How the Sharing Economy Is Rescuing China’s Small Manufacturers From Digital Failure

Deep inside China’s vast manufacturing landscape, millions of small and medium-sized manufacturing enterprises are fighting a quiet battle for survival in the digital age. A new study published in Frontiers in Manufacturing Technology reveals just how steep that battle is, and offers a surprising weapon: the sharing economy. According to the research, led by Ji Xiaosheng of Hangzhou City University together with colleagues at Zhejiang University, the production capacity domain of China’s sharing economy reached 1.2548 trillion yuan in 2022, accounting for 32.75 percent of the entire sharing economy market and making it the second-largest sector by market size. That enormous pool of shared industrial capacity, the authors argue, may hold the key to unlocking digital transformation for the small firms that form the backbone of Chinese manufacturing.

The stakes could hardly be higher. More than 90 percent of all manufacturing enterprises in China fall into the small and medium-sized category, which means the digital fate of the entire sector rests on their shoulders. Yet official statistics paint a sobering picture. According to the Analysis Report on Digital Transformation of Small and Medium-sized Enterprises (2021) cited in the study, 79 percent of small and medium-sized enterprises remain in the exploratory stage of digital transformation, 12 percent have reached the practical stage, and only 9 percent have achieved deep application. In other words, the overwhelming majority of these firms have barely begun a journey that the researchers describe as a long-term, systematic project, one in which significant results typically materialize only after three to five years or longer.

Why do so many small manufacturers stall? The study identifies five interlocking barriers. First, leadership often lacks sufficient understanding of digital transformation; many executives are unsure why sensors should be installed on equipment or what industrial big data is actually for, and they prefer projects that deliver quick returns over multi-year restructuring of production methods, management models, and business models. Second, digital talent is desperately scarce. On average, digital-related personnel account for only 20 percent of total human resources in Chinese enterprises, and most of those specialists are concentrated in large firms that can offer generous salary packages that small enterprises simply cannot match. Third, the transformation foundation is weak: only 38 percent of small and medium-sized manufacturing enterprises have adopted identification technologies such as barcodes and RFID for data acquisition, 22 percent have integrated and shared key business systems, and a mere 4.5 percent use big data analysis to guide production. Fourth, transformation funds are insufficient, since small asset scales and chronic financing difficulties leave many firms unable to sustain the high costs of transformation through its middle and later stages. Fifth, and perhaps most culturally entrenched, many small manufacturers habitually go it alone, rarely collaborating or sharing resources with other enterprises, a legacy of workshop origins where inter-firm collaboration had little visible impact on profitability.

This pattern of isolation has produced what the researchers call information islands, where data and knowledge are not shared across departments, let alone across companies. With the rise of new-generation information and communication technologies such as artificial intelligence, big data, cloud computing, and blockchain, the authors contend that relying on going it alone is no longer realistic for sustainable profit growth. The millions of small and medium-sized enterprises that have gone bankrupt in the past three years, they note, are sobering examples of what happens when firms stick to old conventions in a rapidly digitizing economy.

The theoretical engine behind the study combines two classic frameworks from strategic management: the Resource-Based View and Dynamic Capabilities Theory. The Resource-Based View, rooted in Barney’s 1991 work on firm resources and sustained competitive advantage, holds that competitive advantage stems from access to valuable, rare, inimitable, and non-substitutable resources. Dynamic Capabilities Theory, developed by Teece and colleagues, focuses on a firm’s ability to sense, seize, and reconfigure resources in dynamic environments. The sharing economy, the authors argue, directly addresses both lenses. Platforms offering advanced technologies, specialized expertise, and data resources give small firms access to resources they could never possess internally, while real-time market information flowing through these platforms sharpens their sensing capabilities and their flexible models support rapid reconfiguration of operations.

To translate theory into practice, the study proposes three types of sharing economy platforms. The first is a chain-hosting shared manufacturing platform, built on leading enterprises, which improves collaboration efficiency across the upstream and downstream of an industrial chain, enabling sharing from raw material procurement and research and development design through production, logistics, marketing, and after-sales service. The second is an industry-brain-like collaborative sharing platform, constructed on industries within a region that have significant advantages, which leverages platform effects to drive the digital transformation of entire industrial clusters, particularly helping firms with weaker capabilities, while also providing services such as talent training and technological innovation. The third is a shared service cloud platform built on the industrial internet, which combines cloud computing with industrial internet interfaces to provide high-quality, low-cost microservices and computing power in a cloud-based manner, enabling small firms to go to the cloud and use the cloud without massive upfront investment. Complementing these platforms, the study describes the data middle platform, an enterprise-level sharing service hub that integrates data sources, establishes unified data standards and models, provides real-time decision support, promotes business innovation, and ensures data security, thereby breaking down the information barriers that have traditionally separated departments and systems.

The empirical heart of the paper is a case study of Company F, a small enterprise in the elevator industry specializing in sales, installation, and maintenance, which engaged with a shared service platform built by Company L, an industrial internet enterprise recognized by the Ministry of Industry and Information Technology as a specialized, refined, and innovative small giant. Company L’s platform relies on key technologies including the Internet of Things, intelligent perception, cloud intelligence, and 5G edge computing to enable digital collaborative sharing across the entire elevator industrial chain. Through IoT devices such as smart sensors, smart elevator gateways, and AI cameras, enterprise customers can collect elevator status data and maintenance information online. The platform analyzes the collected data and issues early warning notices to determine the possible time and location of elevator failures, arranging personnel to reach the scene in time to avoid breakdowns. A low-code development platform further allows business departments to build applications by dragging and dropping, reducing the application building threshold and shortening project development cycles.

The quantitative results are striking. Analyzing Company F’s financial records from 2015 to 2022 with Microsoft Excel, the researchers found that the mean main business profit margin rose from 32.65 percent before transformation (2015 to 2018) to 45.94 percent after transformation (2019 to 2022), while mean total asset turnover increased from 0.64 to 0.84. Simple linear regression with time as the independent variable confirmed these effects statistically: for profit margin, the regression yielded a significant positive slope of 13.29 with an R-squared of 0.62 and p less than 0.05, supporting the hypothesis that digital transformation via a sharing service platform significantly increases profitability; for total asset turnover, the slope was 0.20 with an R-squared of 0.74 and p less than 0.01, confirming enhanced operational efficiency. The journey was not without turbulence. In 2020, facing the unforeseen circumstances of the COVID-19 pandemic, Company F saw its main business profit margin and net profit growth rate decline by 32.98 percent and 117.85 percent respectively compared with 2019. But the leadership swiftly adjusted strategy, collaborated with Company L to formulate a new digital transformation plan, and maintained rapid growth in main business income and net profit over the following two years, with main business income growth reaching 20.97 percent in 2022.

Qualitative findings reinforced the numbers. Thematic analysis of interview transcripts with Company F’s digitalization head and technical staff, alongside observational notes from site visits, was conducted using NVivo version 12. Initial open coding identified recurring concepts such as real-time monitoring and cost savings from 15 interview segments and 10 observation entries, which were grouped into categories during axial coding and refined into three key themes: enhanced maintenance efficiency, streamlined spare parts management, and increased collaboration. Real-time monitoring appeared in 80 percent of interviews, and staff emphasized the platform’s role in anticipating failures before they happen and reducing warehouse clutter. The researchers concluded that Company L’s sharing service platform mitigated technological and financial barriers by enabling predictive maintenance and lean inventory practices, directly supporting the study’s central proposition that sharing economy platforms facilitate digital transformation in small and medium-sized manufacturing enterprises.

The implications extend well beyond a single elevator firm, and well beyond China. The authors suggest that the chain-hosting model could be adapted in regions such as Southeast Asia or Europe to bolster small firm integration into global supply chains, while the shared service cloud platform’s low-cost digital solutions could empower resource-constrained enterprises in middle-income countries to leapfrog technological barriers. At the same time, the study cautions that significant regional disparities exist within China itself, with coastal provinces often possessing more developed digital infrastructure and a more established culture of technological adoption than inland regions, creating an uneven playing field that demands targeted policy interventions. The researchers also acknowledge limitations: the evaluation timeframe is limited, and future research should employ longitudinal approaches tracking enterprises over five to ten years to understand how the benefits and challenges of sharing-economy-facilitated transformation unfold over time. For now, the message for millions of small manufacturers is clear: the resources they lack may already exist on a platform they have yet to join, and the collaborative sharing of technology, talent, and data may be the most affordable path through the digital gate.

Subject of Research: Digital transformation barriers among Chinese small and medium-sized manufacturing enterprises and how sharing economy platforms overcome them, with statistical evidence from an elevator industry case study.

Article Title: Overcoming digital transformation barriers: Chinese SMMEs in the sharing economy

Article References: Xiaosheng, J., Hua, Z., Jin, Z., AL-Bukhaiti, K., & Wan, A. (2025). Overcoming digital transformation barriers: Chinese SMMEs in the sharing economy. Frontiers in Manufacturing Technology, 5, Article 1495840. https://doi.org/10.3389/fmtec.2025.1495840

Image Credits: AI Generated

DOI: 10.3389/fmtec.2025.1495840

Keywords: digital transformation, sharing economy, SMMEs, manufacturing, China, data sharing, shared service platform, Industry 4.0, elevator industry, regression analysis, Overcoming, digital