Research in Action
From corporate governance to career services, marketing and finance, Lindner faculty are pioneering research and practical applications of artificial intelligence that redefine how businesses operate, make decisions and connect with people.
Finding AI's Place in the Executive Suite
Department Head and Professor of Management Joanna Campbell, PhD, laid the groundwork for the evolving role of AI in corporate governance at the 2024 European Academy of Management Conference with her keynote address “Towards a Holistic View of Corporate Governance.”
Campbell explored the evolving role of AI in corporate governance, highlighting its implications for decision-making, risk management, efficiency and more. The discussion centered the rise of “agentic technologies” that augment human agency in decision-making.
Key takeaways included the increasing pressure on corporate leaders to engage in social issues and the challenges of AI ethics, bias and oversight. Overall, the presentation raised critical questions about AI’s future in boardrooms, including the potential for AI-powered directors.
Career Services, Empowered by AI
Experiential Learning & Teaching Excellence Assistant Director Susan Bailey presented “AI to the Rescue: Saving Time in Career Services to Focus on What Matters” at the Cooperative Education and Internship annual conference.
After participating in Lindner’s annual Communities of Practice (CoP), Bailey began toying with ways to use AI to teach students and to streamline her own work processes. In her presentation, she explores ways to use AI tools to analyze student data to tailor career service strategies; to improve processes like resume review, interview preparations and salary negotiation; and to integrate into the classroom curriculum.
“My approach with AI has always been practical and twofold: how can it help our students, and how can it help us, so we have more time to focus on the important things like our students?” said Bailey.
When Tone of Voice Matters
Research indicates that consumers are more easily persuaded to act in specific ways by people who are similar to them in looks, behavior and beliefs. But does similarity’s effect on persuasion extend to similarity in how people sound? The article “Vocal Similarity, Trust, and Persuasion in Consumer-Recommender Interactions” from Kimberly Hyun, PhD, assistant professor of marketing, explores how similarity in voice influences consumer choice.
To investigate this phenomenon, Hyun and her fellow researchers used machine learning data from Shark Tank pitches and Kickstarter campaigns to show how a spokesperson’s or entrepreneur’s voice that is closer to the audience’s or investor’s results in higher persuasion, leading to greater business success.
The research provides a deeper understanding of consumer-persuader interactions, including new tools for vocal analytics. Sales and customer relations can particularly benefit from this research.
“This paper has a clear practical implication for increasing persuasion by both human and AI recommenders. This research calls for a focus on personalization when matching a persuader (salesperson) to a consumer. For instance, call centers could capture a customer’s voice in real time and simultaneously adapt the AI agent’s voice to be more similar to the customer’s,” noted Hyun.
AI's Financial Revolutions
AI financial advisors. Satellite imagery for inventory management. Tone analysis to predict stock movements. The next wave of business technologies is here and they’re shaping every business sector, including finance. Mehmet Saglam, PhD, Johnson Professorship, associate professor of finance, is working to understand the full spectrum of AI’s impact in the financial sector.
Of particular interest to Saglam is retail trading. Multiple AI interventions allow for the investigation of retail trading with real-world implications for the investment market. Retail traders can create market instability by reacting to news and buying or selling en masse. This was evident in the past with GameStop, where the stock price surged dramatically due to coordinated buying. Retail trading can push stock prices away from their fundamental values, leading to potential losses when prices correct.
By analyzing transaction data, AI can predict whether a trade was made by a retail trader. Retail traders tend to hold onto stocks that are losing value, which is a behavioral bias that can lead to suboptimal investment decisions. Conversely, retail traders often sell stocks that are performing well too soon, missing out on potential future gains. Understanding retail trading patterns helps analyze behavioral biases and improve investment strategies. Identifying retail trades can help in assessing their impact on market dynamics and price movements.