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A fresh look at integrating design thinking with behavioral economics in market research


blackboard, think, education, idea, school, solution, creativity,  lightbulb, learning | Pikist

Market researchers have embraced many approaches over the years in their quest to understand, and thereby influence, human behavior. More than a decade ago, design thinking, with its emphasis on empathy and customer centricity caught the attention of market researchers.
Methods such as direct observation, co-creation between consumers and marketers, and rapid prototyping became standard tools in research design. Since then, however, behavioral economics, with its emphasis on uncovering cognitive biases, has displaced much of design thinking in market research. We believe this is unfortunate, as a richer approach to market research today lies in the integration of the two disciplines.

Behavioral economics seeks to understand how biases and mental shortcuts, or heuristics, influence decision making. Design thinking focuses on understanding the external world and how the perceived possibilities it presents create the context, or “choice architecture” for decision making. Together these two disciplines help researchers dive deeper insights into the “Whys” behind peoples choices and judgements.


Design Thinking Concepts

Design Doing with Don Norman. A transcript of Episode 125 and 126 of… | by  UX Podcast | Medium
The Design of Everyday Things | Chapter 5 - Human Error No, Bad Design |  Don Norman - YouTube

In his seminal work, The Design of Everyday Things (1988), Don Norman discusses two important underpinnings to design thinking – discoverability and mental models. Discoverability refers to the ability of an individual to effectively interact with a product of design. It is the result of thorough understanding and application of five fundamental design concepts: affordances, signifiers, constraints, mappings and feedback. When something is easily discoverable it creates a positive user experience and satisfaction.

Mental models are representations in peoples’ minds of how things work, whether tangible objects or abstractions. Mental models are critical drivers of decision making and behavior. One example would be the understanding of consumers as to how deductibles and co-insurance in insurance plans work. Their mental model of how health insurance works could be accurate or flawed. In either case, however, the model will impact their behavior.

Overlapping Heuristics with Design Thinking

Heuristics, as popularized in behavioral economics, are best described as the mental shortcuts people use to make judgements and decisions. In essence, heuristics represent simple cognitive routines. The mental models of design thinking may be thought of as simplified representations of how a complex world works. It is in these parallel processes of simplification that behavioral economics and design thinking begin to overlap.

Both heuristics and mental models may lead one to make incorrect judgments or less than optimal decisions, but they are both essential to the way one functions in the world. It is for this reason that researchers should consider working with both disciplines in deciphering human behavior. Behavioral economics’ heuristics keep the researcher aware of the systematic and predictable biases in judgments people can make, while design thinking’s mental models ground these biases in perceptions of how actual decision-making environments operate.

Changing Behavior

Heuristics and mental models go beyond providing insights into human behavior and decision making. They also provide guidance for positively influencing behavior. Consider the decision journey framework shown in the figure on the next page, adapted from a buying process model developed in the 1980s by Stephen King (the adman, not the author):

This framework depicts a considered decision-making process in which the consumer:

  • Encounters a cue calling for a judgment or action
  • Imagines the outcomes of a decision or judgment
  • Searches for additional information to guide the decision
  • Makes a choice or judgment
  • Evaluates the decision or judgment in light of experienced outcomes and
  • Updates mental models as needed

Mental models and heuristics fuel each stage of the decision-making process shown in this framework. And at the center of this framework sit affordances (the complete inventory of what the environment offers) and nudges (prompts for specific judgments or behaviors – such as ads, coupons, tweets, bios, etc.) which further define the environment in which decision making takes place. In design theory terminology, these nudges can be viewed as signifiers or cues as to how to interact with the affordance. In this framework affordances and nudges serve as placeholders for the actions that can be leveraged to impact any stage of the decision-making process.

In order to construct robust research programs that provide insights into human behavior and guidance for driving behavioral change, one must organize research insights into a high-level framework such as what we’ve illustrated here.

Creating optimal research to populate such a framework can be a difficult task for market researchers as the research must elicit both procedural and declarative knowledge from respondents. Procedural knowledge is used in performing a specific task, while declarative knowledge includes the facts, opinions, perceptions and self-knowledge of the individual. A questionnaire based research study can be effective in gathering declarative knowledge but accessing procedural knowledge is better suited to simulation where immersing the individual in a virtual task environment can more readily illicit the thought processes actually used in making a choice. Observing these “in-the-moment” thought processes can offer insights into heuristics and mental models that are traditionally difficult to verbalize. Affordances and nudges can also be manipulated in experiments to understand exactly how readily decision making can be influenced.

Within this integrated framework it is possible to synthesize research insights and provide guidance for how choice architectures can be reassembled in ways that can predictably and positively impact behavior.

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