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Designing a Digital Nudge

While a number of researchers have suggested guidelines for selecting and implementing nudges in offline contexts, information systems present unique opportunities for harnessing the power of nudging. For example, Web technologies allow real-time tracking and analysis of user behavior, as well as personalization of the user interface, and both can help test and optimize the effectiveness of digital nudges; moreover, mobile apps can provide a wealth of information about the context (such as location and movement) in which a choice is made. Given these advantages, information systems allow rapid content modification and visualization to achieve the desired nudging effect.

Drawing on guidelines for implementing nudges in offline contexts, we now highlight how designers can create digital nudges by exploiting the inherent advantages of information systems. Just as developing an information system follows a cycle, as in, say, the systems development life cycle—planning, analysis, design, and implementation—so does designing choices to nudge users (see Figure 5)—define the goal, understand the users, design the nudge, and test the nudge. We discuss each step in turn, focusing on the decisions designers must make.

Studies have demonstrated that when consumers miss an opportunity to purchase a product for a significantly reduced price, they are less likely to purchase this product later for its regular price or for a less significantly reduced price. Two possible explanations for this inaction-inertia effect were considered: avoidance of regret (reluctance to purchase the product represents an attempt to avoid regret over missing the better price) and price contrast (reluctance to purchase the product results from a simple price comparison process). The results of 3 experiments favored the avoidance-of-regret explanation.

Figure 5. Designing digital nudges follows a cycle; based on Datta and Mullainathan and Ly et al.

Step 1: Define the goal. Designers must first understand an organization's overall goals and keep them in mind when designing particular choice situations. For instance, the goal of an e-commerce platform is to increase sales, the goal of a governmental taxing authority's platform is to make filing taxes easier and encourage citizens to be honest, and the goal of project creators on crowdfunding platforms is to increase pledges and overall donation amounts. These goals determine how choices are to be designed, particularly the type of choice to be made. For example, subscribing to a newsletter is a binary choice—yes/no, agree/disagree—selecting between items is a discrete choice, and donating monetary amounts is a continuous choice, though it could also be presented as a discrete choice. The type of choice determines the nudge to be used (see the table here). The choice architect, however, must consider not only the goals but also the ethical implications of deliberately nudging people into making particular choices, as nudging people toward decisions that are detrimental to them or their wellbeing is unethical and might thus backfire, leading to long-term negative effects for the organization providing the choice. In short, overall organizational goals and ethical considerations drive the design of choice situations, a high-level step that influences all subsequent design decisions.

Step 2: Understand the users. People's decision making is susceptible to heuristics and biases. Heuristics, commonly defined as "rules of thumb,"can facilitate human decision making by reducing the amount of information to be processed when addressing simple, recurrent problems. Conversely, heuristics can influence decisions negatively by introducing cognitive biases—systematic errors—when one faces complex judgments or decisions that should require more extensive deliberation.Researchers have studied a wide range of psychological effects that subconsciously influence people's behavior and decision making. In addition to the middle-option bias, decoy effect, and scarcity effect described earlier, common heuristics like the "anchoring-and-adjustment" heuristic, or people being influenced by an externally provided value, even if unrelated; the "availability" heuristic, or people being influenced by the vividness of events that are more easily remembered; and the "representativeness" heuristic, or people relying on stereotypes when encountering and assessing novel situations, influence how alternatives are evaluated and what options are ultimately selected. Other heuristics and biases that can have a strong effect on choices include the "status quo bias," or people tending to favor the status quo so they are less inclined to change default options; the "primacy and recency effect," or people recalling options presented first or last more vividly, so those options have a stronger influence on choice; and "appeals to norms," or people tending to be influenced by the behavior of others.Understanding these heuristics and biases and the potential effects of digital nudges can thus help designers guide people's online choices and avoid the trap of inadvertently nudging them into decisions that might not align with the organization's overall goals.

Step 3: Design the nudge. Once the goals are defined (see Step 1: Define the goal) and the heuristics and biases are understood (see Step 2: Understand the users), the designer can select the appropriate nudging mechanism(s) to guide users' decisions in the designer's intended direction. Common nudging frameworks a designer could use to select appropriate nudges include the Behavior Change Technique Taxonomy,NUDGE, MINDSPACE, and Tools of a Choice Architecture. Selecting an appropriate nudge and how to implement it through available design elements, or user-interface patterns, is determined by both the type of choice to be made—binary, discrete, or continuous—and the heuristics and biases at play; see the table for examples. For example, a commonly used nudge in binary choices is to preselect the desired option to exploit the status quo bias. When attempting to nudge people in discrete choices, choice architects can choose from a variety of nudges to nudge people toward a desired option. For example, in the context of crowdfunding, with the goal of increasing pledge amounts, choice architects could present the desired reward option as the default option; add (unattractive) choices as decoys; present the desired option first or last to leverage primacy and recency effects; or arrange the options so as to present the preferred reward as the middle option. When attempting to nudge people in continuous choices (such as when soliciting monetary donations), choice architects could pre-populate input fields (text boxes) with a particular value so as to exploit the "anchoring and adjustment" effect. Likewise, when using a slider to elicit numerical responses, the position of the slider and the slider endpoints serve as implicit anchors. Presenting others' choices next to rewards to leverage people's tendency to conform to norms or presenting limited availability of rewards to exploit the scarcity effect can be used to nudge people in binary, discrete, or continuous choices.

As the same heuristic can be addressed through multiple nudges, in most situations, designers have a variety of "nudge implementations" at their disposal. Unlike in offline environments, implementing nudges in digital environments can be done at relatively low cost, as system designers can easily modify a system's user interface (such as by setting defaults, displaying/hiding design elements, or providing information on others' pledges). Likewise, digital environments enable dynamic adjustment of the options presented on the basis of certain attributes or characteristics of the individual user (such as when a crowdfunding platform presents particular rewards depending on the backers' income, gender, or age). Notwithstanding the choice of nudges, designers should follow commonly accepted design guidelines for the respective platforms (such as Apple's Human Interface Guidelines and Microsoft's Universal Windows Platform design guidelines) to ensure consistency and usability.

Big-data analytics can be used to analyze behavioral patterns observed in real time to infer users' personalities, cognitive styles, or even emotional states.

Step 4: Test the nudge. Digital environments allow alternative designs to be generated easily, so their effects can be tested quickly, especially when designing websites. The effectiveness of digital nudges can be tested through online experiments (such as A/B testing and split testing). Testing is particularly important, as the effectiveness of a nudge is likely to depend on both the context and goal of the choice environment and the target audiences. For example, a digital nudge that works well in one context (such as a hotel-booking site like https://www.booking.com) may not work as well in a different context (such as a car-hailing service like https://www.uber.com); such differences may be due to different target users, the unique nature of the decision processes, or even different layouts or color schemes on the webpages; a hotel may use colors and shapes that evoke calmness and cleanliness, whereas a car-hailing service may use colors and shapes that evoke speed and efficiency. As choice architects have various nudge implementations at their disposal, thorough testing is thus imperative for finding the nudge that works best for a given context and users.

Especially in light of the increasing focus on integrating user-interface design and agile methodologies, using discount usability techniques (such as heuristic evaluation, as introduced by Nielsen) is often recommended to support rapid development cycles (see, for example, Jurca et al.). Likewise, agile methodologies include the quick collection of feedback from real users. However, such feedback from conscious evaluations should be integrated with caution because the effects of nudges are based on subconscious influences on behavior, and experimental evaluations can provide more reliable results. If a particular nudge does not produce the desired effect, a first step for system designers is to evaluate the nudge implementation to determine whether the nudge is, say, too obvious or not obvious enough (see Step 3: Design the nudge). In some instances, though, reexamining the heuristics or biases that influence the decision-making process (see Step 2: Understand the users) or even returning to Step 1: Define the goal and redefining the goals may be necessary (see the sidebar, "Questions Designers Need to Address").

Conclusion

Understanding digital nudges is important for the overall field of computing because user-interface designers create most of today's choice environments. With increasing numbers of people making choices through digital devices, user-interface designers become choice architects who knowingly or unknowingly influence people's decisions. However, user-interface design often focuses primarily on usability and aesthetics, neglecting the potential behavioral effects of alternative designs. Extending the body of knowledge of the computing profession through insights into digital nudging will help choice architects leverage the effects of digital nudges to support organizational goals. Choice architects can use the digital nudging design cycle we have described here to deliberately develop such choice environments.

Figure. Applying the digital nudging design cycle (selected examples).

One final note of caution is that the design of nudges should not follow a "one-size-fits-all" approach, as their effectiveness often depends on a decision maker's personal characteristics.In digital environments, characteristics of users and their environment can be inferred from a large amount of data, allowing nudges to be tailored. System designers might design the choice environment to be adaptive on the basis of, say, users' past decisions or demographic characteristics. Likewise, big-data analytics can be used to analyze behavioral patterns observed in real time to infer users' personalities, cognitive styles, or even emotional states. For example, Bayesian updating can be used to infer cognitive styles from readily available clickstream data and automatically match customers' cognitive styles to the characteristics of the website (such as through "morphing"). Designers of digital choice environments can attempt to "morph" digital nudges on the basis of not only the organizational goals but also users' personal characteristics.

Any designer of a digital choice environment must be aware of its effects on users' choices. In particular, when developing a choice environment, designers should carefully define the goals, understand the users, design the nudges, and test those nudges. Following the digital-nudging design cycle we have laid out here can help choice architects achieve their organizational goals by understanding both the users and the potential nudging effects so intended effects can be maximized and/or unintended effects minimized.