嘉宾介绍
2022年毕业于香港城市大学商学院，获资讯系统专业博士学位。主要研究兴趣为商业人工智能，机器行为，人工社会智能。研究成果发表在Scientometrics, ACM SIGMIS Database，情报学报等国内外期刊。
讲座介绍
With the groundbreaking advancements in large language models (LLMs), the domain of consumer-brand relationship (CBR) management has witnessed a progressive integration of LLMs into various customer-brand interactions. This integration has given rise to a personalized and dynamic paradigm of brand relationship management. However, current LLMs research predominantly focuses on physical or intellectual capabilities, while social intelligence remains largely ignored. This study investigates the application of LLMs for individual-level CBR measurement through analyzing online customer reviews. We propose the RICE (Real-time Individual Consumer-brand relationship mEasurement) approach that leverages LLMs to measure CBRs. Using a dataset from a Chinese fashion brand, we demonstrate that RICE achieves high consistency with human annotations in measuring CBRs and significantly enhances the prediction of customer purchase behaviors. Our research provides an effective CBR measurement approach for both academic research and practical applications in CBR management during the AI era.
