Fashion Printing Technology Diffusion: Big Data Analytics

Autor: Marguerite Moore, Yanan Yu, Lisa Parillo-Chapman
Rok vydání: 2020
Předmět:
Zdroj: Pivoting for the Pandemic.
DOI: 10.31274/itaa.11718
Popis: Digital printing technology (DPT) represents a core innovation that is currently revolutionizing the global decorated apparel market by automating printing process, facilitating customization, and reducing energy and production lead time. However, the fundamental understanding of the emerging DPT market remains unexplored due to its novelty. This study aims to identify DPT diffusion patterns over the past decade in the U.S. market and establish a predictive user profile using social media based data analytics along with data mining and traditional statistical modeling. The visualized DPT diffusion pattern depicts an s-shaped curve, which highlight the propensity that as new technology evolves over time. Additionally, the outcome profile suggests that likely DPT adopters reside in locations that reflect higher levels of education (bachelor’s degrees or higher), relatively young populations (i.e., between 19-34 years of age), proportionately higher incomes generated from art and design occupations, but lower levels of household incomes.
Databáze: OpenAIRE