Since the inception of business, organizations have searched for clues to help identify and select successful leaders. They have searched for men and women of vision with that rare combination of traits that help them serve as motivator, business driver, and authority figure. The concept of leadership has been widely observed and frequently studied, but a thorough understanding of what defines successful leadership has always remained just out of reach.
I wanted to find the answer(s) to the age-old question, “What makes a great leader?” After studying the behavioral attributes of thousands of business leaders, the resulting data could reveal commonalities that define strong leadership. What similar patterns or behaviors might possibly be found over and over again? By forming a concise “leadership recipe,” the never-ending search for quality leaders could finally be simplified to a standardized set of characteristics that might help predict successful leadership in any organization. But could science and behavioral psychology be successfully applied to extract these leadership “revelations” from the data?
I centered my investigation on 30 behavioral leadership models that were used across 24 unique companies encompassing 4,512 business leaders from all performance levels. These companies included several from the Fortune 500 list. Each of the 30 leadership models was analyzed to identify the most common behaviors that differentiate higher-performing leaders from low-performing leaders. The findings compiled from this data set revealed new evidence that must serve as a foundational piece of every leadership hiring or training endeavor.
Expectations of the Study
Leadership is a concept that is difficult to capture. You know it when you see it, but it is difficult to quantify. The components of leadership are often examined and observed, but the ability to predict successful leadership has thus far avoided the confines of a repeatable recipe. Many approaches have been used in an attempt to document commonalities among successful leaders, but only with mixed results at best. Taking a new approach to the issue, I set out to study the behavioral characteristics of successful leaders in comparison to leaders of lower performance levels. The two main objectives of this study were:
- To identify the three most important behaviors that are predictive of leadership performance.
- To identify the level or degree of the three most common behaviors that are predictive of leadership performance.
Behavioral Leadership Models
Before discussing the study findings, it is important to lay the groundwork of this study using the behavioral leadership model. The behavioral leadership model is the cornerstone to this research study since it is designed to capture the behavioral preferences of successful leaders currently working in the position. Essentially, the behavioral leadership model captures the unique combination of behaviors that predicts success. Each unique model was created using the same methodology, but the customization was made possible by using performance data related to a specific position. To create a behavioral leadership model, each organization used the following three-step process.
Define Success– Traditionally, leadership success is determined by education, experience, potential, or other non-performance related measures. For this study, success was determined by actual performance on the job. We want to better understand the behaviors of the real leaders who produce results on a daily basis.
To keep the study focused on leadership productivity, each company defined success based on their business practices, and their leaders were evaluated on their ability to produce the desired business results. Those who did not produce the desired outcomes were considered ineffective leaders while others who produced the desired results were considered successful leaders. Each organization utilized specific performance data captured from those leaders actively engaged in the leadership role. The types of performance data collected ranged from subjective data (i.e., performance evaluations, soft achievement ratings, etc.) to objective data (i.e., store sales, percent to plan, profit metrics, etc.).