Journal of Hunan University Natural Sciences

This study investigates the relationship between environmental, social, and governance (ESG) disclosure, corporate governance mechanisms, and firm value in listed companies. As capital markets increasingly integrate non financial information into valuation processes, ESG disclosure and governance quality have become critical signals for investors. Using panel data from publicly listed firms and employing panel regression techniques, this research examines whether ESG disclosure and corporate governance enhance firm value, measured by Tobin’s Q. The findings provide empirical evidence that ESG disclosure has a positive and significant effect on firm value. Furthermore, corporate governance mechanisms not only directly enhance firm value but also strengthen the valuation effect of ESG disclosure. These results support signaling theory and agency theory, suggesting that transparent ESG reporting and effective governance reduce information asymmetry and agency conflicts, thereby increasing market valuation. The study contributes to the corporate finance and sustainability literature by integrating ESG disclosure and governance perspectives in explaining firm value, with important implications for managers, investors, and policymakers.

Keywords: Environmental, Social, and Governance (ESG) Disclosure; Corporate Governance Mechanisms; Firm Value; Tobin’s Q; Panel Data Analysis.

DOI https://doi.org/10.55463/issn.1674-2974.53.4.4

This study examines the role of artificial intelligence (AI) adoption in enhancing innovation and competitiveness among small and medium-sized enterprises (SMEs) operating in the creative industries of the Special Region of Yogyakarta, Indonesia. Despite the growing strategic importance of AI for SMEs, empirical evidence from developing economies remains limited, particularly regarding how institutional and organizational factors jointly shape AI-driven competitiveness. To address this gap, this study proposes an integrative model that positions government support, organizational capacity, and inter-firm collaboration as antecedents of AI adoption. Data were collected from creative industry SMEs through a structured questionnaire. The measurement instrument captured organizational capacity, collaboration, government support, AI adoption, innovation, and competitiveness. Data analysis was conducted using Partial Least Squares–Structural Equation Modeling (PLS-SEM). The results indicate that both organizational capacity and collaboration play critical roles in facilitating AI adoption among creative SMEs. Government support emerges as a key enabling factor by strengthening internal capabilities and fostering collaborative networks. Furthermore, AI adoption is found to significantly stimulate product and process innovation, which in turn enhances SME competitiveness. Innovation functions as an essential mechanism through which AI adoption translates into superior competitive outcomes rather than acting as an isolated technological investment. The study concludes that AI adoption should be understood as a strategic capability embedded within a broader ecosystem of organizational readiness, collaboration, and policy support. For creative SMEs, effective AI utilization requires coordinated efforts across technological, organizational, and institutional dimensions to generate sustainable innovation and long-term competitive advantage.

Keywords: Artificial Intelligence Adoption; Small and Medium-Sized Enterprises (SMEs); Creative Industries; Innovation Performance; Firm Competitiveness; Developing Economies.

DOI https://doi.org/10.55463/issn.1674-2974.53.4.3

This study investigates the dual role of cocoa as both a cultural resource and an economic asset within the Dayak Kenyah community in Lung Anai, East Kalimantan, Indonesia. Existing research on cocoa in Indigenous contexts in Kalimantan remains limited, with most studies focusing on broader agrarian issues, particularly oil palm and coal extraction.
Adopting an ethnographic approach based on field observations and in-depth interviews, this study demonstrates that cocoa extends beyond its function as a market commodity. It is embedded in cultural practices that shape collective identity, social relations, and local ecological knowledge. Cocoa cultivation and consumption are closely linked to ritual activities and community gatherings, reinforcing social cohesion and the community’s relationship with nature.
The study further examines the socio-economic implications of Indonesia’s new capital development, Ibu Kota Nusantara (IKN), which introduces both opportunities and challenges for the Dayak Kenyah community. These include pressures on land rights, shifts in livelihood strategies, and processes of cultural and identity transformation.
The findings contribute to interdisciplinary discussions on Indigenous economies, cultural sustainability, and rural transformation, highlighting the importance of integrating local knowledge systems into national development agendas.

Keywords: Cocoa production; Dayak Kenyah community; indigenous livelihoods; cultural sustainability; rural transformation; Ibu Kota Nusantara (IKN).

DOI https://doi.org/10.55463/issn.1674-2974.53.3.13

Concrete bridge crack detection plays a critical role in intelligent infrastructure inspection and structural safety assessment. However, existing automated approaches still face significant challenges, including the high miss rate of fine cracks, strong interference from complex backgrounds, and insufficient boundary delineation accuracy.
To address these limitations, this study proposes a novel two-stage collaborative detection–segmentation framework that integrates an enhanced YOLOv11 detector with a dual-branch DualUNet architecture. The framework follows a “coarse localization–guided fine segmentation” strategy. In the detection stage, YOLOv11 is improved by incorporating a P2 high-resolution feature layer and a C2PSA attention module, enabling accurate localization of fine cracks while effectively suppressing background noise and generating reliable region-of-interest (RoI) priors.
In the segmentation stage, a dual-branch DualUNet with a shared ResNet34 encoder is developed. The global branch captures contextual semantic information from the full image, whereas the RoI branch refines local crack features by integrating detection priors with a Multi-Scale Squeeze-and-Excitation (MSSE) module. This design enhances the representation of fine structural details and mitigates the limitations of single-stage segmentation methods.
To address the severe class imbalance caused by sparse crack pixels, a hybrid loss function combining weighted binary cross-entropy and Dice loss is adopted. Additionally, a boundary supervision mechanism is introduced to improve contour accuracy.
Experimental results on the Crack500 dataset demonstrate that the proposed framework achieves superior performance, with recall and precision reaching 0.79 and 0.75, respectively. Compared with baseline models, the proposed method improves Boundary IoU by 26%, indicating significantly enhanced edge delineation. Visual results further confirm that the method effectively suppresses background interference while preserving crack continuity, making it suitable for practical bridge inspection applications.

Keywords: Concrete crack detection; Bridge inspection; YOLOv11; Dual U-Net; Attention mechanism; Boundary segmentation.

DOI https://doi.org/10.55463/issn.1674-2974.53.4.2

This study examines the relationship between inventory optimization and cost reduction within strategic logistics management, with the aim of identifying how effective inventory practices enhance operational efficiency and reduce overall logistics costs. In the context of increasing pressure for cost efficiency, the study addresses a gap in the literature concerning the integrated role of inventory management in achieving both cost savings and service performance improvements.
A structured literature review was conducted, drawing on academic publications, industry reports, and case studies. The analysis focuses on inventory optimization techniques, cost reduction strategies, and emerging technological solutions, using a thematic synthesis approach to identify key patterns and relationships.
The findings demonstrate that inventory optimization contributes significantly to reducing warehousing and transportation costs through improved inventory turnover and minimized holding levels. The adoption of advanced technologies, including AI-driven forecasting and RFID systems, further enhances inventory accuracy and operational performance. In addition, optimized inventory systems improve customer service outcomes by increasing order fulfillment rates and reducing stockouts. However, the study also identifies key challenges, particularly high implementation costs and organizational resistance to change.
This study contributes to the literature by providing an integrated perspective on the role of inventory optimization in cost efficiency and logistics performance. The findings highlight the importance of aligning inventory management practices with broader supply chain strategies. Future research should further examine the long-term impact of digital technologies on inventory optimization and cost structures.

Keywords: Inventory optimization; Logistics cost reduction; Supply chain management; Artificial intelligence; RFID; Operational efficiency.

DOI https://doi.org/10.55463/issn.1674-2974.53.1.14