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Determining the Importance of Factors for
Transport Modes in Freight Transportation
Author: Wan Liu
November 2016
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II
Author Wan Liu
Student number 4347463
University Delft University of Technology
Program Master Technology, Policy and Management (TPM)
Specialization Supply Chain Management
Graduation Committee:
Chairman: Prof. dr. L.A. Tavasszy
Delft University of Technology
Faculty of Technology and Management (TPM)
Section Transport Policy and Logistical Organization
First supervisor: Dr. J. Rezaei
Delft University of Technology
Faculty of Technology and Management (TPM)
Section Transport and Logistics
Second supervisor: Dr. G. van de Kaa
Delft University of Technology
Faculty of Technology and Management (TPM)
Section Economics of Technology and Innovation
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Preface
This report is my graduation research project for the master degree program of
Management of Technology at Delft University of Technology. This research
was conducted at Technology, Policy and Management Faculty of TU Delft
between February 2016 and November 2016. This research would not have
been completed without the help of my three committee members. I would like
to express my sincere gratitude to my first supervisor Dr. Jafar Rezaei for his
continuous support and patient guidance during the research, and sincerely
appreciate his encouragement during the hard times when I was frustrated in
the middle of data-collecting process, and his encouragement helps me to
continue pursuing my research without struggling with the regret about the days
I have wasted. I am also very grateful to the chairman of my graduation
committee Prof. dr. Ir. Lorant Tavasszy for helping me nail the subject of my
thesis and providing me enlightening feedbacks and books. I would like to
express my sincere gratitude to my second supervisor Dr. Geerten van de Kaa
for his invaluable and detailed feedbacks during the execution of this research.
Moreover, I would like to thank my parents for their absolute support and
patience, in particular, through these nine months I was often in bad mood.
Special thank also goes to my friends for their unconditional help and
understanding.
Wan Liu
November 2016
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Abstract
The road transportation has been overly used in freight transportation for
decades, and it has undesirable effects on the environment. Nowadays, with
the ever-increasing awareness of environmental issues which is mainly caused
by road freight transportation, intermodal transport is thus promoted in order to
reduce greenhouse gas emissions. But, even though many policies promoting
the use of intermodal transport have been proposed, they have less impact to
trigger shippers to shift mode from road transport to intermodal transport. The
main reason might be that the real requirements of shippers towards transport
mode are not well understood, hence this research is to investigate freight
transport mode choice from their perspectives. The requirements for transport
modes are abstracted into a set of factors, and knowing the perceived
importance assigned to each factor is helpful to understand what should be
improved in intermodal transport. In this research, the literature review
regarding freight transportation mode is done aiming to generate an exhaustive
list of decisive factors, and these factors are transport cost, door-to-door travel
time, on-time reliability, flexibility, frequency, and reduction of CO2-emission.
However, apart from these six factors, characteristics of the freight itself do play
a role as a premise in freight transport mode choice, and factors are possibly
perceived differently regarding different types of freights, therefore, four type of
freights are chosen, which are freights from manufacturing industry, agriculture
industry, perishable food industry, and chemical industry. Best-Worst method
which is a Multiple Criteria Decision Making method is chosen to conduct data
analysis, and online questionnaires are sent to the respondents which are
divided into three groups: practitioners, industry experts, and professors. And,
since this research mainly focus on two regions: Europe and the United States,
all respondents are chosen from these two regions. The results of data analysis
indicate an overall ranking of all factors, where transport cost is viewed as the
most important, closely followed by on-time reliability, and reduction of CO2-
emission is viewed as the least important. Moreover, through the comparison
of the general perception of factors regarding four types of freights, it can be
seen that one or more factors are perceived differently based on four types of
freights. Besides, different groups of respondents do perceive specific factor
differently, and perceptions of practitioners and professors differ a lot. Since
these two types of comparison analysis have not been done in the previous
literature, so this research is the first study to provide a perspective for
understanding factors from the perceptions of different types of respondents
and in terms of different types of freights. Besides, by including reduction of
CO2-emission, this research provides an overview of this ever-increasing
important factor.
Keywords: Freight transport mode choice, CO2-emission, intermodal transport,
best worst method (BWM), multi-criteria decision-making (MCDM), comparison
analysis
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Table of Contents
Chapter 1 Introduction ................................................................................................................ 1
1.1 Research objective and research question ..................................................................... 3
1.2 Research relevance ............................................................................................................. 3
1.2.1 Practical relevance ....................................................................................................... 3
1.2.2 Scientific relevance ...................................................................................................... 4
1.3 Research framework ........................................................................................................... 5
Chapter 2 Literature review ....................................................................................................... 8
2.1 Outline of the considered literature ................................................................................... 8
2.2 Decision-makers and involved stakeholders ................................................................... 9
2.2.1 Characteristics of freights considered by decision-makers .................................. 12
2.2.2 Behavioral analysis theory ........................................................................................ 13
2.3 Considered criteria in the existing literature .................................................................. 14
Chapter 3 Methodology ............................................................................................................ 26
3.1 Research design ................................................................................................................ 26
3.2 Multi-criteria decision-making .......................................................................................... 27
3.3 Best-Worst method ............................................................................................................ 29
3.4 Data collection and preliminary preparation .................................................................. 31
3.4.1 Data collection ............................................................................................................ 31
3.4.2 Collection of respondents .......................................................................................... 32
3.5. Questionnaires .................................................................................................................. 33
Chapter 4 Analysis ..................................................................................................................... 37
4.1 Data analysis ...................................................................................................................... 37
4.1.1 Weights and Ranking ................................................................................................. 37
4.1.2. Comparison Analysis ................................................................................................ 38
4.2 Data interpretation ............................................................................................................. 39
4.2.1 General Results .......................................................................................................... 39
4.2.2 Differences across four types of industries............................................................. 44
4.2.3 Differences across three types of respondents ..................................................... 54
Chapter 5 Conclusion and Recommendation ..................................................................... 64
5.1 Conclusion .......................................................................................................................... 64
5.2 Limitation ............................................................................................................................. 67
5.3 Recommendation ............................................................................................................... 68
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5.4 Suggestions for future research ...................................................................................... 70
Bibliography................................................................................................................................... 71
Appendix A .................................................................................................................................... 79
Appendix B .................................................................................................................................... 86
1
Chapter 1 Introduction
Driven by rapid global industrialization and ever-increasing demand for freight
movements, freight transportation has become a major source of air pollution.
In the Europe, greenhouse gas emission (GHG) produced during freight
transportation stands for 75% of the total emissions from all transportation
sources. Among the freight transport modes, the most commonly used mode is
the road transport contributing the most to GHG, and it occupies about 74.9%
of the total inland freight transport in the European Union, while rail transport
stood at 18.2% and the remainder (6.9%) of the freight transport was carried
along inland waterways (Eurostat, 2014). Besides, during the last decade,
member states of European Union have witnessed a sharp increase in road
freight transport from 1,526 billion tonne-kilometers (tkm) in 2000 to 1,692
billion tonne-kilometres (tkm) in 2012. Its variation corresponds to an increase
of 11.2%, which is much greater than the tonne-kilometers of all transport
modes (7.3%) (Gleave et al., 2015). Road transport easily outpaces the other
freight transport modes. However, compared to the other freight transport
modes, road transport is the least environmental-friendly and sustainable one
as the amount of GHG it produces in terms of the same delivery distance is
much higher than the amount of GHG produced by other modes. (Lammgård,
C., 2007). Therefore, from the environmental perspective, a major problem
caused by the overuse of road transport in freight transportation is the indirect
effects in terms of global warming because of increased emission of GHG,
where the carbon dioxide (CO2) accounts for the major part (Lammgård, C.,
2007). In addition to environmental angle, the ever-increasing figure of freight
road-transport also results in terrible congestion on western European
highways, therefore causing significant costs for society (Blauwens, Vandaele,
Van de Voorde, Vernimmen, & Witlox, 2006).
In order to relieve the aforementioned negative consequences, decision-
makers should be inspired to choose other alternative modes instead of uni-
road transportation. Thus, European Union proposed White Paper in 2001 in
order to shift freight transport from unimodal road transport to intermodal
transport1 . By doing so, the use of other modes, such as ship or rail, is
increased, and as pre- or post- haulage the use of truck in intermodal
transportation is always considerably reduced to an extent that economic
benefits can be achieved given that the cost of road transportation is always
1 Intermodal transport refers to the movement of goods in one and the same loading unit by a
sequence of at least two transportation modes, which uses ship or train for main haulage and
only use truck for pre- or post- haulage since ship and rail transport cannot provide door-to-
door transportation without the help of trucks (Crainic & Kim, 2007).
2
higher than the costs of other two modes. But this European Transport Policy
faces a difficult task which involves rebalancing the modal split, while
meanwhile maintaining European trade flow competitiveness (Maria, Raquel, &
Leandro, 2011). However, according to Gleave et al. (2015) the policies and
programs appear to have less impact to trigger a significant modal shift, as the
road transport is still the dominant transport mode, and its share of total freight
transportation increases from 43% in 2000 to 45% in 2012; followed by shipping
as the second important mode, but since 2000 the share of shipping has
remained generally constant about 37%; the share of rail has been significantly
decreased (at between 10% and 12%).
While the main issue is how to trigger the modal shift from uni-road
transportation to intermodal transportation, since most shippers are still willing
to use road transportation even though many policies including White Paper
and investments are in effect. Thus, knowing how decision-makers choose
what they choose in freight transportation modes is crucial to make effective
policies and investments in order to trigger a modal shift given the evidence
that if one wants to make measures to be effective enough to influence the
behavior of shippers, one needs to understand shippers’ behavior first (Dries,
Cathy, & Van Lier, 2013), which is usually categorized into behavioral analysis
of freight mode choice decision in existing studies. When making the modal
choice decision, shippers assess one transport mode based on whether their
expectations for factors (criteria) such as transport cost and door-to-door travel
time, that transport modes are characterized by, are met up with that considered
mode. To be specific, the transport modal choice decision is based on the trade-
off among these criteria since during the decision-making process some factors
(criteria) are actually conflicting. Thus, it is crucial to know the preference of
decision-makers towards the factors (criteria), which is the main objective of
this research.
To conclude, this research will study how people with different, but important
and freight transportation-related, backgrounds perceive the criteria of transport
modes regarding the different commodity types of freights. Transport cost, door-
to-door travel time, on-time reliability, flexibility, frequency, and reduction of
CO2-emission are chosen as important criteria through exclusively and
thoroughly literature review. Four commodity types are chosen including
manufacturing freights, agriculture freights, perishable foods and chemical
freights, and industry experts, professors and practitioners are chosen to
represent three types of respondents. This research will be conducted with the
help of online questionnaires, and the data will be mainly collected from the
European Union and the United States. The Multiple Criteria Decision Making
(MCDM) method will be used to analyze the collected data, and thus the
research questions will be answered in the following corresponding chapters.
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The outline of this research is as follows. The first chapter discusses the
research objectives, questions and the approach of this research. The second
chapter presents the analysis of existing researches that relate to the interest
of this research. The third chapter is about discussing the method used in this
research to collect and analyze data. The fourth chapter presents the results of
analyzed data and the data interpretation with the help of comparison analysis.
At the end, conclusions and recommendations are presented in chapter 5.
1.1 Research objective and research question
The research objective of this study is 1. To determine the importance of criteria
considered in the freight transport modal choice decision. 2. To compare the
importance of one criterion across four types of industries.
In order to achieve the research objective, the research question is proposed
as:
How important are the criteria of transport modes in the decision of freight
transport mode choice and whether there is a difference in the importance of
one criterion among manufacturing industry, agriculture industry, perishable
food industry, and chemical industry?
The following sub-questions are formulated in order to answer the research
question:
1. Which transport criteria are considered by shippers when making mode
choice decisions?
2. How to determine the importance of chosen criteria?
3. Whether perceptions of different groups of respondents differ regarding
one criterion?
4. How can the importance of criteria be used to increase the
competitiveness of intermodal transport?
1.2 Research relevance
1.2.1 Practical relevance
As mentioned in the introduction, the ever-increasing transportation congestion
and the environmental problem cannot be ignored, and the main cause which
is the exploitation of the use of the uni-road transportation should be eliminated.
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Thus, policies promoting the shift from the uni-road transportation to the
intermodal transport have been proposed such as White Paper in the European
Union. Whereas, given the impact of White Paper the most freight
transportation are still mainly done by truck, and decision-makers seem
reluctant to choose other modes. Understanding why decision-makers choose
what they choose as a transport mode is essential to understand decision-
makers’ demand and preferences. Thus, by knowing decision-makers’
demands and preferences, governments can make an appropriate and effective
policy to trigger a modal shift. Thus, this research will study the factors (criteria)
which are considered by decision-makers in their modal choice decision.
Therefore, this research has a practical relevance by aiming to solve the
practical issue which is the difficulty in inspiring decision-makers to shift from
unimodal road transport to intermodal transport.
1.2.2 Scientific relevance
As mentioned in the introduction, analyzing the importance of criteria of
transport modes which are considered by shippers can help policy makers
understand why shippers choose one mode instead of another and which
criterion is perceived as the most important by shippers. Many previous studies
have already been conducted in this field. However, few studies consider the
CO2-emission as one decisive criterion, and the extent to which shippers are
willing to shift their transport mode regarding CO2-emission as a criterion has
only been marginally researched in mode choice decision literature (Fries,
2009). As argued in the introduction part, since given the fact that the
environmental aspect is accepting more and more attention these years and its
importance is expected to increase in the near future, the CO2-emission should
be included as an important criterion of transport mode, because in order to
precisely measure the shippers’ preference, it is essential to include important
criteria that might impact the modal choice and leave out other unimportant
ones (Hensher, Rose, & William, 2005). Since the existing literature including
CO2-emission as a criterion is still in its infancy, therefore incorporating CO2-
emission as one important criterion in this study will bridge the knowledge gap
of existing researches.
Moreover, existing studies often include shippers, or freight forwarders, and
quite often they combine freight forwarders and shippers together as one target
population. The research done by Choi, Chung, & Lee (2013) includes not only
shippers, freight forwarders, but also researchers, but these three types
population are viewed together as one target population in that research.
However, it seems researchers rarely compare perceptions of these three
groups with regard to one specific modal criterion. Not to mention perception of
scholars themselves, scholars who conduct such freight transport modal choice
5
study seem lack of interest in comparing their perception with other two types
of decision-makers. While, few existing studies include this type of comparison
in their research, and even though one research includes the comparison, such
as the research of Bergantino and Bolis (2008), it just compares its result, which
is based on freight forwarders, to the result of another paper which is based on
practitioners. But, it is clear that these two studies have different study sectors
and approaches, which makes the comparison less valid. Thus, having such
comparison among three groups in one research is in need, and such
comparison helps in understanding whether the perceptions of these three
types of respondents tend to converge or to diverge. Having comparison
analysis in terms of three types of respondent is necessary, as evidenced by
Duan, Rezaei, Tavasszy, and Chorus (2015) that the perception will vary when
it comes to different types of users such as shippers, freight forwarders, and
scholars. Besides, physical characteristics of freights are what create the first
threshold for freight transport modal choice (Roberts, 2012). And, according to
Gleave et al. (2015), it is useful to categorize the transport of freights in terms
of different commodity categories because the freight transport modal choice is
often closely related to the characteristics of the freights transported. While
existing literature rarely compares the perception of one criterion based on
different type of freights. Therefore, in this research four types of freight will be
included, and the comparison analysis regarding the perception of one criterion
across four industries will be presented.
To conclude, this research is the first study that includes the aforementioned
two types of comparison analyses in the field of freight transport mode choice,
which provides a new perspective to understand the decision of freight modal
choice not only in terms of decision-makers’ specific backgrounds but also in
terms of different characteristics of freight types. Therefore, this research
actually fills the knowledge gap that previous literature has not conducted such
comparison analyses in their studies.
1.3 Research framework
The main goal of this research is to evaluate the importance of criteria which
are often considered in the freight transport modal choice, and investigate
whether the perceived importance of one criterion will differ across different
types of industries. Hence, the following steps, as shown in figure1, must be
taken to achieve the goal of the research. The initial step is to review existing
literature covering subjects including the study of freight transport modal choice,
analysis of important modal criteria/ factors considered in freight transport
modal choice, and Multiple Criteria Decision Making (MCDM) methods. After
literature review is done, the first research sub-question is answered by having
a list of important criteria considered in freight transport modal choice.
6
Therefore, after this step, the important criteria are identified and MCDM is
chosen as a research method for analyzing data.
Data collection is the second step where the questionnaire is designed based
on chosen MCDM method and the identified criteria, and by sending
questionnaires to the chosen respondent groups, respondents’ opinion
regarding the importance ratio of the criteria can be collected, which will answer
the second sub-question. The third step is to apply the MCDM method to the
collected data, therefore getting the importance/ weight of each criterion. The
fourth step is to analyze and interpret the output from the third step, which
generates findings that answer the main research question and the third sub-
question. And in the fourth step, the findings of this research are discussed by
reflecting on the conclusions from the previous studies which are mentioned in
the literature review, and the similarity and difference between the findings of
this research and the findings of previous researches will be underlined.
Conclusions and recommendations will be drawn in the final step where the
fourth sub-question will be answered, and in this step suggestions for the future
research will also be presented.
7
Figure1 Research approach
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Chapter 2 Literature review
In order to generate an exhaustive and exclusive criteria list, the literature
review is conducted, and the unambiguous definition of each chosen criterion
is detailed in this chapter. Information about the current study of freight transport
mode choice and important criteria has been gathered from the literature review.
Thus, the important criteria which are often considered in the existing literature
will be listed and explained, among which the relatively more important criteria
will be chosen. The first sub-questions- Which criteria of transport modes are
considered by shippers when making mode choice decisions? -will be
answered in this chapter. Literature explaining the reason why shippers should
switch from road transportation to other modes will also be presented. Moreover,
as shippers refer to the decision-makers who make freight transport modal
choice decision, whereas not every freight transport modal choice decision is
made by shippers, in other specific cases the freight transport decision might
be made by different decision-makers, such as carriers and freight forwarders.
Thus, the involved decision-makers will be discussed in the 2.2. While apart
from decision-makers, other stakeholders are also involved in the freight
transport mode choice since they may have impact on the decision shippers
make, for example the influence exerted by governments and scholars etc..
These stakeholders will also be detailed in the 2.2, and the reason why
assessing and improving important criteria will bring benefits to these
stakeholders will be explained either. Besides, within the 2.2, the fact that
decision-makers often consider characteristics of freights will be discussed, and
the underlying theory of decision-makers’ behavior will be explained.
In addition, the underlying concept will be set for the fourth sub-question- How
can the importance of criteria be used to increase the competitiveness of
intermodal transport?. The current situation of the modal split in freight
transportation will be explained. Besides, based on the literature it will be
demonstrated for the underlying concept of the fourth sub-question that
shippers agree on the important criteria confirmed by researchers, and changes
in these criteria have an impact on freight transportation modal choice decisions.
The fourth sub-question will be finally answered in the conclusion part of
chapter 5.
2.1 Outline of the considered literature
The benefits of shifting from truck to other modes can be represented in many
9
areas, such as reduced highway congestion, reduced pavement preservation
costs, improved safety and air quality, and better-utilized existing infrastructure
(the Florida Department of Transportation Rail Planning & Safety Office). This
is the reasoning that existing literature often uses as the argument to justify
their study of freight transport modal choice or split.
Apart from four researches (Vannieuwenhuyse, Gelders, & Pintelon,2003;
Beuthe et al., 2005; Dries et al., 2013; Umut & Semih, 2008) which are based
on Multi-criteria-decision-making method, the rest of literature reviewed in this
study generally uses discrete choice model combined with stated preference
technique which is used for data collection. Table 1 presents the 15 studies
which estimate the transport modal criteria shippers consider. Among them,
four studies strongly support the conclusion of the bibliographical review of Feo-
Valero, García-Menéndez, and Garrido-Hidalgo (2011a) that the most
commonly used transport criteria are transport cost, travel time, frequency, on-
time reliability, and losses and damages. And, the set of criteria considered by
the rest of the researches is in line with this conclusion. Besides, among other
thirty studies which are not presented in table 1, four researches (Fries, 2009;
Lammgård, 2007; Regmi & Hanaoka, 2015; Zhang, Boardman, Gillen, &
Waters, 2005) point out that the ever-increasing important environmental factor,
which is often measured by using CO2-emission as an indicator, should not be
ignored in freight transport modal choice due to the increasing societal attention
and governmental regulations. Thus, CO2-emission is also included as one
important criterion in table1.
2.2 Decision-makers and involved stakeholders
According to García-Menéndez and Feo-Valero (2009), in the study of freight
transport modal choice the most critical issue is to pinpoint the right decision-
maker first, which is a quite difficult task compared to the decision-maker
identification in passenger transport since the user of the service and the
decision-maker are often the same person, namely passengers. While, there is
a large number of actors involved in shipping freights, therefore making it
difficult to identify the decision maker in freight transportation. The decision-
maker of freight transport modal choice normally consist of three categories
including shippers, carriers, and the receiver. To be specific, shippers refer to a
group of people who have a shipment which needs to be delivered, such as
freight forwarders, logistics operators, and third-party logistics, etc.; Carriers
are the agents, such as trucking company, rail company, and barge company,
etc., who move the shipment from the shippers to the receivers by themselves;
the receiver refers to the agent to whom the freight is delivered (Fries &
10
Patterson, 2008). And, Fries and Patterson (2008) also pointed out that two
different types of shippers must be distinguished which are private shippers
who transport shipments by using their own transportation modes and end-
shippers who completely outsource freight transport activities, and the research
mentions that these two types of shippers do have different focus regarding
freight transport demand.
While, regarding each specific freight transport case, decision makers from one
of these three categories will make decisions. It is worth mentioning that these
three categories of decision-makers are not necessarily mutually exclusive. To
conclude, in many researches, shippers are viewed as the decision-maker. De
Jong, Bakker, Pieters, and Wortelboer (2004) and Bergantino and Bolis (2004)
pointed out that the decision related to the mode of freight transport is made by
shippers. Moreover, it is also concluded that more than half of the decisions
regarding the freight transport are made by shippers (UNESCAP Secretariat,
2000). Hence, the aforementioned researches support the reasoning for
incorporating shippers and carriers as target populations, and in this research
decision-makers who are from the one of these two fields and are in charge of
transportation of freights, by using their own transportation modes, are grouped
as practitioners.
Danielis, Marcucci, and Rotaris (2005) mentioned that in a global and
competitive environment which is characterized by complex logistics and
supply chain structures, it is important for many different stakeholders to assess
firms’ value of service for freight transport. Carriers, for example, might take
advantage of knowing firms’ willingness to pay for specific service
characteristics, namely criteria in this research, so that they can customize their
services according to customers’ preference and differentiating their services,
therefore strengthening their own competitive position (Danielis et al. 2005). As
mentioned at the beginning of this chapter, not only are decision-makers
involved in the freight transport mode choice, other stakeholders who directly
or indirectly influence decision-makers’ preference of alternatives are also
involved in this decision-making process. After assessing the important criteria
perceived by shippers and carriers, other stakeholders, who might influence
shippers, such as the government, will make better investment decision and
regulations to motivate shippers to choose other transport modes instead of
uni-road transportation. For example, in order to motivate shippers to shift from
road transport to other modes, the government intervention and the government
investment are two methods to exert an influence on freight transport modal
choice (Jeffs & Hills,1990).
This study includes three type of respondents, which are industry experts,
practitioners, and scholars. Practitioners refer to people who not only make
freight modal choice decision but also use their own transportation modes to
11
transport freights, which means they do not outsource the freight transportation
to other companies. Therefore, according to the aforementioned findings from
the research of Fries and Patterson (2008), practitioners, in this research, can
thus be represented by private shippers and carriers. Industry experts in this
research are defined as a group of people who works in the third-party-logistics-
company or logistics consultancy company and do not transport freights
themselves, such as freight forwarders who organizes shipments by
outsourcing freights transportation. The reason for particularly dividing actual
decision-makers into two groups- industry experts and practitioners- is that
these two type decision-makers have different focus regarding freight transport
demand when considering freight transport mode (Fries & Patterson, 2008),
and due to different working environments and capacities, people representing
industry experts, such as freight forwarders, often play a role as experts in
logistics-related decision-making process, while practitioners, such as carriers,
tend to work in the field and thus might cannot see the whole decision-making
process in a strategical way that industry experts do. In addition, compared to
practitioners, industry experts acquire more logistics-related know-how and
professional perspectives, while compared to professors, industry experts own
more practical knowledge and freight transportation-related working
experiences. Thus, industry experts can even be viewed as the interface
between practice-focused practitioners and technology-focused professors,
therefore it is interesting to know how industry experts actually perceive the
criteria when making a decision of freight transport mode.
While, scholars who are specialized in logistics-related field, compared to other
two types of respondents, might rarely have practical experience about freight
transport modal choice, but do have the updated information and scientific
methods and especially a full-view of technologies and logistics, which
suggests that they can make modal choice decision by considering more
aspects which would not be perceived by practitioners and industry experts.
Furthermore, their studies seem to be the main source for governments and
policy makers to analyze the current situation of freight transportation, and thus
they can help governments to decide whether to choose investment policy or
intervention policy, therefore influencing the transport modal choice made by
practitioners and industry experts. Correspondingly, scholars generally get
updated information, such as survey data, from interviews with practitioners and
industry experts. To conclude, as mentioned in the section 1.2.2 people from
different working backgrounds might perceive the criteria differently and thus
assign the different importance regarding one specific criterion, and since
existing studies mostly only choose practitioners as the target population, it
might be informative to know how professors and industry experts perceive the
criteria. And, comparing the importance of criterion perceived by the three types
of respondents and finding the possible difference in their perceived importance
regarding one criterion might present a more comprehensive picture and
12
interesting perspectives for future study of freight transport modal choice.
2.2.1 Characteristics of freights considered by decision-makers
The idea of considering the physical characteristics and requirements of
freights is what creates the first threshold for freight transport modal choice
(Roberts, 2012). Additionally, according to Gleave et al. (2015) it is useful to
categorize the transport of freights in terms of different commodity categories,
because the choice between rail and road is often related to the characteristics
of the freights transported. For example, it would be uneconomical if Firm A
plans to ship twenty tons of chemical freights by using truck when the rail mode
is available. Besides, According to García-Menéndez (2009) and de Jong et al.
(2004) the impact of different characteristics of freights on preferences of
criteria should also be considered in studies of freight transport modal choice,
and the set of criteria should be considered separately in terms of different types
of freights regarding their different characteristics.
According to Jeffs and Hills (1990), the characteristics of a shipment have at
least an equal importance on a shippers’ demand criteria, and four most
important aspects are concluded as 1. Physical condition and external
dimension (size); 2. Commodity value; 3. Perishableness; 4. Hazardousness.
Their research mentions that physical conditions and external dimension of
freights might have a direct impact on freight transport modal choice in terms
of the availability of modes to fit, for example, the extremely heavy or
voluminous freights, and such special characteristics even might not allow a
modal choice to exist since, for example, only one designated mode, such as
rail, can transport these extremely heavy or voluminous freights. Thus, it can
be concluded that the principal first level of modal choice depends on the nature
of freights, namely characteristics of freights (Roberts, 2012).
Moreover, characteristics of freights especially play an important role if the
freights have a special trait, for example, perishable characteristics are linked
to high requirements of door-to-door travel time because of the limited durability
of the perishable freights. Therefore in that case the short travel time is
appreciated by shippers, and shippers will give higher priority to the mode with
the shortest travel time. Due to the limited time this research just includes four
types of freights, which are freights from manufacturing industry, freights from
agriculture industry, perishable foods, and freights from chemical industry.
13
2.2.2 Behavioral analysis theory
The behavior of freight transport mode choice can be viewed as similar to the
behavior of purchasing a product, and the behavior of decision-makers is thus
assumed to be rational given that it is within the decision-makers' "bounded
rationality" which can be explained in the sense that decision-makers' behavior
is rational within the limits of his cognitive and learning capacities and also
within the limits of available information (Craig, 1973). Furthermore, Greeno,
Sommers, and Wolff (1977) proposed that, just as a product, all transport
modes can be conceived of, in abstract, their attributes which are represented
by a set of criteria. Besides, Winston (1981) mentioned that decision-makers'
behavior is based on a utility function, and profit or cost, which are included in
the utility function, are a function of qualitative criteria such as on-time reliability,
flexibility, and so forth, because these qualitative criteria are often related to the
potential risk that leads to extra costs, for instance, the low on-time reliability
might cause a late shipment which then leads to extra costs. Thus, Winston
addressed that qualitative criteria have the importance with regard to the
decision-makers' utility, and in order to achieve the minimum-cost solution,
decision-makers tend to consider these criteria when selecting a freight
transport mode. This finding is in line with the research of Arunotayanun and
Polak (2007) which found that freight modal choice has been studied based on
two theories: operations research techniques and utility maximization theory,
and the utility maximization theory has been used, in general, more common,
because the logistics decision-making process is extremely sophisticated and
criteria, such as on-time reliability and frequency, play a significant role.
To conclude, all the aforementioned researches actually study the decision-
makers’ behavior based on microeconomic theory, particularly based on utility
maximization. Moreover, some previous researches also concluded that when
decision-makers choose freight transport mode they, at first, consider the utility
value of each mode, and then they choose a suitable mode by comparing the
measured utility value of one mode with the one of another mode (Ben-Akiva &
Lerman 1985; Train 2003). The research of Das, Aeppli, Cook, and Martland
(1999) also supports this conclusion, and it also shows that consumers’
behavior is particularly relevant to understand how decision-makers select
between competing transportation modes, and decision-makers are expected
to select the mode that will minimize the total cost. The underlying theory
explaining the behavior of decision-makers in freight transport mode choice is
the utility maximization which views the transport cost as the cost and
qualitative criteria to be related to the potential cost that will become the real
cost once the chosen mode fails to satisfy the requirements of one specific
criterion, for instance, terrible on-time reliability of the chosen mode might incur
extra costs for compensating stockout. Thus, decision-makers attempt to
minimize the total cost by considering the criteria of each mode and thus
14
choosing the mode which might incur less cost, therefore maximizing their utility.
Hence, it is necessary to know which important criteria decision-makers
consider in the decision of freight transport mode choice. The next section will
identify the important criteria.
2.3 Considered criteria in the existing literature
Regarding the criteria of freight transport modes a decision-maker considers,
researchers appear to agree on transport cost, door-to-door travel time,
frequency, flexibility, on-time reliability, and loss and damage, because they are
the most commonly incorporated in the literature (Marcucci & Scaccia, 2004;
Zotti & Danielis, 2004; Punakivi & Hinkka, 2006; Bergantino & Bolis, 2007).
Moreover, the bibliographical review of Feo-Valero et al. (2011a) which is based
on 31 papers also concludes that the aforementioned six criteria are the most
commonly used transport criteria. On the other hand, from a practical
perspective, it also appears that decision-makers in the real life tend to consider
these six criteria according to the interview conducted by Zotti and Danielis
(2004). The below table presents the times of appearances of the
aforementioned criteria in the previous literature and transport modes and the
target population that literature considers.
It is commonly accepted that freight transport mode choice of shippers is
influenced not only by the pure economic criteria, such as transport cost and
door-to-door travel time, but also by more qualitative factors including frequency,
on-time reliability, flexibility, loss and damage etc. (Witlox & Vandaele, 2005).
This finding was confirmed in the earlier research of Jeffs and Hills (1990) who
mentioned that the research only including generalized costs as the main
criterion fails to explain adequately the prevalence of road freight in the UK, and
the authors suggested that other criteria should also be considered in the study
of freight transport modal choices. Moreover, the criteria that influence the
freight transport mode choice can be generally divided into two groups: (1)
economic criteria which are quantitative criteria including door-to-door time and
transport cost; and (2) quality of service criteria which are qualitative criteria,
such as flexibility and frequency. While some researches confirmed that
transport cost is the most important criteria, and Beuthe et al. (2005) even
concluded that all weights of non-cost qualitative criteria weigh as equal as the
weight of transport cost. But, interestingly, this conclusion is contradicted by
Danielis et al. (2005) who concluded that there is a strong preference for quality
criteria over cost. It is possible that the reason causing this contradiction is that
these two studies use the different group of respondents, which suggests that
respondents having different working background might perceive the criteria
differently.
15
Fries and Patterson (2008), Dries et al. (2013), Fries (2009), Lammgård (2007),
Regmi and Hanaoka(2015), and Zhang et al.(2005) argued that the ever-
increasing important factor- CO2-emission - should be included in freight
transport modal choice due to the increasing societal concern and
governmental regulations. Moreover, it seems that most existing literature uses
environmental perspectives as a reason to justify the intention of their research,
but few of them really include environmental-related factor into the criteria set.
Hence, as mentioned in 1.2.2, CO2-emission is included as one important
criterion in this study, and thus it is listed in table 1. After the below table, each
criterion will be explained regarding the previous literature, and in the end
decisive criteria will be chosen from the criteria presented in the below table.
Table 1: criteria considered in the literature
Target
populatio
n
Considered
modal
alternatives
Door-to-
door
travel
time
Transpo
rt cost
On-time
Reliabilit
y
Freque
ncy
Flexibilit
y
Loss&
damage
CO
2-
em
mis
sion
Shinghal &
Fowkes
(2002)
Indian
firms from
six
different
product
sectors
Road,
intermodal,
and rail
transport.
x x x x
Vannieuwe
nhuyse
et al.
(2003)
Flemish
shippers
and
logistics
providers
Road, inland,
and rail
transport
x x x x x x
Beuthe
(2005)
Belgian
shippers
Rail, road,
waterways,
short-sea
shipping and
their inter-
and multi-
modal
combinations
x x x x x x
Marcucci &
Scaccia
Italian
logistics
Train, ship
and inter-
x x x x x x
16
(2004) personnel modality
transport.
Zotti &
Danielis
(2004)
Mechanic
s
companie
s in the
Italian
region
Road and
intermodal
transport
x x x x x x
Punakivi &
Hinkka
(2006)
Logistics
service
providers
Ship, road,
air, railroad
transport
x x x
Bergantino
& Bolis
(2007)
Italian
freight
forwarder
s
Road and
maritime ro-
ro transport
x x x
Fries &
Patterson
(2008)
Canadian
shipping
manager
s
Road, rail,
and
intermodal
transport
x x x x
García‐
Menéndez
& Feo‐
Valero(200
9)
Spanish
exporters
and
freight
forwarder
s
Short-see
shipping and
road
transport
x x
Norojono &
Young
(2003)
Freight
companie
s in Java
Rail and road
transport
x x x
Chiara,
Deflorio, &
Spione
(2008)
Italian/Fr
ench
transport
operators
Road only
mode and
intermodal-
rail
transportation
x x
Feo-Valero
et al.(2016)
Spanish
producer
s and
distributor
s
Road and
intermodal-
rail
transportation
x x x x
Brooks,
Puckett,
Hensher,
& Sammon
s (2012)
Australia
n
shippers
Road, rail,
and coastal
shipping
x x x
17
Maria et
al.(2011)
Spanish
freight
forwarder
s
Road and
maritime-
intermodal
transportation
x x x x
Dries et al.
(2013)
Belgian
shippers,
freight
forwarder
s
Road, barge-
intermodal,
and rail-
intermodal
transportation
x x x x
Transport cost
Transport cost, which is an indispensable criterion incorporated in many
previous studies of freight transport modal choice, is the main criterion driving
the choice of decision-makers and has a negative effect on its selection
probability. Vannieuwenhuyse et al. (2003) investigated the perception of
Belgian logistics decision maker regarding the choice of transport modes by
using a survey, and thus concluded that transport cost is one of the criteria
having the highest weight. Both Macharis and Bontekoning (2004) and Caris et
al. (2008) included the transport cost as the main criteria in the transport mode
choice and also the route choice. Moreover, the importance of the transport cost
is also confirmed by Beuthe et al. (2005), who mentioned that all weights of
non-cost qualitative criteria weigh as equal as the weight of transport cost, and
their research also points out that Belgian shippers clearly view the transport
cost as the main criteria in the transport mode choice. This perspective is also
underlined by Feo-Valero, Garcia-Menendez, Saez-Carramolino, and Furio-
Prunonosa (2011b) who concluded that transport cost is the only reason to
stimulate shippers who use the hinterland rail connection to shift their transport
mode, and this conclusion is based on the fact that 81% of freight forwarders
uses the low cost of the rail transport as the main reason for their transport
mode choice. Cullinane and Toy (2000) conducted a survey of 75 bibliographic
references about route and transport mode choice, showing that transport cost,
together with door-to-door travel time and reliability, are consistently referenced
and often considered as most relevant factors. Another extensive survey
consisting of 246 interviews with freight forwarders, which is conducted by Grue
and Ludvigsen (2006), is to identify the determinants of mode and route choice
in the intra-European freight transport market, and its result shows that the
transport cost and reliability are chosen as the most relevant transport mode
choice criteria. However, the research of Bouffioux et al. (2006) also shows that
transport cost with the weight of 64% largely overruns other qualitative criteria
such as flexibility (6%) and frequency(below 5%), indicating the highly
perceived importance of transport cost.
18
On the other hand, there also exists few literature which does not include
transport cost in freight transport mode choice. It mainly because that its
researcher only wants to study the qualitative criteria in terms of monetary value,
hence researcher deliberately avoids including transport cost (Zamparini,
Layaa, & Dullaert, 2001).
It boils down to the fact that transport cost is the most important criterion in
freight transport mode choice, and the times of its appearance in the previous
literature is the highest among the times of appearance of other criteria.
Although, other qualitative criteria play an important role in freight transport
mode choice either, but their impact on the modal choice decision is often not
big enough to actually stimulate a modal shift towards intermodal transport, but
their importance should not be ignored.
Door-to-door travel time
Travel time is often considered by a large body of previous literature as an
important criterion in freight transport mode choice (Cullinane & Toy 2000; Zotti,
& Danielis 2004; Feo-Valero, Espino, & Garcia 2011; Garcia-Menendez & Feo-
Valero 2009; Norojono & Young 2003). The reason that most previous studies
focus on travel time is concluded by Zhang et al. (2005), which is that travel
time can be clearly defined by researchers and easily understood by
respondents. While, this reason is slightly contradicted by some researchers
who refer travel time using different names in their studies, making the definition
of travel time quite ambiguous. Chiara et al. (2008) adopted travel time, while
some researchers used door-to-door transport time (de Jong et al., 2004;
Beuthe et al., 2005). Danielis and Marcucci (2007) used transit time in their
study. Even though different names have been used for travel time by these
researchers, the underlying concept of travel time, that those names are based
on, is still the same, which is the total travel time of door-to-door delivery of
freights also including loading and unloading operations and transshipment
time in case of intermodal transportation. Therefore, in this study, the term,
namely door-to-door travel time, is used, and to keep consistency all the
different names of travel time used by previous studies are replaced by door-
to-door travel time thereafter.
The monetary value of door-to-door travel time is investigated by Maria et al.
(2011). It shows 6.82 Euros per hour, which means one is willing to pay for 6.82
Euros per shipment in order to reduce the door-to-door travel time, therefore
indicating that door-to-door travel time is a relatively important factor when
choosing the freight transport modes. While, Massiani, Danielis, and Marcucci
(2007) concluded that the value of door-to-door travel time saving viewed by
shippers depends on the sensitivity of the customers to the product delivery
schedule. Therefore, customers who are very sensitive to the availability of
freights will have an impact on the freight forwarders so that the freight
19
forwarders will place high value to the reduction of door-to-door travel time. This
sensitivity of door-to-door travel time can be explained by many factors
including customer requirements, characteristics of freights etc. Perishability,
one of the characteristics of perishable freights, plays an important role in terms
of reduction of door-to-door travel time, and thus it increases the importance of
short door-to-door travel time due to the limited durability of such freights
(Brooks et al, 2012). For instance, due to the short shelf life required for
perishable goods, there is a high value at 12.19 Euros per shipment an hour,
making door-to-door travel time a relatively important criterion in deciding
transport modes (Maria et al., 2011). This finding is also supported by Fries
(2009), who concludes that when considering the perishable freights or high
value freights shippers tend to give higher priority to door-to-door travel time
requirements. Thus, this study will incorporate the perishable freights, and
investigate whether there is the difference in the perception of one specific
criterion regarding this type of freights and other types of freights.
On-time reliability
On-time reliability is defined in terms of the percentage of shipments that arrive
at their final destination on time. The previous literature generally agrees that
transport cost and reliability are most relevant for shippers. Rapp trans and Ivt
(2008) pointed out that shippers tend to weight on-time reliability about 20-100%
higher than transport cost and up to 14 times higher than door-to-door travel
time. While, transport cost seems to be of higher relevance than on-time
reliability only in the building materials freights. Das et al (1999) found that on-
time reliability is the most important decisive factor considered in the modal
choice in a study of the Indian freight market, and, especially for shippers of
chemical goods which require highly reliable transport flow, reliability is of
particular importance. This finding is also in line with the research conducted
by Danielis et al. (2005) who concluded that there is a high willingness to pay
for quality criteria in freight transport service, especially for on-time reliability.
Feo et al. (2011) pointed out that on-time reliability influences shippers in
making freight transport mode choice based on the case study of Spanish traffic.
Moreover, Fries (2009), Grue and Ludvigsen (2006), and Beuthe et al. (2005)
also concluded that on-time reliability is the most relevant transport criteria, and
Beuthe et al. addressed that on-time reliability appears to be the most important
criterion. De Jong et al. (2004) incorporated on-time reliability in their study
since on-time reliability is viewed to have an increasing importance in the mode
choice. Besides, McGinnis (1990) included on-time reliability as one of key
criteria based on the comparison of twelve studies on mode choice process,
and then the conclusion is made that nearly twenty years’ empirical research of
freight transport choice, in terms of a wide range of methodologies and different
industries and with regional and national samples, indicates that shippers in the
20
United States, in general, perceive qualitative criteria more important than
transport cost. Additionally, Murphy and Hall (1995) later updated McGinnis’
research and ranked on-time reliability as the first important criterion.
The requirements of on-time reliability required by shippers are influenced by
some factors in various ways. With the increasing adoption of Just-in-time (JIT)
processes in many firms, the on-time reliability is assigned with higher values
(de Jong, 2004). The trip distance also plays an important role in influencing
the sensitivity of on-time reliability in such a way that increasing distances will
decrease sensitivity in requirements of on-time reliability due to the possible
higher delay risk in terms of long distance transportation (Fries, 2009). Thus, it
can be concluded that the adoption of JIT processes and transport time will
have an impact on the requirements of on-time reliability required by shippers.
Moreover, requirements of on-time reliability can also be affected by the
characteristics of freights transported. Witlox and Vandaele (2005) mentioned
that on-time reliability is particularly important for the company who produces
cooling machines, and it even surpasses transport cost. This study will answer
whether the preference of on-time reliability will differ regarding different groups
of freights by answering the main research question.
Frequency
Zamparini et al.(2001) defined frequency in terms of the number of shipments
offered by a transport company or freight forwarders in a determined period of
time. Frequency also appears to be an important criterion in mode choice,
especially for shippers who make frequent and low volume shipments (Shinghal
& Fowkes, 2002). The research carried out by Combes (2012) even further
strengthens these findings. Based on about 3,000 shippers in France, this
research indicated that frequency of shipments seems to play an important role
in determining modal choice and shipment size. Moreover, according to the
study of roll-on/roll-off 2 railways between France and Italian alps, the
investigation shows that 9% of shippers are willing to choose the combined roll-
on/ roll-off where 10 departures happen per day. While, 4% of shippers would
like to choose combined roll-on/ roll-off where 4 departures happen per day.
Garcia-Menendez (2004) concluded that together with transport cost, door-to-
door travel time, the role of frequency is a determinant of modal choice due to
the growing importance of efficiency in logistics. Chiara et al. (2008) concluded
that high frequency might cause shippers to shift transport modes.
2 Roll-on/roll-off (RORO or ro-ro) ships are vessels designed to carry wheeled cargo, such
as cars, trucks, semi-trailer trucks, trailers, and railroad cars, that are driven on and off the ship
on their own wheels or using a platform vehicle, such as a self-propelled modular transporter”
(Wikipedia ).
21
In some existing studies, willingness-to-pay (WTP)3 and monetary values have
been frequently used to quantify the subjective value of qualitative factors,
which help stakeholders understand the freight transportation market more
directly and clearly (Rodrigo & Satish, 2014). According to Bergantino and Bolis
(2007) who conducted the research among Italian freight forwarders, frequency
is perceived as the most important parameter together with on-time reliability.
In that research, the frequency is presented in terms of monetary value, and
freight forwarders appear to value 1% improvement in frequency at about 33
euros. Moreover, Witlox and Vandaele (2005) mentioned that the plastic
producing company is willing to pay an extra 0.0045 euro per ton-km for an
increasing frequency up to 27.6 shipments per week. Hence, according to these
studies frequency, as one of qualitative factors, plays an important role in
deciding freight transport modes.
Flexibility
Flexibility is defined as the ability of a company to respond quickly and efficiently
to changing customer needs in inbound and outbound delivery, support, and
services (Day, 1994). While, in the literature of freight transport modes, it is
often defined as the number of unplanned shipments which are operated
without excessive delay. Flexibility is commonly included as a quality criterion
in previous literature (Bolis & Maggi, 2003; Witlox & Vandaele, 2005;
Vannieuwenhuyse et al., 2003; Marcucci & Scaccia, 2004; Zotti & Danielis,
2004; Massiani, 2007). As flexibility is incorporated as a criterion, its significant
relevance is estimated. However, it also appears that the importance of
flexibility always turns out lower than criteria like transport cost and door-to-
door travel time.
In the research of Vannieuwenhuyse et al. (2003), flexibility is regarded as one
of top five performance criteria regarding freight transport modes by the
shippers and logistics providers, and it is assigned with the weight of 7.05,
which is quite lower than the weights of transport cost (8.34) and transport time
(7.61). Norojono and Young (2003) pointed out that quality and flexibility of
service are major factors in determining the freight transport mode choice, and
policies which can improve the flexibility and quality of service provided by the
particular mode may considerably increase the use of that mode. Flexibility is
a qualitative criterion, whereas it is also estimated in terms of monetary value
in the research of Zamparini et. al (2001). Their research mentions that flexibility
seems to be an irrelevant criterion regarded by the sample of Tanzanian firms
3 Willingness-to-pay values indicate how much a company is willing to pay for an improvement
in qualitative factors and how much the same company wishes to receive as compensation
once there is an inferior performance of that qualitative factors. (Witlox & Vandaele , 2005)
22
since its value is less than 0.002 US$/ ton-km.
CO2-emission
During the last decades, environmental perspective has been received much
attention in the freight transportation, and CO2-emission is the crucial part of it.
Such environmental problem is mainly contributed by CO2-emissions from the
transport and logistics process. Therefore, shippers tend to consider this
criterion when making freight transport mode decision, and the importance of
CO2-emission is supposed to become more significant in the future.
Beuthe et al. (2005) mentioned that rail and shipping transport are more
environmental-friendly in comparison with the truck, hence policy makers
attempt to promote a shift from truck to those two modes in order to curb the
increasing transport pollution. Moreover, according to Lammgård (2007) the
intermodal transportation of rail or ship is also more environmental-friendly
compared to unimodal truck transport because truck used in the intermodal
transportation is only adopted for pre-or post-haulage to complete the door-to-
door transportation which cannot be done by unimodal rail or unimodal ship
transportation. Thus, Dutch national policy attempts to choose the most
effective and sustainable modes instead of truck, and it tries to improve the
coordination among different modes, therefore decreasing the CO2-emission
(Topteam Logistiek, 2011). The European Union also planned to shift transport
mode towards more sustainable modes to meet its objectives which are to find
the cleaner and more efficient transport system (The European Union, 2011).
While, not only does policy makers pay attention to this environmental
perspective, but also stakeholders including shippers, customers, and carriers
concern about this environmental issue caused by the freight transportation.
According to Fries (2009) shippers are willing to pay for the reduction of
greenhouse gasses, therefore setting “green image” for their company to be
better differentiated from other companies. In addition to this, Beltran et al.
(2012) also mentioned the significant role that CO2-emission plays in the freight
mode choice decision made by shippers who have the feeling of “warm glow”
and thus consider it related to the socially responsible entrepreneurship.
But less existing literature considers the CO2-emission as an important criterion
deciding the freight transport mode choice. Among the literature including CO2-
emission as a criterion, some of them conclude that the importance of CO2-
emission is the least significant in comparison with other criteria. Platz (2008)
concluded that only do shippers consider environmental benefits from a
microeconomic perspective when considering for marketing or public relation.
Besides, according to Konings and Kreutzberger (2001) shippers rarely
concern about the environmental issues, whereas it is expected that the
23
sustainability aspect will become a competing quality dimension in the future.
Dries et al. (2013) mentioned that compared to the transport cost, door-to-door
travel time and on-time reliability CO2-emission has the relatively minor weight
in modal choice. However, some literature does admit the significant
importance of CO2-emission. Lammgård (2007) concluded that in addition to
the transport cost which is valued as the highest, the weight of CO2-emission
was taken into account to a high degree, therefore indicating that emphasizing
CO2-emission may help in raising the interest and priority of using the efficient
transport modes such as intermodal transport. Considering CO2-emission as
the only decisive criterion, Beltran et al. (2012) found that the CO2-emission
has a significant willingness-to-pay value of 71 euro/ton for its decrease. Fries
(2009) also mentioned that the Swiss shippers are willing to pay 1.52 euro for
a percentage point decrease in CO2-emissions.
However, the majority of existing literature has not attempted to quantify the
CO2-emission and incorporate it as an important criterion in the decision-
making process. Feo-Valero et al. (2011b) mentioned that there are no freight
forwarders who actually consider the environmental perspective as the reason
to shift towards the rail transport. Whereas, in the light of the evidences that
freight transportation is responsible for most of the increase in CO2-emission
and the rail- or ship- intermodal transport is more sustainable than unimodal
truck transport, it is expected that in the near future CO2-emission will
continuously attract interest from stakeholders and become a significant
criterion in freight transport mode choice (López-Navarro, 2014).
Damage and Loss
Damage and loss are defined in terms of the percentage of commercial value
loss due to damage, theft, and accidents (Witlox & Vandaele, 2005). Some
previous studies also consider safety and security to be aspects of quality, and
thus the absence of loss and damage play a pivotal role in freight transport
mode choice. Patterson et al. (2007) found that the damage and loss are ranked
higher than on-time reliability and transport cost. Besides, Witlox and Vandaele
(2005) also emphasized the importance of eliminating loss and damage in the
decision of mode choice given that each damage and loss represents a tangible
loss in terms of the value of freights, and the conclusion is that the more
handling operations the freight transport includes, the higher the chance of loss
or damage is. While, from the other side, the research of Feo-Valero et al.
(2011a) shows that there is a diminishing interest in the damage and loss
criterions since the improved transport technology and infrastructure and the
widely-used containers largely increase the level of freight transport service,
and underlines that the use of containers has a positive impact to eliminate
damage and loss. Furthermore, Danielis and Marcucci (2007) even underlined
that shippers are willing to tolerate a minimal damage and loss. Thus, the
24
conclusion can be drawn that due to the increasing use of containers the
damage and loss are largely eliminated from freight transportation, which
suggests that adopting containers in freight transportation has a positive impact
for decreasing appearances of loss and damage (Feo-Valero et al., 2011a).
Selection of criteria
Door-to-door travel time and transport cost are largely incorporated in previous
literature as important criteria in freight transport mode choice, and the
importance of on-time reliability, frequency, and flexibility have been
consistently approved by most existing literature. While, CO2-emission is
barely mentioned in the major literature. However, rising concerns of society for
CO2 emission can no longer be ignored, and companies, nowadays, have a
moral obligation to adopt the sustainable way to operate their business. Besides,
customers appear to value the green image that companies present and to be
aware of the considerable effect of CO2-emission the road transport generates.
Hence, decision-makers tend to incorporate CO2-emission as an important
criterion when deciding freight transport mode. Therefore, reduction of CO2-
emission will become an important criterion considered in the future research,
and by measuring respondents’ preferences towards it, it can be explicitly seen
whether respondents are willing to reduce CO2-emission when considering
transport modes. To conclude, this research will include reduction of CO2-
emission as an important criterion together with other five criteria which are
transport cost, door-to-door travel time, flexibility, frequency, and on-time
reliability. The damage and loss will not be incorporated in this research. There
are two reasons explaining why there is no need to include damage and loss
as an important criterion. 1. Since this research studies the freight transport
mode choice under the situation that containers are used as loading units to
carry freights, and in the same line of reasoning concluded by Feo-Valero et al.
(2011a) the use of containers eliminates the appearance of loss and damage,
or largely reduce the possibility of damage and loss to the minimal level that
decision-makers are willing to tolerate (Danielis & Marcucci, 2007). Therefore,
in that case, the importance of damage and loss is expected to be neglected
given there is small possibility of happening of loss and damage with the help
of containers, hence there is no need to incorporate this criterion in this
research; 2. With respect to the tighter regulation of cargo screening and more
attention paid to freight transportation, the safety and security of freights is no
more of an issue today (Roberts, 2012), which further ensures the absence of
damage and loss in freight transportation. So, it can be concluded that given
the setups of this research where containers are used during freight
transportation, the importance of damage and loss to the transport modal
choice is diminished so that damage and loss is not chosen as the important
criterion in this research.
25
To conclude, this chapter lists the chosen criteria and explains the reason why
these criteria are chosen by conducting the literature review, and the chosen
criteria are transport cost, door-to-door travel time, on-time reliability, flexibility,
frequency, and reduction of CO2-emission. Moreover, due to the specific
characteristics owned by different types of freights, this research divides
freights into four segments which are freights from manufacturing industry, from
agriculture industry, from perishable foods industry, and from chemical industry.
The next chapter focuses on the discussion of the methodology used in this
research, and setup steps for collecting data, such as designing the
questionnaire, are also presented.
26
Chapter 3 Methodology
Based on the knowledge from the literature review of chapter 2, this chapter will
focus on research planning which is an important part of implementation of
research. The underlying theories, which is multi-criteria-decision-making
theories, will be introduced, followed by the chosen method, which is Best-
Worst method, and the reason for choosing this method will also be explained
in the following section. The steps of conducting this research (figure 2) will be
introduced at first, and then the theory and methods will be explained.
Additionally, the process of selecting target population and the data collection
process will also be presented. At the end, the sample of questionnaires will be
partially explained.
3.1 Research design
To conduct this research in a rigorous manner, six steps should be strictly
followed. The first step is to pinpoint the problem, which is then transformed to
the explicit statement of the research question and the research objective. The
second step is to find the theories and methods to support this research, which
is namely what this chapter presents. In the third step, the questionnaire should
be designed based on the requirements of Best-worst method, and target
population should be selected, and after questionnaires are sent to those
respondents the data will be collected, which is the fourth step. In the fourth
step, the Best-Worst method will be used to analyze the collected data set.
Finally, the fifth step is to interpret the data, and therefore based on the results
of this step the research question will be answered. Figure 2 represents the
aforementioned steps.
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Figure 2 Steps of the research
3.2 Multi-criteria decision-making
Decision-making process is the thing that happens in the daily life, and
everyone consciously or unconsciously repeats this process. The choice is
made based on the decision maker's preference and evaluation. The aim of the
decision-making process is to choose among alternatives or options in order to
attain optimal results or objectives (Forman & Selly, 2010). Saaty and Vargas
(2012) concluded that decision makers attempt to find a way to assign weights
to the alternatives and then choose the most optimal one. Different decision
makers value the criteria involved differently. Practitioners and industry experts
face freight transport mode choice in their daily life, while from the scholar side,
researchers also face the decision of choosing freight transport mode when
conducting related studies. And, it is possible that due to the different
preferences of criteria perceived by these three types of groups, their final
decision of choosing transport mode might be different either. Multi-Criteria
Decision-Making (MCDM) method is suitable for this study since it is often used
for estimating how one makes decisions considering multiple criteria when
some criteria are qualitative and some criteria are conflicting. Furthermore, in
the case of this research where reduction of CO2-emission is included, MCDM
28
is appropriate since Vannieuwenhuyse et al. (2003) concluded that
environmental impact is mostly intangible, so it can only be incorporated by
more sophisticated combination methods such as multi-criteria analysis
methods. Besides, using multi-criteria decision making (MCDM) can also bridge
the literature gap that there are few existing studies using MCDM to analyze
mode choice in freight transportation. MCDM allows the inclusion of the
preferences, regarding qualitative criteria, of the decision maker (Dries, 2013).
In order to answer the research question, the importance of door-to-door travel
time, transport cost, on-time reliability, frequency, flexibility, and reduction of
CO2-emission should be determined in such a way that the numerical
importance is attached to each criterion, and this can be done by using the
MCDM.
Many MCDM methods have been proposed in existing literature including AHP
(analytical hierarchy process), ANP (analytical network process), TOPSIS (a
technique for an order of preference by similar to ideal solution), and WPM(the
weighted product model), while each method has its own characteristics. In
general, MCDM problems can be classified into two different categories: 1.
Problems with a finite number of alternative solutions which is the multi-criteria
attribute decision-making problem, and 2. Problems with an infinite number of
alternative solutions which is the multi-objective decision-making problem
(Zimmermann, 1991). Regarding our research question, the alternatives are
finite, therefore, the first type of MCDM problem is similar to the research
question of this study, so the multi-attribute decision-making method should be
chosen.
Furthermore, the most widely-used multi-attribute decision-making method is
the Analytical hierarchy process (AHP). AHP decomposes a complex MCDM
problem into a system of hierarchies, and it uses the technique of pairwise
comparison to elicit the numerical evaluation of qualitative phenomena from
decision makers (Saaty, 1980, 1994). By using the pairwise comparison, the
relative preference of alternatives with respect to criteria can be derived. After
getting the weights from the comparison of the criteria, the overall value for
each alternative will be calculated based on the weights for the criteria.
Although the AHP is a very famous approach, it has a big drawback which is
the inconsistency. Such inconsistency is caused by the unstructured way of
comparisons which are conducted by using pairwise comparison-based
methods. On the other side, AHP also requires a large amount of data. As
mentioned in the introduction part the reason why freight transport mode choice
is barely studied compared to passengers transport mode choice is that the
freight transport mode choice require a large data set, which is proved too hard
to collect because of budget restrictions and the fact that shippers are reluctant
to provide information regarding the cost of transport (García-Menéndez, 2009).
In the same line of reasoning, the big data set AHP requires is really hard to
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achieve especially in this freight transport modal choice study. Therefore, the
Best-or-Worst method (BWM) is considered in our study, because by using the
specific structured pairwise comparison it remedies the inconsistency issue
which AHP cannot solve, and compared to some other MCDM methods, BWM
requires fewer comparison data (Rezaei, 2015, 2016). Moreover, according to
the research of Rezaei (2015), it shows that BWM performs significantly better
than AHP not only in terms of consistency ratio, but also with respect to other
evaluation criteria such as minimum violation, total deviation, and conformity,
therefore generating more reliable results. The BWM has been successfully
applied to several multi-criteria decision-making problems such as water
scarcity management (Chitsaz & Azarnivand, 2016), supplier selection (Rezaei,
Nispeling, Sarkis, & Tavasszy, 2016), freight bundling configuration (Rezaei,
Hemmes, & Tavasszy, 2016), technological innovation (Gupta & Barua, 2016),
supplier segmentation (Rezaei, Wang, & Tavasszy, 2015), supply chain
sustainability in oil and gas industry (Sadaghiani, Ahmad, Rezaei, & Tavasszy,
2015), efficiency of university-industry (Salimi& Rezaei, 2016), and business
continuity management systems (Torabi, Giahi, & Sahebjamnia, 2016).
3.3 Best-Worst method
Best-worst method (BWM) is a new method proposed to solve multi-criteria
decision-making problems (Rezaei, 2015, 2016). Compared to other MCDM
methods, BWM has two aforementioned advantages, which is also the main
reason for choosing this method in this research. In the following section, the
steps of conducting the Best-Worst method (BWM) are explained, and the
requirements of conducting BWM are also presented. For the explicit and
detailed introduction of Best-Worst method, we refer to (Rezaei, 2015, 2016)
3.3.1 Steps of BWM
According to Rezaei (2015, 2016), five steps of the BWM method will be
described below.
Step 1. A set of decision criteria should be determined first. In this step, a set
of criteria {𝑐1, 𝑐2, 𝑐3,…., 𝑐𝑛} is chosen to make a decision. For example, in this
research, the set of decision criteria is { 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑟𝑡 𝑐𝑜𝑠𝑡𝑐1 , 𝑑𝑜𝑜𝑟 − 𝑡𝑜 −
𝑑𝑜𝑜𝑟 𝑡𝑟𝑎𝑣𝑒𝑙 𝑡𝑖𝑚𝑒𝑐2, 𝑜𝑛 −
𝑡𝑖𝑚𝑒 𝑟𝑒𝑙𝑖𝑎𝑏𝑖𝑙𝑖𝑡𝑦𝑐3 , 𝑓𝑙𝑒𝑥𝑖𝑏𝑖𝑙𝑖𝑡𝑦𝑐4, 𝑓𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦𝑐5,𝑟𝑒𝑑𝑢𝑐𝑡𝑖𝑜𝑛 𝑜𝑓 𝐶𝑂2 𝑒𝑚𝑖𝑠𝑠𝑖𝑜𝑛𝑐6}.
Step 2. The best criterion (e.g. most desirable, most important) and the worst
criterion (e.g. least desirable, least important) should be determined. In this step,
the decision maker just picks the best and worst criteria in general, and there
is no comparison to be made yet.
30
Step 3. The preference of the best criterion over all the other criteria should be
determined by using a number between 1 and 9 where 1 means equal
preference between the best criterion and another criterion, and 9 means the
extreme preference of the best criterion over another criterion. The result of this
step is the vector of Best-to-others which would be:
𝑨𝑩= (𝑎𝐵1, 𝑎𝐵2, 𝑎𝐵3,…, 𝑎𝐵𝑛),
Where 𝑎𝐵𝑗 indicates the preference of the best criterion B over criterion j, and
it can be deduced that 𝑎𝐵𝐵=1.
Step 4. The preference of all criteria over the worst criterion is determined by
using a number between 1 and 9. The result of this step is the vector of others-
to-worst which would be:
𝑨𝑾 = (𝑎1𝑊, 𝑎2𝑊, 𝑎3𝑊, … , 𝑎𝑛𝑊)𝑇,
Where the 𝑎𝑗𝑊 indicates the preference of the criterion j over the worst criterion
W. It also can be deduced that 𝑎𝑊𝑊=1.
Step 5. The optimal weights (𝑤1 ∗, 𝑤2 ∗, 𝑤3 ∗, …, 𝑤𝑛 ∗) should be calculated. The
optimal weights of criteria will satisfy the following requirements:
For each pair of 𝑤𝐵/𝑤𝐽 and 𝑤𝑗/𝑤𝑊, 𝑤𝐵/𝑤𝐽= 𝑎𝐵𝑗 and 𝑤𝑗/𝑤𝑊= 𝑎𝑗𝑊.
Therefore, in order to meet these conditions for all j, we should minimize the
maximum among the set of {|𝑤𝐵 − 𝑎𝐵𝑗𝑤𝑗|, |𝑤𝑗 − 𝑎𝑗𝑊𝑤𝑤|}, The problem can be
formulated as follows:
min 𝑚𝑎𝑥𝑗 {|𝑤𝐵 − 𝑎𝐵𝑗𝑤𝑗|, |𝑤𝑗 − 𝑎𝑗𝑊𝑤𝑤|}
subject to
∑ 𝑤𝑗𝑗 =1
𝑤𝑗≥0, for all j (1)
Problem (1) can be transferred to the below linear programming problem:
min𝜉𝐿
subject to
|𝑤𝐵 − 𝑎𝐵𝑗𝑤𝑗|≤𝜉𝐿, for all j
|𝑤𝑗 − 𝑎𝑗𝑊𝑤𝑊|≤𝜉𝐿, for all j (2)
∑ 𝑤𝑗𝑗 =1
𝑤𝑗≥0, for all j
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This problem (2) is a linear problem, therefore providing a unique solution to
the problem. After solving the problem (2), the optimal weights
(𝑤1 ∗, 𝑤2 ∗, 𝑤3 ∗, …, 𝑤𝑛 ∗) and 𝜉𝐿∗ are obtained. 𝜉𝐿∗ can be directly considered
as an indicator of the consistency of the comparison in this model, and the
closer the value of 𝜉𝐿∗ is to zero, the higher the consistency is, and thus the
more reliable the comparisons become.
3.4 Data collection and preliminary preparation
3.4.1 Data collection
Data collection is the process of gathering data in a systematic and rigorous
way. According to Sapsford and Jupp (1996), in order to ensure that data
collected are both defined and accurate and that following decisions based on
arguments embodied in the findings are valid, the formal data collection process
is indispensable. Data can be generally classified into two groups: primary and
secondary data. Primary data refers to the data which are collected for the first
time by researchers (Parab, 2015). And, this primary data can be collected by
using unstructured interviews, structured interviews and questionnaires
(Sekaran & Bougie, 2010). While secondary data are those which have already
been gathered and thus are available, hence researchers do not need to collect
them again (Sekaran & Bougie, 2010).
For this research, since there is no existing secondary data, the primary data
will be collected by using the questionnaire as a data collection method. The
questionnaire is a set of pre-formulated questions for collecting information from
respondents, and it can be conducted by mail, telephone, face-to-face
interviews, handouts, emails, or web-based questionnaires (Data Collection
Methods for Program Evaluation: Questionnaires). According to this research,
it means the questionnaire will be designed in such a way that the preference
of respondents about transport modal criteria will be elicited. The questionnaire
is made by using an online questionnaire tool called SurveyGizmo®, and the
emails attached with the link referred to the questionnaire have been sent to
the chosen respondents, because this distribution method is efficient and
appropriate for accessing the large amount of respondents, and meanwhile the
respondents can fill the questionnaire when they are free. Moreover, due to its
web-based characteristics, the questionnaire can protect the privacy of
participants by keeping their responses anonymous. On the other hand, this
questionnaire distribution method also has a big drawback which is the low
response rate due to multiple reasons, for instance: people are reluctant to
32
invest their time in filling questionnaires, and due to the fact that their responses
are anonymous they might not feel any pressure and motivation to fill the
questionnaire. Therefore, given the aforementioned drawbacks, the number of
chosen respondents should be as large as possible in order to get more
responses. The following section will explain how the respondents are chosen.
3.4.2 Collection of respondents
In order to distribute questionnaires, the set of respondents should be selected
first, which is the preliminary preparation of collecting data. As mentioned in the
2.2 three types of respondents will be considered in this research, including
practitioners, industry experts, and scholars. Thus, in addition to google
searching engine, Linkedin is used in the process of collecting respondents by
using its built-in searching engine to find the relevant respondents having
logistics and transportation related titles. Different keywords are used to search
different type of respondents based on the aforementioned assumption that
practitioners are fieldwork-focused and industry experts are consultancy-
focused, while both of them can be viewed as decision-makers of freight
transport modal choice. The keywords used in searching practitioners are
logistics providers, carriers, private shippers, logistics executive, logistics
manager, logistics coordinator, and director of logistics. And, their certified skills
should include at least logistics, supply chain management, transport, or supply
chain, therefore suggesting they have a know-how of logistics and can make a
sensible decision on freight transport mode. Besides, to ensure that the chosen
person is still doing the freight transportation activities the status of their current
career will be checked in the “experience” list. Regarding the industry experts,
keywords such as logistics analyst, freight forwarders, logistics management
specialist, logistics specialist, shipping consultant, logistics supervisor, and
freight broker are used, and also personnel who currently works for the third-
party logistics company (such as C.H. Robinson) or logistics consultancy
company (such as Leanlogistics) with the related logistics titles and working
experiences will also be chosen as industry experts. Additionally, these people
should also have certified “top skills”, which are shown on their Linkedin page,
including logistics, supply chain, and transportation. The campus website,
published papers in the logistics field, and Linkedin are used together to find
scholars. Keywords such as logistics and supply chain management are used
to search scholars, and in each logistics-related paper, the authors’ contact
information including email address and their department will be presented,
among them the scholars from logistics or transportation-related department
will be chosen. Besides, the application named FindThatLead, which is installed
in google chrome, is used for extracting the email address of the selected
person in his/her Linkedin page, and after extracting email address
MailTester.com is used to check whether the email address is the valid one. At
the end, 1072 respondents are selected in total, which include 555 practitioners,
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317 industry experts, and 200 professors.
3.5. Questionnaires
In order to minimize bias in research, principles focusing on three areas are
behind the process of questionnaire design (Sekaran & Bougie, 2010). The first
principle is about the wording of the questions. The second relates to the way
that a question will be formulated, such as length of questions, sequencing of
questions, and open-ended/-closed questions. The third refers to the general
layout and format of the questionnaire. Our questionnaire is designed based on
these three principles, and it is made in such a way that respondents think about
the freights from different industries in general and think about the transport
modal choice process in particular with regard to four types of freights. The
following section presents the structure of our questionnaire.
The first page has an introduction which briefly explains the purpose of
this research and addresses that how their responses will contribute to
the research. Moreover, it is also clearly stated that their responses will
be treated with great confidentiality and fully anonymous. In the end, the
involved researchers are represented.
The second page starts with the short introduction of the background
information related to the following question. For instance, as four type
of freights are included in this research, the second page which relates
to the manufacturing industry will ask respondents to suppose that they
are shippers and are going to transport the container full of machines
(freights from manufacturing industry). Meanwhile, three transport
modes are available which are truck, rail, and ship. Following the
introduction of background information, the respondents will be asked to
choose the best criterion and the worst criterion from the six criteria.
Then, based on what the respondent choose, the web-based
questionnaire will present the corresponding following questions which
are 1. The preference of comparison between the chosen best criterion
and the other criteria. 2. The preference of comparison between the
other criteria and the chosen worst criterion. And these two questions
will be presented by using “TextBox Grid” that presents questions in the
column and row, therefore making the comparison more explicitly.
According to the requirements of the BWM method, the respondents
should pick a number between 1 and 9 to show their preference, thus, a
statement indicating the meaning of each number between 1 and 9 is
attached to each comparison question. The statement used in the
questionnaire is listed below.
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Definition of 1 to 9 measurement scale:
1: Equal importance 7: Very strongly more important
3: Moderately more important 9: Extremely more important
5: Strongly more important 2,4,6,8: Intermediate values
The third, fourth, and fifth page are structured in the same way as the
second page, and each page represents a different industry. So, three
pages are followed to separately represent agriculture industry,
perishable foods industry, and chemical industry.
The sixth page contains the demographical questions such as the
question asking the job titles when it is the questionnaire targeting
practitioners, and when it comes to the scholars, questions asking their
current position in the university will be presented. And at the end, the
small text box is provided for respondents to write down their feedback.
Followed by the last page which is the “Thank you page”.
Four questions with regard to manufacturing industry and an example of a
model answer will be presented below to explicitly demonstrate how the
questionnaire works. Besides, this sample questions are from the questionnaire
which will be sent to practitioners, so the below background information for four
questions asks respondents to suppose that they are shippers (figure 3).
Figure 3 BWM questionnaire- the introduction of background information
After reading the background information, the respondent chooses on-time
reliability as the most important criteria in the first question (figure 4).
Figure 4 BWM questionnaire- the first question
Then the second question will jump out without the option of on-time reliability
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since the respondent already chose it as the most important. In this question,
the flexibility is chosen as the least important criteria (figure 5).
Figure 5 BWM questionnaire- the second question
Then, according to what respondent chose in the first question, the web-based
questionnaire will automatically generate the ranking row (figure 6). And the
number between 1 and 9 are filled to indicate the preference between on-time
reliability and other criteria.
Figure 6 BWM questionnaire- the third question
Finally, in the fifth question the respondent should compare the other criteria to
the least important one which is flexibility, and the ranking column is
automatically generated based on the first and second question (figure 7).
Because the best-to-worst criterion comparison is already conducted in the
fourth question, so the best criterion (on-time reliability) is intentionally excluded
in the fifth question in order to keep data having a good consistency.
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Figure 7 BWM questionnaire- the fifth question
All these five questions are compulsory to be filled in, and only the
demographical questions are optional questions. The format of the rest parts
with regard to other three industries has the same structure, and other two types
of questionnaires sent to industry experts and scholars are all in the same
structure with this sample questionnaire, only some keywords are changed
according to which type of respondents the questionnaire is sent to. And the
reason why three types of questionnaires are prepared is that by doing so the
feedback data will be automatically classified into three groups: practitioners,
industry experts, and scholars. The details of the whole questionnaire are given
in appendix A (on the page 79).
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Chapter 4 Analysis
The questionnaires have been sent separately into three groups corresponding
to three types of respondents, and the data collection process runs about two
months. In total out of 1,072 actors, 51 have responded, of which 1
questionnaire is excluded because of missing data. Among all 50 respondents,
there are 20 practitioners, 16 professors, and 14 industry experts. In total, the
response rate is approximate to 5 percent. In the beginning, the results will be
analyzed by using BWM, and all the calculations are done according to the
requirements of the BWM method. The results of the importance ranking of the
six criteria and their corresponding weights in terms of specific industry and
specific type of respondents will be presented (see table 2). Then, the Mann-
Whitney U test is chosen to investigate whether there, for one specific industry,
exists differences in the weights of one criterion across three groups of
respondents, and the Wilcoxon signed-rank test is used to test whether there,
for one specific type of respondents, exists differences in the weights of one
criterion across four types of industries. To answer the research questions, the
results of Mann-Whitney U test and Wilcoxon signed-rank test will be
interpreted together with the weights presented in table 2. Furthermore, in the
comparison analysis section the findings are discussed by reflecting on the
findings from the previous researches which are mentioned in chapter 2.
4.1 Data analysis
In this section, the collected data is analyzed by using BWM, and thus weights
of all criteria and their consistency and standard deviations are obtained. The
results are presented in table 2, and bar charts are further used to make the
results more vivid.
4.1.1 Weights and Ranking
As explained in section 2.3, six criteria are incorporated in this research, which
are transport cost, door-to-door travel time, on-time reliability, flexibility,
frequency, and reduction of CO2 emission. The table2 presents the final results
from collected survey by using BWM method. It included five segments which
are 1. overall results, 2.results based on three types of respondents, 3. results
based on four types of industries, 4. general categorization grouped into three
types of respondents, and 5. general categorization grouped into four types of
industries, and all these segments from the table 2 are visualized by
representing figure 8 to figure 17.
The first segment of the table, visualized in figure 8, is generated by considering
38
results from all respondents and all industries, therefore there are six overall
weights regarding six criteria. While, the second segment grouped results into
three groups with regard to three types of respondents, therefore making the
comparison between perceptions of different groups of respondents more
explicit (see visualization in figure 17). In contrast to the second segment, the
third segment partition the results based on four groups of industries, and thus
the comparison in the importance of one criterion across industries can be
easily observed (see figure 12), and also the ranking of weights of all criteria
from one industry can be compared to the one from another industry.
The fourth segment which firstly categorize all the data into three groups in
terms of three groups of respondents shows how one group of respondents
perceives importance of the criteria regarding four industries, such as from the
perspective of industry experts whether the ranking of weights of criteria in the
manufacturing industry differs with the one in the perishable food industry (see
figure 9, 10, and 11). While, in the fifth segment where all data are first
categorized into four groups regarding four types of industries, in each specific
industry it can be seen that whether there exists difference in the perception of
one criterion across three types of respondents, such as in the manufacturing
industry whether professors have the different ranking of weights of the criteria
compared to industry experts (see figure 13, 14, 15, and 16). Table 2 includes
average weights, standard deviation, and consistency (𝜉𝐿∗). As mentioned in
the methodology, the highest average weight of a criterion indicates that this
criterion is viewed as the most important compared to the other criteria, and the
more closer the value of the consistency is close to zero the more reliable the
results are.
4.1.2 Comparison Analysis
As mentioned in 4.1.1, even though from table 2 and figure 9-11 various
differences can be seen, but in order to know whether these differences are
statistically significant, the comparison analysis is required. Thus, the signed-
rank test and Mann-Whitney U test are chosen to analyze 1) regarding one
specific commodity type whether there is a difference in the weights of one
criterion across perceptions of three groups of respondents and 2) regarding
one group of respondents whether there is a difference in the weights of one
criterion across four types of industries. 3) in general, whether the perceived
importance of one criterion differs based on different industries. 4) in general,
whether different groups of respondents consider the importance of one
criterion differently.
The non-parametric test is chosen because the sample size of this research, in
general, is quite small, therefore the normality assumption required by the
parametric test cannot be tested and supported. Moreover, because three
39
groups of participants have the different sample size, the comparison analysis
regarding three groups of respondents adopts the Mann-Whitney U test. While,
the signed-rank test is chosen for the comparison analysis across four types of
industries since four groups of industry have the same sample size, and it is
worth mentioning that the sign test is also chosen since it can be used without
an assumption about the symmetry of differences which is required by signed-
rank test. So in the case that one comparison analysis fails this required
assumption, the sign test is conducted in place of signed-rank test. Software,
which is namely SPSS, is used to conduct the non-parametric analysis.
The tables generalizing all the p-value from comparison analysis are presented
from table 3 to table 11, and the p-value which has a significance level less than
5% is underlined. In the next section, these tables will be used together with the
above-mentioned table 2 and figures (8-17) to present findings from data
analysis.
4.2 Data interpretation
4.2.1 General results
According to table 2 and figure 8, the research question- how important are the
criteria of transport modes in the decision of freight transport mode choice- can
be answered. In general, the transport cost is viewed as the most important
criterion with the average weight of 0.246, and on-time reliability (0.242) is
slightly lower than transport cost. The ranking of transport cost is in line with the
results of Vannieuwenhuyse et al. (2003) which shows that transport cost has
the highest weight. Besides, regarding the literature related to the valuation
approach, the ranking of on-time reliability is supported by the research of
Danielis et al. (2005) which concludes that there is a high willingness to pay for
qualitative criteria especially for on-time reliability, suggesting the high
importance of it. However, the ranking of on-time reliability in this research
slightly differ with the finding of Murphy and Hall (1995) which concludes that
on-time reliability, instead of transport cost, appears to be the most important
criterion. Door-to-door travel time with the weight of 0.206 ranks third, which is
in line with the research of Rapp Trans and Ivt (2008) that shows door-to-door
travel time is observed to be of minor relevance compared with transport cost
and on-time reliability. Furthermore, the finding of these top-three criteria is
already proved by Cullinane and Toy (2000) who conducted a survey of 75
bibliographic references and concluded that these three criteria are consistently
referenced and often considered as most relevant factors. Moreover, it is worth
mentioning that Beuthe et al. (2005) concluded that all weights of non-cost
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qualitative criteria weigh as equal as the weight of transport cost, while, in this
research the weight of transport cost (0.246) just slightly exceeds the on-time
reliability (0.242) by 0.004, not to mention another qualitative criterion-the door-
to-door travel time- which has the weight of 0.206. This divergence might be
caused by the different underlying methodology of surveys, and the method
used in Beuthe et al. requires that detailed monetary value should be assigned
to their chosen criteria, which might make respondents tend to care more about
transport cost. In contrast, without assigning any monetary value to the criteria
in surveys, our findings can be concluded that all weights of qualitative criteria
are definitely higher than the weight of transport cost, suggesting the high
perceived importance of qualitative criteria.
Ranking as the fourth important criterion, flexibility (0.123) is slightly more
important than frequency (0.112), but both the weights of these two criteria
largely exceed the weight of reduction of CO2-emission (0.07). The finding that
flexibility (0.123) is of less important is against the conclusion drawn by
Norojono and Young (2003) which indicates that flexibility is found to be very
significant in determining the freight transport mode choice and even mentions
that improving flexibility for particular modes might result in considerable
improvements in the use of that mode. However, the finding of flexibility in this
research is in line with the research of Zamparini et. al (2001) which proposes
that flexibility seems to be an irrelevant criterion due to its value less than 0.002
US$/ ton-km. Besides, the finding that frequency gets relatively low importance,
especially compared to on-time reliability which has the weight that almost
doubles the weight of frequency, is against the research from Bergantino and
Bolis (2008) which shows that frequency is perceived as the most important
parameter together with on-time reliability, but from the other side, the research
of Bouffioux et al. (2006) shows that frequency is viewed by shippers as the
least important with the weight below 5%, and the weight of flexibility(6%)
slightly overruns it, which is quite in line with the relative ranking between
flexibility (12.3%) and frequency(11.2%) in this research.
It can also be seen that reduction of CO2-emission gets the lowest weight,
therefore showing the less concern respondents have towards it, which is also
in line with the outcomes of existing literature that all agrees on this conclusion,
such as the research of Konings and Kreutzberger(2001) which mentions that
shippers rarely concern about the environmental issue. It should be admitted
that even though the reduction of CO2-emission is actually of interest to some
stakeholders, such as governments and professors, the general results are
largely affected by real decision-makers since the amount of practitioners and
industry experts (34) largely exceeds the amount of professors (16), suggesting
a perception gap between real decision-makers and professors. In addition, it
41
is worth mentioning that the consistency of the general result is quite high since
its value is low (0.116), indicating the high reliability of the general results (see
table 2). Besides, the error bars representing standard deviations are also
presented in figure 8.
Table 2 Mean weights and standard deviation of criteria Transport
Cost
Door-to-
door
travel time
On-time
Reliability
Flexibility Frequency Reductio
n of CO2
emission
Consistency
(𝜉𝐿∗)
Overall results
(50 respondents )
0.246
(0.127)
0.206
(0.109)
0.242
(0.11)
0.123
(0.063)
0.112
(0.051)
0.07
(0.064)
0.116
(0.087)
Results based on three types of respondents
Industry experts 0.240
(0.134)
0.228
(0.12)
0.243
(0.11)
0.106
(0.042)
0.114
(0.053)
0.069
(0.068)
0.14
(0.106)
Professors 0.27
(0.129)
0.186
(0.098)
0.234
(0.118)
0.113
(0.044)
0.1
(0.043)
0.097
(0.083)
0.111
(0.072)
practitioners 0.232
(0.12)
0.208
(0.086)
0.248
(0.104)
0.143
(0.079)
0.118
(0.055)
0.051
(0.029)
0.104
(0.082)
Results based on four types of industries
Manufacturing industry 0.279
(0.118)
0.174
(0.086)
0.237
(0.113)
0.135
(0.08)
0.103
(0.052)
0.071
(0.061)
0.115
(0.088)
Agriculture industry 0.279
(0.124)
0.218
(0.102)
0.2
(0.082)
0.116
(0.059)
0.114
(0.053)
0.074
(0.078)
0.107
(0.09)
Perishable foods industry 0.135
(0.06)
0.272
(0.102)
0.278
(0.098)
0.128
(0.063)
0.126
(0.052)
0.061
(0.044)
0.115
(0.09)
Chemical industry 0.293
(0.126)
0.160
(0.073)
0.254
(0.128)
0.114
(0.042)
0.102
(0.046)
0.076
(0.07)
0.127
(0.083)
General categorization grouped into three types of respondents
Industry
experts
Manufacturi
ng
0.294
(0.137)
0.177
(0.091)
0.244
(0.102)
0.120
(0.042)
0.101
(0.059)
0.063
(0.048)
0.144
(0.107)
Ranking 1 3 2 4 5 6
Agriculture 0.282
(0.120)
0.223
(0.132)
0.207
(0.093)
0.095
(0.038)
0.113
(0.055)
0.078
(0.097)
0.131
(0.112)
Ranking 1 2 3 5 4 6
42
Perishable
goods
0.116
(0.025)
0.321
(0.114)
0.257
(0.109)
0.103
(0.044)
0.143
(0.048)
0.059
(0.042)
0.139
(0.110)
Ranking 4 1 2 5 3 6
Chemical 0.270
(0.141)
0.188
(0.093)
0.262
(0.128)
0.105
(0.044)
0.100
(0.044)
0.076
(0.075)
0.145
(0.106)
Ranking 1 3 2 4 5 6
Professors Manufacturi
ng
0.3
(0.107)
0.161
(0.057)
0.241
(0.114)
0.110
(0.037)
0.084
(0.046)
0.104
(0.087)
0.1
(0.075)
Ranking 1 3 2 4 6 5
Agriculture 0.327
(0.122)
0.204
(0.108)
0.167
(0.06)
0.093
(0.039)
0.103
(0.041)
0.105
(0.096)
0.108
(0.068)
Ranking 1 2 3 6 5 4
Perishable
goods
0.151
(0.089)
0.263
(0.099)
0.280
(0.099)
0.119
(0.051)
0.110
(0.029)
0.077
(0.059)
0.103
(0.077)
Ranking 3 2 1 4 5 6
Chemical 0.301
(0.125)
0.116
(0.05)
0.249
(0.156)
0.130
(0.042)
0.104
(0.053)
0.1
(0.092)
0.132
(0.072)
Ranking 1 4 2 3 5 6
Practitioners Manufacturi
ng
0.254
(0.115)
0.183
(0.102)
0.230
(0.12)
0.163
(0.110)
0.119
(0.049)
0.051
(0.028)
0.107
(0.083)
Ranking 1 3 2 4 5 6
Agriculture 0.24
(0.12)
0.225
(0.075)
0.220
(0.084)
0.147
(0.069)
0.123
(0.06)
0.046
(0.027)
0.089
(0.088)
Ranking 1 2 3 4 5 6
Perishable
goods
0.135
(0.046)
0.246
(0.088)
0.29
(0.091)
0.151
(0.075)
0.128
(0.065)
0.05
(0.029)
0.107
(0.085)
Ranking 4 2 1 3 5 6
Chemical 0.303
(0.121)
0.176
(0.058)
0.253
(0.109)
0.109
(0.04)
0.102
(0.042)
0.057
(0.035)
0.112
(0.073)
Ranking 1 3 2 4 5 6
General categorization grouped into four types of industries
Manufacturing Industry
experts
0.294
(0.137)
0.177
(0.091)
0.244
(0.102)
0.120
(0.042)
0.101
(0.059)
0.063
(0.048)
0.144
(0.107)
Ranking 1 3 2 4 5 6
43
Professors 0.3
(0.107)
0.161
(0.057)
0.241
(0.114)
0.110
(0.037)
0.084
(0.046)
0.104
(0.087)
0.1
(0.075)
Ranking 1 3 2 4 6 5
Practitioners 0.254
(0.115)
0.183
(0.102)
0.230
(0.12)
0.163
(0.110)
0.119
(0.049)
0.051
(0.028)
0.107
(0.083)
Ranking 1 3 2 4 5 6
Agriculture Industry
experts
0.282
(0.120)
0.223
(0.132)
0.207
(0.093)
0.095
(0.038)
0.113
(0.055)
0.078
(0.097)
0.131
(0.112)
Ranking 1 2 3 5 4 6
Professors 0.327
(0.122)
0.204
(0.108)
0.167
(0.06)
0.093
(0.039)
0.103
(0.041)
0.105
(0.096)
0.108
(0.068)
Ranking 1 2 3 6 5 4
Practitioners 0.24
(0.12)
0.225
(0.075)
0.220
(0.084)
0.147
(0.069)
0.123
(0.06)
0.046
(0.027)
0.089
(0.088)
Ranking 1 2 3 4 5 6
Perishable
goods
Industry
experts
0.116
(0.025)
0.321
(0.114)
0.257
(0.109)
0.103
(0.044)
0.143
(0.048)
0.059
(0.042)
0.139
(0.110)
Ranking 4 1 2 5 3 6
Professors 0.151
(0.089)
0.263
(0.099)
0.280
(0.099)
0.119
(0.051)
0.110
(0.029)
0.077
(0.059)
0.103
(0.077)
Ranking 3 2 1 4 5 6
Practitioners 0.135
(0.046)
0.246
(0.088)
0.29
(0.091)
0.151
(0.075)
0.128
(0.065)
0.05
(0.029)
0.107
(0.085)
Ranking 4 2 1 3 5 6
Chemical Industry
experts
0.270
(0.141)
0.188
(0.093)
0.262
(0.128)
0.105
(0.044)
0.100
(0.044)
0.076
(0.075)
0.145
(0.106)
Ranking 1 3 2 4 5 6
Professors 0.301
(0.125)
0.116
(0.05)
0.249
(0.156)
0.130
(0.042)
0.104
(0.053)
0.1
(0.092)
0.132
(0.072)
Ranking 1 4 2 3 5 6
Practitioners 0.303
(0.121)
0.176
(0.058)
0.253
(0.109)
0.109
(0.04)
0.102
(0.042)
0.057
(0.035)
0.112
(0.073)
Ranking 1 3 2 4 5 6
44
Figure 8 general rating of criteria
4.2.2 Differences across four types of industries
This comparison analysis attempts to ensure whether, from the perspective of
one specific type of respondents, the importance of the criterion regarding one
industry, such as agriculture industry, will be different with the importance of the
counterpart criterion in another industry, such as perishable food industry.
Therefore, the data set is, at first, categorized into three groups in terms of three
types of respondents, and in each group, the weights of criterion regarding four
industries are compared in pair, therefore leading to six pairs. Then, the general
comparison analysis based on industries considered by all respondents will be
present in table 6, and In this section the research question- whether there is a
difference in the importance of criterion among manufacturing industry,
agriculture industry, perishable food industry, and chemical industry- can be
answered by combining the visualized data from table 2 (see figure 9, 10, 11
and 12) and results from the signed-rank test (see table 3, 4, 5, and 6). The
tables of p-values will be presented in the following section, and the detailed
information can be found in Appendix B.
1. From the perspective of industry experts
Regarding the figure 9 it can be seen that, in general, industry experts view the
door-to-door travel time in the perishable food industry the most important, and
perceive the transport cost in the manufacturing industry as the second
24.60%
20.60%
24.20%
12.30%11.20%
7%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
Transport cost Door-to-doortravel time
On-timereliability
Flexibility Frequency Reduction ofCO2-emission
General ranking of criteria
45
important. It is evident that compared to other criteria in all industries, reduction
of CO2-emission always gets the lowest importance no matter in which industry,
but it, in particular, gets the lowest importance in the perishable food industry.
Moreover, regarding all four industries the importance of frequency is in general
slightly higher than the importance of flexibility, which is worth mentioning since
professors and practitioners really perceive these two criteria in another way
around. However, the finding of frequency is against the research of Bergantino
and Bolis (2007) which shows that industry experts perceive frequency as the
most important parameter together with on-time reliability, while in this research
the importance of frequency is largely exceeded by the one of on-time reliability.
With regard to table 3, in total there are 9 comparisons that get significant
differences, which are underlined. Regarding transport cost, there are in total
three significant differences in the comparison between 1. Manufacturing and
perishable industry; 2. Agriculture and perishable food industry; 3 perishable
food and chemical industry. In each aforementioned comparison, the weight of
transport cost in one industry differs with the weight of transport cost in another
industry. And regarding figure 9, it can be concluded that transport cost in
perishable food industry always gets the lowest importance compared to the
transport cost in other three industries. The weights of door-to-door travel time
differ across 1. Manufacturing and perishable food industry; 2. Agriculture and
perishable food industry; 3. Chemical and perishable food industry. The
importance of door-to-door travel time in perishable food industry ranks the
highest compared to the one in other three industries, and the one in
manufacturing industry is the lowest. The importance of on-time reliability only
differs with its counterparts across agriculture and perishable food industry, and
the one in the perishable food industry is higher than the one in the agriculture
industry. When it comes to frequency, its importance differs across
manufacturing and perishable food industry, and across perishable food and
chemical industry, and the importance of frequency in perishable food industry
is the highest.
46
Figure 9 Importance of criteria based on different industries
Table 3 The results of P-value regarding industry experts
For industry
experts
Transport
cost
Door-to-
door
travel time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Manufacturing
vs. agriculture
industry
P-value
,650
,173
,581 ,116 ,221
1,000
Manufacturing
vs. perishable
industry
P-value
,002
,006
,267
,382 ,004
,196
Manufacturing
vs. chemical
industry
P-value
,791
1,000 ,551
,300
,706
,791
Agriculture vs.
perishable
food industry
P-value
,002
,019
,023
,463
,055
1,000
Agriculture vs.
chemical
industry
P-value
,730
,510
,433
,826
,346
,778
Perishable
food vs.
,002
,013
,826
,925
,003
,581
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
Transport cost Door-to-doortravel time
On-timereliability
Flexibility Frequency Reduction ofCO2-emission
Industry Experts
Manufacturing Agriculture Perishable Chemical
47
chemical
industry
P-value
2. From the perspective of professors
According to figure 10, in general professors view the transport cost in
agriculture industry as the most important, and the reduction of CO2-emission
in perishable food industry gets the lowest importance. It seems that, regarding
four industries in total, frequency only slightly ranks higher than reduction of
CO2-mission, while in the manufacturing industry the importance of reduction
of CO2-emission even exceeds the importance of frequency, which is a
perception worth mentioning here since from the perspectives of other two
groups, reduction of CO2-emission is always the least important no matter in
which industry.
According to table 4, the statistically significant differences appear most
frequently in the comparison between manufacturing and perishable food
industry, where the weights of five criteria in manufacturing industry separately
differ with their counterparts in perishable food industry. These five criteria are
transport cost, door-to-door travel time, on-time reliability, frequency, and
reduction of CO2-emission. Weights of door-to-door travel time, on-time
reliability, and frequency in perishable food industry are significantly higher than
their separate counterparts in manufacturing industry, while the weights of
transport cost and reduction of CO2-emission in manufacturing industry are
separately higher than their counterparts in perishable industry. Professors
perceive the door-to-door travel time differently across manufacturing and
chemical industries, and the one in manufacturing industry gets higher
importance than the one in chemical industry. Three significant differences exist
in the comparison between agriculture and perishable food industry where
professors view the importance of transport cost in agriculture industry
significantly higher than the one in perishable food industry; where the
importance of on-time reliability in perishable food industry ranks largely higher
than the one in agriculture industry; where the flexibility in perishable food
industry gets the higher importance than its counterpart in agriculture industry.
The importance of transport cost in chemical industry is significantly different
with the one in perishable food industry, and the one in chemical industry is
relatively higher than the one in perishable food industry. While, the importance
of door-to-door travel time in perishable industry is significantly higher than the
one in chemical industry.
48
Figure 10 Importance of criteria based on different industries
Table4 The results of P-value regarding professors
For professors Transport
cost
Door-to-
door
travel time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Manufacturing
vs. agriculture
industry
P-value
,607
,158
,088 ,256 ,064
,875
Manufacturing
vs. perishable
industry
P-value
2,44E-4
,005
,023
,600 ,003
,011
Manufacturing
vs. chemical
industry
P-value
,532
,009 ,955
,607
,607
,607
Agriculture vs.
perishable
food industry
P-value
,001
,084
,001
,035
,177
,180
Agriculture vs.
chemical
industry
P-value
,638
,424
,256
,057
1,000
,509
Perishable
0%
5%
10%
15%
20%
25%
30%
35%
Transport cost Door-to-doortravel time
On-timereliability
Flexibility Frequency Reduction ofCO2-emission
Professors
Manufacturing Agriculture Perishable Chemical
49
food vs.
chemical
industry
P-value
,001 ,001 ,302 1,000 1,000 ,119
3. From the perspective of practitioners
As illustrated in figure 11, unlike professors who give the highest importance to
the transport cost in agriculture industry, practitioners view the transport cost in
chemical industry as the most important criterion and reduction of CO2-
emission in agriculture industry as the least important. While, the latter
perception differs with the perspectives of industry experts and professors who
both think that regarding all four industries the importance of reduction of CO2-
emission in agriculture industry is the highest and the one in perishable industry
is the lowest. And, it can be seen that, in general, there is a relatively large gap
between frequency and reduction of CO2-emission, showing that the
practitioner’s view with regard to the relative importance of frequency and of
reduction of CO2-emission is approximate to the industry experts’ view.
It can be seen from table 5, unlike professors who perceive that 5 criteria in
manufacturing industry differ with their separate counterparts in perishable food
industry, practitioners only perceive the importance of transport cost differently
across these two industries, and the transport cost in manufacturing industry
gets the higher importance compared to the one in perishable food industry.
Moreover, except for the comparison of transport cost between manufacturing
and agriculture industry, other five comparisons shows that transport cost is
perceived differently based on different industry. And amongst these
comparisons, the transport cost in chemical industry gets the highest weight,
while the one in perishable food industry, as usual, gets the lowest weight. The
importance of door-to-door travel time in chemical industry is significantly lower
than the one in agriculture industry and the one in the perishable food industry
which is the highest. Practitioners perceive that the importance of on-time
reliability in agriculture industry significantly differs from the one in perishable
food industry which is the second highest in general. Frequency is viewed
differently across the agriculture and chemical industry, and it gets the relatively
higher importance in agriculture industry.
50
Figure 11 Importance of criteria based on different industries
Table5 The results of P-value regarding practitioners
For
practitioners
Transport
cost
Door-to-
door
travel time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Manufacturing
vs. agriculture
industry
P-value
,159
,648
,359 ,370 ,794
,099
Manufacturing
vs. perishable
industry
P-value
,002 ,057
,359
,911 ,627
,115
Manufacturing
vs. chemical
industry
P-value
,012
,881 ,145
,073
,100
1,000
Agriculture vs.
perishable
food industry
P-value
,003
1,000
,013
,171
,778
,324
Agriculture vs.
chemical
industry
P-value
,019
,005
,648
,167
,033
,167
Perishable
food vs.
,000
,008
,149
,332
,076
,629
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
Transport cost Door-to-doortravel time
On-timereliability
Flexibility Frequency Reduction ofCO2-emission
Practitioners
Manufacturing Agriculture Perishable Chemical
51
chemical
industry
P-value
4. General differences across four types of industries
The research question can be answered in this section. According to figure
12 where all respondents’ perspectives are summarized together, for the
importance of transport cost, there is a quite significant difference between
the one in chemical industry, which is the highest, and the one in perishable
food industry, which is the lowest, and table 6 shows this is a statistically
significant difference. The importance of transport cost in manufacturing is
as same as the one in agriculture industry, and both of them are significantly
different with the one in perishable industry. Moreover, it is also shown that
the importance of transport cost in chemical industry is significantly different
with the one in manufacturing industry. The highest importance given to door-
to-door travel time is in perishable food industry, and the lowest one goes to
the chemical industry, which is further evidenced by table 6 that there is
actually a significant difference between these two industries regarding door-
to-door travel time. This finding regarding door-to-door travel time in
perishable food industry is in line with the conclusion drawn by Maria et al.
(2011) which indicates that due to the limited durability of perishable foods,
the door-to-door travel time becomes a relatively important criterion in this
industry. Moreover, Fries (2009) also indicated that due to the limited
durability of perishable foods, perishableness is linked with door-to-door
travel time requirements, and he also added that only in perishable foods
industry is door-to-door travel time observed to be of major relevance, which
supports the finding that door-to-door travel time ranks second in perishable
food industry (see figure 12 ). And, this finding can be explained by the
research of Ire and Rapp Trans (2005) which indicates that when shippers
transport the short-dated freights they tend to give higher priority to
requirements of door-to-door travel time, because door-to-door travel time is
influenced by the time delay between a customer's ordering of freights and
the shipment of that freights. Furthermore, Rodrigo and Satish (2014)
pointed out that if perishable foods are delayed beyond the maximum
delivery threshold, then the supply chain processes are possibly delayed,
therefore leading to the high risk for perishable foods to get damaged.
It is interesting that for door-to-door travel time only one comparison does
not have a significant difference which is the comparison between
manufacturing and chemical industry, and except for this comparison, other
52
5 comparisons all have significant differences. Among them, it can be seen
that door-to-door travel time is perceived significantly differently across
agriculture and perishable foods industry. The finding is in line with the
results of Wanders (2014) which underlines that differences in preference
regarding door-to-door travel time are found between agriculture freights and
perishable foods. Regarding on-time reliability, only two comparisons have
significant differences, showing that the perceived importance of on-time
reliability in manufacturing industry significantly differ with the one in
perishable food industry, and the one in agriculture industry is significantly
different with the one in perishable food industry where the former is the least
and the latter is the highest (see table 6).
According to table 6, all respondents perceive the importance of flexibility in
agriculture industry significantly different with the one in perishable food
industry, and the one in perishable food industry is higher than the one in
agriculture industry. For frequency, except the comparison between
manufacturing and chemical industry, other comparisons all have the
significant differences. Therefore, the importance of frequency in
manufacturing industry is perceived different with the one in agriculture
industry and also with the one in perishable food industry; the importance of
frequency in agriculture industry differs with the one in perishable food
industry and with the one in chemical industry; the perceived importance of
frequency in perishable food industry differs with the one in chemical industry.
Moreover, among frequency in those industries, the one in perishable food
industry gets the highest importance, while the one in chemical industry gets
the lowest. To conclude, the comparison between manufacturing and
perishable food industry and the comparison between agriculture and
perishable food industry respectively have five significant differences, and
these two comparisons have the most differences compared to other four
comparison-pairs. Therefore, the conclusion can be drawn that perishable
foods are perceived very differently compared to the freights from agriculture
industry, which is supported by the research of Wanders (2014) which
includes agriculture & food sector as one commodity group, but at the end
admits that there exists differences in preference within the agriculture & food
sector itself, and underlined that shippers transporting perishable foods must
be distinguished from shippers who do not because when transporting
perishable foods shippers tend to give a higher value to short door-to-door
travel time, which is also in line with our finding that door-to-door travel time
ranks the second important in perishable food industry. While, Wanders also
mentioned in his research that the importance of transport cost, on-time
reliability, and CO2-emission do not significantly differ across perishable
foods and the manufacturing industry, which is against our finding that these
three criteria in perishable foods industry do significantly differ with their
counterparts in manufacturing industry. This divergence might be explained
53
by the different definitions given to the commodity group, because in his
research the perishable foods are together with the agriculture freights to
form one commodity group, while in this research agriculture freights and
perishable foods are separated into two commodity groups in order to
accurately present respondents’ preferences.
When it comes to the reduction of CO2-emission, its importance in chemical
industry ranks the highest, while its importance in perishable food industry
ranks the lowest. This finding is supported by the research of Fries (2009)
which shows that the reduction of CO2-emission in the chemical industry
gets the highest weight compared with reduction of CO2-emission in
agriculture, perishable food and manufacturing industry. In addition,
according to Fries, if the freights have the higher specific value and are
placed in the higher position of the value creation chain, then shippers tend
to be more willing to pay for a reduction of CO2-emission. From our findings,
the conclusion can be drawn that when transporting chemical freights
decision-makers tend to assign relatively high importance to reduction of
CO2-emission. From table 6 it can be seen that the statistically significant
difference exists in the comparison between the importance of reduction of
CO2-emission in manufacturing industry and the one in perishable food
industry, indicating that respondents seem to consider reduction of CO2-
emission differently regarding these two industries.
Figure12 results based on four types of industries
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
Transport cost Door-to-doortravel time
On-timereliability
Flexibility Frequency Reduction ofCO2-emission
Comparisons among four types of commodities
Manufacturing Agriculture Perishable Chemical
54
Table 6 The results of P-value regarding all respondents
Hypothesis
Test Summary
Transport
cost
Door-to-
door
travel
time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Manufacturing
vs. agriculture
industry
P-value
,363
,010
,770 ,078
,041
,307
Manufacturing
vs. perishable
food industry
P-value
,000
,000
,013
,978 ,002
,004
Manufacturing
vs. chemical
industry
P-value
,035
,775
,227
,191
,846
,386
Agriculture vs.
perishable
food industry
P-value
,000
,010
,000
,004
,044
,658
Agriculture vs.
chemical
industry
P-value
,216
,001
,123
,645
,050
,626
Perishable
food vs.
chemical
industry
P-value
,000
,000
,148
,461
,003
,074
4.2.3 Differences across three types of respondents
This comparison analysis aims to investigate whether, for each industry, there
is a difference in perceived importance of criterion across three groups of
respondents. Therefore, the data set which is generated by using BWM is first
categorized into four groups regarding four types of industries, and within each
industry group dependent variable is defined as the weight of each criterion,
and the independent variable is defined as three groups of respondents which
are industry experts, professors, and practitioners. In each industry, three pair-
comparisons will be made, which are the comparison between industry experts
55
and professors, the comparison between industry experts and practitioners,
and the comparison between professors and practitioners.
In this section, at first, under one specific industry the three groups of
respondents will be compared to each other in terms of their perception
regarding one specific criterion, since there are four industries, so four sections
will be presented below, plus one section which aggregates in terms of all four
industries. Thus the third sub-question- Whether perceptions of different groups
of respondents differ regarding one criterion?- can be answered by combining
the visualized data from table 2 (see figure 13, 14 15, 16, and 17) and p-values
of comparison analysis in table 7, 8, 9, 10, and 11. The detailed tables can be
found in Appendix B.
1. Comparison analysis in manufacturing industry
Regarding figure 13, when considering manufacturing industry, the person who
gives the overall highest importance is the professor who views the transport
cost as the most important, and both industry experts and practitioners also
choose transport cost as the most important criterion. Moreover, all of them
agree that on-time reliability is the second important criterion in manufacturing
industry, while the reason might be explained by the feedback from one
respondent of practitioner group. This respondent mentions that the
manufacturing materials he moves are usually heavy equipment such as cranes,
which means on-time delivery to the job site is extremely important. Additionally,
according to Danielis et al. (2005), their research concludes that regarding
manufacturing firms qualitative criteria are even preferred over transport cost.
It can be concluded that on-time reliability is quite important when it comes to
manufacturing freights. Industry experts and practitioners perceive the
reduction of CO2-emission as the least important criterion, while professors
view the frequency as the least important and assign the relatively higher
importance to the reduction of CO2-emission. And, according to table 7,
professors’ perception about frequency differs with practitioners’ perception
towards frequency. All three groups agree with the ranking of top-three criteria,
which starts from transport cost, followed by on-time reliability, and the third
important criterion is door-to-door travel time.
56
Figure 13 Importance of criteria based on different type of respondents
Table 7 The results of P-value in manufacturing industry
manufacturing
industry
Transport
cost
Door-to-
door
travel time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Industry
experts vs.
professors
P-value
,918 ,580 1,000 ,637 ,473 ,355
Industry
experts vs.
practitioners
P-value
,278 ,907 ,654 ,727 ,164 ,538
Professors vs.
practitioners
P-value
,185 ,916 ,728 ,404 ,006 ,130
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
Manufacturing Industry
Practitioners Professors Industry experts
57
2. Comparison analysis in agriculture industry
According to figure 14, compared to the top-three ranking of criteria in
manufacturing industry, all respondents perceive it in a slightly different way
when considering agriculture industry, where the transport cost still ranks
the most important, but followed by the door-to-door travel time, and the on-
time reliability ranks as the third important. It is interesting that when
considering agriculture industry, unlike professors and industry experts who
give the transport cost significantly higher importance compared to the on-
time reliability and door-to-door travel time, practitioners seem to view these
top-three criteria almost as important as the same. To conclude, all
respondents agree with these three criteria to be top-three important. This
time, professors still give the higher importance to the reduction of CO2-
emission, compared to the importance of frequency and flexibility, and view
the flexibility as the least important, meanwhile industry experts and
practitioners choose reduction of CO2-emission as the least important
criterion. And according to table 8, in agriculture industry professors’
perception frequently differs with the practitioners’ perception, and the
differences of their perceptions appear in transport cost, flexibility, and
reduction of CO2-emission. Moreover, regarding flexibility practitioners also
have statistically different perception with industry experts.
Figure 14 Importance of criteria based on different type of respondents
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
Agriculture Industry
Practitioners Professors Industry experts
58
Table 8 The results of P-value in agriculture industry
Agriculture
industry
Transport
cost
Door-to-
door
travel time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Industry
experts vs.
professors
P-value
,377 ,854 ,334 ,886 ,951 ,131
Industry
experts vs.
practitioners
P-value
,154 ,538 ,829 ,018 ,359 ,538
Professors vs.
practitioners
P-value
,047 ,156 ,059 ,015 ,338 ,013
3. Comparison analysis in perishable foods industry
While, in perishable food industry, the top-three ranking of criteria totally
changes (see figure 15). Professors and practitioners both rank on-time
reliability as the most important, followed by the door-to-door travel time, while
industry experts view door-to-door travel time as the most important and view
on-time reliability as the second important. It is worth mentioning that it is the
first time that frequency is ranked as the third important criterion, which is done
by industry experts, and flexibility is also viewed as the third important criterion
by practitioners. Both practitioners and industry experts perceive the time-
related criteria as top-three important criteria. To conclude, the ranking of these
time-related criteria is generally supported by existing studies, and it can be
concluded that in perishable food industry, time-related criteria do attract the
highest importance. And the finding of door-to-door travel time which ranks, in
general, the highest by industry experts can be supported by the research
(Brooks et al, 2012) that mentions perishability, as one of the characteristics of
perishable freights, plays an important role in terms of reduction of door-to-door
travel time, and thus it increases the importance of short door-to-door travel
time due to the limited durability of such freights. Besides, Maria et al. (2011)
further explained it by using an example that due to the short shelf life required
for perishable goods, there is a high value at 12.19 Euros per shipment an hour,
59
therefore making door-to-door travel time a relatively important criterion in
deciding transport modes. For transport cost, professors view it as the third
important, while both industry experts and practitioners rank it as the fourth
important. Even though professors give the relatively higher importance to
reduction of CO2-emission compared to industry experts and practitioners, all
of them perceive it as the least important criterion. According to table 9, industry
experts and practitioners perceive the importance of door-to-door travel time
differently.
Figure 15 Importance of criteria based on different type of respondents
Table 9 The results of P-value in perishable food industry
Perishable
food industry
Transport
cost
Door-to-
door
travel time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Industry
experts vs.
professors
P-value
,224 ,110 ,759 ,377 ,052 ,697
Industry ,135 ,015 ,630 ,135 ,309 ,752
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
Perishable Foods Industry
Practitioners Professors Industry experts
60
experts vs.
practitioners
P-value
Professors vs.
practitioners
P-value ,844 ,844 ,940 ,404 ,639 ,439
4. Comparison analysis in chemical industry
All respondents agree on the same top-three ranking of criteria concluded in
manufacturing industry, where on-time reliability is viewed as the second
important criterion (see figure 16). This is in line with the conclusion drawn by
Das et al (1999) that mentions on-time reliability is viewed especially important
for chemical freights since such freights require highly reliable transport flow.
Moreover, according to the feedback from one respondent of practitioners
group, chemical freights are the freights that customers wait until the last minute
to order, which means that a late delivery will hold up their production, therefore
leading extra costs. And, this kind of cost often exceeds the price margins which
is additionally paid to shippers for guaranteeing higher on-time reliability (Fries,
2009). Hence, this feedback clearly explains why on-time reliability ranks so
high in the chemical industry, and thus supports our finding. It can be seen that
all respondents agree that reduction of CO2-emission is the least important
criterion, while compare to industry experts and practitioner, professors still give
the relatively high importance to it (see figure 16). According to table 10,
industry experts differ with professors in door-to-door travel time and flexibility,
and, in contrast to professors, industry experts tend to give higher importance
to door-to-door travel time, while compared to industry experts, professors
assign higher importance to flexibility. Moreover, professors and practitioners
also perceive these two criteria differently. Regarding door-to-door travel time,
practitioners give the relatively higher importance than professors do, and when
considering flexibility professors tend to give higher importance than
practitioners do.
61
Figure 16 Importance of criteria based on different type of respondents
Table 10 The results of P-value in chemistry industry
Chemical
industry
Transport
cost
Door-to-
door
travel time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Industry
experts vs.
professors
P-value
,608 ,010 ,473 ,017 ,951 1,000
Industry
experts vs.
practitioners
P-value
,436 ,986 ,904 ,959 ,877 ,545
Professors vs.
practitioners
P-value ,888 ,003 ,223 ,036 ,863 ,352
5. General comparison analysis between different groups of
respondents
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00%
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
Chemical Industry
Practitioners Professors Industry experts
62
When considering all industries, the top-three ranking of criteria differs among
three types of respondents. For industry experts, the on-time reliability and
transport cost are both viewed as the most important, and the third important
criterion is door-to-door travel time. In contrast to industry experts, professors
rank transport cost as the most important, followed by the on-time reliability,
and perceive the door-to-door travel time as the third important criterion.
Practitioners view on-time reliability as the most important criterion, followed by
transport cost, and they view the door-to-door travel time the third important. It
is interesting to see that actual decision-makers do perceive the qualitative
criterion (on-time reliability) as the most important when selecting freight
transport mode, while professors tend to concern more about the monetary
criterion (transport cost). As usual, reduction of CO2-emission still gets the
lowest importance from the perspectives of all respondents, even though
professors always give a relatively higher importance to it (see figure 17). It can
be seen from table 11 that industry experts and professors perceive door-to-
door travel time differently; the perception of industry experts and the
perception of practitioners differ regarding flexibility; practitioners and
professors perceive differently not only in door-to-door travel time but also in
reduction of CO2-emission. The divergence of perception regarding reduction
of CO2-emission between practitioners and professors is in line with the
situation where scholars, such as Lammgård (2007) who concluded that in
addition to the transport cost the weight of CO2-emission was taken into
account to a high degree, care about CO2-emission and gives it high concern,
while according to most previous researches practitioners show less interest to
reduction of CO2-emission. The research of Konings and Kreutzberger (2001)
concludes that practitioners rarely concern about reduction of CO2-emission,
which is in line with our finding. And, for our finding showing that industry
experts perceive CO2-emission less important, it is supported by the research
of Feo-Valero et al. (2011b) which mentioned that environmental perspective is
not a strong reason to stimulate industry experts to choose intermodal transport.
The third sub-question is answered in this section.
63
Figure 17 Importance of criteria based on different type of respondents
Table 11 The results of P-value regarding four industries
Four
industries
Transport
cost
Door-to-
door travel
time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
Industry
experts vs.
professors
P-value
,184 ,046 ,645 ,214 ,239 ,159
Industry
experts vs.
practitioners
P-value
,779 ,689 ,844 ,021 ,707 ,197
Professors vs.
practitioners
P-value ,053 ,022 ,189 ,247 ,086 ,002
0.00% 5.00% 10.00% 15.00% 20.00% 25.00% 30.00%
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
Comparisons among three groups of respondents
Practitioners Professors Industry experts
64
Chapter 5 Conclusion and Recommendation
In this chapter, the conclusions drawn in this study are presented, and research
questions are answered. Furthermore, the limitation of this research is
mentioned, and recommendations are provided. At the end, the suggestions for
the future research are proposed.
5.1. Conclusion
How important are the criteria of transport modes in the decision of
freight transport mode choice
To evaluate the importance of the chosen criteria, the questionnaires were used
to collect data from industry experts, professors, and practitioners in the fields
of freight transportation modal choice. After the collected data is analyzed by
using BWM, this research question is answered in chapter 4. And, the results
demonstrates that transport cost ranks highest with the weight of 24.6%,
followed by on-time reliability with the weight of 24.2%, and door-to-door travel
time ranks (at 20.6%) as the third important. Moreover, flexibility (at 12.3%),
frequency (at 11.2%) and reduction of CO2-emission (at 7%) are successively
viewed as the fourth, fifth and sixth important. To conclude, transport cost, on-
time reliability and door-to-door travel time are the top-three important criteria,
and especially transport cost and on-time reliability do receive similar and
significantly high importance. Besides, the weights of top-three criteria all
exceed 20%, which shows the relatively big gap between the third criterion
(door-to-door travel time) and the fourth criterion (flexibility). Reduction of CO2-
emission gets the lowest weight which is almost three times less than the weight
of door-to-door travel time, suggesting that respondents might lack the relevant
knowledge regarding environmental perspectives.
Whether there is a difference in the importance of criterion among
manufacturing industry, agriculture industry, perishable foods industry,
and chemical industry?
This research question is answered in the comparison analysis section of
chapter 4 by using signed-rank test. It can be seen that door-to-door travel
time and frequency are perceived significantly different across manufacturing
and agriculture industry, and these two criteria get relatively higher weight in
agriculture industry. There exists five differences between manufacturing and
perishable foods industry, which means that, except flexibility, each criterion is
perceived significantly differently across these two types of industries. These
five criteria are transport cost, door-to-door travel time, on-time reliability,
65
frequency, and reduction of CO2-emission, where on-time reliability, door-to-
door travel time and frequency in perishable foods industry are viewed
significantly more important than their counterparts in manufacturing industry.
This is due to the fact that perishable foods have high requirements for time-
related criteria, therefore respondents tend to rate these criteria higher in
perishable foods industry. Conversely, transport cost and reduction of CO2-
emission in manufacturing industry are rated higher than their counterparts in
perishable foods industry. Furthermore, there is only one significantly difference
in perceptions of transport cost between manufacturing industry and chemical
industry, and transport cost in the chemical industry is rated higher than its
counterpart in the manufacturing industry. Five differences also exist between
agriculture and perishable foods industry, and these differences exist in
transport cost, door-to-door travel time, on-time reliability, flexibility, and
frequency. And, time-related criteria such as on-time reliability, door-to-door
travel time, flexibility, and frequency in perishable foods industry are rated
higher than their counterparts in the agriculture industry, while only is transport
cost in agriculture industry rated higher than its counterpart in perishable foods
industry. In the comparison between agriculture and chemical industry, on-time
reliability in the chemical industry is rated significantly higher than on-time
reliability in the agriculture industry, while frequency in the agriculture industry
is viewed higher than the one in the chemical industry. Perishable foods
industry and chemical industry differ with regard to transport cost, on-time
reliability, and frequency, and except transport cost other two criteria in
perishable foods industry get higher importance than their counterparts in
chemical industry.
To conclude, there exists at least one difference in the importance of criterion
across manufacturing industry, agriculture industry, perishable foods industry,
and chemical industry. Moreover, the comparison between manufacturing and
perishable foods industry and the comparison between agriculture and
perishable foods industry do have the most differences, which suggests that
respondents tend to consider one specific criterion very differently in perishable
foods industry compared to manufacturing and agriculture industry.
Which transport criteria are considered by shippers when making mode
choice decisions?
In chapter 2, the literature review is conducted to generate an exhaustive and
exclusive criteria list which answer this sub-research question. After discussing
all the criteria that are frequently studied in existing literature and are approved
to be much relevant and important, in the end six important criteria are chosen
including transport cost, on-time reliability, door-to-door travel time, flexibility,
frequency, and reduction of CO2-emission. The reason why reduction of CO2-
66
emission is included is due to its ever-increasing importance and the motivation
of this research which is to bridge the knowledge gap that few existing studies
include CO2-emission as one important criterion.
How to determine the importance of criteria of transport modes?
Chapter 3 and chapter 4 together give the answer to this sub-research question.
At first, BWM method is chosen, and questionnaires are designed according to
the requirements of BWM method. After sending questionnaires, BWM is used
to analyze the collected data, therefore generating the importance of criteria.
Whether perceptions of different groups of respondents differ regarding
one criterion?
The answer to the third sub-question is generated from chapter 4 where the
BWM-analyzed data and comparison analysis are used together. Only the
statistically significant difference, supported by comparison analysis, will be
used to answer this question. For the comparison between practitioners and
professors, practitioners rated door-to-door travel time significantly higher than
professors, while professors rated reduction of CO2-emission significantly
higher than practitioners. This indicates a potential education or information gap
where professors could inform practitioners of the importance of reduction of
CO2-emission in freight transport modal choice. Meanwhile, industry experts
and professors also hold different perception towards door-to-door travel time,
and industry experts give the significantly higher importance to it. And, it is
worth mentioning that regarding all three groups of respondents, the one who
assign door-to-door travel time the lowest importance is the professor. The
perception of industry experts differs with the perception of practitioners with
respect to flexibility, where practitioners rated flexibility significantly higher than
industry experts. While, this difference might be explained by the fact that
practitioners have intimate knowledge of the fieldwork operation that motives
their relatively high perception of flexibility, and industry experts rated flexibility
low because they were potentially unfamiliar with the fieldwork operation.
To conclude, practitioners and professors have two differences in perceptions,
while only one difference in perception can separately be found in the
comparison between industry experts and practitioners and in the comparison
between industry experts and professors. Thus, this finding actually meet the
expectation of this research, since this research suppose that practitioners and
professors would have most differences in perceptions and compare industry
experts as the interface between other two groups. It can be underlined that
different groups of respondents do perceive specific criterion differently, and the
perception of practitioners and the one of professors differ a lot.
67
How can the importance of criteria be used to increase the
competitiveness of intermodal transport?
Based on the findings of this research, since transport cost, on-time reliability
and door-to-door travel time receive relatively high importance, thus the policy
that improves on-time reliability and decreases door-to-door travel time and
transport cost for a particular mode might result in considerable improvements
in the use of that mode. Therefore, in order to promote the use of rail and barge
for transporting freights, governments should propose the policy which reduces
the transport cost of rail and barge or increase the transport cost for using uni-
road transport. Moreover, it might be that more facilities such as ports should
be built in order to increase the number of barges, therefore increasing on-time
reliability for barge-intermodal transport. Furthermore, since this research also
indicates that apart from reduction of CO2-emission, frequency and flexibility
are considered as less important, so it can be drawn that if, for instance,
governments want shippers to choose rail intermodal transport, instead of
increasing flexibility and frequency for rail service, governments should focus
on how to increase the quality of top-three criteria of rail service. Hence, to
increase the competitiveness of intermodal transport, knowing that transport
cost, on-time reliability and door-to-door travel time are the criteria that shippers
care about most helps governments to make efficient and effective policy in
such a way that less important criteria can be ignored to some extent.
Last but not least, this research also shows that the perceptions of practitioners
and professors significantly differ towards reduction of CO2-emission, which
suggests that practitioners might lack the relevant knowledge regarding the
environmental issues caused by freight uni-road transportation. And, due to this
kind of knowledge gap practitioners tend to choose uni-road transportation
without the consideration to reduce CO2-emission. Thus, this finding makes it
clear that in order to make shippers voluntarily choose intermodal transport, just
increasing the quality of top-three criteria might not be enough, and
practitioners should be educated in terms of environmental perspectives and
the contribution that intermodal transport makes to reduce greenhouse gas
emission.
5.2 Limitation
Although this research has reached its aim, there still are some unavoidable
limitations. Due to the time limit, only two regions Europe and the United States
was included, and also due to the low response rate of the online questionnaire
there comes the first limitation. This limitation is that this research was
conducted only based on a small size of the population which consists of only
68
50 respondents, and since this study segregate population into three group of
respondents so the population in each group is even smaller, which leads that
the non-parametric statistical test is chosen to analyze the collected data. While,
the non-parametric test is less powerful than the parametric test. Thus, given
the small sample size, the findings of this research should be considered
tentative, which might provide a new perspective for interested researchers.
The second limitation is the one pointed out by a professor who suggests that
regarding perishable food industry, it should be specifically mentioned whether
the meat, used as an example of perishable foods in the questionnaire, would
be transported frozen or chilled which indicates the level or time of perishability,
and it has a substantial impact on transport mode choices. Therefore, the
example regarding perishable food industry given in the questionnaire might
restrict the consideration of respondents towards some criteria, and it should
have been specified in terms of the level or time of perishability.
5.3 Recommendation
Based on the findings of this research, some recommendations are proposed
in this section for governments, policy makers, decision-makers, and
researchers. The recommendations are:
1. Through the findings of top-three criteria and the relatively smaller
weights assigned to last three criteria, the preference of respondents can
be clearly seen. This finding suggests that modal-shift policies should
focus mainly on acknowledged important criteria of the mode to be
promoted. Thus when making policy to promote the use of intermodal
transport, policymakers should focus on improving the quality of top-
three criteria for intermodal service, especially of transport cost and on-
time reliability, since these two criteria have similar weights which are
still relatively higher than the weight of door-to-door travel time. On the
other side, by knowing that transport cost is viewed as the most
important, policy makers can even increase the transport cost for uni-
road transport, therefore indirectly promoting the use of intermodal
transport. Furthermore, since the finding also shows that the perceptions
of industry experts and practitioners differ regarding flexibility, and
practitioners give it relatively higher weight, hence if the policy includes
the adjustment of flexibility for intermodal service, it might be better that
these two types decision-makers should be treated differently, for
instance: practitioners receive the option of intermodal service with high
level of flexibility.
2. It can also be seen that among three groups of respondents, professors
perceive reduction of CO2-emission highly important, while, conversely,
69
practitioners assign it with the lowest weight. So, it suggests that there
might exist a knowledge gap regarding environmental perspectives
between practitioners and professors, and not knowing terrible
environmental issues brought by uni-road transport might be one reason
that practitioners care less about the reduction of CO2-emission. And,
this work should be done by governments and researchers in such a way
that with the help of academic argumentation from researchers,
governments can educate companies about the environmental issues
and the proposed methods for solving these issues. Besides, as
mentioned in section 2.3, shippers might choose the intermodal
transport with less CO2-emission when they consider this action as a
green image for their company to relate to socially responsible
entrepreneurship. Thus, only bridging the knowledge gap for
practitioners might not be enough, and their clients should also be
educated to feel the responsibility for reducing the greenhouse gas
emission. And, by doing so customers might give credit for practitioners
who choose the intermodal transport, therefore promoting the use of
intermodal service by more and more practitioners.
3. One finding shows that perception of one specific criterion regarding
perishable foods industry differs with its counterpart regarding other
three types of industries, especially in terms of time-related criteria such
as door-to-door travel time, on-time reliability, flexibility, and frequency.
With this knowledge, it can be concluded that when it comes to the
perishable food industry, an improved quality in time-related criteria,
such as on-time reliability or flexibility, for the intermodal service will
make it more attractive. Furthermore, the result also shows that there is
a difference in perception across these four types of industries,
suggesting that respondents have different requirements for one
criterion regarding four types of industries. Therefore, for the
transportation of freights from one specific industry, the intermodal
service should be made in such a way that criteria which one specific
type of freights cares about most should be improved in terms of quality,
and satisfying the requirements of that specific type of freights is the
premise for shippers to choose intermodal transport. So, intermodal
services should be customized according to customers’ requirements
regarding their specific freight type, and by differentiating its services
based on different requirements of different freights the intermodal
transportation can strengthen their own competitive position.
70
5.4 Suggestions for future research
This study mainly focuses on the demand side in freight transport modal choice,
namely the expectation of decision makers towards transport modes. So the
requirements of decision-makers regarding all six criterion can be seen. Future
researches can be conducted with respect to supply side of freight
transportation which is how decision-makers perceive actual services that one
specific mode provides, which are also represented in terms of criteria. Then
the future research can combine their findings and the conclusions of this
research to investigate whether there is a gap between the expected quality of
criteria and actual quality of criteria in terms of each mode, which might be the
interesting subject since the failure of satisfying decision-makers’ requirements
might also be the reason for the difficulty of shifting from uni-road transport to
intermodal transport. In addition, if the future research studies the delivered
quality of transport modes, the group of professors which is included in this
research is not recommended to include as one group of respondents, since
professors do not directly experience service of transport modes.
71
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Appendix A
In this research, in order to collect data from respondents, the on-line tool named
surveygizmo® is used for designing questionnaire. The biggest advantage of this tool that
the email-based or word-based questionnaire cannot compete with is that its logical
sentence can be set for each question to make the whole questionnaire interactive. In the
following pages, the sample of questionnaire is provided, and it is worth mentioning that
the actual online-questionnaire is more interactive, which means that which kind of
question will appear next really depends on the respondent’s answer to the former question.
80
“Determining the Importance of Factors for Transport Modes
in Freight Transportation”
Dear Sir/ Madam
We are conducting a research at Delft University of Technology, aiming to evaluate the
importance of factors in freight transport modes considering freights from four industries
(manufacturing industry, agriculture industry, perishable food industry, and chemical
industry). Given your expertise in the field related to this topic, we feel that you have unique
experience and know-how that can help us achieve the aim.
This questionnaire requires approximately 10 minutes to complete. We assure you that all
the information you provide will be treated with the greatest possible confidentiality and
fully anonymous. Your participation represents a valuable contribution to this study, and we
thank you for your cooperation. In addition, if you are interested in the outcome of our
research, we are willing to share the outcome with you, and if you have any questions
regarding the questionnaire or the research, please do not hesitate to contact us.
Thank you.
Sincerely ,
Wan Liu(MSc Student)
Prof. Dr. Ir. Lorant Tavasszy
Dr. Jafar Rezaei
Dr. Geerten van de Kaa
Email: [email protected]
81
Freights from manufacturing industry
1. Suppose, as a shipper, you will transport the containers full of machines (freights
from manufacturing industry). The following factors are considered to select the most
appropriate transport mode from the feasible modes truck, rail, and ship for transporting these
freights.
The below listed factors are important for deciding which transport mode to use, in your opinion,
what are the Most and the Least important factors?
Factors MOST Important Least Important
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
2. You have selected XX factor as the MOST IMPORTANT factor. Could you please indicate
your preference of this factor over the other factors. Use a number between 1 and 9 to show
the preference of the MOST IMPORTANT factor over the other factors (1 represents equal
importance and 9 shows that the most important factor is extremely more important than the
other one. Please check below statements for detailed information of 1 to 94).
Factors
Most important
Transport
cost
Door-
to-door
travel
time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
3. You have selected XX factor as the LEAST IMPORTANT factor. Could you please indicate
your preference of the other factors over this factor. Use a number between 1 and 9 to show
the preference of the other factors over the LEAST IMPORTANT factor (1 represents equal
importance and 9 shows that another factor is extremely more important than the least
important factor. Please check below statements for detailed information of 1 to 9).
Least important
Factors
Transport cost
Door-to-door travel time
4 Definition of 1 to 9 measurement scale:
1: Equal importance 7: Very strongly more important
3: Moderately more important 9: Extremely more important
5: Strongly more important 2,4,6,8: Intermediate values
82
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
Freights from the agriculture industry
1. Suppose, as a shipper, you will transport the containers full of cereals (freights
from agriculture industry). The following factors are considered to select the most appropriate
transport mode from among the feasible modes truck, rail, and ship for transporting these
freights.
The below listed factors are important for deciding which transport mode to use, in your opinion,
what are the Most and the Least important factors?
Factors MOST Important Least Important
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
2. You have selected XX factor as the MOST IMPORTANT factor. Could you please indicate
your preference of this factor over the other factors. Use a number between 1 and 9 to show
the preference of the MOST IMPORTANT factor over the other factors (1 represents equal
importance and 9 shows that the most important factor is extremely more important than the
other one. Please check below statements for detailed information of 1 to 95).
Factors
Most important
Transport
cost
Door-
to-door
travel
time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
3. You have selected XX factor as the LEAST IMPORTANT factor. Could you please indicate
your preference of the other factors over this factor. Use a number between 1 and 9 to show
the preference of the other factors over the LEAST IMPORTANT factor (1 represents equal
importance and 9 shows that another factor is extremely more important than the least
5 Definition of 1 to 9 measurement scale:
1: Equal importance 7: Very strongly more important
3: Moderately more important 9: Extremely more important
5: Strongly more important 2,4,6,8: Intermediate values
83
important factor. Please check below statements for detailed information of 1 to 9).
Least important
Factors
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
Freights from perishable food industry
1. Suppose, as a shipper, you will transport the containers full of meat (freights from perishable
food industry). The following factors are considered to select the most appropriate transport
mode from among the feasible modes truck, rail, and ship for transporting these freights.
The below listed factors are important for deciding which transport mode to use, in your opinion,
what are the Most and the Least important factors?
Factors MOST Important Least Important
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
2. You have selected XX factor as the MOST IMPORTANT factor. Could you please indicate
your preference of this factor over the other factors. Use a number between 1 and 9 to show
the preference of the MOST IMPORTANT factor over the other factors (1 represents equal
importance and 9 shows that the most important factor is extremely more important than the
other one. Please check below statements for detailed information of 1 to 96).
Factors
Most important
Transport
cost
Door-
to-door
travel
time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
6 Definition of 1 to 9 measurement scale:
1: Equal importance 7: Very strongly more important
3: Moderately more important 9: Extremely more important
5: Strongly more important 2,4,6,8: Intermediate values
84
3. You have selected XX factor as the LEAST IMPORTANT factor. Could you please indicate
your preference of the other factors over this factor. Use a number between 1 and 9 to show
the preference of the other factors over the LEAST IMPORTANT factor (1 represents equal
importance and 9 shows that another factor is extremely more important than the least
important factor. Please check below statements for detailed information of 1 to 9).
Least important
Factors
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
Freights from chemical industry
1. Suppose, as a shipper, you will transport the containers full of chemical freights. The
following factors are considered to select the most appropriate transport mode from among the
feasible modes truck, rail, and ship for transporting these freights.
The below listed factors are important for deciding which transport mode to use, in your opinion,
what are the Most and the Least important factors?
Factors MOST Important Least Important
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
2. You have selected XX factor as the MOST IMPORTANT factor. Could you please indicate
your preference of this factor over the other factors. Use a number between 1 and 9 to show
the preference of the MOST IMPORTANT factor over the other factors (1 represents equal
importance and 9 shows that the most important factor is extremely more important than the
other one. Please check below statements for detailed information of 1 to 97).
7 Definition of 1 to 9 measurement scale:
1: Equal importance 7: Very strongly more important
3: Moderately more important 9: Extremely more important
5: Strongly more important 2,4,6,8: Intermediate values
85
Factors
Most important
Transport
cost
Door-
to-door
travel
time
On-time
reliability
Flexibility Frequency Reduction
of CO2-
emission
3. You have selected XX factor as the LEAST IMPORTANT factor. Could you please indicate
your preference of the other factors over this factor. Use a number between 1 and 9 to show
the preference of the other factors over the LEAST IMPORTANT factor (1 represents equal
importance and 9 shows that another factor is extremely more important than the least
important factor. Please check below statements for detailed information of 1 to 9).
Least important
Factors
Transport cost
Door-to-door travel time
On-time reliability
Flexibility
Frequency
Reduction of CO2-emission
86
Appendix B
1. Comparison analysis regarding three backgrounds of respondents
(Mann-Whitney u test)
For manufacturing industry
1. Comparison between industry experts and professors
Test Statisticsa
weightTC weightFR weightCO2 weightOT weightFL weightTT
Mann-Whitney U 109,000 94,500 89,500 112,000 100,000 98,000
Wilcoxon W 245,000 230,500 194,500 248,000 236,000 234,000
Z -,125 -,728 -,936 ,000 -,499 -,582
Asymp. Sig. (2-tailed) ,901 ,467 ,349 1,000 ,618 ,560
Exact Sig. [2*(1-tailed Sig.)] ,918b ,473b ,355b 1,000b ,637b ,580b
a. Grouping Variable: profession
b. Not corrected for ties.
2. Comparison between industry experts and practitioners
Test Statisticsa
weightTC weightFR weightCO2 weightOT weightFL weightTT
Mann-Whitney U 114,000 105,000 128,000 133,000 136,000 143,000
Wilcoxon W 345,000 210,000 359,000 364,000 241,000 248,000
Z -1,111 -1,415 -,640 -,472 -,370 -,135
Asymp. Sig. (2-tailed) ,266 ,157 ,522 ,637 ,711 ,893
Exact Sig. [2*(1-tailed Sig.)] ,278b ,164b ,538b ,654b ,727b ,907b
a. Grouping Variable: profession
b. Not corrected for ties.
3. Comparison between professors and practitioners
Test Statisticsa
weightTC weightFR weightCO2 weightOT weightFL weightTT
Mann-Whitney U 124,000 79,000 118,000 156,000 140,000 164,000
Wilcoxon W 355,000 215,000 349,000 387,000 276,000 300,000
Z -1,350 -2,729 -1,533 -,368 -,859 -,123
Asymp. Sig. (2-tailed) ,177 ,006 ,125 ,713 ,391 ,902
Exact Sig. [2*(1-tailed Sig.)] ,185b ,006b ,130b ,728b ,404b ,916b
a. Grouping Variable: profession
87
b. Not corrected for ties.
For agriculture industry
1. Comparison between industry experts and professors
Test Statisticsa
weightco2 weightTC weightFR weightFL weightTT weightOT
Mann-Whitney U 75,500 90,000 110,000 108,000 107,000 88,000
Wilcoxon W 180,500 195,000 246,000 213,000 243,000 224,000
Z -1,518 -,915 -,083 -,166 -,208 -,998
Asymp. Sig. (2-tailed) ,129 ,360 ,934 ,868 ,835 ,318
Exact Sig. [2*(1-tailed Sig.)] ,131b ,377b ,951b ,886b ,854b ,334b
a. Grouping Variable: profession
b. Not corrected for ties.
2. Comparison between industry experts and practitioners
Test Statisticsa
weightco2 weightTC weightFR weightFL weightTT weightOT
Mann-Whitney U 128,000 104,000 119,500 77,500 128,000 140,000
Wilcoxon W 359,000 335,000 224,500 182,500 233,000 245,000
Z -,640 -1,448 -,926 -2,341 -,640 -,236
Asymp. Sig. (2-tailed) ,522 ,148 ,354 ,019 ,522 ,814
Exact Sig. [2*(1-tailed Sig.)] ,538b ,154b ,359b ,018b ,538b ,829b
a. Grouping Variable: profession
b. Not corrected for ties.
3. Comparison between professors and practitioners
Test Statisticsa
weightco2 weightTC weightFR weightFL weightTT weightOT
Mann-Whitney U 88,000 103,000 136,000 89,000 121,000 106,000
Wilcoxon W 319,000 334,000 272,000 225,000 257,000 242,000
Z -2,453 -1,993 -,981 -2,423 -1,441 -1,901
Asymp. Sig. (2-tailed) ,014 ,046 ,326 ,015 ,149 ,057
Exact Sig. [2*(1-tailed Sig.)] ,013b ,047b ,338b ,015b ,156b ,059b
a. Grouping Variable: profession
b. Not corrected for ties.
Perishable food industry
1. Comparison between industry experts and professors
Test Statisticsa
88
weightTC weightTT weightOT weightFL weightFR weightco2
Mann-Whitney U 82,500 73,500 104,500 90,500 65,500 102,500
Wilcoxon W 187,500 209,500 209,500 195,500 201,500 207,500
Z -1,228 -1,603 -,312 -,895 -1,936 -,395
Asymp. Sig. (2-tailed) ,219 ,109 ,755 ,371 ,053 ,693
Exact Sig. [2*(1-tailed Sig.)] ,224b ,110b ,759b ,377b ,052b ,697b
a. Grouping Variable: profession
b. Not corrected for ties.
2. Comparison between industry experts and practitioners
Test Statisticsa
weightTC weightTT weightOT weightFL weightFR weightco2
Mann-Whitney U 102,000 75,000 132,000 102,000 116,000 137,500
Wilcoxon W 207,000 306,000 237,000 207,000 347,000 368,500
Z -1,516 -2,425 -,505 -1,516 -1,044 -,320
Asymp. Sig. (2-tailed) ,130 ,015 ,613 ,130 ,296 ,749
Exact Sig. [2*(1-tailed Sig.)] ,135b ,015b ,630b ,135b ,309b ,752b
a. Grouping Variable: profession
b. Not corrected for ties.
3. Comparison between professors and practitioners
Test Statisticsa
weightTC weightTT weightOT weightFL weightFR weightco2
Mann-Whitney U 161,000 161,000 165,000 140,000 152,000 142,000
Wilcoxon W 297,000 392,000 396,000 276,000 288,000 373,000
Z -,215 -,215 -,092 -,859 -,491 -,798
Asymp. Sig. (2-tailed) ,830 ,830 ,927 ,390 ,624 ,425
Exact Sig. [2*(1-tailed Sig.)] ,844b ,844b ,940b ,404b ,639b ,439b
a. Grouping Variable: profession
b. Not corrected for ties.
Chemical industry
1. Comparison between industry experts and professors
Test Statisticsa
weightTC weightTT weightOT weightFL weightFR weightco2
Mann-Whitney U 99,000 51,000 94,000 55,000 110,000 112,000
Wilcoxon W 204,000 187,000 230,000 160,000 215,000 248,000
Z -,541 -2,538 -,749 -2,371 -,083 ,000
Asymp. Sig. (2-tailed) ,589 ,011 ,454 ,018 ,934 1,000
Exact Sig. [2*(1-tailed Sig.)] ,608b ,010b ,473b ,017b ,951b 1,000b
89
a. Grouping Variable: profession
b. Not corrected for ties.
2. Comparison between industry experts and practitioners
Test Statisticsa
weightTC weightTT weightOT weightFL weightFR weightco2
Mann-Whitney U 117,000 139,000 136,000 138,000 135,500 122,500
Wilcoxon W 222,000 349,000 241,000 348,000 240,500 332,500
Z -,805 -,035 -,140 -,070 -,158 -,613
Asymp. Sig. (2-tailed) ,421 ,972 ,889 ,944 ,875 ,540
Exact Sig. [2*(1-tailed Sig.)] ,436b ,986b ,904b ,959b ,877b ,545b
a. Grouping Variable: profession
b. Not corrected for ties.
3. Comparison between professors and practitioners
Test Statisticsa
weightTC weightTT weightOT weightFL weightFR weightco2
Mann-Whitney U 155,000 70,000 121,000 94,000 154,000 130,000
Wilcoxon W 365,000 206,000 257,000 304,000 290,000 340,000
Z -,159 -2,867 -1,242 -2,102 -,191 -,956
Asymp. Sig. (2-tailed) ,873 ,004 ,214 ,036 ,848 ,339
Exact Sig. [2*(1-tailed Sig.)] ,888b ,003b ,223b ,036b ,863b ,352b
a. Grouping Variable: profession
b. Not corrected for ties.
1. General comparison analysis based on three types
respondents (mann-whitney)
a. Industry experts vs. professors
Test Statisticsa
weightTC weightTT weightOT weightFL weightFR weightCO2
Mann-Whitney U 1539,500 1413,000 1704,500 1556,000 1568,000 1524,500
Wilcoxon W 3135,500 3493,000 3784,500 3152,000 3648,000 3120,500
Z -1,328 -1,994 -,460 -1,242 -1,178 -1,407
Asymp. Sig. (2-tailed) ,184 ,046 ,645 ,214 ,239 ,159
a. Grouping Variable: profession
b. Industry experts vs. practitioners
Test Statisticsa
90
weightTC weightTT weightOT weightFL weightFR weightCO2
Mann-Whitney U 2176,500 2149,500 2195,500 1719,500 2155,000 1948,000
Wilcoxon W 5416,500 5389,500 3791,500 3315,500 3751,000 5188,000
Z -,281 -,400 -,197 -2,302 -,376 -1,291
Asymp. Sig. (2-tailed) ,779 ,689 ,844 ,021 ,707 ,197
a. Grouping Variable: profession
c. Professors vs. practitioners
Test Statisticsa
weightTC weightTT weightOT weightFL weightFR weightCO2
Mann-Whitney U 2079,000 1990,500 2233,000 2272,000 2133,000 1795,000
Wilcoxon W 5319,000 4070,500 4313,000 4352,000 4213,000 5035,000
Z -1,934 -2,290 -1,315 -1,158 -1,717 -3,076
Asymp. Sig. (2-tailed) ,053 ,022 ,189 ,247 ,086 ,002
a. Grouping Variable: profession
2. Comparison analysis regarding four industries (Wilcoxon signed-
rank test+ sign test)
1. From the perspective of industry experts (14 respondents)
There are nine comparisons that have statistically significant difference:
1.the comparison between manufacturing and perishable food industry
regarding transport cost criterion; 2. the comparison between manufacturing
and perishable food industry regarding door-to-door travel time criterion; 3.
the comparison between manufacturing and perishable food industry
regarding frequency criterion; 4. the comparison between agriculture and
perishable food industry regarding transport cost; 5. the comparison
between agriculture and perishable food industry regarding door-to-door
travel time; 6. the comparison between agriculture and perishable food
industry regarding on-time reliability; 7. the comparison between chemical
and perishable food industry regarding transport cost; 8. the comparison
between chemical and perishable food industry regarding door-to-door
travel time; 9. the comparison between chemical and perishable food
industry regarding frequency.
2. From the perspective of professors (16 respondents)
There are eleven comparisons having statistically significant difference,
which are: 1. the comparison between manufacturing and perishable food
91
industry regarding transport cost; 2. the comparison between manufacturing
and perishable food industry regarding door-to-door travel time; 3. the
comparison between manufacturing and perishable food industry regarding
on-time reliability; 4. the comparison between manufacturing and perishable
food industry regarding frequency; 5. the comparison between
manufacturing and perishable food industry regarding reduction of CO2-
emission; 6. the comparison between chemical and manufacturing industry
regarding door-to-door travel time; 7. the comparison between agriculture
and perishable food industry regarding transport cost; 8. the comparison
between agriculture and perishable food industry regarding on-time
reliability; 9. the comparison between agriculture and perishable food
industry regarding flexibility; 10. the comparison between chemical and
perishable food industry regarding transport cost; 11. the comparison
between chemical and perishable food industry regarding door-to-door
travel time.
3. From the perspective of practitioners (20 respondents)
There are in total nine significantly differences: 1. The comparison between
manufacturing and perishable food industry regarding transport cost; 2. The
comparison between manufacturing and chemical industry regarding
transport cost; 3. The comparison between agriculture and perishable food
industry regarding transport cost; 4. The comparison between agriculture
and perishable food industry regarding on-time reliability; 5. The comparison
between agriculture and chemical industry regarding transport cost; 6. The
comparison between agriculture and chemical industry regarding doo-to-
door travel time; 7. The comparison between agriculture and chemical
industry regarding frequency; 8. The comparison between perishable foods
and chemical industry regarding transport cost; 9. The comparison between
perishable foods and chemical industry regarding door-to-door travel time.
4. From the perspectives of all respondents (50 respondents)
Summarizing all the perspectives from 50 respondents, there are eighteen
significant differences: 1. The comparison between manufacturing and
agriculture industry regarding door-to-door travel time; 2. The comparison
between manufacturing and agriculture industry regarding frequency; 3. The
comparison between manufacturing and perishable food industry regarding
transport cost; 4. The comparison between manufacturing and perishable
food industry regarding door-to-door time; 5. The comparison between
manufacturing and perishable food industry regarding on-time reliability; 6.
The comparison between manufacturing and perishable food industry
regarding frequency; 7. The comparison between manufacturing and
perishable food industry regarding reduction of CO2-emission; 8. The
comparison between manufacturing and chemical industry regarding
transport cost; 9. The comparison between agriculture and perishable food
92
industry regarding transport cost; 10. The comparison between agriculture
and perishable food industry regarding door-to-door travel time; 11. The
comparison between agriculture and perishable food industry regarding on-
time reliability; 12. The comparison between agriculture and perishable food
industry regarding flexibility; 13. The comparison between agriculture and
perishable food industry regarding frequency; 14. The comparison between
agriculture and chemical industry regarding door-to-door travel time; 15.
The comparison between agriculture and chemical industry regarding
frequency; 16. The comparison between perishable foods and chemical
industry regarding transport cost; 17. The comparison between perishable
foods and chemical industry regarding door-to-door travel time; 18. The
comparison between perishable foods and chemical industry regarding
frequency.
