Exploring the Impact of Generative AI on Cross-Border E-Commerce Brand Building in Chinese Tianjin's Manufacturing Sector

TL;DR

This paper explores how generative AI enhances productivity and brand building in Chinese Tianjin's manufacturing sector's cross-border e-commerce, highlighting the moderating role of strategic e-commerce approaches.

Contribution

It develops and tests a comprehensive model demonstrating AI's impact on productivity and branding, emphasizing the moderating effect of cross-border e-commerce strategies.

Findings

Generative AI significantly boosts productivity.

Productivity positively influences brand building.

Cross-border strategies moderate AI's impact on branding.

Abstract

This study investigates the influence of generative artificial intelligence (AI) on the brand construction of cross-border e-commerce companies in the manufacturing industry in Tianjin, China. We examine the direct effects of generative AI on productivity, the mediating role of productivity in the relationship between generative AI and brand building, and the moderating influence of cross-border e-commerce strategies by developing and testing a comprehensive model. Based on data collected from 210 manufacturing firms in Chinese Tianjin, the results show that generative AI significantly increases productivity, which positively affects branding. Moreover, cross-border e-commerce strategies were found to moderate the impact of generative AI on branding, underscoring the importance of these strategies for using AI technologies to compete successfully in the global marketplace. This study provides valuable theory, empiricism and practical contributions to understanding the role AI plays in manufacturing and electronic commerce. Besides, this study tests several hypotheses to quantify these impacts using a structured model that consists of independent, dependent, mediating and moderating variables. Information is collected through a comprehensive survey of manufacturers in Chinese Tianjin and analyzed to test our proposed model. This study was analyzed and summarized using quantitative analysis, regression and structural equations (PLS-SEM).