Search Results (1,451)

The rapid expansion of e-commerce has transformed market access in developing economies, yet its impact on the spatial structure of market participation remains insufficiently understood. While existing studies primarily examine welfare outcomes such as income growth and consumption smoothing, few investigate how digital platforms reshape the balance of market entry between urban and rural areas. Drawing on New Economic Geography and platform economics theory, this study proposes that e-commerce development rebalances urban–rural market vitality through three associative pathways: alleviating rural capital constraints, improving rural innovation environments, and promoting agricultural-industry agglomeration. Using county-level panel data covering 2725 Chinese counties from 2011 to 2022, we employ a Double Machine Learning (DML) framework to examine the association between designation as an “E-commerce into Rural Comprehensive Demonstration County” and changes in the urban–rural market vitality balance (URMAR). The results indicate that demonstration county designation is associated with a statistically significant reduction in urban–rural market disparity, as measured by both the Theil index and the absolute difference in new enterprise registrations. The directional URMAR indicator further reveals that this convergence is driven primarily by accelerated rural enterprise formation. Subsample analysis confirms that the rebalancing interpretation holds across counties with different baseline market structures. Mechanism analysis provides suggestive evidence consistent with all three proposed associative pathways. Heterogeneity analysis further reveals that these effects are stronger in economically developed eastern regions, in counties linked to higher-tier cities, and in secondary and tertiary industries. These findings advance a market-structure perspective on digital development that complements existing welfare-based approaches and offer policy insights for fostering balanced regional development through targeted digital and complementary investments. Full article

The rapid growth of publicly available digital services increases the need for scalable security assessment. This is particularly important for software directly used by end users, such as Android applications. Due to staff shortages and financial constraints, small and medium-sized enterprises are often unable to test their applications for vulnerabilities. One possible support mechanism is the use of large language models (LLMs) to assist testers during such assessments. The aim of this study was to investigate the possibility of using an LLM as an interactive guide for dynamic application security testing (DAST) of Android applications. For this purpose, five LLM-based systems were compared: Gemini 2.5 Flash, GPT-oss 120B, Llama 3.3 70B, Qwen 3 32B, and Trinity Large Preview accessed via OpenRouter. The models were evaluated on intentionally vulnerable Android applications using weighted step-level scoring and three selected exploit guidance scenarios. In the main guidance experiment, Gemini achieved the highest combined Fully Discovered and Partially Discovered (FD + PD) detection rate of 79.1% in the representative run, while repeated runs for selected models showed limited aggregate variability. The results also indicate that more detailed prompts improve the quality of generated guidance. The findings suggest that LLMs can serve as interactive guides for DAST testing of Android applications, although they should be treated as supporting tools rather than standalone security-testing systems. Full article

To investigate the heterogeneous interactions between various agricultural business entities (abbreviated as ABEs, including farmers, cooperatives and enterprises) and agricultural land transfer (abbreviated as ALT) in China, this study constructs a spatial simultaneous equation model based on the GS3SLS method and applied to data from 30 provinces in 2012–2020. The results show the following: (1) ABEs and ALT demonstrate significant bidirectional positive correlations at the intra-regional level, especially among farmers, while cooperatives and enterprises exhibit more pronounced spatial spillover effects. (2) Despite overall positive correlations, negative interactions emerge in specific entities of some regions (e.g., central China’s ALT among farmers vs. central China’s ABEs among farmers, and eastern China’s ABEs among enterprises vs. neighboring ABEs among enterprises). Conversely, cooperatives maintain universally positive ABE-ALT interactions, peaking in central/western regions. (3) The co-development of ABEs and ALT exhibits temporal heterogeneity: the growth in the number of farmer ABEs lags behind their agricultural land transfer (ALT), whereas for cooperatives and agricultural enterprises, ALT lags behind their growth in numbers. This indicates that the relationship between agricultural operators (“human”) and land transfer (“land”) needs to be reconfigured. The heterogeneous interactive relationships revealed in this study provide a solid theoretical basis for formulating differentiated and precise policies on the transfer of agricultural land and the coordination of various operating entities, so as to efficiently promote agricultural modernization. Full article

Small and medium-sized enterprises (SMEs) frequently depend on spreadsheet-based financial reporting due to limited budgets and constrained access to enterprise analytics systems. As transaction volumes increase, manual profit and loss computation becomes time-intensive and prone to inconsistencies. This study proposes and evaluates a modular robotic process automation (RPA) framework designed to enhance spreadsheet-centric financial analytics without requiring enterprise system replacement. The framework is implemented as a unified pipeline using UiPath. Statistical anomaly detection mechanisms are integrated to identify abnormal revenue deviations and expense spikes in operational data. Experimental benchmarking compares manual spreadsheet processing with automated workflow execution using execution time, error exposure, reporting latency, and scalability as evaluation criteria. Empirical evaluation across five datasets spanning 300 to 3000 transactions demonstrates time reductions of 88.6% to 95.5% and error reductions of 93.3% to 95.5% relative to manual spreadsheet processing. Scalability analysis confirms linear growth of automated runtime with transaction volume, in contrast to the superlinear growth observed in manual processing. A cost feasibility analysis further indicates that lightweight RPA can significantly reduce operational costs in SME environments up to 88.6%. The study contributes a structured automation architecture that integrates spreadsheet automation with statistical monitoring to support financial oversight and decision support. The findings suggest that interface-level automation provides a viable transitional pathway for SMEs seeking incremental digital transformation while preserving existing spreadsheet infrastructures. Full article

Enterprise green transformation is a strategic response to emerging development concepts and high-quality growth, as well as a key approach to achieving symbiotic integration between firms and their social environment. Using a sample of Chinese A-share listed companies from 2008 to 2023, this paper examines the relationship between corporate social responsibility (CSR) disclosure and green transformation. It further explores the underlying mechanisms, focusing on the role of patient capital as an external governance mechanism within the green governance environment. Empirical results show that CSR disclosure significantly promotes corporate green transformation. Mechanism tests reveal that this effect operates through two channels: alleviating agency costs and easing financing constraints, with patient capital playing a positive moderating role. Additional analyses indicate that the promoting effect of CSR disclosure on green transformation is particularly pronounced in competitive and polluting industries. Full article

This paper examines an algorithm and evaluates the upper limits of technical parameters for step-by-step management of forecast coverage for aggregated generation from solar power plants (SPPs) in Ukraine, given the high share of renewable energy sources in the integrated power system of Ukraine. The relevance of the research is due to the growth in the installed capacity of SPPs, stricter requirements for forecasting accuracy, and the full financial responsibility of producers for imbalances in accordance with the current electricity market model. The problem is formulated as a special case of a hierarchically controlled quasi-dynamic power system, accounting for technological, energy, and economic constraints. The objective function is defined as the minimisation of the total hourly measure of discrepancy between the forecast and actual volumes of electricity supplied, whilst ensuring power balance through energy storage systems and flexible generation. The numerical implementation was carried out using the “SOPS” software and information complex. The input data used were hourly indicators of the forecasted and actual generation of Ukraine’s solar power plants for 2021–2025, published by the state-owned enterprise “Guaranteed Buyer”. Hourly, daily and monthly operating parameters for aggregated solar power generation in 2025 have been calculated. The calculations show that the maximum hourly mismatch between forecasted and actual solar generation in 2025 reached 3116 MW, while the maximum daily mismatch exceeded 19.8 GWh. Under the assumed operating conditions, an energy storage system with 30,000 MWh capacity and flexible generation of up to 7500 MW enabled full imbalance compensation, achieving IMB(t) = 0 for all hourly intervals in the analysed case. The required volumes of flexible generation and the operating parameters of the storage systems have been determined. The practical significance of the results lies in their potential use for operational planning of the operating modes of solar power plants, energy storage systems, and flexible generation on a daily and hourly basis, as well as for justifying technical and economic decisions aimed at reducing imbalances. The results obtained confirm the effectiveness of the proposed step-by-step control algorithm and demonstrate the potential to minimise imbalances through the rational coordination of solar power plants, energy storage systems, and flexible generation capacities. Full article

The New Energy Industry (NEI) is a strategic sector for harmonizing the energy-economy-environment nexus, yet it faces challenges from high technological complexity and industrial fragmentation. DIT enables enterprises to transition from passive response to proactive implementation of systematic adjustments. This study explores how Digital Intelligence Technology (DIT) drives integrated innovation within the sector. Utilizing a panel dataset of 750 listed firms from 2007 to 2023, we examine the influence of DIT through mediation, threshold, and moderation models. Our findings indicate that DIT significantly facilitates integrated innovation by expanding corporate capabilities and reducing information asymmetries. This impact is primarily contingent upon R&D intensity and government subsidies. Notably, R&D intensity exhibits a triple-threshold effect (3.82%, 7.47%, and 7.88%); specifically, once R&D investment surpasses 7.88%, DIT generates a multiplier effect with internal capabilities, substantially accelerating the integration of internal and external innovation elements. Furthermore, State-Owned Enterprises (SOEs) demonstrate superior efficiency in leveraging DIT for innovative outcomes compared to non-SOEs, bolstered by policy advantages. Government subsidies further mitigate resource constraints and enhance risk-bearing capacity. These results provide critical strategic insights for NEI to refine R&D management and bolster human capital, ultimately helping the industry achieve a balance between economic growth and environmental sustainability. Full article

This study rigorously examines the impact of political instability (POI) on firm performance (FPER) using high-dimensional panel data from 2006 to 2022, drawn from the World Bank Enterprise Survey (WBES) for 60 economies. Using advanced machine learning-based econometric techniques, including Double-Selection LASSO Regression (DSLR) and Partialing-Out LASSO Regression (POLR), the analysis reveals that POI significantly reduces FPER across the sampled countries. These findings remain robust across a series of validation tests, including alternative estimation approaches such as Cross-Fit Partialing-Out LASSO Regression (CF-POLR) and Bayesian Model Averaging (BMA), the use of alternative FPER proxies—employment growth (FEMG), innovation (IINN), and labor productivity (FLP)—and the substitution of POI with government regulation (REG). Mediation analysis further indicates that operational costs (OCOST) and firm investment (FINV) significantly and partially mediate the total effect of POI on FPER. In contrast, financial constraints (FCST) do not emerge as a significant mediator. The moderation analysis shows that political connections (PC) substantially attenuate the negative impact of POI on FPER. Heterogeneity analyses demonstrate that small, young, and capital-intensive firms are more severely affected by POI than medium and large, older, technology-intensive, and labor-intensive firms. Additionally, firm ownership-based heterogeneity indicates that state-owned enterprises experience slightly stronger adverse effects from fluctuations in POI than non-state-owned firms. Based on these empirical insights, policymakers need to promote institutional stability and provide direct support for vulnerable young and small firms to reduce the adverse effects of POI on FPER. Ultimately, this boosts economic flexibility in politically unstable markets by managing key growth drivers. Full article

In recent years, China’s digital economy has become a key engine for high-quality development. Assessing the operational efficiency of leading digital enterprises is crucial for optimizing resource allocation and promoting sectoral growth. However, existing research largely remains at regional or industry levels and typically reports efficiency scores without diagnosing the root sources of inefficiency. To fill this gap, this study measures the operational efficiency of 99 firms selected from China’s Top 100 Digital Economy list (2017–2022) using the BCC-DEA model, and analyzes their dynamic evolution via kernel density estimation. The findings reveal a fluctuating upward trend in overall efficiency, and that the gap in overall technical efficiency primarily originates from scale efficiency rather than pure technical efficiency. The kernel density peak exhibits a “rise–decline–rise” pattern, indicating existing but narrowing efficiency differences among firms. By decomposing efficiency, this study further classifies firms into four types, revealing that inefficiency is heterogeneous. This paper makes three main contributions. First, it identifies scale efficiency as the main source of efficiency gaps. Second, it classifies firms into four types, revealing that inefficiency is heterogeneous. Third, it uses kernel density estimation to track the dynamic evolution of efficiency, showing a narrowing efficiency gap but a persistent superstar effect. Two policy implications follow: firms with low pure technical efficiency should focus on management training and technology adoption, while firms with low scale efficiency should pursue scale expansion through mergers or partnerships. Full article

Nowadays, Global Value Chains (GVCs) play a vital role in job creation, income generation, knowledge diffusion, and productivity growth. However, significant disparities exist across countries in terms of their integration into GVCs, and Tunisia is no exception to this pattern. In this regard, the question about factors that influence GVCs’ participation is yet to be discussed, to formulate and implement appropriate strategies and reforms. Thus, using firm-level data from the 2025 World Bank Enterprise Survey, this paper examines the role of female leadership and innovation in determining Tunisian firms’ participation in GVCs. Participation in GVCs is captured by a dummy variable indicating the firm’s export and import status. Estimation results from the logit model show that female representation in decision-making positions significantly increases the likelihood of firms’ participation in GVCs. The results also highlight the importance of process innovation in GVC participation, while product innovation appears to have no significant effect. Notably, when firms combine both types of innovation, their likelihood of joining GVCs increases further. Regarding control variables, firm size appears to be an important determinant, as larger firms display a greater tendency to participate in GVCs. The findings further indicate that firm certification and foreign equity participation significantly promote integration into GVCs, while corruption constitutes a major constraint on the integration of Tunisian firms. From a policy perspective, these findings highlight the need to rethink industrial policies, with a stronger focus on process innovation as a key lever of productive sector modernization. Achieving this transformation also requires the development of an inclusive policy ecosystem that supports meaningful and sustainable progress in female’s leadership representation. Full article

Green trade is increasingly central to reconciling trade growth with climate goals and to driving industrial upgrading in major trading economies. The essential of green trade promotion depends not only on trade scale but also on the technological sophistication and trade diversification of green products. Environmental regulation is a primary policy instrument guiding firms toward greener production and trade. This study investigates whether strengthening environmental regulation can promote green trade from the perspectives of scale, technological structure, and diversification. To this end, we first construct a comprehensive green product list and then calculate firm-level and provincial-level green trade indicators using detailed product-level customs transaction data from 2000 to 2020. These trade indicators are further matched with enterprise operating and financial data to build an integrated panel dataset, upon which fixed-effects regression models are employed to quantify the effects of environmental regulation on green trade. Our empirical results show that enhanced environmental regulation can promote the expansion of green export scale, with the effects amplifying as product technology levels advance. Regarding diversification, the promoting effect of environmental regulation on green imports surpasses that on exports. Furthermore, this study highlights that the combined effect of various types of environmental regulations can better promote the comprehensive development of green trade. Strengthened environmental regulation can enlarge the green export scale of private-owned, foreign-invested, and small-scale enterprises. These findings provide policy implications for combining differentiated environmental regulations with green trade incentives to promote green trade development. Full article

With the in-depth advancement of the “Digital China” initiative, policies and research discourses related to the digital economy have continued evolved, making it necessary to systematically examine their stage-specific characteristics and underlying logic from a long-term perspective. Accordingly, this study adopts information society theory as the analytical framework and selects the annual series of reports on China’s digital economy development published by the China Academy of Information and Communications Technology (CAICT) from 2015 to 2024 as the research corpus. Using text mining techniques and Latent Dirichlet Allocation (LDA) topic modeling, this paper conducts a longitudinal examination of the stage-wise systemic evolution of key topics in China’s digital economy development. The findings indicate that over the past decade, the topic structure of China’s digital economy has followed a clear evolutionary trajectory, progressing from “informatization-driven development” to “platform expansion,” and subsequently to “data factors and institutional governance.” In the early stage, the focus was on information infrastructure development and industrial integration; the middle stage shifted toward the platform economy and enterprise growth; more recently, the emphasis has increasingly been placed on the construction of data factor markets and the improvement of governance frameworks. This process of topic evolution not only reflects changes in the practical forms of the digital economy but also reveals the ongoing adjustment of the state’s cognitive framework and governance logic regarding digital economy development. These findings provide empirical evidence for understanding the systemic evolution of China’s digital economy over time. By identifying the stage-specific pathways of China’s digital economy, this study extends the application of information society theory within this context and provides new empirical evidence for understanding the evolutionary logic underlying high-quality digital economy development. Full article

Digital Payment Tools (DPTs) are increasingly promoted as catalysts for financial inclusion, operational efficiency, and sustainable growth among Small and Medium Enterprises (SMEs) in developing economies such as Bangladesh. However, prior research predominantly treats DPT adoption as a static decision, offering limited insights into how pre-adoption perceptions evolve into post-adoption satisfaction and continued use, particularly from a sustainable development perspective. Addressing this gap, this study aims to examine the determinants of SME entrepreneurs’ satisfaction (SAT) and continued intention to use (CIU) DPTs by integrating the Technology Acceptance Model (TAM) and the Post-Adoption Model (PAM), with SAT and sustainability attitude (SUS) positioned as a mediating mechanism linking digital payment usage to long-term sustainable behavior. Data were collected from 219 SME entrepreneurs in Bangladesh and analyzed using Structural Equation Modeling. Results reveal that Perceived Ease of Use (PEOU) directly impacts Perceived Usefulness (PU) and SAT but does not impact CIU. Although PU does not influence CIU, it has a significant impact on SAT, which in turn influences CIU. Additionally, SAT fully mediates the relationship between PEOU and PU with CIU. Moreover, PEOU and PU have a significant impact on SAT through the partial mediation of SUS, while SUS fully mediates the relationship between PU and CIU. By bridging pre-adoption and post-adoption perspectives and embedding sustainability within the digital payment adoption framework, this study advances understanding of how DPTs contribute to sustainable SME development in emerging economies. The insights offer practical implications for Fintech developers and policymakers aiming to enhance long-term adoption and impact of DPTs in emerging economies. Full article

This study examines the drivers and challenges/barriers faced by built-environment professionals in applying circular economy (CE) principles within Maiduguri, Nigeria’s housing delivery system, a city recovering from prolonged conflict. Using a mixed-method approach, including a literature review, an interview and a questionnaire administered to construction professionals (n = 188), the research assesses awareness and practical implementation. Key drivers for CE adoption include regulatory incentives, increased research funding, potential cost savings, and rising environmental awareness. Major barriers, however, consist of limited technical expertise, weak policy enforcement, and financial constraints. The analysis also reveals significant gaps in on-site waste management and resource recovery practices. To address these issues, this study recommends targeted capacity-building programmes, stronger policy frameworks, and enhanced multi-stakeholder collaboration. Small and Medium Enterprises (SMEs) should be supported to venture into engineering waste recycling and management. These measures aim to promote core CE practices, such as waste minimisation, reuse, recycling, and remanufacturing within the construction industry, aligned with Sustainable Development Goal (SDG) 11 (Sustainable Cities and Communities). The research concludes that integrating CE strategies can foster sustainable housing development in Maiduguri, supporting environmental protection, socio-economic growth, and increased resilience of the built environment in post-conflict contexts. Full article

This study examines how heritage-based small and medium enterprises (SMEs) cope with economic shocks and institutional constraints in a semi-urban context. The study focuses on identifying context-specific barriers that shape SME growth and sustainability in heritage-based, semi-urban settings. Using a mixed-methods design, survey data from 200 SMEs were analyzed with PLS-SEM, and 20 semi-structured interviews were examined through thematic analysis, collected in Kashan, Iran (March–May 2025). We find that inflation and limited access to finance are the primary barriers to firm growth, followed by regulatory delays and administrative complexity. Qualitative findings reveal five recurring adaptive routines, short-cycle cash management, cooperative input purchasing, product simplification/micro-pivoting, reliance on local networks, and minimalist digitalization, that operate in a discernible temporal sequence to sustain firm continuity. By integrating resource-based and institutional perspectives, the paper advances meso-level theorizing on SME resilience and proposes a set of low-cost, actionable policy measures (e.g., streamlined e-licensing, targeted mobile microfinance, and buyer–supplier matchmaking) for local authorities and development practitioners. Full article