Lead Firms Drive Digital Transformation and Value Co-Creation in Industrial Chains

Research Background

In the era of digital transformation, B2B e-commerce platforms have become pivotal in driving industrial change. This study by Jiayi Lu and Xinyi Chen from the School of Management at Nanjing University of Posts and Telecommunications explores how “chain leader” firms use these platforms to transform industrial chains and create new quality productive forces. The research addresses the gaps in existing literature, particularly in understanding the role of lead firms as enablers and ecosystem builders.

The digital age has brought about significant changes in how businesses operate, with B2B e-commerce platforms serving as a critical arena for fostering new productive forces. Unlike consumer internet, which focuses on individual users, B2B platforms aim to empower entire industries, enabling value creation and transformation. However, many small and medium-sized enterprises (SMEs) are hesitant to adopt digital technologies due to the challenges of leaving traditional paths or their limited industry influence. In contrast, core enterprises and integrated platforms, with their extensive customer base and strong market position, naturally become leaders and enablers of this transformation.

The study identifies three key theoretical gaps in current research:
1. A systematic lack of perspective on the role of “chain leader” firms as integrative hubs.
2. An outdated understanding of the “chain leader” role, failing to account for the transition from controllers to ecosystem builders and enablers.
3. Limited applicability of theories derived from B2C contexts to B2B industrial internet platforms.

To address these gaps, the authors selected three representative “chain leader” firms—Alibaba, Sany Heavy Industry, and Haier COSMOPlat—and used a multiple-case comparative analysis to explore the mechanisms of “chain leader-driven, digital enablement, and value co-creation.” The goal is to provide a comprehensive framework that explains how these mechanisms drive the formation of new quality productive forces.

The context of the supply chain and AI decision-making is crucial here. As industries move towards more data-driven and interconnected systems, the role of lead firms becomes even more significant. These firms not only facilitate the flow of information and resources but also set the standards and practices for the entire ecosystem. Prior approaches often focused on individual firm performance, neglecting the broader systemic impact. By examining the role of lead firms, this study aims to fill this gap and provide a more holistic view of digital transformation and value co-creation in industrial chains.

Key Findings

The study reveals three core mechanisms through which lead firms drive digital transformation and value co-creation: data-driven operations, network synergy, and intelligent decision-making. Each mechanism plays a crucial role in transforming business models and enhancing overall productivity.

Data-Driven Mechanism: Breaking Information Silos and Creating New Decision Paradigms

The data-driven mechanism involves leveraging data to break down information silos and create a new paradigm for decision-making. By integrating and analyzing data from various sources, lead firms can make more informed and efficient decisions. For example, Alibaba’s integration of e-commerce, finance, logistics, and cloud computing data creates a highly coordinated cross-business data ecosystem. This enables high-precision demand forecasting, which drives agile supply chains. Similarly, Sany Heavy Industry uses its Root Cloud platform to build an information system and digital factory, achieving operational intelligence and cost optimization.

90% of the data collected by Alibaba and Sany Heavy Industry is integrated into a central data platform, significantly improving decision accuracy and efficiency. The use of high-precision demand prediction models has led to a 37% increase in production efficiency, demonstrating the power of data-driven mechanisms in creating new quality productive forces.

The method principle behind the data-driven mechanism is the integration of diverse data sources into a unified platform. This allows for real-time analysis and decision-making, reducing the reliance on historical data and manual processes. The key design logic is to create a centralized data repository that can be accessed and utilized by various stakeholders within the ecosystem. The experimental setup involved collecting and integrating data from multiple business units, and the evidence shows a significant improvement in decision-making and operational efficiency.

Compared to related work, this study provides a more detailed and practical approach to data integration and utilization. While previous studies have highlighted the importance of data, they often lacked concrete examples and specific metrics. This study, by providing detailed case studies and quantifiable results, offers a more robust and actionable framework for data-driven operations.

Network Synergy Mechanism: Reconstructing Industrial Ecosystems and Enabling Value Co-Creation

The network synergy mechanism involves building an open and trustworthy digital industrial ecosystem. Lead firms, such as Haier COSMOPlat, connect diverse entities like suppliers, distributors, research institutions, and financial service providers in a single, highly coordinated digital space. This not only transforms the traditional linear organizational model but also shifts the competitive logic from zero-sum games to win-win scenarios.

Haier COSMOPlat, for instance, connects over 900,000 global enterprises, creating a comprehensive empowerment system. By integrating dealers and implementing unified strategies, the platform significantly reduces costs and uncertainties. It has also set 11 international standards, marking its evolution from an enabler to a rule-definer in the industry.

Empirical studies, such as those by Chen and Pan (2024), provide quantitative support for the benefits of network synergy. Their research shows that B2B e-commerce platforms can enhance export trade stability by diversifying markets, with a 15% increase in trade stability observed after the adoption of B2B platforms.

The method principle of the network synergy mechanism is to create a collaborative and interconnected ecosystem. The key design logic is to leverage the platform to integrate and coordinate various stakeholders, enabling them to work together more effectively. The experimental setup involved connecting and integrating different entities into a single digital space, and the evidence shows a significant reduction in costs and uncertainties, as well as an increase in trade stability.

Compared to related work, this study provides a more comprehensive and practical approach to network synergy. While previous studies have discussed the importance of collaboration, they often lacked specific examples and measurable outcomes. This study, by providing detailed case studies and quantifiable results, offers a more robust and actionable framework for network synergy.

Intelligent Decision-Making Mechanism: Optimizing Global Resource Allocation and Enhancing Supply Chain Resilience

The intelligent decision-making mechanism leverages advanced technologies like digital twins to create virtual models that interact with the physical world in real-time. This allows for simulation and optimization across the entire supply chain, shifting from experience-based to data and algorithm-driven decision-making.

In practice, intelligent decision-making enhances procurement, production, logistics, and commercial processes. For example, machine learning enables predictive maintenance and high-precision demand forecasting, while operations research optimizes procurement, production, and logistics plans. In the commercial domain, AI-driven sales predictions, dynamic pricing, and precise marketing strategies improve market efficiency. These applications result in a 20% increase in transaction conversion rates and a 18% improvement in customer retention on B2B platforms, enhancing the overall resilience of the supply chain.

The method principle of the intelligent decision-making mechanism is to use advanced technologies to simulate and optimize decision-making. The key design logic is to leverage digital twins and other advanced analytics to create a virtual model of the supply chain, allowing for real-time simulation and optimization. The experimental setup involved implementing these technologies in various stages of the supply chain, and the evidence shows a significant improvement in transaction conversion rates and customer retention.

Compared to related work, this study provides a more detailed and practical approach to intelligent decision-making. While previous studies have highlighted the importance of advanced technologies, they often lacked specific examples and measurable outcomes. This study, by providing detailed case studies and quantifiable results, offers a more robust and actionable framework for intelligent decision-making.

Limitations

While the study provides valuable insights, it also faces several limitations that need to be addressed. These include issues related to data standardization, the long return on investment for digital initiatives, and potential risks associated with platform monopolies and opaque algorithmic pricing.

• Data Standardization: The lack of unified data standards hinders the seamless integration of data across different entities. This can be mitigated by developing and adopting industry-wide data standards and protocols. For example, establishing a common data format and ensuring interoperability between different systems can help overcome this limitation. The impact of this limitation is significant, as it can lead to data silos and reduced efficiency. Mitigations include the development of standardized data formats and the promotion of data sharing agreements. The absence of standardized data formats can result in data fragmentation, making it difficult to derive meaningful insights and hindering the effectiveness of data-driven operations. To mitigate this, industry consortia and regulatory bodies can collaborate to develop and enforce common data standards, ensuring that data from different sources can be easily integrated and analyzed.
• Long Return on Investment: The extended period required to see returns on digital investments may deter some firms from fully embracing digital transformation. Government incentives and subsidies can help offset the initial costs and encourage broader adoption. The impact of this limitation is that firms may be hesitant to invest in digital initiatives, leading to slower adoption and missed opportunities. Mitigations include government grants, tax incentives, and public-private partnerships to share the financial burden. Additionally, lead firms can play a role in reducing the financial burden by offering shared services and infrastructure, thereby lowering the entry barrier for smaller firms. For example, Alibaba’s provision of SaaS tools and data platforms can help SMEs access advanced technologies without incurring high upfront costs.
• Platform Monopolies and Opaque Pricing: The risk of platform monopolies and non-transparent algorithmic pricing can distort value distribution. Regulatory oversight and transparency requirements can help ensure fair and equitable practices. The impact of this limitation is that smaller firms may be at a disadvantage, and the overall ecosystem may suffer from unfair practices. Mitigations include antitrust regulations, transparent pricing policies, and regular audits to ensure compliance. Ensuring transparency in pricing algorithms and promoting competition among platforms can help prevent monopolistic practices and ensure a level playing field for all participants. For instance, regulatory bodies can mandate the disclosure of pricing algorithms and conduct regular audits to ensure fairness and transparency.
• Regional Digital Divide: Disparities in digital infrastructure and access can limit the reach and effectiveness of digital transformation. Addressing this requires targeted investments in underdeveloped regions and policies to bridge the digital divide. The impact of this limitation is that certain regions may be left behind, leading to uneven economic development. Mitigations include government-led initiatives to improve digital infrastructure, training programs, and subsidies for technology adoption in underdeveloped areas. For example, governments can invest in broadband infrastructure and provide training programs to help SMEs in rural areas adopt digital technologies. This can help ensure that the benefits of digital transformation are distributed more equitably across different regions.
• Data Ownership and Interdepartmental Coordination: Unclear data ownership and poor interdepartmental coordination can hinder the implementation of digital strategies. Establishing clear guidelines and fostering collaboration between departments can help overcome these barriers. The impact of this limitation is that data may be misused or not fully utilized, leading to inefficiencies. Mitigations include the development of data governance frameworks, regular interdepartmental meetings, and the appointment of data stewards to manage data assets. Clear data ownership and governance policies can help ensure that data is used effectively and securely. For example, lead firms can establish data stewardship roles and implement regular interdepartmental meetings to ensure that data is managed and utilized in a coordinated manner.

Practical Implications

The study offers several practical implications for supply chain and AI practitioners, including the need for open and platform-based digital enablement systems, strategic integration of SMEs into digital ecosystems, and innovative financial services to support the transition.

• Open and Platform-Based Digital Enablement Systems: Lead firms should develop open, platform-based digital enablement systems. This includes packaging core capabilities as standardized SaaS services or industrial apps, establishing cross-organizational digital standards, and creating fair profit-sharing mechanisms. For example, Alibaba’s approach of providing SaaS tools and data platforms can lower the entry barrier for SMEs and enhance their efficiency. Specifically, lead firms can offer modular and scalable solutions that can be easily integrated into existing systems, making it easier for SMEs to adopt and benefit from digital technologies. By providing standardized SaaS services, lead firms can help SMEs access advanced technologies without the need for significant upfront investments. This can include tools for supply chain management, predictive analytics, and process automation, enabling SMEs to improve their operational efficiency and competitiveness.
• Strategic Integration of SMEs into Digital Ecosystems: SMEs should proactively integrate into the digital ecosystems led by lead firms. This involves accessing open SaaS tools and data platforms, focusing on specialized and unique capabilities, and strengthening data asset management. By becoming indispensable “specialized experts,” SMEs can gain more influence in value distribution. For instance, a small manufacturer can use data analytics to optimize production and negotiate better terms with lead firms and financial institutions. SMEs can also leverage the data and insights provided by the platform to improve their operational efficiency and competitiveness. By focusing on their core competencies and using data-driven insights, SMEs can differentiate themselves and become valuable partners in the digital ecosystem. This can involve developing niche products, optimizing production processes, and leveraging data to make informed decisions, thereby enhancing their value proposition and negotiating power.
• Innovative Financial Services: Financial institutions should innovate their service models to support the digital transformation. This includes developing data-driven credit assessment models, offering embedded financial services, and exploring blockchain-based financing solutions. For example, a bank can use B2B platform data to provide tailored financing options and automate the financing process, thereby reducing the reliance on traditional collateral and improving the accessibility of capital for SMEs. Additionally, financial institutions can collaborate with lead firms to offer integrated financial services, such as supply chain financing and trade finance, to support the entire ecosystem. By leveraging the data available on B2B platforms, financial institutions can develop more accurate and dynamic credit assessment models, enabling them to offer more flexible and tailored financing solutions. This can include short-term working capital loans, invoice financing, and trade finance, helping SMEs to manage their cash flow and invest in growth opportunities.
• Government Policy Support: Governments should focus on developing and supporting digital talent and infrastructure. This includes aligning educational programs with the needs of the digital economy, providing subsidies and incentives for digital adoption, and facilitating the exchange of knowledge and best practices. For example, governments can partner with universities and industry associations to develop training programs for digital skills, and offer grants and tax breaks to companies that invest in digital technologies. This will help create a supportive environment for digital transformation and ensure that the benefits are widely shared. By investing in digital infrastructure and education, governments can help bridge the digital divide and ensure that all regions and sectors have the opportunity to benefit from digital transformation. This can include initiatives to improve broadband access, provide training in digital skills, and offer financial incentives for the adoption of digital technologies.
• Collaborative Innovation and Knowledge Sharing: Lead firms, SMEs, and financial institutions should collaborate to foster innovation and knowledge sharing. This can be achieved through the establishment of innovation hubs, joint research and development projects, and the sharing of best practices. For example, lead firms can host innovation workshops and hackathons to bring together different stakeholders and generate new ideas. This will help create a vibrant and dynamic ecosystem that can adapt to changing market conditions and drive continuous improvement. By fostering a culture of collaboration and innovation, the entire ecosystem can benefit from the collective knowledge and expertise of its members. This can include initiatives to promote open innovation, such as hackathons, innovation challenges, and joint R&D projects, as well as the establishment of platforms for sharing best practices and lessons learned.

Source: https://doi.org/10.12677/ecl.2025.14124267