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Personalised feedback and eco-driving: An explorative study
Transportation Research Part C 58 (2015) 760–771
Contents lists available at ScienceDirect
Transportation Research Part C journal homepage: www.elsevier.com/locate/trc
Personalised feedback and eco-driving: An explorative study R.F.T. Brouwer a,⇑, A. Stuiver a, T. Hof a, L. Kroon a, J. Pauwelussen b, B. Holleman a a b
TNO, Kampweg 5, Soesterberg, The Netherlands TomTom Telematics, Amsterdam, The Netherlands
a r t i c l e
i n f o
Article history: Received 15 September 2014 Received in revised form 28 April 2015 Accepted 28 April 2015 Available online 2 July 2015 Keywords: Green driving support Driver behaviour change Value orientation Goal orientation Adaptive HMI Personalisation Driver segments Acceptance
a b s t r a c t Conventional road transport has negative impact on the environment. Stimulating eco-driving through feedback to the driver about his/her energy conservation performance has the potential to reduce CO2 emissions and promote fuel cost savings. Not all drivers respond well to the same type of feedback. Research has shown that different drivers are attracted to different types of information and feedback. The goal of this paper is to explore which different driver segments with specific psychographic characteristics can be distinguished, how these characteristics can be used in the development of an ecodriving support system and whether tailoring eco-driving feedback technology to these different driver segments will lead to increased acceptance and thus effectiveness of the eco feedback technology. The driver segments are based on the value orientation theory and learning orientation theory. Different possibilities for feedback were tested in an exploratory study in a driving simulator. An explorative study was selected since the choice of the display (how and when the information is presented) may have a strong impact on the results. This makes testing of the selected driver segments very difficult. The results of the study nevertheless suggest that adapting the display to a driver segment showed an increase in acceptance in certain cases. The results showed small differences for ratings on acceptation, ease of use, favouritism and a lower general rating between matched (e.g., learning display with learning oriented drivers) and mismatched displays (e.g., learning display with performance oriented drivers). Using a display that gives historical feedback and incorporates learning elements suggested a non-verifiable increase in acceptance for learning oriented drivers. However historical feedback and learning elements may be less effective for performance oriented drivers, who may need comparative feedback and game elements to improve energy conserving driving behaviour. Ó 2015 Published by Elsevier Ltd.
1. Introduction 1.1. Providing feedback to change driving behaviour The negative impact of road transport on the environment has been well documented (see, e.g., Fuglestvedt et al., 2008). Much technological development has been focussed on increasing the fuel efficiency of engines and a number of support systems have been developed to aid the driver in a more eco-friendly driving style. On the latter topic, research has shown that providing feedback is a powerful tool for instigating a behaviour change (e.g. Fischer, 2008; Allcott and Mullainathan,
⇑ Corresponding author. Tel.: +31 888 665 969. E-mail address: [email protected] (R.F.T. Brouwer). http://dx.doi.org/10.1016/j.trc.2015.04.027 0968-090X/Ó 2015 Published by Elsevier Ltd.
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2010; Stern, 2011). Stimulating eco-driving using feedback technologies has the potential to reduce CO2 emissions and promote fuel cost savings of 1–8% (Tulusan et al., 2012). On an operational level (Michon, 1985) eco-driving refers to implementation of eco-driving techniques while driving which encompasses keeping the speed down, efficient gear shifting, anticipatory, calm and steady driving, and efficient braking (Strömberg et al., 2015). In-vehicle feedback technologies provide drivers with real-time, continuous feedback about their eco-driving behaviours. A recent naturalistic driving study shows an average decrease of between 2% and 8% in fuel consumption, depending on feedback design (Stillwater and Kurani, 2013). From interviews with 46 drivers Stillwater and Kurani (2013) conclude that the information content of the display (e.g., maximising MPG or savings, or minimising CO2) plays an important role in stimulating drivers to drive economically. Different drivers seem to be attracted to different types of information and feedback. He et al. (2010) argue that presenting feedback in the same way to differently motivated drivers may not be that effective. In their reflection on different energy feedback technologies they bring forward that an improvement would be to consider the specific values and goals of each individual when providing feedback to drivers. As such, the visualisation could provide personalised feedback of the positive results of eco-driving behaviours, highlighting for example for one person health benefits and for another person monetary gains. This is in line with Anable’s (2005) work on segmenting the driver population based on psychographic variables (e.g., attitudes, values, personal norms) instead of based on demographic variables (e.g., gender, age). Also, in the field of consumer behaviour and marketing it is common practice to approach different people in different ways because they are motivated by different factors (Wedel and Kamakura, 1998). The goal of the research described in this paper is to explore whether tailoring eco feedback technology to different driver segments with specific psychographic characteristics will lead to increased acceptance and effectiveness of the eco feedback technology. The remainder of this paper starts with a short discussion of the value orientation theory and learning orientation theory. Both these theories offer a good starting point to distinguish different driver segments. Based on insights from these theories we describe four driver segments and propose four variants of the same eco feedback display that, supposedly, match the different driver segments. These different displays were tested with respect to acceptance while drivers drove in a driving simulator. The results and implications of this explorative study are discussed. 1.2. Value orientation theory Values are principles that guide and influence behaviour. Values are seen as enduring behavioural ideals that are firmly incorporated within persons. As behavioural ideals, values guide a person’s sense of what is good and bad and which goals to pursue (Rokeach, 1973). Regarding understanding pro-environmental behaviour three types of values are important: pro-self, pro-social, and ecocentric values (Stern, 2000; De Groot and Steg, 2008, 2009; Van Vugt et al., 1995; Stern et al., 1993). People with a strong pro-self value orientation will especially take into account the costs and benefits of pro-environmental behaviour for them personally. People with a strong pro-social value orientation include in their decision on behaving pro-environmentally the perceived costs and benefits for other people. People with a strong ecocentric value orientation will consider the perceived costs and benefits for the ecosystem and biosphere when deciding to act pro-environmentally (De Groot and Steg, 2009). Stern and Dietz (1994), however, found that in a general population sample the ecocentric value orientation does not differentiate from social-altruism. Therefore, and in line with He et al. (2010) who suggested to improve feedback by matching it to an individual’s values, we hypothesise that matching the eco feedback technology to people’s value orientation (pro-self vs. pro-social) will increase the attractiveness and effectiveness of eco-feedback technologies. 1.3. Goal orientation theory Motivating people to drive eco-friendly can be considered to be a learning process. Drivers will need to learn new skills, which hopefully will form into habits. Receiving feedback can therefore be seen as an important part of the learning process. Eco feedback technology aims to make drivers learn the connection between their driving behaviours and certain outcomes, for example, the amount of fuel they use. Feedback is an essential component of learning, and its principles are rooted in educational theory (Darby, 2001). Goal orientation theory is a social-cognitive theory of achievement motivation and originally examines the reasons why children engage in their school work (Dweck, 1986). Earlier work of Dweck (1986) contrasted two types of goal orientations: learning goal orientation and performance goal orientation. People with a learning goal orientation wish to acquire additional knowledge or master new skills. They are self-referential, focusing on the development of skill and competence relative to the task and one’s own past performance. People with a performance goal orientation want to demonstrate their competence and to make a good impression on others. They are trying to outperform others and strive to be the best in a group (Harackiewicz and Elliot, 1993). We assume that aligning the provided feedback to people’s goal orientation (learning orientation vs. performance orientation) increases attractiveness and effectiveness of eco feedback technology. 1.4. Four driver segments To enable developing and testing tailored eco feedback, we used insights from these theories to distinguish four driver segments based on their value orientation and goal orientation.
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Driver segment 1: Egoistic learner We assume that a driver from this driver segment has a pro-self value orientation and learning goal orientation. The egoistic learner takes into account personal benefits and costs (e.g., fuel saving, prolonged lifespan of vehicle), and has a desire to improve his/her eco-driving behaviour relative to his/her own past performance. Driver segment 2: Altruistic learner A driver from this driver segment has a pro-social value orientation and learning goal orientation. The altruistic learner is conscious of the needs of and benefits for others, and takes these into account when making decisions. He/She is motivated by improving his/her eco-driving behaviour relative to his/her own past performance. Driver segment 3: Egoistic performer A driver from this driver segment has a pro-self value orientation and performance goal orientation. The egoistic performer takes into account personal benefits and costs (e.g., fuel saving, prolonged lifespan of vehicle), and likes to compare to and outperform others. Driver segment 4: Altruistic performer A driver from this driver segment has a pro-social value orientation and performance goal orientation. The altruistic performer is conscious of the needs of and benefits for others, and takes these into account when making decisions. At the same time he/she likes to compare to and outperform others. We use these four distinguished driver segments to develop four variants of eco feedback technology that we think would appeal to these different driver segments. In a match–mismatch experiment (see Section 2), we tested two of these variants. 1.5. Four variants of the eco-driving feedback technology tailored to four driver segments Variant 1 is adapted to driver segment 1: the egoistic learner We hypothesise that the egoistic learner will be attracted and influenced by feedback technology that shows his/her personal gains in terms of money saved, and that displays his/her progress and performance of eco-driving behaviours relative to his/her own past performance. Variant 2 is tailored to driver segment 2: the altruistic learner We hypothesise that the altruistic learner will be attracted and influenced by feedback technology that shows self and other drivers gains in terms of environmental benefits, and that displays his/her progress and performance of eco-driving behaviours relative to his/her own past performance. Variant 3 is tailored to driver segment 3: the egoistic performer We hypothesise that the egoistic performer will be attracted and influenced by feedback technology that shows his/her personal gains in terms of money saved, and that displays his/her progress and performance of eco-driving behaviours relative to others’ past performance. Variant 4 is adapted to driver segment 4: the altruistic performer We hypothesise that the altruistic performer will be attracted and influenced by feedback technology that shows self and other drivers gains in terms of environmental benefits, and that displays his/her progress and performance of eco-driving behaviours relative to others’ past performance. The different forms of possible feedback related to goal orientation and value orientation are depicted in Fig. 1. 1.6. Design and testing of eco feedback displays To explore our assumptions we tested three displays: a basic display, a display tailored to drivers with a learning goal orientation (variant 1 or 2) and a display tailored to drivers with a performance goal orientation (variant 3 or 4).1 The displays were designed by varying historical vs. comparative feedback and learning vs. game element (see Figs. 3 and 4). These displays were presented to drivers in an experimental study carried out in a driving simulator. All three variants of the eco feedback display presented feedback about the optimal eco speed and the actual speed by presenting a green field in the display. The optimal eco speed was calculated by a complex algorithm developed in the European project ecoDriver. The 1 It was beyond the scope of this study to fully develop and test four personalised displays. The purpose of this study was to explore whether we were able to find support for some of the assumptions we made.
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Fig. 1. Feedback strategies adapted to the different driver segments.
eco speed algorithm uses the vehicle’s environment and current driving behaviour to estimate optimal speed range for a section of the trip, taking several aspects into account such as speed limits, preceding vehicles, and road slope. A detailed description of the eco speed algorithm can be found in Seewald et al. (2013). The basic display presented feedback about the optimal green speed and actual speed (see Fig. 2) by displaying a green triangle around the ideal speed range. The display tailored to drivers with a learning goal orientation presents feedback that focuses on personal improvement (see Fig. 3).2 The presented feedback focuses on personal progress of the driver by showing the competence level based on a five star rating. When the driver obtains five stars on a competence level, he/she proceeds to the next level. The display tailored to drivers with a performance goal orientation presents feedback that focuses on the driver’s performance relative to the performance of other drivers; see Fig. 4. We explored the attractiveness and effectiveness of these three displays in a driving simulator study and investigated whether drivers with learning respectively performance goal orientations accepted the displays specifically tailored to them to a larger degree. We also examines whether drivers drove more eco-friendly, that is, complied more often with the presented optimal eco speed. 2. Method 2.1. Participants 26 Participants took part in the experiment. They were all professional truck drivers. On average participants were 50.8 years old, with a standard deviation of 12.2. Only two female truck drivers participated in this experiment. On average participants had 28.2 years of driving experience, with a standard deviation of 13.1. The least experienced driver had 8 years of experience. Based on replies to a questionnaire, drivers were divided into two balanced groups of either learning goal orientation or performance goal orientation (see Hibberd et al., 2013). 2.2. Test vehicle The experiment was performed in the TNO truck driving simulator. This simulator was selected due to benefits from previous eco-driving research performed in the same simulator. In particular, relevant models for the vehicle to assess the optimal eco-driving speed were already developed. The TNO truck driving simulator is the 7th generation of driving simulators developed at TNO (Fig. 5). The mock-up consisted of a DAF CF cabin and was mounted on a six degrees of freedom motion platform. Steering wheel and pedals were equipped with control loading to simulate power steering and pedal characteristics, their positions were measured and sent to the car model. The visualisation was a combination of hard and software components. The entire 2
The logo shown in Figs. 3 and 4 is from the ecoDriver project. This study was part of that project (see acknowledgements).
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Fig. 2. Basic display presenting eco speed feedback.
Fig. 3. Display for drivers with learning goal orientation showing personal improvement.
Fig. 4. Display for driver with a performance goal orientation showing other drivers performance.
visualisation had a range of 180° front view and a 120° back view. The ecoDriver feedback was displayed on a tablet mounted at the top of the instrument cluster in the middle of the dashboard. 2.3. Experimental design A within subject design was used, exposing participants to four conditions; three display conditions and a baseline. All drivers started with a warm-up phase to get used to the driving simulator, followed by the baseline condition, in which they drove without an ecosystem installed. Subsequently, all participants drove with the basic display. Then half of the participants drove first with the learning goal orientation display and then with the performance goal orientation display, and the other half of the participants vice versa.
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Fig. 5. The TNO truck driving simulator.
2.4. Description of scenario A long haul scenario was defined as a basis for a realistic driving environment. This started with 3.8 km of 60 km/h rural road, followed by 9.2 km of 80 km/h two-lane motorway and finally 3.9 km 60 km/h two-lane rural road. On the route, the driver was confronted with a series of traffic events: a section with road works, traffic lights, a sharp curve, motorway entry and exits, a limited maximum speed, right turn at intersection and vehicles slowing down in front of the driver (see Fig. 6). A lead truck was used to manage traffic events. This lead truck slowed down to create some of the events. Driver’s reactions may differ between events and between drivers. Thus, even with relatively tightly scripted events, variation between drivers and between events might have occurred. 2.5. Experimental procedure Participants first received an explanation of the experiment and procedure, after which they signed an informed consent. Next they were asked to fill in a questionnaire which contained questions on demographical information and their goal orientations. Subsequently, participants drove all conditions as described above. After the baseline condition they had to fill in a small questionnaire to establish their workload in the baseline situation. In addition, participants also filled out questionnaires after each drive with one of the three displays, to measure the perceived usefulness, acceptance, ease of use and mental workload. When they finished the last drive, there was one last questionnaire in which participants were asked to rate the different displays. The whole experiment took about 1.5 h per participant. Participants were paid for their participation in the experiment. 2.6. Questionnaires and variables Learning and performance goal orientations were assessed using the two 8-item scales developed by Button et al. (1996). Several performance indicators were recorded to obtain an indication of drivers’ experience of the three different displays. Both subjective and objective measures were taken into account. The focus was on the questionnaire data, to gain more insight into the perceived differences between the different displays and the preference of the participants.3 The objective data, compliance with optimal eco speed, were analysed to study the eco-driving performance of drivers, giving insight into the effects of the eco-driving feedback on driving behaviour. We did not include simulated fuel usage as a variable in the analysis, because it is difficult to get realistic fuel usage data from a driving simulator. 2.7. Compliance Eco-driving behaviour of drivers was measured by calculating the difference between the displayed optimal eco speed and the actual operating speed of the driver (in km/h). The smaller the difference between optimal eco speed and actual speed, the more the driver complied with the optimal eco speed. Therefore a lower score means less difference and better performance. During the baseline, no optimal eco speed feedback was given to the drivers but they were told to drive as they normally would. Even though the participants received no feedback in the baseline condition, the optimal eco speed was calculated. Based on this calculation we established participants’ room for improvement. If the participants already drove 3 The number of participants was low for a between subjects study with subjective data. Due to the extensive testing period of almost four hours it was not feasible to test more than 30 participants. An extensive test procedure was necessary to expose the participants to the different in a semi-realistic setting and give them time to actually use it and try to compete with ‘others’. Based on how much time the sessions took, combined with the exploratory nature of the current research, we decided it would take a dis-proportionate amount of time to increase the scale of the current experiment to make thorough between group testing possible. To be able to do proper statistical analysis with a between-subjects test would imply increasing the participant number to at least 80 or more. Moreover whether we find a difference in feedback also depends on the design of the displays.
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Fig. 6. Examples of events in the driving simulator environment.
at the optimal eco speed, improvement is not possible, and then it is difficult to tell whether participants responded to the displays differently. 3. Results The objective of this experiment was to explore whether tailoring the display to match the goal orientation of the driver increased attractiveness of the display and compliance with the optimal eco speed. The results on acceptance, usefulness, satisfaction and compliance are described below. Cronbach’s alphas (a measure for internal consistency) for different sets of related items were well above .7 (minimum of 0.772). 3.1. Acceptance In general, participants rated the basic display and the performance goal orientation display more acceptable than the learning goal orientation display. However, looking at Fig. 7, where the results are averaged within groups, it can be seen that participants with a learning goal orientation rated the basic display overall lower, and that in fact participants with a performance goal orientation rate the learning variant as less acceptable. 3.2. Usefulness Participants rated the performance goal orientation display as the most useful in general, see Fig. 8. Participants seemed not to have the feeling that the device limited their freedom. In general they thought they saved more fuel than normally and stated that they drove deliberately more eco-friendly. Also most participants felt that the system was useful in general.
Fig. 7. Average acceptance scores for the different displays by participant group.
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Fig. 8. Average scores on usefulness per item for the three different displays.
Fig. 9. Average usefulness scores for the three different displays by participant group.
Fig. 9 shows the usefulness scores for both groups of participants separately. These results show that participants with a performance goal orientation perceive the learning goal orientation display as the least useful. Participants with a learning goal orientation found both tailored displays more useful than the basic display. 3.3. Ease of use Ease of use was determined by a questionnaire of 5 items. Overall, the basic display and the performance goal orientation display were rated more easy to use than the learning goal orientation display. Looking into the different items of the scale shows that the displays scored moderately on being encouraging and on accuracy (see Fig. 10). In addition, for these two items the differences between the displays were small. With regard to understanding of information and usefulness of the feedback, the basic display and the performance goal orientation display scored better than the learning goal orientation display. Participants indicated that the basic display was the easiest to use. Fig. 11 shows the ease of use scores for both groups of participants separately. Similar to the usefulness results we see that participants with a performance goal orientation perceive the learning goal orientation display as the least easy one to use. Participants with a learning goal orientation do not show this distinction, suggesting that they find both displays equally easy to use. After driving with all displays, participants were asked which display was their favourite. Fig. 12 shows the frequency of choices separated by participant group. It can be seen that participants with a performance goal orientation indeed had a preference for the matched display. In addition participants with a learning goal orientation had a (minor) preference for the matched display. A considerable number of participants with a performance goal orientation disliked the learning goal orientation display, while this is not the case for participants with a learning goal orientation. Participants with a learning orientation do not
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Fig. 10. Average scores on ease of use per item for the three different displays.
Fig. 11. Average ease of use scores for the three displays per participant group.
show a negative rating for the mismatched display when compared to the basic display. The basic display was rated equally often as favourite by both groups. 3.4. Rating of the displays Participant were asked to give a score for all displays on a scale from 1 to 10. Fig. 13 shows the mean ratings per participant group, indicating, as seen previously, that participants with a learning goal orientation rate all displays almost the same, while participants with a performance goal orientation rate the matching performance goal orientation display clearly higher. 3.5. Eco-driving performance measured by compliance with optimal eco speed Fig. 14 shows the results for compliance with optimal eco speed for both groups of participants separately. A low value means better eco-driving, that is, a smaller difference between optimal eco speed and actual speed. The figure depicts the compliance score computed over the whole trip. On average, participants drove closer to the optimal eco speed when they received feedback by the displays then when they drove without the displays.
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Fig. 12. Number of participants that choose basic/learning/performance display as their favourite per participant group.
Fig. 13. Mean ratings for each display per participant group.
3.6. Compliance clustered by what drivers indicated their favourite interface Drivers indicated what their favourite interface was. Instead of clustering them in groups by the psychological constructs from the questionnaire, they can also by clustered into groups according to their preference for an interface. Each interface was preferred by some drivers but the three groups differ in size (see Fig. 12). The basic interface was preferred by eight drivers, the learning oriented interface by seven and the performance interface by nine drivers. Mean compliance within these groups indicates whether drivers followed the eco-advice better when driving with the system they preferred. Again no statistical tests were performed so it is unclear whether the differences are due to chance or are real differences. Fig. 15 however does not show that the preferred interface led to a clearly higher compliance. 4. Discussion Providing feedback is a powerful tool for instigating a behaviour change. The theory behind personalising feedback shows that there are possibilities to increase the effectiveness of feedback by tailoring the feedback to a person. Stillwater and Kurani (2013), for example, found that decreases in fuel consumption were dependent on feedback. In other words, different drivers seem to be attracted to different types of information and feedback. Presenting personalised feedback may be more effective than presenting the same feedback to all drivers. We presented a driver segmentation based on driver’s goal orientation and value orientation. In a driving simulator we investigated whether feedback based on goal orientation (learning orientation vs performance orientation) would increase acceptance of such feedback. The results of a driving simulator study
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Fig. 14. Compliance with optimal eco speed per participant group (lower score means smaller difference between optimal eco speed and actual speed).
Fig. 15. Compliance with optimal eco speed per participant group clustered by what drivers indicated as their favourite interface (lower score on compliance means smaller difference between optimal eco speed and actual speed).
suggested that a performance oriented group gave a (mismatched) learning oriented display a lower rating on acceptation, ease of use, favouritism and a lower general rating. A learning oriented group rated both adapted systems (matched and mismatched) higher than the performance group did in general and rated the adapted systems also higher than a basic display on acceptation, general rating and favoured. Overall eco-driving performance, indicating how much the drivers adapted their speed to the optimal eco speed, was better with the adapted displays. To prove that personalised feedback indeed works requires an interaction between type of display and type of group. For our study it means that a performance oriented driver prefers a performance oriented display (match) above a learning oriented display (mismatch) while a learning oriented driver prefers a learning oriented display (match) above a performance oriented display (mismatch). We found no such interaction. Learning oriented drivers seemed to prefer personalised displays (performance or oriented) while performance oriented drivers seemed to prefer the performance display or the basic display. The outcome of the experiment strongly depends on the chosen display. This is less of a problem for situations in which differences can be found in directions that are expected, but more so in situations where no (clear) results are found as in the present study (e.g., the lack of difference between the two adapted displays for the learning oriented group or the lack of difference between basic and performance displays for the performance oriented group). Designing displays that fit the target group is a pre-requisite for finding differences. The purpose of this experiment was to find differences within and between performance and learning oriented groups of drivers. Within the scope of the present study displays were designed
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that seemed to fit a priori the groups optimally. The results however indicated that the chosen implementations (displays) did not differentiate clear enough between the two segments of drivers. Despite certain limitations in this study the results suggest that adapting the display to a driver segment increases acceptance in certain cases. Using a display that gives historical feedback and incorporates learning elements increased the acceptance for learning oriented drivers.4 Furthermore the results showed that a learning oriented display may be less effective for performance oriented drivers, who may need comparative feedback and game elements. As stated the difficulty with this type of research is the implementation (the displays) of the feedback to different driver segments. The implementation is what is tested while we are interested whether the feedback based on the driver segments themselves was correct. The outcome of the experiment gives some support that the driver segments and feedback are on the right track. In a follow-up project we will test different implementations of the different types of feedback of the driver segments to come up with displays that clearer can differentiate between learning and performance oriented drivers. Acknowledgements The authors would like to acknowledge that this research was performed within the ecoDriver project (see www.ecodriver-project.eu) which is co-funded by the European Commission, DG INFSO, within the Seventh Framework Programme under grant agreement n° 288611. FP7-ICT-2011-7: Information and Communication Technologies Low carbon multi-modal mobility and freight transport. The contribution of TNO to the ecoDriver project was further supported by the Dutch Ministry of Infrastructure and the Environment. References Allcott, H., Mullainathan, S., 2010. Behavior and energy policy. Science 327, 1204–1205. Anable, J., 2005. ‘Complacent car addicts’ or ‘aspiring environmentalists’? Identifying travel behaviour segments using attitude theory. Transp. Policy 12 (1), 65–78. Button, S.B., Mathieu, J.E., Zajac, D.M., 1996. Goal orientation in organizational research: a conceptual and empirical foundation. Organ. Behav. Hum. Decis. Process. 67, 26–48. Darby, S., 2001. Making it obvious: designing feedback into energy consumption. In: Energy Efficiency in Household Appliances and Lighting. Springer, pp. 685–696. De Groot, J.I., Steg, L., 2008. Value orientations to explain beliefs related to environmental significant behavior how to measure egoistic, altruistic, and biospheric value orientations. Environ. Behav. 40 (3), 330–354. De Groot, J.I., Steg, L., 2009. Mean or green: which values can promote stable pro-environmental behavior? Conserv. Lett. 2 (2), 61–66. Dweck, C.S., 1986. Motivational processes affecting learning. Am. Psychol. 41 (10), 1040. Fischer, C., 2008. Feedback on household electricity consumption: a tool for saving energy? Energ. Effi. 1, 79–104. Fuglestvedt, J., Berntsen, T., Myhre, G., Rypdal, K., Bieltvedt Skeie, R., 2008. Climate forcing from the transport sectors. Proc. Nat. Acad. Sci. USA (PNAS) 105 (2), 454–458. Harackiewicz, J.M., Elliot, A.J., 1993. Achievement goals and intrinsic motivation. J. Person. Soc. Psychol. J. Person. Soc. Psychol. 65 (5), 904–915. He, H., Greenberg, S., Huang, E., 2010. One Size Does not Fit all: Applying the Transtheoretical Model to Energy Feedback Technology Design. CHI’10, pp. 927–936. Hibberd, D., Jamson, H., Jamson, S., Pauwelussen, J., Obdeijn, C., Stuiver, A., Brignolo, R., Barberi, C., Iviglia, A., Mazza, M., 2013. D12.1: Multi-modal in-vehicle and nomadic device eco-driving support for car drivers and truck drivers. ecoDriver Project.. Michon, J.A., 1985. A critical view of driver behavior models: what do we know, what should we do? In: Human Behavior and Traffic Safety. Springer, US, pp. 485–524. Rokeach, M., 1973. The Nature of Human Values. The Free Press, New York. Seewald, P., Ivens, T., Spronkmans, S., 2013. D23. 1: Report on Test Scenarios for Validation of on-line Vehicle Algorithms. ecoDriver Project. . Stern, P.C., 2000. Toward a coherent theory of environmentally significant behaviour. J. Soc. Issues 56, 407–424. Stern, P.C., 2011. Contributions of psychology to limiting climate change. Am. Psychol. 66 (4), 303–314. Stern, P.C., Dietz, T., 1994. The value basis of environmental concern. J. Soc. Issues 50 (3), 65–84. Stern, P.C., Dietz, T., Kalof, L., 1993. Value orientations, gender, and environmental concern. Environ. Behav. 25 (5), 322–348. Stillwater, T., Kurani, K.S., 2013. Drivers discuss ecodriving feedback: goal setting, framing, and anchoring motivate new behaviors. Transp. Res. Part F: Traffic Psychol. Behav. 19, 85–96. Strömberg, H., Karlsson, I.M., Rexfelt, O., 2015. Eco-driving: drivers’ understanding of the concept and implications for future interventions. Transp. Policy 39, 48–54. Tulusan, J., Steggers, H., Staake, T., Fleisch, E., 2012. Supporting eco-driving with ecofeedback technologies: recommendations targeted at improving corporate car drivers’ intrinsic motivation to drive more sustainable. In: Energy Informatics 2012 (EI 2012), Atlanta, Georgia, United States, October 2012. Van Vugt, M., Meertens, R.M., Van Lange, P.A.M., 1995. Car versus public transportation? The role of social value orientations in a real-life social dilemma. J. Appl. Soc. Psychol. 25 (3), 258–278. Wedel, M., Kamakura, W.A. (Eds.), 1998. Market Segmentation: Conceptual and Methodological Foundations. Kluwer Academic Publishers, Dordrecht.
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Although in the present study for learning oriented drivers this was also the case for a performance oriented display.
Contents lists available at ScienceDirect
Transportation Research Part C journal homepage: www.elsevier.com/locate/trc
Personalised feedback and eco-driving: An explorative study R.F.T. Brouwer a,⇑, A. Stuiver a, T. Hof a, L. Kroon a, J. Pauwelussen b, B. Holleman a a b
TNO, Kampweg 5, Soesterberg, The Netherlands TomTom Telematics, Amsterdam, The Netherlands
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Article history: Received 15 September 2014 Received in revised form 28 April 2015 Accepted 28 April 2015 Available online 2 July 2015 Keywords: Green driving support Driver behaviour change Value orientation Goal orientation Adaptive HMI Personalisation Driver segments Acceptance
a b s t r a c t Conventional road transport has negative impact on the environment. Stimulating eco-driving through feedback to the driver about his/her energy conservation performance has the potential to reduce CO2 emissions and promote fuel cost savings. Not all drivers respond well to the same type of feedback. Research has shown that different drivers are attracted to different types of information and feedback. The goal of this paper is to explore which different driver segments with specific psychographic characteristics can be distinguished, how these characteristics can be used in the development of an ecodriving support system and whether tailoring eco-driving feedback technology to these different driver segments will lead to increased acceptance and thus effectiveness of the eco feedback technology. The driver segments are based on the value orientation theory and learning orientation theory. Different possibilities for feedback were tested in an exploratory study in a driving simulator. An explorative study was selected since the choice of the display (how and when the information is presented) may have a strong impact on the results. This makes testing of the selected driver segments very difficult. The results of the study nevertheless suggest that adapting the display to a driver segment showed an increase in acceptance in certain cases. The results showed small differences for ratings on acceptation, ease of use, favouritism and a lower general rating between matched (e.g., learning display with learning oriented drivers) and mismatched displays (e.g., learning display with performance oriented drivers). Using a display that gives historical feedback and incorporates learning elements suggested a non-verifiable increase in acceptance for learning oriented drivers. However historical feedback and learning elements may be less effective for performance oriented drivers, who may need comparative feedback and game elements to improve energy conserving driving behaviour. Ó 2015 Published by Elsevier Ltd.
1. Introduction 1.1. Providing feedback to change driving behaviour The negative impact of road transport on the environment has been well documented (see, e.g., Fuglestvedt et al., 2008). Much technological development has been focussed on increasing the fuel efficiency of engines and a number of support systems have been developed to aid the driver in a more eco-friendly driving style. On the latter topic, research has shown that providing feedback is a powerful tool for instigating a behaviour change (e.g. Fischer, 2008; Allcott and Mullainathan,
⇑ Corresponding author. Tel.: +31 888 665 969. E-mail address: [email protected] (R.F.T. Brouwer). http://dx.doi.org/10.1016/j.trc.2015.04.027 0968-090X/Ó 2015 Published by Elsevier Ltd.
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2010; Stern, 2011). Stimulating eco-driving using feedback technologies has the potential to reduce CO2 emissions and promote fuel cost savings of 1–8% (Tulusan et al., 2012). On an operational level (Michon, 1985) eco-driving refers to implementation of eco-driving techniques while driving which encompasses keeping the speed down, efficient gear shifting, anticipatory, calm and steady driving, and efficient braking (Strömberg et al., 2015). In-vehicle feedback technologies provide drivers with real-time, continuous feedback about their eco-driving behaviours. A recent naturalistic driving study shows an average decrease of between 2% and 8% in fuel consumption, depending on feedback design (Stillwater and Kurani, 2013). From interviews with 46 drivers Stillwater and Kurani (2013) conclude that the information content of the display (e.g., maximising MPG or savings, or minimising CO2) plays an important role in stimulating drivers to drive economically. Different drivers seem to be attracted to different types of information and feedback. He et al. (2010) argue that presenting feedback in the same way to differently motivated drivers may not be that effective. In their reflection on different energy feedback technologies they bring forward that an improvement would be to consider the specific values and goals of each individual when providing feedback to drivers. As such, the visualisation could provide personalised feedback of the positive results of eco-driving behaviours, highlighting for example for one person health benefits and for another person monetary gains. This is in line with Anable’s (2005) work on segmenting the driver population based on psychographic variables (e.g., attitudes, values, personal norms) instead of based on demographic variables (e.g., gender, age). Also, in the field of consumer behaviour and marketing it is common practice to approach different people in different ways because they are motivated by different factors (Wedel and Kamakura, 1998). The goal of the research described in this paper is to explore whether tailoring eco feedback technology to different driver segments with specific psychographic characteristics will lead to increased acceptance and effectiveness of the eco feedback technology. The remainder of this paper starts with a short discussion of the value orientation theory and learning orientation theory. Both these theories offer a good starting point to distinguish different driver segments. Based on insights from these theories we describe four driver segments and propose four variants of the same eco feedback display that, supposedly, match the different driver segments. These different displays were tested with respect to acceptance while drivers drove in a driving simulator. The results and implications of this explorative study are discussed. 1.2. Value orientation theory Values are principles that guide and influence behaviour. Values are seen as enduring behavioural ideals that are firmly incorporated within persons. As behavioural ideals, values guide a person’s sense of what is good and bad and which goals to pursue (Rokeach, 1973). Regarding understanding pro-environmental behaviour three types of values are important: pro-self, pro-social, and ecocentric values (Stern, 2000; De Groot and Steg, 2008, 2009; Van Vugt et al., 1995; Stern et al., 1993). People with a strong pro-self value orientation will especially take into account the costs and benefits of pro-environmental behaviour for them personally. People with a strong pro-social value orientation include in their decision on behaving pro-environmentally the perceived costs and benefits for other people. People with a strong ecocentric value orientation will consider the perceived costs and benefits for the ecosystem and biosphere when deciding to act pro-environmentally (De Groot and Steg, 2009). Stern and Dietz (1994), however, found that in a general population sample the ecocentric value orientation does not differentiate from social-altruism. Therefore, and in line with He et al. (2010) who suggested to improve feedback by matching it to an individual’s values, we hypothesise that matching the eco feedback technology to people’s value orientation (pro-self vs. pro-social) will increase the attractiveness and effectiveness of eco-feedback technologies. 1.3. Goal orientation theory Motivating people to drive eco-friendly can be considered to be a learning process. Drivers will need to learn new skills, which hopefully will form into habits. Receiving feedback can therefore be seen as an important part of the learning process. Eco feedback technology aims to make drivers learn the connection between their driving behaviours and certain outcomes, for example, the amount of fuel they use. Feedback is an essential component of learning, and its principles are rooted in educational theory (Darby, 2001). Goal orientation theory is a social-cognitive theory of achievement motivation and originally examines the reasons why children engage in their school work (Dweck, 1986). Earlier work of Dweck (1986) contrasted two types of goal orientations: learning goal orientation and performance goal orientation. People with a learning goal orientation wish to acquire additional knowledge or master new skills. They are self-referential, focusing on the development of skill and competence relative to the task and one’s own past performance. People with a performance goal orientation want to demonstrate their competence and to make a good impression on others. They are trying to outperform others and strive to be the best in a group (Harackiewicz and Elliot, 1993). We assume that aligning the provided feedback to people’s goal orientation (learning orientation vs. performance orientation) increases attractiveness and effectiveness of eco feedback technology. 1.4. Four driver segments To enable developing and testing tailored eco feedback, we used insights from these theories to distinguish four driver segments based on their value orientation and goal orientation.
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Driver segment 1: Egoistic learner We assume that a driver from this driver segment has a pro-self value orientation and learning goal orientation. The egoistic learner takes into account personal benefits and costs (e.g., fuel saving, prolonged lifespan of vehicle), and has a desire to improve his/her eco-driving behaviour relative to his/her own past performance. Driver segment 2: Altruistic learner A driver from this driver segment has a pro-social value orientation and learning goal orientation. The altruistic learner is conscious of the needs of and benefits for others, and takes these into account when making decisions. He/She is motivated by improving his/her eco-driving behaviour relative to his/her own past performance. Driver segment 3: Egoistic performer A driver from this driver segment has a pro-self value orientation and performance goal orientation. The egoistic performer takes into account personal benefits and costs (e.g., fuel saving, prolonged lifespan of vehicle), and likes to compare to and outperform others. Driver segment 4: Altruistic performer A driver from this driver segment has a pro-social value orientation and performance goal orientation. The altruistic performer is conscious of the needs of and benefits for others, and takes these into account when making decisions. At the same time he/she likes to compare to and outperform others. We use these four distinguished driver segments to develop four variants of eco feedback technology that we think would appeal to these different driver segments. In a match–mismatch experiment (see Section 2), we tested two of these variants. 1.5. Four variants of the eco-driving feedback technology tailored to four driver segments Variant 1 is adapted to driver segment 1: the egoistic learner We hypothesise that the egoistic learner will be attracted and influenced by feedback technology that shows his/her personal gains in terms of money saved, and that displays his/her progress and performance of eco-driving behaviours relative to his/her own past performance. Variant 2 is tailored to driver segment 2: the altruistic learner We hypothesise that the altruistic learner will be attracted and influenced by feedback technology that shows self and other drivers gains in terms of environmental benefits, and that displays his/her progress and performance of eco-driving behaviours relative to his/her own past performance. Variant 3 is tailored to driver segment 3: the egoistic performer We hypothesise that the egoistic performer will be attracted and influenced by feedback technology that shows his/her personal gains in terms of money saved, and that displays his/her progress and performance of eco-driving behaviours relative to others’ past performance. Variant 4 is adapted to driver segment 4: the altruistic performer We hypothesise that the altruistic performer will be attracted and influenced by feedback technology that shows self and other drivers gains in terms of environmental benefits, and that displays his/her progress and performance of eco-driving behaviours relative to others’ past performance. The different forms of possible feedback related to goal orientation and value orientation are depicted in Fig. 1. 1.6. Design and testing of eco feedback displays To explore our assumptions we tested three displays: a basic display, a display tailored to drivers with a learning goal orientation (variant 1 or 2) and a display tailored to drivers with a performance goal orientation (variant 3 or 4).1 The displays were designed by varying historical vs. comparative feedback and learning vs. game element (see Figs. 3 and 4). These displays were presented to drivers in an experimental study carried out in a driving simulator. All three variants of the eco feedback display presented feedback about the optimal eco speed and the actual speed by presenting a green field in the display. The optimal eco speed was calculated by a complex algorithm developed in the European project ecoDriver. The 1 It was beyond the scope of this study to fully develop and test four personalised displays. The purpose of this study was to explore whether we were able to find support for some of the assumptions we made.
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Fig. 1. Feedback strategies adapted to the different driver segments.
eco speed algorithm uses the vehicle’s environment and current driving behaviour to estimate optimal speed range for a section of the trip, taking several aspects into account such as speed limits, preceding vehicles, and road slope. A detailed description of the eco speed algorithm can be found in Seewald et al. (2013). The basic display presented feedback about the optimal green speed and actual speed (see Fig. 2) by displaying a green triangle around the ideal speed range. The display tailored to drivers with a learning goal orientation presents feedback that focuses on personal improvement (see Fig. 3).2 The presented feedback focuses on personal progress of the driver by showing the competence level based on a five star rating. When the driver obtains five stars on a competence level, he/she proceeds to the next level. The display tailored to drivers with a performance goal orientation presents feedback that focuses on the driver’s performance relative to the performance of other drivers; see Fig. 4. We explored the attractiveness and effectiveness of these three displays in a driving simulator study and investigated whether drivers with learning respectively performance goal orientations accepted the displays specifically tailored to them to a larger degree. We also examines whether drivers drove more eco-friendly, that is, complied more often with the presented optimal eco speed. 2. Method 2.1. Participants 26 Participants took part in the experiment. They were all professional truck drivers. On average participants were 50.8 years old, with a standard deviation of 12.2. Only two female truck drivers participated in this experiment. On average participants had 28.2 years of driving experience, with a standard deviation of 13.1. The least experienced driver had 8 years of experience. Based on replies to a questionnaire, drivers were divided into two balanced groups of either learning goal orientation or performance goal orientation (see Hibberd et al., 2013). 2.2. Test vehicle The experiment was performed in the TNO truck driving simulator. This simulator was selected due to benefits from previous eco-driving research performed in the same simulator. In particular, relevant models for the vehicle to assess the optimal eco-driving speed were already developed. The TNO truck driving simulator is the 7th generation of driving simulators developed at TNO (Fig. 5). The mock-up consisted of a DAF CF cabin and was mounted on a six degrees of freedom motion platform. Steering wheel and pedals were equipped with control loading to simulate power steering and pedal characteristics, their positions were measured and sent to the car model. The visualisation was a combination of hard and software components. The entire 2
The logo shown in Figs. 3 and 4 is from the ecoDriver project. This study was part of that project (see acknowledgements).
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Fig. 2. Basic display presenting eco speed feedback.
Fig. 3. Display for drivers with learning goal orientation showing personal improvement.
Fig. 4. Display for driver with a performance goal orientation showing other drivers performance.
visualisation had a range of 180° front view and a 120° back view. The ecoDriver feedback was displayed on a tablet mounted at the top of the instrument cluster in the middle of the dashboard. 2.3. Experimental design A within subject design was used, exposing participants to four conditions; three display conditions and a baseline. All drivers started with a warm-up phase to get used to the driving simulator, followed by the baseline condition, in which they drove without an ecosystem installed. Subsequently, all participants drove with the basic display. Then half of the participants drove first with the learning goal orientation display and then with the performance goal orientation display, and the other half of the participants vice versa.
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Fig. 5. The TNO truck driving simulator.
2.4. Description of scenario A long haul scenario was defined as a basis for a realistic driving environment. This started with 3.8 km of 60 km/h rural road, followed by 9.2 km of 80 km/h two-lane motorway and finally 3.9 km 60 km/h two-lane rural road. On the route, the driver was confronted with a series of traffic events: a section with road works, traffic lights, a sharp curve, motorway entry and exits, a limited maximum speed, right turn at intersection and vehicles slowing down in front of the driver (see Fig. 6). A lead truck was used to manage traffic events. This lead truck slowed down to create some of the events. Driver’s reactions may differ between events and between drivers. Thus, even with relatively tightly scripted events, variation between drivers and between events might have occurred. 2.5. Experimental procedure Participants first received an explanation of the experiment and procedure, after which they signed an informed consent. Next they were asked to fill in a questionnaire which contained questions on demographical information and their goal orientations. Subsequently, participants drove all conditions as described above. After the baseline condition they had to fill in a small questionnaire to establish their workload in the baseline situation. In addition, participants also filled out questionnaires after each drive with one of the three displays, to measure the perceived usefulness, acceptance, ease of use and mental workload. When they finished the last drive, there was one last questionnaire in which participants were asked to rate the different displays. The whole experiment took about 1.5 h per participant. Participants were paid for their participation in the experiment. 2.6. Questionnaires and variables Learning and performance goal orientations were assessed using the two 8-item scales developed by Button et al. (1996). Several performance indicators were recorded to obtain an indication of drivers’ experience of the three different displays. Both subjective and objective measures were taken into account. The focus was on the questionnaire data, to gain more insight into the perceived differences between the different displays and the preference of the participants.3 The objective data, compliance with optimal eco speed, were analysed to study the eco-driving performance of drivers, giving insight into the effects of the eco-driving feedback on driving behaviour. We did not include simulated fuel usage as a variable in the analysis, because it is difficult to get realistic fuel usage data from a driving simulator. 2.7. Compliance Eco-driving behaviour of drivers was measured by calculating the difference between the displayed optimal eco speed and the actual operating speed of the driver (in km/h). The smaller the difference between optimal eco speed and actual speed, the more the driver complied with the optimal eco speed. Therefore a lower score means less difference and better performance. During the baseline, no optimal eco speed feedback was given to the drivers but they were told to drive as they normally would. Even though the participants received no feedback in the baseline condition, the optimal eco speed was calculated. Based on this calculation we established participants’ room for improvement. If the participants already drove 3 The number of participants was low for a between subjects study with subjective data. Due to the extensive testing period of almost four hours it was not feasible to test more than 30 participants. An extensive test procedure was necessary to expose the participants to the different in a semi-realistic setting and give them time to actually use it and try to compete with ‘others’. Based on how much time the sessions took, combined with the exploratory nature of the current research, we decided it would take a dis-proportionate amount of time to increase the scale of the current experiment to make thorough between group testing possible. To be able to do proper statistical analysis with a between-subjects test would imply increasing the participant number to at least 80 or more. Moreover whether we find a difference in feedback also depends on the design of the displays.
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Fig. 6. Examples of events in the driving simulator environment.
at the optimal eco speed, improvement is not possible, and then it is difficult to tell whether participants responded to the displays differently. 3. Results The objective of this experiment was to explore whether tailoring the display to match the goal orientation of the driver increased attractiveness of the display and compliance with the optimal eco speed. The results on acceptance, usefulness, satisfaction and compliance are described below. Cronbach’s alphas (a measure for internal consistency) for different sets of related items were well above .7 (minimum of 0.772). 3.1. Acceptance In general, participants rated the basic display and the performance goal orientation display more acceptable than the learning goal orientation display. However, looking at Fig. 7, where the results are averaged within groups, it can be seen that participants with a learning goal orientation rated the basic display overall lower, and that in fact participants with a performance goal orientation rate the learning variant as less acceptable. 3.2. Usefulness Participants rated the performance goal orientation display as the most useful in general, see Fig. 8. Participants seemed not to have the feeling that the device limited their freedom. In general they thought they saved more fuel than normally and stated that they drove deliberately more eco-friendly. Also most participants felt that the system was useful in general.
Fig. 7. Average acceptance scores for the different displays by participant group.
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Fig. 8. Average scores on usefulness per item for the three different displays.
Fig. 9. Average usefulness scores for the three different displays by participant group.
Fig. 9 shows the usefulness scores for both groups of participants separately. These results show that participants with a performance goal orientation perceive the learning goal orientation display as the least useful. Participants with a learning goal orientation found both tailored displays more useful than the basic display. 3.3. Ease of use Ease of use was determined by a questionnaire of 5 items. Overall, the basic display and the performance goal orientation display were rated more easy to use than the learning goal orientation display. Looking into the different items of the scale shows that the displays scored moderately on being encouraging and on accuracy (see Fig. 10). In addition, for these two items the differences between the displays were small. With regard to understanding of information and usefulness of the feedback, the basic display and the performance goal orientation display scored better than the learning goal orientation display. Participants indicated that the basic display was the easiest to use. Fig. 11 shows the ease of use scores for both groups of participants separately. Similar to the usefulness results we see that participants with a performance goal orientation perceive the learning goal orientation display as the least easy one to use. Participants with a learning goal orientation do not show this distinction, suggesting that they find both displays equally easy to use. After driving with all displays, participants were asked which display was their favourite. Fig. 12 shows the frequency of choices separated by participant group. It can be seen that participants with a performance goal orientation indeed had a preference for the matched display. In addition participants with a learning goal orientation had a (minor) preference for the matched display. A considerable number of participants with a performance goal orientation disliked the learning goal orientation display, while this is not the case for participants with a learning goal orientation. Participants with a learning orientation do not
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Fig. 10. Average scores on ease of use per item for the three different displays.
Fig. 11. Average ease of use scores for the three displays per participant group.
show a negative rating for the mismatched display when compared to the basic display. The basic display was rated equally often as favourite by both groups. 3.4. Rating of the displays Participant were asked to give a score for all displays on a scale from 1 to 10. Fig. 13 shows the mean ratings per participant group, indicating, as seen previously, that participants with a learning goal orientation rate all displays almost the same, while participants with a performance goal orientation rate the matching performance goal orientation display clearly higher. 3.5. Eco-driving performance measured by compliance with optimal eco speed Fig. 14 shows the results for compliance with optimal eco speed for both groups of participants separately. A low value means better eco-driving, that is, a smaller difference between optimal eco speed and actual speed. The figure depicts the compliance score computed over the whole trip. On average, participants drove closer to the optimal eco speed when they received feedback by the displays then when they drove without the displays.
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Fig. 12. Number of participants that choose basic/learning/performance display as their favourite per participant group.
Fig. 13. Mean ratings for each display per participant group.
3.6. Compliance clustered by what drivers indicated their favourite interface Drivers indicated what their favourite interface was. Instead of clustering them in groups by the psychological constructs from the questionnaire, they can also by clustered into groups according to their preference for an interface. Each interface was preferred by some drivers but the three groups differ in size (see Fig. 12). The basic interface was preferred by eight drivers, the learning oriented interface by seven and the performance interface by nine drivers. Mean compliance within these groups indicates whether drivers followed the eco-advice better when driving with the system they preferred. Again no statistical tests were performed so it is unclear whether the differences are due to chance or are real differences. Fig. 15 however does not show that the preferred interface led to a clearly higher compliance. 4. Discussion Providing feedback is a powerful tool for instigating a behaviour change. The theory behind personalising feedback shows that there are possibilities to increase the effectiveness of feedback by tailoring the feedback to a person. Stillwater and Kurani (2013), for example, found that decreases in fuel consumption were dependent on feedback. In other words, different drivers seem to be attracted to different types of information and feedback. Presenting personalised feedback may be more effective than presenting the same feedback to all drivers. We presented a driver segmentation based on driver’s goal orientation and value orientation. In a driving simulator we investigated whether feedback based on goal orientation (learning orientation vs performance orientation) would increase acceptance of such feedback. The results of a driving simulator study
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Fig. 14. Compliance with optimal eco speed per participant group (lower score means smaller difference between optimal eco speed and actual speed).
Fig. 15. Compliance with optimal eco speed per participant group clustered by what drivers indicated as their favourite interface (lower score on compliance means smaller difference between optimal eco speed and actual speed).
suggested that a performance oriented group gave a (mismatched) learning oriented display a lower rating on acceptation, ease of use, favouritism and a lower general rating. A learning oriented group rated both adapted systems (matched and mismatched) higher than the performance group did in general and rated the adapted systems also higher than a basic display on acceptation, general rating and favoured. Overall eco-driving performance, indicating how much the drivers adapted their speed to the optimal eco speed, was better with the adapted displays. To prove that personalised feedback indeed works requires an interaction between type of display and type of group. For our study it means that a performance oriented driver prefers a performance oriented display (match) above a learning oriented display (mismatch) while a learning oriented driver prefers a learning oriented display (match) above a performance oriented display (mismatch). We found no such interaction. Learning oriented drivers seemed to prefer personalised displays (performance or oriented) while performance oriented drivers seemed to prefer the performance display or the basic display. The outcome of the experiment strongly depends on the chosen display. This is less of a problem for situations in which differences can be found in directions that are expected, but more so in situations where no (clear) results are found as in the present study (e.g., the lack of difference between the two adapted displays for the learning oriented group or the lack of difference between basic and performance displays for the performance oriented group). Designing displays that fit the target group is a pre-requisite for finding differences. The purpose of this experiment was to find differences within and between performance and learning oriented groups of drivers. Within the scope of the present study displays were designed
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that seemed to fit a priori the groups optimally. The results however indicated that the chosen implementations (displays) did not differentiate clear enough between the two segments of drivers. Despite certain limitations in this study the results suggest that adapting the display to a driver segment increases acceptance in certain cases. Using a display that gives historical feedback and incorporates learning elements increased the acceptance for learning oriented drivers.4 Furthermore the results showed that a learning oriented display may be less effective for performance oriented drivers, who may need comparative feedback and game elements. As stated the difficulty with this type of research is the implementation (the displays) of the feedback to different driver segments. The implementation is what is tested while we are interested whether the feedback based on the driver segments themselves was correct. The outcome of the experiment gives some support that the driver segments and feedback are on the right track. In a follow-up project we will test different implementations of the different types of feedback of the driver segments to come up with displays that clearer can differentiate between learning and performance oriented drivers. Acknowledgements The authors would like to acknowledge that this research was performed within the ecoDriver project (see www.ecodriver-project.eu) which is co-funded by the European Commission, DG INFSO, within the Seventh Framework Programme under grant agreement n° 288611. FP7-ICT-2011-7: Information and Communication Technologies Low carbon multi-modal mobility and freight transport. The contribution of TNO to the ecoDriver project was further supported by the Dutch Ministry of Infrastructure and the Environment. References Allcott, H., Mullainathan, S., 2010. Behavior and energy policy. Science 327, 1204–1205. Anable, J., 2005. ‘Complacent car addicts’ or ‘aspiring environmentalists’? Identifying travel behaviour segments using attitude theory. Transp. Policy 12 (1), 65–78. Button, S.B., Mathieu, J.E., Zajac, D.M., 1996. Goal orientation in organizational research: a conceptual and empirical foundation. Organ. Behav. Hum. Decis. Process. 67, 26–48. Darby, S., 2001. Making it obvious: designing feedback into energy consumption. In: Energy Efficiency in Household Appliances and Lighting. Springer, pp. 685–696. De Groot, J.I., Steg, L., 2008. Value orientations to explain beliefs related to environmental significant behavior how to measure egoistic, altruistic, and biospheric value orientations. Environ. Behav. 40 (3), 330–354. De Groot, J.I., Steg, L., 2009. Mean or green: which values can promote stable pro-environmental behavior? Conserv. Lett. 2 (2), 61–66. Dweck, C.S., 1986. Motivational processes affecting learning. Am. Psychol. 41 (10), 1040. Fischer, C., 2008. Feedback on household electricity consumption: a tool for saving energy? Energ. Effi. 1, 79–104. Fuglestvedt, J., Berntsen, T., Myhre, G., Rypdal, K., Bieltvedt Skeie, R., 2008. Climate forcing from the transport sectors. Proc. Nat. Acad. Sci. USA (PNAS) 105 (2), 454–458. Harackiewicz, J.M., Elliot, A.J., 1993. Achievement goals and intrinsic motivation. J. Person. Soc. Psychol. J. Person. Soc. Psychol. 65 (5), 904–915. He, H., Greenberg, S., Huang, E., 2010. One Size Does not Fit all: Applying the Transtheoretical Model to Energy Feedback Technology Design. CHI’10, pp. 927–936. Hibberd, D., Jamson, H., Jamson, S., Pauwelussen, J., Obdeijn, C., Stuiver, A., Brignolo, R., Barberi, C., Iviglia, A., Mazza, M., 2013. D12.1: Multi-modal in-vehicle and nomadic device eco-driving support for car drivers and truck drivers. ecoDriver Project.
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Although in the present study for learning oriented drivers this was also the case for a performance oriented display.