Analysis of online CSR message authenticity on consumer purchase intention in social media on Internet platform via PSO-1DCNN algorithm

• Main Authors: Li, Man, Liu, Fang, Abdullah, Zulhamri
• Format: Article
• Published: Springer Science and Business Media Deutschland GmbH 2024
• Online Access: http://psasir.upm.edu.my/id/eprint/105711/; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85162024658&doi=10.1007%2fs00521-023-08739-y&partnerID=40&md5=9c611322558314094aa5b73400e16c03

With the deepening of the research on corporate social responsibility (CSR), CSR has become increasingly important to enterprises. It can affect consumers’ willingness to purchase enterprise products. Corporate social responsibility activities are used by many multinational corporations as a competitive business strategy to build a favorable corporate image in the eyes of consumers. Social media, made possible by developments in web and mobile technology, ushered in a new era of advertising on digital platforms. In this context, it has become an important topic to study the relationship between the authenticity of online CSR communication messages and consumers’ purchase intentions. This work proposes an algorithm based on PSO-1DCNN joint optimization to analyze the impact of online CSR information authenticity on consumers’ purchase intention in social media on the Internet platform. First, this work uses one-dimensional convolutional neural network (1DCNN) to model the relationship between the two. This model uses multichannel convolution feature, BN and Dropout strategy to promote performance for the model. Secondly, this work designs optimization measures from inertia weight and learning factor to build an improved particle swarm optimization algorithm (IPSO). Third, this work uses IPSO to optimize the initial network parameters of 1DCNN to build IPSO-1DCNN. The model has stronger convergence ability and convergence speed, in addition, it also has stronger global optimization ability. Fourthly, systematic experiments are carried out for the IPSO-1DCNN designed in this work, and the experimental data verify the superiority of this method. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.