2012. Vol.3, Special Issue, 1070-107 8
Published Online October 2012 in SciRes (http://www.SciRP.org/journal/ce) http://dx.doi.org/10.4236/ce.2012.326161
Copyright © 2012 SciRes.
School Choice of Computing Students: A Comparative
Perspective from Two Universities
Rex P. Bringula1, Ma. Ymelda C. Batalla1, Shirley D. Moraga1,
Lester Dave R. Ochengco1, Kyle N. Oha gan1, Rolando R. Lansigan2
1College of Computer Studies and Sy stems, Uni versity of the East, Manila, Philippines
2College of Computer Studies, National University, Manila, Philippines
Email: rex_ b ringula@yahoo. com
Received July 18th, 2012; revised August 20 th, 2012; accepted August 30th, 2012
This descriptive study utilized a validated questionnaire to determine the profile of two sets of students
and their level of consideration in deciding to enroll in their University. It also determined whether their
level of consideration in deciding to enroll in their University significantly differed from each other. It
was found out that most of the University of the East (UE) and National University (NU) respondents
were male respondents taking up Information Technology. They did not have a home province, lived in
Manila and Quezon City, lived in family-owned houses, belonged to a family with five family members,
and travelled at least an hour in going to school through jeepneys. On the other hand, they were different
in terms of family monthly income (most of the UE respondents belonged to a family with a higher family
monthly income) and number of family members who studied in the University (most of the NU respon-
dents had at least one member who studied in the same university). It was also noted that more than a
quarter of NU respondents lived near their school. UE and NU respondents agreed that they considered
nine and five, respectively, of the eleven institutional image indicators in deciding to enroll in the univer-
sity. UE respondents had the highest consideration on admission process and course offering while NU
respondents had the highest consideration on scholarships and grants. Test of difference between means
revealed that the level of considerations of the respondents on the institutional image indicators signifi-
cantly differed in nine out of the eleven indicators. Thus, the null hypothesis stating that there is no sig-
nificant difference in the level of consideration of the respondents in deciding to enroll in the two univer-
sities in terms of institutional image indicators is partially rejected. Conclusions, recommendations, and
limitations of the study were also discussed.
Keywords: Competition; Computing Students; Information Technology; Institutional Image; School
Choice; School Marketing
Education is regarded as a very important commodity. Such
importance has been viewed in individual and national levels.
In the individual level, a person can become confident, aware,
and active in the society (Ur Rehman et al., 2010; Vaduva et al.,
2011). In the national level, education plays an important role
in the socio-economic development of the country (Ur Rehman
et al., 2010). It can accelerate economic growth (Ur Rehman et
al., 2010), and develop the human resources skills (Ur Rehman
et al., 2010; Ud Din et al., 2011) of the nation. A nation with
uneducated people could be left behind in every field of life
(Bashir et al., 2011).
Higher education institutions (HEIs) are committed to serv-
ing these demands. However, HEIs all over the world are ex-
periencing financial problems in sustaining their daily opera-
tions. Banya & Elu (2001) reported that HEIs in Sub-Saharan
African lacked resources and needed multiple means of funding.
In Pakistan, lack of financial resource was accounted as one the
impediments in enhancing the quality of public higher educa-
tion (Khan & Iqbal, 2011). It was observed in the Middle East
and North Africa (MENA) countries that the demand for higher
education was increasing (Nahas, 2011). Along with this ob-
servation, HEIs in MENA countries, except Lebanon, were also
facing financial problems (Nahas, 2011). In Jordan, the non-
control over student tuition fees and student enrollment posed
funding constraints to HEIs (Kanaan et al., 2011). Universities
are striving to continue serving the demand for education
stimulated by demographic changes, technological innovations,
and a more competitive labor market environment at a time of
tight budgetary constraints (Fahim & Sami, 2011).
Private HEIs (PHEIs) are financed through gifts, grants, re-
search contracts (Leifner, 2003), but primarily through tuition
fees (Leifner, 2003; Ho & Wang, 2011; Kanaan et al., 2011;
Kabbani & Salloum, 2011). Tuition fees greatly depend on
student enrollment. A significant decrease in the student en-
rollment could greatly affect the finances of the schools.
Universities in the Philippines offering degree and non-de-
gree programs in computing (Information Technology, Com-
puter Science, Information Systems, and Associate in Computer
Technology) are also experiencing the same problem. This is
aggravated by the fact that the interest in a career in program-
ming is declining (De Raadt, 2004) and few women are at-
tracted to the field of computing (De Raadt, 2004; Tsagala &
Kordaki, 2007; Leiviskä & Siponen, 2010). Other problems
were related to the antisocial image of technology courses, the
cyclic nature of demand on the degree, and the notion of out-
R. P. BRINGULA ET AL.
sourcing of technology jobs overseas (Lenox et al., 2008). The
recent moratorium of the Commission of Higher Education
(CHED) of the Philippines on computing degree programs also
significantly affects the enrollment on the said degrees. Also,
the imminent outcomes on university enrollment of the imple-
mentation of K-12 program of the Philippine government will
also be felt in the future. As such, PHEIs are now actively in-
forming possible enrollees what they could offer. PHEIs are
now driven towards competition brought by these political and
Philippine HEIs are now employing marketing techniques
and business models to inform possible enrollees. However,
there is a dearth of studies on what students consider in decid-
ing to enroll in a school in Philippine setting. Though market-
ing strategies might work, the lack of solid and sound basis for
such strategies is apparent. As a consequence, school choice in
a local setting is almost unknown. Thus, this study was con-
Toward these goals, the study sought answers to the follow-
ing questions: 1) What is the profile of the freshmen in terms of
gender, degree program, home province, city home location,
type of house residence, household income, number of family
members, number of family members studying in the same
university, travel time from house to school, and mode of
transportation? 2) What is the level of consideration of the re-
spondents in deciding to enroll in the University in terms of
institutional image indicators, such as Tuition Fee, Tuition Fee
Payment Scheme, Admission Process, Schedule of Classes,
Course Offering, Facilities, Faculty Profile, Scholarships and
Grants, Kinship Patronage, Security in Campus, and Accessi-
bility? and 3) Is there a significant difference in the level of
consideration of the respondents in deciding to enroll in the two
Universities in terms of the institutional image indicators?
The paper was subdivided into five main sections in order to
answer the above questions. In Literature Review section, stud-
ies conducted related to this research were presented. This sec-
tion also served as basis in the formulation of the Research
Paradigm and Hypothesis of the study. The Methodology part
presented how data were analyzed and followed immediately
by the Findings and Discussion. The summary of t he study was
presented in Conclusions, Recommendations, Limitations, and
Literature shows that competition has benefits. Harrison
(2005a) and Novak (2006) commented that competition is a
powerful tool that can be used in the school sector to benefit the
consumers (i.e., students) and raise productivity. Competition
may contribute to improve the quality of education (Gabriel et
al., 2008; Bukowska & Siwinska-Gorzelak, 2011). School ad-
ministrators are compelled to make improvements and devel-
opments in their schools because of pressure from competitors
and the need to attract students (Harrison, 2005b; Duarte et al.,
2010). Most schools that cannot cope with the competition are
likely to be left behind in attracting students. Unsatisfied stu-
dents may drop out (Belanger et al., 2002) and may move to a
superior private school (Harrisson, 2005a). In other words,
schools in the different parts of the world are stimulated by
competition and are promoting choice among consumers
(Gabriel et al., 2008).
Szekeres (2010: p. 429) commented that “the climate of
competition for students in the higher education sector makes it
imperative that institutions increase their marketing efforts,
both domestically and internationally, to help sustain student
numbers.” This makes competition over possible enrollees
stiffer (Szekeres, 2010). They are now competing for students
(Duarte et al., 2010) in terms of not only their abilities and cog-
nitive skills but also their social background (Robert, 2010).
This resulted in applying a number of standard marketing prac-
tices in higher education institutions (Szekeres, 2010).
Universities are employing marketing strategies to inform
possible enrollees on what they could offer. It was found out
that successful recruitment strategies could attract students
(Kerstetter, 2011). Rizvi & Khan (2010) clarified that market-
ing in schools is not only about informing their clients or cus-
tomers, but also knowing that what is required by the clients or
customers thus ensuring that the schools give ultimately the
product and service they need while maintaining its quality.
Schools are maintaining and looking for ways to improve their
image (“the organization’s portrait made in the mind of a con-
sumer”) and reputation (“the degree of trust or distrust in an
organization’s ability to meet customers’ expectations on a
given attribute”) (Nguyen & LeBlanc, 2001: p. 305).
Many articles were written to determine what were consid-
ered by the students in choosing a school. Hu and Hossler
(2000) found out that tuition fee was directly related to the
preference of students in choosing a school. Fee, a component
of price, was one of the factors in choosing a school in England
(Maringe, 2006). The studies of Jackson (1980) and Pereda et al.
(2007) also had similar findings. Flexibility of tuition fee pay-
ment was also considered (Maringe, 2006). Salahuddin et al.
(2008) also confirmed through factor analysis that flexibility of
tuition fee payment was found to be one of the important fac-
tors in choosing a private university.
Students would apply in schools that would accept them
(Drewes & Michael, 2006). For example, one of the most im-
portant choice factors in choosing a school of Canadians in the
United States was grade requirements (McCarthy et al., 2012).
This shows that students considered the admission procedures
and policies of the school. This was found to be consistent in
the study of Sidin et al. (2003). Students also had consideration
for schedule of classes that allotted time for extra-curricular
activities (Parker et al., 2007), and offered face-to-face and
web-based study (Hagel & Shaw, 2008).
Kerstetter (2011) advocated that degree offerings of a school
should be one of the foci of marketing strategies. This was
found to be correct in the literature. Reddy (2011) found out
that academic offerings were one of the factors in choosing a
business school of Indian students. Canadian students studying
in the United States also considered this factor in choosing a
school (McCarthy et al., 2012). Meanwhile, Pauline (2010)
revealed that Lacrosse players of the National Collegiate Ath-
letic Association chose a college that could provide an aca-
demic program that would lead to career opportunities after
The overall impression of quality education was significantly
correlated (positively) with the quality and accessibility of the
IT facilities. It also predicted students’ satisfaction (Brewer &
Carnes, 2008; Nadiri et al., 2009). This factor was also sup-
ported by Shah and Nair (2010), and McCarthy et al., (2012).
Quality of teaching staff (Shah & and Nair, 2010; McCarthy et
al., 2012) was also an important choice consideration for the
students since the kind of teachers greatly affected the learning
Copyright © 2012 SciRes. 1071
R. P. BRINGULA ET AL.
outcomes of the students.
Scholarships and grants for financially challenged yet bright
students could also contribute to the institutional image of the
University. This factor was also found to influence school
choice (Pauline, 2010; Kerstetter, 2011; McCarthy et al., 2012).
Kinship patronage in the forms of recommendations of friends
and family members was also found to influence school choice
(McCarthy et al., 2012).
Students spent most of their time in school. In the study of
Warrington (2005 cited in Andre-Bechley, 2007), it was found
out that school location was important to working parents be-
cause they were concerned about the violence or crime in the
area. Thus, security was considered an important factor of
school choice (Belanger et al., 2002).
Dahari & Ya (2011) argued that young children should not
spend long periods of time in going to school. They found out
that parents in Malaysia would send their children to schools
close to their homes or close to their workplace. Thus, accessi-
bility of school was considered a factor in school choice.
Research Paradigm and Hypothesis
The foregoing review of related literature and studies served
as basis for the formulation of the research paradigm below.
Figure 1 shows the two sets of respondents—computing
freshmen from the University of the East and those from Na-
tional University. A validated questionnaire provided data on
their demographic profile and their level of consideration in
deciding to enroll with respect to the eleven institutional image
indicators. With the use of proper statistical tools, the differ-
ence between their levels of consideration in deciding to enroll
in the University with regard to the eleven institutional image
indicators was also determined. To this end, it was hypothe-
sized (H0) that there is no significant difference in the level of
consideration of the respondents in deciding to enroll in the two
universities in terms of the institutional image indicators.
Research Design, the Subjects, Sample Size, and
The study employed a descriptive design. It used a descrip-
tive-survey as the research instrument. Freshmen taking up
Information Technology (IT)-related degree and non-degree
programs from the National University and University of the
East answered the questionnaire. There were 147 (5 class sec-
tions) and 667 freshmen (18 class sections) at the National
Institutional Image Indicators
1. Tuition Fee
2. Tuition Fee Payment Scheme
3. Admission Process
4. Schedule of Classes
5. Cours e Offe ring
7. Faculty Profile
8. Scholarships and Grants
9. Kins hip Pat ron age
10. Security in Campus
The research paradigm.
University (NU) and University of the East (UE), respectively.
Sloven’s formula with an error of 5% was utilized to compute
for the sample sizes. The sample size for NU was 110. To ac-
commodate a low return rate, 120 were distributed and 113
were retrieved. These were all used in the analysis.
On the other hand, one hundred (100) students who partici-
pated in the pretest of the questionnaire were deducted from the
total population of UE respondents. Thus, the actual population
considered for UE was 567 and its computed sample size was
235. Two hundred eighty-eight (288) forms were distributed to
accommodate a low return rate. Two hundred seventy-seven
(277) were retrieved and all of these were used in the analysis.
Students were randomly selected through their class sections.
The class sections were written on a piece of paper and were
randomly picked out. Students (regardless of age, gender, etc.)
of the selected sections answered the questionnaire. Table 1
shows the details of forms distributed and retrieved per section.
(The names of the sections were changed to protect the privacy
of the students.)
Research Instrument, Validation of the Research
Instrument, and Statistical Treatment of Data
The questionnaire consisted of two parts. The first part gath-
ered data on the profile of the respondents in terms of name,
gender, degree program, home province, etc.
The second part of the questionnaire determined the level of
consideration in deciding to enroll in the university with regard
to institutional image indicators, such as tuition fee, tuition fee
payment scheme, admission process, schedule of classes,
course offering, facilities, faculty profile, scholarships and
grants, kinship patronage, security in campus, and accessibility.
Questions were based on the last stage of school choice—ma-
triculation stage (Chapman, 1986). All questions began with the
phrase “I decided to enroll in the university because…”. They
could respond from 1 (Disagree) to 5 (Strongly agree) (See
Table 2). The mean and mean ranges (shown in Table 2) were
utilized to determine the level of consideration in the choice of
The questionnaire was pretested to 100 freshmen. Confusing
and vague words were revised based on the pretest. The validity
and reliability of questionnaire were determined through factor
Distributed and retrieved survey forms.
A 40 38 A 30 29
B 39 37 B 30 27
C 12 12 C 30 29
D 40 38 D 30 28
E 39 37
F 38 37
G 40 39
H 40 39
TOTAL288 277 TOTAL 120 113
Copyright © 2012 SciRes.
R. P. BRINGULA ET AL.
The 5-point scale, its mean range, and verbal interpretation.
Weight/Scale Mean Range Verbal Interpretation
5 4.51 - 5.00 Strongly agree
4 3.51 - 4.50 Agree
3 2.51 - 3.50 Moderately agree
2 1.51 - 2.50 Slightly agree
1 1.00 - 1.50 Disagree
and Cronbach alpha analyses. Factor analysis determined the
validity of the questions that made up each variables (Dancey &
Reidy, 2002) while Cronbach alpha analysis determined the
internal consistency of the questions (Alese & Owoyemi, 2004).
Factor and Cronbach’s alpha analyses revealed that all con-
structs were found to be valid (factor loading 0.50) and reli-
able ( 0.70) (George & Mallery, 2009; Pallant, 2001). Table
3 shows the validi ty and reliability of the construct s.
Frequency counts, percentages, and mean were utilized to
describe the data. Maps were used to show the place of resi-
dence of the respondents. Test of difference (independent sam-
ples) was used to determine whether there was a significant
difference in the level of consideration in deciding to enroll in
the University. Test of difference (independent samples) is used
“when the participants perform in only one of two conditions,
i.e., an independent, between-participants or unrelated design”
(Dancey & Reidy, 2002: p. 206). A 5% level of probability
with 95% reliability was adopted to determine the degree of
significance of the findings.
Findings and Discussion
Profile of the Respondents
Table 4 shows the profile of the respondents. The profile of
NU and UE respondents shared common characteristics as
shown in Table 4. Most of the respondents were taking up de-
gree program in Information Technology (NU: f = 95% or 84%;
UE: f = 227% or 82%), male (NU: f = 74% or 65%; UE: f =
208% or 75%), do not have a home province (NU: f = 38% or
34%; UE: f = 77% or 28%), lived in family-owned house (NU:
f = 50% or 44%; UE: f = 164% or 59%) in the city of Manila
(NU: f = 62% or 55%; UE: f = 110% or 40%), and belonged to
a family with five family members family-owned house, and
spent an hour or less (NU: f = 97% or 86%; UE: f = 110% or
76%) in going to school by means of jeepneys (NU: f = 73% or
65%; UE: f = 204% or 74%).
Meanwhile, the profile of respondents of the two universities
also differed in some aspects. Most NU respondents belonged
to families whose monthly income did not exceed Php 40,000
(about US $1000) while UE respondents belonged to families
whose monthly income was at least Php 40,000. In terms of
number of family members who studied in the same university,
fifty-one percent (51%) (f = 57) of NU respondents had at least
one member of the family who studied in NU. On the other
hand, fifty-four percent (54%) (f = 151) of UE respondents did
not study in UE.
The average travel time of NU respondents (average travel time
= 40 minutes) was shorter than that of UE respondents (average
travel time = 54 minutes). Twenty-six percent (f = 29% or 26%)
of NU respondents could reach the school by walking while
only a small portion (f = 26% or 9%) of UE respondents was
living within in the vicinity of the school. The disparity was
attributed to the location of the two universities (See Figures 2
and 3. Maps were taken from Google Map). Ocular inspections
revealed that residential houses were situated near NU. Thus,
students living near NU would only reach the school in less
than five minutes. On one hand, UE is situated in a more com-
mercialized area where business establishments outnumber the
Level of Consideration in Deciding to Enroll in the
Table 5 shows that NU respondents rated “Agree” (Admis-
sion Process = 3.71, Schedule of Classes = 3.59, Course Offer-
ing = 3.77, Faculty Profile = 3.78, and Scholarship and Grants
= 3.86) on five of eleven institutional image indicators. The
findings support the findings of Drewes & Michael (2006), and
Sidin et al. (2003) on Admission Process; Parker et al. (2007),
Validity and reliability of constructs.
Institutional Image Indicators Factor
Tuition Fee* 0.779 0.709
Tuition Fee Payment Scheme 0.875 0.781
Admission Proc e ss 0.692 0.758
Schedule of Classes 0.751 0.832
Course Offering 0.810 0.820
School Facilities 0.637 0.831
Faculty Pro f ile 0.777 0.844
Scholarships and Grants 0.850 0.760
Kinship Patronage 0.814 0.738
Security in Campus* 0.697 0.719
Accessibility 0.698 0.795
Note: *One item was deleted.
Map Showi ng the Location of the U niversity of t h e East.
Copyright © 2012 SciRes. 1073
R. P. BRINGULA ET AL.
Profile of the respondents.
Profile of the Respondents
f % f %
Informa tion Technology 95 84 22782
Computer Science 18 16 48 17
Associate i n Computer Technology - - 2 1
Male 74 65 20875
Female 39 35 69 25
None 38 34 77 28
Region III 27 24 32 12
Region IV-A 15 13 69 25
Other Provinces 33 29 99 35
Manila 62 55 11040
Quezon City 18 16 56 20
Other Parts of Metro Manila 33 29 11140
Type of House Residence in the City
Family-Owned 50 44 16459
Apartment-Rented 30 27 46 17
Others 33 29 67 24
Family Monthly Income
Less than or E q ual to Php 40,000 77 68 13749
Greater than or Equal to Php 4000 36 32 14051
An Hour or Less 97 86 11076
More than an Hour 16 14 67 24
*Average Travel Time: NU = 40 min;
UE = 54 min
Mode of Transportation
Jeepney 73 65 20474
Walking Distance 29 26 26 9
LRT/MRT 20 18 95 34
Average Number of Family Members 5 - 5 -
Number of Family Members Studied in
None 56 49 151 54
At Least One (1) 57 51 12 646
Map showing the location of the national university.
Level of consideration in deciding t o enroll in the university.
NU Respondents UE Respondents
Instituti onal Image
Indicators MeanV. I.* Mean V. I.*
Tuition Fee 3.00 Moderately
agree 3.28 Moderately
Payment Scheme 3.38 Moderately
agree 3.77 Agree
Admission Proc es s 3. 71 Ag re e 4.04 Agree
Schedule of C l asses3.59 Agree 3.97 Agree
Course Offeri ngs 3.77 Agree 4.04 Agree
agree 3.85 Agree
Faculty Profile 3.78 Agree 4.03 Agree
Grants 3.86 Agree 3.99 Agree
Kinship Patronage 2.76 Moderately
agree 2.97 Moderately
Security in C ampus3.08 Moderately
agree 3.54 Agree
agree 3.56 Agree
OVERALL MEAN3.42 Moderately
agree 3.73 Agree
Note: *V. I. = Verbal Interpretation.
and Hagel & Shaw (2008) on Schedule of Classes; Kerstetter
(2011), Reddy (2011), McCarthy et al. (2012), and Pauline
(2010) on Course Offering; Shah & Nair (2010), and McCarthy
et al. (2012) on Faculty Profile; and Pauline (2010), Kerstetter
(2011), and McCarthy et al. (2012) on Scholarships and Grants.
Scholarships and Grants got the highest mean rating while
Kinship Patronage (mean rating = 2.76, “Moderately agree”)
got the lowest mean rating. Follow-up interviews with the NU
respondents confirmed that they decided to enroll in their Uni-
versity because of the financial assistance and the prestige of
the scholarship provided by their school.
As shown in Table 1, most of NU respondents belonged to a
Php 40,000 or less-family monthly income. The average tuition
fee per semester for IT-related degree programs in NU is about
Php 32,000 (US $800). Thus, a high consideration on Scholar-
Copyright © 2012 SciRes.
R. P. BRINGULA ET AL.
ship and Grants can be attributed to the financial capability in
paying tuition fees of the NU respondents. This implies that NU
should inform possible enrollees, especially those who belong
to a Php 40,000 or less family income, about the financial as-
sistance they could offer.
Meanwhile, UE respondents “Agree” that they considered
nine institutional indicators in choosing their school. These
indicators were Tuition Fee Payment Scheme (mean rating =
3.77), Admission Process (mean rating = 4.04), Schedule of
Classes (mean rating = 3.97), Course Offering (mean rating =
4.04), Facilities (mean rating = 3.85), Faculty Profile (mean
rating = 4.03), Scholarship and Grants (mean rating = 3.99),
Security in Campus (mean rating = 3.54), and Accessibility
(mean rating = 3.56). Admission Process and Course Offering
got the highest mean ratings while Kinship Patronage got the
lowest mean rating.
Studies found to be consistent with these findings are given
Tuition Fee Payment Scheme—Maringe (2006), Salahuddin
et al. (2008), McCarthy et al. (2012)
Admission process—Drewes & Michael (2006), and Sidin
et al. (2003)
Schedule of Classes—Parker et al. (2007), and Hagel &
Course offering—Pauline (2010), Kerstetter (2011), Reddy
(2011), and McCarthy et al. (2012)
Facilities—Brewer & Carnes (2008), Nadiri et al. (2009),
Shah & and Nair (2010), and McCarthy et al. (2012)
Faculty profile—Shah & and Nair (2010), and McCarthy et
Scholarship and Grants—Pauline (2010), Kerstetter (2011),
and McCarthy et al. (2012)
Security in Campus—Warrington (2005 cited in Andre-
Bechley, 2007), and Belanger et al. (2002)
Accessibility—Dahari & Ya (2011)
Admission Process refers to the convenience of admission
(i.e., from acquiring an application form) to enrollment (i.e.,
selecting subjects and paying tuition fees) procedures. This
shows that respondents decided to enroll in UE because of its
fast and convenient admission procedures. Moreover, Course
Offering in UE portrays a good image as perceived by the re-
spondents. Course Offering refers to the prestige (e.g., high
level of accreditation status, demand and popularity of the de-
gree program, and government-recognized degree programs) of
the degree programs offered by the University.
These findings reveal that UE portrays the best institutional
image in terms of its Admission Procedures and Course Offer-
ings. These findings also suggest that UE administrators should
continuously find ways to improve the admission procedures
and to seek higher level accreditation status of its IT-related
degree programs. In other words, they should direct their mar-
keting strategies to those students whose major considerations
in deciding to enroll in the University are convenient and fast
admission procedures, and established IT-related degree pro-
Difference between Level of Consideration in
Deciding to Enroll in the University of the
Table 6 shows the test of difference (independent samples)
between the level of consideration in deciding to enroll in the
Test of difference between level of consideration in deciding to enroll
in the university of UE and NU respondents.
Instituti onal Image
Indicators Difference between
Means (D) (UE – NU) t-valueaSig.
Tuition Fee 0.27306 3.269 0.001
Tuition Fee Payment
Scheme 0.39774 4.288 0.000
Admission Proc e ss0.33680 3. 43 0 0. 00 1
Schedule of Classes0.38712 4.02 9 0.000
Course Offerings 0.27020 2.781 0.006
Facilities 0.42006 4.847 0.000
Faculty Pro f i le 0 .24907 2.722 0.007
Grants 0.13051 1.462 0.144
Kinship Patronage0.20930 1.852 0.065
Security in Campus0.45384 4.214 0.000
Accessibility 0.30542 2.582 0.011
Note: adgrees of freedom (df) = 388.
University of UE and NU respondents with regard to the insti-
tutional image indicators. Responses in terms of Scholarship
and Grants (D = 0.13051, t(388) = 1.462, p-value > 0.05) and
Kinship Patronage (D = 0.20930, t(388) = 1.852, p-value > 0.05)
were not found to have significant difference.
On the other hand, it was found out that there were signifi-
cant differences on the responses on the following nine institu-
tional image indicators.
In terms of Tuition Fee, UE respondents had higher con-
sideration (D = 0.27306) in deciding to enroll in their Uni-
versity than NU respondents. The t-value of 3.269 (df = 388)
with an associated p-value of 0.001, which is less than 0.05
level of significance, shows that the difference is unlikely to
have arisen from sampling error. Similarly, UE respondents
also had higher consideration in deciding to enroll in their
school than NU respondents in terms of Tuition Fee Pay-
ment Scheme (D = 0.39774, t(388) = 4.288, p-value < 0.05).
This is attributed to the scholarships and grants received by
NU respondents. Informal interviews with two NU teachers
confirmed that most of computing freshmen were scholars.
NU respondents tend to have lower considerations (and
conversely, UE had higher consideration) on these institu-
tional image indicators since their financial assistance from
scholarships and grants would cover the cost of their educa-
tion. Therefore, they were less concerned about paying their
The same reason also can also be attributed to the higher
consideration of UE respondents on Course Offerings (D =
0.27020, t(388) = 2.781, p-value < 0.05), Facilities (D =
0.42006, t(388) = 4.847, p-value < 0.05) and Faculty Pro-
file (D = 0.24907, t(388) = 2.722, p-value < 0.05). Since
most UE respondents were “paying clientele”, they wanted
to get the best out of their tuition fees. They expected more
credible course offerings, up-to-date facilities, and qualified
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R. P. BRINGULA ET AL.
The differences between the responses of UE and NU re-
spondents in terms of Admission Process (D = 0.33680,
t(388) = 3.430, p-value < 0.05) and Schedule of Classes (D
= 0.38712, t(388) = 4.029, p-value < 0.05) were found to be
significant. The positive difference (D) indicates that UE
respondents had higher consideration in deciding to enroll
in their University than NU respondents. It can be noted
that Admission Process refers to the convenience and fast
procedures in enrolling in the University while Schedule of
Classes refers to the flexibility and convenience of class
schedules of the University. The higher consideration of UE
respondents on these indicators can be attributed to the
home location of the UE freshmen. Sixty percent of UE re-
spondents were from other parts of Metro Manila (see Ta-
ble 4). An enrollment procedure that could not be finished
in one day meant more frequent visits to the University and
higher cost of transportation. Schedule of classes not suited
to the students resulted in poor attendance in class or in
dropping the subject. Therefore, fast and convenient admis-
sion procedures, as well as flexible and expedient class
schedules would be very beneficial to commuting enrollees.
The home location of the respondents also explains the
significant difference in their level of consideration in de-
ciding to enroll in the University in terms of Accessibility.
UE respondents had higher consideration than NU respon-
dents in this indicator (D = 0.30542, t(388) = 2.582, p-value
< 0.05) since UE respondents live farther than NU respon-
dents. Thus, a school which can be easily accessed (i.e.,
convenience of getting to the school, short travel time, di-
verse modes of transportation in going to school, and roads
leading to the school are in good condition) would be a
greater consideration for those who live farther.
It was found out that UE respondents had higher considera-
tion in Security in Campus (D = 0.45384) than NU re-
spondents. The result is unlikely to have arisen from sam-
pling error (t(388) = 4.214, p-value < 0.05). This can be at-
tributed to the location of the school. UE is situated in a
more commercialized area (see Figure 2). The vibrant
economic activities in the area fuel the fast circulation of
money which, in turn, attracts people from all walks of life
including lawless individuals, or groups. Unfortunately, as a
result, students could be victims of these lawless individuals
or groups. The home location of the respondents could
partly explain the difference.
School administrators (particularly, UE administrators) could
consider these indicators to direct marketing strategies for pos-
sible enrollees. School administrators could stress that students
could receive the best value of their money and students could
settle their tuition fees in an easy-payment-plan. As such, poli-
cies could be formulated on how to meet this easy-payment-
plan and constantly look on ways on how to improve this pay-
ment scheme. They could also stress that students’ fees were
utilized to serve them better in terms of pursuing higher ac-
creditation status, providing state-of-the-art facilities, and hiring
qualified faculty members.
Careful consideration should also be given in terms of ad-
mission process and schedule of classes. A long and unmoving
line of enrollees during admission can be an indication that
there is something wrong in the admission processes. This may
leave a negative impression on possible enrollees that could
have visited the school for admission inquiries. Home location
of the students and the ease of access in going to school may
warrant a flexible schedule of classes. The University can offer
varied class sessions (e.g., morning, afternoon, or evening ses-
sions) that can cater to commuters and non-commuters alike.
Lastly, security within and outside the University justifies the
need for the visibility of security guards, local law enforcers,
and policemen. This institutional image serves not only the
students but also their parents.
As a summary, the discussions above show that the findings
can be translated into practice. These have implications on the
marketing and policy formulation of the Universities in order to
attract possible enrollees. The findings also suggest that school
choice varies even from the same type of respondents (i.e.,
students). In other words, other students may have had higher
considerations in deciding to enroll in the University because
they are more meticulous in choosing a school.
Conclusion, Recommendations, Limitations, and
It was found out that UE respondents had higher considera-
tions than NU respondents in deciding to enroll in the Univer-
sity based on nine out of eleven institutional image indicators.
They had higher considerations in terms of the Tuition Fee,
Tuition Fee Payment Scheme, Admission Process, Schedule of
Classes, Course Offerings, Facilities, Faculty Profile, Security
in Campus, and Accessibility. Thus, the null hypothesis stating
that there is no significant difference in the level of considera-
tion of the respondents in deciding to enroll in the two Univer-
sities in terms of the institutional image indicators is partially
rejected. In other words, school choice differs even among the
same type of respondents.
The findings of the study could serve as a basis in directing
the marketing and operational planning of the schools. The two
universities could focus on their strongest institutional image
indicators while improving on their weakest. Students and par-
ents would be informed on what the schools could offer. This
could lead to a wholesome competition between schools, the-
reby directly giving benefits to the consumers (e.g., students).
The indicators considered could also serve as basis for the for-
mulation of survey forms in determining freshmen satisfaction
on the services provided by the Universities. This could pin-
point which institutional indicators were met and which were
Since the study is relatively a pioneering study in school
choice in the Philippines, it recognizes its limitations. The
findings of the study are limited only to two Universities with
IT-related programs. The comparison of school choice among
three or more Universities offering IT-related programs can be
initiated. This would greatly contribute to a better understand-
ing of the school choice of possible IT freshmen. Second, the
questionnaire was based on the last stage of school choice.
Institutional image indicators not considered in the last stage
might be considered in the previous stages. Furthermore, future
studies could determine which institutional image indicators
could be considered in each stage.
Third, in this study, the demographic profile of the respon-
dents was used only for the purpose of describing the respon-
dents. It did not look into the possibility that the profile of the
respondents could also affect school choice (Jimenez & Sa-
las-Velasco, 2000). Consequently, school choice based on the
particular profile of the respondents (e.g., income, gender, fam-
ily size, etc.) can also be investigated. These studies can further
Copyright © 2012 SciRes.
R. P. BRINGULA ET AL.
illuminate the differences found in this study.
Fourth, it is interesting to note that even though UE provides
various scholarship programs, freshmen from NU did not opt to
enroll at UE. The study could not answer this gap due to the
limitations of the design of the study and of the questionnaire.
The questionnaire only focused on what the respondents con-
sidered in choosing their school without referencing to other
schools. Thus, it is unclear whether they had considered enroll-
ing in UE or not, or they never had the chance to know that UE
offered such scholarships. It is recommended that the quantita-
tive approach employed in this study be augmented by a quali-
tative approach. In this manner, the research gap mentioned
above could be filled or addressed accordingly.
Lastly, a study on school choice based on the reputation (e.g.,
Daily et al., 2010; Padlee et al., 2010; Pauline, 2010) of the
school could be explored.
The authors are greatly thankful to the invaluable help of
Vice-Chairman Jaime J. Bautista, Dr. Ester A. Garcia, Dr. Oli-
via C. Caoili, Dean Rodany A. Merida, Dr. Socorro R. Vil-
lamejor, NU faculty members, Gregorio Punla, Jr., and to all
students who participated in the study.
Alese, B. K., & Owoyemi, S. O. (2004). Factor analytic approach to
internet usage in South—Western Nigeria. Journal of Information
Technology Impact, 4, 171-188.
Andre-Bechley, L. (2007). Finding space and managing distance: Pub-
lic school choice in an urban California district. Urban Studies, 44,
Banya, K., & Elu, J. (2001). The World Bank and financing higher
education in Sub-Saharan Africa . Higher Education, 42, 1-34.
Bashir, Z., Mojoka, M. I., & Mahmood, T. (2011). Comparison of
quality indicators in public and private secondary schools of Hazara
Region, Pakistan. Interdisciplinary Journal of Contemporary Re-
search in Business, 2, 157-172.
Belanger, C., Mount, J., & Wilson, M. (2002). Institutional image and
retention. Tertiary Education and Management, 8, 217-230.
Brewer, P., & Carnes, L. (2008). The perceived impact of physical
facilities on the student learning environment. Business Education
Digest, 17, 3-21.
Bukowska, G., & Siwinska-Gorzelak, J. (2011). School competition
and the quality of education: Introducing market incentives into pub-
lic services: The case of Poland. Economics of Transition, 19,
Chapman, R. G. (1986). Toward a theory of college selection: A model
of college search and choice behavior. Advances in Consumer Re-
search, 13, 246-250.
Dahari, Z. B., & Ya, M. S. (2011). Factors that influence parents’
choice of pre-schools education in Malaysia: An exploratory study.
International Journal of Business and Social Science, 2, 115-128.
Daily, M. C., Farewell, S., & Kumar, G. (2010). Factors influencing the
university selection of international students. Academy of Educa-
tional Leadership Journal, 14, 59-75.
Dancey, C. P., & Reidy, J. (2002). Statistics without Maths for Psy-
chology using SPSS for Windows. Essex, England: Pearson Educa-
De Raadt, M. (2004). Searching for tomorrow’s programmers. Issues in
Informing Science and Information Technolog y, 1, 597-603.
Drewes, T., & Michael, C. (2006). How do students choose a university?
An analysis of applications to universities in Ontario, Canada. Re-
search in Higher Education, 47, 781-800.
Duarte, P. O., Alves, H. B., & Raposo, M. B. (2010). Understanding
university image: A structural equation model approach. Interna-
tional Review on Public and Nonprofit Marketing, 7, 21-36.
Fahim, Y., & Sami, N. (2011). Adequacy, efficiency and equity of
higher education financing: The case of Egypt. Prospects, 41, 47-67.
Gabriel, S., Cosmin, M., & Marius, P. (2008). What is wrong with our
schools? A guide to education reform. Annals of the University of
Oradea, Economic Science Series, 2, 449-454.
George, D., & Mallery, P. (2009). SPSS for Windows step by step: A
simple guide and reference 16.0 update (9th ed.). Boston: Pearson
Hagel, P., & Shaw, R. (2008). The influence of delivery mode on con-
sumer choice of university. European Advances in Consumer Re-
search, 8, 531-536.
Harrison, M. (2005a). Policy forum: Government policy and perform-
ance of schools competition, regulation and private schools. The
Australian Economic Review, 38, 66-74.
Harrison, M. (2005b). Public problems, private solutions: School
choice and its consequences. Cato Journal, 25, 197-215.
Ho, H.-f., & Wang, F.-Y. (2011). Prestige, parallel or predatory— Pr ic in g
strategies amongst Taiwanese universities. International Journal of
Marketing Studies, 3, 67-77.
Hu, S., & Hossler, D. (2000). Willingness to pay and preference for
private institutions. Research in Higher Education, 41, 685-701.
Jackson, G. A. (1980). How students pay for college: Temporal and
individual variation. Highe r Education, 9, 619-632.
Jimenez, J. D. D. & Salas-Velasco, M. (2000). Modeling educational
choices: A binomial logit model applied to the demand for higher
education. Higher Education, 40, 293-311.
Kabbani, N., & Salloum, S. (2011). Implications of financing higher
education for access and equity: The case of Syria. Prospects, 41,
Kanaan, J. H., Al-Salamat, M. N., & Hanania, M. D. (2011). Political
economy of cost-sharing in higher education: The case of Jordan.
Prospects, 41, 23-45. doi:10.1007/s11125-011-9179-5.
Kerstetter, K. M. (2011). Investigating high school band recruitment
procedures using educational marketing principles. Journal of Band
Research, 46, 1-17.
Khan, F., & Iqbal, M. Z. (2011). Impediments in enhancing the quality
of education in public sector higher educational organizations. Inter-
disciplinary Journal of Contemporary Research in Business, 2,
Leiviskä, K., & Siponen, M. (2010). Attitudes of sixth form female
students toward the IT field. SIGCAS Computers and Society, 40,
34-49. doi: 10.1007/s11125-011-9183-9.
Liefner, I. (2003). Funding, resource allocation, and performance in
higher education system s. Higher Education, 46, 469- 489.
Lenox, T. L., Woratschek, C. R., & Davis, G. A. (2008). Exploring
declining CS/IS/IT enrollments. Information Systems Education
Journal, 6, 1-11.
Maringe, F. (2006). University and course choice: Implications for
positioning, recruitment and marketing. International Journal of
Educational Management, 20, 466-479.
McCarthy, E. E., Sen, A. K., & Garrity, B. F. (2012). Cross-border
education: Factors influencing Canadian students’ choice of institu-
tions of higher education in the United States. Global Conference on
Business and Finance Proceedings, 7, 446-457.
Nadiri, H., Kandampully, J., & Hussain, K. (2009). Students’ percep-
Copyright © 2012 SciRes. 1077
R. P. BRINGULA ET AL.
Copyright © 2012 SciRes.
tions of service quality in higher education. Total Quality Manage-
ment, 20, 523-535. doi:10.1080/14783360902863713
Nahas, C. (2011). Financing and political economy of higher education:
The case of Lebans in students’ retention decisions. Prospects, 41,
Nguyen, N., & LeBlanc, G. (2001). Image and reputation of higher
education institutions in students’ retention decisions. The Interna-
tional Journal of Educational Mana gement, 15, 303-311.
Novak, J. (2006). Choice matters: What needs to change to make
schools competitive? Policy, 22, 23-28.
Padlee, S. F., Kamaruddin, A., & Baharun, R. (2010). International
students’ choice behavior for higher education at Malaysian private
universities. International Journal of Marketin g Stu dies, 2, 202-211.
Pallant, J. (2001). SPSS survival manual: A step by step guide to data
analysis using SPSS for Windows version 10. Buckingham: Open
Parker, R. S., Cook, S., & Pettijohn, C. E. (2007). School choice attrib-
utes: Positioning a private school. Services Marketing Quarterly, 28,
Pauline, J. (2010). Factors influencing college selection by NCAA
Division I, II, and III Lacrosse players. Journal of Research in
Health, Physical Education, R ecr ea ti on , Sport & Dance, 5, 62-69.
Pereda, M., Airey, D., & Bennett, M. (2007). Service quality in higher
education: The experience of overseas students. Journal of Hospital-
ity, Leisure, Sport & Tourism Education, 6, 55-67.
Reddy, M. (2011). Determinants of student choice of business schools
in India: A factor analytic investigation. International Journal of
Management, 28, 751-762.
Rizvi, S. A. A., & Khan, M. N. (2010). The uniqueness of educational
marketing. Journal of Economics and E ngineering, 4, 39-43.
Robert, P. (2010). School origin, school choice, and student perform-
ance. Educational Research and Evaluation, 16, 107-129.
Salahuddin, M., Islam, R., Akbar, D., & Raihan, K. M. (2008). How
can we increase the quality of private schools in Bangladesh from the
perspectives of students and managers? International Journal of
Management Perspectives, 2, 1-17.
Shah, M., & Nair, C. S. (2010). Enrolling in higher education: The
Perceptions of stakeholders. Journal of Institutional Research, 15,
Sidin, S. Md., Hussin, S. R., & Soon, T. H. (2003). An exploratory
study of factors influencing the college choice decision of under-
graduate students in Malaysia. Asia Pacific Management Review, 8,
Szekeres, J. (2010). Sustaining student numbers in the competitive
marketplace. Journal of Higher Educational Policy and Management,
32, 429-439. doi:10.1080/1360080X.2010.511116.
Tsagala, E., & Kordaki, M. (2007). Critical factors influencing secon-
dary school pupil’s decisions to study computing in tertiary educa-
tion: Gender differences. Education and Information Technologies,
12, 281-295. doi:10.1007/s10639-006-9026-0.
Ud Din, M. N., Murtaza, A., & Khan, F. (2011). Comparative study of
quality of education in public and private secondary schools in
NWFP. Interdisciplinary Journal of Contemporary Research in Busi-
ness, 2, 416-429.
Ur Rehman, N., Khan, J., Tariq, M., & Tasleem, S. (2010). Determi-
nants of parents’ choice in selection of private schools for their chil-
dren in District Peshawar of Khyber Pakhunkhwa Province. Euro-
pean Journal of Scien tific Research, 44, 177- 187.
Vaduva, S. A., Fotea, I. S., Corcea, M., & Lazar, M. (2011). Beyond
satisfaction: Engaging students and faculty in a Romanian business
school. The Marketing Management Journa l, 21, 207-213.