Mix one part advanced analytics with one part Expectancy Theory, and the result should be an organizational culture that you can shape.
Why do people do what they do? As a leader, this question must keep you up at night sometimes.
You go through great pain and expense to create a mission, a clear vision, a strategy, and a base philosophy for how people should act and yet, some employees don't seem to get the memo -- or the training, emails, posters, town hall addresses, or anything else you spent a king's ransom to produce.
The data scientists in your organization are key to solving this problem, because you can apply advanced analytics to motivational theory when understanding and shaping your organization's culture. We'll look at an oldie-but-goodie motivational theory called Expectancy Theory, and explore how data science can be used to undergird its application within your organization.
What did you expect?
Originally submitted by Victor Vroom in the 1960s, Expectancy Theory attempts to explain why people behave a certain way in your organization. As with most theories, its practical application can be challenging, but that's where data science comes in.
Expectancy Theory is broken down into three beliefs that the performer has about taking an action: expectancy, instrumentality, and valence.
- Expectancy is a person's belief that they will achieve a goal. For instance, you decided to read this article because, in part, you felt you could complete the article and understand it.
- Instrumentality is the belief that achieving your goal will produce some sort of reward. By reading this article, you'll learn information that you can apply within your organization.
- Valence is how much value you place on the reward. If this information helps in solving one of your challenges, then it's absolutely valuable.
Expectancy Theory can have a significant effect within your organization if applied effectively. Behavioral change specialists like myself use Vroom's theory and derivatives thereof to help organizations navigate through transformative change.
It's one thing to have a mission, vision, and strategy, but if your employees don't support your implementation with their behaviors, you'll never get to your intended destination. Expectancy Theory gives us a frame with which to understand their behavior, and data science gives us a powerful technology for its application -- one that most leaders don't have. Let's explore how data science can power each of the three beliefs of Expectancy Theory.
From potential to kinetic
The biggest area where data science can help is in the actual expectancy belief. When contemplating a behavior, a person must feel like they can accomplish their goal and control is a large contributor to this belief.
For instance, a salesperson can do all the right things and still end up with no sale; this can be frustrating. Data science can run an experimental design that will help the employees and the leaders understand exactly how much control their behavior has over the outcome. Using statistical methods, data scientists can determine causation between behavior and outcome: not only the effect but also the degree of influence. These insights help shape attitudes and reinforce the belief system.
Data science can also help with instrumentality. Your employees must have confidence in your reward system.
Earlier in her career, my wife was a hotel sales manager, working primarily on commission. Her general manager didn't pay her hard-earned commissions, and her boss told her not to make waves. How is she supposed to be motivated to make more sales under these conditions?
Data science can help with transparency by making this data visible to everyone. Connecting the data points between performance and rewards (compensation, bonuses, etc.) provides an important service to the organization, notwithstanding its analytic banality.
And finally we come to valence: How much do people care about these rewards? That's an important question that can be answered with data science, especially in a large organization. How often do leaders attempt to incentivize their workforce with meaningless rewards? Any change professional knows the answer is a lot! The value of any reward is in the eye of the beholder. A $100 gift card to Playland might mean the world to one person and almost nothing to someone else.
One area data science can help is with sentiment analysis around the company's reward system. As its name implies, sentiment analysis helps you gauge how the organization feels about your rewards. If the sentiment is good, there's no need to intervene. However, if certain areas of the reward system aren't valued, it may be time to take a closer look.
One of the biggest frustrations leaders have is their organizations zig when they should have zagged. If you're a leader that uses data science and you have or anticipate this issue, you'd be remiss if you didn't apply some of your analytic capability to understand and influence your organization's collective behavior.
You should use data science to fortify the three beliefs that comprise Expectancy Theory: expectancy, instrumentality, and valence. The next step is to use that information to take specific action to monitor and correct course if necessary.
And get some sleep -- a nice mixture of data science and Expectancy Theory works much better than chamomile tea.
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