Academic Journal of Computing & Information Science, 2023, 6(1); doi: 10.25236/AJCIS.2023.060107.

## Calculation Method’s Research of Interruption Probability of Online Economic Transaction Based on Bayes Information Fusion

Author(s)

Xuemei Yu

Corresponding Author:
Xuemei Yu
Affiliation(s)

Beijing Polytechnic, Beijing, 100176, China

### Abstract

The problem of economic transaction interruption under the crisis of online economic transaction interruption is analyzed. It is pointed out that there is an unreliable supplier in the online economic transaction manufacturer. When the supplier fails, there is a problem in the selection of two standby reliable suppliers, and the customer service factor is taken into account. By using a Bayes information fusion method and lingo software, we establish a mathematical model to calculate the procurement cost, supplier transaction cost and customer shortage cost and solve the optimal selection scheme. Finally, a sensitivity analysis is carried out on the relevant scenario parameters to obtain the numerical test and sensitivity analysis results. The expected total income of online economic transactions is the largest in the CPD model by comparing the four models of online economic transactions. Compared with three non-centralized online economic transaction models, the expected total income of online economic transactions is the largest in the NGPD model.

### Keywords

Bayes, Information fusion, Online trading, Economic risk, Transaction interruption

### Cite This Paper

Xuemei Yu. Calculation Method’s Research of Interruption Probability of Online Economic Transaction Based on Bayes Information Fusion. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 1: 42-51. https://doi.org/10.25236/AJCIS.2023.060107.

### References

[1] Hs A, Ah B, D H. 2020, A novel linguistic approach for multi-granular information fusion and decision-making using risk-based linguistic D numbers. Information Sciences, 530(11):43-65.

[2] Pozzato G, M Müller, Formentin S. 2020, Economic MPC for online least costly energy management of hybrid electric vehicles. Control Engineering Practice, 102(2):104534.

[3] Gutt D, Neumann J, Zimmermann S. 2019, Design of review systems – A strategic instrument to shape online reviewing behavior and economic outcomes. The Journal of Strategic Information Systems, 28(2):104-117.

[4] U Tandon, Kiran R, Sah A N. 2019, Customer satisfaction as mediator between website service quality and repurchase intention: An emerging economy case. Operations Research, 59(1-2):155-156.

[5] Shah A M, Yan X, Shah S, et al. Exploring the impact of online information signals in leveraging the economic returns of physicians. Journal of Biomedical Informatics, 2019, 98(10):103272.

[6] Santos J, Yip C, Thekdi S. 2020, Workforce/Population, Economy, Infrastructure, Geography, Hierarchy, and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience. Risk analysis, 40(1):43-67.

[7] Weber F, Lehmann J, Graf-Vlachy L. 2019, Institution‐Infused Sensemaking of Discontinuous Innovations: The Case of the Sharing Economy. Journal of Product Innovation Management, 36(5):632-660.

[8] Pozzato G, M Müller, Formentin S. 2020, Economic MPC for online least costly energy management of hybrid electric vehicles. Control Engineering Practice, 102(11):104534.

[9] Sun G. 2020, Research on the cooperative development of university and industry economy based on Internet of Things technology. Transactions on Emerging Telecommunications Technologies, 12(5):e3917.

[10] Weber F, Lehmann J, Graf-Vlachy L. 2019, Institution‐Infused Sensemaking of Discontinuous Innovations: The Case of the Sharing Economy. Journal of Product Innovation Management, 36(5):632-660.

[11] Laoutaris N. 2019, Why Online Services Should Pay You for Your Data? The Arguments for a Human-Centric Data Economy. IEEE Internet Computing, 23(5):29-35.

[12] Paik Y, Kang S, Seamans R. 2019, Entrepreneurship, innovation, and political competition: How the public sector helps the sharing economy create value. Strategic Management Journal, 40(4):503-532.

[13] Rasha, Makhlouf. 2020, Cloudy transaction costs: a dive into cloud computing economics. Journal of Cloud Computing, 9(1):1-11.

[14] Uy A, Hn A, Jk A. 2020, Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases. Future Generation Computer Systems, 103(6):58-78.

[15] Bourguignon H, Gomes R, Tirole J. 2019, Shrouded transaction costs: must-take cards, discounts and surcharges. International Journal of Industrial Organization, 63(MAR.): 99-144.

[16] Palmeira M. 2021, The interplay of micro-transaction type and amount of playing in video game evaluations. Computers in Human Behavior, 115(5):106609.

[17] Erdin E, Cebe M, Akkaya K. 2020, A Bitcoin payment network with reduced transaction fees and confirmation times. Computer Networks, 172(12):107098.

[18] Jaiswal D P, Kumar S, Mukherjee P. 2020, Customer Transaction Prediction System. Procedia Computer Science, 168(11):49-56.

[19] Hb A, Rg B, Jt C. 2019, Shrouded transaction costs: must-take cards, discounts and surcharges. International Journal of Industrial Organization, 63:99-144.

[20] Puri V, Sachdeva S, Kaur P. 2019, Privacy preserving publication of relational and transaction data: Survey on the anonymization of patient data1*. Computer Science Review, 2019, 32(MAY):45-61.

[21] Nti I K, Adekoya A F, Weyori B A. 2021 A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction. Journal of Big Data, 8(1).