An empirical study on the behavior of e-commerce strategic planning based on deep learning algorithm

Abstract

On the basis of large-scale literature research, the evaluation and element model for the successful implementation of e-commerce are established, and the key elements (customer, strategy, leadership, technology) and the evaluation elements (system quality, system quality, information quality, service quality) affect the success of e-commerce. First, learn the effective features of the items from the content data through deep learning in advance, and then transform the learned features into the learning task of the collaborative filtering target, and add balance and no relevant constraints to the e-commerce strategic planning behavior values of users and items, using alternating optimization algorithms to learn the value of e-commerce strategic planning behavior and fine-tuning the deep network, and finally get the compact and informative e-commerce strategic planning behavior value of users and items, effectively solving the data sparse problem and cold start in the collaborative filtering algorithm problem. Secondly, the combination of conceptual model and structural equation model has innovated research methods and introduced structural equation model method, which effectively handles the complex relationship between multi-dimensional variables and revises and verifies the hypothetical model. Through path analysis, the interaction and influence between key success factors, success evaluation factors, and successful implementation of e-commerce are explored, and useful attempts are made to expand relevant research data analysis methods.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

References

  1. Aringhieri R, Duma D, Fragnelli V (2018) Modeling the rational behavior of individuals on an e-commerce system. Oper Res Perspect 5:22–31

    Google Scholar 

  2. Bai X (2017) Research on the customer churn model of e-commerce based on the improved combined intelligent algorithm. C E Ca 42(4):1460–1464

    Google Scholar 

  3. Chen C (2015) Research on college students’ safety awareness and behavior in food consumption: an empirical study based on the college students in Suzhou. Adv J Food Sci Technol 7(12):977–982

    Article  Google Scholar 

  4. Choi JG, Che MS (2016) An empirical study on the relationship of personal optimistic bias and information security awareness and behavior in the activity of information ethics. Inst Secur Cryptol 17(5):538–547

    Google Scholar 

  5. Dai T, Wen D (2016) An empirical study on the customer channel choice behavior in the overall process of shopping under 020 mode. Int J Web Portals 8(1):13–31

    Article  Google Scholar 

  6. Dhote S, Vichoray C, Pais R (2020) Hybrid geometric sampling and AdaBoost based deep learning approach for data imbalance in E-commerce. Electron Commer Res 20:259–274

    Article  Google Scholar 

  7. Ding K, Jiang P, Zheng M (2017) Environmental and economic sustainability-aware resource service scheduling for industrial product service systems. J Intell Manuf 28:1303–1316

    Article  Google Scholar 

  8. Mocanu E, Mocanu DC, Nguyen PH (2019) On-line building energy optimization using deep reinforcement learning. IEEE Trans Smart Grid 10(4):3698–3708

    Article  Google Scholar 

  9. Menning L, Garg G, Pokharel D (2017) Communications, immunization, and polio vaccines: lessons from a global perspective on generating political will, informing decision-making and planning, and engaging local support. J Infect Dis 216(1):24–32

    Article  Google Scholar 

  10. Guo M (2017) An empirical study on user acceptance behavior of mobile location-based service. Boletin Tecnico/Tech Bull 55(11):352–357

    Google Scholar 

  11. Hamamoto M (2019) An empirical study on the behavior of hybrid-electric vehicle purchasers. Energy Policy 125(2):286–292

    Article  Google Scholar 

  12. Jipp M, Ackerman PL (2016) The impact of higher levels of automation on performance and situation awareness: a function of information-processing ability and working-memory capacity. J Cogn Eng Dec Mak 10(2):138–166

    Article  Google Scholar 

  13. Myint K, See-Ziau H, Husain R (2016) Dental students’ educational environment and perceived stress: the University of Malaya experience. Malays J Med Sci 23(3):49–56

    Google Scholar 

  14. Qu B, Zhang Z, Zhu X (2015) An empirical study of morphing on behavior-based network traffic classification. Secur Commun Netw 8(1):68–79

    Article  Google Scholar 

  15. Ramírez-Martínez IF, Gallardo-Matienzo G, Mita-Arancibia Á et al (2015) Estrategias de aprendizaje según los enfoques de aprendizaje en estudiantes del internado rotatorio de la Facultad de Medicina de la Universidad San Francisco Xavier de Chuquisaca (Sucre, Bolivia). Fem Revista De La Fundación Educación Médica 18(1):15–25

    Article  Google Scholar 

  16. Tang M, Wu Z (2015) Research on the mechanisms of big data on consumer behavior using the models of C2C e-commerce and countermeasures. Afr J Bus Manage 9(1):3–15

    Google Scholar 

  17. Vandecasteele F, Vandenbroucke K, Schuurman D (2017) Spott: on-the-spot e-commerce for television using deep learning-based video analysis techniques. Acm Trans Multimed Comput Commun Appl 13(3s):1–16

    Article  Google Scholar 

  18. Wang T, Liu K, Chen J (2018) Study on the image object recognition and simulation based on deep learning algorithm. Paper Asia 1(9):14–16

    Google Scholar 

  19. Wang S, Wang J, Zhao S (2019) Information publicity and resident’s waste separation behavior: an empirical study based on the norm activation model. Waste Manage 87:33–42

    Article  Google Scholar 

  20. Wu D, Wu Q, Yin X (2020) Lameness detection of dairy cows based on the YOLOv3 deep learning algorithm and a relative step size characteristic vector. Biosyst Eng 189:150–163

    Article  Google Scholar 

  21. Yang G, Xu N, Hong Z (2018) Identification of navel orange lesions by nonlinear deep learning algorithm. Engenharia Agrícola 38(5):783–796

    Article  Google Scholar 

  22. Yang P, Ma Z, Zhang C (2020) A high-performance deep learning algorithm for the automated optical inspection of laser welding. Appl Sci 10(3):933–946

    Article  Google Scholar 

  23. Zhang C, Zheng X, Zhao H (2017) An intervention study of college student’ dietary behavior based on the health action process approach[J]. J Hyg Res 46(5):755–760

    Google Scholar 

  24. Zhang H (2016) Research and modelling on the e-commerce consumer behavior based on intelligent recommendation system and machine learning. Int Technol Manag 7:61–63

    Google Scholar 

  25. Zhou H, Zuo G, Wan X (2017) An empirical study on the behavior of motor imagery based on mental rotation. J Biomed Eng 34(2):173–179

    Google Scholar 

Download references

Acknowledgements

This work is supported by the Program of Liaoning Social Science Planning Fund Project: Research on the Reform and Development of Urban Public Utilities PPP in Northeast China (Grant No.L19CJL004).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Zilong Huang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ren, X., He, J. & Huang, Z. An empirical study on the behavior of e-commerce strategic planning based on deep learning algorithm. Inf Syst E-Bus Manage (2021). https://doi.org/10.1007/s10257-021-00504-9

Download citation

Keywords

  • E-commerce
  • Key success factors
  • Evaluation
  • Structural equation model
  • Deep learning algorithm
  • Data sparse