In the realm of evolving e-commerce sales channels, the e-commerce sale of agricultural products has become a vital avenue for cherry farmers. However, a notable discrepancy exists between the intentions and actual behaviors of cherry farmers regarding e-commerce participation. In this study, binary logistic regression and interpretive structural model were used, and the cherry producing area of Yantai City, Shandong Province, China, was taken as the study area, and a total of 501 actual valid questionnaires were returned, and the validity rate of the questionnaires was 95.1 per cent. The results of the study show that the deviation of cherry farmers’ willingness and behavior is mainly affected by age, frequency of online shopping, whether to participate in e-commerce training, and whether to join a cooperative in farmers’ individual characteristics, revenue expectations and profit expectations in behavioral attitudes, government publicity and neighborhood effects in subjective norms, e-commerce use in perceived behavioral attitudes, the number of agricultural population in household resource endowment and logistics costs and e-commerce training in external scenarios Impact. On this basis, the 11 influencing factors are analyzed in depth and three transmission paths are analyzed. The study further proposes recommendations to enhance the translation of cherry farmers’ e-commerce intentions into action, such as bolstering e-commerce promotion, increasing the frequency of training, improving supporting infrastructure, and reducing logistics costs.

1. Ajzen, I. (2011). The theory of planned behavior: Reactions and reflections. Psychology & Health, 26(9), 1113–1127. https://doi.org/10.1080/08870446.2011.613995
2. Beckman, J., Ivanic, M., Jelliffe, J. (2021). Market impacts of Farm to Fork: Reducing agricultural input usage. Applied Economic Perspectives and Policy, 44(4), 1995–2013. https://doi.org/10.1002/aepp.13176
3. Chao, P. E. N. G., Biao, M. A., Zhang, C. (2021). Poverty alleviation through e-commerce: Village involvement and demonstration policies in rural China. Journal of Integrative Agriculture, 20(4), 998–1011. https://doi.org/10.1016/S2095-3119(20)63422-0
4. Chen, C., Gan, C., Li, J., et al. (2023). Linking farmers to markets: Does cooperative membership facilitate e-commerce adoption and income growth in rural China? Economic Analysis and Policy, 80, 1155–1170. https://doi.org/10.1016/j.eap.2023.09.040
5. Chen, S., Liu, W., Song, H., et al. (2023). Government‐led e‐commerce expansion project and rural household income: Evidence and mechanisms. Economic Inquiry, 62(1), 150–174. Portico. https://doi.org/10.1111/ecin.13167
6. Chen, Z., Chen, J., Zhang, Z., et al. (2019). Does network governance based on banks’ e-commerce platform facilitate supply chain financing? China Agricultural Economic Review, 11(4), 688–703. https://doi.org/10.1108/caer-06-2018-0132
7. Cui, M., Pan, S. L., Cui, L. (2019). Developing community capability for e‐commerce development in rural China: A resource orchestration perspective. Information Systems Journal, 29(4), 953–988. Portico. https://doi.org/10.1111/isj.12241
8. Dong, L., Zulfiqar, F., Yaseen, M., et al. (2024). Why are kiwifruit farmers reluctant to adopt eco-friendly green food certification? An investigation of attitude-behavior inconsistency. Journal of Agriculture and Food Research, 16, 101106. https://doi.org/10.1016/j.jafr.2024.101106
9. Fecke, W., Danne, M., Musshoff, O. (2018). E-commerce in agriculture—The case of crop protection product purchases in a discrete choice experiment. Computers and Electronics in Agriculture, 151, 126–135. https://doi.org/10.1016/j.compag.2018.05.032
10. Gao, J., Zeng, Y., Liu, M. (2024). Policy interventions and market innovation in rural China: Empirical evidence from Taobao villages. Economic Analysis and Policy, 81, 1411–1429. https://doi.org/10.1016/j.eap.2024.02.015
11. Grimmer, M., Miles, M. P. (2016). With the best of intentions: a large sample test of the intention‐behaviour gap in pro‐environmental consumer behaviour. International Journal of Consumer Studies, 41(1), 2–10. https://doi.org/10.1111/ijcs.12290
12. Huang, C.C., Jin, H., Zhang, J., et al. (2020). The effects of an innovative e-commerce poverty alleviation platform on Chinese rural laborer skills development and family well-being. Children and Youth Services Review, 116, 105189. https://doi.org/10.1016/j.childyouth.2020.105189
13. Hwang, Y. (2010). The moderating effects of gender on e-commerce systems adoption factors: An empirical investigation. Computers in Human Behavior, 26(6), 1753–1760. https://doi.org/10.1016/j.chb.2010.07.002
14. Karine, H. A. J. I. (2021). E-commerce development in rural and remote areas of BRICS countries. Journal of Integrative Agriculture, 20(4), 979–997. https://doi.org/10.1016/S2095-3119(20)63451-7
15. Khan, F., Ahmed, W., Najmi, A., et al. (2019). Managing plastic waste disposal by assessing consumers’ recycling behavior: the case of a densely populated developing country. Environmental Science and Pollution Research, 26(32), 33054–33066. https://doi.org/10.1007/s11356-019-06411-4
16. KIM, E., Jun, J., Hyun, J. H. (2022). The competencies of sellers in e-commerce and innovative sales activities for sales performance. Journal of Distribution Science, 20(1), 99–108.
17. Knuth, M. J., Khachatryan, H., Hall, C. R. (2021). How Consistent Are Consumers in Their Decisions? Investigation of Houseplant Purchasing. Behavioral Sciences, 11(5), 73. https://doi.org/10.3390/bs11050073
18. Leong, C., Pan, S. L., Newell, S., et al. (2016). The Emergence of Self-Organizing E-Commerce Ecosystems in Remote Villages of China: A Tale of Digital Empowerment for Rural Development. MIS Quarterly, 40(2), 475–484. https://doi.org/10.25300/misq/2016/40.2.11
19. Li, B., Ding, J., Wang, J., et al. (2021a). Key factors affecting the adoption willingness, behavior, and willingness-behavior consistency of farmers regarding photovoltaic agriculture in China. Energy Policy, 149, 112101. https://doi.org/10.1016/j.enpol.2020.112101
20. Li, B., Yin, Z., Ding, J., et al. (2020). Key influencing factors of consumers’ vegetable e-commerce adoption willingness, behavior, and willingness-behavior consistency in Beijing, China. British Food Journal, 122(12), 3741–3756. https://doi.org/10.1108/bfj-11-2019-0834
21. Li, F., Zang, D., Chandio, A. A., et al. (2023b). Farmers’ adoption of digital technology and agricultural entrepreneurial willingness: Evidence from China. Technology in Society, 73, 102253. https://doi.org/10.1016/j.techsoc.2023.102253
22. Li, G., Qin, J. (2022a). Income effect of rural E-commerce: Empirical evidence from Taobao villages in China. Journal of Rural Studies, 96, 129–140. https://doi.org/10.1016/j.jrurstud.2022.10.019
23. Li, L., Guo, R., Yang, J. (2023a). The deviation between the behavior and willingness of rural residents to use clean energy and its influencing factors in China. Energy for Sustainable Development, 77, 101324. https://doi.org/10.1016/j.esd.2023.101324
24. Li, L., Zeng, Y., Ye, Z., et al. (2021b). E‐commerce development and urban‐rural income gap: Evidence from Zhejiang Province, China. Papers in Regional Science, 100(2), 475–495. https://doi.org/10.1111/pirs.12571
25. Li, X.; Sarkar, A.; Xia, X.; Memon, W.H. (2021c). Village Environment, Capital Endowment, and Farmers’ Participation in E-Commerce Sales Behavior: A Demand Observable Bivariate Probit Model Approach. Agriculture, 11, 868.
26. Li, X., Guo, H., Jin, S., et al. (2021d). Do farmers gain internet dividends from E-commerce adoption? Evidence from China. Food Policy, 101, 102024. https://doi.org/10.1016/j.foodpol.2021.102024
27. Li, W.; He, W. (2024) Revenue-increasing effect of rural e-commerce: A perspective of farmers' market integration and employment growth. Econ. Anal. Pol, 81, 482.
28. Li, Y., Wu, Q., Zhang, Y., et al. (2022b). Spatial Structure of China’s E-commerce Express Logistics Network Based on Space of Flows. Chinese Geographical Science, 33(1), 36–50. https://doi.org/10.1007/s11769-022-1322-0
29. Lin, H., Li, R., Hou, S., et al. (2021). Influencing factors and empowering mechanism of participation in e-commerce: An empirical analysis on poor households from Inner Mongolia, China. Alexandria Engineering Journal, 60(1), 95–105. https://doi.org/10.1016/j.aej.2020.06.010
30. Liu, M., Min, S., Ma, W., et al. (2021). The adoption and impact of E-commerce in rural China: Application of an endogenous switching regression model. Journal of Rural Studies, 83, 106–116. https://doi.org/10.1016/j.jrurstud.2021.02.021
31. Lorca, P., De Andrés, J., García-Diez, J. (2019). Impact of E-Commerce Sales on Profitability and Revenue. The Case of the Manufacturing Industry. Engineering Economics, 30(5), 544–555. https://doi.org/10.5755/j01.ee.30.5.21254
32. Luo, X., Niu, C. (2019). E-Commerce Participation and Household Income Growth in Taobao Villages. World Bank, Washington, DC. https://doi.org/10.1596/1813-9450-8811
33. Ma, W., Rahut, D. B., Sonobe, T., et al. (2024). Linking farmers to markets: Barriers, solutions, and policy options. Economic Analysis and Policy, 82, 1102–1112. https://doi.org/10.1016/j.eap.2024.05.005
34. Ma, W., Zhou, X., Liu, M. (2019). What drives farmers’ willingness to adopt e‐commerce in rural China? The role of Internet use. Agribusiness, 36(1), 159–163. Portico. https://doi.org/10.1002/agr.21624
35. Mei, Y., Mao, D., Lu, Y., et al. (2020). Effects and mechanisms of rural E‐commerce clusters on households’ entrepreneurship behavior in China. Growth and Change, 51(4), 1588–1610. Portico. https://doi.org/10.1111/grow.12424
36. Mican, D., Sitar-Tăut, D. A., Moisescu, O. I. (2020). Perceived usefulness: A silver bullet to assure user data availability for online recommendation systems. Decision Support Systems, 139, 113420. https://doi.org/10.1016/j.dss.2020.113420
37. Nguyen, T. T. P., Zhu, D., Le, N. P. (2015). Factors influencing waste separation intention of residential households in a developing country: Evidence from Hanoi, Vietnam. Habitat International, 48, 169–176. https://doi.org/10.1016/j.habitatint.2015.03.013
38. Park, E., Luo, Y., Trouth, F., et al. (2021). Charting the Future of E-Grocery: An Evaluation of the Use of Digital Imagery as a Sensory Analysis Tool for Fresh Fruits. Horticulturae, 7(9), 262. https://doi.org/10.3390/horticulturae7090262
39. Qiao, T., Qiu, Z. (2024). The dynamic process of social capital transformation and the emergence of E-commerce diffusion networks. Journal of Rural Studies, 105, 103101. https://doi.org/10.1016/j.jrurstud.2023.103101
40. Qiu, X., Jin, J., He, R., et al. (2022). The deviation between the willingness and behavior of farmers to adopt electricity-saving tricycles and its influencing factors in Dazu District of China. Energy Policy, 167, 113069. https://doi.org/10.1016/j.enpol.2022.113069
41. Quintal, V. A., Lee, J. A., Soutar, G. N. (2010). Risk, uncertainty and the theory of planned behavior: A tourism example. Tourism Management, 31(6), 797–805. https://doi.org/10.1016/j.tourman.2009.08.006
42. Schulze Schwering, D., Isabell Sonntag, W., Kühl, S. (2022). Agricultural E-commerce: Attitude segmentation of farmers. Computers and Electronics in Agriculture, 197, 106942. https://doi.org/10.1016/j.compag.2022.106942
43. Sheng, J., Lu, Q. (2020). The influence of information communication technology on farmers’ sales channels in environmentally affected areas of China. Environmental Science and Pollution Research, 27(34), 42513–42529. https://doi.org/10.1007/s11356-020-10203-6
44. Song, Y. (2016). Institutionalizing Rural Women’s Political Participation in China. Asian Women, 32(3), 77. https://doi.org/10.14431/aw.2016.09.32.3.77
45. Su, L., Peng, Y., Kong, R., et al. (2021). Impact of E-Commerce Adoption on Farmers’ Participation in the Digital Financial Market: Evidence from Rural China. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1434–1457. https://doi.org/10.3390/jtaer16050081
46. Turner, M. D. (2016). Rethinking Land Endowment and Inequality in Rural Africa: The Importance of Soil Fertility. World Development, 87, 258–273. https://doi.org/10.1016/j.worlddev.2016.06.014
47. Wang, C., Wu, X., Chen, T. (2023). Neighborhood effect, virtual social networks, and farmers’ social e‐commerce participation behavior. Agribusiness. Portico. https://doi.org/10.1002/agr.21885
48. Wang, Y., Tian, Y., Nasrullah, M., et al. (2024). Does social capital influence farmers’ e-commerce entrepreneurship? China’s regional evidence. Electronic Commerce Research. https://doi.org/10.1007/s10660-023-09794-2
49. Wang, Z., Zhang, B., Yin, J., et al. (2011). Willingness and behavior towards e-waste recycling for residents in Beijing city, China. Journal of Cleaner Production, 19(9–10), 977–984. https://doi.org/10.1016/j.jclepro.2010.09.016
50. Warfield, J. N. (1973). Binary Matrices in System Modeling. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(5), 441–449. https://doi.org/10.1109/tsmc.1973.4309270
51. Yang, S., Wang, H., Wang, Z., et al. (2021). The Nexus between Formal Credit and E-Commerce Utilization of Entrepreneurial Farmers in Rural China: A Mediation Analysis. Journal of Theoretical and Applied Electronic Commerce Research, 16(4), 900–921. https://doi.org/10.3390/jtaer16040051
52. Zeng, Y., Guo, H., Yao, Y., et al. (2019). The formation of agricultural e‐commerce clusters: A case from China. Growth and Change, 50(4), 1356–1374. Portico. https://doi.org/10.1111/grow.12327
53. Zhang, B., Fu, Z., Wang, J., et al. (2017). Effect of householder characteristics, production, sales and safety awareness on farmers’ choice of vegetable marketing channels in Beijing, China. British Food Journal, 119(6), 1216–1231. https://doi.org/10.1108/bfj-08-2016-0378
54. Zhang, D., Chen, S., Zhou, N., et al. (2023). Location optimization of fresh food e-commerce front warehouse. Mathematical Biosciences and Engineering, 20(8), 14899–14919. https://doi.org/10.3934/mbe.2023667
55. Zhang, Y. (2019). Application of improved BP neural network based on e-commerce supply chain network data in the forecast of aquatic product export volume. Cognitive Systems Research, 57, 228–235. https://doi.org/10.1016/j.cogsys.2018.10.025
56. Zhang, Y., Halder, P., Zhang, X., et al. (2020). Analyzing the deviation between farmers’ Land transfer intention and behavior in China’s impoverished mountainous Area: A Logistic-ISM model approach. Land Use Policy, 94, 104534. https://doi.org/10.1016/j.landusepol.2020.104534
57. Zheng, S., Yu, L., Fu, H. (2023). Has Rural E-Commerce Increased Potato Farmers’ Income? Evidence from the Potato Home of China. Potato Research, 67(1), 15–35. https://doi.org/10.1007/s11540-023-09614-y