Did you know the human brain is actually set up to handle information on a narrative level? Turns out it’s not just that facts are more interesting and engaging when they’re embedded in a story — we’re actually hard-wired to organize and understand information better when it’s delivered in a context that includes progression of thought and memorable key points.
Presented with the challenge of explaining the meaning and methodology behind the data- and dialogue-driven model of effective leadership, I decided weaving abstract concepts, factual elements and research findings into a narrative sequence of events was the ideal way to disseminate the information.
The Beginning: Life and Death of a Company
The story begins years ago, when I worked as an HR generalist for a Boulder, Colo., start-up company that grew from 100 to 1,500 people over two years. The company went public while I was there, and the culture changed quickly. At the core of the fast growth and success was employee voice. The interplay of ideas and information was fluid and fast, and this interaction led to the success of the firm.
When the company went public, the level of bureaucracy increased, and the voice of the employee became lost. Top executives had too many voices bombarding them (investors, accountants, media, etc.), and the flow of information was altered. When employee voice was lost, the company’s performance declined. Eventually, this company went out of business.
This experience led me down a new career path in research. I signed up for a doctoral program, graduated and joined the faculty at Cornell University. It was there I started a large-scale research project designed to discover whether the lessons I learned at that Colorado start-up applied to other organizations.
Research that Grew from the Death Experience
What I learned from the research provides the content for the next chapter. In 1993, I started collecting data from thousands of firms, examining the factors that predicted long-term performance. My focus was on initial public offerings (IPOs), which I like to call the “fruit flies of management” — they represent a large variety of industries, they are young and old, they are all over the world, and they live and die quickly.
I started the IPO studies in an effort to understand what predicted outcomes such as stock price growth, earnings per share growth, sales growth and survival. I chose survival because I knew the financial community would argue the right or wrong of other measures, but “alive or dead” would be unequivocal.
Assisted by a small team, I studied the prospectus of all firms going public in multiple years from 1988 to 2000. We coded more than 300 variables from each prospectus, including factors the financial community considered important to performance, as well as our “people management” items, and we sent surveys to the executives. We tracked which firms lived and died and how they died. We added financial performance measures to the database. Our first discovery was that the most powerful predictor of long-term performance was what we called “HR value”: the degree to which a firm valued people vis-à-vis other assets.
One Discovery Leads to Another
When the findings hit the press, lots of people had opinions about it. The critics came out in force, and we extended the research to studies within organizations. The second phase of the research focused on this question: “What is it about valuing people that really makes a difference?”
We found valuing people alone was inadequate for success, but firms that valued people under conditions of high urgency outperformed their peers. The plot thickened — we found that as urgency increased, firms that matched urgency levels with practices aimed at valuing people succeeded. It was this balance that led to success.
The CEO Challenge
Once CEOs started getting interested in the story, they asked more from the team. They wanted to know how we could help them improve the performance of their organizations. They challenged us to turn the research into something tangible.
Our first project was for the CEO of Indus International, which was launched with a very long employee survey. The data helped us refine our measures, but it didn’t thrill the CEO — it was too much data, too late and not focused on his present needs. The upshot was an ultimatum: Provide more value or lose Indus’ participation in the study.
At the time, we knew the following:
I still thought data was the key to our next step in this work, but I could not conduct long surveys. Thus, I had to make some choices. Given what we knew, our focus became sense of urgency, measured via survey questions and by studying rates of change in organizations.
Our research led us to the insight that sense of urgency at the firm level could be operationalized at the individual level as employee energy. Energy — which is a more employee-friendly word than “urgency” — reflected what we were studying as sense of urgency at the enterprise level. Energy, at its core, is basically motivation (in fact, our theoretical models come out of the motivation literature). The unique nature of our measurement of energy, however, found you can have too much of it — you can be overly energized, which can be likened to too much energy sent to a light bulb or exercising at a level above your target heart rate.
Turning the Page on Our Data Strategy
The use of energy and the discovery of the importance of variance led us to propose changing our data strategy. In 1996, we conducted our first “pulse survey” using only two questions. We asked employees to rate their energy at work and then comment about what affected their energy.
Here’s what we learned from these early studies:
We also came to know that the measurement process we developed to deal with the frustrations of a CEO and management team at Indus led to an intervention that affected the two keys to firm-level success we discovered in our research.
Our data-collection process focused on measuring sense of urgency (via energy). The data process shed light on what was affecting energy, so managers could make swift and tactical changes to keep sense of urgency at the right level. At the same time, the act of talking to employees about the data, taking action based on the data and engaging in dialogue made people feel more valued.
The data and dialogue process that grew from our research gave employees voice in a way that led to immediate positive changes in the organization. The use of data and dialogue affected sense of urgency and value and led to improved performance.
The Conclusion: Art Meets Science in Data- and Dialogue-Driven Leadership
This story started in 1976. It is now more than 30 years later, and what began as an insight about employee voice led to a journey that ended with the development of data- and dialogue-driven leadership methodology. Since doing the original studies, I have worked on developing technology to support the data and dialogue processes, created training programs to help managers learn to use data and dialogue in their daily work and conducted more in-depth studies with firms going through mergers, IPOs, acquisitions, etc.
The advancement of technology provides an opportunity to take the data and dialogue processes and tools to a new level, where managers not only are engaging in dialogues with their employees but where employees share data and use their own dialogues to innovate, improve performance, advance their own knowledge and careers and more.
We’ve also learned, however, that even with all the technology available, the dialogue part of the model is the most powerful. This means teaching managers how to have meaningful conversations remains a critical skill.
The art of dialogue, combined with the science and technology of data, promises to help improve organizational performance in any industry, anywhere in the world.
– Theresa M. Welbourne, Ph.D., is founder, president and CEO of eePulse Inc., adjunct professor of executive education at the University of Michigan Business School and editor in chief for Human Resource Management. She can be reached at firstname.lastname@example.org.