Sion Pickering - Masters Dissertation - 19.04.2016

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MSc Marketing
Masters Dissertation
SESSION 2015/16
Title
EXPLORING THE FACTORS
INFLUENCING CYCLING UPTAKE AS A
FORM OF ACTIVE TRANSPORT IN
EDINBURGH BASED EMERGING ADULTS
Author
SIÔN ERYL PICKERING
40188009
,
Supervisor: Professor John Ensor

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Abstract
Introduction. As the level of physical activity in emerging adults drops, related
health problems such as obesity and cardiovascular disease are on the rise. Current
UK policies aim to increase physical activity through adapting the public’s behaviour
to increase Active Transport (AT) as a form of commuting. However, there is a lack
of research into the barriers and motivators (influencing factors) to AT within the
emerging adulthood population, a point that is especially true of studies focusing on
cycling as a mode of transport. This study considered the influencing factors to
cycling uptake within the emerging adult population, as well as the difference
between genders and across the Stages of Change model (SoC).
Methodology. A mixed methods design was utilised, consisting of Study I - an
exploratory qualitative study utilising semi-structured interviews with upstream,
midstream, and downstream individuals. These interviews identified 21 individual
factors in three overarching themes: Personal Factors, Social Factors, and
Environmental Factors. Subsequently Study II utilised an online quantitative
questionnaire to consider these factors within broader the emerging adult population
of Edinburgh.
Results. Inconsistencies emerged between the respondents of the two studies,
suggesting that there is a disparity between upstream policy makers and
downstream emerging adults. While there were few differences in influencing factors
to cycling uptake between the genders, those that did emerge contradict previous
research. Across the SoC, there was significant difference between 12 of the 21
factors examined, suggesting that initiatives designed to overcome these factors
would lead to an increase in cycling uptake within the studied population.
Conclusion. It is concluded that there is a clear difference between the influencing
factors to cycling of emerging adults and those of other generations. However, due
to the sampling limitations of this study, these findings will need to be verified in
larger populations before greater generalisability can be assumed. It is essential for
additional research to explore these factors in greater detail across a variety of
geographical locations, with the view of guiding the development of relevant AT
policies and campaigns.

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Table of Contents
Chapter 1: Introduction............................................................................................1
1.1 Area of Study ................................................................................................ 1
1.2 Factors Influencing Cycling Uptake............................................................... 2
1.3 Geographical Consideration.......................................................................... 3
1.4 Theoretical Background ................................................................................ 3
1.5 Introduction to the Methodology.................................................................... 4
1.6 Aims and Objectives ..................................................................................... 4
1.7 Chapter Summary......................................................................................... 5
Chapter 2: Literature Review...................................................................................6
2.1 Factors Influencing Cycling Uptake............................................................... 6
2.1.1 Personal Factors..................................................................................... 6
2.1.2 Physical Environment ............................................................................. 6
2.1.3 Social Factors ......................................................................................... 8
2.2 Gender Differences in Cycling....................................................................... 9
2.3 Active Transport in Emerging Adults........................................................... 10
2.4 Theoretical Background .............................................................................. 11
2.4.1 The Health Belief Model........................................................................ 11
2.4.2 The Theory of Reasoned Action & Theory of Planned Behaviour......... 12
2.4.3 Trans Theoretical Model of Health Behaviour....................................... 12
2.4.4 Behavioural Models in Relation to Cycling Uptake ............................... 14
2.5 Chapter Summary....................................................................................... 15
Chapter 3: Methodology ........................................................................................16
3.1 Philosophical Rational................................................................................. 16
3.2 Study I......................................................................................................... 17
3.2.1 Overview............................................................................................... 17
3.2.2 Sample.................................................................................................. 18
3.2.3 Design................................................................................................... 19
3.2.4 Materials ............................................................................................... 19
3.2.5 Analysis ................................................................................................ 20
3.2.6 Ethical Considerations .......................................................................... 20
3.3 Study II........................................................................................................ 21
3.3.1 Overview............................................................................................... 21
3.3.2 Sample.................................................................................................. 21
3.3.3 Design & Materials................................................................................ 22

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3.3.4 Analysis ................................................................................................ 23
3.3.5 Ethical Considerations .......................................................................... 23
Chapter 4: Results.................................................................................................24
4.1 Study I......................................................................................................... 24
4.2 Study II........................................................................................................ 25
4.2.1 Demographics....................................................................................... 25
4.2.2 Influencing Factors in Emerging Adults................................................. 26
4.2.3 Gender.................................................................................................. 30
4.2.4 Stages of Change ................................................................................. 34
Chapter 5: Discussion ...........................................................................................41
5.1 Key Findings ............................................................................................... 41
5.2 Influencing Factors for Emerging Adults ..................................................... 41
5.3 Disparity between Upstream and Downstream Sources ............................. 43
5.4 Influencing factors between genders........................................................... 44
5.5 Influencing factors across SoC.................................................................... 47
Chapter 6: Conclusion...........................................................................................49
6.1 Overview ..................................................................................................... 49
6.2 Practical Contributions ................................................................................ 49
6.2.1 General barriers for emerging adults .................................................... 49
6.2.2 Disparity between upstream, midstream, and downstream sources..... 51
6.2.3 Gender differences ............................................................................... 52
6.3 Theoretical Contributions ............................................................................ 52
6.4 Study Limitations and Future Research ...................................................... 53
6.5 Chapter Summary....................................................................................... 55
References............................................................................................................ lvi
Appendices.........................................................................................................lxviii
Appendix A – Study I Interview design & example interview questions...........lxviii
Appendix B – Study I Interview Debrief Sheet ................................................. lxix
Appendix C – Map of the City of Edinburgh Council Boundary......................... lxx
Appendix D – Study II Questionnaire Layout & Analysis Coding ..................... lxxi

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Chapter 1: Introduction
1.1 Area of Study
Lack of physical activity is considered the second greatest risk factor of diseases
worldwide, leading to many negative health factors including cardiovascular
diseases and certain types of cancer (Douglas et al., 2011; Organisation for
Economic Co-operation and Development, 2010; World Health Organisation, 2003).
As the UK population ages, the effects from this ill-health is putting increased strain
on the National Health Service (NHS England, 2013; Office for National Statistics,
2014a). In turn this has brought about a governmental push to increase Active
Transport (AT) – traveling using non-motorised transport – be it commuting to work
or for leisure. Considering that an average of 1.1 hours of an individual’s day is spent
commuting, there is clear opportunity to increase physical exercise levels during this
time (Schafer & Victor, 2000). AT has been demonstrated to improve public health
in a variety of countries (Götschi et al., 2015; Rabl & de Nazelle, 2012; Rojas-Rueda
et al., 2013). In a recent review, Brown et al. (2016) discussed the difficulty in
comparing studies in the field of AT, concluding that there little consistency when
describing AT. For example many studies disregard the differences between
walking and cycling (Gordon-Larsen et al., 2009; Lindström, 2008; Wagner et al.,
2001) which is misleading as cycling is revealed to be a more intense form of
exercise than walking, with a strong positive relationship between regular cycling
and cardiorespiratory fitness leading to an increased life expectancy of between 3
and 14 months (de Hartog et al., 2010; Oja et al., 1991; Oja et al., 2011).
In the UK, the modal share of cycling has declined significantly since 1971
(Goodman, 2013) with fewer than 3% of journeys completed by bicycle each day
compared to roughly 70% by car (Department for Transport, 2005; Office of National
Statistics, 2014a). This is significantly lower than the Netherlands where between
28% and 36% of the population state they regularly use a bicycle (European
Commission, 2014; Pucher & Dijkstra, 2003). In fact, within the European Union
(EU), the UK sits among the lowest percentiles when it comes to cycling, with just
six EU countries recording a lower percentage of daily bicycle journeys in 2014
(European Commission, 2014). Though there are differences between countries
there is also significant disparity to the uptake of cycling within the UK, with 29% of

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adults in Cambridge cycling to work compared to 0.3% in Merthyr Tydfil (Office of
National Statistics, 2014a). In Scotland, the cycling rate is estimated to be between
1 and 1.5% of all journeys (Parkin, Ryley, & Jones, 2007; Scottish Government,
2010).
1.2 Factors Influencing Cycling Uptake.
Research confirms clear differences in influencing factors to cycling, including
external aspects such as poor infrastructure (Mullan, 2013; Pucher, Dill, & Handy,
2010) and the weather (Nankervis, 1999), as well as internal factors such as
perception of risk (Mullan, 2013), social norms (Daley & Rissel, 2011; Gatersleben
& Haddad, 2010) and attitudes to cycling (Gatersleben & Appleton, 2007). Though
these factors are often considered independently, it is likely that there is a strong
overlap between factors (Mullan, 2013). There are also variations in cycling uptake
between genders, with twice as many men cycling than women (Office of National
Statistics, 2014a) suggesting that the factors influencing cycling uptake encountered
by men and women are different (Dickinson et al., 2003; Garrard, Rose, & Lo, 2008;
Macmillan et al., 2014).
When considering the current literature, the majority of research focuses on AT in
children and adolescents (Dessing et al., 2014; Faulkner et al., 2009; Forman et al.,
2008; Nelson et al., 2008) or individuals in later life (Parkin, Ryley, & Jones, 2007).
This has created a gap in the literature investigating the use of AT in those aged 18
to 29 – otherwise known as emerging adulthood (Arnett, 2000). Between 1991 and
2001, 18 – 29 year olds had the greatest level of obesity within the UK population
(Huang et al., 2003; Liu, Mizerski, & Soh, 2012), with a lack of physical activity being
attributed as a key factor to this problem (Keating et al., 2005; Poobalan et al., 2012).
One explanation for this low activity level is the significant life events that occur
during this ‘critical development period’ such as moving out of the family home,
enrolling in university, and starting a family (Crombie et al., 2009; Laska et al., 2009;
Scheiner & Holz-Rau, 2013). The latter of these is supported by data from Eurostat
(2015) which indicates that 51.6% of first births within the EU occurred for mothers
aged 20 – 29 years old - a figure closely mimicked in Scotland (ISD Scotland, 2015).
It is believed that 18 – 29 year olds sit within the generation defined as ‘Millennials’
(Pirie & Worcester, 1998), and are considered more environmentally conscious than
their predecessors, whilst many will be educated to University degree level also

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(Pirie & Worcester, 1998). In all this suggests that this generation will be more willing
to change their travel behaviour compared to previous generations. However, with
the need for increased connectivity across all aspects of their daily lives - including
whilst commuting (American Public Transportation Association, 2013) – suggesting
that the influencing factors to cycling for 18 to 29 year olds may not be as
straightforward to policy makers as first thought. As emerging adults mature, it is
imperative that policy makers take note of the potential for variety in factors
influencing cycling uptake otherwise they may find their policies become redundant.
With the apparent failure to reduce transport related emissions, Lucas and
Pangbourne (2014) note that the existing transport policies may already be
outdated.
1.3 Geographical Consideration
Few studies into the influencing factors to cycling have been carried out in Scotland,
and those that have focus on population segments based on broad “life stages”
(Cass & Faulconbridge, 2016; Kirby & Inchley, 2009; Ryley, 2005). However,
Edinburgh is a compact city, measuring roughly 10 miles in diameter (Local
Government Boundary Commission for Scotland, 2013), and as such many of the
commuting journeys are not likely to exceed the distance a person can easily cover
by bicycle (British Medical Association, 2012). Coupled with a large student
population, known to be high bicycle trip generators (Martens, 2004; National
Records of Scotland, 2015; Rodriguez & Joo, 2004; Tolley, 1996), this suggests that
there is potential to increase the cycling rate in Edinburgh. Contrary to this,
Edinburgh is also known for its undulating terrain which can lead to a reduction in
cycling (City of Edinburgh Council, 2016a; Vandenbulcke et al., 2011). Taking all
these factors into consideration, it is apparent that Edinburgh is an ideal location to
study the factors that influence cycling uptake in emerging adults.
1.4 Theoretical Background
There are many theories that attempt to explain why individuals take up cycling as
a form of AT. When considering behaviour change in a health context, the Health
Belief Model, Theory of Reasoned Action, Theory of Planned Behaviour, and the
Trans Theoretical Model of Health Behaviour (TTM) are well utilised (Taylor et al.,
2006). The latter of these is underpinned by the Stages of Change model (SoC)
which maps out six distinct steps along the behaviour change pathway (Prochaska

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& DiClemente, 1982). Within the TTM, influencing factors to cycling uptake are
integral in balancing the expected benefits against the perceived costs to
implementing a new behaviour (Nigg et al., 2012).
1.5 Introduction to the Methodology
Using a mixed-method design, this study aims to add to existing literature by
investigating the influencing factors to cycling uptake in young adults (defined as
those aged 18 – 29) based in Edinburgh. Due to a lack of previous literature on this
target group, it is not clear which factors will be significant to cycling uptake within
the chosen population, and so an initial exploratory study is required (Study I).
Following the completion of this primary study, a second study (Study II) is
undertaken utilising a quantitative methodology to analyse whether the influencing
factors to cycling uptake in 18 – 29 year olds explored in Study I are consistent
across a larger population.
1.6 Aims and Objectives
The purpose of this study is to look at the perceived factors influencing cycling
uptake to a place of work or study for emerging adults (defined as 18 – 29 years
old) living in Edinburgh. This is divided into key research questions as follows:
1. What are the barriers and facilitators (influencing factors) for cycling to a
place of work or study in emerging adults?
2. Is there a difference in the perceived influencing factors to entry in emerging
adults compared to those of AT policy influencers?
3. Is there a difference in the influencing factors to cycling in Edinburgh between
males and females?
4. Is there a difference in the influencing factors to cycling in Edinburgh across
the SoC?
When reframed as research objectives, the outcomes of this study become:
1. To determine the perceived influencing factors to entry for policy influencers.
2. To establish the perceived influencing factors to entry within the emerging
adult population of Edinburgh.
3. To evaluate the perceived influencing factors to cycling of the policy makers
to those of the emerging adult population of Edinburgh.
4. To compare these findings between genders as well as across the SoC.

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The achievement of these objectives will aid in answering the research questions
set out above. By looking at a novel subject group, both in terms of age range as
well as location, this study will be able to compare and contrast to similar studies
whilst also adding to the overall field of behaviour change theories within Social
Marketing.
1.7 Chapter Summary
This paper comprises a total of six chapters, including the introduction (Chapter 1).
Chapter 2 will consist of a review of previous literature, focusing on the influencing
factors to cycling uptake as well as the theoretical background focusing on the Trans
Theoretical Model of Health Behaviour (TTM) and its use in increasing cycling
uptake. Chapter 3 contains the methodology, stating the research philosophy before
detailing Study I and Study II. A brief explanation is included before outlining the
sampling method, research design, materials, analysis, and the ethical
considerations for each study. Chapter 4 details the results of these studies, starting
with the explored factors from Study I. This is followed by the quantitative findings
of Study II, including relevant statistical results and graphical representations.
Chapter 5 consists of the discussion, in which the findings of Study I and Study II
are debated in relation to the initial project aims and objectives. The final chapter,
Chapter 6, discusses the practical implications of the paper as well as the
contribution of the project to the field of behaviour change and social marketing as
a whole. The paper concludes with the projects limitations and recommendations
for future research.

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Chapter 2: Literature Review
2.1 Factors Influencing Cycling Uptake
There are many influencing factors to cycling uptake set out in the literature, with
Parkin, Ryley, and Jones (2007) grouping these into three overarching themes –
personal, environmental, and social factors. These broad themes have been
supported by the findings of Simons et al. (2014) who looked at AT uptake in 18 -
25 year olds and Titze et al. (2007) who established similar categories when
considering the cycling rates of Australian university students.
2.1.1 Personal Factors
Personal factors are those that are directly influenced by an individual such as
access to a bicycle (Molina-Garcia, Castillo, & Sallis, 2010; Ogilvie & Goodman,
2012) as well as perceived speed and cost benefits compared to alternative modes
of travel (Mullan, 2012). One of the largest personal factors seen in previous
literature is the perceived personal safety of individuals whilst cycling (Fishman,
Washington, & Haworth, 2012; Greig, 2012; Pooley et al., 2013). When looking at
an Australian sample, Fishman, Washington and Haworth (2012) also identified that
the mandatory use of a helmet while cycling also acted as a barrier to cycling. This
is counterintuitive as helmets are reported to reduce injury (Attewell, Glase, &
McFadden, 2001; Thompson, Rivara, & Thompson, 2000), whilst also increasing
the perception of personal safety (Fyhri & Phillips, 2013). One possible explanation
is that Fishman, Washington and Haworth (2012) analysed users of a public bicycle
sharing scheme (PBSS) which have markedly different user groups to private
bicycle users, with many PBSS users utilising the service for leisure rather than to
commute, and so may not be representative of the wider cycling community
(Beecham & Wood, 2014; Castillo-Manzano, Castro-Nuño, & López-Valpuesta,
2015; Goodman & Cheshire, 2014; Ogilvie & Goodman, 2012).
2.1.2 Physical Environment
When considering factors related to the physical environment, infrastructure is one
of the most commonly noted factors (Pucher, Dill, & Handy, 2010; Scheepers et al.,
2014). This includes aspects such as access to segregated cycle paths (Beecham
& Wood, 2014), secure storage facilities (Titze et al., 2007) and facilities at the

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journey end-point (Buehler, 2012). Snizek, Sick-Nielsen, and Skov-Petersen (2013)
superimposed the locations of positive and negative experiences of cyclists over
their regular commuting route, revealing a clear link between positive experiences
and on-route cycle facilities such as segregated cycle paths and attractive natural
environments. In contrast, negative experiences were linked to bus stops, densely
populated areas, and un-signalled junctions, all of which suggests that there is a
strong link between infrastructure and personal factors such as safety. This is
supported by Vandenbulcke et al. (2011) who identified that the quality of
infrastructure, and the corresponding reduction in accidents, has a significant effect
on cycling across Belgium. The same study also revealed that environmental factors
such as flat terrain leads to significantly increased rates of cycling, which could be
explained by the increase in physical exertion required to overcome undulating
terrain. Interestingly, larger and busier roads were not associated with negative
experiences in Denmark (Snizek, Sick-Nielsen, & Skov-Petersen, 2013), which is
counter to Guell, Panter, and Ogilvie (2013) who revealed perceptions of the UK’s
busy roads to be more dangerous. This contrast could be due to a lower risk of
cyclist fatality on major roads in Denmark compared to the UK (Statistics Denmark,
2016).
It is believed that travel distance is often the most significant physical environment
barrier to cycling (Heinen, Maat, & Van Wee, 2011), with the British Medical
Association (2012) suggesting that eight kilometres (approximately five miles) is
easily achievable by most adults. On the other hand Hansen and Nielsen (2014)
established that a significant number of Danish commuters travelled far longer
distances by bicycle than proposed in other studies. The explanation given by
participants included the introduction of good facilities at the journey end-point
(including bicycle parking and shower facilities), as well as practical reasons such
as being quicker than alternative transport methods, whilst also improving their
health and reducing stress. Hansen and Nielsen also revealed that a great number
of these commuters cycled year round which is interesting as Bergström and
Magnusson (2003) concluding there was a 47% drop in bicycle trips during the
winter months, in part due to the lower light levels, lower temperature, and higher
precipitation levels encountered during this period. Not all studies support these
findings, with Nankervis (1999) comparing the actual weather against a daily bicycle

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count at one Australian location. It was concluded that there was no difference in
cycling levels with varying weather conditions when considering actual weather data
against levels of cycling, though the use of a single location bicycle count as a proxy
for cycling levels is unlikely to give accurate cycling rates. One study that analysed
the effects of weather in more depth was conducted by Flynn et al. (2012). This
study compared travel diaries of 163 participants from across a single American
state and identified a 1° drop in temperature led to a reduction of 3% in the likelihood
of cycling. However, this sample has a high percentage of females (63%) which
could skew data as there is a distinct gender divide in those who cycle to work
(Dickinson et al., 2003) which will be discussed shortly. Alongside this the weather
data collected looked at the geographical area as a whole, and did not take into
account the potential effects of ‘micro-climates’. It could be that it is not weather per
say that affects the rate of cycling, but the effects that weather has on other barriers
such as the perception of danger. The study by Bergström and Magnusson (2003),
noted earlier, concludes that improving cycleway maintenance in winter, and so
lowering perceived risk, could lead to an increase in bicycle trips of 18%. Mullan
(2013) demonstrated that this increased perception of risk was a major factor in why
a small sample of Irish leisure cyclists did not cycle to work, although the perceived
risks of Mullan’s subjects also included factors such as “…dangerous, inconsiderate,
and intolerant drivers…” (pg. 2) as well as the weather. When weather was
mentioned in these interviews alongside the increased perceived danger, it was also
linked to factors such as wet clothes and unpleasant cycling – which could be
overcome with better facilities at the work place. Buehler (2012) established that the
combined availability of secure bicycle parking, shower facilities, and lockers greatly
increased the likelihood of cycling within a large sample from Washington DC.
Unfortunately, though this study collected data from a large population, the use of
the one-day travel diary in this study leaves many internal and external variables
unaccounted for compared to a study looking at cycling over a longer time span,
with Richardson (2003) demonstrating that travel diaries are more reliable over five-
days rather than one.
2.1.3 Social Factors
Of the three broad themes set out by Parkin, Ryley & Jones (2007), literature into
the effects of the social factors to cycling uptake are fewer in number. Bartle, Avineri,

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and Chatterjee (2013) identified that a sense of community and trust was important
within an online cycle route-sharing community. This supports eariler work which
concludes that cycling has a strong reliance on word of mouth (Bartle et al., 2009).
When looking at Scottish school-children, Kirby and Inchley (2009) conclude that
the social influence for younger individuals stems mainly from their parents,
suggesting that programmes and facilities should be aimed at parents as well as
younger people when attempting to increase cycling in this age range. However,
due to the increased autonomy realised during the transition from secondary school
to emerging adulthood (Crombie et al., 2009; Laska et al., 2009) the same parental
influences are unlikely to have such a significant effect in older individuals. Results
from a self-reported questionnaire across a Belgian population indicate that
increasing social support and ‘modelling’ leads to an increase in cycling (De Geus
et al., 2008). The same social support has proved to be effective in increasing
cycling rates in an Australian student sample (Titze et al., 2007), though there were
significant interactions between environmental and personal factors in this latter
study also which could alter the effect of the social factors. Fishman, Washington,
& Haworth (2012) identified that non-users of PBSS in Australia would be more likely
to use the PBSS if they saw others using it. In part this is down to increased
awareness of the PBSS’s availability but it could also be linked to the perceived gain
in personal safety due to the rise in cyclist numbers (Jacobsen, 2003).
2.2 Gender Differences in Cycling
When considering cycle commuting, it is essential to evaluate the significant
difference in uptake between genders. It is indicated that females equate to only
about a third of regular cycling commuters (Dickinson et al., 2003), though both
Rosenbloom (2006) and Crane (2007) discuss the apparent convergence of male
and female cycling commuter numbers in recent years. Rosenbloom goes on to
state that this convergence is in part due to a change in the role of women both in
the workplace as well as at home. Reasons for this continued gender disparity
includes lower perceptions of safety (Macmillan et al., 2014) and more complex trips
for females – for example taking in the school-run (Dickinson et al., 2003) - while
women are less likely to use specific cycling clothing compared to men and are more
likely to cycle on segregated off-road paths (Garrard, Rose, & Lo, 2008; Steinbach
et al., 2011). This could link in with the stereotypes of cyclists as being “rule breakers

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and risk takers” (Daley & Rissel, 2011), which acts as a significant barrier to the
uptake of cycling in women. Dickinson et al.’s (2003) study identified that financial
incentives have a significant influence on increasing cycling rates in women, with
Wardman, Tight, and Page (2007) suggesting that a daily payment of £2 would lead
to a near doubling of the rate of cycling for both men and women. On the other hand
Dowling (2000) confirmed that disincentives such as increased parking charges
have little effect on women, which could in part be due to the complex journeys often
undertaken by women leading to a perceived necessity to use the car more
frequently (Dickinson et al., 2003).
2.3 Active Transport in Emerging Adults
As mentioned in the introduction, much of the current literature considers AT in
younger or older generations (Dessing et al., 2014; Parkin, Ryley, & Jones, 2007).
When comparing older and younger generations, Aldred, Woodcock, and Goodman
(2015) established that younger individuals were more likely to cycle than those over
55 years old, but this conclusion was based on a single census response with no
additional detailed analysis. Simons et al. (2013) considered the benefits and
barriers to cycling in a younger cohort (15 – 19 year olds) and identified that the
most significant influences included increased autonomy, low costs and short travel
time – all factors also seen in older populations (Parkin, Ryley, & Jones, 2007). What
is notable however is that personal safety and health benefits did not feature highly
in the uptake of AT for this younger cohort, which suggests that there could be
inconsistency to barriers to cycling uptake within younger individuals. Unfortunately
it may not be possible to extrapolate this to the UK population due to the lower
overall rate of cycling and increased cyclists fatality rate in the UK compared to
Belgium (European Commission, 2014; Gill & Goldacre, 2009). However, Kirby and
Inchley (2009) established that personal safety, social factors, and reduction in
environmental impact from AT was of great importance to a large group of Scottish
school children, supporting Simons et al. (2013).
As part of the millennial generation, 18-29 year olds are believed to have
significantly different view and ideals than preceding generations, specifically
around sustainability (Pirie & Worcester, 1998). With such a low level of cycling
within the UK population (Office of National Statistics, 2014a; Scottish Government,
2010) it is important to examine whether there is a disparity between the perceived

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barriers of upstream policy makers, midstream policy influencers, and downstream
cycling commuters.
2.4 Theoretical Background
2.4.1 The Health Belief Model
Policies designed to increase AT are often based on a variety of behaviour change
models, with the Health Belief Model (HBM) arguably the most influential (Becker et
al., 1977) (Figure 2.1). However, it is clear that the HBM relies heavily on socio-
cultural aspects, suggesting that all behaviour emanate from these. Although it is
believed that socio-cultural elements such as gender and socio-economic status do
play a role in cycling uptake in the UK (Dickinson et al., 2003; Goodman, 2013),
both Dickinson et al. (2013), and Goodman (2013) demonstrate that these factors
are not the only predictors of cycling uptake. The HBM has also been criticised for
providing a weak predictive power of behaviour change (Harrison, Mullen, & Green,
1992), with Zimmerman and Vernberg (1994) concluding that the HBM should be
considered a list of variables rather than a relationship-based theory.
Figure 2.1. Diagrammatic representation of the Health Belief Model. Based on
Becker et al. (1977). Illustrates that Socio-demographic factors play a significant role
in behaviour change.

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2.4.2 The Theory of Reasoned Action & Theory of Planned Behaviour
Due to these flaws additional models have been developed including the Theory of
Reasoned Action (TRA) (Douglas, Fishbein, & Ajzen, 1977) which was revised by
Ajzen (1985) to form the Theory of Planned Behaviour (TPB) (Figure 2.2). The TPB
focused less on environmental factors of behaviour change, instead concentrating
on internal factors such as attitude and subjective norms. There are criticisms to the
TRA and TPB also, such as the lack of consideration for the social nature of human
interaction (Kippax & Crawford, 1993), with Taylor et al. (2006) arguing that both
models are biased towards individualistic interpretations of human behaviour. When
considering the behaviour change of cycling uptake, it is apparent that nether the
TRA or TPB consider major factors mentioned earlier such as the effect of relevant
cycling infrastructure (Pucher, Dill, & Handy, 2010), as well as environmental factors
such as the weather (Bergström & Magnusson, 2003) and topography
(Vandenbulcke et al., 2011). Although it could be argued that it is not specifically the
physical environment aspects themselves that must be considered, but the
individual’s attitude towards them.
Figure 2.2. Diagrammatic representation of the Theory of Reasoned Action
(TRA) and the Theory of Planned Behaviour (TPB). Based on Douglas, Fishbein,
& Ajzen (1977); and Ajzen (1985). Illustrates the lack of social nature of human
interaction within these models, as described by Kippax and Crawford (1993).
2.4.3 Trans Theoretical Model of Health Behaviour
Subsequently a model that examines these criticisms was put forward by Prochaska
and DiClemente (1982) – the Trans Theoretical Model of Health Behaviour (TTM)

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(Figure 2.3). Based around the Stages of Change (SoC) the TTM was developed
to assist in smoking cessation (Prochaska & DiClemente, 1983) and has more
recently been used for a variety of key health behaviour changes such as the
promotion of exercise and activity (Marshall & Biddle, 2001). One of the key benefits
of the TTM over previous models is that, as it can directly influence behaviour
change, it can be tested more rigorously through its use in intervention programmes
(Taylor et al., 2006). However, it is also argued that due to the significant differences
in intentions and goals between the behaviour of smoking cessation and behaviour
adaptation (Nigg et al., 2012), TTM may not be as well suited to altering positive
behaviours such as increasing cycling uptake.
Figure 2.3. Diagrammatic representation of the Trans Theoretical Model of
Health Behaviour including the Stages of Change Model. Based on Prochaska
and DiClemente, (1982). Illustrates the significance of the Decisional Balance on
successful progression through the SoC.
Within the TTM, the importance of weighing up the expected benefits against the
perceived costs of the new behaviour is central with this ‘Decisional Balance’ for
individuals in earlier SoC skewed towards negative factors (i.e. barriers) with the
opposite being true in later stages (Nigg et al., 2012) (Figure 2.4). When applied to
cycling as a form of AT, Nkurunziza et al. (2012) established that low bicycle costs,
increased bicycle quality, and receiving adequate training are all key motivators for
those in the contemplation stage, with the same motivators not identified in those in
the pre-contemplation stage. However, the definitions of the various SoC used in
the Nkurunziza et al. (2012) study differ significantly from previous research raising
the potential for erroneous conclusions. In addition, this study was conducted in
Tanzania – considered one of the world’s poorest economies (Central Intelligence

14
Agency, 2016) and so the influencing factors within this population will likely vary
considerably to those in the UK and other developed countries.
2.4.4 Behavioural Models in Relation to Cycling Uptake
Gatersleben and Appleton (2007) established that those in the maintenance stage
were affected less by poor facilities at the destination compared to those in other
stages. This research went on to illustrate that developing specific plans for action
as well as positive reinforcement and encouragement over a two-week period led to
a greater uptake of cycling as a form of AT in those already in the contemplation
stage of the SoC model. 42% of the journeys analysed by Gatersleben and Appleton
(2007) made reference to the weather – either positively or negatively –
demonstrating that the weather has a significant effect when considering the uptake
of cycling. Another interesting finding is that traffic safety was mentioned less
frequently towards the end of the two-week study period. This suggests that
although safety is a key barrier in those who are in the pre-contemplation or
contemplation stage, once action is taken the individuals perceptions change
quickly. It could be that there is a stigma attached to cycling that enhances this lack
of safety which is supported by Daley and Rissel (2011) who revealed four main
stereotypes linked to cycling, one of which is “Dangerous”.
Figure 2.4. The flow of positive and negative factors across the Stages of
Change. Adapted from Nigg et al (2012). Illustrates the greater influence of
negative factors (marked in orange) to behaviour uptake in the early SoC compared
to the greater influence of positive factors (marked in blue) in the later SoC.
Precontemplation Contemplation Preperation Action Maintenance
Perception of Positive Factors
Perception of Negative Factors
RelativeScore
Stage of Change

15
Another way to motivate progression through the SoC model is to encourage
incentives such as a free breakfast (Rose & Marfurt, 2007) or disincentives such as
increased cost of car parking at the destination (Buehler, 2012), both of which
influence the TTM’s Decisional Balance. Rose and Marfurt (2007) also revealed that
partaking in a social event such as a ‘cycle-to-work day’ progressed many of those
in the contemplation stage of cycling to the action stage – with 27% of those who
had cycled to work for the first time during this event still cycling five months later.
However, as this study gathered data from those who are in the action stage (i.e.
have cycled into work at least once in the past year) it would be interesting to
examine whether the same incentives affect those in the pre-contemplation and
contemplation stages. From the results of Rose and Marfurt (2007) it appears that
there is a difference in the way males and females approach the TTM’s Decisional
Balance, with more females continuing to cycle after five months than males (30%
compared to 22%) which could signal that there are more females in the
contemplation stage. Alternatively there could be a barrier to the action stage that
is having a stronger inhibitory effect on females than on males in the same stage
such as the higher perceived risk to cycling seen by women (Mullan, 2013; van
Bekkum et al., 2011).
2.5 Chapter Summary
This chapter has considered the current literature surrounding influencing factors to
cycling uptake as a whole, focusing on the three overarching themes set out by
Parkin, Ryley, and Jones (2007). Subsequently a review of the specific factors that
may attribute to the lower number of female cyclists compared to males was
evaluated. A reflection on the limited literature regarding active transport in
emerging adults is discussed, before concluding with an exploration of the relevant
theoretical models that underpin behavioural change – the HBM, TRA, TPB, and the
TTM - with specific regard the behaviour change models related to increasing
cycling as a mode of transport.

16
Chapter 3: Methodology
3.1 Philosophical Rational
There are two main strategies of research methodology – Qualitative and
Quantitative, with deep-rooted contrasting epistemological and ontological
considerations to each (Table 3.1) (Bryman & Bell, 2015). The merits of each
strategy are well documented with quantitative strategies focusing on the
measurement of fine differences between individuals while qualitative strategies
focus on collecting deep, rich data from their subjects which can be beneficial for
building theories (Bryman & Bell, 2015; Patton, 2002). Due to the replicability of
quantitative studies, it is possible to compare findings across different groups and
studies more easily than qualitative strategies (Grosvenor, 2000), especially as
qualitative strategies often contain smaller sample sizes, leading to reduced
generalisability of the findings (Bryman & Bell, 2015). Due to the established
research into the influencing factors to cycling as a form of AT noted in the literature
review, coupled with the difficulty in establishing an individual’s SoC through
qualitative means (Nkurunziza et al., 2012), it would appear that a quantitative
approach would be most appropriate for this study. However, as the proposed age
range (18 – 29 year old) is not well studied (Simons et al., 2014), there is potential
for unexpected factors that influence cycling uptake to be identified, a point
supported by the lack of increased cycling uptake due to existing policies (Cycling
Scotland, 2015). Quantitative research is often used where responses are
anticipated, which could lead to a wording bias (Clifton & Handy, 2001). In contrast
to quantitative strategies, qualitative research can study the range of attitudes
present (Grosvenor, 2000), which could be of great use when looking at complex
travel behaviour such as cycling as a form of AT (Beirão & Sarsfield Cabral, 2007;
Poulenez-Donovan & Ulberg, 1994). Grosvenor (2000) goes on to note that
qualitative research is very useful in novel situations – such as the influencing
factors for cycling uptake in 18 - 29 year olds.

17
Orientation Quantitative Research Qualitative Research
Principle Deductive, testing of theory Inductive, generation of theory
Epistemological Natural Science Model, in
particular positivism
Interpretivist
Ontological Objectivism Constructionism
Table 3.1. Differences in underlying constructs between qualitative and
quantitative strategies. Adapted from Bryman and Bell (2015 pg. 28).
From this it is clear that a qualitative strategy must be incorporated in order to
explore the influencing factors to cycling in young adults, whilst also utilising the
repeatability and comparability of quantitative strategies (Grosvenor, 2000; Bryman
& Bell, 2015). It has been debated whether these two fields should be seen as
mutually exclusive, or whether the underlying constructs are more complimentary
than originally thought (Johnson & Onwuegbuzie, 2004). In turn this has led to the
development of the mixed method approach which considers the epistemological
and ontological considerations set out in Table 3.1 as interchangeable rather than
absolute (Bryman & Bell, 2015). In an earlier edition, Bryman and Bell (2011)
discuss the benefits gained from cross-checking the findings from one methodology
against those of another (triangulation), a point that is supported by Grosvenor
(2000) who stated that qualitative research can complement quantitative research.
Overall it is believed that mixed method studies provide broad investigatory insights
compared to either quantitative or qualitative methods alone (Easterby-Smith et al.,
2012), and so the research in this paper follows an equal-weighted qualitative
followed by quantitative approach (as set out by Morgan in the Priority-Sequence
Model, 1998), with the methodology split into two studies as described below.
3.2 Study I
3.2.1 Overview
When considering the three main qualitative methodologies - observation studies,
focus groups, and interviews - there are benefits and limitations to each (Bryman &
Bell, 2015). As it is not possible to directly observe an individual’s perceptions, an
observational methodology is not considered (Clifton & Handy, 2001). Although data
from focus groups are enhanced by participant interactions (Patton, 2002),

18
interviews are considered superior as there is a decrease in the potential for the
social desirability effect, improvement in participant confidentiality, and reduction in
the number of participants required (Clifton & Handy, 2001; Grosvenor, 2000). Due
to the exploratory nature of Study I’s research objective, it was decided that a semi-
structured qualitative interview was the most appropriate methodology, allowing for
flexibility in participant responses, as well as the exploration of new themes as they
became available, whilst also focusing the interview on the aims of the project
(Bryman & Bell, 2015).
3.2.2 Sample
The sample was selected by an opportunity method through contacts of the main
researcher. Participation was initially sought from nine individuals, of which four
responded positively to involvement in the study. No further contact was received
from the other five. In order to create a large enough sample size, a snowball
sampling method was utilized with the four interviewees, which yielded a further four
participants.
Similar studies have signaled that information saturation occurs between 12 and 16
participants (Mullan, 2013), however due to the difficulty in recruiting suitable
candidates, a sample size of eight upstream and midstream sources was
considered sufficient for the methodology utilized in this study, without depleting
resources from the project as a whole (Baker & Edwards, 2012). In order to ensure
that information saturation had occurred within this sample (Trotter, 2012), a small
number of downstream sources (emerging adults aged 18 – 29) were also
interviewed. Two of these downstream participants were male, and one was female.
A fourth interview was scheduled with an additional downstream female, however it
was not possible to arrange this in the available timescale.
In total the sample consisted of six males and five females, with an age range of 21
to 73 year old at time of interview. All upstream and midstream participants were
currently working on, or had previous experience of projects that influenced policies
related to cycling in Edinburgh, either directly (upstream) or indirectly (midstream).
All downstream sources had first-hand experience of cycling in and around
Edinburgh.

19
3.2.3 Design
A semi-structured interview style was implemented for Study I. This followed a
predetermined interview guide, developed in line with recommendations from
Morgan and Krueger (1998) as utilized by Simons et al. (2013), with the following
structure:
1. Study introduction. A brief introduction, including gathering informed
consent.
2. Starting questions. Simple questions to put the interviewee at ease and
gain an understanding of their involvement in cycling related projects.
3. The main body of the interview. Focusing on the influencing factors to
cycling, with additional points considering the demographics of cyclists in
Edinburgh, as well as the marketing campaigns promoting cycling in
Edinburgh.
4. Concluding remarks. Included opportunity for the interviewee to add any
additional points missed during the previous stage, as well as a full participant
debrief about the study.
A copy of the interview guide used as well as example questions is included in
Appendix A. The interview was terminated once the interviewer was satisfied all of
these points had been covered sufficiently by the participant. All participants were
given contact details of the main researcher in the event that they had additional
information to add at a later date. Subsequently two participants (“E” and “J”)
emailed additional points which were added to their respective interview transcript.
Upon completion of the interview all participants received a copy of the Interview
Debrief Sheet which explained the study in greater depth (Appendix B).
Interviews took place in a convenient location for the interviewee and consisted of
their place of work (5), a local café (4), and their home (2). All locations where
considered for both privacy and low noise prior to commencing the interviews.
Interviews lasted between 15 minutes and 50 minutes.
3.2.4 Materials
All interviews were recorded using a Lenovo S8-50F Tablet running the Smart Voice
Recorder Application (SmartMob, 2014) through the Android 5.0.1 operating system
(Google, 2015). The files were recorded at 44 kHz to ensure greatest recording
clarity. A short pilot study prior to commencing interviews ensured sufficient audio

20
data recording to allow for accurate transcription. Following the completion of the
interview short sections of the audio file were immediately checked for sufficient
quality and any additional notes taken by the interviewer were recorded as
necessary. All interviews were subsequently transcribed by the main researcher
within 24 hours of the interview to ensure that accurate recall occurred for any less
audible sections.
3.2.5 Analysis
Upon successful transcription, all data was coded in NVivo 10 (QSR International,
2014) using a thematic analysis approach. As this study was designed to test
existing theories rather than generate new theory, the grounded theory approach
(Strauss & Corbin, 1998) was not undertaken. However, in order to accurately reflect
the topics raised in the data, the thematic analysis undertaken followed the initial
steps outlined in the grounded theory approach, a method described by Braun and
Clarke (2006), as follows:
1. Familiarization with the data set. Development of initial ideas by listening to
interviews and transcribing the data.
2. Establishment of basic codes (called sub-codes). These sub-codes were
based on the relevant segments that arose frequently within the transcripts.
Segments of the interviews were allocated into these sub-codes. Transcript
segments could be assigned to multiple sub-codes.
3. Development of codes. The grouping of sub-codes into larger related sets of
codes.
4. Emergence of themes. These codes were subsequently grouped together into
broad themes. These themes were developed based on the research aims set
out in the introduction.
3.2.6 Ethical Considerations
In order to prevent any professional harm occurring to the interviewees, all names
and distinguishing features (occupations, workplaces etc.) were anonymized by the
main researcher at the transcription stage. All participants gave full informed
consent to being recorded and to their voluntary participation in the study prior to
commencing of the interview.

21
3.3 Study II
3.3.1 Overview
When considering different types of quantitative methodology, self-completing
questionnaires are not affected by interviewer variability, whilst also being quicker
and more convenient to gather data from greater numbers of participants (Bryman
& Bell, 2015). However, Bryman and Bell (2015) also note that questionnaires must
be shorter than other methods in order to increase the response rate, potentially
leading to a reduction in the quality of the data collected. Though it is also not
possible to evaluate how much consideration the respondent gives to completing
online questionnaires - especially when compared to interviews or focus groups
(Bryman & Bell, 2015) - these concerns can be reduced by the careful design of
questionnaires (Bryman & Bell, 2015).
3.3.2 Sample
As this study considered the influencing factors in the Edinburgh population,
questionnaires were targeted at emerging adults who lived within the City of
Edinburgh Council boundary, as defined by the Local Government Boundary
Commission for Scotland (2013) (Appendix C). Due to the limited timescale of this
project Study II was designed to gather a total sample size of 100-150 completed
questionnaires. In order to assess the research question pertaining gender
differences in the influencing factors to cycling, an equal number of male and female
respondents was sought. The questionnaire was distributed to cycling organizations
to ensure that cyclists from across the SoC were included within the sample.
The sample was selected using an opportunity sample of the main researchers’
network. A link to the online questionnaire was distributed via email and social media
platforms, with a two-week time limit for questionnaire completion implemented due
to project time restraints. Alongside this the online questionnaire link was sent out
to relevant membership groups such as the mailing list of local cycling organizations.
Subsequently participants were encouraged to forward the questionnaire link onto
additional relevant individuals through a snowball sampling method (Bryman & Bell,
2015).

22
3.3.3 Design & Materials
A self-completing questionnaire was developed with the following main topics:
1. Basic Demographics. Items focused on ensuring participant eligibility for the
study (age, home location, physical impairments), as well items pertaining to the
research aims of gender differences.
2. Respondents Current “Stage of Change” (1 item). This item was based on
the questionnaire item utilized by Curry et al. (1992) for assessing SoC of dietary
fat reduction.
3. Perceptions of influencing factors to cycling uptake. A total of 21 items
including the physical environment (10 items), personal factors (8 items), and
social factors (3 items). These items were selected based on the findings from
Study I, with each item based on a 7-point scale (Strongly Agree to Strongly
Disagree). To avoid a response bias, all factor items were randomly assigned
to be worded in a positive or negative manner.
4. Life events and incentivizing events (2 items). These items where based on
the findings from Study I. The incentivizing event items consisted of eight
events, each of which utilized a 7-point scale (Very Likely to Very Unlikely) as
to what extent each event would motivate respondents, while the life events
question was open ended (up to three answers per respondent).
To avoid participants omitting questions unintentionally, all questions required an
answer before continuing – although an opt-out (prefer not to answer) was given
throughout. The questionnaire was arranged following the guidelines set out by
Bryman and Bell (2015, Pg. 221) in order to increase the completion rate. This
allowed simple questions at the start of the questionnaire (e.g. basic demographics),
before leading onto more challenging items pertaining factors and life events. No
contact details were collected for individual participants, which led to the inability to
follow up partially completed questionnaires. A full copy of the questionnaire is
included in Appendix D, including analysis coding references.
Prior to data collection of the questionnaire, a small pilot study was conducted with
three emerging adults (aged 21 - 27) to ensure all questions read correctly and the
questionnaire ran as expected. The pilot study participants did not partake in the
data collection stages for Study I or Study II. Following this pilot study the wording

23
and layout for two of the questions regarding influencing factors and the life events
questions were altered to increase question clarity (Appendix D).
3.3.4 Analysis
Data was collected online before being exported into Excel for coding. The full
coding layout for all questions except the final question (regarding life events) is
outlined in Appendix D. Respondents geographical eligibility was evaluated using
the online software BatchGeo (2016) to ensure respondents home postcode was
within the City of Edinburgh Council Boundary (Government Boundary Commission
for Scotland, 2013). The responses for the open ended question were grouped
according to the codes from Study I. At this stage incomplete data as well as
ineligible responses are removed from the data set, leaving 105 completed
questionnaires for analysis. Further data analysis was conducted in SPSS 20.0
(IBM, 2014) with the corresponding statistical tests outlined in Table 3.2 being
conducted to a statistical significance of p ≤ 0.05. Actual commuting distance was
calculated in a straight line between the postcodes of the respondents’ home and
place of work or study using online software provided by FreeMapTools (2016).
Aim Statistical Test
Compare influencing factors within emerging adults Moods Median Test
Compare influencing factors between genders Chi-Squared
Compare influencing factors across the SoC model Chi-Squared
Table 3.2. Statistical analysis conducted in SPSS 20.0 (IBM, 2014) for each of
the research aims of Study II.
3.3.5 Ethical Considerations
To comply with Edinburgh Napier University Ethical Committee regulations, the
questionnaire was generated using the NoviSurvey software (2016). All participants
gave full informed consent before commencing the questionnaire. Participants were
informed that they were free to stop participating at any point without fear of negative
consequences, with no data being attributable to any individual. No personal details
were collected during the questionnaire except for basic demographic information
necessary to consider the research aims set out earlier.

24
Chapter 4: Results
4.1 Study I
Three overarching themes emerged from the interview data – personal factors,
physical environment, and social factors, with a total of 21 influencing factors
(codes) (Figure 4.1). All 21 codes emerged across both upstream and midstream
participants, with no new codes being developed solely from the downstream
interviews. This allowed the three downstream interviews to be analysed alongside
the upstream and midstream interviews in order to give a broader perspective of
these codes. When looking at the codes overall those that are noted most frequently
include personal safety (noted by all 11 participants), infrastructure (noted by 10
participants), and weather (noted by 9 participants). Codes noted least frequently
are sustainability, bicycle theft, and funding (for training or infrastructure) all of which
were noted by 3 participants each, with bicycle theft closely related to bicycle
storage infrastructure.
PhysicalEnvironment
Storage at home
Storage at journey
end
Distance
Road quality
Other bike
facilities
Weather
Cycle path
network
Signage
Topography
Road and cycle
structure
convergence
SocialFactors
Culture
Self-image
Social aspect
to cycling
Figure 4.1. Overarching themes, split into the 21 coded factors (barriers and
facilitators) from Study I interviews.
PersonalFactors
Confidence
Access to bike /
equipment
Physical Exertion
/ Health
Quicker than
alternatives
Cheaper than
alternatives
Personal safety
Enjoyment
Other
commitments

25
Eight incentivising events were mentioned within the interviews, as well as the
specific barriers to cycling uptake these are designed to overcome (Table 4.1).
Incentivising event Barrier event is designed to overcome
Bicycle Breakfast (2) Low social component
Bicycle Buddy Scheme (4) Low confidence
Bicycle Maintenance (2) Lack of knowledge
Social Cycle Rides (3) Low social component
Personal Cycling Skills Training (7) Low confidence
Workplace Challenges (2) Low social component
“Bike to Work” Scheme (1) Access to bicycle
Table 4.1. The coded incentivising events that emerged from Study I
interviews as well as the barriers these events are designed to overcome.
Number of individual interviewees noting each event in brackets.
4.2 Study II
4.2.1 Demographics
A total of 194 participants completed the survey. Of these 89 were discarded as they
did not fit the required criteria. This led to a total of 105 participants with 50 female
(48%) and 54 male (51%). One participant did not disclose their gender (1%).
A total of 104 participants estimated their daily commute to their place of work or
study, of which 91.5% estimated it was equal to or below 8km. This figure rose to
94% in females compared to 87% in males (Table 4.2). Only 82 respondents input
a full postcode for both their home and place of work or study. Comparisons of the
estimated distance and actual distance identified a statistically significant difference
between the two distance measurements (t = -4.522, df = 82, p<0.01) with both
males (t = -3.115, df = 80, p=0.03) and females (t = -4.305, df = 80, p<0.01)
overestimating their distance to their place of work or study.

26
Female Male Total
Distance (x) Est. Act. Est. Act. Est. Act.
x < 2km 17 15 9 6 26 21
2km < x ≤ 4km 11 16 14 18 25 34
4km < x ≤ 6km 12 5 16 8 28 13
6km < x ≤ 8km 7 2 8 5 15 7
8km < x 3 3 7 4 10 7
Total 50 41 54 41 104 82
Table 4.2. Frequency distribution of respondents’ estimated and actual
commuting distance to place of work or study divided by gender. Est. =
Estimated Distance, Act. = Actual Distance.
4.2.2 Influencing Factors in Emerging Adults
Moods Median Test results for all 21 questionnaire items relating to the influencing
factors to cycling, as well as the eight incentivising events are displayed in Table
4.3. A significant effect (p ≤0.05) was established in four of the influencing factors
to cycling uptake: cycling confidence, storage at end point, other end point facilities,
and topography; and four of the incentivising events: personal bicycle skills (likely
to motivate), advice for cyclists (unlikely to motivate), bicycle maintenance courses
(unlikely to motivate), and social rides (likely to motivate) (Figure 4.2a & b). The
open-ended question regarding Life Events yielded one category not considered in
the questionnaire: starting a family.

27
Table 4.3. Chi-Squared results for perceptions of influencing factors and
incentivising events for 18 – 29 year olds based in Edinburgh. M= Median
Value, X2 = Moods Median Test, p = Significance Level (* = p ≤0.05; ** = p ≤0.01;
*** = p ≤0.001). Degrees of Freedom for all factors analysed are 1. b All values are
less than or equal to the median and so unable to perform Moods Median Test.
Influencing Factor M X2 p
Cycling Confidence 1.00 5.396 0.020*
Storage at Home 2.00 1.233 0.267
Distance 7.00b - - - - - -
Road Quality 3.00 3.476 0.062
Storage at End Point 6.00 7.168 0.007**
Other End Point Facilities 4.00 8.68 0.003**
Weather 4.00 0.322 0.571
Availability of Cycle Path 4.00 3.726 0.054
Signage of Cycle Routes 4.00 0.418 0.518
Topography 5.00 19.017 <0.001***
Link Between Roads and Cycle
Paths
4.00 1.299 0.254
Monetary Savings 2.00 0.257 0.612
Access to a Bicycle 2.00 0.214 0.643
Enjoyment from Cycling 5.50 0.154 0.695
Quicker Than Alternatives 2.00 0.445 0.505
Personal Safety 3.00 0.268 0.605
Health Benefits 2.00 0.488 0.485
Other Commitments 6.00 0.503 0.478
Negative Cyclist Self Image 4.00 0.063 0.802
Social Aspect 4.00 0.01 0.919
Positive Self Image 2.00 0.001 0.976
Incentivising Events
Personal Bicycle Skills 4.00 10.255 0.001***
Bicycle Breakfast 6.00 0.509 0.475
Advice for Cyclists 5.00 7.913 0.005**
Bicycle Maintenance Courses 5.00 8.625 0.003**
Social Cycle Rides 4.00 5.417 0.020*
Workplace Challenges 4.00 1.425 0.233
“Cycle to Work” Scheme 5.00 1.234 0.267
Access to a Cycling Mentor 3.00 0.002 0.964

28
Participant Response Participant Response
Confidence
I am confident I could regularly commute
to my place of work / study by bicycle.
Figure 4.2a. Graphic representation of significant participant responses by total
percentage towards influencing factors to cycling uptake (Table 4.3).
Storage at Journey End Point
I do not have a safe place to leave my
bicycle at my place of work / study.
Topography
As a city, Edinburgh is too hilly to cycle
around.
Other Facilities
Excluding the bicycle storage facilities, I
feel that the other facilities offered by my
place of work / study are adequate for
the needs of cyclists.
Percentage(%)
Percentage(%)
Percentage(%)
Percentage(%)

29
Participant Response
Figure 4.2b. Graphic representation of significant participant responses by
total percentage towards incentivising events to cycling uptake (Table 4.3).
Question posed: Which of the following would motivate you to cycle to your place
of work / study more regularly.
Advice for Cyclists
Bicycle Maintenance Courses Social Rides
Personal Bicycle SkillsPercentage(%)
Percentage(%)
Percentage(%)
Percentage(%)

30
4.2.3 Gender
Chi-Squared analysis results for the perceptions to each influencing factors and
incentivising events between genders are displayed in Table 4.4. A significant effect
(p ≤ 0.05) was established in four of the influencing factors to cycling uptake
between genders: bicycle storage at the journey end point, additional facilities at the
journey end point, topography, and link between road and bicycle paths; each of
which were skewed towards a greater negative perception by females, except
topography were males agreed more strongly that Edinburgh was too hilly (Figure
4.3a). Two of the eight incentivising events were perceived by females as significant
motivators - bicycle maintenance sessions and access to the cycle to work scheme
- while personal bike skills, advice for cyclists, and access to a cycling mentor were
perceived by males as significant motivating events (Figure 4.3b).

31
Table 4.4. Chi-Squared results for perceptions of influencing factors &
incentivising events between males and females. M= Median Value, IQR = Inter
Quartile Range, X2 = Chi-Squared value, Df = Degrees of freedom, p = Significance
Level (* = p <0.05; ** = p <0.01; *** = p <0.001).
Influencing Factor
Female Male
X2 Df pM IQR M IQR
Cycling Confidence 2.00 1.00 1.00 1.00 11.362 8 0.182
Storage at Home 2.00 1.25 2.00 2.00 8.775 6 0.187
Distance 6.50 1.00 7.00 1.25 7.963 6 0.241
Road Quality 3.00 1.00 3.00 3.00 7.39 6 0.286
Storage at End Point 5.50 3.00 6.00 4.00 14.579 7 0.042*
Other End Point Facilities 4.00 2.00 3.50 4.00 18.386 7 0.010**
Weather 4.50 4.25 3.00 5.00 10.251 6 0.114
Availability of Cycle Path 5.00 3.00 4.00 3.25 8.851 6 0.182
Signage of Cycle Routes 4.00 2.25 4.00 2.50 5.551 7 0.593
Topography 5.00 2.25 6.00 2.25 22.977 6 0.001**
Link Between Roads and
Cycle Paths
4.50 2.00 4.00 3.00 15.666 7 0.028*
Monetary Savings 2.00 3.00 2.00 2.00 5.562 6 0.474
Access to a Bicycle 2.00 3.00 1.00 2.00 5.167 6 0.523
Enjoyment from Cycling 5.00 3.00 6.00 2.25 1.471 7 0.983
Quicker Than
Alternatives
2.00 2.00 2.00 3.25 12.441 6 0.053
Personal Safety 3.00 2.00 3.00 3.00 11.055 7 0.136
Health Benefits 2.00 1.00 1.50 1.00 4.815 7 0.683
Other Commitments 6.00 3.00 6.00 2.00 4.257 6 0.642
Negative Cyclist Self
Image
2.00 1.50 2.00 4.00 4.444 6 0.617
Social Aspect 4.00 3.00 4.00 3.00 3.248 7 0.861
Positive Self Image 4.00 1.00 4.00 1.50 7.664 6 0.264
Incentivising Event
Personal Bicycle Skills 4.00 4.00 2.00 3.00 22.113 6 0.001**
Bicycle Breakfast 6.00 3.00 6.00 5.00 10.215 6 0.116
Advice for Cyclists 5.00 1.00 4.00 3.00 17.473 6 0.008**
Bicycle Maintenance
Courses
6.00 2.00 5.00 3.00 13.259 6 0.039*
Social Cycle Rides 4.50 2.00 4.00 3.00 10.781 6 0.095
Workplace Challenges 4.00 2.25 5.00 2.00 8.246 6 0.221
“Cycle to Work” Scheme 5.00 3.00 5.00 2.00 16.961 6 0.009**
Access to a Cycling
Mentor
3.00 2.00 3.00 3.00 11.851 5 0.037*

32
Storage at Journey End Point
I do not have a safe place to leave my
bicycle at my place of work / study.
Other Facilities at End Point
Excluding the bicycle storage facilities, I
feel that the other facilities offered by my
place of work / study are adequate for the
needs of cyclists.
Topography
around.
Link between Road and Cycle Paths
The road network in Edinburgh is well connected
to the cycle path network.
Figure 4.3a. Graphic representation of influencing factors with significant
differences by total percentage between genders (from Table 4.4). Green bars
represent males, blue bars represent females.
Percentage(%)
Percentage(%)
Percentage(%)
Percentage(%)

33
Figure 4.3b. Graphic representation of incentivising events with significant
differences by total percentage between genders (from Table 4.4). Green bars
represent males, blue bars represent females.
Personal Bicycle Skills Advice for Cyclists
Bicycle Maintenance Sessions Access to the Cycle to Work
Scheme
Access to a cycling mentor
Percentage(%)
Percentage(%)
Percentage(%)
Percentage(%)Percentage(%)

34
4.2.4 Stages of Change
The SoC model described by Prochaska and DiClemente (1982) notes that
behaviours must be achievable by all. To comply with this assumption participants
who’s estimated commuting distance was over 8km (seen as achievable by the
British Medical Association, 2012) were excluded from the SoC analysis, leaving a
total of 95 respondents. Due to the low number of respondents in the contemplation,
preparation, and action stages (accounting for 19% of the total sample) it was not
possible to accurately analyse these stages and so all subsequent SoC analysis
focuses on comparing the pre-contemplation and maintenance stages only. The
participant count for these groups is displayed in Figure 4.4. There was no
significant difference in the numbers of males and females in each SoC [X2 = 0.230
(2, N = 94), p = 0.892].
Chi-Squared analysis results for the perceptions of each influencing factors and
incentivising events between pre-contemplation and maintenance SoC are
displayed in Table 4.5. A significant effect (p ≤ 0.05) was established in 12 of the
influencing factors to cycling uptake between SoC, with pre-contemplators
perceiving stronger agreement with cycling confidence, distance, weather,
topography, financial gains, access to a bicycle, enjoyment from cycling, quicker
Figure 4.4. Number of respondents for pre-contemplation and maintenance
SoC, displayed by gender. Respondents who answered ‘prefer not to disclose’ (1)
are excluded from this figure. No significant effect was established between genders
across the two SoC. Green bars represent males, blue bars represent females.
Count
Stage of Change

35
than alternatives, other commitments, negative cyclists self-image, and those in the
maintenance stage agreeing strongly with link between the road and cycle paths
and personal safety (Figure 4.5a). There was significant interaction between the
SoC for two of the incentivising events studied: bicycle maintenance courses and
access to a cycling mentor, with those in the maintenance stage less likely to agree
that these events would be of benefit to them compared to pre-contemplators
(Figure 4.5b).

36
Table 4.5. Chi-Squared results for perceptions of influencing factors and
incentivising events between the two SoC. PC = Pre-Contemplation Stage, Main
= Maintenance Stage. Med = Median, IQR = Inter-Quartile Range, X2 = Chi-Squared
value, Df = Degrees of freedom, p = Significance Level (* = p<0.05; ** = p<0.01; ***
= p<0.001).
PC Main
Influencing Factor Med IQR Med IQR X2 Df p
Cycling Confidence 2.00 3.00 1.00 0.00 27.606 5 <0.001***
Storage at Home 2.50 4.00 2.00 2.00 7.031 5 0.218
Distance 6.00 3.50 7.00 1.00 19.185 5 0.002**
Road Quality 3.00 2.75 3.00 2.00 4.346 6 0.630
Storage at End Point 6.00 3.00 6.00 4.00 8.487 7 0.292
Other End Point Facilities 4.00 1.75 3.00 2.00 11.566 7 0.116
Weather 6.00 3.00 2.00 3.00 31.472 6 <0.001***
Availability of Cycle Path 4.00 2.75 4.00 2.25 5.374 6 0.497
Signage of Cycle Routes 4.00 2.75 3.00 3.00 7.627 7 0.367
Topography 4.00 3.50 6.00 2.00 14.239 6 0.027*
Link Between Roads and
Cycle Paths
4.00 1.00 4.00 2.00 14.408 7 0.044*
Monetary Savings 3.00 4.00 1.00 2.00 18.116 5 0.003**
Access to a Bicycle 6.00 5.50 1.00 1.00 41.457 6 <0.001***
Enjoyment from Cycling 4.00 2.75 6.00 2.00 29.634 4 <0.001***
Quicker Than
Alternatives
4.00 4.00 1.00 1.00 29.747 6 <0.001***
Personal Safety 3.50 2.75 3.00 2.00 19.327 6 0.004**
Health Benefits 2.00 2.00 1.00 1.00 7.608 4 0.107
Other Commitments 5.50 2.75 7.00 1.00 18.936 6 0.004**
Positive Self Image 6.00 3.75 2.00 2.00 38.558 6 <0.001***
Social Aspect 4.00 2.00 4.00 2.25 7.072 6 0.314
Negative Self Image 3.00 3.00 4.00 1.00 12.157 6 0.059
Incentivising Event
Personal Bicycle Skills 2.00 3.50 4.00 3.00 5.271 6 0.510
Bicycle Breakfast 5.00 4.50 7.00 2.25 9.119 6 0.167
Advice for Cyclists 5.00 3.50 5.00 2.00 4.516 6 0.607
Bicycle Maintenance
Courses
4.00 3.00 6.00 2.00
22.891 6 0.001***
Social Cycle Rides 4.00 3.00 4.50 2.00 5.285 6 0.508
Workplace Challenges 3.50 3.00 4.00 1.25 10.043 6 0.123
“Cycle to Work” Scheme 4.00 4.00 5.00 2.00 11.094 6 0.086
Access to a Cycling
Mentor
2.00 2.75 4.00 2.00 13.392 5 0.020*

37
Confidence
I am confident I could regularly commute
to my place of work / study by bicycle.
Distance
It is too far from my home to place of
work / study for me to commute by
bicycle.
Weather
I would cycle to my place of work / study
whatever the weather.
Topography
around.
Figure 4.5a. Graphic representation of influencing factors with significant
differences by total percentage between SoC (from Table 4.5). Pre-Contemplation
SoC is represented by blue bars, Maintenance SoC is represented by green bars.

38
Figure 4.5a (Continued). Graphic representation of influencing factors with
significant differences by total percentage between SoC (from Table 4.5). Pre-
Contemplation SoC is represented by blue bars, Maintenance SoC is represented by
green bars.
Link between road and cycle
paths
The road network in Edinburgh is well
connected to the cycle path network.
Reduction in cost compared to
other transport modes
I can save money by cycling to my place
of work / study.
Access to bicycle
I have access to a bicycle whenever I
need it.
Enjoyment of cycling
I do not enjoy cycling to my place of work
/ study.
Percentage(%)
Percentage(%)

39
Figure 4.5a (Continued). Graphic representation of influencing factors with
significant differences by total percentage between SoC (from Table 4.5). Pre-
Contemplation SoC is represented by blue bars, Maintenance SoC is represented by green
bars.
Participant ResponseParticipant Response
Quicker than alternative models
Cycling to my place of work / study takes less
time than alternative transport modes.
Personal safety
I do not feel safe from other road / path
users when riding my bicycle in Edinburgh.
Too many other commitments
I have too many other commitments to
cycle to my place of work / study.
Positive self-image
I consider myself to be a cyclist.
Percentage(%)
Percentage(%)

40
Figure 4.5b. Graphic representation of incentivising events by total percentage
with significant differences between SoC (from Table 4.5). Pre-Contemplation
SoC are represented by blue bars, Maintenance SoC are represented by green bars.
Bicycle Maintenance
Percentage(%)
Access to a cycling mentor
Percentage(%)

41
Chapter 5: Discussion
5.1 Key Findings
This study aimed to examine the factors influencing cycling uptake in emerging
adults. The findings confirm the difference in influencing factors within this age
group (Research Question 1), and explores the disparity between policy makers
and downstream users (Research Question 2). The finding also partially confirms
the differences to perceptions between genders (Research Question 3), as well as
across the SoC model (Research Question 4).
5.2 Influencing Factors for Emerging Adults
The primary aim of this study was to examine the factors influencing cycling uptake
in a sample of those aged 18-29 in Edinburgh. The findings confirm the difference
in these factors within this age group compared to younger school children and
adolescents (Kirby & Inchley, 2009) and older individuals (Parkin, Ryley, & Jones,
2007). When considering the factors explored in Study II it is clear that there is a
discontentment amongst emerging adults with the current storage facilities at
journey end locations, though not in regards to additional facilities such as showers,
changing facilities, and lockers for cyclist. This is supported within Study I:
“…a lot of organisations, not all, are taking steps to make sure people
can change, have a shower, and have somewhere to keep all their kit.”
(Upstream Source)
This supports the findings of Buehler (2012) who identified a greater likelihood of
cycling to work when secure bicycle parking and showering facilities were available
compared to bicycle storage facilities alone. It is likely that the majority of these end-
point facilities are regulated directly by employers rather than the local council. With
many UK employers having a negative view of implementing low cost options (e.g.
additional cycling facilities - Potter et al., 1999), it may be necessary for Edinburgh
Council to assist the development of these schemes by promoting the additional
benefits of AT to employers. This could include improved employee health (de
Hartog et al., 2010; Oja et al., 2011) leading to fewer worker sick days and increased
productivity (Centre for Disease Control and Prevention, 2013).

42
What is interesting is that safe bicycle storage at home is not a significant barrier for
emerging adults, suggesting that the initiatives put in place by the City of Edinburgh
Council noted in Study I such as the “communal bicycle huts” (Midstream Source)
are having the desired effect. Another explanation is that journey end point storage
may be less secure or there is potentially more uncertainty surrounding its
availability, a point which is reinforced from Study 1:
“I guess guaranteed space as well, where you’re going. If there is going
to be somewhere to safely lock up [your bicycle].” (Downstream Source)
Another noteworthy non-significant factor is commuting distance. This is counter to
previous research, with Heinen, Maat, and Van Wee (2011) concluding that many
individuals perceive their commute to be too far to cycle. However, Keijer and
Rietveld (2000) indicated that journeys under 2km are also less attractive to cyclists
as there is no significant gain in time or convenience. With 25% of respondents in
Study II noting their regular commute to be under 2km (Table 4.2), it could be that
Edinburgh is in fact too compact for commuting by bicycle to be seen as beneficial.
This is reinforced in Study I, with the comments:
“…if you have someone that only has half a mile, a mile maybe to walk
to wherever they’re going, maybe isn’t going to benefit that much
bearing in mind the hassle of getting the bike out…” (Midstream Source)
“In the centre of Edinburgh people don’t necessarily need to cycle. If
you can walk around… then I don’t know if they would cycle.”
(Downstream Source)
As noted by Mullan (2012), it is likely that there is interaction between these factors,
and so reducing additional barriers such as the pressure felt by individuals to buy
expensive equipment could increase cycling in those that commute under 2km. As
many policies regard AT as a whole – grouping walking and cycling together
(Gordon-Larsen et al., 2009; Wagner et al., 2001), it is likely that cycling initiatives
do not focus on individuals commuting under 2km as they are likely to walk instead
(Millward et al., 2013), and so are still actively transporting themselves. However,
additional benefits to cycling such as increased intensity of cycling compared to
walking (Oja et al., 1999) should be promoted alongside the aspects of speed and
convenience. Edinburgh’s hilly topography is perceived as a significant barrier to

43
cycling uptake in emerging adults, potentially as an increased level of physical
exertion is required to overcome these (Mullan, 2013) and could leave individuals
in need of additional facilities such as showers at their journey end point (Buehler,
2012). This was summed up by one source:
“…People don’t want to arrive at work sweaty.” (Midstream Source)
Although physical exertion is linked to increased health benefits (Götschi et al.,
2015), and so it is perhaps interesting to note that the motivator of increased health
outcomes from cycling does not have a significant effect on cycling levels.
Potentially respondents do not perceive bicycle commuting as a form of exercise, a
point that is supported by Mullan (2013) who concluded that this perception was
due to the low intensity and duration of commutes. It must be noted that the
participants in Mullan’s study were leisure cyclists who had participated in a longer
“cycling event (of between 50km and 160km in length), and so it is likely that these
individuals use their bicycles more regularly than just during their commute, which
could alter their perceptions.
5.3 Disparity between Upstream and Downstream Sources
The second aim of this study was to consider whether there was a difference in the
perceived factors to cycling uptake between policy makers (upstream sources),
policy influencers (midstream sources), and emerging adults (downstream
sources). No novel factors were introduced from the interviews with upstream and
midstream sources compared to previous literature (Figure 4.1). This suggests that
the factors perceived by policy makers to affect emerging adults are similar to those
that affect other age groups such as school children (Kirby & Inchley, 2009) and
over 55’s (Parkin, Ryley, & Jones, 2007) and support the three overarching themes
of personal factors, physical environment, and social factors, set out by Parkin,
Ryley, and Jones (2007). As no additional factors were introduced during the
analysis of downstream interviews, this enhances the statement that upstream and
midstream sources do comprehend the factors affecting cycling uptake emerging
adults. However, when these factors are considered across a larger downstream
population, as in Study II, only four of the 21 influencing factors differed significantly.
Though it in not possible to compare the findings of the two studies statistically, it is
possible to infer that the factors to cycling uptake affecting emerging adults in

44
Edinburgh are not well understood by upstream and midstream sources, and so it
is clear that there is a need for increased communication between policies makers
and downstream users.
It could be argued that the downstream interviews conducted in Study I should have
exposed different influencing factors, though this could be due to the small number
of downstream interviews conducted. These interviews were also of shorter duration
compared to upstream and midstream sources, leading to fewer coded segments
in the subsequent thematic analysis. In part this is due to the interview format
utilised, which was not adapted from the interviews of upstream and midstream
sources. A more beneficial method of examining the beliefs of downstream sources
may have been to conduct focus groups as these can help individuals to define
problems, and allows for individuals to reveal their perspectives differently when
compared to individual interviews (Bryman & Bell, 2011; Hutt, 1979). Unlike
upstream and midstream sources, downstream sources are less likely to have a
vested interest and so participation in a focus group would have fewer confidentiality
implications.
5.4 Influencing factors between genders
The third aim of this study was to consider the factors influencing cycling uptake
between genders. This aim was successful in comparing males and females with
some clear differences between the genders. Comparison of the questionnaire
responses between males and females demonstrated alternative opinions with
regard to the facilities at journey end point, topography, and the link between cycle
paths and roads (Table 4.4). From this it is possible to conclude that the journey
end point facilities put in place are more adequate for the needs of females
compared to males, a factor that is supported by one downstream source:
“…they’ve been trying to action to improve facilities at work for cyclists.
For instance there’s been hairdryers installed at work to help [female]
cyclists…”
A second explanation is that, as fewer female respondents cycled to their place of
work or study, these non-cycling females do not recognise the additional facilities
that would be of benefit to regular female commuter cyclists. Alternatively, as
females are less likely to utilise cycle specific clothing (Steinbach et al., 2011), there

45
is less necessity for these additional facilities, and so the facilities in place are
considered adequate. Unfortunately it is not possible to examine these relationships
further within this study as the questionnaire items considering the perceptions of
additional facilities was too broad, though a future study could focus more
specifically on which additional facilities are required by both men and women.
There is no significant relationship between the SoC and genders in Study II, though
six of the Study I interviewees mentioned gender as an influencing factor in cycling
uptake, for example one interviewee noted:
“…I mean it is seen as having a gender component in that more men
than women cycle…” (Midstream Source)
However the gender equality was noted within these interviews also:
“…there are plenty of women who cycle. You see quite a broad range.”
(Upstream Source)
The findings from Study I and Study II indicate that the reported convergence of
cycling uptake between genders is true (Crane, 2007; Rosenbloom, 2006), with one
interviewee noting the lack of female role models as an explanation for the slow
change in this trend:
“…When it comes to cycling men have a lot more role models than
females.” (Downstream Source)
This suggests a strong social element to cycling, a point supported by Study II with
social rides motivating respondents to cycle (p=0.020), though there was no
significant difference between genders (p=0.095), nor was the influencing factor
‘social aspect’ perceived as significant within emerging adults (p=0.919). However,
when considering the trends seen within the incentivising events of Study II, there
is no significant difference between genders in events linked to increasing social
aspects of cycling – bicycle breakfast, social group rides, and workplace challenges.
Overall this suggests that social influences are not seen as an important factor for
cycling uptake in males or females, supporting the low number of previous studies
considering this factor (Parkin, Ryley & Jones, 2007).

46
A second explanation for the convergence of cycling between the genders is that
the priorities of females in the emerging adulthood stage of life are changing
(Rosenbloom, 2006). This is supported within Study II, with no significant difference
between genders for two key perceptions; personal safety (p=0.136), and too many
other commitments (p=0.642), which is counter to previous literature (Dickinson et
al., 2003; MacMillan et al., 2013). When considering personal safety it is argued
that, with females perceiving the links between road and cycle networks to be
adequate, then their perceived level of personal safety also increases. This is
supported as these linking segments are seen as a significant point of negative
cycling experiences (Snizek et al., 2013) though it could be argued that there would
be a significant difference between the genders for the item regarding the extensity
of the cycling network as a whole (availability of cycle path), which there is not
(p=0.182).
The lack of significant difference between genders for additional commitments
suggests that emerging adults are less likely to have multiple journey points (e.g.
dropping children off at school) seen by Dickinson et al. (2003) to be a key
explanation for lower female cyclists. In part this is supports Rosenbloom (2006)
and Crane (2007) who describe the convergence of female cyclists as part of a
larger culture shift in females entering the workplace and being seen less as the
lone parent figure, while the steady increase in the mothers age at first births from
26.4 in 1973 to 30.0 years in 2013 (Office of National Statistics, 2013) also supports
this explanation. The interviews in Study I partially support this, as the major
transition points discussed focused predominantly on the movement away from
home or into a place of work, however one interviewee did note that starting a family
would negatively alter the cycling behaviour of emerging adults:
“I think maybe a lot happens when you have kids… you can find yourself
slipping back to the easy alternative… that is jumping in the car…”
(Midstream Source)
From the open ended question in Study II it is apparent that becoming a parent is
still considered to have a significant effect on cycling levels in emerging adults,
noted by six of respondents (three males and three females), but not all of those
said it would negatively impact on their cycling habits, with respondents also stating

47
that they would positively change their cycling behaviour to encourage their children
to cycle. This is promising as it confirms support for the significant social influence
parents have on a child’s cycling uptake (Kirby & Inchley, 2009). Unfortunately, the
item regarding life events was poorly worded within this questionnaire, with several
participants noting confusion with the question. To ensure accuracy in the
occurrence of life events, it is essential for a more detailed questionnaire focusing
on life events be utilised for future studies. One example of such a questionnaire is
given in Salmelo-Aro, Aunola, and Nurmi (2007).
5.5 Influencing factors across SoC
The final aim of this study was to consider the factors influencing cycling uptake
across the SoC model within the TTM (Prochaska & DiClemente, 1982; 1983). This
aim was successful in comparing respondents in the pre-contemplation and
maintenance SoC. Unfortunately the survey respondents were not broad enough to
examine the influencing factors for individuals in the contemplation, preparation, or
action SoC. Although it was not possible to fully explore the differences in
perceptions of factors across all SoC there are still important findings to consider
from this research objective. 12 of the 21 factors differed significantly between the
pre-contemplation and maintenance stages, including aspects within the physical
environment, personal factors, and social factors. When considering the TTM model
it is clear that these factors influence the Decisional Balance (Prochaska &
DiClemente, 1982), with more negative aspects being considered at the earlier SoC
(pre-contemplation) compared to later SoC (maintenance), a point that supports the
conclusions of Nigg et al. (2011) when looking at physical activity as a whole.
The significant difference across SoC for factors of the weather and personal safety
support the work of Gatersleben and Appleton (2007). It could be that weather
barriers are formed by individuals within the pre-contemplation SoC as an excuse
for not cycling, a point that Garrard (2009) revealed in a sample of car drivers. It is
not possible to confirm this within the current study, but it would be possible by
focussing on the perceptions of influencing factors across the SoC for regular users
of alternative modes of transport such as car-drivers and public transport users.
Distance is also significantly different between the pre-contemplation and
maintenance SoC, which supports Heinen, Maat, and Van Wee (2011), especially
with respondents in this study significantly overestimating their commuting distance.

48
It could be that, once in the maintenance SoC, the physical exertion required is less
than when first starting out due to higher fitness levels (Stinson & Bhat, 2005).
However, with no difference between SoC when looking at the questionnaire item
regarding health benefits, this explanation is not supported within Study II. Another
explanation could be that once in the maintenance SoC, individuals are aware of
more direct routes that are available to cyclists only. This is supported in Study I:
“…if a [person] drives then they don’t necessarily know there is a
different route…” (Midstream Source)
Although it is not possible to confirm this from the data gathered in Study II. Caution
must also be taken as the actual commuting distance was considered as a straight
line between respondents’ home and their place of work or study. A more accurate
method of measuring commuting distance could have been utilised such as GPS
tracking or online route sharing software (Bartle et al., 2013; Broach et al., 2012;
Dessing et al., 2014). However, within these alternative methods there is potential
for a subject bias effect to occur, which would need to be addressed prior to
undertaking such a study.
When considering the personal factors that differ between the pre-contemplation
and maintenance SoC, those in the maintenance SoC believe they derive clear
benefits from cycling including lower costs and reduced commuting time when
compared to alternative transport modes. In order to increase the progression
through the SoC it is recommended to focus marketing campaigns towards these
factors. This could be done through schemes such as the “Bike to Work” scheme
where individuals can claim a lower cost bicycle, or through local councils and
workplaces offering advice and information on routes that would reduce the time
spent commuting compared to other modes of transport. However, the perceptions
of the questionnaire item considering the “Bike to Work” scheme displayed no
significant difference between the SoC suggesting that this scheme is no longer
offering a significant benefit, especially when compared to the perceived additional
motivation gained from a bicycle maintenance course. In part this latter point could
be linked to reducing costs and increasing commuting speed as it is reported that
well-maintained bicycles are more cost effective in the long term, whilst also
reducing the physical exertion required (Sustrans, 2015), factors which may not be
apparent to those in the pre-contemplation SoC.

49
Chapter 6: Conclusion
6.1 Overview
This study set out to explore the influencing factors to cycling uptake in emerging
adults based in Edinburgh. From the findings of Study I and Study II it is apparent
that there is a variation in the perception of influencing factors within this under
studied population compared to previous generations. These findings should assist
the development of worthwhile policies aimed at increasing cycling uptake within
the Edinburgh population. However, further exploration is required to ensure that
new policies will be successful in positively influencing the levels of cycling as a
form of AT within the wider emerging adult population.
6.2 Practical Contributions
6.2.1 General barriers for emerging adults
This study uncovers influencing factors that can be directly targeted with relevant
campaigns, whilst also suggesting incentivising events that are likely to prove
effective in increasing cycling as a form of AT within the emerging adult population.
These campaigns could have wide reaching impacts on health such as reducing
obesity levels and other related negative health factors (e.g. cardiovascular
disease) (World Health Organisation, 2003) which are ever more apparent within
this age range (Huang et al., 2003; Liu, Mizerski, & Soh, 2012). A secondary effect
will be the reduction of overall population health problems due to the increase in air
quality from fewer motorised vehicles (Yim & Barrett, 2012).
Emerging adults in Edinburgh find the hilly topography a major barrier to cycling
uptake. Though it is not possible to remove this barrier entirely it might be possible
to lessen its impact in several ways. The first is through increasing awareness of
alternative routes utilising marketing communications targeting emerging adults
specifically, as well as better signage of these routes for cyclists. Some
organisations already provide such information, for example Edinburgh Napier
University (ENU) suggests accessing its campuses via the flatter, segregated off-
road bicycle path along the canal (Edinburgh Napier University, 2016). Though
these routes are likely to be slightly longer in distance, it is possible that using a
segregated path would boost cycling confidence, while increased cycling would lead

Sion Pickering - Masters Dissertation - 19.04.2016

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