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.

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.

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.