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Functional Digital Nudges: Identifying Optimal Timing for Effective Behavior Change

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Digital nudges hold enormous potential to change behavior. Despite the appeal to consider timing as a critical factor responsible for the success of digital nudges, a comprehensive organizing framework to guide the design of digital nudges considering nudge moment is yet to be provided. In this paper, we advance the theoretical model to design digital nudges by incorporating three key components: (1) Identifying the optimal digital nudge moment (2) Inferring this optimal moment and (3) Delivering the digital nudge at that moment. We further discuss the existing work and open research avenues.
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Functional Digital Nudges:
Identifying Optimal Timing for
Eective Behavior Change
Aditya Kumar Purohit
University of Neuchâtel
Neuchâtel, NE, Switzerland
aditya.purohit@unine.ch
Adrian Holzer
University of Neuchâtel
Neuchâtel, NE, Switzerland
adrian.holzer@unine.ch
ABSTRACT
Digital nudges hold enormous potential to change behavior. Despite the appeal to consider timing as
a critical factor responsible for the success of digital nudges, a comprehensive organizing framework
to guide the design of digital nudges considering nudge moment is yet to be provided. In this paper,
we advance the theoretical model to design digital nudges by incorporating three key components: (1)
Identifying the optimal digital nudge moment (2) Inferring this optimal moment and (3) Delivering
the digital nudge at that moment. We further discuss the existing work and open research avenues.
INTRODUCTION
Thaler and Sunstein [
24
] suggested that policymakers can design nudges to promote change in
behavior among citizens. They defined a nudge as "any aspect of the architecture of choice that
changes people’s behavior in a way predictable without prohibiting all options or significantly changing
their incentives" [
24
]. Nudges assist in modifying the behavior by changing the way we see things and
making a person more sensitive to one option [
13
]. Consumers accept nudges because they retain their
freedom of choice [
19
]. Exhibiting the low-cost advantage with the potentiality to shape behavior [
12
],
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government organizations have successfully used nudges in public wellness campaigns [
25
], such
as promoting smoking cessation [5], influencing food choices [28], or promoting pro-environmental
consumption behavior [1].
The omnipresence of mobile devices with online communication has enabled the creation of
digital nudges [
3
]. These digital nudges use online technologies such as SMS, notifications, mobile
KEYWORDS
Timing; Digital Nudge; Behav-
ioral change interventions; Nudge moment
applications, and gamification to encourage people to act in the desired way. Research shows that
digital nudges can have propelling eect on behavior and influence decisions of the individual [
17
].
An example of such a nudge is the gamification of physical activity by providing mobile application
users with badges and points when they reach specific goals to increase physical activity [2].
However, the success of the receptivity of nudges has shown to be varying depending on their
timing [
6
]. Deploying early nudges could lead to forgetfulness and provision of late nudges could
shrink the time available for action [
21
]. Even though this issue has been raised, identifying the
appropriate time to turn a potential digital nudge into what we call a functional digital nudge, that is
a digital nudge that can eectively change behavior, is mainly an unresolved issue [
16
]. In this paper,
we advocate for the investigation of this issue and propose open research avenues.
Figure 1: A theoretical model for design-
ing digital nudges for behavior change.
Components 1,2,3,4 - from the existing the-
oretical model by Schneider et al. [20].
Components 2.1, 2.2, 3.1 - added to ad-
vance the theoretical framework
DESIGNING FUNCTIONAL DIGITAL NUDGES
Schneider et al. [
20
] provided a general framework for designing digital nudges. The design process
proposed to design digital nudges was similar to the systems development cycle (planning, analysis,
design, implementation) - see Figure 1. Step 1 in the cycle was to define the goal - Investigating the
organization’s goal (e.g., increasing sales, increasing pledges). Step 2 was to understand and recognize
user heuristic and biases (availability heuristic, representativeness heuristic). Step 3 was concerned
about designing the nudges and Step 4 dealt with testing the nudges. Assessing the significance
of the timing of digital nudges, we propose to extend this framework by including three additional
components : (1) Step 2.1 identifying the optimal digital nudge moment (2) Step 2.2 inferring the
optimal digital nudge moment (3) Step 3.1 delivering the digital nudge at the optimal moment.
IDENTIFYING THE OPTIMAL DIGITAL NUDGE MOMENT (2.1)
In health education research, McBride, Emmons, and Lipkus [
14
] introduced the notion of teachable
moments to motivate individuals to change their behavior. A teachable moment has been described
as "naturally occurring health events thought to motivate individuals to adopt risk-reducing health
behaviors spontaneously." For example, An intervention for smoking cessation for women could be
appropriate during the perinatal period [
18
]. Sunstein mentioned that timing maers for reminder
type nudges [
23
]. For Instance, in an oline seing, Sinning and Gillitzer [
21
] presented theoretical
and empirical evidence on how dierent timings of nudges had an impact on tax payments. However,
the specific payment behavior was investigated in a field experiment using a simple reminder leer
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and suggested to use early reminders. Nevertheless, this study was confined to tax payment behavior
and did not identify a specific nudging sweet spot. From the literature, we grasp the importance of
timing to the success of digital nudges beyond reminders. Table 1 presents studies indicating how
significant the timing is for various digital nudges.
Table 1: Significance of Timing for
various digital nudges
Type of digital nudge Sig. Delivery Medium
Provision of Information[9] High PDA(1)
Feedback[4] High Mandometer(2)
Reminder[8] High Mobile Phone(3)
Default[10] Low Email(4)
Game rewards[26] High Games(5)
Associated goals :
(1) - Beer dietary decision making
(2) - Normalize eating behavior
(3) - Increase payment of court fines
(4) - Increase web survey participation
(5) - Create sense of value and accomplishment
The studies selected are from diverse disciplines like Information systems, Medical research, Policy
Analysis, and Digital games. Besides the research by Intille et al. [
9
] that concentrated on "Just-
in-Time" technology, other studies do not explicitly work on adapting digital nudges with timing.
Nevertheless, we infer from studies the importance of timing digital nudges to achieve the goal. For
instance, to motivate an incremental dietary behavior change, Intille et al. [
9
] proposed that the
information should be provisioned on a PDA (personal digital assistant) when the user was at the
point of purchase (nudge moment). Another study investigated how to modify eating behavior for
weight loss in obese adolescents [
4
]. The researchers provided participants with real-time feedback
during meals (nudge moment) to slow down their eating speed.
Figure 2: Classification of nudge moments
Open research avenues. In recent work, Meske and Poho [
16
] argued that the timing of nudge is
an essential aspect of persuasion and present nudging tools are missing this crucial facet. Furthermore,
it is still unclear to which behaviors timing of digital nudges is indispensable. Understanding optimal
timing will lead to the identification of nudge moments. To simplify the understanding of the nudge
moments, we have inferred various contexts from communication studies and classified them as
potential dimensions of nudge moments in five categories (Figure 2). A nudge moment can include one
or more of these dimensions. For each moment, the nudge could be timed before, during, or aer it
occurs. Digital nudge designers should implement validation techniques to validate the eectiveness
of the digital nudges at dierent times.
Example scenario. Imagine Tom, a 22-year-old design major. Tom agonizes with his increasing
weight. His doctor has suggested maintaining a healthy diet by increasing protein intake and reducing
carbohydrates. A nudge moments can be the moment right before Tom decides where to eat at lunch
to nudge his restaurant choice to a beer option. Another moment would then be right when he
receives the menu in a restaurant to nudge his choice to a healthier option. This moment would have
a combination of location context (a restaurant), situational context (lunchtime, aer entering the
restaurant), and behavioral context (siing down, having received the menu).
INFERRING THE OPTIMAL DIGITAL NUDGE MOMENT (2.2)
Inferring the timing for the digital nudge implies that designers understand the digital context of
the target user concerning soware and hardware. As digital devices have become an intrinsic part
of our lives, mobile sensing technologies can be employed to interpret user behavior. The mobile
phones equipped with sensors can record sociability and mobility behaviors [
7
]. In a study by Mehl,
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Gosling and Pennebaker [
15
], an Electronically Activated Recorder (EAR) was used to infer users
social interaction, daily activities, and mobility paerns. Similarly, Wang et al. [
27
], inferred the
studying behavior of the students using combinations of mobile sensing technologies. For instance, a
Microphone to determine if the environment was noisy or silent. An accelerometer can be used to
ascertain if the phone is in activity by the user. GPS and WiFi data to ascertain if the student was in
the library or study area. Inferring time of the day is trivial for any digital device. However, inferring
past/current or future activity as well as context can be challenging. Sensors can help identify certain
behaviors, such as sleeping or running, and particular contexts, such as location.
Open research avenues. Using smartphone sensing, researchers have overcome the challenges of
traditional methods of inferring behavior. However, it is still a challenge to monitor behavior paerns
throughout a day across wide-ranging components of behavioral science. Another research avenue
should further investigate how to combine dierent sensor technologies to operate as behavioral
predictors and infer the behavior of user post-intervention. At the same time, the data required for
predicting and inferring behaviors should be minimized as privacy is a significant concern.
Example scenario. In the case of Tom, the location context (inside a restaurant) can be inferred with
GPS, the situational context (lunchtime, aer entering the restaurant) with a clock and a timer, and
the behavioral context (siing down) with a gyroscope and accelerometer. All these sensors are part
of any smartphone. However inferring the exact moment when Tom is about to decide what to eat
is not trivial (e.g., aer receiving the menu). One could imagine that this could be inferred using an
RFID chip in the menu which would be activated when Tom is nearby.
DELIVERING THE DIGITAL NUDGE AT THE OPTIMAL MOMENT (3.1)
Aer inferring the nudge moments, it is crucial to identify a suitable digital form for the delivery
of digital nudges. Today various digital devices like smartphones [
11
] and fitness trackers [
22
] can
leverage digital nudges to steer people in a particular direction using delivery methods such as sound
and vibration notifications. Furthermore, user interfaces use banners, badges and other visible icons to
draw user aention online. It should be noted that the delivery mode is not limited to personal devices
and can include connected and ambient objects (e.g., lights, public displays, connected fridges).
Figure 3: Proximity of device with user
concerning various forms to deliver digi-
tal nudges
Open research avenues. Even though many delivery methods exist, the form in which digital nudge
is most useful for a particular behavior needs further investigation. For instance, In which form will
the feedback nudge be most eective on various devices for commuting behavior, through push
notification, vibration or text SMS? An understanding of the most eective form of delivery for various
digital nudge types for target behaviors will assist in reducing the aempts of trial and error and save
the time of nudge designers. In Figure 3, we identify that special aention is needed to be given to the
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device’s proximity to the self when choosing dierent forms to deliver digital nudges. In particular,
research should investigate unorthodox delivery methods through connected objects which are bound
to become more and more present in our homes, cars, and cities.
Example Scenario. In Tom’s example, the digital nudge could be to gamify his food consumption by
allowing him to track his food intake habits and set his goals. This nudge can be delivered directly
through a push notification on his phone when he is about to order. The notification could direct him
to his food tracking app. If the menu itself was a connected tablet, it could directly show how each
option would aect Tom’s score.
CONCLUSION
Digital technologies can harness the power of nudges by making them a useful tool for behavior
change. It is important to identify favorable timing of digital nudges by taking advantage of mobile
devices and online communication. In this paper, we aempted to provide a framework that could
assist in designing timely functional digital nudges and a roadmap of open questions that still need
to be addressed. It is relevant to note that the success of several digital nudges relies on their timely
delivery. Future research can consider the open research avenues presented in this paper, imparting
us with a temporal sweet spot for digital nudging.
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