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safety
Article
Preventing Tire Blowout Accidents: A Perspective on
Factors Affecting Drivers’ Intention to Adopt Tire
Pressure Monitoring System
Kai-Ying Chen 1and Chih-Feng Yeh 2, *
1Department of Industrial Engineering and Management, National Taipei University of Technology,
Taipei 10608, Taiwan; kychen@ntut.edu.tw
2College of Management, National Taipei University of Technology, Taipei 10608, Taiwan
*Correspondence: t9749001@ntut.org.tw; Tel.: +886-09-3310-1443
Received: 8 February 2018; Accepted: 5 April 2018; Published: 9 April 2018
Abstract:
The aim of this study is to explore whether risk perception or anticipated regret is
responsible for intensifying the participants’ intention to adopt a tire pressure monitoring system
(TPMS) to prevent a tire-related accident, and whether the optimism bias has a moderator effect
between risk perception/anticipated regret and intention. With 274 valid questionnaires and PLS-SEM
(partial least squares structural equation modeling) analysis, the results indicate a significant positive
relationship between risk perception and intention to adopt TPMS, but not between anticipated regret
and intention. The moderator effect of optimism bias on risk perception and anticipated regret is
not found in the model. The findings will prove useful for public service advertising campaigns by
providing a basis for an understanding of the role of cognitive and emotional factors in tire-blowout
accident prevention, thereby increasing the motivation for drivers in Taiwan to take advantage of the
protection afforded them by using TPMS.
Keywords:
in-vehicle technology; tire pressure monitoring system; risk perception; anticipated regret;
optimism bias; road safety
1. Introduction
Tire pressure must be checked regularly before driving to ensure that tires are properly inflated.
Drivers typically do not check the tire pressure unless they notice unusual vehicle tire performance.
According to the roadside survey PMDT (potential mechanical defect tests) in South Africa, 40% of
vehicles on suburban roads and 29% of vehicles on highways had mechanical defects which could
potentially result in an accident [
1
]. Velupillai and Guvenc [
2
] argued that approximately three-fourths
of all automobiles operate with at least one underinflated tire, and visual checks are often insufficient
to determine if the tires are underinflated. A survey, RRCGB (Reported Road Casualties Great Britain),
found that around 2% of fatal road accidents and casualties over the five-year period from 2008 to
2012 had tire defects recorded as a contributory factor, as did around 1% of serious and slight injury
reported accidents and casualties [
3
]. Similarly, Table 1shows that over the 10-year period from 2007
to 2016, 809 people were killed in accidents on super highways, and the condition of the vehicle tires
had been the contributory factor to cause 9.52% (77) of people dead in accidents in Taiwan (Ministry
Of Transportation and Communication (MOTC), Taiwan, 2017).
Safety 2018,4, 16; doi:10.3390/safety4020016 www.mdpi.com/journal/safety
Safety 2018,4, 16 2 of 14
Table 1. Injury accidents and casualties involving tire-related factors, 2007–2016, Taiwan.
Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Accidents (freq).
Accumulate
%
total
110
88 64 67 62 58 66 60 77 67 719
tire-related
7 11 8 9 6 4 5 5 4 3 62 8.62
Casualties (people) 1648 (176)
10.68
Fatal total
112
98 82 74 72 68 71 73 89 70 809
tire-related
7 11 11 13 9 4 5 5 9 3 77 9.52
Serious total 86
104 102
75 47
103
70 83 89 80 839
tire-related
13 16 23 17 2 4 8 11 4 1 99
11.80
Tires running in flat can damage the tires and lead to an unexpected blowout. Ockert, Johannes,
and Grobbelaar [
1
] noted that a substantial number of road accidents are associated with mechanical tire
imperfections. Statistics provided by Michelin Tire show that tire blowouts result in 23,000 collisions
and 535 fatalities each year [
4
]. Ratrout [
5
] pointed out that statistics on road security personnel
indicate that 13% of the accidents they attended were caused by tire blowouts or tread separation.
Obviously, a significant number of vehicles operate with tire pressures below the manufacturer’s
specifications. Properly inflated tires will ensure the shortest braking distance, a reduction in blowouts,
the mitigation of hydroplaning for better road handling, and an increase in tire life [6].
The purpose of a tire pressure monitoring system (TPMS), as an aspect of in-vehicle technology
(IVT), is to help drivers maintain the correct tire pressure for their vehicles and thereby reduce the
risk of tire-related accidents. TPMS also alerts drivers to pressure loss, tires running at low pressure,
tire failure, inflating tire, and location of wheels [
6
–
9
]. Many countries are planning to take similar
action requiring vehicles to have a TPMS sensor installed on every wheel [
10
]. Taiwan has taken action
with a mandatory TPMS requirement, which came into effect on 1 November 2014. Although all new
passenger vehicles now have TPMS installed by the manufacturer, there are still about 6.5 million
older vehicles in Taiwan without TPMS which run the risk of tire-related accidents (Ministry Of
Transportation and Communication (MOTC), Taiwan, 2015).
Most previous research on road safety has focused on risky driving behavior [
11
–
24
], with
some researches being carried out on risk perception [
16
,
19
,
25
–
33
] and IVTs [
34
–
45
]. Although
IVTs have been discussed in areas such as seat-belt use [
46
–
52
], backing aid systems, and rear-view
video cameras [
53
] and event-triggered videos [
54
], to the best of our knowledge, a comprehensive
investigation of the intention to use TPMS is lacking. According to previous models conducted
by Li et al. [
55
], the research model in this study was based on risk perception, anticipated regret,
and optimism bias. The purpose of this study is to explore the correlations between risk perception,
anticipated regret, and intention, and to determine which of these factors is most likely responsible
for the intention to use TPMS. The discussion includes the topic of whether the optimism bias has a
moderator effect between risk perception/anticipated regret and intention.
2. Method and Theory Description
2.1. Risk Perception
Risk perception concerns how people perceive and understand risks [
56
], and it can influence
consumer behavior [
57
]. Sjöberg et al. [
58
] pointed out risk perception as the subjective assessment of
the probable occurrence of a specific type of accident and our degree of concern as to the consequences.
Many studies have shown that the tendency to take a certain risk is decreased when people have
information regarding a specific high-risk situation. Nordgren et al. [
59
] stated that the subject of how
people make judgments about risks has been of longstanding interest, in terms of how risk perception
influences our decisions and behaviors, particularly as the perception of risk is often at odds with the
actual risk a behavior entails. While risk perception is theoretically based on two basic judgments,
Safety 2018,4, 16 3 of 14
the probability that a negative outcome will occur and the severity of its consequences [
60
], there
are other factors which can have a significant impact on people’s risk perception. One such factor
concerns the risk characteristics which refer to features of the risk [
61
]. Previous studies have suggested
that young drivers are more likely to underestimate the probability of the specific risks in certain
traffic situations [
26
]. Milech et al. [
62
] argued that young drivers tend to perceive the hazards in
traffic less holistically and so overestimate their own driving skills [
63
]. Rundmo and Iversen [
30
]
pointed out that the great majority of studies on young drivers’ risk perception have concluded that
the misperception of hazards, as well as the optimism bias related to one’s own driving skills, are
core causal factors in traffic accidents, and that this limitation in perception and the processing of
information can result in misjudgments and bad perceptions. The results of traffic safety research
have also shown that a driver’s behavior is mainly determined by how information is perceived
and processed, i.e., the perception of risk is a cognitive or belief-based judgment. Risk perception is
often related to certain preventive actions. Stasson and Fishbein [
64
] argued that the hypothesis that
perceived risk is related to seatbelt use has little support. Fhaner and Hane [
65
] found correlations
ranging from
−
0.02 to 1.3 between a composite perceived risk score and seatbelt usage. However,
the present study has posited that the intention to use TPMS is a preventive action which, by detecting
the tire pressure, protects a driver against tire blowouts and decreases the incidence of accidents.
According to previous studies conducted by Becker [
66
] and Rogers [
67
], reasoning that people are
inclined to take preventive action if they believe that inaction significantly increases their risk. Thus,
we have suggested that the higher the perceived probability that a negative tire-related accident will
occur and the higher the perceived severity of the consequences, the more positive the influence on the
intention to use TPMS. Therefore, we hypothesize that:
Hypothesis 1 (H1). Risk perception positively influences the intention to use TPMS.
2.2. Anticipated Regret
While the vast majority of studies have focused on the cognitive factors that influence
decision making, a growing body of research has emphasized the importance of emotions [
55
].
Previous studies on the relationship between emotions and decision making have focused on
anticipated emotions [
68
–
71
], such as disappointment or regret, which might arise from counterfactual
comparisons; while typically not experienced in the immediate present, they are expected to be
experienced in the future [
72
]. Regret is an unpleasant emotion that is said to be experienced when:
(1) an obtained outcome compares negatively to a possible other outcome; and (2) there is a sense of
personal responsibility over the obtained negative outcome [
73
]. Loomes and Sugden [
69
] pointed
out that an important feature of regret is that it can be anticipated and taken into account when
making decisions. An action-based theory called “regret theory”, a model of decision making with
anticipatory aspects of regret, formulated by Bell [
68
] and Loomes and Sugden [
69
], suggested that
the utility of a chosen option additionally depends on the feelings evoked by the outcomes of the
rejected options. Consequently, people compare the actual outcome with what the outcome could have
been if a different choice had been made, and experience emotion as a consequence. These emotional
consequences of decision making are future anticipated and taken into account when making decisions
in situations of uncertainty [55].
Several studies on road safety have shown that anticipated regret influenced participants towards
the safer, risk-aversion choice [
74
–
76
]. Anticipated regret may predict the intention to perform a
behavior, whether by action or inaction [
77
–
81
]. Sheeran and Orbell [
80
] have found that anticipated
regret increased the consistency between participants’ intentions and their behavior, as failure to act
was associated with adverse effects. Therefore, anticipated regret might not only intensify intention,
but it could also increase the likelihood of intentions being acted on. Conner, Sandberg, McMillan,
and Higgins [
81
] conducted their research on how far anticipated regret influenced the intention to
quit smoking and found significant positive correlations. Thus, we hypothesize that:
Hypothesis 2 (H2). Anticipated regret positively influences the intention to use TPMS.
Safety 2018,4, 16 4 of 14
2.3. Optimism Bias
The concept of optimism bias is that people perceive their likelihood of experiencing negative
events to be less when compared with an average individual or a group which is similar to them [
82
].
This concept has been obtained for a wide variety of health risks [
83
]. In a study of AIDS-related
risks, Van der Pligt et al. [
84
] found that for groups aware of their relative risk status, high-risk groups
gave higher ratings of their own risk than low-risk groups, and all groups showed an optimism
bias and thought their risks were lower than those of an average person of their age and gender.
Harre et al. [
85
] found that a higher perception of driving skill was associated with a lower perception
of accident risk. Harre and Sibley [
86
] noted that a number of studies have found that drivers tend to
consider themselves superior to their peers in areas such as driving skill, reflexes, judgment, and safety.
Optimism bias can lead to overconfidence [
27
], and lead drivers who believe that they are safer than
the average driver tend to think injury prevention campaigns do not apply to them [
87
]. Martha and
Delhomme [
88
] pointed out that most of the participants expressed comparative optimism regarding
speeding-ticket risk and speeding-induced crash risks. Consequently, we suggest that people who
tend to consider themselves superior to others in driving skills, reflexes, judgment, and safety will
have a lower degree of risk perception, which will in turn affect their intention to use TPMS. Thus,
we hypothesize that:
Hypothesis 3 (H3).
Optimism bias moderates the relationship between risk perception and the intention
to use TPMS.
As noted earlier, optimism bias allows people to perceive the likelihood of their experiencing
negative events to be less when compared with an average individual or a group similar to them. This
can lead to positive feelings and emotions, such as rejoicing, elation, and pride, rather than negative
emotions, such as regret, disappointment, and self-recrimination. In keeping with the context of
our study, we argue that individuals with a high optimism bias have intense positive emotions as
regards tire-related accidents, because they perceive their risks to be lower than that of the average
person. On the other hand, people with a low optimism bias will have negative emotions as regards
tire-related accidents. Thus, optimism bias could affect the relationship between anticipated regret and
the intention to use TPMS. We hypothesize that:
Hypothesis 4 (H4).
Optimism bias moderates the relationship between anticipated regret and the intention
to use TPMS.
2.4. Method
According to relevant research questionnaires and scales used by other scholars, a questionnaire
was designed using a 7-point Likert-scale, ranging from 1 (strongly disagree) to 7 (strongly agree),
to measure participants’ risk perception, anticipated regret, personal optimism bias, and intention
to use TPMS. The risk perception scale was adapted and modified from Rundmo and Iversen [
30
];
the anticipated regret scale was adapted and modified from Abraham and Sheeran [
76
] and Li, Zhou,
Sun, Rao, Zheng, and Liang [
55
]; the personal optimism bias scale was adapted and modified from
Coogan et al. [89]; and the intention scale was adapted from Lee [90].
The participants in this study were individuals over 18 years old. They were randomly selected
from the Greater Taipei Area and asked to fill in a questionnaire online. A total of 302 questionnaires
were completed. After 28 invalid samples were eliminated, 274 valid samples remained. Of the 274
participants, 165 (60.2%) were male and 109 (39.8) were female. Most of the participants were between
the age of 31 and 60 (85.4%), 181 (66.1%) had college degrees, 108 (39.4%) had held a driver’s license
for 11–20 years and drove a car 6–10 years old. Table 2shows the distribution of the basic information
of the valid samples.
Safety 2018,4, 16 5 of 14
Table 2. Demographic information of respondents in this study.
Items N% Items N%
Gender Income (USD/Year)
Male 165 60.2 <20,000 21 7.7
Female 109 39.8 20,000~30,000 85 31.0
Age 30,001~40,000 90 32.8
18~30 8 2.9 40,001~50,000 43 15.7
31~40 88 32.1 50,001~60,000 22 8.0
41~50 88 32.1 60,001~70,000 6 2.2
51~60 58 21.2 70,001~80,000 7 2.6
61~ 32 11.7 Car Age (years)
Education 3~5 91 33.2
high school 17 6.2 6~10 93 33.9
collage 181 66.1 11~15 61 22.3
graduate school 76 27.7 16~20 22 8.0
Time held License license 21~ 7 2.6
<1 10 3.7 Experienced Times
1~10 40 14.6 No experienced 155 56.6
11~20 108 39.4 <5 108 39.4
21~30 62 22.6 6~10 11 4.0
31~40 42 15.3 11~ 0 0.0
40~ 12 4.4 Check Times
No check 113 41.3
<5 148 54.0
6~10 13 4.7
11~ 0 0.0
The investigation into the moderator effects of optimism bias between risk perception/anticipated
regret and the intention to use TPMS involved two steps. First, it was necessary to establish moderators
within the data by using principal component analysis (PCA), in order to find the components that
maximized the variance in the dataset and tease out the factors. The analysis was conducted using
a personal optimism bias scale [
89
], SPSS, to calculate factor 1 and factor 2. To comply with the
research context, we defined factor 1—items OB1, OB2, and OB6—as
general optimism
, and factor
2—items OB3, OB4 and OB5—as
drive skill optimism
to be the interaction terms (shown in Table 5)
in this study.
Second, to understand the correlation between the different degrees of personal optimism bias,
categories were established within the data; a hierarchical cluster analysis using Ward’s method and
Squared Euclidean Distance was performed on the data to tease out clusters of optimism bias.
3. Results
3.1. Model Analysis
The measurement model of this study was evaluated by the SmartPLS used in partial least squares
structural equation modeling (PLS-SEM) for model validation. The main advantage of PLS is that it
does not need to consider the multivariate normal distribution of the sample data or total sample [
91
].
The measurement evaluation is conducted by the least squares method. PLS is a technique using
latent structural equation modeling to estimate the structural model and measurement model [
92
].
Gefen et al. [
93
] suggested that PLS should be selected for the exploration or extension of an existing
structural theory.
The construct reliability (CR) value consists of the validity values of all the measurement variables
to represent the internal consistency of the construct indicators. Higher validity suggests the higher
internal consistency of the indicators. The recommended CR value is above 0.7 [
94
,
95
]. In this
study, CR was in the range of 0.896–0.928, which was above the general recommended value of 0.7.
The average variance extracted (AVE) is the average amount of variation that a latent construct is
Safety 2018,4, 16 6 of 14
able to explain in the observed variables to which it is theoretically related [
96
]. If the AVE of the
latent variable is higher, it means the latent variable has higher degrees of convergence validity and
discriminant validity. When AVE is greater than 0.5, it means the dimension has sufficient convergence
validity [
95
]. The AVE values of the variables were between 0.681 and 0.771, over the threshold of
0.5. Therefore, the measurement questionnaire items of this study had a certain degree of convergent
validity, as shown in Table 3.
Table 3. Interconstruct correlations.
Item Construct CR αAVE 1 2 3 4 5
1. Intention 0.896 0.829 0.743 0.862 - - - -
2. Risk Perception 0.928 0.907 0.681 0.476 0.825 - - -
3. Anticipated Regret 0.910 0.853 0.771 0.328 0.478 0.878 - -
4. General Optimism 0.748 0.719 0.539 0.281 0.362 0.212 0.734 -
5. Drive skill Optimism 0.851 0.820 0.666 0.146 0.189 −
0.181
0.226 0.816
The values on the diagonal represent the square root of the average variance extracted (AVE).
3.2. Testing the Moderator Effects Optimism Bias
Path coefficient analysis was conducted using a PLS algorithm, and t-values were calculated using
the PLS bootstrapping method, which showed the interaction terms to have a negative relationship
to the intention to use TPMS and not reaching a statistically significant level. This meant that the
interaction terms did not have a moderator effect. Furthermore, a slight change in R
2
between the
two models (model A
→
model 0, model B
→
model 0) also showed that there was no significant
interaction. As the output in Table 4shows, the
∆
R
2
values, associated with the interaction terms
in model A (RP
×
GO, AR
×
GO), which considered general optimism (GO) as an interaction term
in the mode, were 0.001 and 0.002, which were not statistically significant levels (F= 0.365, p> 0.05;
F= 0.731, p> 0.05). This meant that there was no significant interaction between general optimism and
risk perception/anticipated regret. The
∆
R
2
values associated with the interaction terms in model B
(RP
×
DO, AR
×
DO), which considered drive skill optimism (DO) as an interaction term in the mode,
were 0.007 and 0.006, also not reaching a statistically significant level (F= 2.568, p> 0.05; F= 2.198,
p> 0.05). This meant that there was no significant interaction between driver skill optimism and risk
perception/anticipated regret.
Table 4. The results of moderator effect.
Variables Direct Effect Moderation Effect
Model 0 Model A1 Model A2 Model A3 Model B1 Model B2 Model B3
Independent variables
Risk perception(RP) 0.419 *** 0.381 *** 0.38 *** 0.371 *** 0.377 *** 0.372 *** 0.357 ***
Anticipated regret(AR) 0.136 * 0.129 * 0.125 * 0.134 * 0.177 * 0.18 * 0.213 *
Moderators
General optimism (GO) 0.119 * 0.112 * 0.113 *
Drive skill optimism(DO) 0.116 0.128 0.119
Interaction terms
RP ×GO −0.026
AR ×GO −0.044
RP ×DO −0.078
AR ×DO −0.08
R20.243 0.262 0.263 0.264 0.26 0.267 0.266
R2difference 0.019 ** 0.001 0.002 0.017 ** 0.007 0.006
F-test 6.95 0.365 0.731 6.2 2.568 2.198
p-value 0.009 0.546 0.393 0.006 0.11 0.14
*p< 0.05; ** p< 0.01; *** p< 0.001.
Safety 2018,4, 16 7 of 14
3.3. Statistical Cluster and Path Coefficient Analysis
The analysis was performed using a personal optimism bias scale [
89
]. The hierarchical cluster
analysis was formed into two clusters. SPSS was used for the calculations and resulted in 106 (38.68%)
in group 1 and 168 (61.32%) in group 2. As Table 5shows, the mean results of each cluster as to
personal optimism bias clearly identified group 1 as high personal optimism and group 2 as low
personal optimism.
Table 5. Table highlighting responses to personal optimism bias scale.
Items Description Group 1 (n= 106) Group 2 (n= 168)
Mean S.D. Mean S.D.
Risk Perception
RP1
Feeling unsafe that you yourself could be injured in a tire-related
traffic accident. 5.56 1.48 5.37 1.43
RP2 Worried for yourself being in a tire-related traffic accident. 5.44 1.46 4.97 1.52
RP3 How probable do you think it is in general for a person to be
injured in a tire-related traffic accident? 4.81 1.53 4.52 1.48
RP4 How probable do you think it is for yourself to be injured in a
tire-related traffic accident? 4.51 1.62 4.29 1.54
RP5 How concerned are you about traffic and are thinking on the
tire-related risks for a person in general? 5.17 1.45 4.8 1.51
RP6 How concerned are you about tire-related traffic risks and are
thinking that you yourself could be victimized? 5.1 1.65 4.68 1.35
Anticipated Regret
AR1 How much regret would you feel if you have tire-related
problems when driving? 5.47 1.23 5.57 1.09
AR2 How likely is it that you would feel regret if you did not check
tires pressure when driving at high speed? 5.46 1.14 5.54 1.16
AR3 If I did not adapt an action to prevent tire-related accident, I
would feel regret. 5.43 1.28 5.59 0.99
Optimism Bias
OB1 Most accidents are caused by people who are less experienced
than myself. 4.93 1.39 3.42 1.59
OB2 I am a safer driver than of my age and gender. 5.61 1.11 5.05 1.39
OB3 Those speed limit rules make no sense for me, as I am a very
precise driver who responses and brakes quickly. 4.26 1.54 2.49 1.21
OB4
I have a Low Risk car that is safe to drive considerably above the
speed limit. 4.71 1.31 2.12 1.02
OB5 There is no danger in following close, as I am a very precise
driver. 4.86 1.19 2.20 1.08
OB6
My emotions influence my driving less than other of my age and
gender. 5.20 1.09 4.08 1.47
Intention
INT1 I intend to use the TPMS. 5.07 1.52 4.47 1.19
INT2 I predict I will use the TPMS in the near future. 4.44 1.48 4.30 1.32
INT3 I plan to use the TPMS in the near future. 4.61 1.58 4.50 1.39
The purpose of the structural model analysis was to explain the research hypotheses and estimate
the path coefficient of constructs in order to examine the relationship between the independent
variables and dependent variables. In the structural model, the quality of the model was determined
by the dependent variables’ overall explanatory power (R
2
) and standardized path coefficient (
β
).
Its values of 0.19, 0.33, and 0.67 represent low, medium, and high explanatory power [
92
], respectively.
This study used bootstrap resampling to estimate the values of the dimensions of PLS and t-tests to
Safety 2018,4, 16 8 of 14
estimate the standard error and significance of the path coefficient. In this study, the R
2
for group 1
and group 2 was 31.1% and 40.7% total variance explanatory power, respectively, in terms of TPMS
adoption intention, and group 2 had more moderate model explanatory power in the model, as shown
in Table 6.
Table 6. Results structural model analysis.
Path
Group 1 Group 2 Significance
n= 106 (R2= 0.311) n= 168 (R2= 0.407) t-Statistic p-Value
(2-Tailed)
β1t-Value β2t-Value
Independent variables
Risk Perception →Intention 0.297 2.020 * 0.552 7.563 *** 1.751 0.081
Anticipated Regret →Intention −0.005 0.005 0.189 1.148 1.228 0.221
Control Variables
Age →Intention 0.038 0.382 0.295 2.938 ** 1.737 0.084
Gender →Intention −0.057 0.615 −0.036 0.685 0.215 0.830
Education →Intention −0.164 1.912 0.067 0.943 2.061 * 0.040
Income →Intention −0.268 2.128 * −0.064 0.577 1.191 0.235
Time held license →Intention 0.046 0.410 −0.133 1.128 1.035 0.302
Car age →Intention −0.254 2.179 * −0.003 0.045 2.145 * 0.033
Experienced →Intention 0.091 1.057 0.083 1.262 0.075 0.941
Check times →Intention 0.220 2.381 * 0.075 1.153 1.327 0.186
*p< 0.05; ** p< 0.01; *** p< 0.001.
Figure 1shows this study’s conceptual framework.
Figure 1. Conceptual model.
According to the path analysis results, all hypotheses relating to the model variables were
confirmed. The risk perception variable in the two groups had a positive relationship; however,
the relationship was more significant in group 2 (
β
2 = 0.552, p< 0.001) than in group 1 (
β
1 = 0.297,
p< 0.05). t-Tests were run to show where the significant differences (t= 1.751, p> 0.05) in the
risk perception between group 1 and group 2 were missing. This meant that individuals with high
personal optimism as well as those with low personal optimism who have a higher risk perception
had more intention to use TPMS. In contrast, the impact of the variable of anticipated regret in
group 1 (
β
1 =
−
0.005) had a slightly negative relationship and in group 2 (
β
2 = 0.189) had a positive
relationship. However, the relationship in neither group was significant. t-Tests were also run to show
Safety 2018,4, 16 9 of 14
where the significant differences (t= 1.228, p> 0.05) in the anticipated regret between group 1 and
group 2 were missing.
Regarding the control variables, there were two variables which had a significant difference
between group 1 and group 2. Education had a negative (
β
1 =
−
0.164) and a positive (
β
2 = 0.067)
relationship in group 1 and group 2, respectively, on the intention to use TPMS; however,
the relationships were not significant. There was, however, a significant difference (t= 2.061, p< 0.05)
between group 1 and group 2, in that clustered high optimism individuals with more education had a
lower intention to use TPMS. Car Age had a negative correlation on intention to use TPMS (
β
1 =
−
0.254,
β
2 =
−
0.045), while the relationship was significant in group 1. There was, however, a significant
difference (t= 2.145, p< 0.05) between group 1 and group 2, in that high optimism individuals with
older cars had a lower intention to use TPMS than low optimism individuals. The results showed that
personal optimism bias had an influence on these two control variables. Age, Experienced, and Check
times in each group had a positive relationship on intention to use TPMS. However, the t-tests showed
there to be no significant difference between group 1 and group 2. The Experienced variable indicated
that both high- and low-optimism individuals with more experience of tire-related accidents had a
stronger intention to use TPMS. The length of time individuals, whether of high or low optimism, held
a driver’s license did not influence their intention to use TPMS.
4. Discussion
The aim of this study was to explore whether risk aversion or anticipated aversion could intensify
the intention to use TPMS to prevent a tire-related accident, and whether the optimism bias had a
moderator effect between risk perception/anticipated regret to the intention to use TPMS. The sample
group comprised 274 participants in the Greater Taipei Area in Taiwan. Based on the questionnaire
statistics and PLS analysis, this study offers the following findings: Regarding risk perception, this
variable in the two groups had a positive relationship; however, the relationship was more significant
in group 2 (
β
2 = 0.552, p< 0.001) than in group 1 (
β
1 = 0.297, p< 0.05). The t-tests showed there was
no significant difference (t= 1.751, p> 0.05) in the risk perception between group 1 and group 2, which
confirmed the hypothesis (H1). This indicated that participants with low personal optimism had
more intention to use TPMS when perceiving the associated risks of not using it than those with high
personal optimism. In contrast, the impact of anticipated regret in group 1 (
β
1 =
−
0.005) had a slightly
negative relationship, but in group 2 (
β
2 = 0.189) a positive correlation. However, the relationship
in neither group was significant. The t-tests indicated a significant difference (t= 1.228, p> 0.05) in
the anticipated regret between group 1 and group 2. Thus, the hypothesis (H2) was not confirmed.
The results could be due to the short driving distance to the workplace. Narrow roads, speed limits,
and traffic jams could make people in Taiwan prefer public transportation, such as MRT (mass rapid
transit), bus, train, etc., instead of driving to work. Thus, the less people drive, the less likelihood that
they would be involved in a tire-related accident. Therefore, people who are driving less would not
feel regret if they do not use TPMS.
Regarding personal optimism bias, the PCA method was used to calculate the results for factor 1
(general optimism) and factor 2 (driver skill optimism), the interaction terms in this study. According to
the results, the F-tests of
∆
R
2
showed that there was no significant interaction between general/driver
skill optimism and risk perception/anticipated regret. Following the results from the cluster groups’
path coefficient analysis, the t-tests showed that there was no significant difference in the risk
perception/anticipated regret between the two groups. We, consequently, confirmed that optimism
bias did not have a moderator effect on perception/anticipated regret and the intention to use TPMS.
Therefore, the hypotheses (H3, H4) were not confirmed. These results meant that the relationships
between risk perception/anticipated regret and intention would not change significantly, whether the
participants had high or low optimism in general or in their driving skill.
The Age, Experienced, and Check time control variables were shown to have positive relationships
in both groups, with older people tending to use TPMS, probably due to risk aversion and the desire to
Safety 2018,4, 16 10 of 14
feel safer. People who had more experience of tire-related accidents and regularly checked their tires
had a higher intention to use TPMS. Car Age and Income level had negative relationships, so that the
older the car being driven, the lower the intention to use TPMS. This could be because people do want
not to pay for TPMS for an old car. On the other hand, people who have higher incomes usually have a
higher class car with more IVTs to protect them than lower income earners, are less likely to use TPMS.
5. Conclusions
Compared to previous relevant studies on IVTs, such as seat-belt use, backing aid systems,
rear-view video cameras, and event-triggered videos, this study has explored the correlation
of intention, risk perception, anticipated regret, and optimism bias variables, while TPMS
acceptance-related areas have not been explored. Our findings showed that risk perception could
consistently explain an individual’s risk aversion and desire to take preventive action. This confirmed
previous studies conducted by Becker [
66
] and Rogers [
67
], which showed that people are inclined
to take preventive action if they believe that inaction significantly increases their risk. This finding
generally can be observed in IVT acceptance. Interestingly, anticipated regret did not support the
hypothesis and did not consistently show that anticipated regret influences participants towards the
safe option or risk aversion on road safety with previous studies [
74
–
76
]. However, whether the
intention to use TPMS can be best served by enhancing anticipated regret or increasing awareness
of the risk of tire-related accidents, optimism bias did not play a significant interaction role between
risk perception/anticipated regret and intention. Additionally, our findings might prove useful for
public service advertising campaigns by providing a basis for understanding the roles that cognitive
and emotional factors play in accident prevention and road safety in Taiwan, and thereby increase the
motivation for drivers to protect themselves by using TPMS.
6. Limitations and Future Research
One of the most important limitations of this work is related with the use of self-report as primary
source of information, such as social desirability, acquiescence, or an inefficient understanding of
the questions [
97
], explaining potential several biasing sources, However, self-reports have inherent
problems and limitations, most notably the problem of common method variance. In this regard,
it is important to remark the use of observational methods, external data sources, or both, for the
supplementary assessment of self-rated perceptions and behaviors, or use of multiple types of
respondents, longitudinal designs, and confirmatory factor analysis that explicitly models method
effects to minimize of the “common method biases” often affecting cross-sectional designs in further
research [
98
]. It is also important to note, however, that risk perception and anticipated regret are not
the only factors involved in intention to use TPMS. Others factors from TAM (technology acceptance
mode), TPB (theory of planned behavior), and PMT (protection motivation theory) continue to be
discussed. Future researchers may focus on other human factors (i.e., intentions, attitudes, beliefs,
optimism, road safety education, etc.) or on investigating the relationships to different types of vehicles
or occupational fleets and integrate the current findings with other streams of research to extend the
existing theory.
Author Contributions:
All authors conceptualized the work. Chih-Feng Yeh conducted the questionnaire design
of the paper and analyzed the results. All authors were involved in the writing of the paper.
Conflicts of Interest: The authors declare no conflict of interest.
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