Using K‐Means Clustering to Explore Farmers' Behavior and Intention Towards Online Credit in China Based on Extended Theory of Planned Behavior

ABSTRACT Although online credit is recognized as one of the promising approaches to solving the problem of difficult access to loans for Chinese farmers in theory, there is still actually a lack of enthusiasm among farmers for its adoption. This study is dedicated to exploring the reasons why farmers don't adopt online credit from the perspective of perceptions. Based on field survey data involving 698 farmers in Shaanxi province, six constructs derived from the extended theory of planned behavior are designed to identify the specific characteristic typologies of farmers’ online credit perceptions using the K‐means clustering method. Furthermore, the characteristics associated with farmers online credit behavior and intention are examined by an ordered logistic regression model. The results of cluster analysis reveal that five groups with differentiated perception characteristics are partitioned and labeled as (1) Socially influenced non‐believers , (2) Socially independent non‐believers , (3) Just see fraud skeptics/Socially independent skeptics , (4) Suspicious but socially influenced , and (5) Neutral independent and no distinctive perception . The results of ordered logistic regression show that distrust and suspicion among farmers towards online credit have significant relationships with shaping their behavior and intention. Concurrently, it cannot be overlooked the association between subjective norms and farmers’ online credit attitudes and perceptions. Additionally, farmers’ online credit behavior and intention also have correlations with various socio‐demographic factors, including gender, age, education, physical health condition, off‐farm work, and broadband coverage.