From helping to make decisions to designing processes, machine learning and artificial intelligence have transformed the way businesses function. Yet, some academics argue that the ways in which academia can use machine learning and artificial intelligence to develop theories and provide them to businesses have been largely ignored.
Because of this, the gap between industry and academia has only widened. In their research paper “Theories in Flux: Reimagining Theory Building in the Age of Machine Learning”, William & Mary Business Professor Monica Tremblay and Rajiv Kohli, the John N. Dalton Memorial Professor of Business at W&M, argue why it is important for this gap to be closed and give examples of how academia can use machine learning and artificial intelligence to close it. The article, which was coauthored by Nicole Forsgren at GitHub, Inc., was published in a special edition of Management Information Systems Quarterly earlier this year.
In addressing the difference between industry and academia, Tremblay and Kohli note that the importance of replicability in theory building for academia does not necessarily apply to industry. When the two went to Silicon Valley to start their research, Tremblay learned that as opposed to academia, “industry does not think that replicability is important because all their decision making is highly contextual and constantly changing. The idea of replicable theories is like having a swiss army knife — it kind of works to help, but not great.
"(Industry) would rather have a precision knife to go in and do exactly what they want it to do. Therefore, academics need to make theories that are more flexible and that can explain a highly contextual scenario.”
In turn, a great middle ground between providing theories in a quick manner while also ensuring that they are accurate, Kohli and Tremblay suggest, is the use of machine learning and artificial intelligence by academia.
“In the past, we would have to do a survey of 200 people, analyze it, and come up with new knowledge," Kohli said. "Now that we have 2 million records that can be analyzed very quickly (with machine learning), we can have more confidence in generating new knowledge without having to do the very rigorous analysis that we are used to doing."
As Kohli and Tremblay have experience in both industry and academia, they know the importance of bringing this way of thinking to W&M's Rayond A. Mason School of Business.
“We want to be a contributor to adding new knowledge," said Kohli. "To do this, we need to be relevant to industry and engage with it. If we are not relevant to them, businesses don’t see us as useful and we will be doing research in our own labs with no connection to the outside world."
According to Tremblay, by creating theories that are useful to industry, which machine learning and AI help ensure, “academia can uncover some really interesting business phenomena that can contribute to practice.”
Clinical Associate Professor of Business Joseph Wilck, who specializes in operations and information systems management, also sees this type of research as beneficial to the Mason School.
“I think what makes the business school a perfect place for machine learning and AI research is to ensure that it is applied across a variety of industries, and that students (graduates) are taking the latest knowledge into the workplace to enable further improvements in these different industries.," said Wilck. "Academia, and in particular business schools, are perfectly positioned and aligned to enable other industries since we are not directly tied to one over the other, and we teach knowledge and skills that are necessary for all industries.”