WCSE

Utilizing Deep Learning Neural Network to Predicting Factors Influencing Actual Purchase for Clothing Apparel During the COVID-19 Pandemic

Abstract— The clothing industry has been increasing in revenue over the years. However, the buying behavior to actually purchase clothing has been affected during the COVID-19 pandemic. Due to the implemented lockdown and the strict health protocols, people have bought less materials. With that, this study utilized Deep Learning Neural Network using Python 3.8 to predict factors influencing actual purchase of clothing apparel during the COVID-19 pandemic. With 457 respondents, there were a total of 26,506 datasets. After data pre-processing, normalization, and optimization, a 95% average accuracy with 2.384 standard deviation was seen for social norm factor, followed by promotion, product, and perceived behavioral control. These factors were considered significant that affected actual purchase intentions for clothing apparel during the COVID-19 pandemic. The findings of this study could be utilized to entice customers to purchase clothing apparels during the COVID-19 pandemic. Lastly, the results and model utilized could be applied for clothing industries worldwide.

Index Terms— deep learning neural network, actual purchase, clothing apparel, COVID-19 pandemic.

Ardvin Kester S. Ong
School of Industrial Engineering and Engineering Management, Mapua University, Philippines
Ma. Janice J. Gumasing
School of Industrial Engineering and Engineering Management, Mapua University, Philippines
Yogi Tri Prasetyo
School of Industrial Engineering and Engineering Management, Mapua University, Philippines
Maria Arielle Cleofas
School of Industrial Engineering and Engineering Management, Mapua University, Philippines

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Cite: Ardvin Kester S. Ong, Ma. Janice J. Gumasing, Yogi Tri Prasetyo, Maria Arielle Cleofas, " Utilizing Deep Learning Neural Network to Predicting Factors Influencing Actual Purchase for Clothing Apparel During the COVID-19 Pandemic, " WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications, pp. 1597-1603, Sanya, China, April 15-18, 2022.