Search Results (3,702)

The highway and bridge industry is facing persistent challenges related to the high turnover of technical personnel, which poses risks to the continuity and sustainability of infrastructure development. Although Geographic Information System (GIS) training has increasingly been advocated as a strategy to stabilize the workforce, its practical application remains relatively limited across China. Drawing on the Conservation of Resources (COR) theory, this study examines whether GIS training is associated with lower turnover intention among technical staff, potentially through enhanced perceptions of career growth and reduced work-related anxiety. Based on 412 valid responses—primarily from technical personnel employed by major infrastructure enterprises such as regional subsidiaries of the China Communications Construction Group (CCCG) and China State Construction Engineering Corporation (CSCEC)—the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the proposed relationships. The findings indicate that GIS training is negatively associated with turnover intention, with career growth partially mediating this association. Additionally, work anxiety moderates the relationship, such that the link between GIS training and turnover intention appears weaker under higher levels of anxiety. This research contributes to bridging the gap between training practices and theoretical understanding, offering insights to inform workforce retention strategies in technology-intensive industries. Full article

This study uses financial data from 872 Chinese listed companies (2018–2022). It tests the triple dividend effect and threshold effect of China’s environmental protection tax (EPT) using high-dimensional fixed effects models and panel threshold models. We document that (1) EPT creates an environmental dividend for Chinese listed companies. It significantly reduces pollution emissions. A 1-unit tax increase reduces LnTPPE by 2.5%. (2) EPT creates a significant innovation dividend. It forces enterprises to improve the quality of authorized patents. A 1-unit tax increase raises patent technological complexity by 0.79%. (3) EPT creates an economic dividend. It significantly improves firm performance. A 1-unit tax increase raises relative corporate revenue by 38.1%. (4) EPT exerts significant threshold effects on micro-level triple dividend outcomes among Chinese listed companies. A heterogeneity analysis shows significant differences in threshold effects between non-heavily polluting and heavily polluting industries. This study confirms that China’s EPT generates a micro-level triple dividend effect alongside coexisting threshold effects for listed companies. This provides literature references for China to design and implement differentiated policies and offers a quantitative empirical case for implementing globally sustainable EPT strategies. Full article

This study examines the role of human capital, technological investment, and institutional quality in promoting economic complexity in South Africa, with implications for sustainable development and the strategic role of Small and Medium Enterprises. Motivated by the growing importance of productive sophistication for long-term development in emerging economies (notably SDG 8 and SDG 9), the study examines both long-run and short-run dynamics using the Autoregressive Distributed Lag approach, with robustness checks via Fully Modified Least Squares, Dynamic Least Squares, and Canonical Cointegration Regression. Structural Vector Autoregression is employed to assess the persistence of shocks, while the Toda–Yamamoto causality test evaluates causality. The results reveal that institutional quality significantly enhances economic complexity in the long run, while technological investment exhibits a negative long-run impact, potentially indicating absorptive capacity constraints within industries. Though human capital and income per capita do not influence complexity in the long run, they have short-term effects, with income per capita having the most immediate influence. Variance decomposition shows that shocks to technological investment are essential for economic complexity, and are the most persistent, followed by human capital and institutional quality. These findings show the need for institutional reforms that lower entry barriers for SMEs in industries, targeted innovation policies that support upgrading, and human capital strategies aligned with driven industrial transformation. The study offers insights for policymakers striving to influence structural drivers to advance sustainable industrial development and achieve the SDGs. Full article

Public–private partnership (PPP) logistics infrastructure projects have become increasingly prevalent globally. Consequently, the effective management of these projects as their concession periods expire presents a crucial challenge for governments, vital for the sustainable management of PPP logistics infrastructure. This study addresses this challenge by focusing on the pivotal post-concession decision: whether the government should self-operate the mature logistics infrastructure or re-delegate its management to a private entity. Our theoretical model, built on a principal–agent framework, first establishes a social welfare baseline under government self-operation and then analyzes delegated operation under symmetric information, identifying efficiency frontiers. Under symmetric information, we find that government self-operation is more advantageous when its own operational efficiency is sufficiently high, irrespective of the private enterprise’s efficiency; conversely, delegating to an efficient private enterprise is optimal only when government operational efficiency is low. We also demonstrate that if the government can directly specify the demand quantity and service level and delegates operation via a fixed fee, the enterprise can be incentivized to align with the social optimum. However, under asymmetric information, potential welfare gains from delegation are inevitably offset by informational rent and output distortion. We further uncover non-monotonic impacts of parameters like the proportion of low-cost firms on social welfare loss and demonstrate how information asymmetry can indirectly compromise the long-term resilience of the infrastructure. Ultimately, our work asserts that delegation is only superior if its potential efficiency gains sufficiently offset the inherent losses stemming from information asymmetry. Full article

Against the backdrop of China’s fast-growing digital economy and its financial inclusion agenda, there is still little city-level evidence on whether the e-CNY pilot accelerates financial deepening at the grassroots. Using a balanced panel of 271 prefecture-and-above cities for 2016–2022, this study employs a staggered difference-in-differences (SDID) design augmented by the synthetic control method (SCM) to rigorously identify the policy effect of the e-CNY pilot. The results show that the pilot program significantly improves urban financial inclusion, contributing to more equitable access to financial services and supporting inclusive socio-economic development. Mechanism analysis suggests that the effect operates mainly through two channels, a merchant-coverage channel and a transaction-scale channel, with the former contributing the majority of the overall effect. Incorporating a migration-based mobility index shows that most studies’ focus on the merchant-coverage effect is amplified in cities under tight mobility restrictions but wanes where commercial networks are already saturated, whereas the transaction-scale channel is largely insensitive to mobility shocks. Heterogeneity tests further indicate stronger gains in non-provincial capital cities and in the eastern and central regions. Overall, the study uncovers a “penetration-inclusion” network logic and provides policy insights for advancing sustainable financial inclusion through optimized terminal deployment, merchant incentives, and diversified scenario design. Full article

Environmental, social, and governance (ESG) evaluation has become increasingly critical for company sustainability assessments, especially for enterprises in the construction industry with a high environmental burden. However, existing methods face limitations in subjective evaluation, inconsistent ratings across agencies, and a lack of industry-specificity. To address these limitations, this study proposes a large language model (LLM)-based intelligent ESG evaluation model specifically designed for the construction enterprises in China. The model integrates three modules: (1) an ESG report information extraction module utilizing natural language processing and Chinese pre-trained language models to identify and classify ESG-relevant statements; (2) an ESG rating prediction module employing XGBoost regression with SHAP analysis to predict company ratings and quantify individual statement contributions; and (3) an ESG intelligent evaluation module combining knowledge graph construction with fine-tuned Qwen2.5 language models using Chain-of-Thought (CoT). Empirical validation demonstrates that the model achieves 93.33% accuracy in the ESG rating classification and an R² score of 0.5312. SHAP analysis reveals that environmental factors contribute most significantly to rating predictions (38.7%), followed by governance (32.0%) and social dimensions (29.3%). The fine-tuned LLM integrated with knowledge graph shows improved evaluation consistency, achieving 65% accuracy compared to 53.33% for standalone LLM approaches, constituting a relative improvement of 21.88%. This study contributes to the ESG evaluation methodology by providing an objective, industry-specific, and interpretable framework that enhances rating consistency and provides actionable insights for enterprise sustainability improvement. This research provides guidance for automated and intelligent ESG evaluations for construction enterprises while addressing critical gaps in current ESG practices. Full article

In an ever-evolving digital environment, organizations must adopt advanced technologies for real-time big data processing to maintain their competitiveness and growth. However, selecting appropriate technologies is a challenge, particularly for small and medium-sized enterprises (SMEs). This study develops a literature review to analyze the methodologies used in the selection of technologies, with a special focus on those associated with the Industry 4.0. Knowledge Development Process-Constructivist (ProKnow-C) method, which was used to build a bibliographic portfolio, examining approximately 3400 articles published between 2005 and 2024, from which 80 were selected for a detailed analysis. The main methodological contributions come from research articles, the ScienceDirect database, the Expert Systems with Applications Journal, studies conducted in Turkey, and publications from the year 2023. The results highlight the predominant use of multi-criteria techniques, emphasizing hybrid approaches that combine various decision-making methodologies. In particular, the analytic hierarchy process (AHP) and TOPSIS methods were employed in 51.25% of the analyzed cases, either individually or in combination. It is concluded that technology selection should be based on flexible and adaptive approaches tailored to the organizational context, aligning long-term strategic objectives to ensure business sustainability and success. Full article

This study explores the internationalization of small and medium enterprises (SMEs), emphasizing the critical role of competent managerial abilities. Specifically, it investigates the sustainable role of managerial capabilities in directly facilitating SMEs’ entry into international markets, or whether these capabilities first assist in risk management and resource utilization, supporting international expansion. We propose that SMEs with skilled and capable managers are better equipped to manage internal risks and leverage available resources, thereby enhancing their internationalization efforts. Drawing on empirical data from 191 Chinese SMEs, our findings support the proposed model, revealing that managerial capabilities contribute to internationalization indirectly—this relationship is fully mediated by risk management and resource utilization. This study recommends that SMEs prioritize building a sustainable management team capable of navigating internal challenges to successfully pursue international growth. Our research contributes to the resource-based view and the Uppsala model of internationalization by contextualizing the role of managerial capabilities, risk management, and resource utilization in the internationalization processes of SMEs. Full article

In the context of small and medium-sized enterprises (SMEs), organizational resilience has emerged as a critical capability for navigating dynamic and turbulent environments. The ability of firms to sustain their performance despite external disruptions, particularly those arising from market and technological change, is paramount for achieving long-term sustainability. This study offers a novel contribution by examining how two key dimensions of environmental turbulence—market turbulence and technological turbulence—moderate the relationship between organizational resilience capacity and sustainability performance. Our empirical findings, based on data from 423 SMEs, demonstrate that while organizational resilience positively correlates with sustainability performance, this relationship is significantly weakened under high levels of market and technological turbulence, indicating a negative moderating effect. These results advance resource-based and dynamic capabilities theory by highlighting the contingent nature of resilience in unstable contexts. Furthermore, this study provides practical guidance. SMEs should strategically invest in resilience-building efforts and continuously adapt their strategies in response to environmental fluctuations. Targeted approaches to managing different forms of turbulence and forming resilience-oriented collaborations can enhance sustainability outcomes. This research makes significant contributions to theory and practice; however, there are limitations that future research should take into account in order to appropriately utilize this study’s findings. Full article

As an environmental policy, the Action Plan of Atmosphere Pollution Control in Beijing-Tianjin-Hebei and Surrounding Areas in Autumn and Winter (Action Plan of APC) was implemented in 2017, with the goal of achieving the sustainable growth of the regional economy. This study examines the effect of the Action Plan of APC on green total factor productivity (GTFP) in the Chinese construction industry employing a difference-in-differences (DID) approach. The findings indicate the following: Firstly, the environmental policy of the Action Plan of APC has significantly improved the GTFP of the aforementioned areas, and the result is still valid after robustness testing; secondly, the dynamic effect testing reveals that the influence follows an increasing trend over time; thirdly, due to the different degrees of marketization, the influence of the Action Plan of APC on GTFP in Chinese construction industry exhibits notable regional heterogeneity. From the perspectives of both the government and enterprises, this study offers recommendations for promoting the GTFP of China’s construction industry. It also provides a novel framework for assessing the effect of environmental policies on the GTFP of the Chinese construction industry. Full article

Despite growing interest in AI technologies, there is a lack of integrated models explaining AI adoption in SMEs from a consumer perspective. This study addresses this gap. Although artificial intelligence (AI) has gained traction in digital innovation strategies, especially among SMEs, existing research lacks integrative models that address cognitive, contextual, and emotional factors driving AI adoption. This study addresses this gap by developing a theoretical model based on TAM and UTAUT2, enhanced with passion, workplace integration, and trust. Drawing on the Technology Acceptance Model and consumer trust theories, the study provides empirical insights into how these factors shape behavioral intentions to adopt AI technologies. The findings aim to inform both theory and practice by highlighting how emerging digital tools affect consumer decision making and engagement across personal and professional contexts. The study contributes to both theory and practice by offering empirical evidence on the drivers of AI adoption and by providing managerial recommendations for SMEs to implement AI-driven personalization responsibly. Full article

Under China’s “Rural Revitalisation” strategy, contract farming faces challenges including farmers’ limited access to advanced technologies and high operational risks for agricultural support enterprises. The collaborative involvement of scientific research institutions offers potential solutions but remains underexplored. This study employs Stackelberg game theory to model a contract farming supply chain under two agricultural assistance modes: enterprise-led (EL) and collaborative assistance with scientific research institutions (CI). We further propose two government subsidy mechanisms: subsidies to enterprises and subsidies to scientific research institutions. The models analyze optimal decisions, supply chain performance, and subsidy efficiency, validated through numerical experiments. Key findings reveal the following: (1) The CI mode enhances agricultural output and farmer revenue but may reduce enterprise profits, deterring collaboration. (2) Government subsidies incentivize enterprise–institution collaboration. Subsidizing scientific research institutions typically improves agricultural productivity and economic benefits more effectively than subsidizing enterprises. (3) Synergistic effects exist among the government subsidy coefficient, cost coefficient of technical assistance, consumer preferences for agricultural quality, and profit-sharing ratio. The latter three parameters significantly influence subsidy model selection. This research provides policy insights for enhancing agricultural assistance efficiency and sustainable contract farming development. Full article

The mining sequence of ionic rare earth mineral mining districts is related to the effective utilization of rare earth mineral resources and the protection of ecological environment. This study establishes an optimization model for the mining sequence of ion-adsorption rare earth mining districts that incorporates environmental costs, using the net present value (NPV) of the mining district and the net present value of environmental costs (C_E) as objective functions. The model is applied to optimize the mining sequence of Mining District L. The results demonstrate that (1) Four algorithms, namely NSGA-II, NSGA-III, IBEA, and MOEA/D, were selected for comparison. The analysis based on the distribution of solutions, hypervolume values (HV), and computational time revealed that the IBEA exhibited superior performance. (2) The IBEA was employed to solve the multi-objective optimization problem, yielding a set of 30 optimal solutions. Different NPVs corresponded to different C_E values, with the C_E value increasing correspondingly as the NPV increased. (3) The weighted method was employed to transform the multi-objective optimization problem into a single-objective formulation. Using a genetic algorithm (GA), the optimal solution yielded a decision variable sequence for mining order as [2, 5, 8, 4, 1, 9, 6, 7, 3, 10, 11], with the net present value (NPV) of mining district profits reaching CNY 76,640.65 million and the environmental cost NPV amounting to CNY 19,469.18 million. Compared with the mining sequence optimization scheme that did not consider C_E, although the NPV decreased by CNY 3.3266 million, the C_E was reduced by CNY 10.6993 million. The mining sequence optimization model with environmental costs constructed in this paper provides a scientific decision-making basis for mining enterprises to consider the mining sequence in mining districts, minimize the damage to the ecological environment, and promote the coordinated progress of resource development and sustainable development. Full article

In the context of pursuing the goal of strategic imperatives of sustainable development, the ESG performance of enterprises has become a key yardstick for measuring their comprehensive environmental contribution and economic efficiency. Enhancing ESG performance has far-reaching significance in promoting green and sustainable development of enterprises and society. Drawing on the upper echelons theory, this paper investigates the impact of the chief executive officer’s (CEO’s) green experience on corporate environmental, social, and governance (ESG) performance, utilizing a sample of publicly listed Chinese companies from 2011 to 2023. The study demonstrates that CEOs with green experience significantly enhance corporate ESG performance, a conclusion that remains consistent following a series of rigorous robustness checks. Mechanistic analysis reveals that CEOs’ green experience primarily facilitates corporate ESG performance enhancement through green innovation initiatives. Furthermore, CEO discretion amplifies the positive influence of green experience on ESG performance. Heterogeneity analysis demonstrates that the influence of the CEOs’ green experience on ESG performance is more pronounced in high-tech enterprises, in markets characterized by lower levels of competition, and in firms situated in regions exhibiting higher degrees of social trust. These findings impart both theoretical and practical implications for enhancing corporate ESG performance and offer novel strategic perspective to advance environmental stewardship, social responsibility, and corporate governance frameworks. Full article

This study aimed to analyze the influence of local politics, village facilitators, recruitment of administrators, training and education, and organizational culture on work motivation and management performance. The study was conducted in Wajo Regency, South Sulawesi Province, Indonesia, utilizing primary data collected from 250 participants, including administrators of village-owned enterprises (BUMDES), community leaders, and representatives from the private sector. The data were analyzed using structural equation modeling (SEM) with the LISREL program. The results indicated that the latent variables of local politics, village facilitator, recruitment of administrators, training and education, and organizational culture had a positive and significant impact on work motivation and management performance. These findings are valuable key indicators and provide essential insights for promoting and driving the BUMDES as a pillar of rural development strategies. Based on these findings, it is recommended that the local government revitalize the local political system, reorient the organizational culture of the BUMDES toward a modern business-oriented culture suited to rural conditions, and enhance the training and education of village facilitators to improve their motivation and performance. This recommendation will empower the BUMDES to promote rural economic improvement and sustainable rural development by enhancing work motivation and management performance. Full article