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A Behavioral Approach to Energy-efficient Driving.

Authors:

Abstract

The increase of energy efficiency and reduction of greenhouse gas emissions have become important targets of EU initiatives. Energy efficient driving techniques can significantly reduce emissions, but practical solutions which are based on scientific findings are scarce. Since mobile apps are versatile in their functionality, characterized by a short time to market and low product development costs, this literature based study develops criteria for effectively supporting energy efficient driving using mobile applications and supports the findings using a proof-of-concept implementation.
14. Symposium Energieinnovation, 10. bis 12. Februar 2016, Technische Universität Graz, www.EnInnov.TUGraz.at
1
A BEHAVIORAL APPROACH TO
ENERGY-EFFICIENT DRIVING
Pascal LÜDERS
1
, Mariya SODENKAMP
2
, Jürgen WENIG2
Introduction
The increase of energy efficiency and reduction of greenhouse gas emissions have become important
targets of EU initiatives (European Commission 2014). Emissions from personal vehicles are a key cause
of worldwide greenhouse gas emissions with 90% of personal transport emissions being caused by private
vehicles (Barkenbus 2010). Energy efficient driving techniques, or eco-driving, can realize energy savings
up to 20% (Stillwater et al. 2012). Combined with behavioral approaches, which are increasingly being
used by governments (Cabinet Office 2012), these techniques represent a promising way to increase
energy efficiency in the transport sector. Yet, practical solutions which are based on scientific findings are
scarce. Since mobile apps are versatile in their functionality, characterized by a short time to market and
low costs, they can represent a solution. The research question therefore is: How can a mobile application
contribute to energy-efficient driving?
Theoretical Background
Eco-driving techniques include smooth acceleration and deceleration, maintaining steady speeds,
coasting to a stop, driving in proper gear and keeping the vehicle in good maintenance (Neumann et al.
2014). Persuasive behavioral elements are user interface elements employed to influence behavior.
Originating in game design, they are being applied in the real world as well. Mechanisms include scoring
systems, badges, rankings (Blohm, Leimeister 2013) and goal-setting (Loock et al. 2013).
Research Method
To determine how an information system can be used effectively to increase energy efficiency in
transportation, the design science research method was employed, backed by a literature review and
market research. The key criterion of design research is that its technology output must be a useful artifact
in the sense that it must be relevant to the practical problem it aims to solve and should contribute to theory
(Hevner et al. 2004). This was ensured by an extensive literature and market research that resulted in
literature grounded design criteria and is demonstrated by a proof-of-concept prototype.
Eco-driving Design Criteria
Based on the literature review in the fields of energy-efficient driving, behavioral mechanisms and
persuasive technology, the criteria for the development of an eco-driving app were derived. The market
research was conducted to build on existing solutions and to avoid reinventing existing approaches.
Criteria (software requirement)
Source
How it can be implemented
High context feedback
Tulusan et al. 2012
Provide immediate feedback to convey the
connection between driving behavior and energy
consumption impact.
Goal setting
Loock et al. 2013;
Stillwater, Kurani 2012
Let the driver define their own consumption goals
to increase energy savings.
Support electric vehicle use /
fight range anxiety
Franke, Krems 2013
Increase awareness of eco-driving as a way to
increase range and save energy, build range
confidence using driving logs and simulations,
create transparency about range requirements,
recommend ideal electric vehicle models and
suggest behavioral adaptations to make electric
vehicle use feasible.
Social pressure/ normative
feedback
Loock et al. 2013
Compare energy saving performance to other
drivers and employ gamification elements.
Table 1: Design criteria for fostering energy-efficient driving (excerpt).
1
c.con Management Consulting GmbH, Altrottstraße 31, 69190 Walldorf, Tel.: +49 171 56 308 52,
pascal.lueders@ccon.com, www.ccon.com
2
Universität Bamberg, An der Weberei 5, 96047, Bamberg, www.uni-bamberg.de,
{Tel.: +49 951 863-2209, mariya.sodenkamp@uni-bamberg.de},
{Tel.: +49 951 863-2286, juergen.wenig@uni-bamberg.de}
14. Symposium Energieinnovation, 10. bis 12. Februar 2016, Technische Universität Graz, www.EnInnov.TUGraz.at
2
The criteria identified for fostering energy efficiency using a mobile app are shown in Table 1 and include
high context feedback, goal-setting, supporting electric vehicle use and applying social pressure to
influence driver behavior. For electric vehicles driving efficiency is an enabler due to the limited energy
supply available to the driver. In addition, eco-driving has a positive effect on the attractiveness of electric
vehicles which suffer from lower range: Since energy-efficient driving increases the distance that can be
driven electrically, it reduces required battery capacity and consequently vehicle purchasing costs. The
market research revealed that there is a lack of applications promoting both energy efficient driving and
the use of electric vehicles.
Discussion & Conclusion
Based on a thorough evaluation of literature and available technology on the market, a framework for a
mobile application to support eco-driving was created which includes high context feedback, goal setting,
electric vehicle use and social pressure. The proof-of-concept prototype serves to demonstrate and
validate the design criteria. There are however limitations to the research conducted, including the lack of
user experiments and determination of realizable energy economies. Yet, the findings represent a high
value for science, as the systematic literature based assessment of design criteria can be the basis for
further research on the relative impact of the criteria. The results possess a high value for practice as well,
such as for vehicle manufacturers and utilities, since the research revealed that mobile solutions
encompassing both aspects of energy efficiency and electric vehicle adoption are very promising, but have
not yet been marketed.
References
[1] Barkenbus, Jack N. (2010): Eco-driving: An overlooked climate change initiative. In Energy Policy 38
(2), pp. 762-769.
[2] Blohm, Ivo; Leimeister, Jan Marco (2013): Gamification. In Business & Information Systems
Engineering 5 (4), pp. 275-278.
[3] Cabinet Office (Ed.) (2012): Applying behavioural insights to reduce fraud, error and debt.
Behavioural Insights Team.
[4] European Commission (2014): Communication from the Commission to the European Parliament
and the Council. Energy Efficiency and its contribution to energy security and the 2030 Framework
for climate and energy policy.
[5] Franke, Thomas; Krems, Josef F. (2013): What drives range preferences in electric vehicle users? In
Transport Policy 30, pp. 56-62.
[6] Hevner, Alan R.; March, Salvatore T.; Park, Jinsoo; Ram, Sudha (2004): Design science in
information systems research. In MIS quarterly 28 (1), pp. 75-105.
[7] Loock, Claire-Michelle; Staake, Thorsten; Thiesse, Frédéric (2013): Motivating energy-efficient
behavior with green IS: an investigation of goal setting and the role of defaults. In MIS quarterly 37
(4), pp. 1313-1332.
[8] Neumann, Isabel; Franke, Thomas; Bühler, Franziska; Cocron, Peter; Krems, Josef F. (2014): Eco-
driving strategies in battery electric vehicle use
what do drivers get to know over time? In :
Proceedings of the European Conference on Human Centred Design for Intelligent Transport
Systems 2014. With assistance of Risser, R., Pauzié, A., Mendoza, L. Vienna, Austria. Lyon:
Humanist Publications.
[9] Stillwater, Tai; Kurani, K. (2012): Goal Setting, Framing, and Anchoring Responses to Ecodriving
Feedback. In UC Davis Institute of Transportation Studies Working Paper UCD-ITSWP-12-03.
[10] Stillwater, Tai; Kurani, Kenneth S.; Mokhtarian, Patricia L. (2012): Cognitive Mechanisms of Behavior
Change in the Case of In-Vehicle Fuel Economy Feedback.
[11] Tulusan, Johannes; Staake, Thorsten; Fleisch, Elgar (2012): Providing eco-driving feedback to
corporate car drivers. In Anind K. Dey, Hao-Hua Chu, Gillian Hayes (Eds.): The 2012 ACM
Conference. Pittsburgh, Pennsylvania, p. 212.
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