JOURNAL ARTICLE
Deep Learning-Based Cause-Related Marketing and the Impact of the Internet on MICE Events in the Context of the Epidemic.
Published In: Fluctuation & Noise Letters, 2024, v. 23, n. 2. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Shi, Kun; Cui, Boshi; Zhao, XinTong; Ma, Yuwei; Yang, Yang; Du, Zewen 3 of 3
Abstract
Since 2019, novel coronavirus pneumonia has been rampant around the world, and when outbreaks occur, Meetings, Incentives, Conferences and Exhibitions (MICE) events are often affected to varying degrees. In addition, in the context of the epidemic, consumers have increasingly taken the participation of MICE in charitable activities as a measure of their social responsibility and judged MICE events as good or bad accordingly. Therefore, the impact of deep learning-based good cause marketing and the Internet on MICE events in the context of the epidemic has attracted much attention. Based on the CiteSpace analysis, this study systematically reviewed the impact system of cause-related marketing on exhibition activities and fitted the neural network model with a single-factor inter-group experiment. The results show that when the complete data set is divided into 70% training set and 30% test set, the model with the training function of Train lm and seven hidden layers performs best in all models. This shows that in the process of charity marketing, the fit between consumers and charity activities determines the attitude and willingness of consumers to participate in charity marketing. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Fluctuation & Noise Letters. 2024/04, Vol. 23, Issue 2, p1
- Document Type:Article
- Subject Area:Marketing
- Publication Date:2024
- ISSN:0219-4775
- DOI:10.1142/S0219477524400200
- Accession Number:177219039
- Copyright Statement:Copyright of Fluctuation & Noise Letters is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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