Impact of Network Structures and Deep Learning on Financial Performance in Buyer-Supplier Networks

Zhang Y., Siau K.

Metaverse entrepreneurship has emerged as an innovative topic alongside the development of generative AI, agentic AI and metaverse. This study conceptualizes meta-entrepreneurship as a novel form of entrepreneurial activity that enables value creation within virtual and physical realms and proposes an analytical theoretical framework based on a systematic literature review, observations, and focus group study. Our framework is structured around three layers (infrastructure, content, and experience) and two domains (metaverse-based operational domain and AI-based production domain), aims to conceptualize “what is meta-entrepreneurship” and identify new possibilities. The research highlights the multifaceted impact of meta-entrepreneurship on individuals, corporations, industries, societies, and countries. Further, we delineate three pathways to achieve meta-entrepreneurship, analyze nine critical challenges, and propose potential future research directions to contribute to the theoretical foundation of this emerging field.

Huang A., Zhuang J., Ren Y., Rao Y., Tsai S.

Supply chain management (SCM) is pivotal in orchestrating the flow of goods and services from suppliers to consumers, fundamentally shaping business operations worldwide. However, traditional SCM faces significant limitations, such as inefficiencies in handling complex data structures and adapting to rapid market changes, which undermine operational effectiveness. The application of deep learning technologies in SCM is increasingly recognized as crucial, offering powerful tools for real-time visibility, predictive analytics, and enhanced decision-making capabilities. We propose a VAE-GNN-DRL network model that integrates Variational Autoencoder (VAE), Graph Neural Network (GNN), and Deep Reinforcement Learning (DRL) to address these challenges by efficiently processing and analyzing complex supply chain data.

Li M., Lin B.

China's robust renewable energy sector expansion has spurred academic inquiry into the underlying driving forces. Recent studies have highlighted the correlation between the growth of renewable energy and project financing and the pivotal role of information management in financing. Based on this, we use business data from Chinese listed companies from 2007 to 2021 to investigate the relationship between renewable energy business development and financing. Furthermore, we introduce social and enterprise information to explore their impact on this relationship. Our findings reveal that: (1) Expansion of renewable energy businesses enhances fund availability mainly through endogenous financing. (2) “Anti-dumping and countervailing” (“Double-anti”) investigations significantly hinder financing for expanding renewable energy businesses, while industrial policies mitigate these effects. (3) Complex international backgrounds combined with domestic market-government interactions promote renewable energy enterprises to apply information management technology for financing.

Zhou Y., Xu Y., Wang Q.

In the process of manufacturing supply chain development, because appropriate governance mechanisms are lacking, manufacturing supply chain stability, integration and other aspects face many challenges. Such as opportunism, “free riding” and data leakage affect supply chain development. Thus, many manufacturing firms are adopting supply chain relationship governance (SCRG) as a strategic to enhance performance. Using data collected from a survey of 295 manufacturing firms, this paper confirms the influence of SCRG on supply chain performance (SCP). The mediating effect of supply chain integration (SCI) and the moderating effect of digital capabilities (DCs) are explored. The results show that SCRG has a beneficial effect on SCP. SCI plays a partial mediating role in SCRG and SCP. DCs positively moderate the relationship between SCRG and SCI. This study is one of the first to explore the role of DCs in the relationship between supply chain partners and the impact on performance. It provides fresh perspectives and real-world evidence for determining the importance of SCRG strategies.

Mohiuddin M., Karuranga E., Cao Y.

This study recommends a suitable model for evaluating supply chain collaboration in the natural forest products industry. We follow a two-step analysis: The first-order measurement model is leveraged to assess collaboration level, and the second-order confirmatory factor analysis develops the collaboration level by using four indicators representing customer and supplier firms as well as two specific indicators for each of them. Four items are common practices for both sides: joint sales forecasting, exchange of basic information, joint planning, and joint delivery improvement. Two practices are highly oriented toward customers: resource sharing of logistics assets and exchange of performance evaluation. Business-to-business practices engaged mostly with suppliers include the implementation of replenishment systems and joint new product development. Collaboration measurement between suppliers and client firms contributes to effectively manage the relationship between the supplier and client firms and can improve the competitiveness of participating firms in the network.

Li P., Yi X., Zhang C., Baležentis T.

Although shadow banking widely exists in the financial systems of various countries, their definitions vary significantly due to specific economic and financial characteristics. This paper classifies Chinese shadow banking into six categories: securities, trust, private lending, banking, fund, and insurance. The AR-GARCH-DCC model is used to measure systemic risk spillover through from an industrial and institutional perspective. The network topology index is employed to analyze risk contagion and further explore influencing factors. Firstly, based on the results of the AR-GARCH-DCC, the estimated dynamic volatility (σ) indicates that shadow banking risk spillover is time-varying, especially in trust and securities. Second, according to the static risk spillover analysis, various institutions play different roles and can transform between risk spillovers and overflowers. Thirdly, eigenvector centrality, leverage, assets, CPI, and macroeconomic prosperity significantly impact shadow banking systemic risk spillover.

Lee D., Kim J., Song S., Kim K.

Discovering sustainable business partnerships is crucial for small and medium-sized companies, where they can realize potential value through operational resources and abilities. Prior studies have mostly focused on predicting and developing new business partners using various machine learning techniques or social network analyses. However, effectively estimating potential benefits from business partnerships is much more valuable to companies. Therefore, this study proposes a method which combines deep learning and network analyses to estimate the potential value of business partnerships for companies. To demonstrate the effectiveness of the proposed method, we expand business partnerships between companies and assess potential value derived from the parenthesis using business transaction data collected from the Republic of Korea. The results suggest that companies can gain more potential value from extended networks when compared to previous ones. Furthermore, potential value results show clear distinctions between industries. Our findings provide evidence that small and medium-sized companies can experience significant benefits by establishing adequate business partnerships.

Xiao Y., Chen Y., Tan J., Cifuentes-Faura J.

The acquisition of innovation performance through social network relationship resources is a common behavior pattern of organizational members. Recent social network research suggests that negative ties may also have a positive impact on organizational innovation compared with positive ties. Based on this, the paper investigates the impact of dissonant tie, which are a combination of problem-solving tie and difficult working tie, on organizational innovation. The empirical results show that dissonant tie promotes organizational innovation performance and are enhanced by digital sensing and digital seizing, while the increasing effect of the dissonant tie on organizational innovation performance is not verified under the moderating effect of digital reconfiguring. The findings are useful for understanding why certain negative ties may promote organizational innovation and performance, and to provide a theoretical basis for how manufacturing companies can use complex social network ties to survive organizational change and enhance organizational adaptability in digital transformation.

Ji X., Su C.

This article aims to optimize the supply chain financing model and address virtual economic risk control by effectively reducing associated risks. To achieve this objective, the backpropagation (BP) neural network model is designed and implemented, promoting the application of intelligent technology in supply chain financing and virtual economic risk control. Initially, a fundamental BP neural network model is developed and evaluated. Subsequently, an Adam-BP neural network model is proposed by optimizing the Adam optimizer, providing substantial technical support for enhancing the supply chain financing model and virtual economic risk control. The research results indicate significant performance improvement after applying Adam optimization to BP, with all indicators in the plant classification dataset surpassing 0.92 and those in the credit card fraud dataset increasing to above 0.9. Thus, the model presented here exhibits exceptional adaptability and offers effective technical support for optimizing the supply chain financing model and virtual economic risk control methods.

Lin S., Chang T., Hsu M.

This research presents the contribution of profitability and asset utilization to a firm's value generation from a financial market viewpoint via slack-based measure network data envelopment analysis (SBM-NDEA). Despite its superiority in performance measurement, SBM-NDEA has a limitation when confronted with new added datasets as it lacks predictive ability. To overcome this, the authors integrate twin support vector machine into it. A manager's attitude toward risks also plays an essential role in efficiency improvement and value generation, but numerical messages do not convey such information. Textual messages with elastic natures thus bring information beyond just numerical messages. To assist users in quantifying risk types, the authors introduced an advanced text analyzer to conjecture a manager's attitude toward each risk. The results show that the performance evaluation model with forecasting capability can shift the manager's role from monitoring the past to planning the future. This study also demonstrates that the model with textual information reaches superior forecasting performance.

Luo P., Ngai E.W., Cheng T.C.

PurposeThis paper examines the relationship between supply chain network structures and firm financial performance and the moderating role of international relations. In this study, which is grounded in social capital theory and applies the perspective of systemic risk, the authors theorize the effects of supply chain network structures on firm performance.Design/methodology/approachThe authors extracted data from two Chinese databases and constructed a supply chain network of the firms concerned based on nearly 4,300 supply chain relations between 2009 and 2018. The authors adopted the fixed effects model to investigate the relationship between supply chain network structures and firm financial performance.FindingsThe econometrics results indicate that network structures, including the degree, centrality, clustering coefficients and structural holes, are significantly related to firm financial performance. A significant and negative relationship exists between international relations and firm financial performance. The authors also find that international relations strongly weaken the relationship between supply chain network structures and firm financial performance.Originality/valueThis study, which collects secondary data from developing countries (e.g. China) and explores the impacts of supply chain network structures on firm stock performance, contributes to the existing literature and provides practical implications.

Li L., Yang S., Chen N.

Taking China's A-share listed manufacturing enterprises from 2014 to 2020 as objects, this paper discusses the impact and mechanism of corporate digital transformation on supply chain relationship transactions from the perspectives of information asymmetry and agency costs. The findings show that digital transformation significantly inhibits the supply chain relational transactions; the mechanism testing results reveal that digital transformation is conducive to the alleviation of information asymmetry and agency costs, which thereby reduces the degree of supply chain relational transactions; the regulatory effect analysis demonstrates that the impact of digital transformation on supply chain relationship transactions becomes more significant in non-high-tech enterprises and enterprises with less fierce industry competition. Finally, this paper confirms that a decline in the proportion of supply chain relationship transactions can significantly reduce the operational risk of enterprises.

Garcia F., Grabot B., Paché G.

Digitalization in the supply chain involves important transformations in the relationships between supply chain partners. Studies investigating these transformations underline the need to create and share new knowledge processes in order to achieve successful digital projects that improve integration and collaboration in the supply chain. This article proposes to make a parallel between the stages of the SECI model concerning knowledge conversion and the different phases of a Supply Chain 4.0 project. By using a longitudinal case study, the authors fill the gap on the dynamics of knowledge transformation in supply chain digital projects. Based on a supplier portal project, the case study presented helps to understand how to ensure that the different partners will get fully involved in the digital project. It provides five managerial recommendations for companies that wish to commit to supplier development and knowledge sharing through digital projects.

Khan M.M., Bashar I., Minhaj G.M., Wasi A.I., Hossain N.U.

Lee D., Kim K.

• We suggest a method for automatically finding potential business partners. • Deep learning is applied to capture transaction patterns between companies. • The proposed model is effective to identify potential business partners. • Recommendation performances of the proposed model superior to conventional ones. Potential business partner (BP) recommendation is one of the most important issues for companies to increase sales opportunities by discovering new candidate buyers. Recommendation at a low cost and automation is especially essential for small and medium businesses. However, identifying potential BPs has been regarded as a challenging task since not only an analysis of business characteristics for each candidate company by human experts is required but also an investigation of all the possible combinations of their matchings is necessary. Therefore, in this paper, we propose novel BP recommendation models, called deep business partner recommendation (DBR) models, that aim to automatically suggest potential BPs. Specifically, deep learning technique is applied to understand hidden transaction patterns between companies with various industrial sectors and product properties through the two-phases involving i) BP relation representation phase and ii) training and testing phase. In the former, for each company, its features including the industrial sector, product property, relative transaction volume, and geographical distance are embedded into a vector for utilizing as the input of the proposed models. In the latter, the proposed DBR models repeatedly use the input values to capture the hidden transaction patterns between companies in the training. In the testing phase, the extensive experiments conducted to evaluate the BP recommendation performances of the proposed DBR models using a real-world dataset consisting of transaction records among companies in South Korea. The experiment results show that the suggested DBR models significantly outperform the conventional models, in terms of the accuracy for the BP recommendation tasks.