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Market-Based Solutions to Vital Economic Issues


Kenan Institute 2024 Grand Challenge: Business Resilience
Market-Based Solutions to Vital Economic Issues

Emerging Technologies


With a growing emphasis on prioritizing user privacy and data protection, says UNC Kenan-Flagler’s Longxiu Tian, information collected directly from customers becomes the key to solving the puzzle of personalization and accurate targeting for marketers.

Longxiu Tian, UNC Kenan-Flagler assistant professor of marketing, shares his expertise in customer strategies and his perspective on firms' attempts to build trust and profitability with innovative consumer data management strategies.

This article examines the role of Generative Artificial Intelligence (GenAI) in the context of marketing education, highlighting its substantial impact on the field. The study is based on an analysis of how GenAI, particularly through the use of Large Language Models (LLMs), functions. We detail the operational mechanisms of LLMs, their training methods, performance across various metrics, and the techniques for engaging with them via prompt engineering.

The Center for Interuniversity Research in Quantitative Economics, known by its French acronym CIREQ, will host an econometrics conference May 10-11 honoring UNC Kenan-Flagler Business School’s Eric Ghysels.

The New York Times examines the views of David Autor, Ford Professor of Economics at MIT and a 2024 Kenan Institute Distinguished Fellow, on artificial intelligence and potential benefits for the middle class.

Technology acquisitions are increasingly prevalent, but their failure rate is notoriously high. Although extant research suggests that collaboration may improve acquisition success, relatively little is known about how firms cultivate collaboration during postmerger integration (PMI) of technology acquisitions. Using inductive multiple-case methods, we address this gap.

For small businesses, AI promises to handle financial and operational tasks, freeing up workers for other duties and creating new efficiencies. We offer seven focal points for small businesses planning for AI integration.

The paper uses structured machine learning regressions for nowcasting with panel data consisting of series sampled at different frequencies. Motivated by the problem of predicting corporate earnings for a large cross-section of firms with macroeconomic, financial, and news time series sampled at different frequencies, we focus on the sparse-group LASSO regularization which can take advantage of the mixed-frequency time series panel data structures. 

This paper explores the ups and downs of innovation and productivity growth in the US economy and potential connections to the ups and downs of business dynamism and entrepreneurship over the last few decades.

The advent of artificial intelligence (AI) tools necessitates the development of human skills that allow workers to use these new technologies to create value that AI tools cannot on their own.

This paper surveys the recent advances in machine learning method for economic forecasting. The survey covers the following topics: nowcasting, textual data, panel and tensor data, high-dimensional Granger causality tests, time series cross-validation, classification with economic losses.

UNC-Chapel Hill Professor Kurt Gray discusses how research can help us understand – and navigate – our rapidly changing professional and social lives.

Centers & Initiatives