The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 1, No. 2, May-June 2013
Deriving Numerous Benefits with the
Abet of Effective Transportation
Network and Design
Dr. Vipul Chalotra*
*Assistant Professor, Department of Commerce, University of Jammu, Udhampur Campus, INDIA. E-Mail: vipulchalotra@gmail.com
Abstract—Effective transportation management contributes a lot to the business in terms of profitability,
market diversifications, speedy and frequent delivery of goods and routine market information. The paper
analyses the benefits of effective transportation network and design. A survey with 152 wholesalers in district
Udhampur of J&K State was done through snowball referral sampling. The research framework was examined
by empirical analysis of primary data collected. Validity and reliability of the scales in the construct were
assessed through BTS and Cronbach-alpha. The data after purification & validation through factor analysis was
subjected to multivariate tools. The results of hierarchal regression model and one way ANOVA revealed that
transportation network & design is influenced by freight rates and inters state tax and transportation network &
design is affected by supplier scheduling. Further, ANOVA table portrayed that wholesalers with different
qualification level differ significantly with regard to adoption of transportation network and design.
Keywords—Business, Design, Network, Transportation, Wholesalers
Abbreviation—Analysis of Variance (ANOVA)
P
I.
INTRODUCTION
ROPER transportation imbibes bundle of benefits in
terms of creating time and place utilities, reducing the
overall costs of the business and other numerous
benefits. Effective transportation management is recognised
as catalyst for calculating the overall firm performance.
Proper transportation leads to placing right product at right
time, meeting incessant demand requirements, covering of
more & more markets, wider distribution along the globe and
promotion of product acceptance. With the growth of
technologies and economies became more sophisticated and
globalised, transport geographers began to embrace on new
ways of understanding the role of transport at local, national
and global scales [Tolley & Turton, 1995]. As a consequence,
there has been a considerable increase in the number of
means & number of transport modes to cater the needs of
high performance product at lower maintenance costs. Then
transport manufacturers have focused their attention on the
minimisation of the life-cycle-cost and on its main
determining factors, in particular reliability, maintainability
and availability of products [Black, 2001]. Transport system
makes products movable through timely and regional efficacy
for promoting value-added under the least cost principle.
Transport affects the results of logistics activities and
influences production & sale. Value of transportation varies
with different industries. For those products with small
volume, low weight and high value, transportation cost
ISSN: 2321 – 242X
simply occupies a very small part of sale and is less regarded;
for those big, heavy and low-valued products, transportation
occupies a very big part of sale and affects profits more and
therefore it is more regarded. Transportation design and
networks results in supply chain effectiveness by lowering
inventory [Barrett et al., 1999], moving timely inbound goods
from supply sites to manufacturing facilities [Carter & Ferrin,
1995], plant efficiencies and delivering quality products to
customers in an cost efficient manner [Giuliano & Narayan,
2003].
II.
REVIEW OF EXISTING LITERATURE
Research on transportation issues succeeded after the mid1980s and took a somewhat sclerotic approach from basic
spatial interaction models to more sophisticated network
approaches to cost effective movement of goods across space
and time [Knowles, 1993; Black, 2003]. The role that
transportation plays in supply chain management system is
more complex than carrying goods for the proprietors. Its
complexity can take effect only through highly quality
management. By means of well-handled transport system,
goods could be sent to the right place at right time in order to
satisfy customers‟ demands. It brings efficacy and builds a
bridge between producers & consumers. Therefore,
transportation is the base of efficiency & economy in
business logistics and expands other functions of logistics
system. In addition, a good transport system performing in
© 2013 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 1, No. 2, May-June 2013
logistics activities brings benefits not only to service quality
but also to company competitiveness. Transportation design
and network planning includes all functions and subfunctions into a system of goods movement in order to
minimize cost, maximize service to the customers which
contributes to the concept of business logistics. The system,
once put in place, must be effectively managed [Ewing et al.,
2003]. Proper transportation management in supply chains
emphasizes on numerous activities such as distance & time
traveled [Holtzclaw, 1990; Schimek, 1996], trip making
frequency [Glaister & Graham, 2002; Giuliano & Narayan,
2003], mode choice [Cervero & Kockelman 1997] and fuel
consumption [Banister et al., 1997; Newman & Kenworthy,
1989]. These attributes include city size [Levinson & Kumar,
1994], land use patterns & densities [Cervero, 1996;
Messenger & Ewing, 1996], street networks and micro &
macro accessibility to activity centres [Cervero, 1994].
The present study analyses the benefits derived by
wholesalers out of effective transportation network and
design.
III. TESTABLE HYPOTHESES
On the basis of in-depth analysis of existing review of
literature and its meaningful conclusions, the following
hypotheses had been emerged in order to make the study
more reliable and responsive. The main hypotheses are:Hyp 1: Transportation network and design is influenced
by freight rates and inter state tax.
Hyp 2: Transportation network and design is affected by
supplier scheduling.
Hyp 3: Wholesalers with different qualification level
differ significantly with regard to adoption of transportation
network and design.
Obj: To analyse the benefits derived out of effective
transportation network and design.
were using/selling/dealing with the products manufactured by
the above stated units. The number of wholesalers identified
under cement (12), pesticide (12), steel (12),
battery/lead/alloy (12), menthol (1), guns (3), conduit pipes
(2), gates/grills/varnish (15), maize/atta/dal mills (22) and
miscellaneous (30). Some of the major wholesalers contacted
were: Surbhi enterprises, M/S Raj Battery Corporation, M/S
DBN Traders, M/s Swastik Enterprises, M/S Binothia
Hardwares, Allied Agencies, Devika Agencies, Samgam
Automobiles, M/S Inder Medical, ESS ESS Traders etc.
4.3. The Survey Instrument
Information was collected by administering self developed
questionnaire prepared after consulting experts and review of
literature which comprised of general information and 10
statements of transportation management. Statements in the
questionnaire were in descriptive form, ranking,
dichotomous, open ended and five-point Likert scale, where 1
stands for strongly disagree and 5 for strongly agree.
4.4. Collection of Data
The primary data were collected by making three to four
visits
for
getting
response
from
respondents.
Snowball/referral sampling method was applied for collecting
data from the respective respondents. The secondary
information was collected from various sources namely
books, empirical papers from online & hard copies of
journals. Various multivariate tools such as Mean, standard
deviation, regression, one way ANOVA were used to test
hypotheses and for drawing meaningful inferences.
4.5. Reliability and Validity of the Instrument
4.5.1. Reliability
Research design and methodology comprises area of
research, nature of data/information (Primary or secondary),
questionnaire/schedule, research tools applied etc. The
research methodology adopted proceeds as follows:
The Cronbach‟s reliability coefficients for all 5 scale items
after applying rotated component matrix represented alpha
value 0.9826 was higher than the criteria of 0.77 obtained by
Gordon & Narayanan (1984) indicating high internal
consistency. Adequacy and reliability of sample size to yield
distinct and reliable factors is further demonstrated through
Kaiser-Meyer-Olkin Measure of Sampling Adequacy that is
0.907 and all factor loadings between items being greater
than equal to 0.55.
4.1. Sampling and Data Collection
4.5.2. Validity
IV. RESEARCH DESIGN AND METHODOLOGY
The primary data for the study were collected from 152
wholesalers selling the products of small manufacturing firms
operating in district Udhampur of J&K State. An in-depth
analysis of these wholesalers was the main purpose of the
present study. The total numbers of respondents contacted for
the study were 152 out of which 127 responded representing
an effective response rate of 83.55%. Their response was the
main basis for drawing meaningful inferences.
4.2. Sampling Technique Applied
Snowball/referral sampling was used for obtaining data from
wholesalers. Only those wholesalers were contacted who
ISSN: 2321 – 242X
The factor obtained alpha reliability higher to 0.50 and
satisfactory KMO value at 0.907, indicating significant
construct validity of the construct [Hair et al., 1995].
V.
DATA ANALYSIS AND INTERPRETATION
The suitability of raw data for factor analysis obtained from
wholesalers is examined through Anti-image, KMO value,
Bartlett‟s Test of Sphercity and (p-value = 0.000), indicating
sufficient common variance and correlation matrix [Dess et
al., 1997; Field, 2000; Field, 2004]. The process of R-Mode
Principal Component Analysis (PSA) with Varimax Rotation
© 2013 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 1, No. 2, May-June 2013
educed 5 statements out of 10 statements originally kept in
the construct/domain of transportation management. The
KMO value (0.907) and Bartlett Test of Sphercity (2974.254)
indicated high acceptable and significant values. Therefore,
factor loadings emerged consistent with conservative criteria,
thereby resulting into good factor solution using Kaiser
Criteria (i.e. Eigen value ≥ 1) with 36.132% of the total
variance explained. The communality for 5 items ranges from
0.908 to 0.979, indicating high degree of linear association
among the variables. The factor loadings range from 0.830 to
0.895 and the cumulative variance extracted is 36.132%. The
factor and its statements emerged is displayed in the Table 1.
A brief description of factor and its statements emerged are as
under:
5.1. Factor (Transportation Network & Design)
Five items underlying this factor consisted of:
“Transportation network is customer responsive”, “Huge
profit margins are kept by transportation agencies”, “Freight
rates & inter state tax influences transportation design”,
“Speed is critical element in transportation” and “Supplier
scheduling affects transportation design”. The mean values
ranges from 4.51 to 4.55 representing significant values. The
mean value for each statement is: Transportation network is
customer responsive (4.51), huge profit margins are kept by
transportation agencies (4.55), Freight rates & inter state tax
influences transportation design (4.52), Speed is critical
element in transportation (4.52) and Supplier scheduling
affects transportation design (4.54). The statement “Huge
profit margins are kept by transportation agencies” emerged
with highest mean value and the statement “Transportation
network is customer responsive” emerged to be the weakest
among all with low mean value. The factor loadings fluctuate
within .830 to .895. The factor loadings for each statement is:
Transportation network is customer responsive (.895), Huge
profit margins are kept by transportation agencies (.866),
Freight rates & inter state tax influences transportation design
(.863), Speed is critical element in transportation (.859) and
Supplier scheduling affects transportation design (.830). The
statement “Transportation network is customer responsive”
emerged with highest factor loadings and the statement
“Supplier scheduling affects transportation design” emerged
to be the weakest among all with low factor loading. The
communalities varied from .908 - .979 indicating significant
values and high degree of linear association among the
variables. The communalities for each statement is:
Transportation network is customer responsive (.908), Huge
profit margins are kept by transportation agencies (.960),
Freight rates & inter state tax influences transportation design
(.979), Speed is critical element in transportation (.947) and
Supplier scheduling affects transportation design (.936). The
statement “Freight rates & inter state tax influences
transportation design” enriched with highest communalities
and the statement “Transportation network is customer
responsive” emerged to be the weakest but communalities of
all variables indicated significant values for the construct.
The basic rationale of wholesalers is to be customer
responsive and to ensure speed in delivery of goods.
Table 1 – Results Showing Factor Loadings and Variance Explained after Scale Purification (Rotated Component Method) for Transportation
Management (Wholesalers‟ Perceptions)
Eigen
Variance
Cumulative
Factor-wise Dimensions
Mean
S.D
F.L
Communality
α
Value
Explained %
Variance %
Transportation network & design
4.52
.604
12.018
36.132
36.132
.9826
Transportation network is customer
4.51
.667
.895
.908
responsive
Huge profit margins are kept by
4.55
.576
.866
.960
transportation agencies
Freight rates & inter state tax
4.52
.601
.863
.979
influences transportation design
Speed is critical element in
4.52
.601
.859
.947
transportation
Supplier scheduling affects
4.54
.577
.830
.936
transportation design
Footnotes: KMO Value =.907; Bartlett‟s Test of Sphercity = 2974.254, df = 190, Sig. =.000; Extraction Method Principal Component
Analysis; Varimax with Kaiser Normalisation; Rotation converged in 3 iterations; „FL‟ stands for Factor Loadings, „S.D‟ for Standard
Deviation and „α‟ for Alpha.
5.2. Profile of Wholesalers Qualification
It has been analyzed that 21 Wholesalers were graduates and
7 Wholesalers were post-graduates. 59 Wholesalers were
metric pass constituting larger chunk of the respondents
contacted. Another group of Wholesalers who were qualified
upto higher secondary were 29 in number again representing
the second highest chunk of response. Those who had done
technical courses were just 11 out of total 127 respondents.
Thus, it becomes clear that the proportion of Wholesalers is
enlightened and educated. Thus, qualification of Wholesalers
ISSN: 2321 – 242X
highlights the awakened nature of Wholesalers representing
good qualified respondents (Table 2).
S.No.
1.
Table 2 – A Brief Profile of Wholesalers Qualification
Variables
Classification
Frequency
Metric
59
Higher Secondary
29
Qualification
Graduation
21
Post graduation
7
Others
11
Total
127
© 2013 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 1, No. 2, May-June 2013
5.3. Regression Analysis
Table 3 shows output from regression analysis to elicit the
impact of transportation network & design on freight rates &
inter state tax. The result of linear regression analysis (Table
3) enticed that the correlation between predictor and outcome
is positive with value of R as .923, which signifies good
correlation between predictor and the outcome. In the model
1, R is .923 which indicates 92% association between
dependent and independent variable. R-Square for this model
is .845 which means that 84% of variation in transportation
management can be explained from the independent variable.
Adjusted R square (.743) indicates that if anytime another
independent variable is added to model, the R-square will
increase. Further beta value reveals significant relationship of
independent variable with dependent variable. Change in R
square is also found to be significant with F-values
significant at 5% confidence level. Thus the hypothesis
“Transportation network and design is influenced by freight
rates and inter state tax” is accepted as represented by its
significance level p < .05.
Table 3 – Regression Model Summary
Std. Error
F value
Model
R
R
AdjustedR2
of Estimate
ANOVA
1.
.923
.845
.743
.2012
61.109
a. Predictors: (Constant), Freight rates & inter state tax
b. Dependent Variable: Transportation network & design
2
Table 4 shows output from regression analysis to elicit
the impact of transportation network & design on supplier
scheduling. The result of linear regression analysis (Table 4)
enticed that the correlation between predictor and outcome is
positive with value of R as .903, which signifies good
correlation between predictor and the outcome. In the model
1, R is .903 which indicates 90% association between
dependent and independent variable. R-Square for this model
is .802 which means that 80% of variation in transportation
management can be explained from the independent variable.
Table 4 – Regression Model Summary
Std. Error
F value
Model
R
R
AdjustedR2
of Estimate
ANOVA
1.
.903
.802
.755
.2111
63.792
a. Predictors: (Constant), Supplier scheduling
b. Dependent Variable: Transportation network & design
To test the fourth and last hypothesis, the qualification of the
respondents was classified into five categories viz., Metric,
Higher secondary, Graduate, Post graduate and others
(Technical courses etc). The results of ANOVA (Table 5)
Description of Qualification
Metric
Higher Sec.
Graduate
Post Graduate
Others
The study provides support to the existing literature and a
new face to the benefits accrued to wholesalers with regard to
transportation management. Proper transportation network &
design is recognized as a vital tool in meeting demands,
targeting customers & positioning products in diverse
markets. The present study findings supports all the
formulated hypotheses and reveals that effective
transportation network and design is influenced by freight
rates and inters state tax and transportation network & design
is affected by supplier scheduling. Further, ANOVA table
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t
.239
15.291
Sig.
level
.000
Sig.
level
.000
β
t
.213
17.126
Sig.
level
.000
revealed that wholesalers with different qualification level
differ significantly with regard to adoption of transportation
network and design (Sig. 0.009) as the p value is less than
.05. Therefore, the last hypothesis is also accepted.
Table 5 – ANOVA for Qualification
Nature of Variable
Sum of Squares
df
Between Groups
11.577
4
Within Groups
10.855
123
Total
21.432
127
VI. CONCLUSION
β
Adjusted R square (.755) indicates that if anytime another
independent variable is added to model, the R-square will
increase. Further beta value reveals significant relationship of
independent variable with dependent variable. Change in R
square is also found to be significant with F-values
significant at 5% confidence level. Thus the hypothesis
“Transportation network and design is affected by supplier
scheduling” is accepted as represented by its significance
level p < .05.
2
5.4. One Way ANOVA
Sig.
level
.000
Mean Square
.394
.278
F
13.477
Sig.
.007
exposed that wholesalers with different qualification level
differ significantly with regard to adoption of transportation
network and design. From the practical perspective, the
government functionaries must take initiatives to organise
trade shows, seminars, workshops, conferences to strengthen
supply chain linkages by integrating fragmented Supply chain
intermediaries with the assistance of wholesalers. Sensitizing
wholesalers through periodic training & education
programmes the need & strategies for profitable
transportation management so that effectuate transportation
decisions can be taken in order to equilibrium demand and
supply.
© 2013 | Published by The Standard International Journals (The SIJ)
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The SIJ Transactions on Industrial, Financial & Business Management (IFBM), Vol. 1, No. 2, May-June 2013
6.1. Limitations of the Study


[7]
The study is area specific and cannot be generalized
for other wholesalers operating in other parts of the
country having dissimilar environmental business
conditions.
The conclusions drawn were not completely free from
biasness for the responses obtained from the different
retailers through surveys. Meaning and concepts of all
scale items was explained to the respondents in local
dialect as majority of them were neo-literate. Though
utmost care was taken to entice correct information, an
element of subjectivity cannot be ruled out as it made
little difference in the originality of ideas obtained in
the field survey and final interpretation.
6.2. Directions for Future Research
Some of the dimensions like quality management, shared
goals & objectives, SCM ethics, warehousing management,
commitment & collaboration, transparency etc. are not
included in the existing study. So, future research can be done
taking into consideration the wholesalers‟ attitude towards
the above dimensions. Future researches can also be
undertaken regarding transportation management from the
perspective of wholesalers and retailers for medium & large
scale industries.
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
L.A. Gordon & Narayanan (1984), “Management Accounting
Systems, Perceived Environmental Uncertainty and
Organisational Structure: An Empirical Investigation”,
Accounting, Organisations and Society, Vol. 19, No. 1, Pp.
330-348.
P.W.G. Newman & J.R. Kenworthy (1989), “Gasoline
Consumption and Cities: A Comparison of United-States Cities
With a Global Survey”, Journal of the American Planning
Association, Vol. 55, Pp. 24-37.
J. Holtzclaw (1990), “Explaining Urban Density and Transit
Impacts on Land Use”, San Francisco, CA, The Sierra Club.
R. Knowles (1993), “Research Agendas in Transport
Geography for the 1990s”, Journal of Transport Geography,
Vol. 1, Pp. 3-11.
R. Cervero (1994), “Rail-Oriented Office Development in
California: How Successful?”, Transportation Quarterly, Vol.
48, Pp. 33-44.
D.M. Levinson & A. Kumar (1994), “The Rational Locator:
Why Travel Times Have Remained Stable”, Journal of the
American Planning Association, Vol. 60, Pp. 319-333.
ISSN: 2321 – 242X
[17]
[18]
[19]
[20]
[21]
[22]
[23]
J.F. Hair, R.E. Anderson, R.L. Tatham & W.C. Black (1995),
“Multivariate Data Analysis”, New Jersey: Prentice Hall, Pp.
87-115.
R.S. Tolley & B.J. Turton (1995), “Transport Systems, Policy
and Planning: A Geographical Approach”, Harlow, UK,
Longman Scientific.
J. Carter & B. Ferrin (1995), “The Impact of Transportation
Costs on Supply Chain Management”, Journal of Business
Logistics, Vol. 16, Pp. 189-211.
R. Cervero (1996), “Mixed Land Uses and Commuting:
Evidence from the American Housing Survey”, Transportation
Research A, Vol. 30, Pp. 361-377.
P. Schimek (1996), “Household Motor Vehicle Ownership and
Use: How Much Does Residential Density Matter?”,
Transportation Research Record, No. 1552, Pp. 120-125.
T. Messenger & R. Ewing (1996), “Transit Oriented
Development in the Sunbelt”, Transportation Research Record,
No. 1552, Pp. 145-452.
G.G. Dess, G.T. Lumpkin & J.G. Covin (1997),
“Entrepreneurial Strategy Making and Firm Performance: Tests
of Contingency and Configurational Models”, Strategic
Management Journal, Vol. 18, No. 9, Pp. 677-695.
R. Cervero & Kockelman (1997), “Travel Demand and the 3ds:
Density, Diversity and Design”, Transportation Research D,
Vol. 2, Pp. 199-219.
D. Banister, S. Watson & C. Wood (1997), “Sustainable Cities:
Transport, Energy and Urban Form”, Environment and
Planning B, Vol. 24, Pp. 125–143.
H.R. Barrett, B.W. Ilbery, A.W. Brown & T. Binns (1999),
“Globalization and the Changing Networks of Food Supply:
The Importation of Fresh Horticultural Produce from Kenya
into the UK”, Transactions of the Institute of British
Geographers, NS 24, Pp. 159-174.
A.P. Field (2000), “Discovering Statistics using SPSS for
Windows”, London, Sage Publications, Pp. 666-679.
W.R. Black (2001), “An Unpopular Essay on Transportation”,
Journal of Transport Geography, Vol. 9, Pp. 1-11.
S. Glaister & D. Graham (2002), “The Demand for Automobile
Fuel: A Survey of Elasticities”, Journal of Transportation
Economics and Policy, Vol. 36, Pp. 1-25.
G. Giuliano & D. Narayan (2003), “Another Look at Travel
Patterns and Urban Form: The US and Great Britain”, Urban
Studies, Vol. 40, Pp. 2295-2312.
W.R. Black (2003), “Transportation: A Geographical
Analysis”, New York, Guilford.
R. Ewing, R. Pendall & D. Chen (2003), “Measuring Sprawl
and its Transportation Impacts”, Transportation Research
Record, No. 1831, Pp. 175-183.
A.P. Field (2004), “Discovering Statistics using SPSS for
Windows”, London, Sage Publications, Pp. 619-672.
Dr. Vipul Chalotra. Working as an Assistant Professor in
Department of Commerce, University of Jammu, Udhampur
Campus for one year i.e. Academic session 2012-2013. He got his
Ph.D., degree in 2012 from the University of Jammu.
© 2013 | Published by The Standard International Journals (The SIJ)
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