Unraveling Biases in Organizational Change
Understanding Choice-Supportive Bias and Selection Bias
Before diving into how these biases operate within organizations, let's first define these two concepts from behavioral economics, and then, we will dig into some approaches that attempt to address them.
Choice-supportive bias
This is the tendency for individuals to retroactively ascribe positive attributes to an option they have selected. It's a cognitive bias that reinforces our decisions, making us believe that our choices are better than they actually are. This bias can lead to skewed perceptions and can obstruct objective decision-making. It can close our minds to alternative solutions and approaches. I am reminded of the saying; never be so firm in your decision that you would not give it up for a better one.
Selection bias
This bias appears when the data used to make a decision or inference does not represent the population it's meant to represent. In other words, if the sample is selected or collected in a way that makes it systematically different from the overall population, any conclusions drawn may be biased and misleading. I can't tell you how many times I have encountered this one.
Biases and Technological Change
In organizations undergoing technological change, these biases can have significant implications. When was the last time your company wasn't going through change?
Choice-supportive bias can manifest when an organization is transitioning to new technologies. I see this often as I help companies move into the Microsoft 365 environment. In a recent project, a client was struggling with an RPA effort. Robotic Process Automation. An entire team was resourced to automate a few internal processes using a separate RPA software solution. In an interview with a team member, they explained the issues with getting the technology in place. We took the details back to the 365 project team, and everything they wanted to do was supported natively by PowerAutomate. Now PowerAutomate was available to the team, and no additional costs were involved, but the team had previously decided that the other software was their choice and had spent months trying to overcome issues. They moved their project from blocked to released once they broke free from their choice-supportive biases.
Selection bias can come into play when gathering and analyzing data to guide the change process. An organization may have a skewed perspective if it relies on feedback or data primarily from tech-savvy employees or early adopters. This could result in overestimating the organization's readiness for change or misunderstanding the support needed for a successful transition. When I am brought into a new client, we usually start with a qualitative understanding phase and then move to a quantitative step. For a recent engagement, the client strongly suggested that we do not perform the quantitative work, saying, 'We already have the insights from the interviews...why bother doing any quant work'. It was stressful, and I had to take them back through the reasoning for our methodological choices. While our qualitative work was good (of course it was), we could not project the findings to 20,000 of their employees. Without the more extensive study, our decisions would be heavily influenced by the selection biases in our qualitative phase.
The Role of Leadership in Recognizing and Addressing Biases
Leaders play a pivotal role in managing biases within their organizations. They are often the ones making strategic decisions, and their attitudes and behaviors can significantly influence the culture and mindset of their teams.
To mitigate choice-supportive bias, leaders must promote and model critical thinking. This can involve questioning assumptions, seeking diverse perspectives, and being open to feedback. It's also essential for leaders to communicate clearly about decisions, including acknowledging uncertainties and potential challenges. This fosters trust and helps prepare the organization for any difficulties during the change process.
For selection bias, leaders can ensure that decision-making processes are inclusive and data-driven. They should advocate for using representative data. They don't need to be masters in statistics but asking a few questions about the sample and margins of error will go a long way. Moreover, leaders can help create a culture that encourages learning and adaptation. By recognizing that biases exist and can influence decisions, leaders can foster an environment where biases are regularly discussed, assessed, and mitigated.
Final thoughts
Change, particularly technological change, is often challenging for organizations. The complexities are further compounded by biases that can distort decision-making and impact the change process. I am reminded of a passage in Adam Grant's book Think Again; "adapting to a changing environment isn't something a company does—it's something people do in the multitude of decisions they make every day."
