TAM-Based Study of Farmers’ Live Streaming E-Commerce Adoption Intentions
Xinqiang Chen, Xiue Zhang, Jiangjie Chen
Xiamen University of Technology Jilin University Jiangnan University
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摘要与影响
Amidst the digital economy surge, live streaming e-commerce of agricultural products has significantly boosted agricultural prosperity. Investigating farmers’ behavioral intentions toward adopting live streaming e-commerce holds critical importance for fostering agricultural healthy and swift growth. Utilizing the Technology Acceptance Model (TAM) as a foundation, this study incorporates three additional variables—government support, platform support, and social learning—to devise a theoretical model. It takes the agriculture-related live streaming e-commerce platform as an example, with 424 Chinese farmers as the sample, to quantitatively assess the factors that impact the intentions to adopt live streaming e-commerce behaviors. The findings indicate that, firstly, the TAM is applicable to the assessment of farmers’ intentions to adopt live streaming e-commerce. Secondly, government support positively impacts perceived usefulness, social learning enhances perceived ease of use, and platform support positively impacts both perceived ease of use and usefulness. Lastly, the technology acceptance extension model applicability varies among farmer groups: government support influence on perceived ease of use is more significant among traditional farmers, social learning impact on perceived ease of use is higher in farmers with higher education levels, and platform support effect on perceived usefulness is stronger among farmers experienced in e-commerce. Therefore, differentiated promotion strategies by the government are necessary, and e-commerce platforms should leverage their technology to offer efficient services and encourage farmer education. A multi-party collaboration model involving the government, platforms, and farmers is essential to collectively foster the healthy development of rural live streaming e-commerce.
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计算机 / AITechnology Adoption and User Behaviour
Digital Marketing and Social Media · Information Retrieval and Data Mining
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