Big data products sharpen market awareness and agility of e-commerce farmers

In China's rapidly evolving rural economy, entrepreneurial farmers, often the backbone of e-commerce growth, face increasing competition, market saturation, and shrinking profit margins. Amid this environment, a recent study published in Sage Open 15(2) underscores a powerful transformation: the use of big data products to boost business intelligence and operational precision.

The study, titled "Impact of Big Data Products on the Entrepreneurial Performance of E-Commerce Farmers: Evidence from China," investigates how the deployment of platform-driven big data tools can uplift the entrepreneurial alertness and dynamic capabilities of e-commerce farmers. Drawing on empirical data from 418 participants across 15 Taobao Villages in Zhejiang Province, the research affirms that the effective use of big data tools like Alibaba’s Business Advisor and JD’s Jingdong Business Intelligence has a direct and significant impact on business outcomes.

In what ways do big data tools improve entrepreneurial competencies?

The study constructs its analysis on the foundation of the Technology Acceptance Model (TAM), expanding it into a tri-dimensional framework that evaluates the usefulness, ease of use, and experiential quality of big data products. These dimensions capture both the rational and emotional evaluations of digital tools by rural entrepreneurs.

Entrepreneurial alertness, the ability to detect new market opportunities, was found to be strongly influenced by farmers’ perception of data tools' usefulness and usability. Farmers who found the tools easy to navigate and helpful in analyzing sales trends or customer preferences exhibited sharper market sensitivity. This enhanced alertness is no longer driven by instinct or past experience, but by real-time data on consumer behavior, competitor activity, and demand fluctuations.

Simultaneously, dynamic capabilities, the ability to adapt to change and reorganize resources effectively, are significantly improved through these tools. Farmers using big data can swiftly adjust product mix, pricing, and marketing strategies based on predictive analytics. The study finds that big data’s experiential features, such as interface friendliness, fun in use, and compatibility with farmers’ routines, substantially contribute to sustained engagement and business transformation.

These improvements are not marginal. The path coefficients from the structural equation model indicate that dynamic capabilities have the most substantial influence on entrepreneurial performance, even more than alertness. This emphasizes that ongoing adaptability, more than momentary insight, determines long-term business success.

What are the implications for digital empowerment in the Global South?

While the study is grounded in China’s Taobao Village model, its implications extend across developing nations where digital infrastructure is emerging but unevenly adopted. The research calls for a nuanced understanding of how product design and user experience shape technology adoption among rural populations. Even if digital tools are available, perceived risks, usability barriers, and misalignment with user preferences can obstruct their effective use.

The authors advocate for inclusive product design that accommodates users with limited digital literacy. Experience-related attributes, such as perceived security, design simplicity, and functional fun, were shown to enhance engagement, especially among risk-averse or older users. These findings are particularly salient for policymakers and platform designers aiming to democratize data access in agriculture and rural enterprise.

Moreover, the study suggests that governmental and institutional support, while necessary, cannot substitute for tools that improve internal competencies. Unlike policies and subsidies, big data tools are continuously accessible and embedded in daily operations. Hence, their potential for sustainable empowerment is higher.

Policy recommendations include expanding rural data infrastructure, subsidizing big data tool access for low-income farmers, and developing public databases that reflect the needs of digitally disadvantaged populations. Programs that train farmers not just in e-commerce, but in interpreting and acting on data, will be critical to scaling these benefits.