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Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães,Portugal, 2017
THE DINU-MODEL –
A PROCESS MODEL FOR THE DESIGN OF NUDGES
Research in Progress
Meske, Christian, University of Duisburg-Essen, Duisburg, Germany,
christian.meske@uni-due.de
Potthoff, Tobias, University of Duisburg-Essen, Duisburg, Germany,
tobias.potthoff@uni-due.de
!
Abstract!
The sociotechnical paradigm legitimates our discipline and serves as core identity of IS. In this study,
we want to focus on IS-induced human behavior by introducing a process model for nudging in IS. In
behavioral economics, the concept of nudging has been proposed, which makes use of human
cognitive processes and can direct people to an intended behavior. In computer science, the concept of
persuasion has evolved with similar goals. Both concepts, nudging and persuasion, can contribute to
IS research and may help to explain and steer user behavior in information systems. We aim for an
integration of both concepts into one digital nudging process model, making it usable and accessible.
We analyzed literature on nudging and persuasion and derived different steps, requirements, and
nudging elements. The developed process model aims at enabling researchers and practitioners to
design nudges in e.g. software systems but may also contribute to other areas like IT governance.
Though the evaluation part of our study has not yet been completed, we present the current state of the
process model enabling more research in this area.
Keywords: Digital Nudging, Nudge, Persuasion, Behavior, Change
1" Introduction"
The Information Systems (IS) discipline is a research field, which draws upon challenges and
problems originating in organizations using information technology. Much of the discipline and its
theories are based on the sociotechnical paradigm, indicating the need to analyze human and technical
factors in the organizational context (Sarker et al., 2013). The sociotechnical paradigm legitimates our
discipline in comparison to computer science and serves as core identity of IS (Benbasat and Zmud,
2003). In this study, we want to focus on the humans’ role in IS research and design by introducing a
process model for ‘nudging’ in IS. In 2009, behavioral economics Thaler and Sunstein have proposed
nudging as a concept of influencing human behavior, which is based on social-psychological and
cognitive theories. They define nudging as “any aspect of the choice architecture that alters people's
behavior in a predictable way without forbidding any options or significantly changing their economic
incentives” (Thaler and Sunstein, 2009, p. 6). However, nudging goes beyond identifying patterns of
behavior. It also constitutes a new view on influencing people’s behavior for their own good. The
founding fathers of nudging and of the related libertarian paternalism, which is a liberty preserving
form of paternalism, show how nudges can be useful in many parts of human life to overcome biases
and flawed thinking (Thaler and Sunstein, 2003). Recently, the concept of nudging has also been
introduced in a Business & Information Systems Engineering catchword as digital nudging revealing a
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 2
high potential for IS research and society e.g. in the fields of e-health, e-commerce or business process
management (Weinmann et al., 2016).
In computer science, the concepts of persuasion and persuasive technology evolved around the turn of
the millennium and were mainly coined by B. J. Fogg (2003). He defined persuasion as “an attempt to
change attitudes or behaviors or both (without using coercion or deception)” (Fogg, 2003, p. 15). As
previous studies suggest, both nudging and persuasion are comparable as they aim at influencing
people to perform a target behavior (Oinas-Kukkonen, 2013). However, the concepts have been
developed individually in different disciplines with partially different goals. Nudging has its roots in
economics and aims at preserving liberty whereas persuasion does not strictly prescribe what is
allowed and what not, and focuses on a technically enabled influence. We question whether nudging
can learn from persuasion and vice versa, and whether it can in combination provide the highest value
for designers of decision situations. In information systems, decision situations include first and
foremost user interfaces of software or web pages (Weinmann et al., 2016). However, nudging can
also contribute to further areas of information systems research e.g. IT adoption and governance
(Larosiliere et al., 2015; Stieglitz et al., 2013). We use the term digital nudging to emphasize on the
digital environment. Digital nudging and our paper may help to (1) explain certain behavior patterns in
information systems and (2) provide solutions for unfavorable behavior. Following the call of
Weinmann et al. (2016, p. 435) to “engage in research on digital nudging”, we argue that both
concepts, nudging and persuasion, need to be compared and integrated thoroughly. Up to now, there
exists no overview of the diverse nudging elements, including guidance when and how to use those. In
persuasion, models have been developed that better operationalize the target of changing behavior but
with less design options as in nudging (Fogg, 2009). We want to close this gap by developing an
application-oriented “digital nudging process model” (DINU Model) based on a systematic literature
review and evaluated and improved with experts. Thereby, behavior change elements from both
streams that are applicable in a digital environment and that meet nudging’s strict conditions are
presented and processed for the design of digital nudges. The article is guided by the research
question: How can nudging and persuasion be combined to enable a digital nudging process model
for behavior change in information systems?
This paper is structured as follows. In section 2, we start with the theoretical background of nudging
and persuasion. In section 3, we describe our research method in detail. Based on the literature
analysis the process model is developed in section 4. The paper ends with a conclusion and outlook to
further research in section 5.
2" Theoretical"Background"
In 2009, Thaler and Sunstein’s book “Nudge” made a starting point for the concept of nudging as a
subject of research in behavioral economics. Their concept builds on the assumption that decisions are
made in choice architectures that are designed by choice architects (Thaler and Sunstein, 2009). They
argue that no design of a choice architecture is neutral as it always influences people in some way.
Consequently, the concept of nudging assumes that the choice architecture can be altered in a way that
makes a desired output or decision more likely. The behavior is predictable due to cognitive biases
that the choice architect can use. In this context, Daniel Kahneman speaks of system 1, which operates
automatically, unconsciously and unreflectively, and system 2 that evaluates consequences in detail
but requires much more cognitive capacity (Kahneman, 2011). Nudges make either use of the
unreflective thinking, or trigger the reflective system for decisions that were otherwise made
unconsciously (Thaler and Sunstein, 2009). It is further stated that the alteration of the choice
architecture is justifiable only if it is to the decision-makers own good and only if it remains a free
decision without any coercion. Thaler and Sunstein (2003) call this concept libertarian paternalism, a
liberty-preserving soft form of paternalism. In this sense nudging is understood as “any aspect of the
choice architecture that alters people's behavior in a predictable way without forbidding any options or
significantly changing their economic incentives” (Thaler and Sunstein, 2009, p. 6). However,
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 3
researchers challenged the lack of intentionality (Hansen and Jespersen, 2013) and the missing goal,
i.e. to benefit the decision-maker (Hausman and Welch, 2010). Even though literature on nudging
often names the government and policy-makers as those who can design and use nudges (e.g. in crisis
situations: Mirbabaie et al., 2014; Stieglitz et al., 2017), companies may also integrate the idea of
libertarian paternalism into their marketing ethics by putting their customers’ or employees’ wellbeing
first (Goldin, 2015; Grüne-Yanoff and Hertwig, 2016; Meske et al., 2016; Room, 2016).
In IS and computer science, and especially in the field of human-computer interaction (HCI), the idea
of persuasion is not completely new. Research with a focus on using design elements to influence
behavior began around the turn of the millennium. Two famous approaches were selected to gain
insight into persuasion: the Fogg Behavioral Model (FBM) (Fogg, 2009) and the Persuasive Systems
Design (Oinas-Kukkonen and Harjumaa, 2009). In their sense, persuasion is a form of human
communication that is aimed at influencing autonomous judgements and actions of others. Fogg
defined “persuasive technology as any interactive computing system designed to change people’s
attitudes or behaviors.” (Fogg, 2003, p. 1) The similarity to nudging is also evident in the exclusion of
coercion or deception (Fogg, 2003, p. 15). Still, persuasive technology is often understood as IT
artefacts exclusively designed to change behavior (e.g. software visualizing accelerated aging for
smokers). It does not primarily address the implementation of persuasive elements in established
information systems e.g. to enhance privacy or increase interaction. In Table 1, we exemplarily
structured the understandings of nudging and persuasive technology to see similarities and differences.
Term
Artefact
Realm
Action
Aim
Target
Predictability
Limitation
Source
Nudging is
any aspect
of the choice
architecture
that
alters
people's
behavior
in a predictable
way
without forbidding any
options or significantly
changing their economic
incentives
(Thaler
and
Sunstein,
2009)
Persuasive
techno-
logy is
any inter-
active
computing
system
design-
ed
to
change
people’s
attitudes or
behaviors
without using coercion or
deception
(Fogg,
2003)
Table 1. Similarities and differences between nudging’s and persuasion’s understanding
Nevertheless, Fogg has provided a model that goes beyond nudging in terms of operationalization. The
FBM foresees three requirements that have to occur simultaneously to facilitate behavior change in the
targeted audience: motivational state, the ability of a person to perform the targeted behavior, and the
presence of an effective trigger (Fogg, 2009). These aspects of persuasion have not yet been
considered in nudging as current nudging tools do not consider the timing of a nudge, the decision-
maker’s motivation or her ability to attend to the nudge. On the other hand, the emergence of nudging
has given rise to debates about ethics since it may constitute a subtle form of manipulation depending
on the kind of nudge (Campbell et al., 2014; Hansen and Jespersen, 2013). This differentiates nudging
from persuasion which does not really know such debates. Although the ethical discussions has not yet
achieved a consensus, we follow Thomas et al. (2013) saying manipulation is seldom subtle and,
therefore, nudging cannot be mistaken for manipulation. Even though we perceive such topics as
important, in this work we do not deepen ethical discussions but focus on the methodology of nudging.
Digital nudging has been introduced as “the use of user-interface design elements to guide people’s
behavior in digital choice environments” (Weinmann et al., 2016, p. 1). However, this definition does
not reflect the importance of a free decision without coercion or a fundamental change of options and
the subtle mode of action. Further, nudges are not necessarily limited to the design of user-interfaces
only, since the form and content of information or messages can also represent a nudge. Hence, we
define digital nudging as a subtle form of using design, information and interaction elements to guide
user behavior in digital environments, without restricting the individual’s freedom of choice.
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 4
3" Method"
3.1" Literature"analysis"and"process"model"
To develop the DINU Model, we analyzed literature on nudging, persuasion and persuasive
technology. We organized the literature search based on the framework by vom Brocke et al. (2009).
In combination with the taxonomy for literature reviews by Cooper (1988) we were able to align the
depth of the search to the goal and target group (see Figure 1). Considering Cooper’s characteristics,
we focus on applications of nudging and similar concepts. The goal of the literature analysis is the
integration of nudging, persuasion and potential further concepts in the form of a process model. The
literature is organized conceptually. Our perspective on the literature is not completely neutral as we
selected those works and parts that could be used for the process model based on our prior knowledge.
The audience are general scholars as nudging can be implemented and, thus, researched in many areas
as well as practitioners/politicians who can use the process model to design and implement nudges in
real world. We choose to include a representative volume of research papers in our analysis because
contents and, especially, nudging elements are often mentioned redundantly so that the representative
volume contributed most to the combination.
Figure 1. Taxonomy of literature reviews (Cooper, 1988); visualization (vom Brocke et al., 2009)
The first phase of the literature review involved articles referring to nudging or nudge in an
explorative search with the databases AISeL, IEEE, Scopus and Google Scholar. The primary
objective was to test the terms for other meanings. In fact, we found different meanings of nudging
e.g. in geology. Adding the keywords prevention, behavio(u)r change or policy in combination with
nudg* limited the results to the intended meaning. Persuasive technology was also searched in
combination with the additional terms prevention, behavio(u)r change or policy. For persuasion we
started with literature of B. J. Fogg (e.g. Fogg, 2003) as it is otherwise difficult to demarcate the term
persuasion. We performed backward and forward searches for all identified works. We carefully
selected a representative set of articles to include those works that describe different elements or
applied nudging and persuasion in different settings. We decided to mainly exclude ethical essays and
opinions in this analysis. We analyzed 30 papers in detail focusing on (1) elements and strategies of
nudging and similar concepts, (2) success factors and (3) examples (see Tables 2 and 3). To develop
the DINU Model we compared the approaches and grouped similar nudging elements iteratively. We
further differentiated between preparatory work, the main design of the nudge, and evaluative tasks.
3.2" Model"evaluation"(in"progress)"
Since we combine results from mainly independent streams of research into one newly created model,
it needs a thorough evaluation with the intended target group e.g. software or process designers who
try to direct people to a certain behavior. For this purpose, we adapted the generic evaluation process
model by Stockmann and Meyer (2014). This model defines three phases in an evaluation project: (1)
Planning, (2) Execution, and (3) Exploitation. The model is supplemented by the Framework for
Evaluation in Design Science Research (FEDS) (Venable et al., 2014). Though the framework depicts
Characteristic
focus(1)
goal(2)
organization(3)
perspective(4)
audience(5)
coverage(6)
Categories
research outcomes research methods theories applications
integration criticism central issues
historical conceptual methodological
neutral representation espousal of position
specialized scholars general scholars practitioners/politicians general public
exhaustive exhaustive and selective representative central/pivotal
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 5
strategies for Design Science Research (DSR), it provides theoretical underpinnings for the evaluation
of the DINU Model as well. At the current state, we have started to plan the evaluation regarding the
first phase of the evaluation process. This phase entails the tasks (1.1) Determining and restricting the
evaluation project and (1.2) Developing the evaluation concept and scheduling. In the following we
will briefly go through some steps that describe the intended evaluation project. Subject of evaluation
will be the derived DINU Model. With the FEDS, evaluations can be distinguished according to two
dimensions: functional purpose and paradigm of the evaluation study (Venable et al., 2014). We
understand our evaluation as summative for the first dimension because it should assess how well the
result match expectations (Wiliam and Black, 1996). As we have carefully developed and iteratively
improved the DINU Model based on prior literature, the next step is a naturalistic evaluation i.e. in
real world with empirical data (Venable et al., 2014). In sum, the goal is to legitimate the
accomplished work by proving the applicability and efficacy of the DINU Model (Stockmann and
Meyer, 2014). We propose and aim at testing the hypothesis that the DINU Model allows an easier
access and design of nudging in information systems.
The evaluation is planned as an internal evaluation project with relevant external stakeholder who
were not involved in the model development (Stockmann and Meyer, 2014). They will be selected due
to their interest and expertise in designing decision situations in the field of information systems,
which is not limited to software architects but may also involve consultants accompanying the
introduction of software. Further, we see high relevance in the avoidance of regretful behavior e.g.
self-disclosure in social media (Binns, 2014; Weinmann et al., 2016). Therefore, we will involve
experts from this field as well. Specifically, we plan to conduct seven to ten semi-structured interviews
with closed and open questions. The participants will see the DINU Model for the first time. Questions
will be framed around the perceived value and usability of the model and its smaller parts in relation to
what the participants have used or known before. They are also invited to recommend changes and
supplements based on their expertise. The main goals of the evaluation are, hence, to prove whether
the experts understand all parts of the model and to identify areas that need improvement from the
application perspective.
4" Preliminary"Results:"The"DINU"Model"
Combining the ideas and elements identified in the literature analysis we created a three-phase process
model to access the nudging environment in a structured manner and provide an easier access to
digital nudging for practitioners and researchers. We separate the digital nudging into the three phases
(1) Analyzing, (2) Designing, and (3) Evaluating including a feedback loop. The whole model is
shown as a summarizing Figure 2 after the following explanations. We use the terms nudger for
choice architect, i.e. the person using the model to design the decision situation, and nudgee for the
decision-maker, e.g. the software user, as those labels can be seen exclusive in the context of (digital)
nudging.
4.1" Analyzing"
First, while collecting and analyzing requirements, the nudger should develop a target behavior for a
defined target audience (Fogg, 2009). She needs to analyze the nudgee in detail to assess reasons of
undesired behavior (e.g. limited cognitive capacity) and consequently goals for digital nudging (e.g.
overcoming flaws). Usually, there are goals like increased productivity which should be achieved
through smaller goals like restoring the intended use. The preference analysis and the focus on welfare
are topics in the field of behavioral economics (Goldin, 2015; Grüne-Yanoff and Hertwig, 2016) that
distinguish nudging from persuasion and should apply for digital nudging as well.
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 6
4.2"Designing"
The design phase is about finding the right elements and situations to design the digital nudge
according to the pre-defined reasons, goals and characteristics of the nudgee. We identified most
elements in nudging literature but also combined and added further components from persuasion.
Additionally, we collected considerations that can improve the element selection and, thus, the success
of the digital nudge. The following Table 2 contains the identified and grouped digital nudging
elements from the literature analysis. Some elements are similar but have not been combined due to
important differences. E.g. customized information focus on the tailoring of information whereas
informing means providing (additional) information in general. In literature, different elements are
often combined and applied simultaneously or successively. We include only those elements that meet
the suggested definition of nudging i.e. they need to be subtle enough and do not alter the choice set.
Table 2 distinguishes between sources that were identified in the nudge context and those related to
persuasion. Sources that are published in IS or computer science outlets but mention nudging were
grouped in the left column as this should highlight the prevalent wording or understanding and not the
discipline. The same applies to Table 3.
Nudging Element
Related to Nudging
Related to Persuasion
Anchoring
(French, 2011; Oullier et al., 2010; Thaler and
Sunstein, 2009)
Customized
information
(Tailoring)
(Johnson et al., 2012)
(Consolvo et al., 2006; Oinas-
Kukkonen and Harjumaa,
2009)
Decision staging
(Tunneling)
(Johnson et al., 2012)
(Oinas-Kukkonen and
Harjumaa, 2009)
Default setting
(Acquisti, 2009; French, 2011; Grüne-Yanoff and
Hertwig, 2016; Hausman and Welch, 2010; Van
der Heijden and Kosters, 2015; Johnson et al.,
2012; Knijnenburg and Kobsa, 2014; Marteau et
al., 2011; Oullier et al., 2010; Sunstein, 2014a;
Thaler and Sunstein, 2009)
Framing
(Acquisti, 2009; French, 2011; Grüne-Yanoff and
Hertwig, 2016; Johnson et al., 2012; Lehner et al.,
2016; Luoto et al., 2014; Oullier et al., 2010;
Thaler and Sunstein, 2009; Thomas et al., 2013;
Wang et al., 2014; Wilkinson, 2013; Zhang and
Xu, 2016)
Informing
(Blitstein et al., 2016; Evans et al., 2014; French,
2011; Marteau et al., 2011; Newell and Siikamäki,
2013; Oullier et al., 2010; Sunstein, 2014b;
Wilkinson, 2013)
Limited time window
(Johnson et al., 2012)
Praise and reward
(Gamification)
(Sohn and Lee, 2007; Toscos
et al., 2006)
Precommitment
strategy
(Lades, 2014; Luoto et al., 2014; Sunstein, 2014b;
Thaler and Sunstein, 2009)
Priming
(Balebako and Cranor, 2014; Sunstein, 2014b;
Thaler and Sunstein, 2009)
Reminders
(Sunstein, 2014b)
(Lee et al., 2006; Oinas-
Kukkonen and Harjumaa,
2009)
Simplification
(Reduction)
(Balebako and Cranor, 2014; Johnson et al., 2012;
Lehner et al., 2016; Oullier et al., 2010; Sunstein,
(Lee et al., 2006)
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 7
2014b; Thaler and Sunstein, 2009)
Social influence
(Social comparison)
(French, 2011; Sunstein, 2014b; Thaler and
Sunstein, 2009; Zhang and Xu, 2016)
(Consolvo et al., 2006; Oinas-
Kukkonen and Harjumaa,
2009; Sohn and Lee, 2007)
Warning
(Balebako and Cranor, 2014; Oullier et al., 2010;
Sunstein, 2014b; Thaler and Sunstein, 2009)
Table 2. Overview of Nudging Elements
Explaining all elements in detail would go beyond the scope of this paper (however, the table provides
an overview about relevant literature for each element). Hence, we will introduce some potentially
new elements or those that are interpreted slightly differently than in persuasion. While the choice set
should remain unchanged in nudging (i.e. it is not allowed to disperse an undesirable option), a
simplification of the choice architecture can be a helpful digital nudge (Balebako and Cranor, 2014;
Sunstein, 2014b). In persuasion, the strategy of reduction is similar but it is not excluded to omit
options (Oinas-Kukkonen and Harjumaa, 2009). We argue that simplification contributes to digital
nudging as long it only refers to a simplification of the decision environment by reducing distractions
but not the options. IT can support people when confronted with too many alternatives (Johnson et al.,
2012). Visual markers can be a simple orientation (Oullier et al., 2010). To simplify and make the
target behavior easier to do, one might also decrease cost and burdens (Fogg, 2009; Thaler and
Sunstein, 2009). Johnson et al. (2012) propose the idea of giving a limited time window for certain
decision that are often hindered by procrastination and inertia. This can be considered a digital nudge
if the decisions can be changed afterwards. Lastly, nudgers can make use of praise and rewards (e.g.
verbal feedback or badges) which can act as a subtle form reinforcement. Though it sounds like
gamification the latter is neither subtle nor does it leave incentives unchanged (Meske et al., 2015). It
can be seen as a tool to increase motivation, though (Wu, 2014).
Besides the nudging elements we have found hints and strategies in the literature which we deem
helpful in selecting the right digital nudging elements summarized in Table 3 as considerations.
Johnson et al. (2012) describe problems that arise in decision environments and certain choice sets that
require specific changes in the choice architecture. Therefore, studying the decision environment
before the intervention may directly point to problems (e.g. attribute overload). Though flawed
thinking often explains undesired behavior it can also be used to digitally nudge people to the intended
behavior if the flaws or cognitive shortcuts are known to the nudger (French, 2011; Oullier et al.,
2010; Thaler and Sunstein, 2009).
Consideration
Related to Nudging
Related to Persuasion!
Ability (Money, physical effort, etc.)
(Fogg, 2009)
Context
(Campbell et al., 2014)
Credibility
(Oinas-Kukkonen and
Harjumaa, 2009)
Motivation
(Fogg, 2009)
Possibility of using human flaws
(French, 2011; Oullier et al., 2010;
Thaler and Sunstein, 2009)
Problems with given choice architecture
(Johnson et al., 2012)
Triggers
(Fogg, 2009)
Table 3. Overview of Considerations
4.3" Evaluating"
The DINU Model’s last step comprises the evaluation of the designed digital nudge after
implementation. Primarily, it should be assessed whether the target behavior is achieved or
modifications of the digital nudge have become necessary. The nudger may also use this step to reflect
on the selected elements. They might have been designed without the intention to confine the nudgee
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 8
in her freedom of choice. Still, unexpected factors can change the decision situation. The complete
DINU Model is displayed in Figure 2, in which the process flow is visualized. Further, the mentioned
aspects for each phase are linked to the respective phases.
Figure 2. Digital Nudging Process Model (DINU Model)
5" Conclusion"
First, in this paper we presented the results and implications of a literature analysis in the fields of
nudging and persuasion. Second, we developed and described a digital nudging process model (DINU
Model). With this model, we aim to provide a tool for the application of digital nudging in the IS field
and to support choice architects in the utilization of nudging elements.
Though we argue that our literature analysis with a representative sample is valuable for the
implementation, this approach is limited in terms of completeness. Further, the literature search on
persuasion appeared problematic due to its multiple meanings. Hence, other corresponding articles
may not be included, also due to the selected search databases. In this paper, we have not yet discussed
ethical and moral concerns about digital nudging in detail, which is of high importance but should be
studied separately. For now, we refer to the discussion paper of Hansen and Jespersen (2013) and call
for future research about the ethical implications of digital nudging.
The important next steps for this research in progress have been presented and comprise the evaluation
and potential adjustment of the DINU Model for nudging with potential users in real-world settings.
The evaluation is based on an evaluation process model and an evaluation framework for DSR.
Refer ences!
Acquisti, A. (2009), “Nudging privacy: The behavioral economics of personal information”, Security
& Privacy Economics, pp. 72–75.
Balebako, R. and Cranor, L. (2014), “Improving app privacy: Nudging app developers to protect user
privacy”, IEEE Security and Privacy, Vol. 12 No. 4, pp. 55–58.
Benbasat, I. and Zmud, R.W. (2003), “The Identity Crisis Within the IS Discipline: Defining and
Communicating the Discipline’s Core Properties”, MIS Quarterly, Vol. 27 No. 2, pp. 183–194.
Binns, A. (2014), “Twitter City and Facebook Village!: teenage girls’ personas and experiences
influenced by choice architecture in social networking sites”, Journal of Media Practice, Vol. 15
No. 2, pp. 71–91.
•Biases
•Cognitive limits
•Flawed thinking
•Distraction
•Direct to intended use
•Overcoming flaws
•Welf are
•Ta rg et au d ie n ce
•Target behavior
•Preferences
•Ability
•Context
•Credibility
•Motivation
•Possibility of using
human flaws
•Problems with given
choice architecture
•Tri g ge r s
•Full freedom of choice
•Preferences respected
•Unchanged incentives
(1) Analyzing
(2) Designing
(3) Evaluating
•Anchoring
•Customized information
•Decision staging
•Default setting
•Framing
•Informing
•Limited time window
•Precommitment
•Priming
•Reminders
•Simplification
•Social influence
•Warning
Elements Considerations
Criteria
Goals
Nudgee
Reasons
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 9
Blitstein, J.L., Cates, S.C., Hersey, J., Montgomery, D., Shelley, M., Hradek, C., Kosa, K., et al.
(2016), “Adding a Social Marketing Campaign to a School-Based Nutrition Education Program
Improves Children’s Dietary Intake: A Quasi-Experimental Study”, Journal of the Academy of
Nutrition and Dietetics, Vol. 116 No. 8, pp. 1285–1294.
vom Brocke, J., Simons, A., Niehaves, B., Reimer, K., Plattfaut, R. and Cleven, A. (2009),
“Reconstructing the Giant: On the Importance of Rigour in Documenting the Literature”, ECIS
2009 Proceedings.
Campbell, W.M., Moore, P. and Sharma, M. (2014), “Cultural Transformation to Support the
Adoption of Green IT”, International Conference on Advanced Information Networking and
Applications Workshops (WAINA), pp. 554–559.
Consolvo, S., Everitt, K., Smith, I. and Landay, J. a. (2006), “Design requirements for technologies
that encourage physical activity”, CHI ’06 Extended Abstracts on Human Factors in Computing
Systems - CHI EA ’06, ACM Press, New York, USA, p. 457.
Cooper, H.M. (1988), “Organizing knowledge syntheses: A taxonomy of literature reviews”,
Knowledge in Society, Vol. 1 No. 1, pp. 104–126.
Evans, W.D., Pattanayak, S.K., Young, S., Buszin, J., Rai, S. and Bihm, J.W. (2014), “Social
marketing of water and sanitation products: A systematic review of peer-reviewed literature”,
Social Science & Medicine, Vol. 110, pp. 18–25.
Fogg, B.J. (2003), Persuasive Technology: Using Computers to Change What We Think and Do,
available at:https://doi.org/10.1007/978-3-540-77006-0.
Fogg, B.J. (2009), “A behavior model for persuasive design”, Proceedings of the International
Conference on Persuasive Technology (PERSUASIVE), available
at:https://doi.org/10.1145/1541948.1541999.
French, J. (2011), “Why nudging is not enough”, Journal of Social Marketing, Vol. 1 No. 2, pp. 154–
162.
Goldin, J. (2015), “Which way to nudge? Uncovering preferences in the behavioral age”, Yale Law
Journal, Vol. 125 No. 1, pp. 226–270.
Grüne-Yanoff, T. and Hertwig, R. (2016), “Nudge Versus Boost: How Coherent are Policy and
Theory?”, Minds and Machines, Vol. 26 No. 1–2, pp. 149–183.
Hansen, P.G. and Jespersen, A.M. (2013), “Nudge and the Manipulation of Choice: A Framework for
the Responsible Use of the Nudge Approach to Behaviour Change in Public Policy”, European
Journal of Risk Regulation, Vol. 1, pp. 3–28.
Hausman, D.M. and Welch, B. (2010), “Debate: To nudge or not to nudge”, Journal of Political
Philosophy, Vol. 18 No. 1, pp. 123–136.
Van der Heijden, J. and Kosters, M. (2015), “From mechanism to virtue: Evaluating Nudge theory”,
RegNet Research Papers, No. 80, pp. 276–291.
Johnson, E.J., Shu, S.B., Dellaert, B.G.C., Fox, C., Goldstein, D.G., Häubl, G., Larrick, R.P., et al.
(2012), “Beyond nudges: Tools of a choice architecture”, Marketing Letters, Vol. 23 No. 2, pp.
487–504.
Kahneman, D. (2011), Thinking , Fast and Slow, Penguin Books, London, GB.
Knijnenburg, B.P. and Kobsa, A. (2014), “Increasing Sharing Tendency Without Reducing
Satisfaction!: Finding the Best Privacy -Settings User Interface for Social Networks”, ICIS 2014
Proceedings, pp. 1–21.
Lades, L.K. (2014), “Impulsive consumption and reflexive thought: Nudging ethical consumer
behavior”, Journal of Economic Psychology, Elsevier B.V., Vol. 41, pp. 114–128.
Larosiliere, G.D., Meske, C. and Carter, L.D. (2015), “Determinants of social network adoption: A
country-level analysis”, HICSS 2015 Proceedings, pp. 3424–3433.
Lee, G., Tsai, C., Griswold, W.G., Raab, F. and Patrick, K. (2006), “PmEB: A mobile phone
application for monitoring caloric balance”, CHI ’06 Extended Abstracts on Human Factors in
Computing Systems - CHI EA ’06, ACM Press, New York, USA, p. 1013.
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 10
Lehner, M., Mont, O. and Heiskanen, E. (2016), “Nudging – A promising tool for sustainable
consumption behaviour?”, Journal of Cleaner Production, Vol. 134, pp. 166–177.
Luoto, J., Levine, D., Albert, J. and Luby, S. (2014), “Nudging to use: Achieving safe water behaviors
in Kenya and Bangladesh”, Journal of Development Economics, Vol. 110, pp. 13–21.
Marteau, T.M., Ogilvie, D., Roland, M., Suhrcke, M. and Kelly, M.P. (2011), “Judging nudging: can
nudging improve population health?”, BMJ, Vol. 342, p. d228.
Meske, C., Brockmann, T., Wilms, K. and Stieglitz, S. (2015), “Gamify Employee Collaboration - A
Critical Review of Gamification Elements in Social Software”, ACIS 2015 Proceedings, pp. 1–15.
Meske, C., Brockmann, T., Wilms, K. and Stieglitz, S. (2016), “Social Collaboration and
Gamification”, in Stieglitz, S., Lattemann, C., Robra-Bissantz, S., Zarnekow, R. and Brockmann,
T. (Eds.), Gamification: Using Game Elements in Serious Contexts, Springer International
Publishing, Cham, pp. 93–109.
Mirbabaie, M., Ehnis, C., Stieglitz, S. and Bunker, D. (2014), “Communication roles in public events
– A case study on Twitter communication”, in Doolin, B., Lamprou, E., Mitev, N. and McLeod, L.
(Eds.), Information Systems and Global Assemblages!: (Re)configuring Actors, Artefacts,
Organizations: IFIP WG 8.2 Working Conference on Information Sysanizations, IS&O 2014,
Springer Berlin / Heidelberg, Auckland, New Zealand, pp. 207–218.
Newell, R.G. and Siikamäki, J. V. (2013), “Nudging Energy Efficiency Behavior: The Role of
Information Labels”, NBER Working Paper Series, Vol. 1 No. 19224, p. 43.
Oinas-Kukkonen, H. (2013), “A foundation for the study of behavior change support systems”,
Personal and Ubiquitous Computing, Vol. 17 No. 6, pp. 1223–1235.
Oinas-Kukkonen, H. and Harjumaa, M. (2009), “Persuasive Systems Design: Key Issues, Process
Model, and System Features”, Communications of the AIS, Vol. 24, pp. 485–500.
Oullier, O., Cialdini, R., Thaler, R.H. and Mullainathan, S. (2010), “Improving Public Health
Prevention with a Nudge”, in Oullier, O. and Sauneron, S. (Eds.), Improving Public Health
Prevention with Behavioural, Cognitive and Neuroscience, Centre for Strategic Analysis, Paris,
France, pp. 38–46.
Room, G. (2016), “Nudge or nuzzle? Improving decisions about active citizenship”, Policy Studies,
Vol. 2872 No. January, pp. 1–16.
Sarker, S., Chatterjee, S. and Xiao, X. (2013), “How ‘Sociotechnical’ is our IS Research? An
Assessment and Possible Ways Forward”, ICIS 2013 Proceedings, pp. 1–24.
Sohn, M. and Lee, J. (2007), “UP health: Ubiquitously persuasive health promotion with an instant
messaging system”, CHI ’07 Extended Abstracts on Human Factors in Computing Systems - CHI
’07, ACM Press, New York, USA, p. 2663.
Stieglitz, S., Mirbabaie, M., Schwenner, L., Marx, J., Lehr, J. and Brünker, F. (2017), “Sensemaking
and Communication Roles in Social Media Crisis Communication”, Proceedings of the 13th
International Conference on Wirtschaftsinformatik (WI), St. Gallen, Switzerland.
Stieglitz, S., Schallenmuller, S. and Meske, C. (2013), “Adoption of social media for internal usage in
a global enterprise”, Proceedings of the IEEE 27th International Conference on Advanced
Information Networking and Applications (AINA), pp. 1483–1488.
Stockmann, R. and Meyer, W. (2014), Evaluation Eine Einführung, UTB, 2nd Editio., Verlag Barbara
Budrich, Opladen & Toronto.
Sunstein, C.R. (2014a), “Nudges and Public Policy”.
Sunstein, C.R. (2014b), “Nudging: A Very Short Guide”, Journal of Consumer Policy, No.
September, pp. 583–588.
Thaler, R.H. and Sunstein, C.R. (2003), “Libertarian Paternalism”, The American Economic Review,
Vol. 93 No. 2, pp. 175–179.
Thaler, R.H. and Sunstein, C.R. (2009), Nudge, Int’l., Penguin Books, London, GB.
Thomas, A.M., Parkinson, J., Moore, P., Goodman, A., Xhafa, F. and Barolli, L. (2013), “Nudging
through technology: Choice architectures and the mobile information revolution”, Proceedings -
Meske and Potthoff / A Process Model for the Design of Nudges
Twenty-Fifth European Conference on Information Systems (ECIS), Guimarães, Portugal, 2017 11
2013 8th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing,
3PGCIC 2013, pp. 255–261.
Toscos, T., Faber, A., An, S. and Gandhi, M.P. (2006), “Chick clique: Persuasive technology to
motivate teenage girls to exercise”, CHI ’06 Extended Abstracts on Human Factors in Computing
Systems - CHI EA ’06, Vol. 31, ACM Press, New York, USA, p. 1873.
Venable, J., Pries-heje, J. and Baskerville, R. (2014), “FEDS: a Framework for Evaluation in Design
Science Research”, European Journal of Information Systems, Vol. 25 No. October 2012, pp. 1–13.
Wang, Y., Leon, P.G., Acquisti, A., Cranor, L.F., Forget, A. and Sadeh, N. (2014), “A field trial of
privacy nudges for facebook”, Proceedings of the 32nd Annual ACM Conference on Human
Factors in Computing Systems - CHI ’14, pp. 2367–2376.
Weinmann, M., Schneider, C. and vom Brocke, J. (2016), “Digital Nudging”, Business & Information
Systems Engineering, Springer Fachmedien Wiesbaden, pp. 1–9.
Wiliam, D. and Black, P. (1996), “Meanings and Consequences: a basis for distinguishing formative
and summative functions of assessment?”, British Educational Research Journal, Vol. 22 No. 5,
pp. 537–548.
Wilkinson, T.M. (2013), “Nudging and manipulation”, Political Studies, Vol. 61 No. 2, pp. 341–355.
Wu, M. (2014), “The Power of a Nudge - Part 1: A Stronger Behavior Driver than Gamification”,
Lithium - Science of Social Blog, available at: https://community.lithium.com/t5/Science-of-Social-
blog/The-Power-of-a-Nudge-Part-1-A-Stronger-Behavior-Driver-than/ba-p/165117 (accessed 19
August 2015).
Zhang, B. and Xu, H. (2016), “Privacy Nudges for Mobile Applications: Effects on the Creepiness
Emotion and Privacy Attitudes”, Proceedings of the 19th ACM Conference on Computer-
Supported Cooperative Work & Social Computing - CSCW ’16, ACM Press, pp. 1674–1688.
