## 4 Results and Discussion
### The Role of Methods  
A large body of studies has examined the adoption of early-generation precision agricultural technologies. These studies typically conduct the ex-post analysis to explain the incentives and challenges involved in adopting an existing technology. However, our review study reveals that the majority of empirical work analyzing the adoption of digital technologies in China utilizes an ex-ante approach. This is largely due to the fact that many digital technologies have not been implemented on a commercial scale, and there is insufficient data available. Ex-ante analysis can be beneficial in evaluating the attributes of the technology and potential obstacles to adoption.  
Unlike traditional technologies, digital agricultural technologies typically have a multifaceted and complex nature. They encompass different components, data acquisition, data analysis, and technology application choices [45]. Adoption of digital technologies is, therefore, more complicated than a simple binary choice decision. Instead, farmers often start with on-farm trials and adopt components sequentially. Stated preference methods, specifically contingent valuation methods and choice experiments, are commonly used to determine a farmer's ex-ante decision (e.g., willingness to adopt or willingness to pay for new agricultural technologies). However, in a contingent valuation survey, as employed in most of the surveyed studies, respondents are typically asked about a technology possessing specific attributes. As a result, this method does not allow for the evaluation of multiple attributes in the adoption decision. A choice experiment is an alternative approach which enables respondents to choose from multiple technology attributes and related outcomes. Furthermore, it can be designed to examine the influence of neighborhood effects on the adoption decision. Learning from extension agents, neighboring farmers and virtual social networks play a strong role in the initial acceptance of new agricultural technology [46]. However, the surveyed studies have rarely explored the influence of social networks and spillovers.  
The implementation of new technologies is a learning endeavor requiring trial evaluation, gradual adoption, review, and modification practices. Optimal frameworks would allow researchers to view adoption as a dynamic process rather than a fixed intention. Researchers would benefit from introducing diverse methodologies into their empirical studies, such as randomized control trials, agent-based models and experiments, and games in surveys. The application of advances in behavior economics can be effective in examining the impact of cognitive factors, social pressure, trust in information providers, and other elements on adoption decisions. Despite recognizing the heterogeneity of the adoption and specificity in terms of technology and local conditions, current research has not yet provided a direct measurement to fill this gap. Therefore, the combination of multi- and transdisciplinary models from economics, digital technology, agronomy, and social psychology are increasingly recommended to explain the adoption decision [47]. Existing empirical studies in China are significantly fewer than those performed in more experienced countries, and they often overlook the complex nature of digital technologies and potential adopters. Hence, research should persist in identifying potential trigger factors and seek to provide insights on the adoption of these emerging technologies, which might differ considerably from traditional technologies.