=Paper=
{{Paper
|id=Vol-1817/paper3
|storemode=property
|title=Do We Choose What We Desire? – Persuading Citizens to Make Consistent and Sustainable Mobility Decisions
|pdfUrl=https://ceur-ws.org/Vol-1817/paper3.pdf
|volume=Vol-1817
|authors=Christopher Lisson,Margeret Hall
|dblpUrl=https://dblp.org/rec/conf/persuasive/LissonH16
}}
==Do We Choose What We Desire? – Persuading Citizens to Make Consistent and Sustainable Mobility Decisions==
https://ceur-ws.org/Vol-1817/paper3.pdf
Do We Choose What We Desire?
– Persuading Citizens to Make
Consistent and Sustainable Mobility Decisions
Christopher Lisson, Margeret Hall
Karlsruhe Institute of Technology, Karlsruhe, Germany
{christopher.lisson, margeret.hall}@kit.edu
Abstract. A dilemma in urban mobility with tremendous effects on citizens’
wellbeing is the unconscious antipode between their short- and long-term
goals. People do not anticipate all consequences of their modal choices and
thus make decisions that might be incoherent with their desires, e.g. taking
their own car due to convenience but causing a congested city. Omnipresent
Information Systems on smartphones provide the necessary information and
coordination capabilities to support people for sustainable and individually
coherent mobility decisions on a mass scale. Building upon extant work in
travel behavior and social psychology, a framework is proposed to coordi-
nate research efforts in the development of persuading measures for sustain-
able mobility decisions. This framework accounts for user heterogeneity,
motivation and wellbeing as influential dimensions in the mobility decision
process. Tied to social influence the derived measures contribute to a behav-
ioral change in people’s mobility behavior leading to a higher wellbeing lev-
el in urban areas.
Key words: Mobility behavior, Wellbeing, Modal Choices, Advanced Trav-
eller Information Systems, Persuasive Technology, Social Influence
1 Introduction
Cities connect people and impact their lives [1, 2]. By now 54% of the world’s popu-
lation live in cities, generate 80% of the world’s GDP, cause 70% of the world’s CO2-
emisson, and consume 66% of the world’s energy [3]. Facilitating human interaction
in time and space, cities are places where people search for opportunities, money, and
a better life [4]. This is not restricted to economic measures but comprises myriad
activities to satisfy heterogeneous human needs [5–7]. Following Ben-Akiva [8] the
pursuit of “a better life” is maintaining and enhancing ‘happiness’ (subjective wellbe-
ing). And since not every need can be satisfied at a single place people need to be
mobile to move towards their desired destination.
Cities are ‘complex interdependent systems of infrastructure, economic, and social
[networks]’ [2]. Because of this interdependency citizens’ mobility behavior impacts
how a city looks, how it changes, and how well people feel inside of it. Considering
limited space inside a city, growing mobility demand, and a habitual behavior towards
using a car results in the conflict between short-term individual interests, e.g. single
car usage because of convenience, and long-term collective interests, e.g. a non-
congested and non-polluted city [9, 10]. Today’s technology – with omnipresent
smartphones and advanced traveller information systems (ATIS) like ridescout1 or
moovel2 – purport to provide a comparable level of mobility and convenience. Com-
bining diverse transportation modes in a mobility chain matching our preferences
[11], they can make individual car use and the related problems thereof obsolete. Chin
and Larsen [12] demonstrate that intelligent demand coordination in a communicating
shared car fleet can provide the same level of mobility with a third of the present
number of cars. Even though these solutions for a better life in cities are available, it
is difficult to convince people to use such ATIS and even more to change their mo-
bility habits permanently [13–15]. While classic financial incentives, e.g. free trans-
portation for one month, are successful in breaking the car habit and introduce new
technologies, they are not suitable as long-term incentives [16, 17].
Drawing upon socio-psychological theories and tying up with Stibe [18] a framework
is proposed to identify measures that are capable to induce individual coherent and
socially desirable mobility behavior by leveraging powers of social influence. Using
motivation and subjective wellbeing as the framework’s main dimensions the under-
lying literature and theoretical concept investigating mobility behavior are exposed in
section 2. While section 3 describes the framework in detail, section 4 concludes and
gives direction to future work.
2 Related Work
2.1 Mobility Decision Chain
The question ‘Which kind of person is likely to choose what transportation mode un-
der which circumstances?’ is complex and still requires more detailed studies [5, 19].
However, such insights are indispensable developing measures that are able to induce
desired behavioral change [14, 20]. Investigating the underlying “mobility decision
chain” enables the identification of levers that can be used to influence this decision.
According to Ben-Akiva [8] all activities are planned and undertaken to satisfy vari-
ous human needs so as to maintain and enhance subjective wellbeing as the ultimate
goal.
Maslow [6, 7] attempted to define these multifaceted needs hierarchically. Though
controversial, the ability to transfer his observations to the context of personal mobili-
ty a basic differentiation into economic and (recently) non-economic related needs is
observable [21, 22]. Examples for the economic related needs are time and cost [23,
24] and for non-economic related needs metal effort, comfort, and flexibility [11, 25,
26].
1
http://www.ridescoutapp.com
2
http://www.moovel.com
Subjective wellbeing (SWB) refers to people’s cognitive and affective evaluation of
their lives’ quality [27]. As a ‘holistic composition’ SWB encompasses individual
related pleasant affects (e.g. happiness) and unpleasant affects (e.g. stress) as well as
to the social position related life satisfaction (e.g. desire to change life) and domain
satisfaction (e.g. role at work, recognition in one’s group) [28]. Considering the hier-
archical aspect one can distinguish between subjective wellbeing on an individual
level, e.g. enjoying a safe financial position, and a communal (or even pro-social)
level, e.g. living in an equal society.
Vallerand [29] operationalizes this influencing chain between needs as the origin and
SWB as the goal in his Hierarchical Model of Intrinsic and Extrinsic Motivation.
Therein social factors – on a global, contextual, and situational level – affect hierar-
chical levels of motivations over mediators and imply consequences on affects, cogni-
tion, and behavior. Conceptualizing personal characteristics as mediators, e.g. psy-
chometric traits, competences or modal preferences, underpins the fact that motiva-
tional measures have to be consistent with the heterogeneous requirements of each
user. Transferring Vallerand’s [29] model to the mobility context provides a higher
granularity over the factor’s influencing the mobility decision. This is in line with
recent mobility behavior studies emphasizing that clustering user groups according to
individually characteristics, e.g. psychometric measures and habitual behavior, has
higher predictive power than pure socio-demographic clustering [5, 21, 30].
2.2 Theory of Planed Behavior and extensions
Besides the mobility chain approach Anable [5] points out that many approaches ori-
entate towards the Theory of Planed Behavior (TPB) [31] and their decomposed ex-
tension (DTPB) [32] to explain choice of transportation modes and thus individuals’
mobility behavior. The DTPB combines intention and innovation research. It more
completely explores the dimensions of subjective norms (i.e. social influence), atti-
tudes (i.e. ease of use), and perceived behavioral control (i.e. self-efficacy) by decom-
posing them into specific belief dimensions. The innovational aspect makes it inter-
esting for the case of the upcoming ATIS technologies. Providing valuable insights
for the incentivizing measurement development they neglect to account for habit, a
factor that has been proved to be highly influential in the context of mobility deci-
sions [13, 20]. The UTAUT 2 model by Venkatesh et al. [33] aims at explaining the
acceptance and usage of technologies. Established in the field of Information Systems
(IS) it successfully explains a higher percentage of variance compared to TPB and
DTPB in the consumer sector. Since the ATIS is the foundation for intelligent de-
mand coordination and thus sustainable model choice recommendation UATUT 2 is
eligible to provide further insights. In addition to habit it also comprises the constructs
performance expectancy, social influence, facilitating conditions, hedonic motivation,
price value and experience.
2.3 Social Cognitive Theory
Previous models cede social influence (SI) a crucial role in explaining the transporta-
tion mode decisions. It is further easy to implement into IS and has low costs com-
pared to financial incentives, so it seems valuable to focus on social influence as a
main dimension to derive behavioral changing measures. This is in line with the re-
search by Stibe [18] who suggests it as a promising approach for behavioral influence
on a mass level. A major source for SI related incentives is the Social Cognitive The-
ory (SCT). It is one of the most powerful theories explaining human behavior – espe-
cially when it comes to mass scale influence, see [34]. It states that an individual's
behavior is related to their observations of others within the context of social interac-
tions, experiences, and outside influences. Thereby the notion of consequences of
other peoples’ actions affects subsequent behaviors [34, 35]. Since the observation
can take place in real or in a virtual environment [36] properly designed ATIS – as
persuasive technologies – can become very effective for inducing behavioral and
attitudinal changes in novel socio-technical contexts [18]. Relevant social influence
aspects are social learning (SL) [34], social comparison (SC) [37], normative influ-
ence (NI) [38], social facilitation (SF) [39], cooperation (CR) [40], competition (CT)
[41]), and recognition (RE) [29].
3 Measurement framework
In order to test the validity and influence of the previous constructs on mobility deci-
sions 25 explorative expert interviews have been conducted and analyzed following
the guidelines by Klein and Myers [42]. The findings have been used to develop and
conduct a pilot study with 408 business students of research pool at a German univer-
sity. The online-survey investigated their motivations for using an ATIS; the attrib-
utes determining their quality perception; typical mobility patterns; as well as their
socio-demographic data, and psychometric measures. According to the processes and
quality criteria suggested by MacKenzie et al. [43] and Chin [44] the constructs have
been evaluated using a SEM-PLS approach. Thereby following results attract particu-
lar attention: 1) Motivational constructs have a significant effect on the intention to
use an ATIS, 2) the motivational constructs can be grouped into categories of eco-
nomic and non-economic factors 3) distinctive cluster of user groups can be derived
based on their Technology Readiness Index 2.0 [45] and their mobility habits, and 4)
the different cluster reveal significantly different factor loadings over the constructs
influencing the intention to use an ATIS. Connecting these findings with the insights
out of wellbeing and SI research results in the conceptualization of a framework for a
systematic derivation of measures that are capable to persuade citizens to make con-
sistent and sustainable mobility decisions, see Fig. 1.
The framework consists of motivation and wellbeing as the main dimensions with
influence on mobility behavior and modal choices. Therein the distinct measures de-
rived from the area of social influence to incentivize sustainable mobility behavior
can be sorted. While motivation ranges from economic (E) to non-economic (NE),
communal (C) and individual (I) goals have been chosen as scale-ends for wellbeing.
Accounting for the user heterogeneity the surfaced areas inside the framework indi-
cate a set of measures that is particularly effective for a specific user type. This fosters
motivational measures to be coherent with each user-type.
communal low criticality
Type_A
CE_1 CNE_1
CE_2 CNE_2
Type_B
economic IE_1 non-economic
INE_1
IE_2 INE_2
high criticality individual
Fig. 1. Measurement Framework
For example, user of Type A is more driven by non-economic altruistic incentives,
while Type B is more likely to respond to individual financial benefits. Using the
portfolio of SI for potential levers this framework can be used to determine and struc-
ture user-type specific measures to incentivize the distinct user types to a sustainable
mobility behavior. Thus Type A can be motivated to make a sustainable mobility
decision by providing the measure that ‘Only 2493 bike shares are required to ramp
the fleet up!’ (derived from CR), while ‘You can safe US$ 30 and 180 minutes com-
pared to your colleagues each week by using a modal mix of bike and underground!’
(derived from SC) motivates user Type B.
The expert interviews further indicate that the more critical a desired activity at the
destination is perceived to be, the more a person tend to refer to economic self-related
measures for his decision. In other words: Stress reduces the tendency to altruism. To
account for the fact the red line comprises influential circumstances, which are in this
case represented by the dimension of ‘criticality’.
4 Conclusion and future work
Citizens may make mobility decisions that might be inconsistent with their goals.
Building upon research in mobility behavior and technology acceptance a pilot study
reveals that motivation and wellbeing have a significant impact on peoples’ mobility
decisions. Both dimensions are main components of our proposed framework for
investigating measures that incentivize people to make sustainable and individually
coherent mobility decisions. Using the theory of social influence to derive these
measures we provide a new perspective to current research in the area of mobility
behavior. Considering the omnipresence of smartphones and ATIS technologies,
where derived measure can be easily implemented, this approach seems promising
when it comes to mass scale persuasion towards sustainable mobility behavior. As a
starting point for future research in this complex area our framework helps coordinat-
ing research efforts while investigating the wide field of possible measures.
Even providing well-founded hints surveys base on peoples’ self-evaluation and in-
herently bear the risk of disclosing unconscious influences [46, 47]. As stated by
Nisbett and Wilson [46] people typically lack insight into their own mental processes
that can lead to the misreport of casual influences. Examples are the unawareness of
the cues relied upon to make a decision and the weighting and integration of cue in-
formation [47–49]. Therefore future research requires laboratory as well as field ex-
periments to investigate the behavior triggering determinants and the motivation
measures’ long-term effects on users’ behavior. A proper experimental design will
also enable to uncover the assumed effects’ strengths in the mobility decision process.
The pilot study has been conducted with a relatively homogeneous user group. There-
fore the next steps require a cross-sectional representative survey in urban and non-
urban areas to gather a wider database of peoples’ psychometric characteristics, their
typical mobility behavior, and their stated motivations to do so. Since cities diverge in
their infrastructures across countries a multinational survey is suggested. This is be-
cause the available infrastructure and socio-economic environment of a city have a
high impact on the prevailing mobility cultures. Based on this data an optimization of
behavioral stable user groups with distinct preferences will be generated, which ena-
bles a specific measurement development for each group. Since the user-type identifi-
cation has to be seamless to be applicable on mass scale the automation of type detec-
tion via geo-spacial data in combination with environmental information will be an
important issue in the future research agenda. Altogether this research will help peo-
ple to make better mobility decisions and thus increase the level of wellbeing in cities.
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