## 4 Results and Discussion
### Identification and Categorization of Factors
#### 4.2.5 Psychological and Behavioral Factors  
Adoption of new technologies is never a purely technical problem. It relies highly the behavior changes of stakeholders, which are influenced by their beliefs and attitudes. A risk-neutral farmer is likely to adopt a technology that results in net increases in operating profits [42]. Many studies show that the perception of profitability with new technologies significantly drives the intention of adoption [31; 34; 37; 38]. In addition to the profitability of alternative technologies, the riskiness of those profits can also impact the decisions of risk-averse farmers [33; 35; 38]. Since novel digital technologies often come with more economic uncertainty and technical difficulty than traditional ones, research has confirmed that the perceived ease of utilizing these technologies increases farmers' adoption of digital technologies [33; 34]. Extrapolated from the discussion in the earlier section, the adoption of DATs is a result of multidimensional considerations. It is positively associated with (1) socioeconomic factors (farmers who are younger, male, healthy, better educated, cooperative members, and have a higher agricultural income and more farming experience), (2) agroecological factors (farmers who own larger cultivated areas), (3) technological factors (the lower cost of technology and access to the digital information), (4) institutional factors (farmers who receive government subsidy or financial services, engage in contract farming, or face pressure for environmental sustainability), and (5) psychological and behavioral factors (farmers who perceive that DATs are profitable and easy to use). It is worth noting that farmer and farm characteristics such as education, income levels, and farm size are considered relatively \"fixed\" determinants and are not often included as potentially important variables in studies conducted in developed countries [40]. Socioeconomic factors are relatively stable within developed countries, which could explain why these factors are not always significant in studies carried out in such countries. However, this review shows that these characteristics are important determinants of farmers' adoptive decision in China. Agriculture in China is often characterized by small-scale farming and significant heterogeneity among different provinces in terms of cultivated area [43]. In recent years, with the continual development of the economy and the introduction of rural revitalization policies, there has been a notable expansion in the farming scale, accompanied by improved health and income standards for rural laborers. In the face of the rapid change in rural operations and the labor force structure, it is still crucial to consider socioeconomic factors in the design of empirical studies. Among all five groups of factors, agroecological factors are the least explored in existing studies. Farm size is the only variable considered in this group. Studies focusing on other countries have found that factors such as soil quality, availability of irrigation water, previous weather shocks, and crop yields are influential determinants in the adoption of DATs. These studies reveal that farmers value agricultural innovations that are adaptable to natural conditions and environmental changes. Overlooking such features in research may lead to a skewed understanding of adoption decisions. The decision of most farmers to adopt new technology is typically driven by its potential to increase profitability or generate direct revenue. As expected, the cost of technology negatively affects adoption. However, some institutional factors such as government subsidies, contract farming, and agricultural extension services can incentivize farmers to adopt new technology despite the associated costs. There is a growing literature going beyond profit maximization as incentives for adoption to examining the role of psychological and behavioral factors in explaining the technology adoption and diffusion process.