ARTIFICIAL INTELLIGENCE AND and machine learning could offer valuable insights into the link between a CEO’s communication style and an organization’s performance. Researchers examining the implications of this possibility include Prithwiraj Choudhury, the Lumry Family Associate Professor of Business Administration, and Tarun Khanna, the Jorge Paulo Lemann Professor, both of Harvard Business School (HBS) in Boston, Massachusetts. Their co-authors include Dan Wang, the David W. Zalaznick Associate Professor of Business, and doctoral
student Natalie Carlson, both of Columbia Business School in New York City.
The team used machine learning to analyze 130 video interviews with CEOs.
The videos were part of Creating Emerging Markets, an oral history project
conducted at HBS by Khanna and Geoffrey Jones, the Isidor Straus Professor of
Business History. The team examined the vocabulary and speech patterns of the
CEOs, from the number of times they used certain words to the extent to which
they moved from subject to subject (what the researchers call “topic entropy”).
Next, the researchers analyzed the emotional content of each speaker’s words—
in particular, how often he or she expressed positive and negative emotions.
Finally, they rated each speaker’s facial expressions across eight emotions:
anger, contempt, disgust, fear, happiness, neutrality, sadness, and surprise.
Because machine-learning technology allowed the researchers to examine
words, tone, and facial expressions simultaneously, they could better detect
what the CEOs didn’t say. For instance, the technology revealed contradictions
between verbal and nonverbal expression. As Choudhury explains in an article
in HBS Working Knowledge, “You might say something positive, but a negative
facial expression may create the opposite meaning.”
Using this data, the researchers identified five communication styles among
these leaders. These include positive-leaning styles such as excitable and dramatic,
and negative-leaning styles such as stern, rambling, and melancholy. The
team then looked for patterns in performance at each leader’s organization. For
example, the higher a leader’s dramatic score, the fewer mergers and acquisitions
took place at that person’s company in the year following the interview.
“With computing power going up stratospherically in the last 20 years, there
are now many opportunities for better analyzing many of the things going on in
business,” says Khanna in HBS Working Knowledge. For example, a leader’s voice
patterns during calls with shareholders could be analyzed for clues about the
firm’s performance. “There is a whole
ocean of data out there,” Khanna
adds, “that people aren’t using.”
Read “Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles.” The paper is forthcoming in
Strategic Management Journal.