research proposal
harishreddy
AT5SamplePS.pdf
Barriers to e-bike uptake –
An investigation of experiences and
perceptions of e-bike riders
in the Australian bicycle market
Master of International Business
Table of Contents
1. Introduction ................................................................................................................................... 4
1.1 Environmental changes ........................................................................................................ 4
1.2 Main characteristics of electric bikes ................................................................................. 4
1.3 Benefits of this research ....................................................................................................... 5
2. Background to the research – Literature review ....................................................................... 6
2.1 Systematic review of e-bike literature................................................................................ 6
2.2 Cycling infrastructure ........................................................................................................... 7
2.3 Safety and e-bikes ................................................................................................................ 8
2.4 Perceptions of safety ............................................................................................................ 9
3. Business Problem ........................................................................................................................ 11
4. Research Problem ....................................................................................................................... 11
5. Conceptual Framework .............................................................................................................. 12
6. Research Objectives ................................................................................................................... 13
7. Research Questions .................................................................................................................... 14
8. Methodology and Data Analysis .............................................................................................. 15
8.1 Research Design ................................................................................................................. 15
8.2 Participating Sampling ....................................................................................................... 15
8.2.1 Around the Bay 2017 (Melbourne) ........................................................................... 16
8.2.2 Tour Down Under 2018 (Adelaide) ........................................................................... 16
8.2.3 Further channels and events ..................................................................................... 17
8.3 Data Collection Method ..................................................................................................... 18
8.4 Survey design ...................................................................................................................... 18
8.5 Data Analysis Method ........................................................................................................ 19
9. Research Budget ......................................................................................................................... 21
10. Research Timeline ................................................................................................................... 21
11. References ............................................................................................................................... 23
Table of Figures
Figure 1: Kalkhoff Integrale i8 e-bike ............................................................................................................... 5
Figure 2: Literature Review – Overview of Literature Considered .......................................................... 7
Figure 3: Spaces for bikes –Street with parallel parked cars (l.)
and bike lane alongside parked vehicles (r.) .................................................................................................. 9
Figure 4: Spaces for bikes –Fully separated bike lane ............................................................................. 10
Figure 5: Interdependences of factors affecting the likelihood to uptake e-bikes ..................... 12
Figure 6: A conceptual framework of key factors influencing the uptake of e-bikes ............... 13
Figure 7: Around the Bay 2016 in Melbourne ........................................................................................... 16
Figure 8: Kalkhoff Bikes and Royale Gazelle display during Tour Down Under 2017 ................ 17
Figure 9: Research budget and Beta distribution .................................................................................... 21
Figure 10: Research timeline ............................................................................................................................ 22
1. Introduction
1.1 Environmental changes
Australia is an extremely large country with a widely spread population – the population is
however concentrated in the major eastern cities. Central Australia (predominately desert) is
not populated (World Fact Book, 2017). The size and sparseness of the population results in
different types of transportation modes. One major form of transportation that is gaining more
popularity in Australia is cycling (Pucher, Buehler and Seinen 2011).
Transnational bicycle manufactures such as Royal Gazelle, Kalkhoff Bikes, Gepida, Haibike or
Focus Bikes have launched new product segments that challenge these needs by offering a
broader variety of bikes for commuter travel or leisure travel (IBIS World 2017). In this context,
for more than one decade bicycles have been electrified and gain interest of cyclist, politicians
and urban city planners – even more every year.
Recently, a remarkable shift has started to occur where citizens starting to take cycling for
every day purposes and commuting as a serious option for transportation (Oakil et al. 2016).
1.2 Main characteristics of electric bikes
In this context, e-bikes also provided new opportunities for people who would not otherwise
consider conventional cycling. This development comes with a change of travel perception
that e-bikes can replace conventional cycling but can also replace journeys that would have
been made by car (Jones, Harms and Heinen 2016).
The definition of e-bikes is regarded as useful and helpful in this paper. When talking about
e-bikes/ electric bikes in general or in particular, this research will focus on Pedal Assist
Systems (PAS) products. A Pedal Assist Systems (PAS) or Pedalecs are equipped with a motor
unit of 250 watts which supports the rider with pedal assist only by pedalling up to 25 km/h
(Fishman Cherry 2016).
In relation to other countries which allow faster bikes (so called Speed Pedalecs or S-Pedalecs)
the continuous rated power must cut out over 25 km/h in Australia due to legal requirements.
(Infrastructure 2017). In the following research I will use the term “e-bike” in order to simplify
this research.
Figure 1 shows an exemplary image of an e-bike equipped with a mid-drive unit and an
integrated battery in the frame.
Figure 1: Kalkhoff Integrale i8 e-bike
1.3 Benefits of this research
This research project aims to understand more about consumer experiences and concerns
about cycling, including riding (or potentially riding) an e-bike.
The findings from this research will be used to understand peoples’ expectations and of cycling,
particularly using e-bikes. Findings may be used to assist research partner PON.BIKE, a global
group of bicycle brands, with information to understand motives and barriers for e-bike uptake
in Australia. It is likely that there will be any benefits for future legal policies and Government
investments to help ensure infrastructure is provided that enables appropriate e-bike use and
experience.
2. Background to the research – Literature review
2.1 Systematic review of e-bike literature
The current literature is related to a wide range of different terms in relation to bicycling. In
order to move forward and have a broad and entire literature perspective, the following key
words are used to focus and increase the quality of data collection:
Keywords: Bicyclist; Bike, Safety; Infrastructure, Bicycle; Pedalec; Electric Bike; E-Bike;
Power assisted bicycles; Pedal Assist Systems; Risk; Cycling experience.
The current literature is related to a wide range of different terms in relation to bicycling.
Relevant literature were collected via a scan of Academic Search Complete, Business Source
Complete, EBSCOhost Research and Google Scholar databases using the terms ‘cycling
experience’, ‘electric bike’, ‘electric bicycle’, ‘e-bike’, ‘Power assisted bicycles’ and ‘pedalec’
conducted between February and April 2017.
This review aim attention at vehicles that are classified as electrically power-assisted cycles
(EPAC) with a maximum power output of 250 watts (Infrastructure 2017), commonly known as
‘e-bikes’, as mentioned previously in Chapter 1.
A review of existing literature relevant to the issue of uptake of electric bikes in Australia
is undertaken in this part to understand factors that influence (e-) bike participation. Given the
complex nature of participation as research topic, this literature review will systematically focus
on different dimensions. As Figure 2 shows relevant literature for this research related to
transportation publications and related to travel psychology and behaviour publications.
This literature is considered in the process of developing the subsequent conceptual
framework in Chapter 4.
Figure 2: Literature Review – Overview of Literature Considered
This following literature review will explain current theories, provide insights about the area of
inquiry (such as major factors and variables in relation to cycling) and show relationships
between these (e. g. differences in methodology).
2.2 Cycling infrastructure
It is universally recognised that bicycles and e-bikes are one essential part in transport modes
by offering an environmentally friendly and active transport option (Johnson and Rose 2016).
Various research has been conducted over the last five years to investigate the influence of
bicycles and e-bikes in general, the behaviour and motivations of cycling and the impact these
have on females and males psychologically for bicycle riding (Fishman, Washington and
Haworth 2014; Jones, Harms and Heinen 2016).
In the study of Crane et al. (2016) e-bike participants made shorter, more frequent trips. This
is in contrast with previous research suggesting that electric bicycle users travel more (Cherry
and Cervero 2007). In particular research has been carried out to determine the bicycle
transport modes detailed by Fyhri and Fearnley (2015). One outcome of their study was that
the ownership and use of e-bikes increase the amount of cycling expressed as both overall
distances and number of trips.
While some sources present e-bikes acting as an intermediate mode, interrupting the
transition from bicycle to bus and from bus to car (Baumann et al. 2008; Cherry et al. 2016),
other researchers provide more complicated discussions about mode shifts behind
infrastructure options (Bagloee, et al. 2016). In their study Johnson and Rose (2016) state that
e-bikes users in Australia are, unlike Europe, more likely shift from car to e-bike not bicycle to
e-bike. Their survey findings provide information that e-bikes in Australia actually mean more
physical exercise not less.
2.3 Safety and e-bikes
E-bikes are challenging for any safety considerations. However, the literature guides to several
potential factors that make it understandable and doable.
First, it is necessary to understand the cultural dimensions in this context. In the current
literature different countries and groups have different perspectives on e-bikes and safety.
One exemplary research for the United Kingdom and the Netherlands pointed out similarities
and differences of the e-biking experience within high (NL) and low (UK) cycling cultures (Jones,
Harms and Heinen 2016). User experiences about safety and e-bikes are likely to be more
positive in high cycling cultures with a strong cycling heritage (e. g. The Netherlands)
compared to potential low cycling cultures and countries like Australia (Lehtonen et al. 2016)
Second, remember that physical condition and age is playing an important role. E-bike riders
are likely to be older than the average for conventional cyclists. While the e-bike enables older
riders to continue to ride and maintain an independent travel option (Johnson and Rose 2015),
older e-bike users are likely to have greater physical weaknesses that impact the severity of
injuries in the event of a crash.
Third, more complex traffic situations are reported difficult for some older bike riders to
navigate (Vlakveld et al. 2014).
All these sources, found in academic journals as well as on commercial websites, highlight
different factors that appear to be a greater safety challenge for e-bikes compared to cyclist.
(Johnson and Rose, 2015; Dozza et al. 2016; BIKE Europe 2017; Schleinitz et al. 2017).
2.4 Perceptions of safety
In the cycling literature, a gap has been identified between subjective risk (people’s individual
perception of risk) and objective risk (the real risk of a critical events such as crash or injury).
Dozza et al. (2016) collected naturalistic e-bike data and analysed objective risk with odds
ratios and gained new knowledge about crash causation of e-bikes. In their study, conducted
in Gothenburg (Sweden), most common conflict occurred with pedestrians (31% of the critical
events), light vehicles (21%), and bicycles (18%). Conflicts with heavy vehicles or infrastructure
(e.g. a pothole on the cycleway) were rare.
In addition, from the same study, a majority of e-bike respondents travelled faster than
traditional cyclist and experienced different conflicts (Dozza and Werneke 2014; Dozza et al.
2016). Interestingly their study highlight that risk increased at intersections and when vehicles
stopped on the bicycle lane (Figure 3).
Figure 3: Spaces for bikes –Street with parallel parked cars (l.) and bike lane alongside parked vehicles (r.)
When looking at overall chances of being involved in a crash, Garrard, Greaves and Ellison
(2010) highlight that cyclists have a higher risk compared to vehicle drivers and passengers.
Surprisingly empirical studies shows that fear of being involved in a crash or collusion is
disproportionate to the actual level of risk to cyclists (Garrard 2011; Johnson et al. 2014).
Another study developed a framework named ‘Fear Iceberg of Bicycle Riding’. This framework
includes different dimensions of fear; beginning from mainly near collisions and harassments
over collisions and minor injuries to serious injuries and fatalities in worst case scenarios
(Garrard 2011).
With the increasingly urbanization of big cities such as Sydney or Melbourne (Figure 4) there
are a many opportunities for urban city planners and multiple benefits of using bicycles for
moving forward, however also a point of potential conflict with psychological barriers and
mindsets in terms of the fear of bicycle riding on Australian roads (Fishman, Washington and
Haworth 2012).
Figure 4: Spaces for bikes –Fully separated bike lane
Different research methods need to be kept in mind when considering the data collection and
information shortfall. Crane et al. (2016) used a multiphase mixed method design. They
conducted qualitative interviews with local residents and retailers before and after the
construction of a new cycleway. Six months later, intercept surveys with 783 cyclists using the
cycleway and 207 pedestrians in the vicinity were also conducted to determine how the
cycleway was being used and received by the community. Other studies (Campbell et al. 2016;
Johnson 2016) used surveys that were delivered online using for example ‘Survey Monkey’.
Other researchers used n-depth interviews to inform the survey development (Bauman et al.
2008).
Little is known about the connectivity of electric bikes and how this may play a role for buying
or using e-bikes. Even though Dozza, Piccinini and Werneke (2016) state in their current
research that e-bikes could be a potential platform for implementing intelligent transport
systems, there are no studies of how wireless communication such as e-bike navigation or
forward collision warning systems can be utilized to mitigate risks and extend the horizon for
e-bike riders.
3. Business Problem
According to a Persistence Market Research report, the global market for bicycles is
anticipated to expand by 38% over the period 2016-2024. E-bikes are expected to be the
leading segment in this development and are foreseen to reflect a CAGR of 4%, to reach a
market valuation of US$ 24.43 Billion by 2024 (PMR 2016).
For PON.BIKE, as global bicycle manufacture of road bikes, mountain bikes and urban bikes,
the e-bike segment is the fastest growing product segment (BIKE Europe 2016). In Australia,
e-bikes are expected to become the major market for generating gross profit within the next
three years. Overall research project sponsor
PON.BIKE Australia, and it’s e-bike brands Kalkhoff Bikes, Royal Dutch Gazelle and Focus Bikes,
is looking to understand (1) how to increase e-bike uptake of personal mobility devices, and
(2) key profile of current and potential e-bikes users. Furthermore PON.BIKE is interested to
understand how travel behaviour changed since purchasing an e-bike and if e-bike owner use
their vehicle as their primary method of commuting.
4. Research Problem
In light of the literature review and the proposed structure to the inquiry, the main information
shortfall can be recognized as the investigation of barriers for e-bike uptake in the Australian
bicycle market.
Data on cycling participation, purpose of travel, (potential) e-bike ownership and cycling
experiences is being collected to increase PON.BIKE’s understanding of the factors influencing
consumers purchase and use of e-bikes.
Figure 5 shows the dependences of different variables on each other. Three user scenarios
have been evaluated for this research: Non-e-bike riders (not interested), potential e-bike
riders (generally interested users but concerned) and existing e-bike riders (potentially
stronger and fearless).
Figure 5: Interdependences of factors affecting the likelihood to uptake e-bikes
The perceived value of e-bikes is highly based on subjective and objective potential barriers
and advantages. These factors can be positively (healthy) or negatively (unsafe event) affect
the likelihood to uptake e-bikes in Australia.
5. Conceptual Framework
As there is a gap within the literature on the e-bikes and safety considerations in Australia, I
suggest that the best approach to addressing these research questions is via a quantitative
design.
As the aim of the research is to evaluate coherences of the dependent variable (likelihood to
uptake e-bikes), different, relevant variables from literature review will be an integrative part
of the research. These are psychographic nature (risk aversion and fear of missing out) and
demographic nature (e.g. gender or age) is in order to use existing in-depth knowledge of
other countries and combine these findings with a local quantitative research design. This
conceptual framework of the psychographic and demographic factors
influencing the uptake of e-bikes is shown in Figure 6.
Figure 6: A conceptual framework of key factors influencing the uptake of e-bikes
This research plan to measure psychographic variables such as Kiasu tendency (Kirsy 2007)
through the 10-item scale developed by Ho et al. (1998). The items were scored on a 5-point
Likert-type and assess the overall tendency towards kiasu (risk aversion) in the context of
potential barriers to uptake e-bikes. The relationship between the cycling behaviour and the
self-reported socio-demographics will be assessed and explained later in Chapter 8.
6. Research Objectives
The aim of the present study is to analyse the dynamics of electric bike ownership and use in
Australia. The evaluation of the two research questions will provide information in relation to
the objectives of the research. This research objective is split into two key specific areas:
Research Objective 1:
Understand current bicycle behaviour –barriers to uptake electric bikes
This objective sets out to identify key factors impacting electric bike sales in Australia. In this
context it would be also beneficial to understand primary use for electric bikes in Australia (by
age group and overall).
Research Objective 2:
Understand how negative perceptions on e-bike ownership and use can be changed
This objective sets out to understand how major barriers can be overcome in order to increase
the usage of e-bikes in (e. g. impact of legal requirements or overall quality of bicycle
infrastructure in Australia)
7. Research Questions
The research questions proposed will explore potential barriers and experiences of e-bike
uptake in Australia. The following part will present the research questions. This includes
rephrased hypotheses and postulated relationships:
Research Question 1:
What is the effect of safety on the likelihood to uptake e-bikes?
This question is, of course, key to understanding the decision making process and the
likelihood to uptake e-bikes. Safety is likely to be relevant and linked to the factors that
decrease or increase overall uptake of e-bikes. The following hypothesis has been drawn:
Hypothesis 1:
The safety factor is having the greatest effect on the likelihood to uptake e-bikes.
It is assumed that there is a strong relationship between safety and e-bikes. Safety may play a
crucial role and have potentially the greatest effect on e-bike uptake. In this context in will be
also investigated if male and female users have similar perceptions about e-bike use and
ownership.
Research Question 2:
What are the likely implications of e-bike ownership on personal mobility?
This question is, of course, key to understanding the likely implications of e-bike ownership of
personal mobility. The following hypothesis has been drawn:
Hypothesis 2:
Personal mobility will significantly increase through the use of e-bikes.
8. Methodology and Data Analysis
8.1 Research Design
The present research will take the form of a quantitative based in the responses to an online
survey.
In order to build a survey that anticipated relevant factors of e-bike user experiences
researchers will conduct a short series of preliminary interviews with industry professionals.
These experts are Dr Marilyn Johnson (Monash University), Graeme Moffett (CEO of PON.BIKE
Australia), Professor Geoff Rose (Institute of Transport Studies, Monash University) and Craig
Richards (CEO of Bicycle Network).
The survey will be designed to capture user experiences or expectations about cycling and e-
bike use or perceptions about use. The survey tests trip attributes, and environmental and
weather conditions in order to answer questions about likelihood to uptake e-bikes Australia.
This research design will use a comparison of means and cross tabulation to analysis the choice
of e-bikes compared to conventional bikes.
This research is interested in gathering information about two populations (gender, sex) in
order to compare them (Comparison of Means). Cross-tabulation tables will be used to provide
a wealth of information about the relationship between the variables.
These variables could be reasons or barriers for e-bike uptake such as ‘riding longer distances’,
‘ride uphill’ or ‘get to the destination faster’. In this context an Independent Samples t Test will
be used to evaluate whether there is statistical evidence that the associated population means
are significantly different. Furthermore this research is going to compare the P-value to the
significance level, and reject the null hypothesis when this P-value is less than the significance
level.
8.2 Participating Sampling
Everyone is welcome to participate the survey whether they own an e-bike, have ridden one
or they’re thinking about riding one. To be eligible to complete this survey, participants need
to be 18 years or older. It is anticipated to achieve participating sampling evenly balanced
between sexes.
Multiple recruitment strategies were used to maximise the number of survey respondents as
explained in the following part.
8.2.1 Around the Bay 2017 (Melbourne)
Around the Bay is a non-competitive fully supported recreational cycling fundraising event
organised by Bicycle Network in Victoria, Australia. It takes place in Melbourne on the 8th
October 2017 and attracting over 10,000 cyclists. This event has cemented its place in bike
riding culture as Australia's biggest one-day bike ride.
Figure 7: Around the Bay 2016 in Melbourne
This recruitment event was chosen based on learning effects and past-experience. Last year,
PON.BIKE teamed up with other partners, to showcase the latest e-bikes, to explain to Aussies
how e-bikes can make cycling more accessible for more people and to let them feel the joy of
e-bikes first-hand via a unique test track as shown in Figure 7. The target is to gain a sample
of at least 150 respondents in order to gain an adequate understanding of e-bike user
experiences. A prize draw involving five $200 Wish Gift cards will be used for survey
respondent as recruitment incentives.
8.2.2 Tour Down Under 2018 (Adelaide)
The Bupa Challenge Tour is the Santos Tour Down Under's annual mass-participation ride. It
takes places on the 14th January 2018 during the Tour Down Under in Adelaide. As Adelaide-
based business, PON.BIKE is key sponsor of the Tour Down Under and can rely on successful
collaboration of the last five years. Last year 43,000 visitors (in scope) travelled specifically to
South Australia to attend the TDU. The target is to gain a sample of at least 200 respondents.
A prize draw involving ten $100 Wish Gift cards and four $150 Barossa Wine Tours will be used
for survey respondent as recruitment incentives.
Figure 8: Kalkhoff Bikes and Royale Gazelle display during Tour Down Under 2017
It should be kept in mind when interpreting results that this is a sample within geographically
limited areas (Melbourne and Adelaide) and potentially biased towards those more pre-
disposed to cycling and therefore may not be generalizable. However, that is a vulnerability
our research sponsor can accept because more than 75% of sales is generated in the four
biggest cities in Australia. Therefore, despite these gaps, sound results are still likely to be
obtainable.
8.2.3 Further channels and events
Focusing on only two events has it’s geographically limitations. Therefore, this given method
generally has its weaknesses. This research is aware of these weaknesses and have thought
through how these vulnerabilities will be compensated. This will be done in two ways:
Dealer Network: First, in collaboration with the bicycle retailer network of PON.BIKE Australia,
15 bicycle dealer in Brisbane, Perth and Sydney were asked to invite their end consumers who
have previously been involved in a store visit. The target is to gain a sample of at least 150
respondents in order to gain an adequate understanding of e-bike user experiences. In order
to ensure higher response rates, PON.BIKE dealer’s, who pro-actively supported the survey
through distribution to their own (potential) customers base, were offered special sales
conditions in October and November 2017 of 10% for the next e-bike order of MY2018 and
20% for the next e-bike order MY2017 or older.
Cycling Magazines: Second, the online link to the survey will be provided via PON.BIKE email
newsletter and key Australian cycling magazines ‘BIKE Europe’, ‘Bicycling Trade’ and ‘Cyclist
Australia/NZ’.
8.3 Data Collection Method
During the major sampling events Tour Down Under (Adelaide) and Around the Bay
(Melbourne), a team of students from University of Adelaide and Monash University conducted
an intercept survey on event facilities via iPads. Respondents were sampled before or after
these events; for example, as they were entering or exiting the venue place Southbank in
Melbourne. Other people would be ignored until the survey was completed. To capture
temporal effects, data were collected during Around the Bay and Tour Down Under between
9 am and 6 pm on both weekend days (Sunday 8th October 2017 and Sunday, 14th January
2018).
It is unlikely that respondents will experience any inconvenience or discomfort. However, if
they you uncomfortable answering any questions, they were educated to feel free to leave that
question blank. If respondents experience distress in relation to the participation in the study
while responding to the survey, they were asked to contact the researcher.
All completed surveys will be password protected and accessible only to the named
researchers in accordance with Adelaide University regulations.
8.4 Survey design
The survey itself will be built via Qualitrics Research Software. The general themes examined
in the survey are: Self-reported cycling experience and skills, benefits and disadvantages of e-
bike riding and feelings of safety. The survey itself will be made of three major sections:
(1) Consumer characteristics – Demographics: This demographic part is a quantitative
tactic used to measure factual information. It will cover demographic questions (age,
gender, employment, education, income, and household residents), licence status,
frequency of driving and proximity of bike lanes or paths to their home location. These
questions help us to gain more knowledge about socio-demographic variables and
local environment characteristics. It will support to test Hypothesis 1 and Hypothesis 2.
(2) Main Effects – Attitude to E-Bikes: This section will contain 5-8 questions. This section
will consist a number of hypothetical question that will ask responds to rank the relative
importance of safety at locations with various cycling and no cycling infrastructure. This
ranking will carried out via Max Diff Scaling in which a number of variables will be listed
and survey respondents are required to state best–worst paired comparisons. It will
test Hypothesis 2.
(3) Consumer characteristics – Risk perception: This psychographic part is a quantitative
tactic used to measure subjective information. This section will contain 5-8 questions
in order to analyse personality traits and lifestyle opinions. This section will be made
up of five-level Likert scale questions that will measure a customer’s perception of e-
bike usage in their e-bike experience. It will also seek to determine reasons stopping
respondents from riding an e-bike and main reasons respondent’s don't ride or don’t
ride more often bikes (multiple-choice options). Several action images (such as
Figure 3) will be used to test respondents’ opinions on risk aversion. . It will test
Hypothesis 1.
There will be an Explanatory Statement as guide for the study. It will explain major benefits of
this study such as assist the opportunity for development of future policies to help ensure
infrastructure is provided that enables safe e-bike use. The survey is targeted to take
approximately 15 minutes.
8.5 Data Analysis Method
Data analysis will be developed through the breakdown of responses from the survey. Data
collection will continue until data concentration was achieved and until patterns and themes
begin to arise and turn up from the responses.
To examine the research question from Chapter 7, a multiple linear regression will be utilised
to determine if the independent variables predict the dependent variable (Kirby and Ross
2007). The independent variables include independent variable Kiasi (attitude of being overly
afraid or timid) and independent variable Kiasu (fear of missing out) and the dependent
variable (likelihood to e-bike uptake) is dependent variable. Finally, the assumptions of
multiple regression—linearity, homoscedasticity and multicollinearity—will be evaluated to
get more information about the barriers to e-bike uptake.
This research will use the F-test to determine whether the set of independent variables
(psychographics) collectively predicts the dependent variable (likelihood to uptake e-bikes). A
t-test will be used to determine the significance of each predictor and beta coefficients will be
used to determine the magnitude of prediction for each independent variable (Nathans et al.
2012).
Additionally, this research will statistical measure how close the data are to the fitted
regression line (R-squared) to test the multiple correlation coefficient of determination. This
will be used to estimate the strength of the relationship between the research model and the
response variable explained before (Gill and Johnson 2010).
A factor analysis will be used for explaining the structure of data by explaining the correlations
between variables. These variables are perceived barriers in user experience of riding an e-bike.
In light of literature review they are: Environmental (Infrastructure), Social (Safety), Social
(Stigma) and Technological. This method will summarises data from the e-bike survey into a
four dimensions by condensing a large number of variables into a smaller set of latent variables
or factors. The specific factors within each group will be determined to a second interview with
Dr Marilyn Johnson from Monash University. These elements will then be tested for relative
importance.
9. Research Budget
Researchers asked all people responsible for the work to estimate optimistic (best), most likely
and pessimistic (worst) budget. A statistical formula (Beta distribution) was used to estimate
budget. This research will use a three point estimates to determine research and resource costs.
The expected budget = (pessimistic budget + 4* expected budget + optimistic budget)/6.
Figure 9: Research budget and Beta distribution
By analysing these estimates, confidence level can be increased in the underlying accuracy of
the budget (Yeo 1990). Using this beta distribution this research project most likely require an
expected budget of $20,917 which is considered satisfactory to cover travel costs, staff
expenses plus administrative costs and travel costs. Furthermore this encompasses
reimbursement for respondents travel and time costs to Interstates in Australia. In order to
successfully meet the aforementioned deadlines above, the budget will be sufficient to provide
relevant resources of the implementation of the research project.
This research is being funded by the conglomerate PON Holdings BV (PON), owner of the
PON.BIKE Group.
10. Research Timeline
The timeframe for the research encompasses 13 months from February 2017 until February
2018. This was deduced from the beginning and end of the researchers postgraduate study,
assuming a year long project with deliverables throughout the year.
Direct costs Optimistic Most Likely Pessimistic
Employees $8.000 $8.000 $9.000
Design $3.000 $5.000 $9.000
Materials $2.500 $2.500 $2.500
Equipment rental $2.500 $2.500 $3.000
Travel $1.800 $2.300 $3.000
Total $17.800 $20.300 $26.500 Beta distribution (pes s i mi s ti c + 4* expected
+ opti mi s ti c)/6. $20.917
Moreover this Gantt planner will anticipate task overlaps and potential delays in the project
planning with planned and smaller project buffer. These buffers are due to peak period of
overall business activities in the bicycle industry between December 2016 and April 2017 –
both for us as PON.BIKE (as bicycle supplier and subsidiary) as well as for our bicycle dealers.
Figure 10 propose a timeline in which four phases were planned including the conceptual,
design, empircal, analytical and dissemination phase. The literature review will be an ongoing
task as to acknowledge sound information of the research and project in an academic way.
Figure 10: Research timeline
Due to budget and time limitations in the highly competitive and volatile bicycle market this
paper consider conducting research in two major bicycle hubs in Melbourne and Adelaide
central area only. Research needs to be carried by the 20th December 2017 in order to use
first key findings for the next global sales meeting and product line-up meeting in Germany
on the 1st February 2018.
11. References
Allen, I.E. and Seaman, C.A., 2007. Likert scales and data analyses. Quality progress, 40(7), p.64.
Bagloee, S.A., Sarvi, M. and Wallace, M., 2016. Bicycle lane priority: Promoting bicycle as a
green mode even in congested urban area. Transportation Research Part A: Policy and
Practice, 87, pp.102-121
Bauman, A.E., Rissel, C., Garrard, J., Ker, I., Speidel, R. and Fishman, E., 2008. Cycling: Getting
Australia Moving: Barriers, facilitators and interventions to get more Australians
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