Project Description
This project consists of two studies. The first study explores how DEI-related content shared by firms on Twitter (now X) influences the digital engagement of their customers. Previous research has shown that brand-generated content on social media plays a critical role in marketing success, and our prior study indicates that DEI-related content generally enhances brands’ digital engagement, with this effect moderated by factors such as the political spectrum of their followers and the type of firm. This new project seeks to further investigate the impact of firms’ DEI strategies on Twitter, focusing on variables such as the authenticity, relevance, and emotional-cognitive aspects of DEI-related content. It also examines whether these strategies affect firms’ financial performance. To support this research, we have developed machine learning models to detect DEI-related content and assess the political spectrum of firm followers. We will also develop new models to measure more nuanced concepts such as authenticity and relevance, as well as econometric models to analyze the effects of these communication patterns. The second study investigates how large language models (LLMs) can predict the outcomes of oral communication between sales representatives and customers. It aims to uncover effective strategies in business communication by applying LLMs to identify key cues for successful conversations. Additionally, we plan to build a system to enhance the communication strategies of sales representatives.
Prerequisites
BUS 202 or equivalent
Special Comments
Project Information (subject to change)
Estimated Start Date: 6/2/2025
Estimated End Date: 8/8/2025
Estimated Project Duration: 10 weeks
Maximum Number of Students Sought: 1
Research Location: Remote
Contact Information: Lingshu Hu (email: lhu@wlu.edu)