Search Results (15,053)

Digital transformation has become a central strategic priority as organizations increasingly rely on digital technologies to redesign business processes, governance structures, and value creation mechanisms in digitally evolving environments. However, existing approaches to digital transformation readiness often rely on additive maturity models or capability inventories that assume transformation capacity increases through cumulative capability development. Such approaches overlook how strategic emphasis must be distributed across transformation domains under governance and resource constraints. This study addresses this limitation by conceptualizing digital transformation readiness as a problem of strategic prioritization rather than cumulative capability accumulation. To operationalize this perspective, the study develops the Strategic Focus Index (SFI), a governance-aligned diagnostic instrument that evaluates how organizations distribute strategic attention across interdependent transformation domains. The index is constructed through a two-round Delphi study involving 53 experts from industry, academia, and the public sector, followed by statistical validation and an illustrative diagnostic application. The findings demonstrate how domain-level prioritization patterns can be systematically interpreted to identify potential imbalances in transformation efforts. By reframing readiness assessment as a prioritization-based diagnostic rather than a linear maturity measure, this study contributes a structured approach for evaluating digital transformation in digital business and platform-based environments. Full article

This study explores the effectiveness of an AI-enabled career preparation platform in enhancing undergraduate students’ awareness of their career readiness and skill development. The research was conducted within a localized context at a comprehensive university in Singapore, introduced as part of a career-preparation exercise for internship exploration and selection, allowing students to self-assess their current competencies and identify gaps vis-à-vis industry requirements. Students first evaluate their perceived knowledge of their skills and the deficiencies they need to address. This platform leverages artificial intelligence to help students profile their skills and discover tailored internship opportunities. By uploading their resumes, students receive a personalized skills profile identifying their relevant competencies. The platform then suggests potential career roles and automatically shows skills for development. Using a retrospective pretest-posttest survey with Likert-scale responses, statistical tests revealed significant improvements across all measured areas. The platform was further assessed across two constructs with high internal consistency, reflecting strong user engagement and satisfaction. Lastly, we highlight the potential of AI-driven tools in supporting student career preparedness and offer insights for further platform improvements. The findings from this study are not assumed to generalize directly to other institutional, cultural, or national settings, but instead offer initial context-specific indications of how such tools may support students’ perceived skill awareness and career planning. Full article

Grassland lake basins are mostly located in arid and semi-arid regions and represent typical ecologically fragile zones. As a representative inland lake in the cold and arid region of northern China, Hulun Lake serves as a crucial node for maintaining the ecological balance of the Hulunbuir grassland. Studying its ecological carrying capacity is particularly key to implementing the philosophy of a holistic approach to the management of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts. Based on data from 2018 to 2024 across four cities (banners, districts) in the Hulun Lake basin, this study constructs an evaluation system to measure ecological carrying capacity across three dimensions—ecosystem support, human activity pressure, and socio-economic response—using the Pressure–State–Response (PSR) model. Spatial analysis and geodetector methods are employed to explore its spatiotemporal differentiation and influencing factors. The findings are as follows: (1) The ecological carrying capacity in the Hulun Lake basin exhibits a significant spatial differentiation pattern, characterized by a gradient of “high in the east, low in the west; high in pastoral areas, low in urban areas.” (2) The overall trend in ecological carrying capacity shows a slow increase amid fluctuations, but the carrying capacity level remains relatively low. (3) The core driving forces of ecological carrying capacity primarily stem from the dimensions of population quality and infrastructure, while the direct influence of agricultural production is relatively limited. (4) Transportation infrastructure plays a strongly influential role as a driving mechanism of ecological carrying capacity in the Hulun Lake basin. Its synergy with factors such as education, information, and industry significantly affects both the ecosystem support capacity and the socio-economic responses of the basin. This study provides a reference for ensuring the ecological security of the Hulun Lake basin. Full article

Against the backdrop of global industrial sustainable transition and the advancement of UN Sustainable Development Goals (SDGs), higher education―a core carrier of sustainable human capital supply―plays a pivotal role in adjusting majors to meet labor market demands, resolving education–industry structural mismatch, and boosting regional sustainable development. From the perspective of “higher education supporting industrial sustainable transition,” this study explores how government Policy Mix Intensity enhances universities’ Major–Industry Alignment and its transmission mechanism, aiming to reveal higher education governance’s sustainable development path. Using panel data from 30 Chinese provinces (2012–2023), we constructed a PMI quantitative index and conducted empirical analysis via a two-way fixed-effects model. The results show the following: (1) high-intensity policy mixes significantly improve alignment, overcoming university organizational inertia and laying an institutional foundation for sustainable education–industry synergy; (2) Policy Mix Intensity acts through three pathways―optimizing capital allocation, deepening industry–education integration, and enhancing dynamic responsiveness―forming a “sustainable factor allocation—sustainable industry-education alignment” logic; (3) policy efficacy is more pronounced in highly marketized Eastern regions and via regulatory tools, reflecting the moderating effect of regional sustainable endowments and policy tool types. This study provides empirical evidence for the “policy mix intensity–sustainable efficacy” transformation mechanism, offers theoretical references and empirical insights from China for the global collaborative realization of SDG4, SDG8, and SDG9 through higher education policy optimization, and proposes that policy design should shift toward factor integration-based sustainable comprehensive governance. Full article

The construction industry is one of the major sources of carbon emissions, and green retrofitting of buildings is an effective pathway to promoting sustainable development in the sector. However, existing research and implementation strategies often struggle to reconcile the needs of governments, businesses, and residents. Therefore, this study proposes a comprehensive research framework that employs bibliometric and text analysis methods to examine implementation barriers in retrofitting projects across four dimensions: policy, cost, technology, and resident satisfaction. The results indicate that retrofitting costs are the primary factor, while technology is a secondary factor. Furthermore, existing policies feature vague technical standards, insufficient incentives, and a lack of differentiation. Conflicts of interest and challenges regarding cost allocation persist throughout the renovation life cycle. Decision-support tools and renovation technologies face limitations and issues regarding applicability. Residents face constraints from multiple factors, including their knowledge base and economic capacity. Based on these findings, the government urgently needs to improve a differentiated policy system and encourage technological R&D and knowledge dissemination. Enterprises must actively respond to policies and optimize their technologies and management practices. Residents need to enhance their energy-saving awareness, participate in retrofitting efforts, and improve their energy consumption behaviors. Full article

Industrial chains in agriculture are currently fragmented and do not support developing resource-based competitive advantages. This is true under the Big Food Framework’s strategic orientation. This research seeks to develop a new analytical framework for evaluating pathways to the integration of agricultural industrial chains and their impact on the performance of companies engaged in food processing in Xinjiang. A mixed-method approach, employing both an exploratory and sequential design, will be used to do this. The primary method of data collection for this study is the case study method, along with the questionnaire method involving 145 agricultural enterprises. From these data, structural equation modeling (SEM) will be used to test the paths of causation among cognitive managers of firms who have implemented the BFF. Evidence will be presented to demonstrate the relationship among three types of integration (vertical, horizontal, and lateral) in the agricultural industrial chain, dynamic capabilities, and company performance. Additionally, network topology and optimization simulations will be conducted to determine how effectively structures are organized in training the respective companies. Important findings revealed in this research include the following: The managerial cognition constructs offered by BFFs play a key role in enhancing the depth and structural balance of industry chain integration. There were complementary performance effects found, and they are related to vertical integration achieving operational efficiency and financial efficiency; horizontal integration improving market competitiveness and brand competitiveness; and lateral integration facilitating innovative growth. Dynamic capabilities are a significant mediating mechanism linking institutional support and digital capability with the depth of integration across different modes of integration. The findings from network optimization suggest that there is a positive effect of balanced connectivity across the different dimensions of integration on overall system efficiency and reduced structural inefficiencies. Based on these findings, the authors recommend that organizations establish governance mechanisms that facilitate coordinated connectivity; strengthen adaptive capabilities within the firm; and promote balanced integration across industrial networks. Future researchers should consider applying these findings to conducting longitudinal studies on network evolution; integrating sustainability measures as part of their analysis; and conducting comparative validation studies across regions or industry systems. Full article

This study addresses the growing necessity to understand how artificial intelligence (AI) competency and soft skills jointly influence organizational innovation and performance in the era of digital transformation. Despite the rapid adoption of AI technologies across industries, organizations continue to face significant challenges in effectively integrating technical AI capabilities with essential human-centric soft skills such as communication, adaptability, and leadership. This gap often limits the realization of AI-driven value and sustainable competitive advantage. The primary challenge in this research area is the lack of comprehensive models that simultaneously examine AI competency and soft skills within a unified framework, particularly in emerging economies where digital maturity varies widely. Existing studies tend to focus either on technical competencies or behavioral factors in isolation, leading to fragmented insights. To address these challenges, this study proposes a novel integrated research model that examines the combined effects of AI competency and soft skills on innovation outcomes and organizational performance. The model is empirically validated using structural equation modeling (SEM), providing robust evidence of the interrelationships among key constructs. The findings reveal that both AI competency and soft skills significantly contribute to innovation capability, which in turn enhances organizational performance. The study offers important theoretical and practical implications by bridging the gap between technical and human dimensions of AI adoption, thereby providing a more holistic understanding of digital transformation success. Full article

Under the dual pressures of global carbon emission reduction and production cost control, energy-intensive industrial enterprises are in urgent need of a balanced low-carbon operation strategy that reconciles economic benefits, environmental performance and production continuity. To address the limitations of existing methods in multi-dimensional objective balancing, this paper proposes a dynamic multi-objective optimization framework for industrial electricity consumption, integrating high-precision load forecasting and optimal scheduling. For load forecasting, an improved dual-gate optimization temporal attention long short-term memory (DGO-TA-LSTM) model is developed, which is modeled based on the one-year hourly electricity operation data (8760 samples) of a high-energy industrial enterprise in southern China, and its performance is verified via three standard metrics—the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE)—compared with five mainstream baseline models. On this basis, when taking time-varying electricity-carbon factors and time-of-use electricity prices as dual guiding signals, a three-objective optimization model minimizing electricity cost, carbon emissions and load deviation is constructed, which is solved by the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), with the Improved Gray Target Decision-Making (IGTD) method introduced to select the optimal compromise solution. Case study results show that the proposed scheme achieved a $1.9$% reduction in electricity cost and a 30% reduction in carbon emissions compared with the unoptimized strategy, providing a feasible and scalable low-carbon operation path for industrial enterprises. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

Under the coordinated implementation of the “dual carbon” goals and digital rural development strategy, digital technology has become a critical support for solving key problems in agricultural carbon reduction and advancing the green and low-carbon transformation of agriculture. Based on panel data from 31 provincial-level regions in China from 2010 to 2023, this study uses the fixed-effect model, mediating the effect model and threshold effect model to systematically examine the impact and transmission mechanism of digital technology on agricultural carbon emission intensity. The results show that: (1) Digital technology markedly lowers agricultural carbon emission intensity, and this conclusion remains steady after endogeneity correction and robustness checks. (2) Digital technology reduces emissions through two core channels: enhancing environmental regulation to constrain high-carbon behaviors via precise monitoring, and improving agricultural socialized services to promote intensive production and lower the adoption threshold of low-carbon technologies. (3) The emission reduction effect of digital technology exhibits a threshold characteristic related to agricultural industrial agglomeration, with the marginal effect of emission reduction showing an increasing trend as the agglomeration level rises. (4) The carbon reduction effect of digital technology shows obvious heterogeneity across grain production functional zones. The inhibitory effect is significant in major grain-producing areas and grain production–consumption balance areas, but not significant in major grain-consuming areas. (5) The carbon reduction effect also presents heterogeneity under different topographic relief conditions. The effect is significant in low-relief areas but not significant in high-relief areas, because complex terrain restricts the construction of digital infrastructure and large-scale application of digital technologies, which further reflects the regulatory role of natural geographical conditions. Accordingly, this paper proposes to strengthen the empowering role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively improve the capacity of agricultural carbon emission reduction and sequestration. Therefore, it is imperative to strengthen the enabling role of digital technology in the green transformation of agriculture, attach importance to regional coordination and differentiated policy design, and comprehensively enhance the capacity of agriculture for carbon emission reduction, sequestration and sustainable development. Full article

This study aims to identify the factors causing employer-induced disruption in construction projects and to examine why contractors do not file claims despite frequently encountering such losses. It also aims to develop a scale with tested reliability and validity to measure the causes of employer-induced disruption. Data for the study were collected through a structured questionnaire administered to architects and civil engineers working on the contractor side in projects conducted under the Public Procurement Law No. 4734. The data obtained in the study were analyzed using SPSS 27.0 and AMOS 24 software. The scale development process included exploratory and confirmatory factor analyses using separate samples following the reliability and validity assessments. The findings indicate that the proposed scale possesses a valid and reliable single-factor structure. Additionally, the results reveal that the most significant reasons for not filing a claim are: the lack of qualified technical staff required for record-keeping, the absence of a clause in the contract regarding disruption, and concerns about the potential deterioration of future employment relations with the employer. This study contributes to the literature by providing a validated measurement tool for assessing employer-related disruptions. It also offers recommendations for improving contract management, the documentation process, and awareness of issues among technical staff. Full article

This study systematically examines the impacts of extreme temperatures on the digital economy development index and the underlying mechanisms based on panel data from 281 prefecture-level cities in China from 2012 to 2023. This study explicitly distinguishes the distinctive adaptive capacity of the digital economy in responding to climate risks. Through global and local spatial autocorrelation analysis, the study finds that both extreme temperatures and the digital economy exhibit significant spatial clustering. This study employs the spatial Durbin model (SDM) and effect decomposition and further incorporates the GS2SLS estimator alongside dual instrumental variables constructed from historical geographic characteristics to address endogeneity, thereby identifying the asymmetrical impacts of extreme heat and extreme cold on the digital economy with great rigor. Specifically, extreme heat fosters short-term local digital demand that is subsequently translated into long-term growth in IT human capital and infrastructure, thereby increasing the DEDI. However, its net spatial effect is inhibitory due to energy crowding out. Extreme cold, by contrast, primarily disrupts supply chains and intensifies energy consumption, with its impact largely confined to the local scope. Green technological innovation mitigates the impact of extreme heat on the digital economy through demand substitution, while, under extreme cold, it manifests as the physical protection of infrastructure. Meanwhile, an optimized industrial structure substantially reduces the economy’s dependence on supply chains, amplifying the promotional effect of extreme temperatures on the digital economy and reflecting the transformation capacity of regions under complex environmental conditions. Heterogeneity analysis demonstrates that the effects of extreme temperatures vary significantly across different urban agglomerations, economic zones, geographic regions and city types. This study not only extends the theoretical framework for the economic assessment of climate risks and spatial econometric analysis to the climate sensitivity of the digital economy but also provides empirical evidence for understanding the complex relationship between climate change and digital economy development and offers references for differentiated policies in a coordinated regional digital economy. Full article

Fully volumetric modular mining structures offer a partial solution to achieving sustainable construction at remote mine sites. Significant logistical challenges arise during road and sea transportation, depending on the size of the prefabricated modules and the remoteness of the site. As an alternative, hybrid modular mining structures comprising various non-volumetric prefabricated components of transportable size, assembled on-site to form complete structures, have previously been proposed. To facilitate hybrid modular structures in the mining industry, the paper reviews the design actions to which mining structures are subjected and evaluates the corresponding structural responses. It also examines existing connections that may be suitable for the hybrid module structures, assessing their effectiveness and safety in connecting prefabricated structural components. Finally, key requirements for connection design are identified to facilitate hybrid assembly. Full article

Electricity infrastructure is widely regarded as a fundamental prerequisite for supporting sustainable industrial development and driving corporate digital transformation under energy constraints. Taking the quasi-natural experiment of changes in electricity supply resulting from the cross-regional power transmission of the Three Gorges Project, and using data from China’s A-share listed manufacturing companies over the period 2000 to 2023, this paper constructs a multi-period difference-in-differences model to investigate whether improvements in electricity infrastructure promote corporate digital transformation and to examine their potential role in supporting sustainable economic development. The empirical results indicate that improvements in electricity infrastructure significantly enhance the level of corporate digital transformation. In the mechanism analysis, the alleviation of financing constraints and the increase in R&D investment play important mediating roles in the process through which electricity infrastructure affects corporate digital transformation. Further heterogeneity analysis reveals that the above effects are more pronounced in non-STAR Market enterprises, labor-intensive enterprises, asset-intensive enterprises, state-owned enterprises, and regions characterized by relatively lower levels of marketization. This study reveals the intrinsic relationship between electricity infrastructure and corporate digital transformation at the micro level, provides empirical evidence for understanding how energy infrastructure supports sustainable digital transformation and enhances long-term economic resilience, and offers policy implications for promoting the coordinated development of energy security and the digital economy. Full article

Growing concerns about global climate change and its negative consequences for communities have put immense pressure on the building industry, which is one of the primary sources of greenhouse gas emissions. Due to the environmental issues associated with the manufacture of sustainable construction materials, alkali-activated concrete (AAC) has emerged as a competitive alternative to cement. To predict the compressive strength (CS) of AAC, four machine learning (ML) models, namely, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were employed in this study using 193 data points. The input variables include Precursor “P” (kg/m³), Blast Furnace Slag “BFS ratio”, Sodium hydroxide “Na” (kg/m³), silicate modulus “Ms”, water content “W” (kg/m³), fine aggregate “FA” (kg/m³), coarse aggregate “A” (kg/m³), and curing time “CT” (day), with CS (MPa) as the output variable. The dataset was checked for stationarity and then normalized to decrease data redundancy and increase integrity. Furthermore, three model combinations were developed based on the relationship between the input and target variables. The XGB-M3 model outperformed all other models with a high degree of accuracy, according to the study’s findings. Specifically, the Pearson correlation coefficient (PCC) was 0.9577, and the mean absolute percentage error (MAPE) was 14.95% during the calibration phase. SHAP, an explainable AI approach that provides interpretable insights into complex AI systems by assigning feature importance to model predictions, was employed. Results suggest the higher predictions from the XGB-M3 and RF-M3 models were largely driven by curing time (CT). Full article