Author: steveshu

Our waitress pleasantly asked, “Would you like small or vacation sized cocktail?”

These were potent words that nudged my wife and I to select 16oz versus 12oz drinks just the other day. We’ve been in the Caymans on vacation, on a “Cay-vay-tion”.

More than thirty years ago, I principally thought like a traditional engineer. 12oz is 1.5 cups. 16 oz is 2 cups.

Then I magically met my wife and started a journey of getting in touch with my feelings. I started to learn about how soft skills and words matter. Sometimes its about cognition and comprehesion of an audience. Other times its about conveying feelings, connections, and story arc.

Returning to lessons from our waitress, the framing of vacation versus small sized is brilliant. One lever with respect to nudge design is being aware of the power of affect. Affect is essentially about immediate feelings that a person experiences (e.g., good or bad), such as based on a nudge. For nudge design, especially be aware of:

1. Reference points (e.g., small versus large)
2. Mental associations (e.g., widespread notions like happiness or other mental associations and metaphors taught in marketing and brand management, such transformation or scarcity/exclusivity)
3. Social (e.g., framing connections to others or others as reference points)
4. Just-in-time (e.g., proximity at the point of decision)

Our waitress hit the nail on the head with a number of these points. So long as she nudges for good, she’ll likely be a winner.

The bottom line is to go beyond where I was as a “traditional engineer”. Consider affect as part of nudge design. Furthermore, build a team with members that are acutely aware of psychology, mental associations, customer experience, and design.

Last night I had a good dinner and conversation with a long-time friend and colleague. We talked about the recently passed Secure Act 2.0 and potential behavioral implications and impacts on the ecosystem and players.

For those unfamiliar with Secure Act 2.0, this legislation covers finance and retirement-related considerations. Just to give some examples without covering the breadth, the Act includes items such as whether employers must provide automatic savings rate escalators in their plans, what escalator caps might be, how student debt might be addressed in the context of savings, and changes in the future age at which retirement savings of an individual must start to be withdrawn.

Each of these changes have potential behavioral implications. Let’s take just one of five behavioral angles I cover at Cornell in my applied behavioral economics courses, namely choice architecture. Defaults are an important tool within the realm of choice architecture and have shown to have big impacts on people’s choices (Carroll et al., 2009; Johnson and Goldstein, 2003; Johnson et al., 2012). Has your company done a behavioral audit on the implications to constituents of specific defaults, such as default values, structure, and outcomes? What if people don’t accept defaults? What happens then in the customer experience? Have you thought about implications to your company? Should your company make any changes?

We also have to acknowledge that consumer choices are often not made in isolation. For example, by increasing the age at which people are required to take minimum distributions from retirement, how might this affect other choices? For example, what impact might it have on how people think about claiming Social Security? Thinking architecture, such as how people construct their preferences using a mixture of fast and slow thinking processes (Payne et al., 1999), is another behavioral angle to consider.

Where does this leave us? In the case of Secure Act 2.0, one way to look at this is in terms of an exogeneous event that constituents have to react to (e.g., employers, advisors, platforms, systems providers, investment managers). However, there will also be those that look at this as an opportunity. There will be some players that will be way more agile than others and able to capitalize on both important behavioral implications and operational tactics.

But this discussion isn’t limited to Secure Act 2.0…

Whether facing an exogeneous event or proactively working an important business problem, here are three strategies informed by behavioral economics that companies and individuals can use:

• To avoid blindspots with group decision making, consider setting up a Red Team to approach problem and think way outside of the box (Cass Sunstein discusses Red Teaming in his book, Wiser). My twist would be that you might consider setting up a Behavioral Red Team or a Red Team with behavioral economics advisor embedded within.
• Anchoring is a powerful force that inhibits change. Use a whiteboard exercise to think about ideal approaches to solving a problem. Maybe it can be part of a Spring-cleaning or offsite event for your company.
• Use behavioral lenses to examine the problem. A choice architecture lens, such as the way defaults are used, is one such lens. But there are other lenses out there, such as the way information is framed. (See Shu et al. for an example of reframing savings decision using pennies and potentially heterogenous treatment effects on people with different income levels).

References:

• Carroll, Gabriel D., James J. Choi, David Laibson, Brigitte C. Madrian, and Andrew Metrick. “Optimal defaults and active decisions.” The Quarterly Journal of Economics 124, no. 4 (2009): 1639-1674.
• Johnson, Eric J., and Daniel Goldstein. “Do defaults save lives?.” Science 302, no. 5649 (2003): 1338-1339.
• Johnson, Eric J., Suzanne B. Shu, Benedict GC Dellaert, Craig Fox, Daniel G. Goldstein, Gerald Häubl, Richard P. Larrick et al. “Beyond nudges: Tools of a choice architecture.” Marketing Letters 23, no. 2 (2012): 487-504.
• Payne, John W., James R. Bettman, David A. Schkade, Norbert Schwarz, and Robin Gregory. “Measuring constructed preferences: Towards a building code.” In Elicitation of Preferences, pp. 243-275. Springer, Dordrecht, 1999.
• Shu, Stephen, Hal Hershfield, Richard Mason, and Shlomo Benartzi. “Reducing Savings Gaps Through Pennies Versus Percent Framing.” (Working Paper 2022).
• Sunstein, Cass R., and Reid Hastie. Wiser: Getting beyond groupthink to make groups smarter. Harvard Business Press, 2015.

While I will address return on investment (ROI) considerations in a future post, probably one of the first rules of thumb I have is to use some degree of portfolio thinking and management processes for implementing behavioral science initiatives.

Research indicates that seemingly promising behavioral interventions sometimes do not work, and it can be hard to predict what will actually work (Milkman et al., 2021). Additionally, some studies indicate that there can be strong wisdom-of-crowds versus individual forecasting performance effects, such as average forecasts outperforming 96% of individual forecasts (DellaVigna and Pope, 2018).

It can be difficult to predict which projects will be successful. There are risk-return tradeoffs and innovating in this space requires process and discipline (which could also result in undesirable outcomes, such as if managers are overly loss averse and fail to innovate).

So based on these premises, some strategies for companies looking to implement behavioral science include:

1. Look for quick wins opportunistically (e.g., when getting started), while also being realistic.
2. Recognize that it can be hard to predict results ex-ante.
3. Develop behavioral intervention generation, vetting, and project portfolio management processes. (This last point is very loaded. I will likely have future posts on different dimensions of just this point. However, one example of portfolio management can be picking several lower-risk projects along with a higher-risk project that could pay off to a much greater extent, e.g., financial or social good).

In summary, to effectively implement behavioral science initiatives, it is important for companies to recognize the unpredictable nature of these interventions and adopt a portfolio approach that includes a process for generating, vetting, and managing projects in order to maximize return on investment and/or other outcome goals of the organization.

References:

• Milkman, Katherine L., Dena Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Pepi Pandiloski, Yeji Park et al. “Megastudies improve the impact of applied behavioural science.” Nature 600, no. 7889 (2021): 478-483.
• DellaVigna, Stefano, and Devin Pope. “Predicting experimental results: who knows what?.” Journal of Political Economy 126, no. 6 (2018): 2410-2456.

In 2023, I’m going to experiment with series of short posts and mixed media that will try to appeal to those that are thinking of starting behavioral economics initiatives or trying to take things to the next level. I have a unique background to bring this type of content as I have worked in the commercial world for more than 30 years while also having an academic research and teaching background in behavioral economics. I will try to leverage an intersection between what I implement as a behavioral economics advisor in the commercial world and what I teach experientially in the classroom.

Current LinkedIn connections and prior students may also feel free to join an Applied Behavioral Economics slack channel. We can use to this as a way to share ideas, post questions, or potentially arrange informal meetups. Please send me an email at [email protected] or if your organization is on an approved domain (e.g., cornell.edu) you should be able to join using the following link: https://join.slack.com/t/appliedbehavi-qow2205/shared_invite/zt-1makq3ojb-GYmDS_eGI50uG_YoLdh10w (link valid for 30 days).