Skip to main content
SpringerLink
Log in

Knowledge acquisition model of mobile payment based on automatic summary technology

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

Abstract

The risks in mobile payment under Fintech have become an urgent problem to be addressed. This paper develops a research framework of knowledge acquisition and explores how automatic summarization technology helps extract knowledge of mobile payment to help managers and users reduce the financial risks. Specifically, we construct the mobile payment domain thesaurus and propose an automatic summary extraction model that integrates Bi-directional Long Short-Term Memory (BiLSTM), Attention Mechanism, and Reinforcement Learning (RL). The model is then used to extract the summary of mobile payment policy documents for knowledge acquisition. Our proposed model performs better than other basic models in Rouge-2, Rouge-4, and Rouge-SU4 indexes. Our study enriches relevant research in the existing literature, facilitates knowledge acquisition in mobile payment, and helps mobile users and managers reduce financial risks in their operations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  1. Chen, M. A., Wu, Q., & Yang, B. (2019). How valuable is FinTech innovation? The Review of Financial Studies, 32(5), 2062–2106.

    Article  Google Scholar 

  2. Dranev, Y., Frolova, K., & Ochirova, E. (2019). The impact of fintech M&A on stock returns. Research in International Business and Finance, 48, 353–364.

    Article  Google Scholar 

  3. Haddad, C., & Hornuf, L. (2019). The emergence of the global fintech market: Economic and technological determinants. Small Business Economics, 53(1), 81–105.

    Article  Google Scholar 

  4. Liu, J., Li, X., & Wang, S. (2020). What have we learnt from 10 years of fintech research? A scientometric analysis. Technological Forecasting and Social Change, 155, 120022.

    Article  Google Scholar 

  5. Sheng, T. (2021). The effect of Fintech on banks’ credit provision to SMEs: Evidence from China. Finance Research Letters, 39, 101558.

    Article  Google Scholar 

  6. Jones, E., & Knaack, P. (2019). Global financial regulation: Shortcomings and reform options. Global Policy, 10(2), 193–206.

    Article  Google Scholar 

  7. Haryadi, D., Kusumawardhana, V. H., & Warnars, H. L. H. S. (2018). The implementation of e-money in mobile phone: A case study at PT bank KEB Hana. In 2018 Indonesian association for pattern recognition international conference (INAPR) (pp. 202–206). IEEE.

  8. Son, I., & Kim, S. (2018). Mobile payment service and the firm value: Focusing on both up-and down-stream alliance. Sustainability, 10(7), 2583.

    Article  Google Scholar 

  9. Du, K. (2018). Complacency, capabilities, and institutional pressure: Understanding financial institutions’ participation in the nascent mobile payments ecosystem. Electronic Markets, 28(3), 307–319.

    Article  Google Scholar 

  10. Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35(1), 220–265.

    Article  Google Scholar 

  11. Iman, N. (2018). Is mobile payment still relevant in the fintech era? Electronic Commerce Research and Applications, 30, 72–82.

    Article  Google Scholar 

  12. Omigie, N. O., Zo, H., Ciganek, A. P., & Jarupathirun, S. (2020). Understanding the continuance of mobile financial services in Kenya: The roles of utilitarian, hedonic, and personal values. Journal of Global Information Management (JGIM), 28(3), 36–57. https://doi.org/10.4018/JGIM.2020070103

    Article  Google Scholar 

  13. Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091.

    Article  Google Scholar 

  14. Lim, S. H., Kim, D. J., Hur, Y., & Park, K. (2019). An empirical study of the impacts of perceived security and knowledge on continuous intention to use mobile fintech payment services. International Journal of Human-Computer Interaction, 35(10), 886–898.

    Article  Google Scholar 

  15. Lonkani, R., Changchit, C., Klaus, T., & Sampet, J. (2020). A comparative study of trust in mobile banking: An analysis of US and thai customers. Journal of Global Information Management (JGIM), 28(4), 95–119.

    Article  Google Scholar 

  16. Wang, L., Luo, X. R., Yang, X., & Qiao, Z. (2019). Easy come or easy go? Empirical evidence on switching behaviors in mobile payment applications. Information & Management, 56(7), 103150.

    Article  Google Scholar 

  17. Wang, Q., Liu, P., Zhu, Z., Yin, H., Zhang, Q., & Zhang, L. (2019). A text abstraction summary model based on BERT word embedding and reinforcement learning. Applied Sciences, 9(21), 4701.

    Article  Google Scholar 

  18. Fan, K., Li, H., Jiang, W., Xiao, C., & Yang, Y. (2018). Secure authentication protocol for mobile payment. Tsinghua Science and Technology, 23(5), 610–620.

    Article  Google Scholar 

  19. Hong, I. B. (2019). Understanding and predicting behavioral intention to adopt mobile banking: The korean experience. Journal of Global Information Management (JGIM), 27(3), 182–202. https://doi.org/10.4018/JGIM.2019070110

    Article  Google Scholar 

  20. Yao, Y., Li, J., & Sun, X. (2021). Measuring the risk of Chinese Fintech industry: Evidence from the stock index. Finance Research Letters, 39, 101564.

    Article  Google Scholar 

  21. Gupta, B. B., & Narayan, S. (2020). A survey on contactless smart cards and payment system: Technologies, policies, attacks and countermeasures. Journal of Global Information Management (JGIM), 28(4), 135–159. https://doi.org/10.4018/JGIM.2020100108

    Article  Google Scholar 

  22. Wang, Y., Hahn, C., & Sutrave, K. (2016). Mobile payment security, threats, and challenges. In 2016 second international conference on mobile and secure services (MobiSecServ) (pp. 1–5). IEEE.

  23. Pal, A., Herath, T., De, R., & Rao, H. R. (2021). Is the convenience worth the risk? An investigation of mobile payment usage. Information Systems Frontiers, 23(4), 941–961.

    Article  Google Scholar 

  24. Albashrawi, M., & Motiwalla, L. (2019). Privacy and personalization in continued usage intention of mobile banking: An integrative perspective. Information Systems Frontiers, 21(5), 1031–1043.

    Article  Google Scholar 

  25. Kulkarni, P. (2019). How to complain to digital payments ombudsman. The Economic Times.

  26. Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290(1), 99–115.

    Article  Google Scholar 

  27. Lundvall, B. Å., & Borrás, S. (2005). Science, technology and innovation policy. The Oxford handbook of innovation (pp. 599–631). Oxford University Press.

    Google Scholar 

  28. Demir, E., & Danisman, G. O. (2021). Banking sector reactions to COVID-19: The role of bank-specific factors and government policy responses. Research in International Business and Finance, 58, 101508.

    Article  Google Scholar 

  29. Liu, Z. (2021). The impact of government policy on macro dynamic innovation of the creative industries: Studies of the Uk’s and China’s animation sectors. Journal of Open Innovation: Technology, Market, and Complexity, 7(3), 168.

    Article  Google Scholar 

  30. Gertler, M., Kiyotaki, N., & Queralto, A. (2012). Financial crises, bank risk exposure and government financial policy. Journal of Monetary Economics, 59, S17–S34.

    Article  Google Scholar 

  31. Choi, T. M., Guo, S., Liu, N., & Shi, X. (2020). Optimal pricing in on-demand-service-platform-operations with hired agents and risk-sensitive customers in the blockchain era. European Journal of Operational Research, 284(3), 1031–1042.

    Article  Google Scholar 

  32. Rush, A. M., Chopra, S., & Weston, J. (2015). A neural attention model for abstractive sentence summarization. arXiv preprint arXiv:1509.00685.

  33. Chopra, S., Auli, M., & Rush, A. M. (2018). Abstractive sentence summarization with attentive recurrent neural networks[EB/OL]. Retrieved March, 21, 2018, from http://aclweb.org/anthology/N/N16/N16-1012.pdf.

  34. Yang, M., Tu, W., Qu, Q., Lei, K., Chen, X., Zhu, J., & Shen, Y. (2019). MARES: Multitask learning algorithm for Web-scale real-time event summarization. World Wide Web, 22(2), 499–515.

    Article  Google Scholar 

  35. Ma, Y., & Li, Q. (2019). A weakly-supervised extractive framework for sentiment-preserving document summarization. World Wide Web, 22(4), 1401–1425.

    Article  Google Scholar 

  36. Alami, N., Meknassi, M., & En-nahnahi, N. (2019). Enhancing unsupervised neural networks based text summarization with word embedding and ensemble learning. Expert Systems with Applications, 123, 195–211.

    Article  Google Scholar 

  37. Mohd, M., Jan, R., & Shah, M. (2020). Text document summarization using word embedding. Expert Systems with Applications, 143, 112958.

    Article  Google Scholar 

  38. Diao, Y., Lin, H., Yang, L., Fan, X., Chu, Y., Wu, D., Zhang, D., & Xu, K. (2020). CRHASum: Extractive text summarization with contextualized-representation hierarchical-attention summarization network. Neural Computing and Applications, 32(15), 11491–11503.

    Article  Google Scholar 

  39. ter Horst, H., Hartung, M., Cimiano, P., Brazda, N., Müller, H. W., & Klinger, R. (2020). Learning soft domain constraints in a factor graph model for template-based information extraction. Data & Knowledge Engineering, 125, 101764.

    Article  Google Scholar 

  40. Mnih, V., Heess, N., Graves, A., & Kavukcuoglu, K. (2014). Recurrent models of visual attention. arXiv preprint arXiv:1406.6247.

  41. Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.

  42. Huang, L. W., Jiang, B. T., Lv, S. Y., Liu, Y. B., & Li, D. Y. (2018). Survey on deep learning based recommender systems. Chinese Journal of Computers, 41(7), 1619–1647.

    Google Scholar 

  43. Peng, M., Yao, Y. L., Xie, Q. Q., et al. (2019). Knowledge representation learning for joint structural and textual embedding via attention-based CNN. Journal of Chinese Information Processing, 33(2), 51–58.

    Google Scholar 

  44. Kumar, A., Chatterjee, J. M., & Díaz, V. G. (2020). A novel hybrid approach of SVM combined with NLP and probabilistic neural network for email phishing. International Journal of Electrical and Computer Engineering, 10(1), 486-493.

    Article  Google Scholar 

  45. Yang, L., Wu, Y. Q., Wang, J. L., et al. (2018). Research on recurrent neural network. Computer Application, 38(S2), 1–6+26.

  46. Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Józefowicz, R., Gray, S., Olsson, C., Pachocki, J., Petrov, M., Pinto, H. P. O., Raiman, J., Salimans, T., Schlatter, J., Schneider, J., Sidor, S., Sutskever, I., Tang, J., Wolski, F. & Zhang, S. (2019). Dota 2 with large scale deep reinforcement learning. arXiv preprint arXiv:1912.06680.

  47. Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489.

    Article  Google Scholar 

  48. Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., Oh, J., et al. (2019). Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature, 575(7782), 350–354.

    Article  Google Scholar 

  49. Song, H. Y., Zhang, W. N., & Liu, T. (2018). DQN-based policy learning for open domain multi-turn dialogues. Journal of Chinese Information Processing, 32(7), 99–108.

    Google Scholar 

  50. Zeng, W., Yu, W. J., Xu, J., et al. (2020). Imitation learning to rank. Journal of Chinese Information Processing, 34(1), 97–105.

    Google Scholar 

  51. Allais, M. (1953). Le comportement de l’homme rationnel devant le risque: Critique des postulats et axiomes de l’école américaine. Econometrica : Journal of the Econometric Society, 21, 503–546.

    Article  Google Scholar 

  52. Ellsberg, D. (1961). Risk, ambiguity, and the Savage axioms. The Quarterly Journal of Economics, 75, 643–669.

    Article  Google Scholar 

  53. Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4(1), 55–81.

    Article  Google Scholar 

  54. Alquliti, W. H., & Ghani, N. B. A. (2019). Convolutional neural network based for automatic text summarization. International Journal of Advanced Computer Science and Applications, 10(1), 200–210.

    Article  Google Scholar 

  55. Li, W. Y., Liu, B., Zhang, W., et al. (2020). An automatic summarization model based on deep learning for Chinese. Journal of Guangxi Normal University (Natural Science Edition), 38(2), 51–63.

    Google Scholar 

  56. Wang, W. (2020). Generated automatic summary method based on C-R neural network. Computer & Digital Engineering, 48(1), 112–118.

    Google Scholar 

  57. Lin, C. Y., & Hovy, E. (2000). The automated acquisition of topic signatures for text summarization. In COLING 2000 volume 1: The 18th international conference on computational linguistics.

  58. Hou, L., Hu, P., & Bei, C. (2017). Abstractive document summarization via neural model with joint attention. In National CCF conference on natural language processing and Chinese computing (pp. 329–338). Springer.

  59. Tan, J., Diao, Y., Qi, R., & Lin, H. (2020). CCDM2020+ 62: Automatic summarization of Chinese news text based on bert-PGN model. Computer Applications, 1, 10.

    Google Scholar 

  60. Nallapati, R., Zhou, B., Gulcehre, C., & Xiang, B. (2016). Abstractive text summarization using sequence-to-sequence rnns and beyond. arXiv preprint arXiv:1602.06023.

  61. Liu, K., & Wang, H. L. (2019). Research on automatic summarization coherence based on discourse rhetoric structure. Journal of Chinese Information Processing, 33(1), 77–84.

    Google Scholar 

  62. Fang, X., Guo, Y., Wang, Q., et al. (2018). Automatic summary of short text based on Seq2Seq and keywords correction. Computer Engineering and Design, 39(12), 3610–3615.

    Google Scholar 

  63. Patel, D., Shah, S., & Chhinkaniwala, H. (2019). Fuzzy logic based multi document summarization with improved sentence scoring and redundancy removal technique. Expert Systems with Applications, 134, 167–177.

  64. McCreadie, R., Rajput, S., Soboroff, I., Macdonald, C., & Ounis, I. (2019). On enhancing the robustness of timeline summarization test collections. Information Processing & Management, 56(5), 1815–1836.

  65. Villa-Monte, A., Lanzarini, L., Bariviera, A. F., & Olivas, J. A. (2019). User-oriented summaries using a PSO based scoring optimization method. Entropy, 21(6), 617. https://doi.org/10.3390/e21060617.

  66. Ma, Y., & Li, Q. (2019). A weakly-supervised extractive framework for sentiment-preserving document summarization. World Wide Web, 22(4), 1401–1425.

  67. Kimura, T., Tagami, R., & Miyamori, H. (2019). Query-focused summarization enhanced with sentence attention mechanism. In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 1–8). IEEE.

  68. Tazibt, A. A., & Aoughlis, F. (2019). Latent dirichlet allocation-based temporal summarization. International Journal of Web Information Systems, 15(1), 83–102.

  69. Alquliti, W., & Binti, N. (2019). Convolutional neural network based for automatic text summarization. International Journal of Advanced Computer Science and Applications, 10(01), 200–211.

  70. Kosmajac, D., & Kešelj, V. (2019). Automatic text summarization of news articles in Serbian language. In 2019 18th International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1–6). IEEE.

Download references

Funding

Funding was provided by National Natural Science Foundation of China (Grant Nos. 71871172, 71571139).

Author information

Authors and Affiliations

  1. School of Management, Wuhan Textile University, Wuhan, 430000, Hubei, China

    Huosong Xia, Jing Liu & Yuan Wang

  2. Research Center of Enterprise Decision Support, Key Research Institute of Humanities and Social Sciences in Universities of Hubei Province, Wuhan, China

    Huosong Xia

  3. Department of Management, University of North Florida, Jacksonville, FL, 32224, USA

    Justin Zuopeng Zhang & Lakshmi Goel

Authors
  1. Huosong Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Justin Zuopeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lakshmi Goel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Justin Zuopeng Zhang.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, H., Liu, J., Zhang, J.Z. et al. Knowledge acquisition model of mobile payment based on automatic summary technology. Electron Commer Res 24, 131–154 (2024). https://doi.org/10.1007/s10660-022-09553-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-022-09553-9

Keywords

Search

Navigation