Research Methodology Last Final Document

8/6/2019 Research Methodology Last Final Document

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ASSIGNMENT REPORT ON:-

EFFICIENCY ANALYSIS OF GDGWI BUS SERVICES

BY:-

HEMLATA

MOHIT PRABHKAR

PEARL DHINGRA

RAJAT OSTWAL

PGDBM Section-A

Under the guidance of

Dr.Suneel arora

Dr. Suneel sharma

2009-2011


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TABLE OF CONTENTS

CONTENTS PAGE NO.

Introduction 3

Title 4

Literature Review 5-9

Research Objectives

Research Methodology

Analysis and Interpretation

Conclusions and Recommendation

Bibliography

Questionnaire


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Introduction

Transport services in an education institution are an integral part of the overall experience of the

students. Even though, these services are primarily meant for the students, college faculty as wellas support staff avail these services. The efficiency of such services, are extremely important in

enriching the overall experience.

In this research report we are attempting to infer the efficiency of the GDGWI bus services. The

G.D. Goenka World School is operating on the same campus as the G.D. Goenka World

institute. There already a very effective system in place for operating the bus services for the

school division of the campus. Up to a 1000 thousand in the school use this service. Which is a

good indicator as to the requirement of a well thought out system in place.

The G.D. Goenka World Institute, has started this year (2009) and is still in process of getting the

system together. In our report we will examine the various factors, which indicate the level of

satisfaction students and other passengers are experiencing. On basis of these, we would try not

only suggest changes in the existing setup but also propose the groundwork for a new system.

The campus boasts an infrastructure worthy of international recognition. The overall

environment is very well thought out, to provide an ideal on-campus experience for the students.

On these basis, the immediate recommendations for the bus services may also stem from

examples of campus facilities experienced by students in the western world. Although practical

implementation of such suggestions would be difficult, the best of these suggestions can be

seriously considered to increase the overall performance of the bus service.


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Immediate Problems

There are some very peculiar problems that are very prominent and thus require our attention

before we describe our problem statement and objectives.

1. LocationAlthough the location is ideal for a campus of this stature, from the perspective of an

Indian environment, it is a factor, which very greatly affects the efficiency of the bus

service provided by the institute.

The campus is located on the road to Sohna city, approximately 25 km away from the

Delhi/NCR students. Thus, the minimum distance is comfortable manageable t ill the

Gurgaon area, beyond which, it becomes a challenge for the management.

2. Student Strength

This being the first year of the institute, it is certain that the bus service will incur a loss.

From a somewhat informal inquiry we have learned that over the past year, out of an

approximate total strength of the 290 students only, 90-100 was the highest number in

terms of bus users, and currently this number is close to 40.

These numbers are only meant to highlight the strength of students using the buses and

by no means is an indication of the facilities. The true scenario will be better observed

over a period of 3-4 years when all batches are simultaneously running with a full

strength.

This is not only a problem for the management, but also provides us a very small number

for the sample size for our primary data. With roughly 40 students availing the bus

service, the scope for accurate predictions for immediate changes becomes very difficult.

3. Route Safety

The route leading to the campus beyond the Gurgaon area is considered unsafe. The last

25 kms of the journey, the route is secluded, no availability of public services as basic as

safe public transport. The condition of the roads is below appropriate standards as well.

The conditions of the road although, still extremely poor and unsafe, drastic changes have

been seen over the past 6 months or so. It would be safe to assume better conditions of

the roads till the next batch comes in next year.

4. The Indian Perspective

From initial interviews of day boarders on campus, we have realized the difference in

perception of the students in India and the western world.

We add this as an immediate concern because we have received some negative feedback

on the quality of the buses. We believed at the start of our research that GDGWI offered

the best buses to the students as compared to other institutes.


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Besides, the population comprising the student body of the current batch does not reflect

the true opinion of students from different financial backgrounds, hence the unexpected

attitude towards bus experience.

It is our deduction that our analysis would definitely disrupt our presumptions about how

the students have perceived the bus facilities.

Research Objectives

1. To identify the possible reasons of student drop-out at GDGWI.

2. To determine various factors responsible for setting up an Effective Bus System in

GDGWI.

3. To arrive at the set of practical guidelines for Improvement in existing bus system at

GDGWI.

Literature Review

1. Title of Survey: The Application of GIS in Education Administration: Protecting Students

from Hazardous Roads

Name of Researchers:

1. Fatemah Admadi Nejad Masouleh

Department of Geoenvironmental

Science

University of Tsukuba

2. Todd Wendell RhoDess

Department of Political ScienceThe Ohio State University

3. Yuji Murayama

Department of Geoenvironmental

Science


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The major social obligations later concluded as O pportunity Cost, borne by SRTUs

which are often discussed in road transport sector are:

y Students Concessions

y Interior rural operations

y Unviable urban schedules

3. Issues when applied to SRUS in terms of capital charge and earnings.

Finally, the paper was concluded with analysis which proved that EVA reflects better

performance of SRTUs than accounting profit, in addition to positive EVA for SRTUs under all

alternatives for the six years considered.

The efficiency of services such as a bus service cant really be judged on the basis of financial

success. The purpose of a college bus service is not to make profits but to provide a comfortable

means of commuting to and from college. The EVA system is a good way to devise a system

wherein factors used to determine performance are focused on the overall bus experience and not

just the financial aspect. Using this as basis we can consider factors like comfort of travel,

overall bus quality, staff etiquette etc. to judge the overall traveling experience of the

passengers.

3. Title of Survey: Local Colleges and the Demand for Higher Education: The Enrollment

Inducing Effects of Location.

Name of Researchers: HOWARD P. TUCKMAN, The author is an assistant professor in the

Department of Economics and a Research Associate in the Institute for Social Research, Florida

State University.

Findings: - The paper studies the effect of distance on the demand for a college. It also analysed

the contradictory findings based upon previous studies which states that the distance from


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college to a students home is inversely related to the demand for college, by Corcoran and

Keller and Russell and Richardson and in another study Sewell and Fenske proves that college

demand is unrelated to distance.

The paper is built up on the estimates of the savings obtainable if a student lives at home and

commutes to school. Comparison of the savings to estimates of the price responsiveness of

college enrolments gave a grounding to the study.

The research declared that proximity of junior college do contribute to augmented enrolments in

institutions of higher education and an increase in proportion of college bound students

choosing to attend a junior class.

We have listed the location as one of the immediate problems, which we assumed to be a deal

breaker for students. This study argues otherwise, and it is true in a way. In spite of the location,

GDGWI, in its first year is seeing nearly 100% enrollment in all streams. The standard of

education offered by an institute does outweigh its minor flaws. Having appreciated the standard

of education offered at GDGWI, it also becomes the responsibility of the management to make

an effort to reduce any problems students might be having because of minor problems like the

institutes bus service.

4. Title of Survey: Encouraging Commuter Student Connectivity.

Name of Researchers: Barbara D. Davis,the University of Memphis, Tennessee

Year of Preparing: 1999

Findings: - On the basis of previous studies, The report starts with analyzing the question; why

commuters are not able to avail the existing opportunities present at college and fail to enjoy the

complete college experience ?

Understanding the problem to be lack of connection to the class, because they usually get to

class right at class time and leave as soon as the class ends. The report concludes by providing

Activity and guidelines to enhance the involvement of commuters in college.


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From our observations and informal interviews with students, we have learned that students who

commute everyday miss out on a lot of opportunities to interact with other students. The

interaction, does not necessarily mean socializing but includes working with peers, attending

classes, extracurricular activities etc. In todays modern day courses, there is hardly any course

which does not involve group assignments, students commuting from far distances make co-

ordination among the group members difficult. Besides these aspects are all a part of the college

experience. This study in a way argues merits of commuting against taking accommodation on

campus. Since, affording accommodation on campus can be a problem for students, the bus

service should be effective to make sure that the students make most of their time in college

without the worry of travel.

5. Title of Survey: Transportation's Future in the Universities

Name of Researchers: ROY J. SAMPSON, Mr Sampton is an Associate Professor of

Transportation, School of Business Administration, University of Oregon, Eugene, Oregon.

Year of Preparing: 1963

Findings: - The journal talks about the lack of enthusiasm of students to study the transportation

curriculum. It emphasized on the importance of transport studies for nation as whole.

Transportation is one of the vital parts behind every nations success.

The study ends with predicting the increase number of enrolment in the transport curriculum in

coming years.

This study highlights the importance of studies in the field of public transport. Lack of

enthusiasm among students to explore this subject is not a very positive indication for the future

where efficient transport services are going to be extremely important. It is debatable to judge the

merits of exploring this subject further, but every little idea helps in making noticeable changes

over a period of time. In todays constantly changing environment, when changes in technology

are seen everywhere, technological changes to help make public transport more efficient are


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always welcome. For the purpose of our study, the lack of research in this area, provides a strong

case for lack of directly relevant literature for our research.

Research Design

A well thought out research design is very important for a successful research. All the steps

involved should be systematically planned out so that all steps can be carried out smoothly. A

sound research design mentions the following aspects very clearly:-

a) Problem Statement

b) Procedures and Techniques for gathering information

c) Population for gathering information

d) Techniques used for analysis

PROBLEM STATEMENT

The purpose of this study is to deduce the factors behind the more than 50% dropouts of students

availing bus services of GD Goenka World Institute within 6 months.

RESEARCH METHODOLOGY

We plan to use following to carry out our research successfully:

DATA :

Primary Data-

To collect the required data, questionnaires are formed for Students, Staff and Transport

Manager of GDGWI respectively.

Semi-structured Interviews was conducted to gather the relevant data.

Observation of daily activities of GDGWI bus services and students response was witnessed

regularly.


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FACTOR ANALYSIS:

Factor analysis is used to reduce the data inorder to get a small set of variables from a large set of

variables.

Secondly, to obtain indexes with variables that measures similar things(conceptually)

We have used EXPLORATORY Factor Analysis wherein we attempt to reveal the underlying

structure of a relatively large set of variables. Our prior assumption is that any indicator may be

associated with any factor.

OUTPUT

Factor Analysis

Notes

Output Created 04-Apr-2010 18:15:49

Comments

Input Data C:\Users\Kd !\Documents\final mohit

and pearl data.spv.sav

Active Dataset DataSet2

Filter

Weight

Split File

N of Rows in Working Data

File

45

Missing Value Handling Definition of Missing MISSING=EXCLUDE: User-defined

missing values are treated as missing.

Cases Used MEAN SUBSTITUTION: For each

variable used, missing values are

replaced with the variable mean.


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Syntax FACTOR

/VARIABLES Quality Punctuality

Etiquette comfort safety opinion

/MISSING MEANSUB

/ANALYSIS Quality Punctuality

Etiquette comfort safety opinion

/PRINT INITIAL CORRELATION

KMO EXTRACTION ROTATION

FSCORE

/FORMAT BLANK(.10)

/PLOT EIGEN

/CRITERIA MINEIGEN(1)

ITERATE(25)

/EXTRACTION PC

/CRITERIA ITERATE(25)

/ROTATION VARIMAX

/SAVE REG(ALL)

/METHOD=CORRELATION.

Resources Processor Time 00:00:00.452

Elapsed Time 00:00:00.485

Maximum Memory Required 5928 (5.789K) bytes

Variables Created FAC1_3 Component score 1

FAC2_3 Component score 2

FAC3_3 Component score 3


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Correlation Matrix

Quality Punctuality Etiquette comfort safety opinion

Correlation Quality 1.000 .303 .071 .441 -.277 -.192

Punctuality .303 1.000 -.198 .539 -.205 .080

Etiquette .071 -.198 1.000 -.173 -.362 .026

comfort .441 .539 -.173 1.000 -.279 -.336

safety -.277 -.205 -.362 -.279 1.000 .313

opinion -.192 .080 .026 -.336 .313 1.000

ANALYSIS INTERPRETATION:

Correlation Matrix measures how suitable is data for Factor Analysis. In the above data as we

can see that we have values like .303.441,.539, etc proves the suitability of data for factor

analysis.

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .532

Bartlett's Test of Sphericity Approx. Chi-Square 53.972

Df 15

Sig. .000

ANALYSIS INTERPRETATION: Kaiser-Meyer-Olkin Measure of Sampling Adequacy :

.532 and Significance:0 approves the Validation of the study.


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Communalities

Initial Extraction

Quality 1.000 .511

Punctuality 1.000 .813

Etiquette 1.000 .855

Comfort 1.000 .774

Safety 1.000 .714

Opinion 1.000 .930

Extraction Method: Principal

Component Analysis.

ANALYSIS

INTERPRETATION:

Communalities are defined as the proportion of its variance explained by the extracted factors.As

above communalities are greater than .5, this proves that extracted factors explain most of the

variance used in the variable being analyzed.

Total Variance Explained

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

dimension0

1 2.172 36.193 36.193 2.172 36.193 36.193

2 1.414 23.565 59.759 1.414 23.565 59.759

3 1.012 16.864 76.623 1.012 16.864 76.623

4 .677 11.291 87.915


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5 .421 7.015 94.929

6 .304 5.071 100.000

Extraction Method: Principal Component Analysis.

ANALYSIS INTERPRETATION: Eigen Value: indicates overall strength of relationship

between a factor and variables. According to Keisler, Eigen value should > 1 or should be

dropped. In the above data, Eigen values are more than 1 for Component 1, 2 and 3,therefore

other components indicates weak relationship between a factor and variables.

Total Variance Explained

Component Rotation Sums of Squared Loadings

Total % of Variance Cumulative %

dimension0

1 1.968 32.807 32.807

2 1.385 23.085 55.891

3 1.244 20.732 76.623

4

5

6


ANALYSIS INTERPRETATION: Three factors

explain 76.623% of variance in the items. Hence,

only three components are significant for the

analysis.


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ANALYSIS INTERPRETATION: Scree plot is a line graph of E Values which indicates the

amount of variance explained by each factor.

In this case, Inflexion point is generated at 2nd

component.


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Rotated Component Matrixa

Component

1 2 3

Quality .642 .259 -.177

Punctuality .862 -.103 .242

Etiquette -.186 .899 .110

Comfort .803 -.350

Safety -.358 -.699 .311

Opinion .960


Rotation Method: Varimax with Kaiser

Normalization.

a. Rotation converged in 4 iterations.


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Component Score Coefficient Matrix

Component

1 2 3

Quality .315 .163 -.018

Punctuality .508 -.058 .342

Etiquette -.094 .681 .173

Comfort .378 -.116 -.184

Safety -.134 -.476 .130

Opinion .121 .078 .823


Rotation Method: Varimax with Kaiser

Normalization.

Component Scores.


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DISCRIMINANT ANALYSIS:

Discriminant analysis is used for analyzing data when the criterion or dependant

variable is categorical and the predictor or independent variables are interval in

nature.

WHY MULTIPLE DISCRIMINANT ANALYSIS??

It studies the differences between groups on the basis of the attributes of the cases

bringing which attributes contribute most to group separation.

Notes

Output Created 06-Apr-2010 03:07:35

Comments

Input Data

Active Dataset DataSet15

Filter

Weight

Split File

N of Rows in Working Data

File

45

Missing Value Handling Definition of Missing User-defined missing values are

treated as missing in the analysis

phase.


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Cases Used In the analysis phase, cases with no

user- or system-missing values for any

predictor variable are used. Cases with

user-, system-missing, or out-of-range

values for the grouping variable are

always excluded.

Resources Processor Time 00:00:01.419

Elapsed Time 00:00:13.599

Variables Created or

Modified

Dis_2 Predicted Group for Analysis 1

Dis1_2 Discriminant Scores from Function 1

for Analysis 1

Number of unweighted cases written to the working file

after classification

45

Analysis Case Processing Summary

Unweighted Cases N Percent

Valid 45 100.0

Excluded Missing or out-of-range

group codes

0 .0

At least one missing

discriminating variable

0 .0

Both missing or out-of-range

group codes and at least

one missing discriminating

variable

0 .0

Total 0 .0

Total 45 100.0


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Group Statistics

Dependant

Mean Std. Deviation

Valid N (listwise)

Unweighted Weighted

Yes Program 1.7778 .84732 27 27.000

time 1.8889 .64051 27 27.000

Discontinue 2.4074 1.73780 27 27.000

hostelmove 3.7037 .54171 27 27.000

No Program 1.5000 .70711 18 18.000

time 1.2778 .46089 18 18.000

Discontinue 1.0556 .87260 18 18.000

hostelmove 3.5000 1.04319 18 18.000

Total Program 1.6667 .79772 45 45.000

time 1.6444 .64511 45 45.000

Discontinue 1.8667 1.58974 45 45.000

hostelmove 3.6222 .77720 45 45.000

ANALYSIS INTERPRETATION:Group statistics table examines whether there are anysignificant differences between groups on each of the independent variables using group

means.By inspecting we can say that Hostel move can be one of the important discriminator.


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Wilks' Lambda F df1 df2 Sig.

Program .970 1.319 1 43 .257

Time .780 12.147 1 43 .001

Discontinue .823 9.279 1 43 .004

hostelmove .983 .737 1 43 .395

ANALYSIS INTERPRETATION:High value of f is produced by time and discontinuation

of bus services which further suggests that they are good discriminators.

Pooled Within-Groups Matrices

Program time Discontinue hostelmove

Correlation Program 1.000 .042 .079 .065

time .042 1.000 -.112 -.279

Discontinue .079 -.112 1.000 .568

hostelmove .065 -.279 .568 1.000


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Analysis 1

Box's Test of Equality ofCovariance Matrices

Log Determinants

Dependant

Rank

Log

Determinant

Yes 4 -2.298

No 4 -4.289

Pooled within-groups 4 -1.789

The ranks and natural logarithms of determinants printed

are those of the group covariance matrices.

ANALYSIS INTERPRETATION: Log

determinants and Boxs M tables basicassumption for DA is that the variances-co-

variance matrices are equivalent.In this case, the log determinants appear similar

which proves this test to be not to be significantso that null hypothesis that the group do not differ

can retained.


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Test Results

Box's M 55.720

F Approx. 4.972

df1 10

df2 6195.276

Sig. .000

Tests null hypothesis of equal

population covariance matrices.

Summary ofCanonical Discriminant Functions

Eigenvalues

Function

Eigenvalue % of Variance Cumulative %

Canonical

Correlation

dimension0 1 .573a 100.0 100.0 .603


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Eigenvalues

Function

Eigenvalue % of Variance Cumulative %

Canonical

Correlation

dimension0 1 .573a 100.0 100.0 .603

a. First 1 canonical discriminant functions were used in the analysis.

ANALYSIS INTERPRETATION:In this case, a canonicalcorrelation of .603 signifies that 36.36% of the variation in the

grouping variable, i.e whether a respondent uses or not the busservices.

Wilks' Lambda

Test of Function(s) Wilks' Lambda Chi-square df Sig.

dimension0

1 .636 18.564 4 .001

ANALYSIS INTERPRETATION Wilks lambda signifies the importance of the discriminantfunction. In this case, it signifies that 63.6% is unexplained.


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Standardized Canonical

Discriminant Function

Coefficients

Function

1

Program .145

Time .769

Discontinue .699

hostelmove -.019

ANALYSIS INTERPRETATION : Time is the strongest predictor while Discontinue was nextin importance as a predictor as they have larger coefficients.

Structure Matrix

Function

1

Time .702

Discontinue .614

Program .231

hostelmove .173


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Structure Matrix

Function

1

Time .702

Discontinue .614

Program .231

hostelmove .173

Pooled within-groups

correlations between

discriminating variables

and standardized canonical

discriminant functions

Variables ordered by

absolute size of correlation

within function.

Canonical Discriminant

Function Coefficients

Function

1

Program .182

Time 1.335

Discontinue .480

hostelmove -.024

(Constant) -3.307

Unstandardized

coefficients


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Functions at Group

Centroids

Dependant Function

1

dimension0

yes .604

no -.906

Unstandardized canonical

discriminant functions

evaluated at group means

Group centroids tableA further way of interpreting discriminant analysis results is to describe each group in

terms of its profi le, using the group means of the predictor variables. These group meansare called centroids. These are displayed in the Group Centroids table In our

example, still using the bus services have a mean of .604 while not using bus service produce amean of 906.

Cases with scores near to a centroid are predicted as belonging to that group.


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Classification Statistics

Classification Processing Summary

Processed 45

Excluded Missing or out-of-range

group codes

0

At least one missing

discriminating variable

0

Used in Output 45

Prior Probabilities forGroups

Dependant

Prior

Cases Used in Analysis

Unweighted Weighted

dimension0

yes .600 27 27.000

No .400 18 18.000

Total 1.000 45 45.000

Separate-Groups Graphs


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Alternate Hypothesis

Another approach we havent been able to pursue for our research is due to the lack of time. We

suggest this method for any future studies related to this topic. This proposed method can be

used as performance parameter for evaluating bus services other beyond the scope of university

campuses.

We propose that pick-up points of all passengers be recorded and plotted on network diagrams.

Network diagrams would give an indication to the optimum paths from these locations to the

destination. Various simple tools such as google maps can be used to measure these distances.

In addition to google maps there are also other GPS based services like map-quest can also be

used to check validity of value of distances. Once these distances have been calculated, we could

implement the likert scale based satisfaction evaluation we have implemented in our study. This

analysis will give levels of satisfaction of customers across different locations and differentiation

can be made on satisfaction levels across the different routes.

This approach for judging the performance of a bus service explores the performance at a deeper

level. The results from this approach can help the researchers to focus more closely on the low

satisfaction routes to come up with more effective recommendations to improve the service.

Students across different routes can have different opinions on aspects of the overall traveling

experience. This method allows incorporation of suggestions at a much closer level to each

passenger.


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Observations

In our research we have focused on the studying the levels of satisfaction received by passengers

using the college bus service. Through this weve have which factors are affecting the change in

the dependent variable. This study has given us insight into which areas can be targeted for

immediate changes to see results.

Quality of the bus service for instance is a strong factor, which affects the overall opinion of a

passenger in favor the using the bus service. Safety, however does not rate as highly to be

considered a strong enough factor.

During the study, it would seem that all factors chosen as parameters would result out to be

significantly important in shaping the opinion of the passenger but surprisingly from our

findings, we learn that across a sample size different people have strongly different views on a

simple concept of a university bus service.

Alternate hypothesis

There is another aspect we have left unexplored in our research due to lack of time, which we

feel deserves mentioning. To calculate the levels of satisfaction of consumers


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Recommendations for proposed model

y The strongest factor being the bus quality, it is our suggestion is the use of the low-floor

buses which are available with the college. These buses are more comfortable and more

fuel efficient also.

y Steps need to be taken to reduce the average travel time of the passengers. The routes

taken by the buses need to be redesigned so as to avoid redundancy of pickup points, i.e.

more than 1 bus should not be running on the same route.

y If reduction of travel time is difficult, more value should be added to the bus experience

so that students can make use of the travel time beneficially. Placing TV screens in buses,

showing selected channels only can be a way to spend time constructively.

y Changes in technology allow setting up of wireless internet on the buses. 3G services are

soon going to be a reality in India and management can look into the option of providing

students wireless internet on the bus to more productively use their travel time. Wastage

of time in traveling is a major concern shared by a majority of daily commuters. This

way they can work on assignments or projects.

y A very efficient but traditional way to measure efficiency is the financial performance of

the bus service. One suggestion to improve the financial situation is to suggest another

perspective to look at the way the bus network can be used. The management of G.D.

Goenka has schools in three locations in and around the Delhi/NCR region, namely,

Vasant Kunj, Rohini, Faridabad and Ghaziabad. We have data to support a substantial

numbers of students from and around these areas. The buses which are used to pick these

students can be parked in these institutions are run from these locations themselves. This

is a sense has 2 major advantages:

a) Saving on fuel expenditure

If this idea is implemented the routes of the buses are essentially halved and thus

saves almost 50% on the fuel expenses. This should give an immediate boost to the

performance.

b) Timings

Punctuality is also a factor of some importance for passengers of the bus service. If

the buses are starting from locations(delhi/ncr) nearer to the students residences they


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REFERENCES AND BIBLIOGRAPHY

1. Fatemah Admadi Nejad Masouleh, Todd Wendell RhoDess, Yuji Murayama,2009. The

Application of GIS in Education Administration: Protecting Students from Hazardous

Roads,[Online].

Available at:

http://web.ebscohost.com.ezproxy.lancs.ac.uk/ehost/detail?vid=1&hid=106&sid=ba308b57-

a5b2-40a0-ad38-

12b0d03ba178@sessionmgr112&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=buh&AN=3

6983055

2. G. Ramesh and T.V. Ramanayya,2007. Economic contribution of Public Passenger

Transportation Organisations An application of EVAR METHODOLOGY.

[Online]

Available at:

http://web.ebscohost.com.ezproxy.lancs.ac.uk/ehost/detail?vid=1&hid=106&sid=8245750e-

51df-4d7a-a826-

f4b81fd598b5@sessionmgr112&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=buh&AN=27

024945

3. Howard P. Tuckman, Local Colleges and the Demand for Higher Education: The

Enrollment Inducing Effects of Location.[Online]

Available at:http://web.ebscohost.com.ezproxy.lancs.ac.uk/ehost/detail?vid=1&hid=106&sid=fcee0975-

cbaa-4a5c-9257-

152a54d24160@sessionmgr111&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=buh&AN=4

511897

4. Barbara D. Davis, the University of Memphis, Tennessee,1999. Encouraging Commuter

Student Connectivity.[Online]

Available at:

http://web.ebscohost.com.ezproxy.lancs.ac.uk/ehost/detail?vid=1&hid=106&sid=6d60141e-

e6a1-408a-ad01-

a9d58a8620bc@sessionmgr104&bdata=JnNpdGU9ZWhvc3QtbGl2ZQ%3d%3d#db=buh&AN=21

69536


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Questionnaire

Efficiency of GDGWI Bus system. Please fill the following questionnaire to help us suggest improvements

in institutes bus service.

1. Name (optional)

2. Program

BBA PGDBM Msc.

3. Address

4. Pick-up Point

5. Travel time

Less than 1 hour 1-2 Hours More than 2 hours

6. Still using bus service?

Yes No

7. When did you discontinue the bus service?

a) August - October

b) October- December

c) January- March

8. When did you move into the college hostel?

a) August- October

b) October - Decemberc) January-March


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Rate the following from questions for overall bus experience

9. Bus quality

Very Satisfied Satisfied Neither Dissatisfied Very Dissatisfied

10.Punctuality

Very Satisfied Satisfied Neither Dissatified Very Dissatified

11.Staff Etiquette


12.Comfort of Travel


13.Route Safety

Very safe safe Neutral unsafe Very unsafe

14.Are you considering leaving the bus service

Strongly Considering mildly conidering cant say Not considering Continuing