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Shopping mall are undergoing a period of crisis, leading to the decline and closure of multiple complexes (Escudero-Gómez, 2024). In recent years, shopping mall formats have continually adjusted to meet consumers' diverse and personalized demands (Sung et al., 2023). Notably, commercial gentrification has had a profound impact on the transformation of business types and operational models in shopping malls, particularly in major cities like Shanghai. It refers to the process where low-income areas are redeveloped into high-end markets, replacing local retail with upscale brands, leading to the loss of local consumers and shifts in regional economic dynamics (Chen and Zhang, 2024). This phenomenon reflects changes in commercial space, consumer behavior, social class, and urban renewal, with global migration further diversifying goods and services in cities like Shanghai, New York, Berlin, and Tokyo.
As primary venues for social commercial activities, shopping malls' transformations have significantly impacted traditional retail and service models in various communities. The transformation of shopping malls, as primary venues for social and commercial activities, has significantly affected traditional retail and service models in diverse communities. Emerging high-end brands may gradually supplant traditional retail models, with this trend exhibiting regional variations. While previous research has investigated the various effects of commercial gentrification, limited attention has been given to the potential uses of artificial intelligence (AI) models, specifically the ChatGPT-4o model, in this area. ChatGPT-4o utilizes advanced deep learning technology to process and analyze extensive data, especially in complex scenarios, thereby addressing the limitations of conventional evaluation methods (Luo et al., 2024). The analytical capabilities of ChatGPT-4o offer a new instrument for examining commercial gentrification, facilitating thorough assessments from various perspectives.
This study attempted to employ both quantitative and qualitative approaches to examine the spatiotemporal development of shopping malls in Shanghai from 2019 to 2024, utilizing the framework of commercial gentrification theory. The study developed ten essential evaluation factors derived from extensive data obtained from public review platforms, encompassing details about shopping centers and their internal categories. The assessment of these factors was conducted using the large language model ChatGPT-4o, which evaluated them according to: brand tier preferences, brand diversity, internationalization level, localization level, degree of digitalization, operational innovation, trend-following tendencies, marketing preferences, event quality, and visitor interaction. The analysis also examined the temporal trends of these factors. Interviews with urban planning experts, business owners, and local residents will yield insights into specific reports on shopping malls. The predictions generated by the AI model will assist in evaluating the accuracy and limitations of ChatGPT-4o in the context of commercial gentrification.
This research would offer an in-depth analysis of Shanghai’s shopping mall transformation through commercial gentrification and explores the practical application of AI models to aid industry professionals in decision-making. Furthermore, the findings will offer policy guidance and strategic recommendations for the sustainable development of commercial areas.
References
Escudero-Gómez, L.A. (2024). Shopping mall challenging decline: Competitive strategies in three case studies from Madrid's urban area, Journal of Retailing and Consumer Services, 79, 103826.
Sung, E., Chung, W.Y., & Lee, D. (2023). Factors that affect consumer trust in product quality: A focus on online reviews and shopping platforms, Humanities and Social Sciences Communications, 10, 766.
Chen, T.L. & Zhang, Y. (2024). Exploring the composition features of commercial gentrification - a case study of Taipei City, Cities, 154, 105391.
Luo, X., Rechardt, A., Sun, G., et al. (2024). Large language models surpass human experts in predicting neuroscience results, Nature Human Behaviour.
| Keywords | Commercial Gentrification, Shopping Malls, GPT-4o, New Methodologies, Business Sustainability |
|---|---|
| Best Congress Paper Award | No |
