Investigating the role of beliefs in influencing the hand-held and hands-free mobile phone use among pedestrians in India-IITB -Velega

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International Journal of Injury Control and Safety
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Investigating the role of beliefs in influencing
the hand-held and hands-free mobile phone use
among pedestrians in India
Ankit Kumar Yadav, Sajid Shabir Choudhary, Nishant Mukund Pawar &
Nagendra R. Velaga
To cite this article: Ankit Kumar Yadav, Sajid Shabir Choudhary, Nishant Mukund Pawar &
Nagendra R. Velaga (2022): Investigating the role of beliefs in influencing the hand-held and hands-
free mobile phone use among pedestrians in India, International Journal of Injury Control and
Safety Promotion, DOI: 10.1080/17457300.2022.2112235
To link to this article: https://doi.org/10.1080/17457300.2022.2112235
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2. Research Article
International Journal of Injury Control and Safety Promotion
Investigating the role of beliefs in influencing the hand-held and hands-free
mobile phone use among pedestrians in India
Ankit Kumar Yadava
, Sajid Shabir Choudharyb
, Nishant Mukund Pawarb
and Nagendra R. Velagab
a
Schepens Eye Research Institute of Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA;
b
Department of Civil Engineering, Indian Institute of Technology (IIT), Mumbai, India
ABSTRACT
Mobile phone distraction is a significant contributor to pedestrian injuries. However, mobile phone
engagement among pedestrians has been scarcely explored in a developing country like India. The
present study utilized the beliefs-based theory of planned behaviour to examine the association
between pedestrian beliefs towards distracted walking (behavioural, normative, and control) and their
mobile phone use frequencies. Based on a survey of 560 pedestrians (64.6% males), it was found that
the major use of mobile phones was for listening to music (30.7%), followed by receiving a call (25%),
making a call (18.9%), texting (9.8%), navigation (8.5%) and internet browsing (7.1%). A series of
multivariate ANOVAs and logistic regression models were developed to investigate the relationships
between the beliefs and frequencies of mobile phone use in hands-free and hand-held conditions.
Significant multivariate differences were found for behavioural and normative beliefs in hands-free
conditions and all three types of beliefs in hand-held conditions. The frequency of mobile phone use
was significantly predicted by normative beliefs (p< 0.001) in the hands-free condition, and by
behavioural (p=0.041) and normative beliefs (p=0.004) in the hand-held condition. The findings may
assist the road safety countermeasures in addressing the issue of pedestrian distraction.
1. Introduction
Technological distractions are quite common among vul-
nerable road users, especially pedestrians (Basch et al.,
2014). With the increasing role of technology in our lives,
distracted pedestrian walking can be commonly observed
where the pedestrians are engaged with their mobile phones
performing several activities such as conversation, texting,
listening to music, taking photographs, web browsing, using
navigation, etc (Lennon et al., 2017). Pedestrian distraction
is an important safety issue since the number of
technology-equipped pedestrians are increasing on daily
basis leading to high likelihood of distraction activities while
walking (Piazza et al., 2020). Smith (2014) found that 53%
of mobile phone owners tend to engage themselves on their
devices while walking. Another study in the United States
of America observed 30% of the pedestrians to be involved
in distracting activities (Barin et al., 2018). In Australia,
Horberry et al. (2019) reported 20% of the pedestrians to
be engaged in mobile phone use while crossing the streets.
The pedestrians’ involvement in technological distractions
increases the likelihood of pedestrian-vehicle collisions
thereby leading to pedestrian injuries and fatalities (Larue
& Watling, 2021). Recent research reported technological
distractions as the prime reason for 24% of the total pedes-
train fatal crashes (Das et al., 2019). The physiological vari-
ations are observed among the distracted pedestrians, such
as their reduced step lengths and increased step widths
(Parr et al., 2014). Moreover, distracting activities diminish
the pedestrians’ attention, visual allocation, situation aware-
ness, and walking speeds (Haga et al., 2015).
1.1. Pedestrian distraction and road safety
In an observational study, Thompson et al. (2013) analyzed
road crossing behaviour of 1102 pedestrians at 20 intersec-
tions in Washington. They reported that the pedestrians
engaged in texting took 0.5 seconds longer and pedestrians
engaged in listening to music took 0.16 seconds longer to
cross each lane as compared to the other pedestrians.
Another study conducted on the campus of the University
of British Columbia examined the cautionary behaviours of
the pedestrians with and without listening to music at
mid-block crosswalks (Walker et al., 2012). The researchers
found that the cautionary behaviours of male pedestrians
were significantly influenced by the presence of music
devices, whereas no significant impact was observed in the
female pedestrians (Walker et al., 2012). Further, with
respect to talking while walking, Hyman et al. (2009) high-
lighted the presence of inattentional blindness among the
pedestrians engaged in talking on their cell phones. In a
field observation, Hatfield and Murphy (2007) compared
the influence of cell phone use on the road crossing
© 2022 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Ankit Kumar Yadav akyadav@meei.harvard.edu; ankitdv588@gmail.com
https://doi.org/10.1080/17457300.2022.2112235
ARTICLE HISTORY
Received 3 April 2022
Revised 16 July 2022
Accepted 8 August 2022
KEYWORDS
Beliefs; distracted
pedestrians; mobile phone;
theory of planned
behaviour

3. 2 A. K. YADAV ET AL.
behaviour of male and female pedestrians at signalized and
unsignalized intersections in Sydney. At signalized crossings,
they found that the crossing speed of female pedestrians
was significantly slower while using the cell phone, whereas
no significant impact was observed on male pedestrians
(Hatfield & Murphy, 2007). At unsignalized crossings, males
were found to be significantly slower while using the cell
phone, whereas females did not show any significant reduc-
tion in their walking speeds (Hatfield & Murphy, 2007).
Furthermore, a recent study by Wells et al. (2018) observed
10,543 pedestrians, out of which one-third were engaged in
distracting activities during road crossing. Amongst all the
pedestrians, they found wearing headphones to have the
highest proportion (19%), followed by text messaging (8%)
and phone conversation (5%). In a field study by Jiang et al.
(2018), the researchers reported that the highest impact on
pedestrian behaviour was caused by texting, followed by
conversation and music distraction, which was observed in
their visual attention and road crossing behaviour.
Mobile phone-induced distraction is a widely researched
theme in the area of pedestrian safety (Schwebel et al.,
2012). A recent research synthesis and meta-analysis high-
lighted that mobile phone involvement while performing
road crossing tasks has detrimental effects on pedestrians
(Simmons et al., 2020). The pedestrians have been found
to have 1.5 higher odds of engaging in distracting activities
compared to the drivers (Ortiz et al., 2017). Both the obser-
vational and laboratory-based studies in a controlled virtual
environment have demonstrated that pedestrians involved
in cell phone use show slower response to stimuli and
reduced attention to the road traffic (Hatfield & Murphy,
2007; Hyman et al., 2009; Neider et al., 2010; Tapiro et al.,
2020; Thompson et al., 2013; Walker et al., 2012; Wells
et al., 2018). This reduction in attention while walking leads
to increased crash risks and pedestrian injuries (Nasar &
Troyer, 2013). This association between the mobile
phone-induced distraction and crash likelihood indicates
the importance of investigating the role of psychological
beliefs behind the decision to engage in mobile phone use
while walking.
Mobile phones are generally used in two modes:
hand-held and hands-free. Previous research on pedestrian
distraction revealed that pedestrian walking behaviour is
different for both the modes since hand-held phone use
induces an additional demand of visual allocation (Thompson
et al., 2013). In a study on university students, the use of
a hand-held phone for conversation was found to be per-
ceived significantly riskier compared to the hands-free
phone use (Prat et al., 2015).
1.2. Theoretical approach
The theoretical approach of the theory of planned behaviour
(TPB) has been widely used in road safety research to
understand the underlying intentions of road users (Ajzen,
1991; Ledesma et al., 2018). The TPB states that an indi-
vidual’s intention to perform an act is defined by the indi-
vidual’s attitude towards the act, the perception of how
others will perceive the act, and the perception about the
degree of control over the act (Ajzen, 1991). Behind these
three predictors of intention (i.e., attitude, subjective norm,
perceived behavioural control) lie the individual’s beliefs
about the particular behaviour. These beliefs can be iden-
tified into three components: behavioural beliefs, normative
beliefs, and control beliefs (White et al., 2010). The attitude
is based on the individual’s beliefs with respect to the pos-
itive and negative outcomes of performing the act (i.e.,
behavioural beliefs). Similarly, subjective norms are based
on the perceived expectations of others (i.e., normative
beliefs) while the perceived behavioural control is governed
by the belief that certain factors would facilitate (act as
motivators) or prevent (act as barriers) them while per-
forming the act (i.e., control beliefs). The implementation
of the belief-based TPB approach helps to identify the
behavioural influences of people, thereby enhancing the
understanding of the motivational factors behind the
behaviour (Elliott et al., 2005; White et al., 2010). TPB
concerns reasoned action/behaviour whereas habit is the
tendency to repeat the past behaviour (Ouellette & Wood,
1998). Even though habit is sometimes added to TPB model
by researchers, the role of ‘past behaviour’ or ‘mobile-phone
related habits’ was not examined in the present study. Our
emphasis was mainly on the beliefs-based TPB model which
was adapted from the past research investigating the
distraction-related beliefs of the drivers (Gauld et al., 2014;
Hill et al., 2021; Przepiorka et al., 2020; Sullman et al.,
2018; White et al., 2010). The reason for using the same
scales and questions was that the findings of the present
work in the context of pedestrians can be compared with
the previous research done using beliefs-based TPB
framework.
1.3. Application of TPB model in pedestrian distraction
research
Recent studies have utilized the traditional TPB model to
investigate the predictors of mobile phone use among pedes-
trians (Barton et al., 2016; Jiang et al., 2017; Koh & Mackert,
2016; O’Dell et al., 2022; Piazza et al., 2019). O’Dell et al.
(2022) found perceived behavioural control as the most influ-
encing predictor of British pedestrians’ intention to cross the
road in distracted state. On the other hand, Piazza et al.
(2019) reported the strongest influence of attitudes on the
road crossing intentions while using a mobile phone among
American pedestrians. On a cohort of college students, Koh
and Mackert (2016) identified subjective norms as the most
significant predictor among the three attributes of TPB influ-
encing the intention to read and send text messages during
walking. Whereas Barton et al. (2016) did not find any sig-
nificant influence of subjective norms on the distracted road
crossing intentions of pedestrians and revealed perceived
behavioural control as the most influencing determinant.
Another study conducted on Chinese students reported that
mobile phone use intention during road crossing was pre-
dominantly influenced by pedestrian attitudes followed by
perceived behavioural control (Jiang et al., 2017).

4. International Journal of Injury Control and Safety Promotion 3
1.4. Previous research on beliefs about mobile phone use
Various studies investigating the mobile phone use while
driving have utilized the belief-based TPB approach to
determine the effect of drivers’ beliefs on their engagement
in the act of distracted driving (Gauld et al., 2014, 2016a,
2016b; Hill et al., 2019, 2021; Przepiorka et al., 2018, 2020;
Sullman et al., 2018; White et al., 2010). A study conducted
on the British drivers revealed a significant influence of
control and behavioural beliefs on the hand-held mobile
phone users, whereas no such effect was observed on the
hands-free mobile phone users (Sullman et al., 2018).
Another study on Australian drivers reported significant
differences between the frequent and infrequent phone users
with respect to all the three types of beliefs among the
hand-held phone users (White et al., 2010). A summary of
previous driver behaviour studies implementing the
belief-based TPB model to investigate driver beliefs is shown
in Table 1. However, the belief-based TPB approach has not
been utilized yet to investigate the mobile phone use among
pedestrians.
1.5. Research gaps, premise, and objectives
Based on the comprehensive literature review, the following
research gaps are identified:
1. Previous studies on pedestrian distraction have
mostly focused on their performance-based and
behavioural aspects (Horberry et al., 2019; Simmons
et al., 2020; Thompson et al., 2013). There is a scar-
city of research investigating the pedestrian beliefs
associated with their engagement in mobile phone
distraction.
2. Studies that examined the pedestrian psychology
behind mobile phone use looked at phone use in
general; however, hand-held and hands-free phone
use were found to have significantly different
influences on pedestrian behaviour (Prat et al.,
2015).
3. The belief-based TPB framework has been extensively
used in driver distraction research, but it has not
been adopted to study pedestrian distraction till now.
4. The majority of the research on pedestrian distrac-
tion has been conducted in developed countries, and
there is a need to explore this aspect in the context
of developing nations, which are more vulnerable to
road fatalities.
In India, 14% of the total road crash fatalities consist of
pedestrians, whereas they account for 17% of road deaths
(Ministry of Road Transport and Highways (MoRTH),
2020). However, various independent studies have reported
a higher estimate of pedestrian fatalities in India. For
instance, Dandona et al. (2020) analyzed the temporal vari-
ations and trends of road fatalities in India between 1990
and 2017 and reported that 35.1% of total road fatalities
in the year 2017 included pedestrians. Further, Hsiao et al.
(2013) conducted interviews of about a million households
in India and stated the proportion of pedestrian fatalities
to be 37% of the total fatalities. A recent cross-cultural
investigation found that the average safety perception of
pedestrians in India is much lower than the pedestrians of
other countries such as Australia, Austria, Canada, Denmark,
Germany, and Switzerland (Yannis et al., 2020). Several
studies on pedestrians in India have identified a significant
influence of mobile phone distraction on the pedestrian
road crossing and gap acceptance behaviour which increases
the severity and occurrence of crash risks (Mukherjee &
Mitra, 2020; Priyadarshini & Mitra, 2018; Vasudevan et al.,
2020). It has been reported from the field observations that
mobile phone use is increasing at an alarming rate among
pedestrians in India (Aranha, 2018; Bhattacharya, 2018).
Even though using a mobile phone while driving is illegal
in India, there is no law regulating the use of mobile phones
among pedestrians. Thus, the issue of distracted pedestrian
walking needs to be given importance to understand the
role of distraction in pedestrian safety, and to identify the
ways to safeguard pedestrians on Indian roads. This high-
lights the need of applying the belief-based TPB framework
to enhance our understanding of the factors influencing the
use of mobile phones while walking among pedestrians in
India. Therefore, the present study aims to identify the
potential predictors of mobile phone use among pedestrians.
To achieve this aim, the following research objectives are
defined:
1. To investigate the role of beliefs in influencing the
use of hand-held and hands-free mobile phones sep-
arately among pedestrians in India.
2. To examine the variations in beliefs between the
frequent and infrequent mobile phone users who use
phones in hand-held and hands-free modes.
2. Methods
2.1. Procedure
The approval for conducting the study was taken from the
Institutional Review Board (IRB) of the Indian Institute of
Technology Bombay (Proposal ID: IRB-2021-021). The study
participants were recruited through word of mouth and
personal communications. An online survey was conducted
to capture the pedestrian’s demographic characteristics,
mobile phone use habits, and beliefs with respect to dis-
tracted walking. The participants were required to be Indian
and above 18years of age. Participation in the study was
voluntary and no compensation was provided to the par-
ticipants. A detailed information sheet was provided at the
beginning of the survey describing the study objectives, and
the participants provided their informed consent before
filling the survey. The survey took around 7-8 minutes to
complete. The data was conducted from March to June
2021, and the participants were requested to provide their
responses based on their pre-COVID behaviour so that the
influence of the pandemic does not affect the study findings.
All the questions in the survey were compulsory; therefore,
there were no missing responses.

5. 4 A. K. YADAV ET AL.
Table
1. An
overview
of
driver
behaviour
studies
implementing
the
belief-based
TPB
model
to
investigate
driver
beliefs.
Study
Drivers’
nationality
Context
Sample
size
Analysis
technique
Key
findings
Elliott
et
al.
(2005)
British
Over-speeding
598
Hierarchical
multiple
regression
Behavioural
beliefs,
normative
beliefs,
and
control
beliefs
explained
41%,
33%
and
38%
of
the
variances
in
intentions
towards
over-speeding.
Warner
and
Åberg
(2008)
Swedish
Over-speeding
162
Multiple
regression
Belief-based
measures
were
successful
in
explaining
31%
of
the
variance
in
intention
of
over-speeding
in
the
urban
driving
environment,
and
44%
in
the
rural
driving
environment.
White
et
al.
(2010)
Australian
Mobile
phone
use
while
driving
769
Multivariate
analysis
of
variance
(MANOVA),
Logistic
regression
analysis
Significant
differences
were
found
between
the
frequent
and
infrequent
mobile
phone
users
with
respect
to
their
behavioural,
normative
and
control
beliefs
in
both
the
hands-free
and
hand-held
mobile
phone
use
mode.
Gauld
et
al.
(2014)
Australian
Texting
while
driving
171
Multivariate
analysis
of
variance
(MANOVA)
Texting
while
driving
was
more
believed
to
be
effective
in
sharing
information
and
using
time
effectively
by
the
frequent
users.
They
showed
higher
beliefs
that
their
texting
behaviour
would
be
affected
by
the
free-flow
traffic.
Sullman
et
al.
(2018)
British
Mobile
phone
use
while
driving
314
Multivariate
analysis
of
variance
(MANOVA),
Logistic
regression
analysis
Compared
to
the
less-frequent
mobile
phone
users,
daily
mobile
phone
users
showed
more
positive
beliefs
about
phone
use,
higher
sense
of
approval
from
the
others,
and
reduced
probability
of
control
beliefs
which
may
stop
them
from
using
a
phone
while
driving.
Gauld
et
al.
(2016a)
Australian
Engagement
in
social
interactive
technology
on
smartphone
114
Hierarchical
multiple
regression
The
drivers’
engagement
in
social
interactive
technology
while
driving
was
found
to
be
influenced
by
the
slow-moving
traffic,
approval
of
the
act
by
friends/
peers,
and
the
positive
feeling
of
receiving
an
expected
communication.
Gauld
et
al.
(2016b)
Australian
Engagement
in
social
interactive
technology
on
smartphone
26
Interview/
focus
group
discussion
Differences
in
behavioural,
normative
and
control
beliefs
were
observed
between
the
three
behaviours
of
initiating,
monitoring
and
responding
to
the
social
interactive
technology.
Hill
et
al.
(2019)
Ukrainian
Texting
while
driving
220
Binary
logistic
regression
Texting
while
driving
was
significantly
associated
with
gender,
higher
mobile
phone
use
habits,
less
demanding
traffic
conditions,
perceived
approval
of
the
act
by
family
members,
and
lesser
prospect
of
receiving
traffic
fines.
Przepiorka
et
al.
(2020)
Polish
Mobile
phone
use
while
driving
298
Multivariate
analysis
of
variance
(MANOVA),
Logistic
regression
analysis
The
control
beliefs
were
found
to
be
significantly
different
between
the
frequent
and
infrequent
mobile
phone
users
in
both
the
hand-held
and
hands-free
conditions.
Hill
et
al.
(2021)
Ukrainian
Mobile
phone
applications
use
while
driving
220
Hierarchical
multiple
regression
Positive
attitudes
and
beliefs
about
perceived
control
on
the
act
were
significantly
related
to
the
use
of
mobile
phone
applications
while
driving.

6. International Journal of Injury Control and Safety Promotion 5
2.2. Participants
The Cochran’s formula was used to estimate the target sam-
ple size in the present study as shown below in Equation
1 (Cochran, 1977):
n
Z pq
d
= = =
2
2
2
2
1 96 0 5 0 5
0 05
384
. . .
.
* *
(1)
Here, d represents the margin of error (5%), p is the
target population (considered as 0.5 for a large population),
q=1 – p, and Z=1.96 for 95% confidence interval. Therefore,
a sample size larger than 384 was targeted during the survey
data collection in the present study.
A total of 560 participants responded to the survey out
of which 9 responses were found to be erroneous and there-
fore were removed. The erroneous responses were identified
using two check questions which were included in different
sections of the survey. In these check questions, the respon-
dents were asked to select a definite response to check
whether they were careful while responding to the survey.
The remaining 551 responses were considered for the data
analysis. The mean age of the sample was 25.85years with a
standard deviation of 5.45years. The sample consisted of 356
males and 188 females, while 7 participants selected ‘prefer
not to say’ in response to the question on gender. 6.2% of
the participants had doctorates, 39.9% were postgraduates,
39.4% were graduates and the remaining 14.5% studied till
pre-university. The majority of the participants were single
(83.8%). About 58% of the sample preferred a hand-held
mobile phone while 42% preferred a hands-free mobile phone.
The sample characteristics are summarized in Table 2.
2.3. Measures
Previous studies such as Sullman et al. (2018), Przepiorka
et al. (2020) and White et al. (2010) have used the
beliefs-based TPB approach to understand distracted driving
behaviour. In this study, the belief model proposed by these
researchers in the context of drivers was used after modi-
fying it with respect to the pedestrians. Along with the
demographic characteristics shown in Table 2, the survey
captured the frequencies of mobile phone use of the pedes-
trians with respect to the type of handset (i.e., hands-free
or hand-held whichever they prefer to use while walking)
as shown in Table 3. The respondents were further classified
into two categories: (a) frequent users (i.e., those with the
frequencies of mobile phone use more than or equal to half
of the time), and (b) infrequent users (i.e., those with the
frequencies of mobile phone use less than half of the time).
The measures related to the pedestrians’ beliefs with
respect to the mobile phone use while walking was classified
into three types of beliefs: (a) behavioural beliefs, (b) nor-
mative beliefs, and (c) control beliefs. The detailed
belief-based questions are mentioned in Table 4 along with
their descriptive statistics for both the handset types. The
participants responded to the belief components on a Likert
scale (where ‘1′ indicated ‘extremely unlikely’ and ‘7′ indi-
cated ‘extremely likely’). The behavioural beliefs enquired
about the attitudes of the pedestrians towards the expected
positive and negative outcomes of distracted walking, the
normative beliefs examined the perceptions about the like-
lihood of the acceptance of the behaviour by the people in
the individual’s social circle (i.e., friends, family members,
etc.), and the control beliefs captured the extent of various
factors (e.g., police presence, crash risk, etc.) in preventing
the distracted walking behaviour of the pedestrians. The
reliability coefficient (Cronbach’s α) was found to be 0.73
for behavioural beliefs, 0.87 for normative beliefs, and 0.84
for control beliefs. Each of the belief items were added up
(with reversed negative outcomes in behavioural beliefs)
and averaged to obtain the three composite measures of
behavioural beliefs, normative beliefs and control beliefs
which were used as the independent variables in the data
analysis. In the analysis process, logistic regression models
were developed for the hand-held and hands-free handset
type with frequent or infrequent mobile phone users as the
dependent variables.
2.4. Data analysis
The data analysis was performed on STATA MP-15 software.
The analytical framework adopted in the present study is
shown in Figure 1. To achieve the first objective, two logistic
regression models were developed separately for hand-held
and hands-free mobile phone users to investigate the role
of behavioural, normative and control beliefs on phone use
frequencies. To achieve the second objective, univariate
analysis was conducted to identify the belief items (for the
three types of beliefs) for which the differences between
the frequent and infrequent mobile phone users were sig-
nificant. In addition, the MANOVA tests were performed
for each belief type to simultaneously examine the variations
across different variables. The MANOVA analysis facilitates
testing for differences without enhancing the risk of Type-I
errors (Sullman et al., 2018; White et al., 2010). To stream-
line the comparison of the study findings with the previous
studies on beliefs conducted in different countries, a similar
analysis technique was adopted as shown in Figure 1
(instead of factor analysis) implemented by the past research-
ers (Gauld et al., 2014; Przepiorka et al., 2020; Sullman
et al., 2018; White et al., 2010).
3. Results
3.1. Pedestrian walking characteristics and distracting
activities
Figure 2 shows the distribution of the main reasons for
walking trips reported by pedestrians. Over 29% of the
participants stated walking for daily commute to their work-
place or the place of education as their main purpose for
walking trips, followed by household activities such as shop-
ping (25%) and exercise (24%). About 51% of the partici-
pants reported their time spent walking per week as one
to five hours (Figure 3). When asked about their crash
history as a pedestrian, 22% stated that they had met with

7. 6 A. K. YADAV ET AL.
a crash while walking on the road whereas 78% were not
involved in a crash in past. Out of the 551 respondents, 8
had a basic keypad phone whereas the remaining 543 owned
a smartphone. The most preferred purpose of using the
mobile phone while walking on the road was found to be
listening to music (30.7%), followed by receiving a call
(25%), making a call (18.9%), texting (9.8%), navigation
(8.5%) and internet browsing (7.1%) respectively.
3.2. Hands-free mobile phone users
3.2.1. Variations in belief items between frequent and
infrequent users
The responses for each of the belief items were compared
between the frequent and infrequent mobile phone users
for both the handset types, as shown in Table 4. Only one
behavioural belief item ‘using time effectively’ was found
to show a significant difference between the frequent and
Table 2. Sample characteristics (N =551).
Variable Categories Frequency Percentage (%)
Gender Male 356 64.6
Female 188 34.1
Prefer not to say 7 1.3
Highest level of education Pre-university 80 14.5
Graduate 217 39.4
Postgraduate 220 39.9
PhD 34 6.2
Marital status Single 462 83.8
Married 88 16.0
Separated 1 0.2
Employment Government job 50 9.1
Private job 109 19.8
Student 350 63.5
Other 42 7.6
Preference of hands-free or hand-held phone while walking on the road Hands-free 230 41.7
Hand-held 321 58.3
Table 3. Distribution of mobile phone use frequencies with respect to the handset type.
Hands-free (n=230) Hand-held (n=321)
Frequency of mobile phone use among pedestrians Frequency Percentage (%) Frequency Percentage (%)
All the time 24 10.4 29 9.0
Most of the time 40 17.4 78 24.3
Half of the time 35 15.2 54 16.8
A few times 103 44.8 150 46.7
Never 28 12.2 10 3.2
Table 4. Descriptive statistics for beliefs with respect to frequency of mobile phone use and type of handset.
Hands-free Hand-held
Beliefs on distracted walking
Frequent users Infrequent users Frequent users Infrequent users
Mean (SD) Mean (SD) p-value Mean (SD) Mean (SD) p-value
Behavioural beliefs n=99 n=131 n=161 n=160
How likely is it that your using a mobile phone while walking in the next week would result in the following?
Using time effectively 4.8 (1.4) 4.0 (1.9) 0.006* 4.7 (1.6) 4.2 (1.7) 0.008*
Being distracted from walking 4.3 (1.6) 3.9 (1.9) 0.138 4.4 (1.8) 4.3 (1.7) 0.451
Being involved in a crash 3.3 (1.7) 3.1 (2.0) 0.391 3.3 (1.8) 2.9 (1.8) 0.054**
Receiving information (e.g., directions, important
news)
4.7 (1.6) 4.8 (1.9) 0.866 5.0 (1.6) 4.5 (1.7) 0.006*
Receiving assistance in an emergency 4.3 (1.9) 4.4 (2.1) 0.647 4.3 (1.9) 4.3 (1.9) 0.887
Being caught and fined by the police 2.6 (1.9) 2.5 (1.9) 0.754 2.4 (1.8) 2.2 (1.7) 0.230
Normative beliefs
How likely is it that the following people or groups of people would approve of your using a mobile phone while walking in the next week?
Friends 5.0 (1.7) 3.8 (2.1) 0.001* 4.9 (1.8) 4.0 (1.9) 0.001*
Family members 4.3 (1.9) 3.7 (2.1) 0.014* 3.9 (1.9) 3.3 (2.0) 0.004*
Partner/boyfriend/girlfriend 4.6 (2.1) 3.4 (2.2) 0.001* 4.2 (1.9) 3.4 (1.9) 0.003*
Work colleagues 4.9 (1.6) 3.8 (1.6) 0.001* 4.5 (1.6) 3.8 (1.8) 0.001*
Drivers 3.6 (1.7) 3.2 (1.7) 0.074** 3.5 (1.7) 3.1 (1.8) 0.077**
Police 3.2 (1.9) 2.9 (1.9) 0.230 2.9 (1.7) 3.0 (1.9) 0.761
Control beliefs
How likely are the following factors to prevent you from using a mobile phone while walking in the next week?
Risk of fines 3.6 (2.1) 3.5 (2.3) 0.921 4.0 (2.2) 3.9 (2.2) 0.781
Demanding walking conditions (e.g., crossing
the road)
4.9 (2.0) 4.3 (2.2) 0.022* 5.5 (1.8) 4.9 (2.1) 0.017*
Risk of an accident 4.7 (2.1) 4.3 (2.3) 0.119 5.0 (1.8) 5.0 (2.1) 0.886
Police presence 4.0 (1.9) 3.7 (2.1) 0.251 3.9 (2.0) 4.1 (2.1) 0.387
Lack of hands-free kit 4.3 (2.1) 3.9 (2.1) 0.237 3.7 (1.9) 4.0 (2.0) 0.093**
Heavy traffic 4.9 (2.1) 4.3 (2.2) 0.024* 5.2 (1.8) 5.0 (2.0) 0.291
*
p0.05,.
**
p0.1, Response (1-7): ‘1’ indicates ‘extremely unlikely’ and ‘7’ indicates ‘extremely likely’.

8. International Journal of Injury Control and Safety Promotion 7
infrequent users (p = 0.006). This indicated that frequent
mobile phone users had a stronger opinion that mobile
phone use while walking leads to effective use of time
compared to infrequent users. With respect to normative
beliefs, all the items showed significant variations between
the frequent and infrequent users, except the perceived
approval of police for mobile phone use while walking
(p = 0.230). The frequent mobile phone users demonstrated
significantly higher perceptions of mobile phone use
approvals from their friends, family, spouse, work col-
leagues, and drivers, compared to the infrequent users. For
the control belief items, frequent and infrequent mobile
phone users differed with respect to the heavy traffic
(p = 0.024) and demanding walking conditions such as
crossing the road (p = 0.022).
3.2. 2. Multivariate analyses of beliefs
Three one-way (frequent versus infrequent users) MANOVA’s
were performed for the three types of beliefs. Significant
multivariate effects were found for the behavioural beliefs
(Pillai's trace = 0.068; F (6, 223) = 2.72; p = 0.014) and
normative beliefs (Pillai's trace = 0.122; F (6, 223) = 5.16;
p0.001). However, no significant multivariate effects were
found between the frequent and infrequent users with
respect to the control beliefs (Pillai's trace = 0.042; F (6,
223) = 1.64; p =0.138).
3.2.3. Impact of beliefs on mobile phone use frequency
As shown in Table 5, the logistic regression model for the
hands-free mobile phone users found that normative beliefs
successfully distinguished between the frequent and infre-
quent mobile phone users (β =0.351, p0.001). The odds
ratio for the normative beliefs indicate that the frequent
users are 1.42 times more likely to believe that their use
of mobile phone while walking would be approved by the
other people. However, no significant roles of behavioural
(p=0.780) and control beliefs (p =0.131) were observed at
a 95% confidence interval. This indicates that the frequency
of mobile phone use among hands-free phone users is less
likely to be governed by the expected outcomes of mobile
Figure 1. Analytical framework of the present study.
Figure 2. Main reason for walking trips reported by the participants.
Figure 3. Distribution of time spent on walking trips per week.

9. 8 A. K. YADAV ET AL.
phone use (i.e., behavioural beliefs) and its prevention causal
factors (i.e., control beliefs).
3.3. Hand-held mobile phone users
3.3.1. Variations in belief items between frequent and
infrequent users
As shown in Table 4, significant variations were observed
between the frequent and infrequent mobile phone users
with respect to the three items of behavioural beliefs namely,
‘using time effectively’ (p=0.008), ‘being involved in a crash’
(p=0.054), and ‘receiving information such as directions,
important news, etc.’ (p=0.006). Similar to the hands-free
condition, significant differences between the frequent and
infrequent phone users were found in the hand-held mobile
phone use condition with respect to all the items of nor-
mative beliefs except the perceived approval of the distracted
walking act by the police (p =0.761). Moreover, two items
of control beliefs, namely, ‘demanding walking conditions’
(p=0.017) and ‘lack of hands-free kit’ (p =0.093) were the
major influencing factors highlighting the differences
between frequent and infrequent mobile phone use.
3.3.2. Multivariate analyses of beliefs
For the hand-held mobile phone users, the MANOVA tests
found the significant multivariate effects for all the three
types of beliefs, with the following test statistics: behavioural
beliefs (Pillai's trace = 0.052; F (6, 314) = 2.89; p =0.009),
normative beliefs (Pillai's trace = 0.082; F (6, 314) = 4.65;
p0.001) and control beliefs (Pillai's trace = 0.044; F (6,
314) = 2.39; p =0.028).
3.3.3. Impact of beliefs on mobile phone use frequency
The logistic regression model for the hand-held phone users
indicated the significant associations of the behavioural
(β = 0.246, p = 0.041) and normative beliefs (β = 0.239,
p=0.004) with the frequency of mobile phone use while
walking on the road (Table 5). The odds ratios suggest that
compared to the infrequent mobile phone users, frequent
mobile phone users have 1.28 times more favourable beliefs
towards distracted walking and have a 1.27 times higher
likelihood of believing that their act of distracted walking
would be approved by their significant others. The control
beliefs did not display any statistical significance in the
logistic regression model for the hand-held mobile phone
users (p=0.544).
4. Discussion
The present study is the first attempt to investigate the
influence of individual beliefs on the engagement in mobile
phone-induced-distracting activities among pedestrians in
India. The study provides substantial evidence that the use
of the mobile phone is quite prevalent among the pedes-
trians, as 47% of them reported using a mobile phone at
least half of the time while walking. Previous studies have
reported mobile phone use among 33%, 10.5%, and 16.6%
of the Chinese, German, and Greek pedestrians respectively
(Ropaka et al., 2020; Vollrath et al., 2019; Zhao et al., 2015).
Technological distractions play a major role in reducing
the mental alertness of the pedestrians on the road ahead
of them leading to collisions (Vollrath et al., 2019). In this
study, the proportion of hand-held mobile phone users was
higher than the hands-free mobile phone users. Previous
research has found that hand-held mobile phone use is
more dangerous than hands-free mobile phone use with
respect to injury severity (Hosking et al., 2009). In an obser-
vational study at an intersection with a length of 3.4 lanes,
Thompson et al. (2013) found that the pedestrians involved
in hand-held phone use took an additional 0.75 seconds,
and hands-free phone users took an additional 1.29seconds
to cross the road compared to the non-distracted pedestri-
ans. This shows that hands-free phone users comparatively
took a longer time to cross and demonstrated better situ-
ational awareness than the hand-held phone users. However,
the relatively lower risk perception of hands-free phone use
among pedestrians is also a matter of concern as significant
safety concerns have been observed even in the case of
hands-free phone use (Redelmeier Tibshirani, 1997).
Listening to music was found to be the most distracting
activity of the pedestrians in the present study. Lee et al.
(2020) observed that pedestrians were less able to recognize
the alerting sound of the vehicles (e.g., car horns and bicycle
bells) while listening to music. Schwebel et al. (2012) found
that the crash probabilities were the highest for the pedes-
trians who were listening to music followed by those who
were texting. It shows that even though the distracting
activity of listening to music requires less cognitive demand
compared to texting or a conversation, constant interruption
Table 5. Logistic regression models for hands-free and hand-held mobile phone users.
Condition Variable Coefficient
Odds
ratio SE z-value p-value
95% CI
Lower Upper
Hands-free (N =230) Behavioural −0.037 0.964 0.132 −0.28 0.780 −0.295 0.221
Normative 0.351 1.421 0.096 3.67 0.001 0.164 0.539
Control 0.140 1.150 0.093 1.51 0.131 −0.042 0.321
Constant −2.086 0.124 0.597 −3.50 0.001 −3.256 −0.916
Log likelihood=−147.978 AIC = 303.956 BIC = 317.708
Hand-held (N =321) Behavioural 0.246 1.279 0.120 2.05 0.041 0.011 0.481
Normative 0.239 1.270 0.083 2.88 0.004 0.076 0.402
Control −0.050 0.951 0.082 −0.61 0.544 −0.214 0.113
Constant −1.605 0.201 0.559 −2.87 0.004 −2.702 −0.508
Log likelihood=−214.603 AIC = 437.207 BIC = 452.292

10. International Journal of Injury Control and Safety Promotion 9
by aural cues makes this activity more challenging from
the perspective of pedestrian safety. This reasoning was
further strengthened in recent work on the reaction time
of distracted pedestrians while encountering green signals,
where the researchers found that the reaction time increased
by 67% for the auditory distractions and 50% in the case
of the visual distractions (Liu et al., 2021).
The present study investigated the associations between
the beliefs and mobile phone use frequencies in hands-free
and hand-held conditions while walking. The significant
multivariate effects of behavioural and normative beliefs
were observed in the hands-free mobile phone use condi-
tion, which was also reported for car drivers by Przepiorka
et al. (2020). However, Sullman et al. (2018) did not find
any significant multivariate effects of the behavioural, nor-
mative, and control beliefs among the car drivers using
hands-free mobile phones. On the other hand, all the three
types of beliefs were found significant in the hand-held
mobile phone use condition, similar to the findings of
White et al. (2010) on car drivers. The study by Sullman
et al. (2018) reported significant multivariate effects of
only behavioural and control beliefs among the hand-held
mobile phone users, whereas only normative and control
beliefs showed significant multivariate effects in the
hand-held condition in Przepiorka et al. (2020). In case
of pedestrians, past studies have reported the significant
role of attitudes (based on behavioural beliefs) and per-
ceived behavioural control (based on control beliefs) on
the engagement with mobile phones during road crossing
(Barton et al., 2016; Jiang et al., 2017; O’Dell et al., 2022;
Piazza et al., 2019). On the other hand, Koh and Mackert
(2016) did not find any significant role of attitudes and
perceived behavioural control on mobile phone use among
pedestrians, which is contradictory to the findings of the
present study. Nevertheless, these studies did not specifi-
cally examine the differences between hand-held and
hands-free phone use among pedestrians (Barton et al.,
2016; Jiang et al., 2017; Koh Mackert, 2016; O’Dell et al.,
2022; Piazza et al., 2019).
Interestingly, it was observed that the frequent mobile
phone users while walking (in both the hands-free and
hand-held condition) are more likely to believe that the
mobile phone use helps in using time effectively compared
to the infrequent users. Similar findings were reported by
Sullman et al. (2018), Gauld et al. (2014), and White et al.
(2010) in the context of car drivers. Moreover, in both the
hands-free and hand-held condition, people involved in
frequent mobile phone distractions were found to have
higher beliefs that their tendency of engaging in distracted
walking would be approved by their friends, family mem-
bers, spouses, and work colleagues similar to the observa-
tions by White et al. (2010). In the end, the logistic
regression models revealed that the frequency of mobile
phone use was significantly predicted by normative beliefs
in the hands-free condition, and by behavioural and nor-
mative beliefs in the hand-held condition. However, Sullman
et al. (2018) and Przepiorka et al. (2020) did not find any
significant role of beliefs in explaining the mobile phone
distraction frequencies in the hands-free mode, whereas
White et al. (2010) reported the significant impact of nor-
mative and control beliefs on the mobile phone use fre-
quencies in the hands-free condition. Further, in the
hand-held condition, Przepiorka et al. (2020) reported a
significant role of behavioural and control beliefs, Sullman
et al. (2018) found the major influence of control beliefs,
and White et al. (2010) demonstrated the substantial impact
of all the three types of beliefs on the frequencies of mobile
phone use among the pedestrians.
In this study, the beliefs about police approval of the act
of distracted walking, being caught and fined by the police,
risk of fines, police presence, and risk of accidents did not
significantly explain the frequency of engagement in mobile
phone use among pedestrians in India. However, the pre-
vious research in the developed countries (such as Australia
and the United Kingdom) has shown the significant impor-
tance of ‘risk of fines’ and ‘risk of accidents’ in predicting
the mobile phone use frequencies (Sullman et al., 2018;
White et al., 2010). This indicates that there is a need of
increasing awareness of the harmful effects of distracted
walking among pedestrians and signifying the importance
of pedestrian safety through mass media campaigns and
roadside advertisements. However, to minimize pedestrian
crashes, individual stand-alone countermeasures may not
be too effective. Recent work by Osborne et al. (2020) exam-
ined the pedestrians’ perspective on the current pedestrian
safety countermeasures. They revealed that the pedestrians
were of the opinion that no single countermeasure can
effectively reduce the crash risk of distracted pedestrians.
They suggested that an integrated approach combining the
elements of separate pedestrian infrastructure, effective leg-
islation, spreading awareness about pedestrian safety issues,
and shared responsibility among the pedestrians would be
beneficial in improving pedestrian safety (Osborne et al.,
2020). Therefore, even though police presence and risk of
fines were not found to play a significant role in the present
study, strict road safety enforcements through police pres-
ence, effective legislations prohibiting the use of mobile
phones while walking, target-oriented distraction deterrence
awareness programs, and heavy fines imposed on distracted
pedestrians are favourable measures towards moulding the
pedestrian beliefs on distracted walking.
To alert the pedestrians during distracted walking, var-
ious pedestrian safety interventions have been suggested by
the researchers in past such as ground-level signal detection
(Kim et al., 2021), Bluetooth beacons (Schwebel et al., 2021),
verbal and auditory warnings (Dreßler et al., 2020; Larue
Watling, 2021), pavement markings (Barin et al., 2018),
etc. However, their acceptance by the pedestrians and their
effects on the pedestrian fatalities need to be evaluated
(Metaxatos Sriraj, 2015). Further, pedestrian safety aware-
ness and campaigns have also been attempted but their
effectiveness is still questionable (Violano et al., 2015). In
addition to these technological, social, and
infrastructure-related interventions, psychological interven-
tions may also play a major role in improving pedestrian
safety. The study findings direct that the psychological

11. 10 A. K. YADAV ET AL.
interventions for pedestrians should target their normative
beliefs for hands-free mobile phone use and the behavioural
and normative beliefs for hand-held mobile phone use.
These psychological interventions have proved to be an
effective tool in minimizing the frequencies of mobile phone
use among the drivers (Elliott et al., 2021); however, there
is a need for more research to evaluate their effectiveness
in case of the pedestrians.
5. Limitations and future research scope
An important limitation of the present work is that the
beliefs were examined with respect to the mobile phone
use in general. Future research can also explain the influ-
ence of beliefs on the specific mobile phone-induced dis-
tracting activities such as calling, texting, etc. Secondly, the
nature of online data collection resulted in inherent limita-
tions, including non-responder bias, social desirability bias,
recruitment bias, as well as technological bias as the people
not familiar with the use of technology were left out.
Moreover, the respondents in this study were found to be
majorly young people which may influence the generaliz-
ability of the study findings to the older population.
However, distracted walking behaviour has been majorly
reported to be observed among the young pedestrians, and
they are also found to be overrepresented in road fatalities
(Horberry et al., 2019; Thompson et al., 2013). Future stud-
ies may consider a wider range of age and equal gender-wise
proportion of participants for better generalizability of the
findings.
In this study, belief-based TPB framework has been used
which does not include all the aspects of the traditional
TPB framework. Therefore, some aspects such as affective
component of attitude and motivation to comply were not
examined in the present study, which can be included in
the future research. In the practical situations, the distrac-
tion due to surrounding road environment (i.e., road infra-
structure and traffic) may also influence pedestrian safety
(Pawar Yadav, 2022). The future studies shall explore the
interactive effects of the technological distractions and road
environment distractions on pedestrian behaviour. Moreover,
capturing actual time of phone use per day and investigating
its association with beliefs associated with pedestrian
behaviour can be an interesting avenue for future research.
While recording crash history in the present study, the
definition of crash was not provided to the participants
which could have led to open interpretations. The present
study focused on the general distracted pedestrian walking
and did not explore the influence of beliefs on distracted
pedestrians performing the road crossing task, which can
be an interesting direction for future research. In the recent
years, a lot of research has been conducted on traffic safety
culture in developing countries (Alhajyaseen et al., 2022;
Suzuki et al., 2022; Timmermans et al., 2019a, 2019b; Yadav
Velaga, 2021a, 2021b). Nevertheless, there is a need to
conduct more detailed investigations on road user behaviour
in developing nations where the road fatalities are signifi-
cantly higher than the developed nations.
6. Conclusions
Overall, the study focused on an important issue of pedes-
trian distraction and examined the association between the
pedestrian beliefs and their frequency of mobile phone use
in hands-free and hand-held conditions. The belief-based
TPB framework was used for this purpose, which has been
commonly used in the case of distracted driving in the past
research (White et al., 2010; Sullman et al., 2018; Przepiorka
et al., 2020, Gauld et al., 2014). The contributions of this
study are twofold: (a) the study provides first-hand evidence
on distracted walking beliefs with respect to the pedestrians
in India, and (b) the utilization of the belief-based TPB
framework has been done for the first time in the context
of pedestrian distraction. The findings highlight the impor-
tance of TPB framework in predicting the mobile phone
use among pedestrians and increase our understanding on
the motivation factors which are likely to influence the
distracted pedestrian walking in Indian context.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
The author(s) reported there is no funding associated with the work
featured in this article.
ORCID
Ankit Kumar Yadav http://orcid.org/0000-0002-5619-4479
Nagendra R. Velaga http://orcid.org/0000-0002-5022-557X
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