The Impact Mechanisms of Data Assetization on Corporate Supply Chain Efficiency

다국어 초록 (Multilingual Abstract)

As an essential production factor and strategic resource of enterprises, data assetization has been endowed with new value and functions in the current wave of digital economy. By systematically integrating, confirming ownership, and realizing the value of decentralized data, data assetization not only helps enterprises reduce costs and improve efficiency internally and optimize processes but also plays a role in external markets by enhancing creditworthiness and creating value, thus providing potential momentum for supply chain optimization. Unlike traditional production factors, data assetization can simultaneously influence three dimensions: information transmission, resource allocation, and financial credit, thereby providing solid support for quality improvement and efficiency enhancement in enterprise supply chain operations. However, most existing studies focus on accounting treatments, capital market performance, or macroeconomic effects of data assetization, with relatively less attention to its micro-mechanisms and heterogeneity at the enterprise level in improving supply chain efficiency. Based on this, this paper systematically analyzes the direct impact of data assetization on enterprise supply chain efficiency and its potential pathways from the perspectives of enterprise operation and supply chain management.
This study uses data from publicly listed companies in China's Shanghai and Shenzhen A-shares over the past decade (2014-2024) to comprehensively examine the influence of data assetization on supply chain efficiency. The research first verifies that data assetization can directly enhance supply chain performance, and further mechanistic analysis reveals that the effects of data assetization exhibit multiple dimensions: First, by alleviating financing constraints, it shortens raw material price lock-in cycles and shifts toward “on-demand procurement,” thereby reducing inventory turnover days. This process not only lowers external financing costs but also improves internal cash flow, helping enterprises optimize upstream and downstream payment periods and comprehensively enhance supply chain efficiency; Second, by improving enterprise adaptability, it enables resource allocation and rapid responses in complex environments characterized by demand fluctuations, technological iterations, or policy shocks; Third, by strengthening innovation capacity, it guides enterprises to identify high-potential R&D directions through data analysis and to leverage data pledges or data trading to acquire external capital, thus increasing R&D intensity without adding fixed assets, driving product and process iterations, and consequently improving supply chain performance; Fourth, by optimizing supply-demand matching and achieving real-time alignment between production scheduling and market demand, it reduces inventory turnaround cycles and supply-demand deviations, thereby significantly improving overall supply chain efficiency. These results demonstrate that data assetization not only exerts a direct promoting effect but also enhances supply chain operation performance through complex indirect mechanisms.
Further heterogeneity analysis indicates that the promoting effects of data assetization on supply chain efficiency vary significantly across different types of enterprises. Large firms, leveraging comprehensive digital infrastructure, cross-enterprise data interfaces, and strong bargaining power within supply chains, can fully realize the efficiency dividends brought by data assetization. In contrast, small and medium-sized enterprises (SMEs) with weaker digital endowments and lacking cross-organizational data collaboration capabilities have difficulties efficiently embedding high-value data into supply chain networks, limiting their effectiveness. Although specialized and innovative SMEs possess technological breakthroughs in niche areas, they are still constrained by “industry barriers” and “scale thresholds”: core enterprises controlling data interfaces, security certification, and governance standards create invisible barriers, making it difficult for SMEs to embed high-value data into upstream and downstream collaborative networks. The characteristic of light assets and fragile cash flows further restrict the implementation of data pledges and supply chain finance products, preventing digital investments from converting into liquidity and production capacity flexibility. These realities further illustrate that the value realization of data assetization in enhancing supply chain efficiency is not universal but deeply linked to firms’ digital capabilities and scale conditions.
In conclusion, this paper reveals the direct effects and multi-dimensional indirect pathways through which data assetization promotes supply chain efficiency at the microenterprise level, and identifies structural constraints across different enterprise types, enriching the intersection of data factor economics and supply chain management research. The findings have significant practical implications: policymakers should improve data ownership rights, platform access standards, and risk compensation mechanisms to help SMEs overcome industry and scale barriers; financial institutions should innovate supply chain finance products based on data assets to enhance firms’ access to funding and liquidity; enterprises should strengthen data governance and cross-enterprise collaboration capabilities to improve the tradability and applicability of data assets. Only through coordinated efforts among government, finance, and enterprises can the potential value of data assetization be fully unleashed, promoting the development of enterprise supply chains toward higher efficiency, agility, and resilience.

국문 초록 (Abstract)

본 연구는 2014년부터 2024년까지의 중국 상하이·선전 A주 상장기업 데이터를 연구 대상으로 하여, 데이터 자산화가 공급망 효율성에 미치는 영향을 실증적으로 검토하였다. 연구 결과, 데이터 자산화는 기업의 공급망 효율성을 직접적으로 향상시킬 뿐 아니라, 다음과 같은 다차원적 경로를 통해 간접적 영향을 미치는 것으로 나타났다.첫째, 자금조달 제약 완화를 통해 원자재 고정가격 계약 기간을 단축하고, 수요 기반의 구매 구조로 전환함으로써 재고 회전일수를 감소시킨다. 이 과정은 외부 자금조달 비용을 절감하고 내부 현금흐름을 개선하여, 상·하류 거래 조건을 최적화하고 전반적인 공급망 효율성을 향상시킨다.둘째, 데이터 자산화는 기업의 적응 능력을 강화하여 수요 변동, 기술 혁신, 정책 충격 등 복합적인 환경 변화 속에서도 자원의 유연한 배분과 신속한 대응을 가능하게 한다.셋째, 혁신 역량을 제고함으로써 데이터 분석을 기반으로 유망한 연구개발 방향을 식별하고, 데이터 담보나 데이터 거래를 통해 외부 자금을 유입시켜 고정자산 부담을 증가시키지 않으면서 연구개발 강도를 높인다. 이는 제품 및 공정 혁신을 촉진하여 공급망 성과를 개선하는 역할을 한다.넷째, 수요와 공급의 정합도를 최적화하여 생산 일정과 시장 수요를 실시간으로 연계함으로써, 재고 회전 주기와 수요-공급 괴리를 완화하고 공급망 전체의 효율성을 현저히 향상시킨다.이상의 결과는 데이터 자산화가 직접적인 촉진 효과뿐 아니라 복합적인 간접 메커니즘을 통해 공급망 운영 성과를 개선함을 보여준다.
이질성 분석 결과, 데이터 자산화의 공급망 효율성 제고 효과는 기업 유형에 따라 현저한 차이를 보였다. 대기업은 완비된 디지털 인프라, 기업 간 데이터 인터페이스, 높은 공급망 협상력을 바탕으로 데이터 자산화의 효율성 효과를 충분히 실현할 수 있다. 반면, 중소기업은 디지털 역량이 부족하고 기업 간 데이터 협력 능력이 제한되어 있어, 데이터 자산이 공급망 네트워크에 효율적으로 내재화되기 어렵다.
특히 ‘전문화·정밀화·특성화·혁신화(专精特新)’ 중소기업의 경우, 세분화된 분야에서 기술 혁신의 우위를 지니고 있음에도 불구하고, ‘산업 장벽’과 ‘규모 한계’라는 이중 제약에 직면해 있다. 핵심 기업이 보유한 데이터 인터페이스, 보안 인증, 거버넌스 기준은 암묵적 장벽으로 작용하여 중소기업이 고가치 데이터를 상·하류 협력 네트워크에 포함시키기 어렵게 만든다. 또한 경량 자산 구조와 취약한 현금흐름은 데이터 담보 및 공급망 금융 상품의 실현을 제약하여, 디지털 투자가 자금 유동성과 생산 탄력성으로 신속히 전환되지 못하게 한다. 이러한 현실은 데이터 자산화의 공급망 효율성 제고 효과가 보편적 현상이 아니라, 기업의 디지털 역량과 규모 조건에 깊이 의존함을 시사한다.
종합적으로, 본 연구는 기업 미시 수준에서 데이터 자산화가 공급망 효율성을 향상시키는 직접적 영향과 다차원적 간접 경로를 규명하고, 기업 유형별 구조적 제약을 식별함으로써 데이터 요소 경제와 공급망 관리의 융합 연구를 풍부하게 하였다.
연구 결과는 다음과 같은 실무적 시사점을 제공한다. 첫째, 정책 입안자는 데이터 권리화, 플랫폼 접속 기준, 위험 보상 메커니즘을 정비하여 중소기업이 산업 및 규모의 장벽을 극복하도록 지원해야 한다. 둘째, 금융기관은 데이터 자산 기반의 공급망 금융 상품을 혁신하여 기업의 데이터 자산 금융 접근성과 유동성을 제고해야 한다. 셋째, 기업은 데이터 거버넌스와 기업 간 협력 역량을 강화하여 데이터 자산의 거래 가능성과 활용 가능성을 높여야 한다. 정부·금융·기업의 협력적 노력을 통해서만 데이터 자산화의 잠재적 가치를 충분히 실현하고, 기업 공급망이 효율적이고 민첩하며 회복력 있는 방향으로 발전할 수 있을 것이다.

목차 (Table of Contents)

• Ⅰ. Introduction 1
• 1. Research Background and Significance 1
• 2. Research Objectives and Questions 4
• 3. Research Contributions and Innovations 6
• Ⅱ. Literature Review 8
• 1. Theoretical Development and Practical Status of Data Assetization 8
• 2. The Data Resources Formation Phase 10
• 1）Stage of Data Commodification 10
• 2）Stage of Data Commodification 11
• 3) Stage of Data Assetization 13
• 3. Review of Research on the Factors Influencing Supply Chain Efficiency 15
• 1) Definitions Related to Supply Chain Management 15
• 2) Research on Supply Chain Efficiency 18
• Ⅲ. Theoretical Foundations and Research Hypotheses 21
• 1. Theoretical Foundations 21
• 1) Supply Chain Management Theory 21
• 2) Information Asymmetry Theory 23
• 3) Financing Constraint Theory 25
• 4) Dynamic Capabilities Theory 27
• 5) Resource Dependence Theory 29
• 2. Theoretical analysis and research hypothesis 31
• 1) Data Assetization and Enterprise Supply Chain Efficiency 31
• 2) The Role of Financing Constraint Mitigation in Digital Assetization and Supply Chain Efficiency 32
• 3) The Role of Corporate Adaptive Capabilities in Digital Assetization and Supply Chain Efficiency 34
• 4) The Role of Innovation Effects in Digital Assetization and Supply Chain Efficiency 36
• Ⅳ. Research Design 40
• 1. Data Sources 40
• 2. Variable Definitions 40
• 1) Dependent Variable 40
• 2) Core Explanatory Variable 41
• 3) Control Variables 43
• 3. Model Construction 45
• Ⅴ. Empirical Analysis 46
• 1. Descriptive Statistics 46
• 2. Correlation Analysis 47
• 3. Multicollinearity Analysis 49
• 4. Baseline Regression 50
• 5. Endogeneity Test 51
• 6. Robustness Tests 53
• 1) Propensity Score Matching (PSM) 53
• 2) Replace the Core Explanatory Variable 54
• 3) Lagged Explanatory Variable Test 55
• 7. Mechanism Tests 56
• 1) Alleviation of Financing Constraints 56
• 2) Enhancement of Adaptive Capacity 58
• 3) Enhancement of Innovation Capability 60
• 4) Optimization of Supply-Demand Matching in the Supply Chain 61
• 8. Heterogeneity 64
• 1) Regional Differences in Digitalization Levels   64
• 2) Industry Heterogeneity 66
• 3) Firm Size Heterogeneity 67
• 9. Further Analysis 69
• 1) Specialized, Innovative, and Small-to-Medium Enterprises (SMEs) 69
• 2) Government Public Data and DID Analysis 70
• 3) Parallel Trends Test 71
• 4) Placebo Test 72
• Ⅵ. Conclusion and Outlook of the Study  74
• 1. Research Conclusion 74
• 2. Policy Implications 76
• 3. Research Limitations 79
• 1) Limitations of the Study 79
• 2) Future Research Directions 79
• References 81
• 국문요지 99