A Robust Prediction of Different Factors Influencing Chinese Consumers Intentions to Purchase Creative Products Using Deep Learning Methodology

The creative products industry in China has evolved over the last several years, shifting from a more conventional model to one that caters to consumers’ unique tastes in fashion. Creative products are in high demand because people want them for more than just aesthetic reasons; they want them to express their personal style. China must change its spending habits to be more sustainable since its fast economic expansion and overconsumption have caused the worst environmental degradation in the world. Consumers’ good attitudes and growing public interest in sustainability belie their stated intentions to act in a way that is compatible with these sentiments. By delving into the attitude-behavior intention gap, this study seeks to reveal the psychological causes and hurdles of sustainable creative product behavioral intents among Chinese consumers. In this paper, a novel approach to consumer intention prediction is presented, Predictive Learning for Consumers Intention Evaluation (PLCIE). To test the efficacy of this model, it is cross validated with the traditional learning technique known as Random Forest Classification (RFC). Undergraduates from several institutions were surveyed statistically using a quantitative study with 1,600 samples. Non-probability sampling approaches, such as judgmental, quota, and convenience samplings, were utilized in the survey administration. Researchers and practitioners in the field of creative product creation may use the present findings to fine-tune sustainable programs and marketing strategies in China, and the perspective behaviour difference framework for responsible customer behavior is validated by the results of this study.