With the increasing demand for interior landscapes, sustainable interior landscape design (SILD) has become part of a desirable lifestyle. However, consumer attitudes and a lack of consciousness and understanding of SILD have led to potentially negative impacts on design decisions regarding the use of sustainability in SILD. This study recruited 405 stakeholders to explore the relationships among value cognition, risk attitude, and decision intention. The value–attitude–behavior (VAB) model method was adopted to evaluate stakeholders’ cognitions and attitudes toward sustainable design and factors that influence sustainable design practices. The results demonstrated a positive correlation between multistakeholders’ value cognition, risk attitude, and SILD decisions. The relationship between value cognition and SILD decision intention is mediated by risk attitude. Furthermore, the value cognition of multistakeholders impacts risk attitude on the practice of SILD, thus changing design decisions. These findings provide insights into the sustainability of interior landscapes and design, particularly in terms of addressing diverse needs within multistakeholder practices.

Ultra-Reliable Low Latency Communication (URLLC) has become crucial in various communication services. It encompasses a broad range of fields, including Internet of Things (IoT) and Internet of Vehicles (IoV), where precise network latency prediction analysis is required to ensure reliability. However, current network latency forecasting models are insufficient in capturing the temporal periodicity and statistical features of network latency. Therefore, we propose a Patching-based Method for Network Latency Forecasting (PatchNF) in the URLLC scenarios. Specifically, to preserve local semantic information, we segment the latency sequence into subsequence-level patches that are used as input tokens for the encoder. Next, we employ dilated convolutions to capture different characteristics of network delay periodicity by utilizing varying receptive fields. To make full use of features with different network delay semantics and improve prediction performance, we introduce a hierarchical approach that exploits network delay semantics at different levels before the execution of the fully-connected mapping. Experimental results on real datasets collected in a URLLC scenario indicate that the proposed method outperforms TS2Vec by 55.2% and Informer by 181.9% in terms of mean squared error (MSE). These results confirm the significant advantage of PatchNF in network latency forecasting.

While identifying firms’ green motivation for responsible innovation is a hot topic in the literature, many questions about these drivers in developing countries still need to be answered. This study aims to empirically influence Chinese construction firms’ primary motivations for adopting responsible innovation. To achieve this goal, a conceptual model was developed using partial least squares analysis and tested on data from contracting firms in the construction industry. This study shows that a combination of green intrinsic and extrinsic motivation factors triggers responsible innovation. However, in the Chinese construction industry, extrinsic incentives, such as recognition, subsidies, and reputation, partly influence responsible innovation through intrinsic motivation. The analysis further suggests that corporate social responsibility moderates the relationship between green motivation and responsible innovation through the ability to adopt appropriate responsible innovation responses by combining intrinsic and extrinsic motivation. Furthermore, firms with more effective resource commitments tend to engage in more responsible innovation activities. This study contributes to a more detailed understanding of the factors that drive responsible innovation.

In the era of artificial intelligence, translating AI-enabled insights into circular business model innovation (CBMI) remains a major challenge for construction firms because implementation depends on workforce transformation rather than technology adoption alone. Drawing on the dynamic capabilities view and the 4I organizational learning framework, this study develops a knowledge-to-skill conversion model linking AI capability (AIC), green knowledge management (GKM), labor skill transformation (LST), government support, and CBMI. Survey data from 498 Chinese construction firms were analyzed using PLS-SEM, multi-group analysis, necessary condition analysis, and fsQCA. The results show that AIC is positively associated with GKM, LST, and CBMI. After LST is included, the average direct relationship between GKM and CBMI becomes non-significant, whereas the serial AIC–GKM–LST–CBMI pathway remains significant, indicating that green knowledge is translated into innovation mainly through workforce enactment. Government support strengthens the conversion of both GKM and LST into CBMI by improving the institutional conditions for knowledge application. Segment analyses show that this mechanism is stronger in industrial construction and infrastructure/heavy civil engineering, while different construction segments reach high CBMI through distinct configurations of AI, knowledge, workforce, and institutional capabilities. Our findings offer practical insights for industry practitioners, demonstrating that building CBMI involves more than simply adopting digital technologies; it also encompasses micro-level mechanisms for reshaping the workforce’s skill structure—specifically, reskilling, work redesign and work adaptability—to enable the transformation of these technologies into innovation.