The process and informatization of networking has spawned a large number of trading platforms, and college students are becoming the main group of online shoppers. This study analyzed the online shopping intention, the perceived risk and perceived ease of use. It formed a summary of the current theoretical system through literature induction. On the basis of this research hypothesis, questionnaires were developed for undergraduates of Hefei University of Technology (Feicui Lake Campus), Anhui University (Qing Yuan Campus) and Anhui Jianzhu University (South Campus). We collate all the data for further model analysis and verify the model based on computer software. The results show that college students' perception of online shopping risk is negatively correlated with their intention to use online shopping, while their perception of online shopping ease of use is positively correlated with their intention to shop online, which is consistent with the theory.

Jing Zhao, Kim Mee Chong, Lin Zhao. A study on influencing factors of online shopping intention of college students in Hefei City. Academic Journal of Business & Management (2024) Vol. 6, Issue 4: 201-211. https://doi.org/10.25236/AJBM.2024.060430.

[1] K.L. Hsiao, C.C. Chen. What drives smartwatch purchase intention? Perspectives from hardware, software, design, and value. Telematics Inform., 35 (1) (2018), pp. 103-113

[2] G. Zhu, Z. Wu, Y. Wang, S. Cao, J. Cao. Online purchase decisions for tourism e-commerce. Electron. Commer. Res. Appl., 38 (2019), Article 100887

[3] M.K. Chang, W. Cheung, V.S. Lai. Literature derived reference models for the adoption of online shopping. Inf. Manag., 42 (2005), pp. 543-559

[4] Vijayasarathy. Predicting consumer intentions to use on-line shopping: the case for an augmented technology acceptance. Inf. Manag., 41 (2004), pp. 747-762

[5] Bauer. Consumer behavior as risk taking. Hancock (Ed.), Dynamic Marketing for a Changing World, American Marketing Association, Chicago (1960), pp. 389-398

[6] Cox. Risk Taking and Information Handing in Consumer Behavior, Harvard University Press, Boston (1967), pp. 1-19

[7] Dowling, S. Richard. A model of perceived risk and intended risk-handling activity. Consum. Res., 21 (1994), pp. 119-133

[8] J.C. Sweeney, N.S. Geoffrey, W.J. Lester. The role of perceived risk in the quality-value relationship: a study in a retail environment. Retail., 75 (1999), pp. 77-105

[9] Forsythe, B. Shi. Consumer patronage and risk perceptions in Internet shopping. Bus. Res., 56 (11) (2003), pp. 867-875

[10] C.M. Chiu, T.G. Wang, Y.H. Fang, H.Y. Huang. Understanding customers' repeat purchase intentions in B2C e-commerce: the roles of utilitarian value, hedonic value and perceived risk Information Systems Journal., 24 (2012), pp. 85-114

[11] S. Kamalul Ariffin, T. Mohan, Y. Goh. Influence of consumers’ perceived risk on consumers’ online purchase intention. Res. Interact. Market., 12 (2018), pp. 309-327

[12] Rosillo-Díaz, F. Blanco-Encomienda, E. Crespo-Almendros. A cross-cultural analysis of perceived product quality, perceived risk and purchase intention in e-commerce platforms. Enterprise Inf. Manag., 33 (2019), pp. 139-160

[13] Y. Zhang, Q. Yang. Assessing hotel decision-making of disabled guests: satisfaction correlation study between online comments’ credibility and perceived risk.First Online 08 April 2019. Electron. Commerce Res. (2019)

[14] B. Choi, O. Kwon, B. Shin. Location-based system: comparative effects of personalization vs ease of use Telematics Inf., 34 (2017), pp. 91-102

[15] Hamid, F.Z. Razak, Bakar, W.S.W. Abdullah. The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government. Procedia Econ. Financ., 35 (2016), pp. 644-649

[16] Nedra, W. Hadhri, M. Mezrani. Determinants of customers' intentions to use hedonic networks: the case of Instagram. Retailing Consum. Serv., 46 (2019), pp. 21-32

[17] Golden, P. Krauskopf. Systematic evaluation of mobile apps. Nurse Pract., 12 (1) (2016), pp. e27-e28