Combined Digital Nudging to Leverage Public Transportation Use

Abstract

The urgency of global climate change is becoming increasingly evident, but prevailing mobility patterns in developed countries still cause severe environmental damage. Therefore, developed countries need to change their mobility patterns fundamentally, such as modal changes to public transportation instead of private car use. Digital nudging in digital mobility applications is a novel and promising way to influence modal changes to public transportation. In this study, we conduct an online experiment with 183 participants who are being nudged toward public transportation trip options. Our results show that combining two different digital nudges significantly affects the choice of public transportation options. By contrast, single nudges do not significantly change the choice of public transportation trips. With our findings, we contribute to the research stream of digital nudging and the transportation literature and provide insights for practice to address the adverse effects of current mobility patterns.

Full text

Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 1
COMBINED DIGITAL NUDGING TO LEVERAGE
PUBLIC TRANSPORTATION USE
Research Paper
Sina Zimmermann, University of Applied Sciences Neu-Ulm, Technical University of
Munich, Germany, sina.zimmermann@hnu.de
Paula Feike, Technical University of Munich, Germany, paula.feike@tum.de
Andreas Hein, Technical University of Munich, Germany, Andreas.hein@tum.de
Heiko Gewald, University of Applied Sciences Neu-Ulm, Germany, heiko.gewald@hnu.de
Abstract
The urgency of global climate change is becoming increasingly evident, but prevailing mobility patterns
in developed countries still cause severe environmental damage. Therefore, developed countries need
to change their mobility patterns fundamentally, such as modal changes to public transportation instead
of private car use. Digital nudging in digital mobility applications is a novel and promising way to
influence modal changes to public transportation. In this study, we conduct an online experiment with
183 participants who are being nudged toward public transportation trip options. Our results show that
combining two different digital nudges significantly affects the choice of public transportation options.
By contrast, single nudges do not significantly change the choice of public transportation trips. With
our findings, we contribute to the research stream of digital nudging and the transportation literature
and provide insights for practice to address the adverse effects of current mobility patterns.
Keywords: Digital Nudging, Online Experiment, Public Transportation, Sustainable Mobility.
1 Introduction
The urgency of global climate change is becoming increasingly evident, and addressing its hazardous
effects is part of the sustainable development goals of the United Nations (United Nations, 2021). In
this regard, mobility significantly contributes to environmental damage and air and noise pollution
(Hauslbauer et al., 2022; Mulley, 2017). For instance, in 2020, the transportation sector was responsible
for more than 20% of global CO2 emissions (Tiseo, 2021). One of the reasons for the adverse effects of
the mobility sector is the use of environmentally harmful modes of transportation, such as driving private
cars instead of using environmentally friendly alternatives like public transportation (Keller, 2022).
Therefore, innovative solutions are necessary to change climate-damaging human behavior in the
mobility sector.
One way to reduce the negative effects of the current mobility patterns is to make more sustainable
mobility modes more efficient. For instance, mobility apps or web applications allow easier and more
convenient use of public transportation by providing real-time information on occupancy rates or current
delays (Zimmermann et al., 2020). In addition, as shown in other contexts, such as fitness tracking or
fostering sustainable behavioral changes in companies, information technology (IT)-enabled solutions
can trigger behavioral changes (Isensee et al., 2022; Sullivan & Lachman, 2017).
One of the promising IT-enabled approaches to changing individual (mobility) behavior is digital
nudging. In general, nudging is defined as “[...] any aspect of the choice architecture that alters people’s

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Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 2
behavior predictably without forbidding any options or significantly changing their economic
incentives” (Thaler & Sunstein, 2008, p. 6). When nudging occurs in the digital realm, such as on
webpages or apps, such an event is referred to as digital nudging and defined as “the use of user interface
design elements to guide people’s behavior in digital choice environments” (Weinmann et al., 2016, p.
433). The recent information systems (IS) literature (e.g., Henkel et al., 2019; Meske & Amojo, 2019;
Weinmann et al., 2016) has discussed the concept of digital nudging. It has also been examined in
diverse other fields, such as psychology (e.g., Taube & Vetter, 2019), business and marketing (e.g.,
Grinstein & Riefler, 2015), and environmental sustainability (e.g., Tiefenbeck et al., 2019). Moreover,
digital nudging promotes sustainable mobility behavior (e.g., Kim et al., 2020; Tussyadiah & Miller,
2019). For instance, digital nudges are used to promote the subscription to public transportation tickets
(Hauslbauer et al., 2022) or to increase the use of public transportation in field experiments
(Anagnostopoulou et al., 2020; Lieberoth et al., 2018).
In the digital realm, implementing more than one nudge at a time to increase their effects is becoming
common (e.g., Camilleri et al., 2019; Loock et al., 2013), as digital nudges can be deployed with less
effort than nudges in an offline setting. For instance, Loock et al. (2013) implemented a combination of
default and goal-setting nudges to induce energy-efficient behaviors among study participants.
However, the effects of combining different digital nudges have not been considered systematically
(Meske & Amojo, 2019; Zimmermann et al., 2021), as exemplified by Guerassimoff and Thomas (2015)
and Abrahamse et al. (2007). Both studies combined feedback, goal-setting mechanisms, and social
comparison nudges to promote energy-efficient behavior but did not address the degree to which the
achieved effects can be attributed to a digital nudge or a combination of two or more.
In addition, changing mobility behavior toward more sustainable options, such as public transportation,
is a complex societal challenge (e.g., Gravert & Collentine, 2021; Hauslbauer et al., 2022; Steg, 2005).
Moreover, former studies often showed mixed results regarding the effects of digital nudging to increase
the use of more sustainable mobility forms, such as public transportation (Cellina et al., 2019).
Therefore, we analyze the effects of digital nudging to increase public transportation use and pose the
following research questions:
1) Can digital nudging increase the choice of public transportation trip options?
2) Can the effect be increased by deploying a combination of two digital nudges?
We conducted an online experiment in Germany with 183 participants to answer these research
questions. We compared the effects of two digital nudges (i.e., decoy and default) and a combination of
both digital nudges for choosing public transportation instead of private car options to a control group.
Our results show that only combining two different digital nudges has significant effects. By contrast,
single nudges do not significantly change the choice of public transportation trips. With our results, we
contribute to the research stream of digital nudging and the transportation literature and provide insights
for practice to address the adverse effects of current mobility patterns.
2 Theoretical Background
2.1 Digital Nudging
Empirical studies showed that psychological triggers could effectively change people’s behavior to
promote public transport use (Hunecke et al., 2010; Möser & Bamberg, 2008). Examples are nudges
that influence people’s behavior without changing economic incentives, relying instead on choice
architecture that refers to the physical or digital environment in which decisions are made. Manipulating
choice architecture affects behavior without forbidding any available options (Thaler et al., 2013). Such
adaptations can influence people consciously or subconsciously and are well suited to changing habitual
choices (Thaler & Sunstein, 2009), for example, people’s mobility behavior. For example, Franssens et
al. (2021) showed that messages in buses that positively label passengers as sustainable could increase

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Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 3
the usage rate of public transportation. Moreover, Fyhri et al. (2021) used nudging mechanisms to
increase the perceived safety of cycle lanes and promote bicycle use.
As more and more choices are being made on screen, nudging has also been transferred to the digital
realm. This is then called digital nudging and is defined as “the use of user-interface design elements to
guide people’s behavior in digital choice environments” (Weinmann et al., 2016, p. 433). Digital nudges
can be deployed, for example, on web pages, apps, or online shops. However, the implementation and
underlying mechanism for behavioral change can vary widely across different digital nudges. For
example, some digital nudges, such as priming or goal setting, are used before action. By contrast, other
nudges, such as default settings or feedback nudges, influence user behavior during or after a decision
or action. Moreover, some digital nudges are mostly combined with other incentives, such as social
norms or framing nudges (Zimmermann et al., 2021).
One type of digital nudging is so-called decoys. Huber et al. (1982) were the first to research the decoy
effect, which can be deployed in decision situations where individuals must choose between two options
that differ along varying dimensions, such as price, sustainability, or quality, but none of the options is
dominant in all dimensions. The resulting trade-off between options makes it difficult for consumers to
decide on one option (Attwood et al., 2020; Momsen & Stoerk, 2014). Introducing a third option during
the selection process - the decoy option - which is similar but inferior to one of the two options, can
unconsciously change a consumer's preferences. The reason is that the superior option of the decoy
appears relatively more appealing than without the decoy (Huber et al., 1982). This effect can be
explained by the observation that consumers often consider irrelevant alternatives in their choices,
influencing the decision-making process and its outcome. A decoy option includes an irrelevant
alternative (Ariely & Wallsten, 1995) and makes the desired choice option more attractive.
Defaults are another kind of digital nudging. Setting defaults preselects the most desired alternative for
the respective situation and influences the presentation of different choices for consumers. This nudge
forces individuals who want to choose differently to invest effort in opting out of the preselected option.
Deciding based on the default option requires the least time and effort (Blumenthal-Barby & Burroughs,
2012). Moreover, defaults are implicit recommendations (Johnson & Goldstein, 2003; Keller et al.,
2020). This case leads to a higher probability of a favorable evaluation and of sticking to the default,
particularly when the decision requires difficult considerations involving money, personal values, and
societal norms (Gollwitzer, 1990; Henkel et al., 2019).
2.2 Related Literature and Hypothesis Development
Increasing sustainable mobility modes is a potential use case for digital nudging. The extant literature
in IS and related research streams showed a growing number of scientific studies on this topic. For
instance, former studies analyzed the effects of digital nudges applied in a flight context. The results of
Székely et al. (2016) provide insights into the effectiveness of default nudges to increase carbon-offset
payments on an online flight booking platform. Sanguinetti and Amenta (2022) used information nudges
on flight emissions on a flight search and booking platform. Their results show that labeling low-
emission flights with “Greenest Flight” could increase the participants' willingness to pay for these
flights.
Within a recreational context, Jariyasunant et al. (2015) used a digital travel feedback system to enforce
more sustainable travel choices, including the choice of transportation mode. Their results show that
digital feedback nudges containing social norms increased the choice of sustainable choices in the
experiment. Moreover, Pihlajamaa et al. (2019) used real-time information to move people from
crowded nature destinations to less crowded areas using occupancy and public transportation
information. Bothos et al. (2016) implemented a recommendation system within mobile apps and used
route planning applications to reduce the overall CO2 emissions of the study participants by proposing
different routes. Additionally, several studies followed a technical approach and developed location-
based recommendations to steer the participants’ mobility behavior using tailored information (e.g., Gao
et al., 2019; Zhu et al., 2017). Although such user-data-informed digital nudges show promising effects,

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they also raise ethical issues like privacy aspects that should be considered when designing and
deploying digital nudges (Sunstein, 2015).
Several studies have also analyzed the promotion of public transportation. For instance,
Anagnostopoulou et al. (2020) nudged users through recommendations, such as “Today it’s sunny! Take
the opportunity to combine bike with public transportation to save CO2 emissions” (Anagnostopoulou
et al., 2020, p. 171) to actively recommend pro-environmental mobility choices to app users. Hauslbauer
et al. (2022) used digital nudges to promote public transport ticket subscriptions with a working contract.
In their experimental study, they deployed default and social norm nudges to increase the subscription
among participants. Their results show no statistically significant effects of the nudges but a positive
trend for the default intervention. Lieberoth et al. (2018) tested different interventions, including
gamification elements and digital nudges, to promote public transportation instead of private car use in
a field experiment. Their results show that nudging can support psychological conversions to behavioral
change, although the impact caused by gamification elements in their experiment showed an even bigger
effect.
The literature on digital decoy nudges is still scarce. Only little empirical evidence on their effects exists
(e.g., Attwood et al., 2020; Momsen & Stoerk, 2014), particularly in a mobility context. Moreover,
extant literature showed mixed results regarding effect sizes from decoy nudges. In the present study,
we chose to test digital decoy nudges to unravel their potential to increase the desired choice option.
Considering the experimental design, the provided mobility scenarios differ in various dimensions, such
as price, emissions, or duration, complicating the decision-making. Decoys can simplify such multi-
dimensional choice options. Therefore, we empirically analyze whether digital decoy nudges can
increase the choice rates of rail and flight options and propose the following hypothesis:
Hypothesis 1: Nudging participants with a digital decoy nudge increases the likelihood that they will
choose a public transportation trip option.
In contrast to the decoy literature, default nudges have been studied intensively. Default nudges create
a status-quo bias toward the preselected choice option and force individuals who want to choose
differently to invest effort in opting out of the preselected option. This case increases the probability of
sticking to the default option (Samuelson & Zeckhauser, 1988). Extant literature showed that default
nudges significantly influence behavioral choices (for an overview see Hummel & Maedche, 2019). For
example, studies analyzed the effects of defaults to promote green online product purchases (Taube &
Vetter, 2019) or energy savings (Loock et al., 2013). In the mobility context, defaults have been used to
raise travelers’ awareness of the environmental impact of their transportation choices (Froehlich et al.,
2010; Sanguinetti et al., 2017), rethink their transportation habits (Anagnostopoulou et al., 2020), and
reduce private car use (Lieberoth et al., 2018; Wunsch et al., 2015). Moreover, Székely et al. (2016)
used default settings to encourage carbon offset payments for flight bookings successfully. Based on
these promising results, we analyze the effects of default nudges to promote public transportation trip
options and propose the following hypothesis:
Hypothesis 2: Nudging participants with a digital default nudge increases the likelihood that they will
choose a public transportation trip option.
As digital nudges can often be implemented with less effort compared with nudges in an offline setting,
combining different digital nudges is becoming more common to increase the expected effects (e.g.,
Camilleri et al., 2019; Loock et al., 2013). Nudges are combined because of an expected amplification
of single-nudging effects. However, the extant literature still needs to systematically consider the effects
of combining different digital nudges (Meske & Amojo, 2019; Zimmermann et al., 2021). In a mobility
context, Anagnostopoulou et al. (2020), Bothos et al. (2016), and Lieberoth et al. (2018) deployed a
combination of different digital nudges, such as recommendations, feedback, or social norms, to
motivate more sustainable mobility behavior like the use of public transportation. However, these former
studies often needed to address the degree to which the achieved effects can be attributed to a digital
nudge or a combination of two or more nudges. Therefore, we first analyze the effects of decoy and

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Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway 5
default nudges alone and then continue to analyze the combined effect. We put forth the following
hypothesis:
Hypothesis 3: Nudging participants with a combination of digital decoy and default nudges increases
the likelihood that they will choose a public transportation trip option.
3 Methodology
3.1 Setting and Experimental Design
We set up an online randomized controlled trial to test our hypotheses. Moreover, we chose a between-
subject experimental design to investigate the effects of digital nudging on the choice behavior of public
transportation trip options over the choice of private car trip options. Creating an interactive and realistic
user interface is essential when designing digital nudges, as every element of the digital choice
environment can influence decisions (Weinmann et al., 2016). Hence, we decided not to use a
standardized online survey tool but to program a custom web application. The website was built using
Django’s free, open-source Python web framework. The Django web framework offers efficiency,
flexibility, and versatility in application development (Django Software Foundation, 2022). On the
website, each participant is tracked by assigning an anonymous session key per participant throughout
the experiment. By starting the website, each participant was randomly assigned to either one of the
treatment groups (i.e., decoy, default, or combined) or the control group without any digital nudges.
Each participant had to choose between public transportation or private car use in three different
scenarios to test the effectiveness of the manipulations. Each scenario contained a fixed start and end
point in Munich (Germany), and the participants could choose between two or three different trip options
for the route. To mimic real-world mobility behavior as well as possible in the experiment, we included
the statement, ‘Once you have found a mode of transport for your route that best matches your real-life
choice, press start route’ (translated). The expected duration and the length of the trips in each scenario
are based on realistic values provided by OpenStreetMap (2022), a free online map provider.
In the three scenarios of the control group, the participants were provided with the best public
transportation and the best private car trip option based on the values of OpenStreetMap (2022). In each
scenario, the public transportation option had a longer travel time than the private car option but was the
more sustainable option. Thus, the two options have different advantages and disadvantages regarding
sustainability and travel time. For the design of the decoy nudge, a second public transportation trip
option was introduced with a longer travel time than the first public transportation option and mode
changes. The default nudges are implemented by preselecting and highlighting the public transportation
option. Finally, we combine the decoy and default nudges. Study participants in this group were shown
the decoy option, and the first public transportation option was preselected and highlighted. Figure 1
depicts the design of the combined nudges. The manipulation of single nudge treatment groups consisted
of the additional decoy option or the preselected and highlighted default option.
In addition to the experimental questions, the website included an additional survey. The survey included
questions about the participant’s demographics, their mobility behavior (e.g., which modes of
transportation they use), and their use of mobility apps (e.g., whether they use mobility apps to plan trips
or check for delays). As the study focuses on sustainable mobility behavior, we also included scales to
assess the participants’ environmental awareness. Therefore, we included two scales from the
environmental attitudes (EA) measurement tool developed by Milfont and Duckitt (2010). In addition,
the scales developed by Smythe and Brook (1980) are used to learn more, specifically about the EA
regarding transportation.
Before launching the study, the intuitive handling and the website’s functionality were tested and
continuously improved in a pre-test with 10 participants. The study participants for the main study were
recruited through Facebook groups and various online tools, such as SurveySwap, SurveyCircle, and
Pollpool. We constrained the data collection to people living in Germany to control for country-specific

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effects. Germany is well suited for our study because it suffers from the negative consequences of
mobility behavior based primarily on private car use. Moreover, the conditions for changing mobility
behavior are good in Germany because several studies (e.g., German Federal Environmental Agency,
2019; Kuhnimhof et al., 2012) showed that the importance of private car ownership and the emotional
attachment to one's car is decreasing in Germany. Moreover, we focus on cases located in a big city in
Germany (Munich), considering that access to public transportation is very good.
Figure 1. Deployment of the decoy and default nudge on the experimental website
3.2 Data Collection and Analysis
The experiment was conducted over 13 weeks, from May to August 2022. A total of 219 participants
started the survey, and 188 completed it. The 29 participants terminated the study immediately after the
start or before the demographic information was filled out. Moreover, two attention checks were
included in the survey to ensure data quality, and five participants were removed because of failed
attention checks. Thus, we have a final sample of 183 participants.
We apply a mixed-effects logistic regression to analyze whether the digital nudges increase the choice
of public transportation trip options. Moreover, we cluster the answers and introduce a random intercept
per participant to control for the several answers per participant. Following Sommet and Morselli (2017),
our model is given as follows:
Pr
= 1(+  +  ++).
The result of the regression 
 reflects the probability that the public transportation option that was
targeted by the nudge(s) is chosen by the participants of the treatments groups (or the respective option
for the control group), whereas  is the index for the participants, and  is the index for the three different
scenarios.  refers to the fixed intercept, and  contains the fixed effects for the different treatment
groups (i.e., decoy, default, combination).  is coded as 1 when a participant chooses the
nudged public transportation trip option and 0 if otherwise.  contains the fixed effects for the different
control variables,  is the random effect that accounts for the correlation of the decisions per subject,
and  represents the residual. The control variables include the participants’ age, gender, EA, number

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of children, individual monthly income, place of residence, and the ownership of a car or public
transportation ticket. For the data preparation, we used Python. The data analysis was performed with
the statistical software R using the integrated development environment RStudio. For the mixed-effects
regression, the package lme4 with the glmer function was used.
3.3 Descriptive Statistics
Table 1 summarizes the descriptive statistics of the participants for each treatment group. The
participants in our study are between 17 and 78 years old, with an average of 29.6 years. Of the
participants, 59.0% are female, and 41.0% are male. Moreover, among the 183 participants, 22 have one
or more children. On average, the participants have a personal net income of €1,800 per month, whereas
25.7% have an income below €1,000 per month, 43.2% have an income of €1,000–€2,000, and 30.6%
have an average net income of more than €2,000 per month. Most (69.4%) live in a large city in
Germany, whereas 16.4% live in medium cities, and 14.2% live in small towns or rural communities.
Demographic Data Control
(n = 44)
Decoy
(n = 46)
Default
(n = 49)
Combined
(n = 44)
Age (SD)
27.9 (8.79)
30.4 (10.56)
30.7 (10.88)
29.4 (10.71)
Gender (in %)
Female
45.5
58.7
65.3
65.9
Male
54.5
41.3
34.7
34.1
Diverse
0.0
0.0
0.0
0.0
Children (SD)
0.1 (0.39)
0.4 (0.86)
0.2 (0.48)
0.1 (0.44)
Net income per month (in €)
1980.2
2079.5
2118.7
1752.1
Place of residence
Large city
79.5
63.0
69.4
65.9
Medium city
9.1
21.7
16.3
18.2
Small town
11.4
10.9
6.1
13.6
Rural community
0.0
4.3
8.2
2.3
Score environmental attitude (SD)
5.3 (1.08)
5.4 (0.98)
5.7 (0.86)
5.6 (0.96)
Car ownership (in %)
56.8
50.0
49.0
56.8
Public transportation ticket (in %)
61.4
43.5
63.3
70.5
Mostly used transportation mode (in %)
Public transportation
36.4
41.3
40.8
40.9
Car
22.7
30.4
20.4
25.0
Bicycle
31.8
17.4
24.5
27.3
Others
0.0
2.2
2.0
2.3
None (only walking)
9.1
8.7
12.2
4.5
Table 1. Descriptive statistics of the demographic data of the participants grouped by the
treatment group

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For the scores of the EA based on the scales of Milfont and Duckitt (2010), we find an average score of
5.52 among all participants with scales ranging from 1 to 7. Our study will use this average score to
classify a below-average EA score as neutral-EA and an above-average score as pro-EA. Of the
respondents, 97.8% have a driving license for passenger cars, and 53.0% own a car. Moreover, 59.6%
have a permanent ticket for public transport. Local public transport is also the most used mode of
transportation by the respondents, with 39.9%. Notably, the survey was conducted during the
introduction of the “nine-euro ticket” in Germany. The German parliament passed the nine-euro ticket
on May 19, 2022, a one-time, time-limited special offer. The nine-euro ticket was valid throughout
Germany on local and regional public transport for June, July, and August 2022 (Müller 2022). Ninety-
three people participated in our study before introducing the nine-euro ticket and 90 people afterward.
Notably, only 51.61% of the respondents had a ticket for local public transport before the introduction,
and 67.78% were after.
4 Results
We start with an initial graphical analysis following the approach of Meske et al. (2020) to gain an
overview of the data. Figure 2 depicts the percentage of participants choosing public transportation and
car options, grouped by control and treatment groups. The initial graphical analysis shows that the public
transportation option is often selected among all control and treatment groups with median values of
80% or higher. Hence, participants who did not experience digital nudging already have a high
willingness to choose public transport in our sample. By adding the nudging interventions, we can
observe that participants select public transportation more often.
The graphical analysis shows a trend for the treatment groups, with higher medians for all three groups
of 83% and up to 97%. Among the treatment groups, we find the smallest effect for the manipulation in
the decoy group regarding the median value and the dispersion among the participants, which can be
deducted from the quantiles in Figure 2. The default and combined nudges lead to a significantly higher
increase in the choice of public transportation options for the medians, whereas the combination of both
nudges is the highest. Moreover, we find that the dispersion among the participants decreases
considerably, meaning that the increase in the median can be ascribed to a large share of the participants
in these two treatment groups.
Figure 2. Public transportation choices among control and treatment groups (in %)
Next, we apply different mixed-effects logistic regression models. Table 2 presents the results. First, we
present the Null model, an intercept-only model with a random intercept for each respondent. Model 1
contains the treatment group added as a fixed effect, and Model 2 has the control variables as fixed

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effects. Models 3 and 4 contain the results for additional analyses: They are similar to Models 1 and 2
but are restricted to participants with an EA score below average.
The results show no significant effects for Model 1, indicating that none of the digital nudging
mechanisms (i.e., decoy, default, and combined) could statistically significantly increase the choice of
public transportation options compared with the control group. However, as in the graphical analysis,
the coefficients for the treatment effects show a positive trend, with the most significant effect for the
combined nudging mechanism. When adding all the control variables in Model 2, we find that the
combination of the two nudges, default, and decoy, has a statistically significant effect on the choice of
the public transportation option. As the coefficients are reported as changes to the log odds ratio, the
coefficient of 3.04 of the combined nudge indicates an approximately 20.9 times higher chance of
selecting the public transportation option than the control group. In addition to the effects based on the
nudging effects, we find that the control variable EA is highly significant, with a coefficient of 1.72.
This result indicates that the score deducted from the EA scales has explanatory power for choosing
sustainable, that is, public transportation options. All other control variables are not statistically
significant.
Based on our findings in Model 2, we run additional analyses to examine the effects based on the EA
scores in more detail. Participants with a pro-EA are more likely to choose the more sustainable
transportation alternative than individuals with an EA below the average EA value. Thus, we focus on
the latter sub-sample with 71 participants to optimize digital nudging from a practical perspective, as it
can be expected that the group with higher EA scores already acts more sustainable. Again, we start
with a graphical analysis of the results for the below-average EA score group (Figure 3).
Figure 3. Public transportation choices among control and treatment groups for participants
with under-average EA scores (in %)
The graphical analysis shows that all median values for the public transportation choices are lower (67%
for the control and 75% - 90% for the treatment groups) and are more dispersed among all groups.
Again, by adding the nudging interventions, we can observe that participants select public transportation
more often. Among the treatment groups, we found the smallest effect for the manipulation in the decoy
group and the highest effect for the combined nudges. The default manipulation leads to a higher median
increase in the public transportation choice option than the decoy nudges. However, the results are more
dispersed, unlike those discussed above for the sample with all participants.
Table 2 shows the regression analysis results for the sub-sample of participants with EA scores below
the average. In Model 3, only the treatment effects for the different nudging manipulations are
considered, which leads again to no significant effects for the coefficients. However, notably, values

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show a positive trend, and the effects are higher than those of Model 1. When considering the regression
analysis of Model 4, again, a significant effect of combining the default and decoy nudges can be
observed. Notably, the individual decoy and default nudges have a more substantial yet statistically
insignificant effect than in Model 2. Finally, although the results for the sub-sample analysis show
similar trends, the effects are similar or stronger, although the regression model contains only 71
participants.
Table 2. Coefficient estimates and standard deviations for the five different logistic regression
models. Null Model: Intercept-only model with random intercept per respondent;
Model 1: Treatment group added as fixed effect; Model 2: Control variables added as
fixed effects; Models 3 and 4; Similar to Models 1 and 2 but restricted to participants
with an EA score below-average.

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5 Discussion
In this study, we have analyzed whether the digital nudges decoy, default, and a combination of both
can increase the choice of public transportation options with three different route planning scenarios in
Munich. Our results show that despite a positive trend, the digital nudges decoy and default did not
significantly increase the likelihood of choosing public transportation options. Therefore, hypotheses 1
and 2 are not supported. However, combining both nudges leads to a statistically significant increase in
our experimental study when other control variables are considered in the regression model. Therefore,
hypothesis 3 is partially supported. Table 3 presents the findings for the hypotheses.
Hypotheses
Findings
Hypothesis 1: Nudging participants with a digital decoy nudge increases the
likelihood that they will choose a public transportation trip option. Not supported
Hypothesis 2: Nudging participants with a digital default nudge increases the
likelihood that they will choose a public transportation trip option. Not supported
Hypothesis 3: Nudging participants with a combination of digital decoy and
default nudges increases the likelihood that they will choose a public
transportation trip option.
Partially
supported
Table 3. Summary of the hypotheses and study results
5.1 Theoretical Contributions
Our findings contribute to the (IS) literature in several ways. First, we present empirical findings on
digital decoy and default nudges in the mobility context. Our results show that the digital decoy nudge
did not increase the choice of public transportation trip options in the regression model. The trend
identified in the graphical analysis of the nudging effects is relatively small. Our analysis of the relevant
literature and former literature reviews shows that no other research study could be found in which
decoy nudges were applied in the digital mobility service providers' field to influence sustainable
mobility choices (Zimmermann et al., 2021). Considering the superordinate field of digital nudging
research on pro-environmental behavior changes, we find consistent results with Momsen and Stoerk
(2014), who tested decoy nudges to reduce energy consumption and did not find significant results. A
potential explanation for our findings might be that decoys are too complex to catch users’ immediate
attention on a website when they are in a decision-making process. By contrast, other digital nudges,
such as defaults, are very prominent and simplify the decision-making process tremendously.
The insignificant findings for the default nudges are rather contradicting when considering the extant
literature. For instance, Hummel and Maedche (2019) reported that default nudges are one of the most
promising behavior change mechanisms in the extant nudging literature. Moreover, former studies in
the mobility context successfully deployed default nudges. For instance, Székely et al. (2016) used
default settings to encourage carbon offset payments for flight bookings, and Stryja and Satzger (2019)
increased the choice of battery-powered rental cars with default settings in an experimental study.
However, evidence that default nudges might not be as effective in digital realms as offline settings also
exists “because people in online environments are confronted much more often with default options and
so have become more cautious” (Meske and Amojo 2019, p.413). Moreover, increasing the use of public
transportation options requires more individual commitment than other contexts that might not demand
such big behavioral changes.
Second, we provide systematic findings on the effect of a combination of two different digital nudges.
To date, the effects of combining different digital nudges have often not been considered systematically
in the extant literature (e.g., Anagnostopoulou et al., 2020; Lieberoth et al., 2018), which leads to

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limitations in terms of the transferability of the findings as why a combination of different digital nudges
lead to behavioral changes is unclear (Meske & Amojo, 2019; Zimmermann et al., 2021). In our study,
the combined digital nudge provides significant results for public transportation options. More
specifically, when adjusting the control variables, participants in the combined group were
approximately 21 times more likely to choose public transportation than participants not exposed to a
digital nudge. However, the individual nudges did not show a significant effect, illustrating that the
interaction between the digital default nudge amplifies each other’s effects. By contrast, most observed
effects seem accountable to the default nudge.
Third, we additionally considered a subgroup of subjects with a below-average EA score based on the
scales of Milfont and Duckitt (2010) and Smythe and Brook (1980). Our results show that environmental
attitude significantly impacts the choice of sustainable mobility options. By contrast, other control
variables (e.g., participant’s age, gender, number of children, or monthly income) did not significantly
influence the decision. Therefore, our results show that the scales are well-suited to predict sustainable
mobility choices. Moreover, we find that the effects of the digital nudges for this sub-sample are more
robust than for the overall sample. This result complements former findings, for instance, of Hunecke
et al. (2010) and Schulz et al. (2021), who pointed out the relevance of mobility target groups based on
attitudinal factors, such as environmental attitude.
5.2 Practical Implications
Our findings are also important for practitioners, such as public transportation companies, city managers,
or designers of mobility apps and other digital platforms used to plan and navigate trips. First, our results
show that the combination of decoy and default can increase the choice of public transportation in cities.
For instance, the findings can be used to integrate these nudges into mobility apps (e.g., Google Maps
or apps offered by public transportation companies) to increase the use of more sustainable modes of
transportation, such as public transport. Moreover, policymakers could use these insights to increase the
effects of political measures, such as promoting the sales of subsidized public transportation tickets with
digital nudging mechanisms. Second, the effects of the nudging interventions in such applications can
differ based on the EA scores of the users, as shown in our results. This finding indicates that although
digital nudging is a promising approach to induce behavior change in individuals’ mobility behavior
(e.g., Franssens et al., 2021; Fyhri et al., 2021), such interventions must be tailored carefully to the
targeted users.
Third, the design of our experimental website can be used as a blueprint or starting point for the further
development of digital nudges. For example, designers of digital mobility service providers can use the
study results to adapt environmental decision-making, improve decision quality, and develop more
effective interventions. The interventions present only minor changes to the existing user interface.
Therefore, digital nudges can be implemented with low implementation and development costs.
However, designers must be aware that applying digital nudges can raise ethical issues, including
privacy, as nudges are often related to manipulating decisions (Schneider et al., 2018). In general,
(digital) nudges should always be deployed in a way that represents the interest of the persons involved
to avoid malicious manipulation, following the guidelines of Thaler and Sunstein (2009). One way to
overcome this issue in practice is, for instance, to make interventions more notable to users so that they
can decide more consciously whether to follow a nudge or to choose a different option. Particularly with
default nudges, designers should consider factors such as the visibility of automatic enrollment and the
ease with which users can opt out (Caraban et al., 2019).
5.3 Limitations and Future Research
This study had several limitations that should be addressed in future research. First, we only collected
data in Germany. Considering that the importance of private car ownership and the emotional attachment
to one’s car is decreasing among the younger generation (e.g., German Federal Environmental Agency,
2019; Kuhnimhof et al., 2012) in Germany, the conditions are promising to change current mobility

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patterns. However, as attitudes toward public transport use vary significantly across countries and
cultures (Kuhnimhof et al., 2012), future studies should examine how these attitudinal differences
influence people’s mobility choices. Moreover, our sample consists of a moderate number of 188
participants. Although we found statistically significant results for the combined nudging interventions,
our sample might not represent larger populations, which should be examined in future research with
larger sample sizes.
Second, the experimental design can only mimic real-world situations without completely reflecting
them. In our experimental study, we implemented only simple user interface elements to distract the
study participants as little as possible and limited the choice options to private cars and public
transportation use. Thus, our findings might only partially be transferable to actual choice decisions, for
instance, when more design elements, such as the price or the date of a trip, are also displayed to users,
or additional mobility options like biking are available. In addition, the availability and accessibility of
public transportation alternatives are prerequisites for behavioral change in the mobility sector. The
fictitious trips the participants were asked to plan were all within or started from Munich in Germany.
In this city, the prerequisite for switching to more sustainable modes of transportation is present.
However, this may be different in other locations. Therefore, the results should not be directly
generalized to other regions with less diverse transportation options but rather taken as a foundation for
future examination of the specific factors that may influence public transportation in more rural areas.
Third, the timing of the study might have influenced the results found and limited the generalization of
the statements. The experiment was conducted in Germany during the spring and summer of 2022. This
period included summer vacations, the COVID-19 pandemic, mandatory masks in public transport, the
relief measure with fuel discounts for gasoline and diesel, and the nine-euro ticket. Therefore, the results
may differ from one experiment to another during the year when there is no pandemic, or other political
measures are taken. Therefore, future studies should examine how political decisions and the restrictions
owing to the COVID-19 pandemic influence public transportation choices.
6 Conclusion
Digital nudging can be a solution to support behavioral change and encourage people to switch to more
environmentally friendly modes of transport. In this study, we conducted an online experiment with a
custom and interactive web application to analyze the effects of decoy and default nudges. We also
addressed the lack of systematic evaluation of combined digital nudges by testing a combination of the
two digital nudges to increase public transportation use. We tested the nudges within a route planning
scenario in a big city in Germany, which offers very good access to public transportation options. Our
results show that the combination of decoy and default nudges could statistically significantly increase
the choice of public transportation options when additionally including control variables. Moreover, our
results show that using decoy and default nudges influences our study participants, indicating that even
small design changes of mobility service providers can influence their mode choice. However, the
individual nudges lack statistical significance, thereby limiting the conclusions related to the general
population.
This study contributes to the theory by providing a deeper understanding of the digital nudging
mechanisms for pro-environmental mobility behavior. For practitioners, our study provides design
implications for implementing digital nudges in web or mobile applications. It provides guidelines for
optimizing the deployment of digital nudges in terms of geographical aspects and target users. Our study
results show that digital nudging in mobility applications can represent another step toward more
sustainable mobility patterns to address urgent environmental challenges worldwide.

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References
Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2007). The effect of tailored information, goal
setting, and tailored feedback on household energy use, energy-related behaviors, and
behavioral antecedents. Journal of Environmental Psychology, 27(4), 265-276.
Anagnostopoulou, E., Urbančič, J., Bothos, E., Magoutas, B., Bradesko, L., Schrammel, J., & Mentzas,
G. (2020). From mobility patterns to behavioural change: leveraging travel behaviour and
personality profiles to nudge for sustainable transportation. Journal of Intelligent Information
Systems, 54(1), 157-178.
Ariely, D., & Wallsten, T. S. (1995). Seeking subjective dominance in multi-dimensional space: An
explanation of the asymmetric dominance effect. Organizational Behavior and Human Decision
Processes, 63(3), 223-232.
Attwood, S., Chesworth, S., & Parkin, B. (2020). Menu engineering to encourage sustainable food
choices when dining out: An online trial of priced-based decoys. Appetite, 149, 104601.
Bothos, E., Apostolou, D., & Mentzas, G. (2016). A recommender for persuasive messages in route
planning applications. 2016 7th International Conference on Information, Intelligence, Systems
& Applications (IISA),
Camilleri, A. R., Cam, M. A., & Hoffmann, R. (2019). Nudges and signposts: The effect of smart
defaults and pictographic risk information on retirement saving investment choices. Journal of
Behavioral Decision Making, 32(4), 431-449.
Caraban, A., Karapanos, E., Gonçalves, D., & Campos, P. (2019). 23 ways to nudge: A review of
technology-mediated nudging in human-computer interaction. Proceedings of the 2019 CHI
Conference on Human Factors in Computing Systems,
Cellina, F., Bucher, D., Mangili, F., Veiga Simão, J., Rudel, R., & Raubal, M. (2019). A large scale,
app-based behaviour change experiment persuading sustainable mobility patterns: methods,
results and lessons learnt. Sustainability, 11(9), 2674.
Django Software Foundation. (2022). The web framework for perfectionists with deadlines. Retrieved
20.02.2022 from https://www.djangoproject.com/
Franssens, S., Botchway, E., De Swart, W., & Dewitte, S. (2021). Nudging commuters to increase public
transport use: a field experiment in Rotterdam. Frontiers in psychology, 12, 633865.
Froehlich, J., Findlater, L., & Landay, J. (2010). The design of eco-feedback technology. SIGCHI
conference on human factors in computing systems,
Fyhri, A., Karlsen, K., & Sundfør, H. B. (2021). Paint it red-a multimethod study of the nudging effect
of coloured cycle lanes. Frontiers in psychology, 1995.
Gao, Q., Trajcevski, G., Zhou, F., Zhang, K., Zhong, T., & Zhang, F. (2019). DeepTrip: Adversarially
understanding human mobility for trip recommendation. Proceedings of the 27th ACM
SIGSPATIAL international conference on advances in geographic information systems,
German Federal Environmental Agency. (2019). Veränderung im Mobilitätsverhalten zur Förderung
einer nachhaltigen Mobilität. Retrieved 06.03.2021 from
https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2019-08-29-
texte_101-2019_mobilitaetsverhalten.pdf
Gravert, C., & Collentine, L. O. (2021). When nudges aren’t enough: Norms, incentives and habit
formation in public transport usage. Journal of Economic Behavior & Organization, 190, 1-14.
Grinstein, A., & Riefler, P. (2015). Citizens of the (green) world? Cosmopolitan orientation and
sustainability. Journal of International Business Studies, 46(6), 694-714.

Combined Nudging for Public Transportation
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway
15
Guerassimoff, G., & Thomas, J. (2015). Enhancing energy efficiency and technical and marketing tools
to change people's habits in the long-term. Energy and buildings, 104, 14-24.
Hauslbauer, A. L., Schade, J., Drexler, C. E., & Petzoldt, T. (2022). Extending the theory of planned
behavior to predict and nudge toward the subscription to a public transport ticket. European
Transport Research Review, 14(1), 1-14.
Henkel, C., Seidler, A.-R., Kranz, J., & Fiedler, M. (2019). How to nudge pro-environmental behaviour:
An experimental study. 27th European Conference on Information Systems (ECIS), Stockholm
and Uppsala, Sweden.
Huber, J., Payne, J. W., & Puto, C. (1982). Adding asymmetrically dominated alternatives: Violations
of regularity and the similarity hypothesis. Journal of Consumer Research, 9(1), 90-98.
Hummel, D., & Maedche, A. (2019). How effective is nudging? A quantitative review on the effect sizes
and limits of empirical nudging studies. Journal of Behavioral and Experimental Economics,
80, 47-58.
Hunecke, M., Haustein, S., Böhler, S., & Grischkat, S. (2010). Attitude-based target groups to reduce
the ecological impact of daily mobility behavior. Environment and behavior, 42(1), 3-43.
Isensee, C., Teuteberg, F., & Griese, K. M. (2022). Exploring the use of mobile apps for fostering
sustainability-oriented corporate culture: A qualitative analysis. Sustainability, 14(12), 7380.
Jariyasunant, J., Abou-Zeid, M., Carrel, A., Ekambaram, V., Gaker, D., Sengupta, R., & Walker, J. L.
(2015). Quantified traveler: Travel feedback meets the cloud to change behavior. Journal of
Intelligent Transportation Systems, 19(2), 109-124.
Keller, S. (2022). Entwicklung des Modal Split im Personenverkehr in Deutschland in den Jahren 2013
bis 2025. Statista. Retrieved 28.10.2022 from
https://de.statista.com/statistik/daten/studie/168397/umfrage/modal-split-im-personenverkehr-
in-deutschland/
Kim, H. B., Iwamatsu, T., Nishio, K.-i., Komatsu, H., Mukai, T., Odate, Y., & Sasaki, M. (2020). Field
experiment of smartphone-based energy efficiency services for households: Impact of advice
through push notifications. Energy and buildings, 223, 110151.
Kuhnimhof, T., Armoogum, J., Buehler, R., Dargay, J., Denstadli, J. M., & Yamamoto, T. (2012). Men
shape a downward trend in car use among young adults—evidence from six industrialized
countries. Transport Reviews, 32(6), 761-779.
Lieberoth, A., Holm, N., & Bredahl, T. (2018). Selective psychological effects of nudging, gamification
and rational information in converting commuters from cars to buses: A controlled field
experiment. Transportation Research Part F: Traffic Psychology and Behaviour, 55, 246-261.
Loock, C.-M., Staake, T., & Thiesse, F. (2013). Motivating energy-efficient behavior with green IS: an
investigation of goal setting and the role of defaults. MIS Quarterly, 1313-1332.
Meske, C., & Amojo, I. (2019). Status Quo, Critical Reflection and Road Ahead of Digital Nudging in
Information Systems Research--A Discussion with Markus Weinmann and Alexey Voinov.
arXiv preprint arXiv:1911.08202.
Meske, C., Amojo, I., & Mohr, P. (2020). Digital Nudging to Increase Usage of Charity Features on E-
Commerce Platforms. Wirtschaftsinformatik (Zentrale Tracks),
Milfont, T. L., & Duckitt, J. (2010). The environmental attitudes inventory: A valid and reliable measure
to assess the structure of environmental attitudes. Journal of Environmental Psychology, 30(1),
80-94.
Momsen, K., & Stoerk, T. (2014). From intention to action: Can nudges help consumers to choose
renewable energy? Energy Policy, 74, 376-382.

Combined Nudging for Public Transportation
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway
16
Möser, G., & Bamberg, S. (2008). The effectiveness of soft transport policy measures: A critical
assessment and meta-analysis of empirical evidence. Journal of Environmental Psychology,
28(1), 10-26.
Mulley, C. (2017). Mobility as a Services (MaaS)–does it have critical mass? Transport Reviews, 37,
247-251.
OpenStreetMap. (2022). OpenStreetMap - Deutschland. Retrieved 30.10.2022 from
https://www.openstreetmap.de/
Pihlajamaa, O., Heino, I., & Kuisma, S. (2019). Nudging towards sustainable mobility behaviour in
nature destinations: Parkkihaukka mobile information service. ITS European Congress.
Brainport, Netherlands,
Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of risk and
uncertainty, 1(1), 7-59.
Sanguinetti, A., & Amenta, N. (2022). Nudging consumers toward greener air travel by adding carbon
to the equation in online flight search. Transportation Research Record, 2676(2), 788-799.
Sanguinetti, A., Kwon, A., Li, Y., Chakraborty, V., Sikand, S., Tarelho, O., Chen, Y., & Amenta, N.
(2017). GreenFLY International Conference of Design, User Experience, and Usability,
Schneider, C., Weinmann, M., & Vom Brocke, J. (2018). Digital nudging: guiding online user choices
through interface design. Communications of the ACM, 61(7), 67-73.
Schulz, T., Böhm, M., Gewald, H., & Krcmar, H. (2021). Smart mobility–an analysis of potential
customers’ preference structures. Electronic Markets, 31, 105-124.
Smythe, P. C., & Brook, R. C. (1980). Environmental concerns and actions: A social-psychological
investigation. Canadian Journal of Behavioural Science/Revue canadienne des sciences du
comportement, 12(2), 175.
Sommet, N., & Morselli, D. (2017). Keep calm and learn multilevel logistic modeling: A simplified
three-step procedure using Stata, R, Mplus, and SPSS. International Review of Social
Psychology, 30, 203-218.
Steg, L. (2005). Car use: lust and must. Instrumental, symbolic and affective motives for car use.
Transportation Research Part A: Policy and Practice, 39(2-3), 147-162.
Stryja, C., & Satzger, G. (2019). Digital nudging to overcome cognitive resistance in innovation
adoption decisions. The Service Industries Journal, 39(15-16), 1123-1139.
Sullivan, A. N., & Lachman, M. E. (2017). Behavior change with fitness technology in sedentary adults:
a review of the evidence for increasing physical activity. Frontiers in public health, 4, 289.
Sunstein, C. R. (2015). Nudging and choice architecture: Ethical considerations. Yale Journal on
Regulation, Forthcoming.
Székely, N., Weinmann, M., & vom Brocke, J. (2016). Nudging People To Pay Co2 Offsets – The Effect
of Anchors in Flight Booking Processes. 24th European Conference on Information Systems
(ECIS), İstanbul,Turkey.
Taube, O., & Vetter, M. (2019). How green defaults promote environmentally friendly decisions:
Attitude‐conditional default acceptance but attitude‐unconditional effects on actual choices.
Journal of Applied Social Psychology, 49(11), 721-732.
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and
happiness. Penguin.
Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving decisions about health, wealth, and
happiness. Penguin.

Combined Nudging for Public Transportation
Thirty-first European Conference on Information Systems (ECIS 2023), Kristiansand, Norway
17
Thaler, R. H., Sunstein, C. R., & Balz, J. P. (2013). Choice architecture. In The behavioral foundations
of public policy (pp. 428-439). Princeton University Press.
Tiefenbeck, V., Wörner, A., Schöb, S., Fleisch, E., & Staake, T. (2019). Real-time feedback promotes
energy conservation in the absence of volunteer selection bias and monetary incentives. Nature
Energy, 4(1), 35-41.
Tiseo, I. (2021). Distribution of global carbon dioxide (CO2) emissions in 2020, by sector. Statista.
Retrieved 25.08.2022 from https://www.statista.com/statistics/1129656/global-share-of-co2-
emissions-from-fossil-fuel-and-cement/
Tussyadiah, I., & Miller, G. (2019). Nudged by a robot: Responses to agency and feedback. Annals of
Tourism Research, 78, 102752.
United Nations. (2021). Goal 13: Take urgent action to combat climate change and its impacts.
Retrieved 04.08.2021 from https://www.un.org/sustainabledevelopment/climate-change/
Weinmann, M., Schneider, C., & Vom Brocke, J. (2016). Digital nudging. Business & Information
Systems Engineering, 58(6), 433-436.
Wunsch, M., Stibe, A., Millonig, A., Seer, S., Dai, C., Schechtner, K., & Chin, R. C. (2015, 2015). What
makes you bike? Exploring persuasive strategies to encourage low-energy mobility.
International conference on persuasive technology,
Zhu, K., Zhang, L., & Pattavina, A. (2017). Learning geographical and mobility factors for mobile
application recommendation. IEEE Intelligent Systems, 32(3), 36-44.
Zimmermann, S., Hein, A., Schulz, T., Gewald, H., & Krcmar, H. (2021). Digital Nudging Toward Pro-
Environmental Behavior: A Literature Review. Twenty-Fifth Pacific Asia Conference on
Information Systems,
Zimmermann, S., Schulz, T., & Gewald, H. (2020). Salient Attributes of Mobility Apps: What Does
Really Matter for the Citizen? Pacific Asia Conference on Information Systems (PACIS),
Dubai, UAE.