The Optimization of Supply Chain Financing for Bank Green Credit Using Stackelberg Game Theory in Digital Economy Under Internet of Things

Zhang, Hui; Zhang, Fengrui; Gong, Bing; Zhang, Xuan; Zhu, Yifan

doi:10.4018/joeuc.318474

Cited by 15 publications

(9 citation statements)

References 37 publications

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“…Then the role of marketing capability in improving organizational performance using market knowledge (Louro, 2023). Adoption of analytics and adaptive capabilities in marketing can improve organizational performance by narrowing the marketing capability gap (Zhang et al, 2023). In addition, Customer and market performance are positively impacted by marketing dynamic capabilities.…”

Section: Dynamic Capability With Marketing Performancementioning

confidence: 99%

Relational Capital Can Improve Marketing Performance With the Mediation of Dynamic Capability: A Theory of Planned Behavior Perspective

Wibisono,

Febriyanti,

Marsuking

et al. 2024

JMUBH

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This study aims to determine whether relational capital can improve marketing performance with dynamic capability mediation. This research discusses relational capital with marketing performance with dynamic capabilities. SEM-PLS with Smart PLS 3.0 software is used in this study to concurrently assess the mediation effect. Through surveys, data from 200 MSMEs with a focus on the coffee industry were gathered. From January to August 2023, questionnaires were given to business managers or marketing teams in Sleman, Yogyakarta. Implications Particularly for coffee enterprises, the acceptance or rejection of hypotheses reveals management ramifications. First, organizations may be able to adjust to the environment more rapidly thanks to dynamic capability. Additionally, it can keep up with and enhance marketing. Third, it aids in the survival of app startups. In a pertinent way, Dynamic Capability conceptualizes both internal and external stakeholders.

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Section: Dynamic Capability With Marketing Performancementioning

confidence: 99%

Relational Capital Can Improve Marketing Performance With the Mediation of Dynamic Capability: A Theory of Planned Behavior Perspective

Wibisono,

Febriyanti,

Marsuking

et al. 2024

JMUBH

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“…Zhang et al [18] intend to improve small and medium-sized enterprises (SMEs) core competitiveness and financing attainability using the DL algorithm by economic globalization. Consequently, the study presents a supply chain symbiosis technique by using DL, economics, and Stackelberg game theory following a status quo analysis of the financing status of SMEs.…”

Section: Related Workmentioning

confidence: 99%

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Aljebreen,

Obayya,

Mahgoub

et al. 2024

IEEE Access

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Currently, there is an emerging requirement for applications related to the Internet of Things (IoT). Though the capability of IoT applications is huge, there are frequent limitations namely energy optimization, heterogeneity of devices, memory, security, privacy, and load balancing (LB) that should be solved. Such constraints must be optimised to enhance the network's efficiency. Hence, the core objective of this study was to formulate the intelligent-related cluster head (CH) selection method to establish green communication in IoT. Therefore, this study develops a chaotic equilibrium optimizer-based green communication with deep learning-enabled load prediction (CEOGC-DLLP) in the IoT environment. The study recognizes the emerging need for IoT applications and acknowledges the critical challenges, such as energy optimization, device heterogeneity, memory constraints, security, privacy, and load balancing, which are essential to enhancing the efficiency of IoT networks. The presented CEOGC-DLLP technique mainly accomplishes green communication via clustering and future load prediction processes. To do so, the presented CEOGC-DLLP model derives the CEOGC technique with a fitness function encompassing multiple parameters. In addition, the presented CEOGC-DLLP technique follows the deep belief network (DBN) model for the load prediction process, which helps to balance the load among the IoT devices for effective green communication. The experimental assessment of the CEOGC-DLLP technique is performed and the outcomes are investigated under different aspects. The comparison study represents the supremacy of the CEOGC-DLLP method compared to existing techniques with a maximum throughput of 64662 packets and minimum MSE of 0.2956.

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“…By increasing the loan utilization rate and lowering the loan utilization threshold, green credit will help promote green technology innovation. With the implementation of green credit policies, enterprises with strong environmental responsibility awareness, high environmental performance, and environmental friendliness will receive more loan funds, thereby creating conditions for their green development [35][36][37]. On the contrary, if a company has characteristics such as heavy environmental pollution, high pollutant emissions, and outdated environmental technology, it is not easy to obtain green credit [38][39][40].…”

Section: Mechanism Analysis and Empirical Testing 41 Mechanism Analysismentioning

confidence: 99%

Can Green Credit Improve the Innovation of Enterprise Green Technology: Evidence from 271 Cities in China

Mi,

Cui,

Zhu

et al. 2024

Systems

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With the promotion of the “carbon neutrality” and “carbon peak” initiatives, green credit plays an important role in helping enterprises to change their high-pollution, high-energy-consumption production methods and establishing a sound green, low-carbon, and circular economic system. This study used spatial correlation analysis and a fixed effects SDM model to examine the spatiotemporal and causal relationship between green credit levels and enterprise green technology innovation in 271 prefecture level cities in China from 2013 to 2021. It found that (1) green credit and green technology innovation levels are both highest in the eastern region, followed by the central region, and exhibit spatial correlation characteristics. The main types of agglomeration are high–high and low–low agglomeration. (2) Green credit has a significant enhancing effect on green technology innovation in enterprises, and this conclusion still holds after robustness and endogeneity tests. (3) There is significant regional heterogeneity in the impact of green credit on green technology innovation, mainly concentrated in the central and western regions. (4) Green credit can significantly increase enterprise R&D investment and enhance the level of green technology innovation through this channel. Finally, some policy implications are provided to the decision-making departments that can be used for reference.

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The Optimization of Supply Chain Financing for Bank Green Credit Using Stackelberg Game Theory in Digital Economy Under Internet of Things

Cited by 15 publications

References 37 publications

Relational Capital Can Improve Marketing Performance With the Mediation of Dynamic Capability: A Theory of Planned Behavior Perspective

Relational Capital Can Improve Marketing Performance With the Mediation of Dynamic Capability: A Theory of Planned Behavior Perspective

Chaotic Equilibrium Optimizer-Based Green Communication With Deep Learning Enabled Load Prediction in Internet of Things Environment

Can Green Credit Improve the Innovation of Enterprise Green Technology: Evidence from 271 Cities in China

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