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Bayesian network in B2B policy application
6th IEEE International Conference on Intelligent Computing and Signal Processing, ICSP 2021 ; : 1031-1035, 2021.
Article in English | Scopus | ID: covidwho-1232283
ABSTRACT
Under the influence of COVID-19, more and more e-commerce companies are in urgent need to understand the causality and impact probability of B2B strategies, and formulate effective cooperative operation strategies. Although recently, few articles believe that B2B strategies are beneficial to improving enterprise profitability. In this paper, data from 362 takeout APP users were surveyed by questionnaire. Through machine learning, Bayesian Networks (BNs) algorithm was adopted to analyze the B2B strategy adopted by the takeout industry for the impact of COVID-19. Firstly, BNs analysis structure is designed according to prior knowledge and market information. Then the maximum likelihood estimation is used to calculate the conditional dependence probability. Finally, the effectiveness of BNs model is verified by training set and test set. The results show that the causal relationship between B2B strategies is highly concentrated in rider's daily health clock, and this strategy is also dependent on green passcode, highlighting the reliability of the windowing strategy. The contribution of this paper is to introduce BNs to B2B strategies and dependent probabilities, specifically providing a feasible way to find the causal relationship between strategies. © 2021 IEEE.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 6th IEEE International Conference on Intelligent Computing and Signal Processing, ICSP 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 6th IEEE International Conference on Intelligent Computing and Signal Processing, ICSP 2021 Year: 2021 Document Type: Article