Prefabricated construction (PC) is increasingly promoted in the construction sector for its potential benefits, including reduced resource assumption and improved quality. Accordingly, Lean methods are popularly applied to PC projects for optimizing operational processes and enhancing their performance in line with strategic objectives. A key factor in effectively implementing Lean to improve strategic control is developing specific strategies and planning that consider their complex interactions. Thus, this paper aims to propose a quantitative network-based model by integrating Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) under complex network theory to develop a Lean implementation framework for effective strategy formulation. Specifically, 17 Lean implementation strategies for PC in the context of the Chinese prefabrication industry were identified via an extensive literature review and expert interviews. Then, ISM-MICMAC quantitatively identifies the direct and indirect relationships among strategies, while subsequent analysis of Topological Structure Weight (TSW) and Structural Degree Weight (SDW), as complex network parameters, is used to evaluate the importance of each strategy. The findings show that the strategic planning for Lean implementation in PC consists of four levels, i.e., foundation, organizational, technical, and control. Selecting appropriate Lean tools and technologies is crucial for PC implementation, which must be built on a top-level management team and foster a Lean culture. Moreover, it involves building a standardized system of processes and activities, enhancing both internal and external collaboration, and continuously improving processes in response to changes. On one hand, this in-depth network-based analysis offers practical insights for PC stakeholders, particularly in China, on Lean implementation in line with PC performance and strategic control and objectives. On the other hand, the network-based model can be future-implemented globally. Additionally, this study expands the current body of knowledge on Lean in PC by exploring the interrelationships of Lean implementation strategies.

New AI technologies have empowered e-commerce personalized recommendation systems, many of which now leverage time-series forecasting to capture dynamic user preferences. However, buyers’ algorithm aversion hinders these systems from realizing their full potential in enabling data-driven decisions. Current research focuses heavily on artifact design and algorithm optimization to reduce aversion, with insufficient attention to the temporal dimensions of human–AI interaction (HAII). To address this gap, this study explores how recommendation accuracy, novelty, and diversity—key attributes in time-series recommendation contexts—influence buyers’ algorithm aversion from a temporal HAII perspective. Data from 205 online survey responses were analyzed using partial least squares structural equation modeling (PLS-SEM). Results reveal that accuracy (encompassing sequential prediction consistency), novelty (balanced with temporal relevance), and diversity (covering long-term preferences) negatively impact algorithm aversion, with perceived usefulness as a mediator. Reduced aversion further facilitates data-driven purchasing decisions. This study enriches the algorithm aversion literature by emphasizing temporal HAII in time-series recommendation scenarios, bridging human factors research with data-driven decision-making in e-commerce.

The digital economy has emerged as a strategic engine driving economic transformation by reconfiguring factor allocation and revolutionizing industrial structures. Digital industrialization (DI) and industrial digitalization (ID) serve as the dual engines of the digital economy. Their coordinated development is crucial for optimizing and upgrading the internal ecosystem of the digital economy, and represents a key breakthrough in addressing regional imbalances in digital economic development. This study constructs a comprehensive evaluation framework to assess the coupling coordination level between DI and ID across 30 Chinese provinces over 2011–2022, and further employs Dagum Gini coefficient, kernel density estimation, Markov chain analysis to investigate regional disparities and dynamic evolution characteristics of the coupling coordination level. The results reveal three key findings: First, the coupling coordination level between DI and ID in China remains relatively low, though it exhibits a steady upward trend overall. Second, significant regional disparities exist in the coupling coordination level, with variations primarily attributable to inter-regional differences. Third, the dynamic evolution of the coupling coordination level demonstrates stability and directionality: the characteristic of path dependence constrains provinces from achieving rapid leapfrog growth in the short term, and the coupling coordination level of adjacent provinces exerts a guiding influence on the transition direction of a given province. This study enriches the existing literature by constructing a systematic framework for analyzing the coordinated development between DI and ID, and provides policy implications for promoting balanced digital economy development across regions.