Satisfaction with travel, goal achievement, and voluntary
behavioral change

Ayako Taniguchi
Associate Professor, Risk Engineering/University of Tsukuba
Ten-noh dai 1-1-1 305 8573 Tsukuba, Japan
taniguchi@risk.tsukuba.ac.jp
Carin Grääs
Market Analyst/Project Manager, Attityd i Karlstad AB
Tynäsgatan 14, SE-65224 Karlstad, Sweden
carin@attitydikarlstad.se
Margareta Friman
Professor, Samot/Karlstad University
Universitetsgatan 2, SE-65188 Karlstad, Sweden
Margareta.friman@kau.se

Correspondence concerning this article should be addressed to Margareta Friman, Karlstad
University, SE-65188 Karlstad, Sweden. Phone: +46 54 700 1168. E-mail:margareta.friman@kau.se1

ABSTRACTThe 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 four weeks. Immediately after completion of
the program, the participants answered a follow-up questionnaire. The Satisfaction with Travel
Scale (STS) was used to measure their travel 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 home to 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.

KEYWORDS: Satisfaction, Goal setting, Soft policy measures, Test traveler, Public transport21. Introduction
The problems of increased motorization have been described by many researchers.
Several researchers have pointed to increased 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 have the intended effects on car traffic (see, forinstance, the effects of road tolls compiled by Li & Hensher, 2012), but these measures are oftenvery costly and not always politically feasible. Examples of soft policy measures include
information campaigns, personal travel planning, and travel policies aimed at causing people tovoluntarily change their travel behavior. The effects of soft policy measures have been evaluatedin 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 modeof travel experience their new way of traveling, or to what extent they succeed in achieving theirtravel change goals.
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 travelchange goal, individuals form implementation intentions entailing commitment to a plan
regarding how to attain the goal (Gärling & Fujii, 2002).
The aim of the present research is to examine the relationship between satisfaction withtravel, goal-achievement, and future goals. We do this as part of a test traveler program (softpolicy measure) whereby car users were given the opportunity to use PT for free for a limited3

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 isfollowed by an outline of the method and the data used for analysis. Next, the results of theregression analyses will be discussed. Finally, we will draw some conclusions and discuss someavenues 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), theutility or satisfaction is derived from observed choices constituting the actual travel behavior. A
recognized problem with this approach, however, is that travel behavior does not always reflect
experiences and satisfaction. For instance, traveling by car can be very stressful due to traffic
jams while cycling 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 of the 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., Eboli & Mazzulla, 2007; del Castillo & Benitez, 2012; 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 affect the 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 and wellbeing in life. Cognitive as well as affective factors are

4

important with regard to how we feel right know, as well as for how we feel about life in general(Diener et al. 1985). Ettema et al. (2011) have taken note of this and 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) withmeasures of affect, the latter being 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 towhich 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 theimplemented soft policy program. In previous studies, behavioral plans and change goals havealready 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 showingthat the reduction in car use is influenced by psychological factors, which are in turn influenced
by environmental factors. This psychological relationship was applied to the present study withthe addition that goal achievement would predict whether or not a future goal is formulated (seeFigure 1).
Insert Figure 1

3. Method
3.1 Participants
A mobility survey was conducted at three workplaces in a Swedish medium-sized region5

(population 310 914). All the employees (8705) were asked about their travel habits. In themobility survey, they were also asked to indicate whether or not they were interested inparticipating in a test traveler program. Among those who signed up for the program (23%),
people who travelled alone to work by car three days a week or more were chosen. As a result
three hundred and twenty-one people participated in 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 fourweeks (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.

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 participatingcompany 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 forparticipating 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/don’t know”. The three most common
reasons for participation were (1) that it was free of charge, (2) environmental reasons and (3) awhillingnes to change travel habits.
The second part asked the respondents to answer questions about progress toward theirgoals. 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 scale6

from 3-4 trips per week to over 11 trips per week (see Table 1). In the analysis, “0” indicated thatthe goal had neither been achieved nor exceeded, while “1” indicated that the goal had either beenachieved or exceeded. Two questions were aimed at describing reasons for achieving or notachieving initial goals. It was possible to choose several reasons from predefined lists. These
reasons are reported in Figure 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 experience and 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 canthink of, it had a very low/very high standard, it worked very well/very poorly, I felt very
hurried/very relaxed, very stressed/very calm, very worried/very confident, I felt very tired/veryalert, very bored/very enthusiastic, and very fed up/very engaged. As mentioned in Table 1,
Cronbach's alpha was high level (= 0.839), and the coefficient of correlations (r square) between
the three components exceeded 0.60. Thus an STS Index was calculated by averaging across thescales.
In the fourth part, the participants were asked questions about their future goals as
regards commuting by PT after the test program. “What is your future goal as regards yourcommute by PT?” The scale used specified the number of trips per week, but they could alsochoose 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 anda 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 forother 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 ofcommutes during a working day (Table 1). The last three variables were measured using

7

categorical data and were thus transformed into continuous data prior to analysis. An average ofthe distance (kilometers) and the frequency of trips (within a weekday/month) was used during
analysis (all data transformations are specified in Table 1).
Insert Table 1

4.

Results

4.1 Sample description
A sample description is given in Table 2. As can be seen, a majority of the participants had morethan 5 km from home to work and undertook a number of trips during a regular work day.
Insert Table 2

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 achieved or exceeded their goals. In
total, 313 reasons were given for achieving initial goals (see Figure 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 Figure 3). Common
reasons reported included no suitable connection being found, shiftwork, and illness.
Of the total number achieving their goals, almost all formulated a future goal (89.4%) ofusing 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.
Insert Figure 2
Insert Figure 3

4.3 Travel satisfaction, goal achievement, and behavioral change8An index of satisfaction with the work commute was formed by averaging across allnine STS scales. Two logistic and two multiple regression analyses were performed in order todetermine the influences of environmental and psychological factors on travel behavior and onachieved and future goals (see Figure 1). Gender and age had no significant effect on the included
variables in the process modell and were therefore not included in the analyses. The distance fromhome to work, the distance from home to the nearest bus stop, and the frequency of trips within aworking day were defined as environmental factors. This is illustrated in Figure 4, which also
includes all the significant paths from the analyses. The regression coefficient for the distancefrom home to the nearest bus stop had a significantly negative effect on satisfaction with travel(see also the complete results for all variables included in the analyses in Table 3). The longer the
distance was to the bus stop - the more 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 workcommute using PT. Satisfaction also had a significantly positive direct effect on future goals ofusing 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.
Insert Figure 4
Insert Table 3

5. Discussion
Understanding which factors determine whether people succeed or fail in changing theirtravel behavior is of fundamental concern in applied transport psychology. The question asked inthis 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 their implementation is9becoming increasingly frequent in Sweden. Getting car users to try PT for a limited time canchange attitudes when the benefits of PT use are discovered (Fujii, Gärling, & Kitamura, 2001;
Pedersen, Friman, & Kristensson, 2011). However, for greater success, these programs should bemonitored 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 forthe success of soft policy measures. One question concerns how travel change goals should beformulated to bring efficiency to travel behavior change. In this program, participants faced aminimum 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 travelchange 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, Friman, & Gärling,2010).
Half of the participants failed to live up to their travel change goals. One reason for thiswas the lack of suitable connections. Thus, we conclude that recruitment to test traveler programis 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 atplanning 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 thisstudy. 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) of goal
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 ongoing10

technological developments may be one way of facilitating 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 thatyou actually did use the bus). Exactly how this should be designed for maximum behavioral
change could be an area for further research.
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 beinvestigated 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 thusincreases the perceived availability of PT.
Goal achievement increases the likelihood of the continued use of PT in the future forcommuting 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 isa 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 relationshipbetween goal intention and the actual outcome sometime after the program has ended.

11

Acknowledgment
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 madepossible by the Royal Swedish Academy of Science and the Japan 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).

12

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15

Figure captions
Figure 1. Process model of voluntary behavioral change
Figure 2. Reasons for succeeding in the initial goal (313 reasons)Figure 3. Reasons for failing in the initial goal (250 reasons)Figure 4. Result of hierarchical multiple and logistic regression analysis

16Environmental
factors

Psychologicalfactors

Travelbehavior

Figure 1.

17

Goalachievement

Future goals

Support from employer

1.0%

Social

1.6%

Do not know

1.9%

Support from family

2.2%

Felt relaxed

3.8%

It bacame an habit

4.5%

Other reasons

7.3%

Fresh air and exercise

8.0%

Flexible working hours…

9.3%

Environmental reasons

9.9%

Convenient

13.1%

Easy to use

16.3%

Good connections

21.1%

0%

5%

10%

Figure 2.

18

15%

20%

25%

⁫ Do not know

0.0%

Carpooling instead of PT

6.6%

Felt stressed

7.8%

Many business trips (complex…

8.6%

Difficult to use

10.2%

Shiftwork

13.3%

Became ill / illness in the family

13.3%

No suitable connections

16.0%

Other reasons

24.2%
0%

10%

20%

Figure 3.

1930%

Figure 4.

20

Table 1. Data transformations
Subject
Distance

Distance

Question
How far is it from your home towork? Enter the number ofkilometers one way.
Estimate the distance from home tothe bus stop you choose/can choose
in order to travel to work.

Frequency oftrips

In general, how frequently do you
need to travel within a distance offive kilometers?

Satisfaction
with travel(STS)

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 I thing can think of-best thing
I can think ofVery low standard-very highstandard
Worked very poorly-worked verywell
Did you achieve or exceed yourtravel change goals?
In general, what was the outcome ofyour work journeys by PT?

Goalachievement
Frequency of
PT use per
week

Scale
Km

Transformation-

1) 0 - 200 m
2) 201-500 m
3) 501-1000 m
4) 1.1 km -3 km
5) More than 3 km
6) Do not know1). More than 5times/week2) 2-5 times / week3) 3-5 times / month4) 1-2 times / month5) 1-2 times / year6) Never-3 to +3
(7 point scale)1) 0.10 km*
2) 0.35 km
3) 0.75 km
4) 2.00 km
5) 5.00 km
6) Missing
1) 20 times/month**2) 14 times/month3) 4 times/month4) 1.5 times/month5) 0.04 times/month6) 0 times/month
The scale was converted from “-3
to +3” to “1 to 7”.
Cronbach's alpha = 0.839
STS Index = summery of scales/9
scales1) Yes
1) 1
2) No
2) 0
1) No trip
1) 0 trips per week***2) 1-2 trips/week2) 1.5 trips per week3) 3-4 trips/week3) 3.5 trips per week4) 5-6 trips/week4) 5.5 trips per week5) 7-8 trips/ week5) 7.5 trips per week6) 9-10 trips/week6) 9.5 trips per week7) 11 trips or more/
7) 11.5 trips per week
week8) Missing8) Other...
9) Missing9) Do not know
Future goal
What is your future goal as regards
1) 1-2 trips /week
1) 1****
your commute by PT?
2) 3-4 trips /week2) 1
3) 5-6 trips /week3) 1
4) 7-8 trips /week4) 1
5) 9-10 trips/week5) 1
6) 11 trips or6) 1
more/week7) 1
7) Be more aware and 8) 0
choose whenever9) Missing
possible10) Missing8) Never9) Other...
10) Do not know*= The 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. ****=1 denotes “yes, there is a future goal” and 0 denotes “no, there
is no future goal”.

21Table 2. Sample descriptives (total sample)
Background factor
GenderFemaleage (years)<36
36 – 45
46-55> 55
Distance from home to work (km)< 3.0
3.0 - 5.0
5.1 - 15.0
> 15.0Distance from Home to bus stop
0-200m201-500m
501-1000m
1.1km-3km
More than 3km
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 / week
More than five 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 / week
more than 11 trips / week

22

N

Percent
254
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

Table 3. Results of multiple and logistic regression analyses
Dependent variables
STS index(multiple linear
regression)

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

Frequency of PT
use(multiple linear
regression)

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

b

t

p

B

w

p

b

t

p

B

w

p

Distance from home to work (km)

0,04

0,53

,600

-

-

-

0,04

0,66

,511

-

-

-

Distance from home to bus stop (km)-0,21 -2,79 ,006

-

-

-

-0,03 -0,43 ,666

-

-

-

-

-

-

Independent Frequency of trips within a work day per monthvariables STS Index-0,01 -0,10 ,919

-

-

-

0,07

1,21

,229

0,15

9,02

,003

0,18

3,06

,003

0,65 16,98 ,000

0,61 10,41 ,000

2,06 11,28 ,001

Goal achievement
Frequency of PT use per week†

Constant term

Goodness of fit, Number of samples(n)14,11 42,70 < .001 -2,15
R2 = 0.04; n = 195

8,87

-

-

,003 -0,35 -0,43 ,670 -7,84 14,82 ,000

r2 = 0.05; n = 195

R2 = 0.46; n = 175

r2 = 0.31; n = 138†: Non-standardized coefficient is described in column b, b= Standardized coefficient, B= Non-standardized coefficient, t= t value, p= pvalue, w= wald statistics value

23