Transportation Research Part F 26 (2014) 10–17

Contents lists available at ScienceDirect

Transportation Research Part F
journal homepage: www.elsevier.com/locate/trf

Satisfaction with travel, goal achievement, and voluntary
behavioral change
Ayako Taniguchi a, Carin Grääs b, Margareta Friman c,⇑a

Risk Engineering/University of Tsukuba, Ten-noh dai 1-1-1, 305 8573 Tsukuba, Japan
Attityd i Karlstad AB, Tynäsgatan 14, SE-65224 Karlstad, SwedencSamot/Karlstad University, Universitetsgatan 2, SE-65188 Karlstad, Swedenb

a r t i c l e

i n f oArticle history:
Received 7 December 2013
Received in revised form 25 April 2014
Accepted 21 June 2014

Keywords:
Satisfaction
Goal setting
Soft policy measures
Test traveler
Public transporta b s t r a c t
The potential effectiveness of soft policy measures aimed at reduced car use depends on
how car users experience these. A common measure being implemented is a free monthly
travel card valid for a limited period on public transport (PT). In this study, a total of 321
car commuters living in Värmland, Sweden were recruited into such a program. The goal
was to use PT between home and work at least three times a week over a period of fourweeks. Immediately after completion of the program, the participants answered a follow-up questionnaire. The Satisfaction with Travel Scale (STS) was used to measure theirtravel experiences. They were also asked to rate their goal achievement. A process model
was used during the analyses. Regression analysis showed that the distance from hometo the nearest bus stop had a signiﬁcantly negative effect on the STS. The STS had a significantly positive effect on goal achievement. Both goal achievement and the STS had a signiﬁcantly positive effect both on PT use and on future goals. This study shows that travel
experiences and goal achievement are important for voluntary behavioral change.
Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction
The problems of increased motorization have been described by many researchers. Several researchers have pointed toincreased emissions (Chapman, 2007), increased congestion (Greene & Wegener, 1997), and increased levels of stress among
travelers (Novaco & Gonzales, 2009) due to increased car travel. In order to reduce the negative effects of the increasingly
accelerating use of private cars, several cities and regions have started to implement hard and soft policy measures. Examples of hard policy measures include changes to infrastructure or road tolls. Hard policy measures have been shown to havethe intended effects on car trafﬁc (see, for instance, the effects of road tolls compiled by Li & Hensher, 2012), but these measures are often very costly and not always politically feasible. Examples of soft policy measures include information campaigns, personal travel planning, and travel policies aimed at causing people to voluntarily change their travel behavior.
The effects of soft policy measures have been evaluated in terms of changes in the numbers of trips made or how many kilometers the participants have traveled using alternative travel modes (Möser & Bamberg, 2008). In a recent review by Richter,
Friman and Gärling (2010), it was concluded that soft transport policy measures are generally effective. Few studies, however, focus on how people voluntarily agreeing to change their mode of travel experience their new way of traveling, or towhat extent they succeed in achieving their travel change goals.
⇑ Corresponding author. Tel.: +46 54 700 1168.
E-mail addresses: taniguchi@risk.tsukuba.ac.jp (A. Taniguchi), carin@attitydikarlstad.se (C. Grääs), margareta.friman@kau.se (M. Friman).
http://dx.doi.org/10.1016/j.trf.2014.06.004
1369-8478/Ó 2014 Elsevier Ltd. All rights reserved.

A. Taniguchi et al. / Transportation Research Part F 26 (2014) 10–17

11

Research into consumer behavior (e.g., Oliver, 2010) shows that satisfying experiences are of importance to people’s willingness, or intent, to continue using a service or product. Thus, a negative travel experience on PT increases the likelihood of
the traveler returning to his/her car. The success of the program is also likely to depend on individual goals. After having set a
travel change goal, individuals form implementation intentions entailing commitment to a plan regarding how to attain thegoal (Gärling & Fujii, 2002).
The aim of the present research is to examine the relationship between satisfaction with travel, goal-achievement, andfuture goals. We do this as part of a test traveler program (soft policy measure) whereby car users were given the opportunity to use PT for free for a limited period.
This article is organized as follows. After brieﬂy reviewing the relevant literature on travel satisfaction, the article discusses a process model for voluntary behavioral change. This is followed by an outline of the method and the data used
for analysis. Next, the results of the regression analyses will be discussed. Finally, we will draw some conclusions and discuss
some avenues for future research.
2. Review of the relevant literature
Satisfaction with travel has been studied from several different starting points and using different methods. In cost-beneﬁt analysis (based on utility-theory, see McFadden, 2001), the utility or satisfaction is derived from observed choices constituting the actual travel behavior. A recognized problem with this approach, however, is that travel behavior does notalways reﬂect experiences and satisfaction. For instance, traveling by car can be very stressful due to trafﬁc jams whilecycling can be very stressful for fear of becoming involved in a trafﬁc accident. Many agree today that experiences, rather
than behavior, provide more insight and a competitive advantage for developing travel services that meet the needs ofthe user.
The focus on travel satisfaction has thus increased during recent years in several ways. One approach is to investigate user
satisfaction with different aspects of the service, or overall travel satisfaction (e.g., del Castillo & Benitez, 2012; Eboli &Mazzulla, 2007; Nathanail, 2008). Another angle is to focus on deviating incidents (so-called critical incidents like missing
the bus due to a lack of information) and their relationship with travel satisfaction (Friman, Edvardsson, & Gärling, 2001;
Friman & Gärling, 2001). Several studies have also begun focusing on activities occurring during the trip and how they affectthe travel experience. Undertaking various activities while traveling, like reading or listening to music, can increase satisfaction (Mokhtarian & Salomon, 2001) or counteract boredom (Ettema, Friman, Gärling, Olsson, & Fujii, 2012).
In-depth studies of people’s travel experiences can beneﬁt from theories of what determines people’s satisfaction andwellbeing in life. Cognitive as well as affective factors are important with regard to how we feel right know, as well as
for how we feel about life in general (Diener, Emmons, Larsen, & Griffen 1985). Ettema et al. (2011) have taken note of thisand developed a measurement instrument (the Satisfaction with Travel Scale) that includes cognitive and affective travel
experiences. Speciﬁcally, this measure combines cognitive judgments (quality of service) with measures of affect, the latterbeing divided into two dimensions with one focusing on positive activation (e.g., enthusiasm–boredom) and the other on
positive deactivation (e.g., hurried–relaxed). Several studies have validated the instrument in its ability to measure car users’,
PT users’, and cyclists’ travel experiences (see for instance Friman, Fujii, Ettema, Gärling, & Olsson, 2013). In addition to measuring travel experiences, it has also been used to study the extent to which commuting affects life satisfaction in general(Olsson, Gärling, Ettema, Friman, & Fujii, 2013). This proven link makes the instrument particularly useful for policy changes
since the aim should be to maintain or, if possible, increase citizens’ life satisfaction.
In this study, we will focus on car users who voluntarily agreed to change their travel behavior. In light of previous
research, travel change goals were formulated within the implemented soft policy program. In previous studies, behavioral
plans and change goals have already proven efﬁcient as methods of reducing car use (e.g., Fujii & Taniguchi, 2005). In addition to previous research, we will focus in this study on the relationship between travel satisfaction and goal achievement.
Taniguchi and Fujii (2007) presented a process model showing that the reduction in car use is inﬂuenced by psychologicalfactors, which are in turn inﬂuenced by environmental factors. This psychological relationship was applied to the present
study with the addition that goal achievement would predict whether or not a future goal is formulated (see Fig. 1).
3. Method
3.1. Participants
A mobility survey was conducted at three workplaces in a Swedish medium-sized region (population 310,914). All theemployees (8705) were asked about their travel habits. In the mobility survey, they were also asked to indicate whether
or not they were interested in participating in a test traveler program. Among those who signed up for the program(23%), people who traveled alone to work by car three days a week or more were chosen. As a result 321 people participatedin the program with the aim of reducing their car use to commute. The participants were offered a free monthly travel card
valid on PT for four weeks (corresponding to 135 Euro). During the test period, the participants agreed to use PT between
home and work at least three times a week. Of the total number of participants (321), 259 answered the follow-up survey
in October 2012, which is analyzed in this study.

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A. Taniguchi et al. / Transportation Research Part F 26 (2014) 10–17

Fig. 1. Process model of voluntary behavioral change.

3.2. Procedure
A survey questionnaire was then administered using a web-based interface. A list of the email addresses of the employees
who were included in the test traveler program was used to send out emails. A web link to the survey and a cover letter
signed by a representative of the participating company were attached. An initial reminder was emailed after a few days
and a second reminder after a week. It was possible to answer the questionnaire over a two-week period.
3.3. Questionnaire
The questionnaire consisted of four parts. Questions aimed at describing reasons for participating in the test traveler program were asked in the ﬁrst part. It was possible to choose several reasons from a list of nine which included ‘‘nothing/do notknow’’. The three most common reasons for participation were (1) that it was free of charge, (2) environmental reasons and(3) a willingness to change travel habits.
The second part asked the respondents to answer questions about progress toward their goals. Questions were asked
about achieving, or exceeding, their initial commitment (three trips per week). Participants exceeding their goals were asked
to specify their travel change goals prior to the test period. All the participants were asked about outcome. Outcome was
ranked on a scale from 3–4 trips per week to over 11 trips per week (see Table 1). In the analysis, ‘‘0’’ indicated that the goal
had neither been achieved nor exceeded, while ‘‘1’’ indicated that the goal had either been achieved or exceeded. Two questions were aimed at describing reasons for achieving or not achieving initial goals. It was possible to choose several reasons
from predeﬁned lists. These reasons are reported in Fig. 2.
In the third part, the Satisfaction with Travel Scale (STS) was applied (Ettema et al., 2011). The STS consists of nine items
which are all rated on seven-point bipolar scales, whereby a positive score coincides with both a positive affective experienceand a higher quality experience, and a negative score coincides with both a negative affective experience and a lower quality
experience. The items are as follows: commuting by PT was the worst/best thing I can think of, it had a very low/very highstandard, it worked very well/very poorly, I felt very hurried/very relaxed, very stressed/very calm, very worried/very conﬁdent, I felt very tired/very alert, very bored/very enthusiastic, and very fed up/very engaged. As can be seen in Table 1, Cronbach’s alpha was high and within an acceptable level (a = 0.839). Thus an STS Index was calculated by averaging across allnine items.
In the fourth part, the participants were asked questions about their future goals as regards commuting by PT after thetest program. ‘‘What is your future goal as regards your commute by PT?’’ The scale used speciﬁed the number of trips per
week, but they could also choose three other options: for instance will be more aware and choose PT whenever possible, will
not use PT at all, and do not know. In the analysis, ‘‘will not use PT at all’’ was coded as 0 and a speciﬁed travel goal (including‘‘will use whenever possible’’) was coded as ‘‘1’’. One question was asked about whether or not the test traveler program
would affect their travel mode choice for other types of trips (leisure and business).
Background data was collected from the mobility survey. This included gender, age, frequency of PT use, distance to work,
distance to the nearest bus stop, and frequency of commutes during a working day (Table 1). The last three variables were
measured using categorical data and were thus transformed into continuous data prior to analysis. An average of the distance (kilometers) and the frequency of trips (within a weekday/month) was used during analysis (all data transformations
are speciﬁed in Table 1).

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A. Taniguchi et al. / Transportation Research Part F 26 (2014) 10–17
Table 1
Data transformations.
Subject

Question

Scale

Transformation

Distance

How far is it from your home to work? Enter the number ofkilometers one way
Estimate the distance from home to the bus stop you
choose/can choose in order to travel to workkm–(1) 0–200 m(2) 201–500 m(3) 501–1000 m(4) 1.1–3 km(5) More than 3 km
(6) Do not know(1) More than 5 times/week(1) 0.10 kma(2) 0.35 km(3) 0.75 km(4) 2.00 km(5) 5.00 km(6) Missing(1) 20 times/monthb(2) 14 times/month(3) 4 times/month(4) 1.5 times/month(5) 0.04 times/month(6) 0 times/monthDistance

Frequency of trips

In general, how frequently do you need to travel within adistance of ﬁve kilometers?

Goal achievement

Very tired–very alert
Very bored–very enthusiastic
Very fed up–very engaged
Very hurried–very relaxed
Very worried–very conﬁdent
Very stressed–very calm
Worst thing I can think of–best thing I can think ofVery low standard–very high standard
Worked very poorly–worked very well
Did you achieve or exceed your travel change goals?

Frequency of PT
use per week

In general, what was the outcome of your work journeys by
PT?

Future goal

What is your future goal as regards your commute by PT?

Satisfaction withtravel (STS)(2) 2–5 times/week(3) 3–5 times/month(4) 1–2 times/month(5) 1–2 times/year(6) Never
3 to + 3 (7 point scale)(1) Yes(2) No(1) No trip
(2) 1–2 trips/week(3) 3–4 trips/week(4) 5–6 trips/week(5) 7–8 trips/ week(6) 9–10 trips/week(7) 11 trips or more/ week(8) Other(9) Do not know(1) 1–2 trips/week(2) 3–4 trips/week(3) 5–6 trips/week(4) 7–8 trips/week(5) 9–10 trips/week(6) 11 trips or more/week(7) Be more aware and choose
whenever possible

The scale was converted from‘‘ 3 to +3’’ to ‘‘1 to 7’’.
Cronbach’s alpha = 0.839
STS Index = summery of scales/
9 scales

(1) 1
(2) 0
(1) 0 trips/weekc(2) 1.5 trips per week(3) 3.5 trips per week(4) 5.5 trips per week(5) 7.5 trips per week(6) 9.5 trips per week(7) 11.5 trips per week(8) Missing(9) Missing(1) 1d
(2) 1
(3) 1
(4) 1
(5) 1
(6) 1
(7) 1
(8) 0
(9) Missing(10) Missing(8) Never(9) Other(10) Do not knowaThe scale was transformed to kilometers and the middle point on each scale level was used in the analyses.
The scale was transformed to months and the middle point on each scale level was used in the analyses.
The middle point on each scale level was used in the analyses.
d1 denotes ‘‘yes, there is a future goal’’ and 0 denotes ‘‘no, there is no future goal’’.
bc

4. Results
4.1. Sample description
A sample description is given in Table 2. As can be seen, a majority of the participants had more than 5 km from home towork and undertook a number of trips during a regular work day.
4.2. Goal achievement and future goals
Thirty percent (78 participants) formulated higher goals than required by the program. Fifty-one percent (n = 117 participants) reported not achieving their initial goals within the program, while forty-nine percent (113 participants) either

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A. Taniguchi et al. / Transportation Research Part F 26 (2014) 10–17

Fig. 2. Reasons for succeeding in the initial goal (313 reasons).

Table 2
Sample descriptives (total sample).
Background factor

N

GenderFemaleAge (years)
<36
36–45
46–55
>55
Distance from home to work (km)<3.0
3.0–5.0
5.1–15.0>15.0
Distance from Home to bus stop
0–200 m201–500 m
501–1000 m
1.1–3 kmMore than 3 km
Frequency of trips (reasons to travel) within a working day
Never
1–2 times/year
1–2 times/month
3–5 times/month
2–5 times/weekMore than 5 times/week
Frequency of PT use
Never
1–2 trips/week
3–4 trips/week
5–6 trips/week
7–8 trips/week
9–10 trips/weekMore than 11 trips/week

254

Percent
80.7

232
14.7
24.1
35.3
25.9
219
3.2
4.6
49.3
42.9
230
22.6
24.3
21.3
12.2
19.6
232
21.1
13.4
25.0
19.4
12.1
9.1
232
13.4
20.3
32.8
13.8
10.8
7.3
1.7

A. Taniguchi et al. / Transportation Research Part F 26 (2014) 10–17

15

Fig. 3. Reasons for failing in the initial goal (250 reasons).

Fig. 4. Result of hierarchical multiple and logistic regression analysis.

achieved or exceeded their goals. In total, 313 reasons were given for achieving initial goals (see Fig. 2). Common reasons for
success included good connections and the fact that PT was easy and convenient to use. Participants not achieving their initial goals gave 256 reasons for this (see Fig. 3). Common reasons reported included no suitable connection being found, shiftwork, and illness during the program.
Of the total number achieving their goals, almost all formulated a future goal (89.4%) of using PT to commute to work. Of
the total number not achieving their goals, 66.4% still formulated a future goal of using PT to commute to work. Nineteen
percent of the participants taking part in the program did not set any future goals for their commute to work using PT.
4.3. Travel satisfaction, goal achievement, and behavioral change
An index of satisfaction with the work commute was formed by averaging across all nine STS scales. Two logistic and two
multiple regression analyses were performed in order to determine the inﬂuences of environmental and psychological factors on travel behavior and on achieved and future goals (see Fig. 1). Gender and age had no signiﬁcant effect on the included
variables in the process model and were therefore not included in the analyses. The distance from home to work, the distance from home to the nearest bus stop, and the frequency of trips within a working day were deﬁned as environmental
factors. This is illustrated in Fig. 4, which also includes all the signiﬁcant paths from the analyses. The regression coefﬁcientfor the distance from home to the nearest bus stop had a signiﬁcantly negative effect on satisfaction with travel (see also thecomplete results for all variables included in the analyses in Table 3). The longer the distance was to the bus stop – the more

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A. Taniguchi et al. / Transportation Research Part F 26 (2014) 10–17

Table 3Results of multiple and logistic regression analyses.
Dependent variables

Independent
variables

Distance from home to work
(km)
Distance from home to bus stop
(km)
Frequency of trips within a work
day per month
STS Index
Goal achievement
Frequency of PT use per week
Constant terma
Goodness of ﬁt. Number ofsamples (n)STS index (multiple
linear regression)

Goal achievement(binary logistic
regression/step-up
procedure)

Frequency of PT use(multiple linear
regression)

b

b

t

p

B

w

p

p

B

w

p

0.04

0.53

.600

–

–

–

0.04

0.66

.511

–

–

–

0.21

2.79

.006

–

–

–

–0.03

0.43

.666

–

–

–

0.01

0.10

.919

–

–

–

0.07

1.21

.229

–

–

–

9.02

.003

0.18
0.61

3.06
10.41

.003
.000

0.15

14.11
42.70 <.001
R2 = 0.04; n = 195

2.15 8.87 .003
r2 = 0.05; n = 195

t

Future goal (binary
logistic regression/
step-up procedure)

0.35
0.43 .670
R2 = 0.46; n = 175

0.65
2.06

16.98 .001
11.28 .001–––
7.84 14.82 .001
r2 = 0.31; n = 138

b = Standardized coefﬁcient; B = Non-standardized coefﬁcient; t = t value; p = p value; w = Wald statistics value.
aNon-standardized coefﬁcient is described in column b.

dissatisﬁed were the participants. Satisfaction with travel had a signiﬁcantly positive effect on the frequency of PT use. The
more satisﬁed the participants were with their travel experience – the more they tended to use PT for their work commute.
Furthermore, the more frequently the participants used PT for their work commute, the likelier it was for them to achieve
their travel change goal (goal achievement). Achieving or exceeding travel change goals had a positively signiﬁcant effect on
formulating future goals for the work commute using PT. Satisfaction also had a signiﬁcantly positive direct effect on futuregoals of using PT to commute to work. A satisfying experience increased the likelihood of the participants wanting to set
future goals for their work commute using PT.
5. Discussion
Understanding which factors determine whether people succeed or fail in changing their travel behavior is of fundamental concern in applied transport psychology. The question asked in this study was whether or not travel satisfaction is important in goal achievement and future use of PT. Test travelers’ programs are a popular soft policy measure and theirimplementation is becoming increasingly frequent in Sweden. Getting car users to try PT for a limited time can change attitudes when the beneﬁts of PT use are discovered (Fujii, Gärling, & Kitamura, 2001; Pedersen, Friman, & Kristensson, 2011).
However, for greater success, these programs should be monitored and implemented with the support of research. Following
and evaluating these programs is one step in the development of future evidence-based programs.
Previous research has shown the importance of travel plans and travel change goals for the success of soft policy measures. One question concerns how travel change goals should be formulated to bring efﬁciency to travel behavior change.
In this program, participants faced a minimum travel change goal in accepting to participate in the program. Several participants adopted this goal while others formulated even higher travel change goals regarding their participation in the program. Theories of motivation converge on the idea that setting a behavioral goal is a key act that promotes goal
achievement (e.g., Ajzen, 1991; Carver & Scheier, 1998). In line with this, the results show that nearly half (49%) of the participants lived up to their travel change goals. This is an improvement on programs not requiring a speciﬁed travel change
goal, where the expected behavioral change is in the order of 5–20% (Richter et al., 2010).
Half of the participants failed to live up to their travel change goals. One reason for this was the lack of suitable connections. Thus, we conclude that recruitment to test traveler program is important. Money should not be spent on participants
who have no real possibility of changing their travel behavior. However, it may also be the case that the participants were
not good at planning their work trips. A weakness of this program was that the set travel change goals were not followed up
with individual behavioral travel change plans. Setting a travel change goal does not always translate successfully into changed travel behavior, which is the main ﬁnding of this study. Commitment to attaining a goal will not necessarily prepare
people for dealing effectively with the self-regulation problems (e.g., planning how to achieve a goal or getting started) ofgoal attainment (Gollwitzer & Sheeran, 2006). Goal attainment could beneﬁt from the formation of a travel plan focusing
on the enactment of the travel change goal. Taking advantage of ongoing technological developments may be one way offacilitating the implementation of travel plans (e.g., an app which reminds you to take the bus or to say no to a car trip,
and which checks that you actually did use the bus). Exactly how this should be designed for maximum behavioral change
could be an area for further research.

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The process model of voluntary behavioral travel change shows that travel satisfaction increases the level of PT use,
enabling a higher degree of goal achievement. What could be done then to increase travel satisfaction with PT? Even though
few environmental factors were included in this study, the results are in line with several other studies (e.g., Eriksson,
Friman, & Gärling, 2008; Stradling et al., 2005) which show that the distance to the nearest bus stop is an important factor.
If this relationship is linear or if there is important step changes remains to be investigated in future studies. Physically moving bus stops closer to home is a problem, however, since this will cause longer routes in conjunction with an increased number of stops. The objective distance to a bus stop must therefore be related to the total travel time using PT. We propose a
research area for the future that focuses on minimizing perceived distance and thus increases the perceived availability of PT.
Goal achievement increases the likelihood of the continued use of PT in the future for commuting to work. Furthermore,
the participants achieving their travel change goals formulated new goals to a greater extent for their future work commute
using PT. A formulated future goal is a goal intention or a self-instruction to use PT, thus increasing the likelihood of the participants actually doing so. Future research could follow up these results and investigate the relationship between goal intention and the actual outcome sometime after the program has ended.
Acknowledgments
This paper was written while Ayako Taniguchi was visiting the Service and Market Oriented Transport Research Group,
Karlstad University, Sweden in 2013. This exchange was made possible by the Royal Swedish Academy of Science and theJapan Society for the Promotion of Science (JSPS). Financial support was also obtained via grant 2004-0297 awarded by
the Swedish Governmental Agency for Innovation Systems (VINNOVA).
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