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Effects of critical incidents on car users’ predicted satisfaction with public transport
Transportation Research Part F 14 (2011) 138–146
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Effects of critical incidents on car users’ predicted satisfaction with public transport q Tore Pedersen a,b, Per Kristensson b, Margareta Friman b,⇑ a b
National Institute of Occupational Health, PO BOX 8149 Dep., N-0033 OSLO, Norway Service Research Center/SAMOT, Karlstad University, Sweden
a r t i c l e
i n f o
Article history: Received 31 August 2009 Received in revised form 25 August 2010 Accepted 11 November 2010
Keywords: Public transport Critical incidents Focusing illusion Affective forecasting Predicted satisfaction
a b s t r a c t The present study examines the hypothesis that car users’ affective forecasts of satisfaction with public transport are biased by a focusing illusion. In Study 1, 54 car users with a stated intent to change travel mode read descriptions of a positive, a negative or a neutral critical incident. They were asked to predict their satisfaction with public transport if the incident occurred. In Study 2, 38 car users with no stated intent to change travel mode read descriptions of a positive or a negative critical incident. They were asked to predict their satisfaction with the service if the incident occurred. The results from Studies 1 and 2 showed that focus on a negative critical incident significantly generated lower predicted satisfaction. Thus, the study show that predicted satisfaction is altered when car users focus on negative critical incidents. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Car usage has increased considerably over the last decades (Gifford & Steg, 2007; Jakobsson, 2004). The number of passenger kilometres per capita has increased by 90% in Western Europe, and drivers make 80% more trips by car than they plan to do when asked in advance (Jakobsson, 2004). In general, the environmental effects of car use are severe (Van Wee, 2007), especially in urban regions where the increase in car usage represents a threat to the quality of urban life (Gifford & Steg, 2007). As there is no immediate sign that car use will not increase further (Sperling & Gordon, 2009), it is important to explore alternative approaches to disclosing the psychological mechanisms underlying people’s choice of travel mode. Although previous research suggests that no intervention is likely to change car users’ travel behaviour (e.g., Shannon et al., 2006), a significant minority of car users state that they are willing to change travel mode (Curtis & Headicar, 1997). The car users’ stated prerequisite for such a travel behaviour change from car to public transport are improved services (Curtis & Headicar, 1997; Eriksson, Friman, & Gärling, 2008; Kingham, Dickingson, & Copsey, 2001), which implies shorter travel times, an increased frequency of service and lower fares. Previous research has thus revealed that at least some car users state that they are willing to change their travel mode towards public transport use if the services are improved. However, it has been shown that habitual car users underestimate their future satisfaction with the service quality level of public transport, as they report larger satisfaction when experiencing the service than they initially predicted they would beforehand (Pedersen, Friman, & Kristensson, in press). Pedersen et al. (in press) suggest that car users’ reluctance to switch transport mode is based on biased predictions about their future
q This research was supported by Grant #2004-02974 from the Swedish Governmental Agency for Innovation Systems (VINNOVA) to the Service and Market Oriented Transport Research Group (SAMOT). ⇑ Corresponding author. Address: Karlstad University/SAMOT, SE-651 88 Karlstad, Sweden. Tel.: +46 54 700 1168; fax: +46 54 836552. E-mail address: [email protected] (M. Friman).
1369-8478/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.trf.2010.11.005
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satisfaction with the public transport service. It has been confirmed in research on subjective well-being that people do not possess an impressive ability to predict the impact of future experiences on the intensity of the accompanying future emotions (e.g., Kahneman & Snell, 1992; Wilson & Gilbert, 2003). Car users usually have little experience and knowledge about public transport, which may explain their biased predictions. People are often prone to focus on what comes to mind easily, without making the effort to think about alternative scenarios and outcomes (Wilson & Gilbert, 2005). Inferring from this, the present study hypothesizes that when car users make affective forecasts about public transport, they focus too much on specific incidents that may not have an important effect on their overall future satisfaction. If car users were to focus their thinking not only on specific incidents but on what their future overall travel will be like, they would probably make more accurate estimates about their future satisfaction. When imagining a change in travel behaviour, car users may overemphasize the effect of critical incidents such as a missed departure at a junction or a successful connection due to the connecting bus driver waiting extra time at the bus stop. Unless car users are able to take incidents that affect satisfaction to a large extent (Friman, 2004) into consideration, they are likely to over- or underestimate how satisfied they will overall be with their decision. Previous research has shown that critical incidents refer to encounters that are particularly satisfying or dissatisfying (e.g., Bitner, Booms, & Tetreault, 1990). For example, service encounters have included two types of constructs: perceptual/cognitive aspects and emotional/affective appraisals (e.g., Bejou, Edvardsson, & Rakowski, 1996; Bitner et al., 1990; Derbaix & Pham, 1991; Friman, 2004; Friman, Edvardsson, & Gärling, 1998; Price, Arnould, & Tierney, 1995; Smith & Bolton, 2002; Stauss, 1992; Van Dolen, Lemmink, Mattsson, & Rhoen, 2001). Studies of perceptual/cognitive processes investigate encounters as they are interpreted and experienced, while the few studies of emotional/affective appraisals examine how individuals respond to and evaluate these encounters. Previous satisfaction research (e.g., Bolton & Drew, 1991; Fornell, 1992; Westbrook & Oliver, 1991) emphasizes the importance of distinguishing overall satisfaction from encounter satisfaction. Encounter satisfaction implies satisfaction with single transactions or encounters with a product or service (e.g., Oliver, 1980; Oliver & Desarbo, 1988), while overall satisfaction (e.g., Bolton & Drew, 1991; Fornell, 1992; Westbrook & Oliver, 1991) implies satisfying or dissatisfying encounters with a product or service over time. In both cases, satisfaction is either defined as an overall judgment of satisfaction or decomposed into satisfaction with performance or quality attributes. It may be assumed that a critical incident influences encounter satisfaction, and when frequently experienced, overall satisfaction (cf., Friman, Edvardsson, & Gärling, 2001). In this vein, a model proposed and tested by Friman et al. (2001) aims at explaining how negative critical incidents affect customer satisfaction. The proposed model posits that the frequencies of remembered negative critical incidents are the sources of cumulative attribute-specific satisfactions, which in turn have direct effects on cumulative overall satisfaction. The model was confirmed in a survey of a representative sample of public transport users and later in an experimental study (Friman & Gärling, 2001). Previous research on affective forecasting has disclosed that people are prone to make biased predictions about the intensity and duration of future emotions, and has identified the focusing illusion as one mechanism responsible for this impact bias (Ayton, Pott, & Elwakili, 2007; Schkade & Kahneman, 1998; Ubel, Loewenstein, & Jepson, 2005; Wilson & Gilbert, 2003; Wilson, Wheatley, Meyers, Gilbert, & Axsom, 2000). Put simply, the focusing illusion causes people to focus too much on a limited range of salient features related to the future event, thereby exaggerating the impact of these features upon their future emotions (Wilson & Gilbert, 2003). If car users’ misperceptions about satisfaction with public transport are caused by a prompting of specific salient features of the service, then introducing such features, embedded in critical incidents in an experimental setting will represent an instance of the focusing illusion, which should generate either larger (if the incident is positive) or lower (if the incident is negative) predicted satisfaction with the service. Studying the potential effects of the focusing illusion in a new context, as well as approaching the focusing illusion from a different angle (e.g., self-initiated behaviour change), is consistent with previous studies’ propositions for future research (e.g. Ayton et al., 2007). In summary, little is known about how car users predict their future satisfaction with public transport. As such, this study induces a focusing illusion upon car users when they predict their future satisfaction with public transport, by introducing a positive, a negative or a neutral critical incident (Friman, 2004). The specific aim is to examine whether one single critical incident is sufficient to alter car users’ predictions about future overall and attribute-specific satisfaction with public transport. If one critical incident is sufficient to cause larger or lower predicted satisfaction, then the results will support the hypothesis that car users are biased by a focusing illusion when predicting future satisfaction with public transport. The study includes car users with an intention to use public transport that is car users who have stated an intention to undertake a self-initiated change in behaviour (Study 1) and car users without a stated intention to use public transport (e.g., no stated intention to undertake a change in behaviour) (Study 2).
2. Study 1 The primary aim of Study 1 was to investigate the effects of critical incidents on predictions about future satisfaction with public transport, with regard to car users with a stated intent to change their travel mode.
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2.1. Method 2.1.1. Participants One hundred forty-three participants in a medium-sized Swedish city (population 109,000) were invited to participate and were randomly assigned to one out of four conditions, that is three experimental conditions and a control group. The participants were prospectively volunteering to sign a contract that would obligate them to leave their car at home, and to use public transport as a daily transport mode to and from their workplace for one full test month. Of the 143 prospective participants, 84 persons completed the web-questionnaire, yielding a response rate of 58.7%. Of these, individuals using the car as a primary transport mode to and from work were included, resulting in a final sample of 54 car-using participants. In the final sample, 24 participants were women and 30 were men. Ninety-three percent were 25 years and older, whereas 7% were 24 years and younger. 2.1.2. Design The study employs a factorial experimental design, where current and predicted satisfaction that is before and after receiving the respective treatment, was measured for car users in the three treatment groups and the control group. It was hypothesized that a negatively framed critical incident would generate lower predicted satisfaction and that a positively framed critical incident would generate larger predicted satisfaction, as compared to a neutral incident and a control condition. 2.1.3. Materials As an experimental manipulation of a critical incident, respondents were presented with one scenario in each group. The scenario consisted of reading a brief description of a critical incident prior to forecasting their future satisfaction with public transport. One critical incident was presented in each group; negatively, positively, or neutrally framed. The incident was related to employee behaviour when switching between two connecting bus lines, and was selected from Friman (2004). The critical incidents were illustrated as an undesired encounter in the negatively framed condition (the driver of the connecting bus do not wait for you although your bus arrives on time), as a desired encounter in the positively framed condition (the driver of the connecting bus waits for you although your bus is arriving late), and as a neutrally framed encounter in the neutral condition (you descend from your bus and then simply ascend the connecting bus). 2.1.4. Procedure The participants were recruited through an information campaign carried out by the local transportation authorities. Information was distributed by mail to both companies and private homes. Radio spots informed about the project on the local radio, and the local transportation authorities also made door-to-door recruitments. Those who signed up as prospective participants in the one-month trial passenger project, were contacted by electronic or traditional mail, and were asked to complete a web-questionnaire. The questionnaire was constructed based on items used in previous research (Friman et al., 2001; Pedersen et al., in press) and consisted of ratings of overall satisfaction, and of satisfaction with safety onboard, safety at stations, travel time, departure frequency, clean and modern vehicles, and number of available seats. The questionnaire consisted of two parts. In the first part of the questionnaire, participants were asked to rate their current satisfaction: ‘‘How satisfied are you currently with . . .?’’. In the second part, participants read a critical incident and then checked the satisfaction scales to predict what their satisfaction with the public transport service would be if they should encounter a similar incident during the trial-passenger period: ‘‘How satisfied do you think you will be with . . .?’’. In the control group participants did not read any incident and were merely asked to rate their predicted satisfaction with public transport. An 11-point rating scale, ranging from 5 (extremely dissatisfied) to 5 (extremely satisfied), was used for all ratings. In addition, demographic items regarding sex and age group were collected. The participants’ main transport mode and frequency of use were checked, as well as their car-use habit measured by the Self-Report Habit Index (Verplanken & Orbell, 2003) which is an instrument containing 12 statements measuring the strength of a specific habit. Responses were made on a 5-point Likert scale ranging from 1 (completely disagree) to 5 (completely agree). The response to each item was then averaged to yield a car use index for each participant. The higher the values were on the index, the stronger the car-use habit. The car users in Study 1 had a mean score of 3.63, which is at the same level of car-use habit as reported in previous research on car users with a stated intent to change current travel mode (Pedersen et al., in press). 2.2. Results Table 1 reports the means and standard deviations (SDs) for current and predicted overall satisfaction and attribute satisfaction for all three treatments and for the control condition. 2.2.1. Current satisfaction A one-way between-subjects ANOVA revealed no significant differences between the four groups on the measure of current overall satisfaction before presented to the treatment, F(3, 50) = 0.07, p = .976. A parallel between-subjects MANOVA performed on current satisfaction with the six attributes yielded an overall significant difference for the current attribute-specific satisfaction ratings, F(18, 125) = 2.27, p = .004, Wilks’ Lambda = .45.
Overall satisfaction Safety onboard Safety at stations Travel time Number of departures Clean and modern Number of seats
Positive incident (n = 14)
Negative incident (n = 12)
Neutral incident (n = 12)
Control (n = 16)
Current satisfaction
Predicted satisfaction
Current satisfaction
Predicted satisfaction
Current satisfaction
Current satisfaction
Mean
(SD)
Mean
(SD)
Mean
Mean
1.21 2.14 2.21 0.79 0.71 0.93 0.36
(2.46) (1.61) (2.05) (3.19) (3.27) (3.36) (2.95)
3.14 2.29 2.79 1.93 1.93 2.00 1.15
(1.10) (2.09) (1.05) (2.50) (2.30) (2.57) (2.88)
1.17 0.92 1.17 0.17 0.33 0.42 1.42
(SD) (1.99) (1.83) (2.52) (3.38) (3.84) (2.11) (2.07)
1.75 1.50 1.75 1.17 0.42 1.09 0.50
(SD) (3.25) (1.78) (2.14) (3.61) (2.91) (2.20) (1.62)
Mean 1.25 1.75 2.17 0.50 2.67 1.33 1.42
Predicted satisfaction
Predicted satisfaction
(SD)
Mean
(SD)
Mean
(SD)
Mean
(SD)
(2.53) (2.26) (1.70) (2.91) (1.92) (2.43) (2.54)
2.82 2.42 2.58 1.00 2.75 1.92 1.50
(2.04) (1.83) (1.17) (2.83) (1.49) (2.31) (1.98)
1.50 1.38 2.38 1.67 1.25 2.25 1.19
(1.75) (1.67) (1.46) (2.53) (2.87) (2.00) (2.89)
2.31 2.38 2.94 2.38 1.80 2.56 1.44
(1.25) (1.20) (0.93) (2.16) (2.11) (1.67) (2.85)
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Table 1 Means and standard deviations for positive, negative and neutral critical incident groups, and control group.
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Univariate ANOVAs however showed no significant differences between the groups on the six attribute-specific satisfaction measures. Thus, the groups did not differ significantly with respect to their baseline current overall or attribute-specific satisfaction. 2.2.2. Predicted satisfaction A one-way between-subjects ANOVA yielded a significant difference between the groups on predicted overall satisfaction, F(3, 49) = 15.89, p = .001. Bonferroni-corrected t-tests at p = 0.05, revealed that the significant difference lies between the negatively framed critical incident and all the other conditions. This result implies that a negatively framed critical incident results in significantly lower predicted satisfaction compared to the predictions made after encountering a positively or neutrally framed critical incident or no critical incident at all (i.e. the control group). A parallel between-subjects MANOVA performed on predicted satisfaction with the six attribute-specific items yielded an overall effect, F(18, 119) = 1.78, p = .036, Wilks’ Lambda = .51. Univariate ANOVAs revealed that the attributes contributing to this effect were predicted satisfaction with safety at stations, F(3, 47) = 3.03, p = .038, travel time F(3, 47) = 4.71, p = .006 and number of departures, F(3, 47) = 4.70, p = .006. Bonferroni-corrected t-tests at p = 0.05 for these three attributes revealed that participants in the negatively framed critical incident condition predicted their satisfaction with the safety at stations and travel time significantly lower than participants in the control condition. In addition, those subjected to the negatively framed critical incident condition predicted their satisfaction with travel time significantly lower than those subjected to the positively framed critical incident condition. Finally, those subjected to the negatively framed critical incident condition predicted their satisfaction with the number of departures significantly lower than those subjected to the neutrally framed critical incident condition. 2.2.3. Effect of the treatments Table 2 reports the effect sizes of overall satisfaction and attribute satisfaction for all between-condition effects. As can be seen in Table 2, the effect between the negatively framed critical incident condition and the control condition exceeds the limits for a large effect (0.80) on overall satisfaction, whereas the effect between the positively framed critical incident condition and the control condition exceeds the limit for a medium effect (0.50) (Hinkle, Wiersma, & Jurs, 2003), indicating that the positively framed critical incident condition may also contain an effect, albeit undetected with regard to statistical significance. The neutrally framed critical incident condition however, lies closer to the limit for a small effect (0.20), indicating that this incident generated an undetected effect above that of the control condition. 2.3. Discussion The aim of Study 1 was to examine the effects of a critical incident on car users’ predictions about future satisfaction with public transport. The participants included in Study 1 had stated an intention to switch their travel mode from car to public transport. The results showed that the negatively framed critical incident resulted in a notably lower predicted satisfaction than the ratings made in the control condition. Contrary to this, the positively framed critical incident did not result in an increased predicted satisfaction compared to the ratings made in the control condition. A possible explanation to these results is car users’ expectations of the service. The car users included in Study 1 had a stated intent to change their current travel mode and had volunteered to partake in a trial user period. Thus, it is most likely that the participants expected the trial period to be a pleasant experience. Subsequently, the anticipation of a pleasant experience may itself have had a stronger effect on their prediction than the positively and the neutrally framed critical incidents, as these conditions did not alter the car users’ predictions. The neutrally framed critical incident resulted in effects similar to the positively framed incident and to that of the control group. Thus, only the negatively framed critical incident, and not the positively and neutrally framed ones, therefore had an effect that changed car users’ predicted satisfaction of the service.
Table 2 Effect sizes (Cohen’s d) between positive, negative and neutral critical incident groups, and control group.
Overall satisfaction Safety onboard Safety at stations Travel time Number of departures Clean and modern Number of seats
Control vs negative d
Control vs neutral d
Control vs positive d
Positive vs negative d
Positive vs neutral d
Negative vs neutral d
2.71 0.72 0.96 2.09 1.40 1.06 1.30
0.40 0.03 0.35 0.87 0.71 0.45 0.04
0.77 0.07 0.15 0.29 0.09 0.38 0.17
3.32 0.57 0.82 1.77 1.34 0.59 1.10
0.26 0.09 0.20 0.57 0.60 0.05 0.22
2.81 0.68 0.65 1.21 2.14 0.55 1.49
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3. Study 2 The primary aim of Study 2 was to eliminate a possible effect of expectation or anticipation on car users’ predictions, due to stated intent to change current travel mode. Thus, car users with no stated preference to change travel mode were recruited. It was hypothesized that a negatively framed critical incident would generate lower predicted satisfaction and that a positively framed critical incident would generate larger predicted satisfaction, as compared to the control condition. 3.1. Method 3.1.1. Participants A sample consisting of 70 undergraduate students and employees at Karlstad University, Sweden, participated in the study on a voluntary basis. Participants were randomly assigned to three approximately equally large groups. An email with a link to a web-questionnaire (positive incident, negative incident or control condition) was sent to the participants, asking them to answer questions about public transport. Of the 70 invitees, 61 completed the questionnaire, yielding a response rate of 87%. The participants were selected for further analyses on the basis of stating in the questionnaire that they possessed a driver’s license and used a car (either driving themselves or as passenger) to commute to the university. Further, those who reported never using a car for commuting and those who reported traveling daily by public transport were excluded, resulting in a final sample of 38 participants that qualified as being general car users. In the final sample, 66% were women and 34% were men, 82% were in the age group 15–34 years, whereas 18% were older. All participants that completed the questionnaire, were rewarded with a lottery coupon, value approximately $1.50. 3.1.2. Design The study employed a factorial experimental design as in Study 1, where current and predicted satisfaction was measured. Study 2 included random assignment to descriptions of a negatively framed and a positively framed critical incident. These two groups of car users were measured before and after receiving the respective treatment. The third group served as control group and did not receive any treatment. 3.1.3. Materials As in Study 1, the scenario consisted of reading a brief description of a critical incident prior to forecasting their future satisfaction with public transport. The critical incidents were illustrated as an undesired encounter in the negatively framed condition (the driver of the connecting bus do not wait for you although your bus arrives on time) and as a desired encounter in the positively framed condition (the driver of the connecting bus waits for you although your bus is arriving late). In the control condition participants only forecasted their future satisfaction. 3.1.4. Procedure The participants were recruited through the university’s website, with a link to the faculty’s participant gateway for persons taking an interest in serving as respondents in psychological studies. The same web-questionnaire as in Study 1 was used which consisted of ratings of overall satisfaction, and of satisfaction with safety onboard, safety at stations, travel time, departure frequency, clean and modern vehicles, and number of available seats. As in Study 1, the questionnaire consisted of two parts. In the first part of the questionnaire, participants were asked to rate their current satisfaction: ‘‘How satisfied are you currently with . . .?’’. In the second part, participants read one of two framings of the critical incident (positive or negative) or were subjected to a control condition with no incident. Participants checked the satisfaction scales to predict what their satisfaction with the public transport service would be if they should encounter a similar incident: ‘‘How satisfied do you think you will be with . . .?’’. In the control condition, participants estimated their predicted satisfaction without being subjected to a critical incident. An 11point rating scale, ranging from 5 (extremely dissatisfied) to 5 (extremely satisfied), was used for all ratings. As in Study 1, demographic items, participants’ main transport mode, and frequency of use, as well as their car-use habit measured by the Self-Report Habit Index (Verplanken & Orbell, 2003) were checked. The car users in Study 2 had a mean score of 2.04 on car-use habit, as measured by the habit index. 3.2. Results Table 3 reports the means and standard deviations (SDs) for current and predicted overall and attribute-specific satisfaction for all three treatments and for the control condition. 3.2.1. Current satisfaction A one-way between-subjects ANOVA revealed that there were no significant differences between the three groups on current overall satisfaction, F(2, 35) = 1.23, p = .304. A parallel between-subjects MANOVA performed on current satisfaction with the six attributes yielded no significant difference for the current attribute-specific satisfaction ratings, F(12, 60) = 0.51, p = .899, Wilks’ Lambda = .82. Thus, the groups did not differ significantly with respect to baseline current overall and attribute-specific satisfaction.
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Table 3 Means and standard deviations for positive and negative critical incident groups, and control group.
Overall satisfaction Safety onboard Safety at stations Travel time Number of departures Clean and modern Number of seats
Positive incident (n = 10)
Negative incident (n = 12)
Control (n = 16)
Current satisfaction
Predicted satisfaction
Current satisfaction
Predicted satisfaction
Current satisfaction
Mean
(SD)
Mean
(SD)
Mean
(SD)
Mean
3.10 2.30 2.40 2.10 1.90 1.70 2.60
(1.37) (2.91) (2.41) (2.85) (2.85) (2.63) (2.01)
3.50 2.60 2.60 3.00 2.60 3.10 2.80
(1.78) (2.55) (1.84) (2.94) (2.95) (1.37) (1.75)
2.17 1.83 1.33 1.58 0.75 0.42 1.92
(2.33) (2.48) (2.96) (3.06) (3.08) (3.32) (2.94)
2.42 1.42 0.17 2.75 1.92 0.08 1.17
Predicted satisfaction
(SD)
Mean
(SD)
Mean
(SD)
(2.19) (2.75) (3.01) (2.09) (2.54) (2.78) (2.98)
3.00 1.69 2.38 2.06 2.00 2.44 2.50
(1.51) (2.75) (2.28) (2.86) (2.42) (2.45) (2.00)
2.88 2.25 2.56 1.75 2.06 3.00 2.63
(1.20) (2.02) (1.71) (2.91) (2.29) (1.55) (1.31)
3.2.2. Predicted satisfaction A one-way between-subjects ANOVA yielded a significant difference between the groups on predicted overall satisfaction, F(2, 35) = 43.03, p = .001. Bonferroni-corrected t-tests at p = 0.05, revealed that the significant difference is between the negatively framed critical incident and the two other conditions. This result implies that a negatively framed critical incident results in significantly lower predicted satisfaction compared to the predictions made after encountering a positively framed critical incident or no critical incident at all (i.e. the control group). A parallel between-subjects MANOVA performed on predicted satisfaction with the six attribute-specific items yielded an overall effect, F(12, 60) = 2.74, p = .005, Wilks’ Lambda = .42. Univariate ANOVAs revealed that the attributes contributing to this effect were predicted satisfaction with safety at stations, F(2, 35) = 4.80, p = .014, travel time F(2, 35) = 14.74, p = .001, number of departures, F(2, 35) = 11.17, p = .001 and clean and modern vehicles, F(2, 35) = 10.17, p = .001. Bonferroni-corrected t-tests at p = 0.05 for these four attributes revealed that participants in the negatively framed critical incident condition predicted their satisfaction with the safety at stations, travel time, number of departures and vehicle condition (clean and modern vehicles) significantly lower than participants subjected to the positively framed critical incident and participants in the control condition. 3.2.3. Effect of the treatments Table 4 reports the effect sizes of overall satisfaction and attribute satisfaction for all between-condition effects. As can be seen in Table 4, the effect between the negatively framed critical incident and the control condition exceeds the limits for a large effect (0.80) on overall satisfaction, whereas the effect between the positively framed critical incident condition and the control condition exceeds the limit for a medium effect (0.50) (Hinkle et al., 2003), indicating that the positively framed critical incident condition may also contain an effect, albeit undetected with regard to statistical significance. 3.3. Discussion The aim of Study 2 was to examine whether a positive and a negative critical incident would generate larger or lower satisfaction respectively among car users with no stated intention to change their current travel mode. The results show that the negatively framed critical incident resulted in notably lower predicted satisfaction than the ratings made of predicted satisfaction in the control condition. As were also the case in Study 1, the positively framed critical incident did not result in increased predicted satisfaction compared to the ratings of predicted satisfaction made in the control condition. A possible explanation to these results may be that neither car users’ with a stated intention to change current travel mode, nor car users with no such stated intentions to change travel mode, is influenced by positive incidents, that is one positive incident per se do not change car users’ preexisting expectations of the public transport service. Thus, only the negative critical incident had an effect that altered the car users’ predicted overall and attribute-specific satisfaction.
Table 4 Effect sizes (Cohen’s d) between positive and negative critical incident groups, and control group.
Overall satisfaction Safety onboard Safety at stations Travel time Number of departures Clean and modern Number of seats
Control vs negative d
Control vs positive d
Positive vs negative d
4.07 0.54 1.56 2.85 2.56 1.98 1.00
0.51 0.23 0.02 0.73 0.09 0.08 0.14
4.20 0.72 1.56 3.63 2.73 2.21 1.06
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4. General discussion The result reported in this study indicate that when car users predict their future satisfaction with public transport, they are affected by negative critical incidents (i.e., leading to a focusing illusion) and as a consequence overlook other factors that will influence their actual satisfaction with travel. In this study, habitual car users were subjected to a critical incident describing a desirable, an undesirable or a neutral staff behaviour. A focusing illusion would imply that the car users relied on the described critical incident and consequently based their valuations of public transport solely on this incident when predicting their overall and attribute-specific satisfaction with public transport. Contrarily, if the focusing illusion had not been the mechanism responsible for influencing the car users’ predictions of satisfaction, then the car users should have recognized that their overall satisfaction with public transport, as well as their attribute satisfaction, will not be influenced by a single instance of a positive or a negative staff behaviour. The results of two experiments show that this was not the case, as the car users’ predicted overall satisfaction was notably lower when exposed to a negative critical incident than in the control condition. In addition, the negative critical incident used in this study made car users predict notably lower satisfaction with several attributes including the condition of the vehicles, which did not pertain to the described incident. How, then, can we then explain the non-significant effect of the positive critical incident? One possibility is that both car users with a stated intention to change current travel mode and car users with no stated intention to change travel mode, have equal expectations of public transport. Another explanation, consistent with previous research in this area (Friman, 2004), is that positive critical incidents simply have less impact than negative critical incidents. Some limitations to this study should be noted. With regard to the validity of the present studies, the internal validity is accounted for by the design of the study, whereas there may be a possible weakness with regard to the external validity. The manipulations were not carried out entirely in a true field setting, which may to some extent potentially weaken the external validity of the studies. However, as the participants in Study 1 had stated an intention to switch to public transport and the same time were volunteering to take part in a future trial passenger project, we argue that this setting resembles the judgment and decision making in real-life, and thus strengthen the potentially weak external validity. Another potential limitation pertains to whether the design and setting of the study is representative of actual, or real-life, judgment and decision making where people have more time to weigh the pros and cons related to specific travel modes. However, if car users are equally prone as people in general are, to making fast and heuristic judgments, the car users may not actually take time to weigh the pros and cons when they predict their future satisfaction with public transport. With regard to sample size, although there were obtained significant results with a low number of participants in each group, a larger number of participants would potentially generate effects also on the positive and neutral incidents. Finally, a remark must be made about the potential limitation inherent in the relationship between predictions about future satisfaction and actual future travel behaviour. Although this relationship has not been fully clarified with regard to public transport, is has been established in other consumer contexts that predictions constitute expectations, that in turn influence future satisfaction ratings (Oliver, 2010), but not necessarily actual future behaviour. Therefore, one may benefit from investigating further the relationship between predictions and actual, future behaviour. To conclude, the present study shows that the experience of one negative critical incident significantly reduces car users’ predicted overall and attribute-specific satisfaction with public transport, and that the presentation of either positive or neutral features do not increase predicted satisfaction significantly. Of interest is the reported medium effect size for the positively framed critical incident, which implies that there may be an undetected effect of this condition in both studies. Previous research has shown that car users’ biased predictions can be corrected by means of making the car users actually experiencing public transport, as car users report larger satisfaction after a trial-passenger period, than what they initially predicted they would beforehand (Pedersen et al., in press). However, carrying out trial passenger projects in order to correct car users’ misperceptions of the potential satisfaction with public transport is a costly affair. As the present study has identified the focusing illusion as the psychological mechanism most likely responsible for car users’ misperceptions of future satisfaction with public transport one should, rather than offering expensive experience-based programs to car users, try to correct the adverse effects of the focusing illusion. Research suggests it is possible to correct mispredictions by means of presenting information in a way that makes people’s evaluations and predictions more accurate. Information presented in such a way are called defocusing techniques (e.g. Ayton et al., 2007; Ubel et al., 2005; Wilson et al., 2000). As people in general often focus on a few salient features of an event, the defocusing technique introduces a broader context in which the activity takes place, thus making it easier for consumers to predict their future satisfaction accurately. One interesting path of research, with respect to the above, would be to introduce a broader context in the domain of public transport and investigate the possible effects of defocusing techniques that introduces features outside the domain that is shifting the angle from the bus ride per se, towards other aspects of daily travel that would remain unaffected by a specific bus ride. This may potentially make car users aware of the fact that the bus ride per se will be absorbed in a larger daily context, i.e. other things in life will compete for ones attention, and the emotional impact of the bus ride will not be as huge as initially expected. Another path of research that would be of interest to investigate is whether an awareness of the process of adaptation (e.g. Ubel et al., 2005) might improve car users predicted satisfaction with public transport. With little doubt, knowledge about various psychological mechanisms underlying preferences and decision making regarding the mode of transport is
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important if one attempts to encourage habitual car users to change their way of travelling, towards use of public transport. Knowledge of these mechanisms, and how to correct for them, will have managerial implications, as one may be able to design information campaigns that can aid car users to make more realistic predictions about their future satisfaction with public transport and thus make them undertake a change towards more frequent public transport use. Acknowledgments The authors would like to express their gratitude to anonymous reviewers for valuable comments on the manuscript, the City of Lund and Skånetrafiken for valuable collaboration. The work on this paper was financially supported by Grant #200402974 from the Swedish Governmental Agency for Innovation Systems (VINNOVA) to Margareta Friman. References Ayton, P., Pott, A., & Elwakili, N. (2007). Affective forecasting: Why can’t people predict their emotions? Thinking & Reasoning, 13, 62–80. Bejou, D., Edvardsson, B., & Rakowski, J. P. (1996). A critical incident approach to examining the effects of service failures on customer relationships: The case of Swedish and US airlines. Journal of Travel Research, 35, 35–40. Bitner, M. J., Booms, B. H., & Tetreault, M. S. (1990). The service encounter: Diagnosing favorable and unfavorable incidents. Journal of Marketing, 54, 71–84. Bolton, R. N., & Drew, J. H. (1991). A multistage model of consumers’ assessments of service quality and value. Journal of Consumer Research, 17, 375–384. Curtis, C., & Headicar, P. (1997). Targeting travel awareness campaigns. Which individuals are more likely to switch from car to other transport for the journey to work? Transport Policy, 4, 57–65. Derbaix, C., & Pham, T. M. (1991). Affective reactions to consumption situations: A pilot investigation. Journal of Economic Psychology, 12, 325–355. Eriksson, L., Friman, M., & Gärling, T. (2008). Stated reasons for reducing work-commute by car. Transportation Research Part F – Traffic Psychology and Behaviour, 11, 427–433. Fornell, C. (1992). A national customer satisfaction barometer: The Swedish experience. Journal of Marketing, 56, 6–21. Friman, M. (2004). The structure of affective reactions to critical incidents. Journal of Economic Psychology, 25, 331–353. Friman, M., Edvardsson, B., & Gärling, T. (1998). Perceived quality of public transport service: Inferences from complaints and negative critical incidents. Journal of Public Transportation, 2, 69–91. Friman, M., Edvardsson, B., & Gärling, T. (2001). Frequency of negative critical incidents and satisfaction with public transport services. I. Journal of Retailing and Consumer Services, 8, 95–104. Friman, M., & Gärling, T. (2001). Frequency of negative critical incidents and satisfaction with public transport services. II. Journal of Retailing and Consumer Services, 8, 105–114. Gifford, R., & Steg, L. (2007). The impact of automobile traffic on quality of life. In T. Gärling & L. Steg (Eds.), Threats from car traffic to the quality of urban life: Problems, causes, and solutions. Amsterdam: Elsevier. Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioural sciences. Boston: Houghton-Mifflin. Jakobsson, C. (2004). Accuracy of household planning of car use: Comparing prospective to actual car logs. Transportation Research Part F – Traffic Psychology and Behaviour, 7, 32–42. Kahneman, D., & Snell, J. (1992). Predicting a changing taste: Do people know what they will like? Journal of Behavioral Decision Making, 5, 187–200. Kingham, S., Dickingson, J., & Copsey, S. (2001). Travelling to work: Will people move out of their cars. Transport Policy, 8, 151–160. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17, 460–469. Oliver, R. L. (2010). Satisfaction. A behavioral perspective on the consumer. New York: M.E. Sharpe. Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. Journal of Consumer Research, 14, 495–507. Pedersen, T., Friman, M., & Kristensson, P. (in press). Affective forecasting: Predicting and experiencing satisfaction with public transport. Journal of Applied Social Psychology. Price, L. L., Arnould, E. J., & Tierney, P. (1995). Going to the extremes: Managing service encounters and assessing provider performance. Journal of Marketing, 59, 83–97. Schkade, D. A., & Kahneman, D. (1998). Does living in California make people happy? A focusing illusion in judgments of Life Satisfaction. Psychological Science, 9, 340–346. Shannon, T., Giles-Corti, B., Pikora, T., Bulsara, M., Shilton, T., & Bull, F. (2006). Assessing commuting habits and potential for modal change. Transport Policy, 13, 240–253. Smith, A. K., & Bolton, R. N. (2002). The effect of consumers’ emotional responses to service failures on their recovery effort evaluations and satisfaction judgments. Journal of Academy of Marketing Science, 30, 5–24. Sperling, D., & Gordon, D. (2009). Two billion cars. Driving toward sustainability. USA: Oxford University Press. Stauss, B. (1992). Attribute-based versus incident-based measurement of service quality: Results of an empirical study in the German car service industry. In P. Kunst & J. Lemmink (Eds.), Quality management in services (pp. 59–78). Maastricht: Van Gorcum. Ubel, P. A., Loewenstein, G., & Jepson, C. (2005). Disability and sunshine: Can hedonic predictions be improved by drawing attention to focusing illusion or emotional adaptation? Journal of Experimental Psychology: Applied, 11, 111–123. Van Dolen, W., Lemmink, J., Mattsson, J., & Rhoen, I. (2001). Affective consumer responses in service encounters: The emotional content in narrative of critical incidents. Journal of Economic Psychology, 22, 359–376. Van Wee, B. (2007). Environmental effects of urban traffic. In T. Gärling & L. Steg (Eds.), Threats from car traffic to the quality of urban life: Problems, causes, and solutions. Amsterdam: Elsevier. Verplanken, B., & Orbell, S. (2003). Reflections on past behavior: A self-report index of habit strength. Journal of Applied Social Psychology, 33, 1313–1330. Westbrook, R. A., & Oliver, R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction. Journal of Consumer Research, 18, 84–91. Wilson, T. D., & Gilbert, D. T. (2003). Affective forecasting. In M. P. Zanna (Ed.). Advances in experimental social psychology (Vol. 35, pp. 345–411). Waterloo, Ontario: Academic Press. Wilson, T. D., & Gilbert, D. T. (2005). Affective forecasting: Knowing what to want. Current Directions in Psychological Science, 14, 131–134. Wilson, T. D., Wheatley, T., Meyers, J. M., Gilbert, D. T., & Axsom, D. (2000). Focalism: A source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 78, 821–836.
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Transportation Research Part F journal homepage: www.elsevier.com/locate/trf
Effects of critical incidents on car users’ predicted satisfaction with public transport q Tore Pedersen a,b, Per Kristensson b, Margareta Friman b,⇑ a b
National Institute of Occupational Health, PO BOX 8149 Dep., N-0033 OSLO, Norway Service Research Center/SAMOT, Karlstad University, Sweden
a r t i c l e
i n f o
Article history: Received 31 August 2009 Received in revised form 25 August 2010 Accepted 11 November 2010
Keywords: Public transport Critical incidents Focusing illusion Affective forecasting Predicted satisfaction
a b s t r a c t The present study examines the hypothesis that car users’ affective forecasts of satisfaction with public transport are biased by a focusing illusion. In Study 1, 54 car users with a stated intent to change travel mode read descriptions of a positive, a negative or a neutral critical incident. They were asked to predict their satisfaction with public transport if the incident occurred. In Study 2, 38 car users with no stated intent to change travel mode read descriptions of a positive or a negative critical incident. They were asked to predict their satisfaction with the service if the incident occurred. The results from Studies 1 and 2 showed that focus on a negative critical incident significantly generated lower predicted satisfaction. Thus, the study show that predicted satisfaction is altered when car users focus on negative critical incidents. Ó 2010 Elsevier Ltd. All rights reserved.
1. Introduction Car usage has increased considerably over the last decades (Gifford & Steg, 2007; Jakobsson, 2004). The number of passenger kilometres per capita has increased by 90% in Western Europe, and drivers make 80% more trips by car than they plan to do when asked in advance (Jakobsson, 2004). In general, the environmental effects of car use are severe (Van Wee, 2007), especially in urban regions where the increase in car usage represents a threat to the quality of urban life (Gifford & Steg, 2007). As there is no immediate sign that car use will not increase further (Sperling & Gordon, 2009), it is important to explore alternative approaches to disclosing the psychological mechanisms underlying people’s choice of travel mode. Although previous research suggests that no intervention is likely to change car users’ travel behaviour (e.g., Shannon et al., 2006), a significant minority of car users state that they are willing to change travel mode (Curtis & Headicar, 1997). The car users’ stated prerequisite for such a travel behaviour change from car to public transport are improved services (Curtis & Headicar, 1997; Eriksson, Friman, & Gärling, 2008; Kingham, Dickingson, & Copsey, 2001), which implies shorter travel times, an increased frequency of service and lower fares. Previous research has thus revealed that at least some car users state that they are willing to change their travel mode towards public transport use if the services are improved. However, it has been shown that habitual car users underestimate their future satisfaction with the service quality level of public transport, as they report larger satisfaction when experiencing the service than they initially predicted they would beforehand (Pedersen, Friman, & Kristensson, in press). Pedersen et al. (in press) suggest that car users’ reluctance to switch transport mode is based on biased predictions about their future
q This research was supported by Grant #2004-02974 from the Swedish Governmental Agency for Innovation Systems (VINNOVA) to the Service and Market Oriented Transport Research Group (SAMOT). ⇑ Corresponding author. Address: Karlstad University/SAMOT, SE-651 88 Karlstad, Sweden. Tel.: +46 54 700 1168; fax: +46 54 836552. E-mail address: [email protected] (M. Friman).
1369-8478/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.trf.2010.11.005
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satisfaction with the public transport service. It has been confirmed in research on subjective well-being that people do not possess an impressive ability to predict the impact of future experiences on the intensity of the accompanying future emotions (e.g., Kahneman & Snell, 1992; Wilson & Gilbert, 2003). Car users usually have little experience and knowledge about public transport, which may explain their biased predictions. People are often prone to focus on what comes to mind easily, without making the effort to think about alternative scenarios and outcomes (Wilson & Gilbert, 2005). Inferring from this, the present study hypothesizes that when car users make affective forecasts about public transport, they focus too much on specific incidents that may not have an important effect on their overall future satisfaction. If car users were to focus their thinking not only on specific incidents but on what their future overall travel will be like, they would probably make more accurate estimates about their future satisfaction. When imagining a change in travel behaviour, car users may overemphasize the effect of critical incidents such as a missed departure at a junction or a successful connection due to the connecting bus driver waiting extra time at the bus stop. Unless car users are able to take incidents that affect satisfaction to a large extent (Friman, 2004) into consideration, they are likely to over- or underestimate how satisfied they will overall be with their decision. Previous research has shown that critical incidents refer to encounters that are particularly satisfying or dissatisfying (e.g., Bitner, Booms, & Tetreault, 1990). For example, service encounters have included two types of constructs: perceptual/cognitive aspects and emotional/affective appraisals (e.g., Bejou, Edvardsson, & Rakowski, 1996; Bitner et al., 1990; Derbaix & Pham, 1991; Friman, 2004; Friman, Edvardsson, & Gärling, 1998; Price, Arnould, & Tierney, 1995; Smith & Bolton, 2002; Stauss, 1992; Van Dolen, Lemmink, Mattsson, & Rhoen, 2001). Studies of perceptual/cognitive processes investigate encounters as they are interpreted and experienced, while the few studies of emotional/affective appraisals examine how individuals respond to and evaluate these encounters. Previous satisfaction research (e.g., Bolton & Drew, 1991; Fornell, 1992; Westbrook & Oliver, 1991) emphasizes the importance of distinguishing overall satisfaction from encounter satisfaction. Encounter satisfaction implies satisfaction with single transactions or encounters with a product or service (e.g., Oliver, 1980; Oliver & Desarbo, 1988), while overall satisfaction (e.g., Bolton & Drew, 1991; Fornell, 1992; Westbrook & Oliver, 1991) implies satisfying or dissatisfying encounters with a product or service over time. In both cases, satisfaction is either defined as an overall judgment of satisfaction or decomposed into satisfaction with performance or quality attributes. It may be assumed that a critical incident influences encounter satisfaction, and when frequently experienced, overall satisfaction (cf., Friman, Edvardsson, & Gärling, 2001). In this vein, a model proposed and tested by Friman et al. (2001) aims at explaining how negative critical incidents affect customer satisfaction. The proposed model posits that the frequencies of remembered negative critical incidents are the sources of cumulative attribute-specific satisfactions, which in turn have direct effects on cumulative overall satisfaction. The model was confirmed in a survey of a representative sample of public transport users and later in an experimental study (Friman & Gärling, 2001). Previous research on affective forecasting has disclosed that people are prone to make biased predictions about the intensity and duration of future emotions, and has identified the focusing illusion as one mechanism responsible for this impact bias (Ayton, Pott, & Elwakili, 2007; Schkade & Kahneman, 1998; Ubel, Loewenstein, & Jepson, 2005; Wilson & Gilbert, 2003; Wilson, Wheatley, Meyers, Gilbert, & Axsom, 2000). Put simply, the focusing illusion causes people to focus too much on a limited range of salient features related to the future event, thereby exaggerating the impact of these features upon their future emotions (Wilson & Gilbert, 2003). If car users’ misperceptions about satisfaction with public transport are caused by a prompting of specific salient features of the service, then introducing such features, embedded in critical incidents in an experimental setting will represent an instance of the focusing illusion, which should generate either larger (if the incident is positive) or lower (if the incident is negative) predicted satisfaction with the service. Studying the potential effects of the focusing illusion in a new context, as well as approaching the focusing illusion from a different angle (e.g., self-initiated behaviour change), is consistent with previous studies’ propositions for future research (e.g. Ayton et al., 2007). In summary, little is known about how car users predict their future satisfaction with public transport. As such, this study induces a focusing illusion upon car users when they predict their future satisfaction with public transport, by introducing a positive, a negative or a neutral critical incident (Friman, 2004). The specific aim is to examine whether one single critical incident is sufficient to alter car users’ predictions about future overall and attribute-specific satisfaction with public transport. If one critical incident is sufficient to cause larger or lower predicted satisfaction, then the results will support the hypothesis that car users are biased by a focusing illusion when predicting future satisfaction with public transport. The study includes car users with an intention to use public transport that is car users who have stated an intention to undertake a self-initiated change in behaviour (Study 1) and car users without a stated intention to use public transport (e.g., no stated intention to undertake a change in behaviour) (Study 2).
2. Study 1 The primary aim of Study 1 was to investigate the effects of critical incidents on predictions about future satisfaction with public transport, with regard to car users with a stated intent to change their travel mode.
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2.1. Method 2.1.1. Participants One hundred forty-three participants in a medium-sized Swedish city (population 109,000) were invited to participate and were randomly assigned to one out of four conditions, that is three experimental conditions and a control group. The participants were prospectively volunteering to sign a contract that would obligate them to leave their car at home, and to use public transport as a daily transport mode to and from their workplace for one full test month. Of the 143 prospective participants, 84 persons completed the web-questionnaire, yielding a response rate of 58.7%. Of these, individuals using the car as a primary transport mode to and from work were included, resulting in a final sample of 54 car-using participants. In the final sample, 24 participants were women and 30 were men. Ninety-three percent were 25 years and older, whereas 7% were 24 years and younger. 2.1.2. Design The study employs a factorial experimental design, where current and predicted satisfaction that is before and after receiving the respective treatment, was measured for car users in the three treatment groups and the control group. It was hypothesized that a negatively framed critical incident would generate lower predicted satisfaction and that a positively framed critical incident would generate larger predicted satisfaction, as compared to a neutral incident and a control condition. 2.1.3. Materials As an experimental manipulation of a critical incident, respondents were presented with one scenario in each group. The scenario consisted of reading a brief description of a critical incident prior to forecasting their future satisfaction with public transport. One critical incident was presented in each group; negatively, positively, or neutrally framed. The incident was related to employee behaviour when switching between two connecting bus lines, and was selected from Friman (2004). The critical incidents were illustrated as an undesired encounter in the negatively framed condition (the driver of the connecting bus do not wait for you although your bus arrives on time), as a desired encounter in the positively framed condition (the driver of the connecting bus waits for you although your bus is arriving late), and as a neutrally framed encounter in the neutral condition (you descend from your bus and then simply ascend the connecting bus). 2.1.4. Procedure The participants were recruited through an information campaign carried out by the local transportation authorities. Information was distributed by mail to both companies and private homes. Radio spots informed about the project on the local radio, and the local transportation authorities also made door-to-door recruitments. Those who signed up as prospective participants in the one-month trial passenger project, were contacted by electronic or traditional mail, and were asked to complete a web-questionnaire. The questionnaire was constructed based on items used in previous research (Friman et al., 2001; Pedersen et al., in press) and consisted of ratings of overall satisfaction, and of satisfaction with safety onboard, safety at stations, travel time, departure frequency, clean and modern vehicles, and number of available seats. The questionnaire consisted of two parts. In the first part of the questionnaire, participants were asked to rate their current satisfaction: ‘‘How satisfied are you currently with . . .?’’. In the second part, participants read a critical incident and then checked the satisfaction scales to predict what their satisfaction with the public transport service would be if they should encounter a similar incident during the trial-passenger period: ‘‘How satisfied do you think you will be with . . .?’’. In the control group participants did not read any incident and were merely asked to rate their predicted satisfaction with public transport. An 11-point rating scale, ranging from 5 (extremely dissatisfied) to 5 (extremely satisfied), was used for all ratings. In addition, demographic items regarding sex and age group were collected. The participants’ main transport mode and frequency of use were checked, as well as their car-use habit measured by the Self-Report Habit Index (Verplanken & Orbell, 2003) which is an instrument containing 12 statements measuring the strength of a specific habit. Responses were made on a 5-point Likert scale ranging from 1 (completely disagree) to 5 (completely agree). The response to each item was then averaged to yield a car use index for each participant. The higher the values were on the index, the stronger the car-use habit. The car users in Study 1 had a mean score of 3.63, which is at the same level of car-use habit as reported in previous research on car users with a stated intent to change current travel mode (Pedersen et al., in press). 2.2. Results Table 1 reports the means and standard deviations (SDs) for current and predicted overall satisfaction and attribute satisfaction for all three treatments and for the control condition. 2.2.1. Current satisfaction A one-way between-subjects ANOVA revealed no significant differences between the four groups on the measure of current overall satisfaction before presented to the treatment, F(3, 50) = 0.07, p = .976. A parallel between-subjects MANOVA performed on current satisfaction with the six attributes yielded an overall significant difference for the current attribute-specific satisfaction ratings, F(18, 125) = 2.27, p = .004, Wilks’ Lambda = .45.
Overall satisfaction Safety onboard Safety at stations Travel time Number of departures Clean and modern Number of seats
Positive incident (n = 14)
Negative incident (n = 12)
Neutral incident (n = 12)
Control (n = 16)
Current satisfaction
Predicted satisfaction
Current satisfaction
Predicted satisfaction
Current satisfaction
Current satisfaction
Mean
(SD)
Mean
(SD)
Mean
Mean
1.21 2.14 2.21 0.79 0.71 0.93 0.36
(2.46) (1.61) (2.05) (3.19) (3.27) (3.36) (2.95)
3.14 2.29 2.79 1.93 1.93 2.00 1.15
(1.10) (2.09) (1.05) (2.50) (2.30) (2.57) (2.88)
1.17 0.92 1.17 0.17 0.33 0.42 1.42
(SD) (1.99) (1.83) (2.52) (3.38) (3.84) (2.11) (2.07)
1.75 1.50 1.75 1.17 0.42 1.09 0.50
(SD) (3.25) (1.78) (2.14) (3.61) (2.91) (2.20) (1.62)
Mean 1.25 1.75 2.17 0.50 2.67 1.33 1.42
Predicted satisfaction
Predicted satisfaction
(SD)
Mean
(SD)
Mean
(SD)
Mean
(SD)
(2.53) (2.26) (1.70) (2.91) (1.92) (2.43) (2.54)
2.82 2.42 2.58 1.00 2.75 1.92 1.50
(2.04) (1.83) (1.17) (2.83) (1.49) (2.31) (1.98)
1.50 1.38 2.38 1.67 1.25 2.25 1.19
(1.75) (1.67) (1.46) (2.53) (2.87) (2.00) (2.89)
2.31 2.38 2.94 2.38 1.80 2.56 1.44
(1.25) (1.20) (0.93) (2.16) (2.11) (1.67) (2.85)
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Table 1 Means and standard deviations for positive, negative and neutral critical incident groups, and control group.
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Univariate ANOVAs however showed no significant differences between the groups on the six attribute-specific satisfaction measures. Thus, the groups did not differ significantly with respect to their baseline current overall or attribute-specific satisfaction. 2.2.2. Predicted satisfaction A one-way between-subjects ANOVA yielded a significant difference between the groups on predicted overall satisfaction, F(3, 49) = 15.89, p = .001. Bonferroni-corrected t-tests at p = 0.05, revealed that the significant difference lies between the negatively framed critical incident and all the other conditions. This result implies that a negatively framed critical incident results in significantly lower predicted satisfaction compared to the predictions made after encountering a positively or neutrally framed critical incident or no critical incident at all (i.e. the control group). A parallel between-subjects MANOVA performed on predicted satisfaction with the six attribute-specific items yielded an overall effect, F(18, 119) = 1.78, p = .036, Wilks’ Lambda = .51. Univariate ANOVAs revealed that the attributes contributing to this effect were predicted satisfaction with safety at stations, F(3, 47) = 3.03, p = .038, travel time F(3, 47) = 4.71, p = .006 and number of departures, F(3, 47) = 4.70, p = .006. Bonferroni-corrected t-tests at p = 0.05 for these three attributes revealed that participants in the negatively framed critical incident condition predicted their satisfaction with the safety at stations and travel time significantly lower than participants in the control condition. In addition, those subjected to the negatively framed critical incident condition predicted their satisfaction with travel time significantly lower than those subjected to the positively framed critical incident condition. Finally, those subjected to the negatively framed critical incident condition predicted their satisfaction with the number of departures significantly lower than those subjected to the neutrally framed critical incident condition. 2.2.3. Effect of the treatments Table 2 reports the effect sizes of overall satisfaction and attribute satisfaction for all between-condition effects. As can be seen in Table 2, the effect between the negatively framed critical incident condition and the control condition exceeds the limits for a large effect (0.80) on overall satisfaction, whereas the effect between the positively framed critical incident condition and the control condition exceeds the limit for a medium effect (0.50) (Hinkle, Wiersma, & Jurs, 2003), indicating that the positively framed critical incident condition may also contain an effect, albeit undetected with regard to statistical significance. The neutrally framed critical incident condition however, lies closer to the limit for a small effect (0.20), indicating that this incident generated an undetected effect above that of the control condition. 2.3. Discussion The aim of Study 1 was to examine the effects of a critical incident on car users’ predictions about future satisfaction with public transport. The participants included in Study 1 had stated an intention to switch their travel mode from car to public transport. The results showed that the negatively framed critical incident resulted in a notably lower predicted satisfaction than the ratings made in the control condition. Contrary to this, the positively framed critical incident did not result in an increased predicted satisfaction compared to the ratings made in the control condition. A possible explanation to these results is car users’ expectations of the service. The car users included in Study 1 had a stated intent to change their current travel mode and had volunteered to partake in a trial user period. Thus, it is most likely that the participants expected the trial period to be a pleasant experience. Subsequently, the anticipation of a pleasant experience may itself have had a stronger effect on their prediction than the positively and the neutrally framed critical incidents, as these conditions did not alter the car users’ predictions. The neutrally framed critical incident resulted in effects similar to the positively framed incident and to that of the control group. Thus, only the negatively framed critical incident, and not the positively and neutrally framed ones, therefore had an effect that changed car users’ predicted satisfaction of the service.
Table 2 Effect sizes (Cohen’s d) between positive, negative and neutral critical incident groups, and control group.
Overall satisfaction Safety onboard Safety at stations Travel time Number of departures Clean and modern Number of seats
Control vs negative d
Control vs neutral d
Control vs positive d
Positive vs negative d
Positive vs neutral d
Negative vs neutral d
2.71 0.72 0.96 2.09 1.40 1.06 1.30
0.40 0.03 0.35 0.87 0.71 0.45 0.04
0.77 0.07 0.15 0.29 0.09 0.38 0.17
3.32 0.57 0.82 1.77 1.34 0.59 1.10
0.26 0.09 0.20 0.57 0.60 0.05 0.22
2.81 0.68 0.65 1.21 2.14 0.55 1.49
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3. Study 2 The primary aim of Study 2 was to eliminate a possible effect of expectation or anticipation on car users’ predictions, due to stated intent to change current travel mode. Thus, car users with no stated preference to change travel mode were recruited. It was hypothesized that a negatively framed critical incident would generate lower predicted satisfaction and that a positively framed critical incident would generate larger predicted satisfaction, as compared to the control condition. 3.1. Method 3.1.1. Participants A sample consisting of 70 undergraduate students and employees at Karlstad University, Sweden, participated in the study on a voluntary basis. Participants were randomly assigned to three approximately equally large groups. An email with a link to a web-questionnaire (positive incident, negative incident or control condition) was sent to the participants, asking them to answer questions about public transport. Of the 70 invitees, 61 completed the questionnaire, yielding a response rate of 87%. The participants were selected for further analyses on the basis of stating in the questionnaire that they possessed a driver’s license and used a car (either driving themselves or as passenger) to commute to the university. Further, those who reported never using a car for commuting and those who reported traveling daily by public transport were excluded, resulting in a final sample of 38 participants that qualified as being general car users. In the final sample, 66% were women and 34% were men, 82% were in the age group 15–34 years, whereas 18% were older. All participants that completed the questionnaire, were rewarded with a lottery coupon, value approximately $1.50. 3.1.2. Design The study employed a factorial experimental design as in Study 1, where current and predicted satisfaction was measured. Study 2 included random assignment to descriptions of a negatively framed and a positively framed critical incident. These two groups of car users were measured before and after receiving the respective treatment. The third group served as control group and did not receive any treatment. 3.1.3. Materials As in Study 1, the scenario consisted of reading a brief description of a critical incident prior to forecasting their future satisfaction with public transport. The critical incidents were illustrated as an undesired encounter in the negatively framed condition (the driver of the connecting bus do not wait for you although your bus arrives on time) and as a desired encounter in the positively framed condition (the driver of the connecting bus waits for you although your bus is arriving late). In the control condition participants only forecasted their future satisfaction. 3.1.4. Procedure The participants were recruited through the university’s website, with a link to the faculty’s participant gateway for persons taking an interest in serving as respondents in psychological studies. The same web-questionnaire as in Study 1 was used which consisted of ratings of overall satisfaction, and of satisfaction with safety onboard, safety at stations, travel time, departure frequency, clean and modern vehicles, and number of available seats. As in Study 1, the questionnaire consisted of two parts. In the first part of the questionnaire, participants were asked to rate their current satisfaction: ‘‘How satisfied are you currently with . . .?’’. In the second part, participants read one of two framings of the critical incident (positive or negative) or were subjected to a control condition with no incident. Participants checked the satisfaction scales to predict what their satisfaction with the public transport service would be if they should encounter a similar incident: ‘‘How satisfied do you think you will be with . . .?’’. In the control condition, participants estimated their predicted satisfaction without being subjected to a critical incident. An 11point rating scale, ranging from 5 (extremely dissatisfied) to 5 (extremely satisfied), was used for all ratings. As in Study 1, demographic items, participants’ main transport mode, and frequency of use, as well as their car-use habit measured by the Self-Report Habit Index (Verplanken & Orbell, 2003) were checked. The car users in Study 2 had a mean score of 2.04 on car-use habit, as measured by the habit index. 3.2. Results Table 3 reports the means and standard deviations (SDs) for current and predicted overall and attribute-specific satisfaction for all three treatments and for the control condition. 3.2.1. Current satisfaction A one-way between-subjects ANOVA revealed that there were no significant differences between the three groups on current overall satisfaction, F(2, 35) = 1.23, p = .304. A parallel between-subjects MANOVA performed on current satisfaction with the six attributes yielded no significant difference for the current attribute-specific satisfaction ratings, F(12, 60) = 0.51, p = .899, Wilks’ Lambda = .82. Thus, the groups did not differ significantly with respect to baseline current overall and attribute-specific satisfaction.
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Table 3 Means and standard deviations for positive and negative critical incident groups, and control group.
Overall satisfaction Safety onboard Safety at stations Travel time Number of departures Clean and modern Number of seats
Positive incident (n = 10)
Negative incident (n = 12)
Control (n = 16)
Current satisfaction
Predicted satisfaction
Current satisfaction
Predicted satisfaction
Current satisfaction
Mean
(SD)
Mean
(SD)
Mean
(SD)
Mean
3.10 2.30 2.40 2.10 1.90 1.70 2.60
(1.37) (2.91) (2.41) (2.85) (2.85) (2.63) (2.01)
3.50 2.60 2.60 3.00 2.60 3.10 2.80
(1.78) (2.55) (1.84) (2.94) (2.95) (1.37) (1.75)
2.17 1.83 1.33 1.58 0.75 0.42 1.92
(2.33) (2.48) (2.96) (3.06) (3.08) (3.32) (2.94)
2.42 1.42 0.17 2.75 1.92 0.08 1.17
Predicted satisfaction
(SD)
Mean
(SD)
Mean
(SD)
(2.19) (2.75) (3.01) (2.09) (2.54) (2.78) (2.98)
3.00 1.69 2.38 2.06 2.00 2.44 2.50
(1.51) (2.75) (2.28) (2.86) (2.42) (2.45) (2.00)
2.88 2.25 2.56 1.75 2.06 3.00 2.63
(1.20) (2.02) (1.71) (2.91) (2.29) (1.55) (1.31)
3.2.2. Predicted satisfaction A one-way between-subjects ANOVA yielded a significant difference between the groups on predicted overall satisfaction, F(2, 35) = 43.03, p = .001. Bonferroni-corrected t-tests at p = 0.05, revealed that the significant difference is between the negatively framed critical incident and the two other conditions. This result implies that a negatively framed critical incident results in significantly lower predicted satisfaction compared to the predictions made after encountering a positively framed critical incident or no critical incident at all (i.e. the control group). A parallel between-subjects MANOVA performed on predicted satisfaction with the six attribute-specific items yielded an overall effect, F(12, 60) = 2.74, p = .005, Wilks’ Lambda = .42. Univariate ANOVAs revealed that the attributes contributing to this effect were predicted satisfaction with safety at stations, F(2, 35) = 4.80, p = .014, travel time F(2, 35) = 14.74, p = .001, number of departures, F(2, 35) = 11.17, p = .001 and clean and modern vehicles, F(2, 35) = 10.17, p = .001. Bonferroni-corrected t-tests at p = 0.05 for these four attributes revealed that participants in the negatively framed critical incident condition predicted their satisfaction with the safety at stations, travel time, number of departures and vehicle condition (clean and modern vehicles) significantly lower than participants subjected to the positively framed critical incident and participants in the control condition. 3.2.3. Effect of the treatments Table 4 reports the effect sizes of overall satisfaction and attribute satisfaction for all between-condition effects. As can be seen in Table 4, the effect between the negatively framed critical incident and the control condition exceeds the limits for a large effect (0.80) on overall satisfaction, whereas the effect between the positively framed critical incident condition and the control condition exceeds the limit for a medium effect (0.50) (Hinkle et al., 2003), indicating that the positively framed critical incident condition may also contain an effect, albeit undetected with regard to statistical significance. 3.3. Discussion The aim of Study 2 was to examine whether a positive and a negative critical incident would generate larger or lower satisfaction respectively among car users with no stated intention to change their current travel mode. The results show that the negatively framed critical incident resulted in notably lower predicted satisfaction than the ratings made of predicted satisfaction in the control condition. As were also the case in Study 1, the positively framed critical incident did not result in increased predicted satisfaction compared to the ratings of predicted satisfaction made in the control condition. A possible explanation to these results may be that neither car users’ with a stated intention to change current travel mode, nor car users with no such stated intentions to change travel mode, is influenced by positive incidents, that is one positive incident per se do not change car users’ preexisting expectations of the public transport service. Thus, only the negative critical incident had an effect that altered the car users’ predicted overall and attribute-specific satisfaction.
Table 4 Effect sizes (Cohen’s d) between positive and negative critical incident groups, and control group.
Overall satisfaction Safety onboard Safety at stations Travel time Number of departures Clean and modern Number of seats
Control vs negative d
Control vs positive d
Positive vs negative d
4.07 0.54 1.56 2.85 2.56 1.98 1.00
0.51 0.23 0.02 0.73 0.09 0.08 0.14
4.20 0.72 1.56 3.63 2.73 2.21 1.06
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4. General discussion The result reported in this study indicate that when car users predict their future satisfaction with public transport, they are affected by negative critical incidents (i.e., leading to a focusing illusion) and as a consequence overlook other factors that will influence their actual satisfaction with travel. In this study, habitual car users were subjected to a critical incident describing a desirable, an undesirable or a neutral staff behaviour. A focusing illusion would imply that the car users relied on the described critical incident and consequently based their valuations of public transport solely on this incident when predicting their overall and attribute-specific satisfaction with public transport. Contrarily, if the focusing illusion had not been the mechanism responsible for influencing the car users’ predictions of satisfaction, then the car users should have recognized that their overall satisfaction with public transport, as well as their attribute satisfaction, will not be influenced by a single instance of a positive or a negative staff behaviour. The results of two experiments show that this was not the case, as the car users’ predicted overall satisfaction was notably lower when exposed to a negative critical incident than in the control condition. In addition, the negative critical incident used in this study made car users predict notably lower satisfaction with several attributes including the condition of the vehicles, which did not pertain to the described incident. How, then, can we then explain the non-significant effect of the positive critical incident? One possibility is that both car users with a stated intention to change current travel mode and car users with no stated intention to change travel mode, have equal expectations of public transport. Another explanation, consistent with previous research in this area (Friman, 2004), is that positive critical incidents simply have less impact than negative critical incidents. Some limitations to this study should be noted. With regard to the validity of the present studies, the internal validity is accounted for by the design of the study, whereas there may be a possible weakness with regard to the external validity. The manipulations were not carried out entirely in a true field setting, which may to some extent potentially weaken the external validity of the studies. However, as the participants in Study 1 had stated an intention to switch to public transport and the same time were volunteering to take part in a future trial passenger project, we argue that this setting resembles the judgment and decision making in real-life, and thus strengthen the potentially weak external validity. Another potential limitation pertains to whether the design and setting of the study is representative of actual, or real-life, judgment and decision making where people have more time to weigh the pros and cons related to specific travel modes. However, if car users are equally prone as people in general are, to making fast and heuristic judgments, the car users may not actually take time to weigh the pros and cons when they predict their future satisfaction with public transport. With regard to sample size, although there were obtained significant results with a low number of participants in each group, a larger number of participants would potentially generate effects also on the positive and neutral incidents. Finally, a remark must be made about the potential limitation inherent in the relationship between predictions about future satisfaction and actual future travel behaviour. Although this relationship has not been fully clarified with regard to public transport, is has been established in other consumer contexts that predictions constitute expectations, that in turn influence future satisfaction ratings (Oliver, 2010), but not necessarily actual future behaviour. Therefore, one may benefit from investigating further the relationship between predictions and actual, future behaviour. To conclude, the present study shows that the experience of one negative critical incident significantly reduces car users’ predicted overall and attribute-specific satisfaction with public transport, and that the presentation of either positive or neutral features do not increase predicted satisfaction significantly. Of interest is the reported medium effect size for the positively framed critical incident, which implies that there may be an undetected effect of this condition in both studies. Previous research has shown that car users’ biased predictions can be corrected by means of making the car users actually experiencing public transport, as car users report larger satisfaction after a trial-passenger period, than what they initially predicted they would beforehand (Pedersen et al., in press). However, carrying out trial passenger projects in order to correct car users’ misperceptions of the potential satisfaction with public transport is a costly affair. As the present study has identified the focusing illusion as the psychological mechanism most likely responsible for car users’ misperceptions of future satisfaction with public transport one should, rather than offering expensive experience-based programs to car users, try to correct the adverse effects of the focusing illusion. Research suggests it is possible to correct mispredictions by means of presenting information in a way that makes people’s evaluations and predictions more accurate. Information presented in such a way are called defocusing techniques (e.g. Ayton et al., 2007; Ubel et al., 2005; Wilson et al., 2000). As people in general often focus on a few salient features of an event, the defocusing technique introduces a broader context in which the activity takes place, thus making it easier for consumers to predict their future satisfaction accurately. One interesting path of research, with respect to the above, would be to introduce a broader context in the domain of public transport and investigate the possible effects of defocusing techniques that introduces features outside the domain that is shifting the angle from the bus ride per se, towards other aspects of daily travel that would remain unaffected by a specific bus ride. This may potentially make car users aware of the fact that the bus ride per se will be absorbed in a larger daily context, i.e. other things in life will compete for ones attention, and the emotional impact of the bus ride will not be as huge as initially expected. Another path of research that would be of interest to investigate is whether an awareness of the process of adaptation (e.g. Ubel et al., 2005) might improve car users predicted satisfaction with public transport. With little doubt, knowledge about various psychological mechanisms underlying preferences and decision making regarding the mode of transport is
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important if one attempts to encourage habitual car users to change their way of travelling, towards use of public transport. Knowledge of these mechanisms, and how to correct for them, will have managerial implications, as one may be able to design information campaigns that can aid car users to make more realistic predictions about their future satisfaction with public transport and thus make them undertake a change towards more frequent public transport use. Acknowledgments The authors would like to express their gratitude to anonymous reviewers for valuable comments on the manuscript, the City of Lund and Skånetrafiken for valuable collaboration. The work on this paper was financially supported by Grant #200402974 from the Swedish Governmental Agency for Innovation Systems (VINNOVA) to Margareta Friman. References Ayton, P., Pott, A., & Elwakili, N. (2007). Affective forecasting: Why can’t people predict their emotions? Thinking & Reasoning, 13, 62–80. Bejou, D., Edvardsson, B., & Rakowski, J. P. (1996). A critical incident approach to examining the effects of service failures on customer relationships: The case of Swedish and US airlines. Journal of Travel Research, 35, 35–40. Bitner, M. J., Booms, B. H., & Tetreault, M. S. (1990). The service encounter: Diagnosing favorable and unfavorable incidents. Journal of Marketing, 54, 71–84. Bolton, R. N., & Drew, J. H. (1991). A multistage model of consumers’ assessments of service quality and value. Journal of Consumer Research, 17, 375–384. Curtis, C., & Headicar, P. (1997). Targeting travel awareness campaigns. Which individuals are more likely to switch from car to other transport for the journey to work? Transport Policy, 4, 57–65. Derbaix, C., & Pham, T. M. (1991). Affective reactions to consumption situations: A pilot investigation. Journal of Economic Psychology, 12, 325–355. Eriksson, L., Friman, M., & Gärling, T. (2008). Stated reasons for reducing work-commute by car. Transportation Research Part F – Traffic Psychology and Behaviour, 11, 427–433. Fornell, C. (1992). A national customer satisfaction barometer: The Swedish experience. Journal of Marketing, 56, 6–21. Friman, M. (2004). The structure of affective reactions to critical incidents. Journal of Economic Psychology, 25, 331–353. Friman, M., Edvardsson, B., & Gärling, T. (1998). Perceived quality of public transport service: Inferences from complaints and negative critical incidents. Journal of Public Transportation, 2, 69–91. Friman, M., Edvardsson, B., & Gärling, T. (2001). Frequency of negative critical incidents and satisfaction with public transport services. I. Journal of Retailing and Consumer Services, 8, 95–104. Friman, M., & Gärling, T. (2001). Frequency of negative critical incidents and satisfaction with public transport services. II. Journal of Retailing and Consumer Services, 8, 105–114. Gifford, R., & Steg, L. (2007). The impact of automobile traffic on quality of life. In T. Gärling & L. Steg (Eds.), Threats from car traffic to the quality of urban life: Problems, causes, and solutions. Amsterdam: Elsevier. Hinkle, D. E., Wiersma, W., & Jurs, S. G. (2003). Applied statistics for the behavioural sciences. Boston: Houghton-Mifflin. Jakobsson, C. (2004). Accuracy of household planning of car use: Comparing prospective to actual car logs. Transportation Research Part F – Traffic Psychology and Behaviour, 7, 32–42. Kahneman, D., & Snell, J. (1992). Predicting a changing taste: Do people know what they will like? Journal of Behavioral Decision Making, 5, 187–200. Kingham, S., Dickingson, J., & Copsey, S. (2001). Travelling to work: Will people move out of their cars. Transport Policy, 8, 151–160. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17, 460–469. Oliver, R. L. (2010). Satisfaction. A behavioral perspective on the consumer. New York: M.E. Sharpe. Oliver, R. L., & DeSarbo, W. S. (1988). Response determinants in satisfaction judgments. Journal of Consumer Research, 14, 495–507. Pedersen, T., Friman, M., & Kristensson, P. (in press). Affective forecasting: Predicting and experiencing satisfaction with public transport. Journal of Applied Social Psychology. Price, L. L., Arnould, E. J., & Tierney, P. (1995). Going to the extremes: Managing service encounters and assessing provider performance. Journal of Marketing, 59, 83–97. Schkade, D. A., & Kahneman, D. (1998). Does living in California make people happy? A focusing illusion in judgments of Life Satisfaction. Psychological Science, 9, 340–346. Shannon, T., Giles-Corti, B., Pikora, T., Bulsara, M., Shilton, T., & Bull, F. (2006). Assessing commuting habits and potential for modal change. Transport Policy, 13, 240–253. Smith, A. K., & Bolton, R. N. (2002). The effect of consumers’ emotional responses to service failures on their recovery effort evaluations and satisfaction judgments. Journal of Academy of Marketing Science, 30, 5–24. Sperling, D., & Gordon, D. (2009). Two billion cars. Driving toward sustainability. USA: Oxford University Press. Stauss, B. (1992). Attribute-based versus incident-based measurement of service quality: Results of an empirical study in the German car service industry. In P. Kunst & J. Lemmink (Eds.), Quality management in services (pp. 59–78). Maastricht: Van Gorcum. Ubel, P. A., Loewenstein, G., & Jepson, C. (2005). Disability and sunshine: Can hedonic predictions be improved by drawing attention to focusing illusion or emotional adaptation? Journal of Experimental Psychology: Applied, 11, 111–123. Van Dolen, W., Lemmink, J., Mattsson, J., & Rhoen, I. (2001). Affective consumer responses in service encounters: The emotional content in narrative of critical incidents. Journal of Economic Psychology, 22, 359–376. Van Wee, B. (2007). Environmental effects of urban traffic. In T. Gärling & L. Steg (Eds.), Threats from car traffic to the quality of urban life: Problems, causes, and solutions. Amsterdam: Elsevier. Verplanken, B., & Orbell, S. (2003). Reflections on past behavior: A self-report index of habit strength. Journal of Applied Social Psychology, 33, 1313–1330. Westbrook, R. A., & Oliver, R. L. (1991). The dimensionality of consumption emotion patterns and consumer satisfaction. Journal of Consumer Research, 18, 84–91. Wilson, T. D., & Gilbert, D. T. (2003). Affective forecasting. In M. P. Zanna (Ed.). Advances in experimental social psychology (Vol. 35, pp. 345–411). Waterloo, Ontario: Academic Press. Wilson, T. D., & Gilbert, D. T. (2005). Affective forecasting: Knowing what to want. Current Directions in Psychological Science, 14, 131–134. Wilson, T. D., Wheatley, T., Meyers, J. M., Gilbert, D. T., & Axsom, D. (2000). Focalism: A source of durability bias in affective forecasting. Journal of Personality and Social Psychology, 78, 821–836.