Machine learning helps to predict leadership performance
Even though many leaders are charismatic, extrovert and transformative, they struggle with their performance if they work in an environment that does not suit them. It’s one of the key findings of a research conducted by Dr. Brian Spisak, dr. Paul van der Laken and Brian Doornenbal, at Vrije Universiteit Amsterdam, and which is published in The Leadership Quarterly.
06/24/2019 | 2:20 PM
Spisak researched how leadership performance can be predicted based on personality tests, using machine learning. With the results of this research, organizations are able to better select on suitable personality traits of leaders, but also make better use of existing data within the organization.
“One of the biggest surprises was that our models became significantly better at predicting when we gave more insight into the context of leaders, such as type of industry and size of organization. It turns out that the environment of an organization adds to being able to predict leadership performance.”
For examples you can look at just about any other leader, such as Theresa May, Mark Zuckerberg and Elon Musk. “They get a lot of credit and criticism for success or failure, when deeper contextual forces are likely at play.” The research however shows that there is a deeper complexity in the situation working independently from the leader. Machine learning can help to better appreciate this complexity, according to the research.
Personalized advertisements, recommendations for music or series; machine learning continues to have an increasing influence on our daily lives. The applications of machine learning, however, continue to expand. It can be used to recognize and use certain patterns, not just in selecting leaders, but also when building teams, making strategic decisions, developing marketing plans or testing the potential of investments.
Spisak sees opportunities to use machine learning to work through large amounts of data within organizations. “Machine learning is a powerful tool and can be used, for example, to analyze the sentiment of interactions of text mining or to unravel the consequences of physical distance through geo-data.” According to Spisak, machine learning has a major influence on future developments of organizations and how they can prepare themselves for those changes.
However, Spisak warns to stay cautious. “Machine learning offers the opportunity to recognize patterns, but that does not mean that we have to act on them. Machine learning is a power engine, but inadvertently using the wrong fuel in the engine has the potential to damage both the engine and the operator.
The article has been published in The Leadership Quarterly.