citationsRead paper

Digital-GenAI-Enhanced HCI in DevOps as a Driver of Sustainable Innovation: An Empirical Framework

Aug 14, 2025

This study investigates the impact of AI-enhanced human–computer interaction (AI-HCI) in DevOps on sustainable innovation performance among A-share internet and technology firms in China. Using a panel dataset of 5,560 firm-year observations from 2018 to 2024, compiled from CNRDS and CSMAR databases, we employ fixed-effects regression models for causal identification. We theoretically articulate and empirically validate— for the first time—three mediating mechanisms through which AI-HCI drives sustainable innovation: (1) operational efficiency gains, (2) enhanced cross-functional knowledge integration, and (3) deepened stakeholder co-participation—thereby establishing a novel digital transformation framework tailored to emerging markets. Results indicate that AI-HCI implementation in DevOps improves firms’ innovation efficiency by 23.7%, providing robust causal evidence and mechanistic insights into how generative AI augments organizational innovation capacity.

0 citationsRead paper

Human-AI Technology Integration and Green ESG Performance: Evidence from Chinese Retail Enterprises

Jul 03, 2025

This study investigates the impact mechanism of human–AI collaborative intelligence integration on retail firms’ green ESG performance. Using annual observations from 5,400 Chinese listed firms (2019–2023) drawn from CNRDS and CSMAR databases, we employ fixed-effects regression and mediation analysis. Results show that AI integration significantly enhances green ESG performance, with approximately 35% of this effect mediated by green technological innovation. Heterogeneity analyses reveal stronger effects among large firms, state-owned enterprises, and firms located in eastern, more developed regions. This research is the first to systematically identify a dual-path mechanism—“technology-driven” and “governance-enhanced”—through which AI enables sustainable development. By bridging AI adoption with ESG outcomes, it extends theoretical understanding of ESG drivers and provides empirical evidence and policy implications for aligning digital transformation with green performance in emerging markets.

0 citationsRead paper

Nexus of Team Collaboration Stability on Mega Construction Project Success in Electric Vehicle Manufacturing Enterprises: The Moderating Role of Human-AI Integration

Jun 04, 2025

This study investigates how team collaboration stability influences project success in megaprojects undertaken by electric vehicle (EV) manufacturing firms, and examines the moderating role of human–machine collaborative integration. Drawing on empirical data from 187 EV project teams in China, we employ structural equation modeling (SEM) to test our hypotheses. Results indicate that team collaboration stability exerts a significant positive effect on project success; moreover, human–machine collaborative integration strengthens this relationship—higher levels of integration amplify the positive impact of stability on success. To our knowledge, this is the first empirical study to identify and validate the moderating mechanism of human–machine collaborative integration between team processes and project performance. By bridging theoretical gaps between traditional team dynamics research and AI-augmented project management scholarship, our findings offer both theoretical grounding and practical guidance for organizational design and technology integration in intelligent construction contexts involving highly complex engineering projects.

0 citationsRead paper

AI-Driven Digital Transformation and Firm Performance in Chinese Industrial Enterprises: Mediating Role of Green Digital Innovation and Moderating Effects of Human-AI Collaboration

May 16, 2025

This study investigates how AI-driven digital transformation affects the performance of Chinese industrial enterprises, focusing on the mediating role of green digital innovation and the moderating effect of human–AI collaboration. Drawing on 6,300 firm-year observations from the CNRDS and CSMAR databases (2015–2022), we employ panel regression and structural equation modeling (SEM) for empirical analysis. Our contribution is threefold: first, we provide robust evidence that AI-driven digital transformation significantly enhances enterprise performance; second, we identify green digital innovation as a critical mediator in this relationship; third, we demonstrate that human–AI collaboration not only positively moderates the direct effect but also strengthens the mediated pathway. Notably, this research pioneers an integrative theoretical framework bridging technology management, environmental sustainability, and organizational theory. The findings offer both conceptual insights and actionable policy implications for synergistic digital–green transformation in industrial contexts.

0 citationsRead paper