See discussions, st ats, and author pr ofiles f or this public ation at : https:www .researchgate.ne tpublic ation280914157 [626393]

See discussions, st ats, and author pr ofiles f or this public ation at : https://www .researchgate.ne t/public ation/280914157
Market orientation: An option for u niversities to adopt?
Article    in  International Journal of Nonpr ofit and V olunt ary Sect or Mark eting · July 2015
DOI: 10.1002/nvsm.1535
CITATIONS
2READS
206
3 author s:
Some o f the author s of this public ation ar e also w orking on these r elat ed pr ojects:
The e volving p attern of the c onc ept of positioning View pr oject
sally b albaaki View pr oject
Trang P . Tran
East Car olina Univ ersity
13 PUBLICA TIONS    169 CITATIONS    
SEE PROFILE
Charles Blankson
Univ ersity of North T exas
120 PUBLICA TIONS    1,752 CITATIONS    
SEE PROFILE
Widy arso R oswinant o
PPM School of Manag ement , Jak arta, Indonesia
5 PUBLICA TIONS    26 CITATIONS    
SEE PROFILE
All c ontent f ollo wing this p age was uplo aded b y Charles Blankson on 20 July 2018.
The user has r equest ed enhanc ement of the do wnlo aded file.

Market orientation: an option for
universities to adopt?
Trang P . Tran1*, Charles Blankson2and Widyarso Roswinanto2
1Department of Management, Marketing, and Information Systems, SUNY Oneonta, USA
2Department of Marketing & Logistics, University of North Texas, USA
The primary objectives of this exploratory paper are to test the concept of market orientation
adapted from related literature in the education context and to examine the effects of market orien-
tation as a second-order factor on university student: [anonimizat]. The revised scale, validated
through exploratory factor analysis and confirmatory factor analysis, constitutes a good fit.Specifically, the new scale is statistically and positively related with student: [anonimizat], indicatingthat market orientation is an important factor that leads to higher student: [anonimizat]. The findingsshow that the degree to which students are satisfied with their choice of university depends signifi-cantly on how market oriented the university is. In other words, the effective application of marketorientation strategy relates to student: [anonimizat], market orientation is an option for universities to adopt. The empiricalresults add to the meager and emerging literature on marketing and branding of universitiesand will be of interest of university administrators and marketing and branding managers ofuniversities. The paper concludes by discussing conclusions, implications, limitations, and
future research.
Copyright © 2015 John Wiley & Sons, Ltd.
Introduction
Organizational research and management and mar-
keting scholars have, over the years, discussed the
importance of developing market orientation (MO)in both profit and nonprofit organizations (Harris,
2001). To this end, in recent years, the concept ofMO has received a great deal of attention from mar-
keting scholars. In fact, while much attention isgiven to discussions about how to improve educa-
tional quality at higher education institutions (i.e.,
universities) by policy makers, media members, aca-demics, and practitioners, there is little research onthe effects of MO within the university context
(Chapleo, 2007, 2015; Merchant, Rose, Moody, &
Mathews, 2015). As Chapleo (2015) maintains, mar-keting and branding remain key in an environment
beset with sweeping changes. Like business
*Correspondence to: Trang P . Tran, SUNY Oneonta, Oneonta,
New York, 13820, USA.E-mail: trang.tran@oneonta.eduInternational Journal of Nonprofit and Voluntary Sector Marketing
Int. J. Nonprofit Volunt. Sect. Mark. (2015)
Published online in Wiley Online Library
(wileyonlinelibrary.com) DOI: 10.1002/nvsm.1535
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

operations, higher education institutions operate
under the influence of a number of external forces,
such as technology and economy. From the techno-logical perspective, the Internet and other techno-
logical advances have created a totally new
perspective for educators (Clark, 1996). New toolsand formats of education have been created in nu-
merous universities to keep up with trends and to
make teaching easier. For instance, virtual officehours are offered at the Department of Chemistry
and Biology at the University of California, Los
Angeles, to enhance faculty and student communi-cations and online discussions (Krieger, 1996).
From the economic perspective, the main source
of finance for state universities is government
funding, but because of the economic downturnthat is rampant in the United States and in several
countries around the world, financial support for
universities at state and federal levels has been tight-ened. In the United States, more universities have
adopted marketing and branding strategies to cope
with the reduction in state funding (Brookes,2003). For instance, in 2012, total state support forpublic universities fell by 7%, resulting in budget
challenges (Merchant et al., 2015). As a conse-
quence, many schools have to adjust their programsaccordingly. Some choose such methods as cancel-
ation of research grants, restriction on scholarship
schemes, and an increase in student tuition( C a r u a n a ,R a m a s e s h a n ,&E w i n g ,1 9 9 8 ) .O t h e r s
start creating new programs, stopping the re-
newal of fixed contracts, annulling new borrow-ing, and raising more revenue (Poprzeczny,
1996; Storey, 1996). Some have chosen to raise
money by extending cooperation with industriesin the form of provision of training services,while others provide professional consultancy ser-
vices. Another preferred option for universities
h a sb e e nt oe x p a n dt h e i rs y s t e mt oo t h e rg e o -graphic areas. For example, Australian universities
partner with Asian universities to tap highly
demanded markets. This cooperation has gener-ated about $2bn in annual revenue for Australian
educational systems (Caruana et al., 1998).Environmentally induced changes are consid-
ered key drivers for universities to review what
they have done and how they could change toembrace this new situation. It is critical for uni-
versities to balance two responsibilities: depen-
dence and independence. The majority ofuniversities are still dependent one way or an-
other on public funding and sponsorship from
the government and its agencies. However, amore important responsibility for these universi-
ties would be to opt out of this situation
altogether and become independent in ways thatcan lead to effectively manage educational andtraining activities and become less dependent on
government funding.
One possible solution that may help universi-
ties pursue the “opting out ”approach and be-
come less reliant on government funding is to
create attractive educational programs, recruitfull-time and effective faculty, increase research
productivity, and improve technological facilities
and laboratories. The ultimate goal is to attractmore students and to strive to satisfy students ’
needs and aspirations. At the same time, it is im-
portant to re-examine internal operations that
characterize the university, identify critical ante-cedents affecting student s atisfaction and aspira-
tions, and find possible solutions that help
enhance the experiences that students have whilepursuing the educational program.
One of those solutions that schools can adopt is
MO (Chapleo, 2015; Merchant et al., 2015). MO is
a key driver of business profitability, innovation,
employees ’commitment, and performance of orga-
nizations (Kirca, Jayachandran, & Bearden, 2005;Noble, Sinha, & Kumar, 2002; Atuahene-Gima,1996; Narver & Slater, 1990). Although several re-
searchers have explored the effects of MO in the
business setting (Baker & Sinkula, 2009; Morgan,Vorhies, & Mason, 2009; Lin, Peng, & Kao, 2008;
Narver & Slater, 1990; Atuahene-Gima, 1996;
Jaworski & Kohli, 1993), unfortunately, little empiri-cal research explores the impact that MO has on an
academic institution (Stokes, 2002; Hammond,Trang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

Webster, & Harmon, 2006; Sargeant, 2008; Hemsley-
Brown & Oplatka, 2010; Fredriksson, 2009).
Using the domain of university as a context, this
exploratory study is to adapt and then
operationalize the original market orientation scale
(MARKOR) developed by Matsuno, Mentzer, andRentz (2000) by proposing a new instrument of
MO capable of being applied in the educational con-
text and to investigate the impact of market orienta-tion of universities on student satisfaction. This
paper is also developed in response to Jacoby ’s
(1978) and Wright and Kearn ’s (1998) suggestion
for marketing scholars to adapt/adopt existingmodels, constructs, frameworks, and definitions in
exploring new insights and generating theory. This
approach is consistent with Boshoff ’s (1999) asser-
tion about the age-old adage that what does not get
measured does not get managed.
The next section of the paper begins with a liter-
ature review of main constructs (MO, and satisfac-
tion) and three sub-dimensions of MO (Intelligence
Generation, Intelligence Dissemination, and Respon-siveness), which was followed by methodologywhere exploratory factor analysis, confirmatory fac-
tory analysis, and structural model were performed.
The paper ends with conclusions, implications, lim-itations, and future research.
Literature review
Market orientation and its application in higher
education
Related literature has emphasized that one of the
major roles that a successful marketing strategy
plays is to translate the organizational goals into real-ity (Felton, 1959; McNamara, 1972; Levitt, 2008). Abody of marketing literature has explored the signif-
icant relationship between MO and an organiza-
tion’s ability to achieve its business goals (Zhou,
Brown, & Dev, 2009; Kirca et al., 2005; Narver &
Slater, 1990). In a market-oriented organization, em-
ployees are more committed to customer satisfac-tion and are able to quickly understand customerneeds and demands, thereby making changes to
satisfy customers. Acknowledging the role of MO
in the organization, researchers have attempted todevelop and measure this construct. A pioneer in
this endeavor is Narver and Slater ’s (1990) study in
which MO is operationalized by three interrelatedbehavioral levels: customer orientation , defined as
“the sufficient understanding of one ’s target buyers
to be able to create superior value for them contin-uously ”(p. 21); competitor orientation , which re-
fers to the case wherein “a seller understands the
short-term strengths and weaknesses and long-termcapabilities and strategies of both the key currentand the key potential competitors ”(p. 21 and 22);
and interfunctional coordination , which means
“the coordinated utilization of company resources
in creating superior value for target customers ”
(p. 22). Positive correlation between the level of
MO within a firm and the ability of the firm toachieve its objectives has been empirically studied
in the literature (Houston, 1986; Baker & Sinkula,
2009; Morgan et al., 2009; Lin et al., 2008; Jaworski
& Kohli, 1993).
The influence of marketing is well documented
in related marketing literature (Harris, 2001; Zhou
et al., 2009; Kirca et al., 2005; Baker & Sinkula,
2009; Morgan et al., 2009; Lin et al., 2008). How-
ever, marketing is not a concept exclusively applied
to business organizations; it can also be employedin nonbusiness
1organizations (Stokes, 2002; Kotler
& Levy, 1969). Early marketing scholars attempted
to look into the role of marketing in nonbusiness or-ganizations and defined marketing as:
the function of the organization that can
keep in constant touch with the organiza-tion ’s customers, assess customers ’needs, de-
velop ‘products ’that meet these needs, and
build a program of communications to ex-press the organization ’sp u r p o s e s( K o t l e r&
Levy, 1969, p. 16).
1Nonbusiness and nonprofit are used interchangeably in market-
ing literature.Market orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

In this setting, nonbusiness marketing deliberation
is viewed as a socially valuable activity (Hammond
et al., 2006).
According to Hammond et al. (2006), the MO
concept can be adopted in the higher educational
environment. Several marketing terms such as value,customers, competitors, pricing, and distribution
have been used in periodical papers, the Association
to Advance Collegiate Schools of BusinessInternational Accreditation Standards, and the
“Baldrige Education Criteria for Performance Excel-
lence ”(Baldrige National Quality Program 2005;
Caplan, 1997; Hammond et al., 2006). The annual
Symposium for the Marketing of Higher Education
has also been organized by the American Marketing
Association. Guidance pertaining to marketing strat-egies in higher educational institutions is now pub-
lished in several journals including the Journal of
Marketing for Higher Education .
Among a series of published sources that bridge
the gap between marketing and higher education
is the Baldrige National Quality Program 2005,which proposes seven key Baldrige criteria integrat-ing the roles of MO and educational performance.
The underlying value that links all Baldrige criteria
is the marketing theory with an emphasis on theconnection between the drivers (top management
emphasis) and the outcome (performance) of MO.
In particular, among seven criteria proposed arethree key benchmarks that point out how important
students are in an educational institution. The first
criterion emphasizes that an organization shouldpay more attention to students and stakeholders.
According to the Baldridge standards, students are
referred to as “key customers. ”The organization
needs to take an appropriate action to bridge the“gaps in performance ”compared with competitors.
The second criterion suggests that requirements and
expectations of students, stakeholders, and themarket must be understood by the organization. It
is important for the organization to strengthen rela-
tionships to retain and satisfy students and stake-holders. Knowledge and information should be
shared and collected among faculty and staff. Thethird criterion revolves around the development of
guidance for the organization to help students learn
and succeed.
Market orientation —the customer perspective
In a market-oriented economy where an organiza-
tion’s existence depends on its customers, the orga-
nization is established to serve customers and meettheir expectations. In this regard, a customer
concept is even more important than a marketing
concept (Lavidge, 1970). Criteria used to assesswhether or not an organization is successful includethe organization ’s ability to tap into the customer ’s
vantage point (Garvin, 1987; Drucker, 1954). The
marketer should view customer preferences as abso-lute standards to evaluate quality performance (Voon,
2006; Esteban, Millán, Molina, & Martín-Consuegra,
2002). A company that is perceived to provide a highservice quality is able not only to avoid customer dis-
pleasure but also to make customers happy by meet-
ing their needs (Boyd et al., 2011).
Needless to say, customer perspectives are indis-
pensable in service quality management. Related lit-
erature in marketing and management takes intoconsideration how important customer perspectivesare in an organization. Esteban et al. (2002) state that
it is necessary that MO be assessed from the stand-
point of customers. The pivotal role of a consumer-defined and customer-perceived MO framework in
service quality management is noted by Krepapa,
Berthon, Webb, and Pitt (2003) and Webb, Webster,and Krepapa (2000). Customers are well aware of
an organization ’s market-focused activities because
they purchase offerings from the organization (Paul,1994). In particular, according to Lundkvist andYakhlef (2004), customer involvement is critical for
service firms in developing a new service.
Student satisfaction
One of the pioneering empirical studies exploring
the domain of service quality is Parasuraman,Trang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

Zeithaml, and Berry ’s (1988) classic framework. The
authors argue that service quality is perceived on
the basis of five gaps, that is, the differences be-tween customer expectation and experience, com-
munications, delivery, service specifications, and
service design. However, in a professional serviceenvironment such as a university, a simpler model
proposed by Brown and Swartz (1989) appears to
be more suitable. Brown and Swartz suggest that be-cause in an academic setting, professionals generally
have common characteristics, such as being well
trained and having advanced degrees; in order tomeet specific requirements, researchers need to in-vestigate the perception of both parties involved,
that is, professionals and clients, and the difference
between client expectation and experience.
The difference between what customers expect
and what they actually experience after they buy
the product is used to measure customer satisfac-tion (Aiello, Czepiel, & Rosenberg, 1977). If the
value of a product that a customer buys is higher
than the value that the customer expects, the cus-tomer becomes happy. However, if the value of aproduct being bought is lower than the level of ex-
pectation, their emotional feeling is beset with dis-
satisfaction. Satisfaction and service quality areclosely correlated. Both constructs are different in
that satisfaction is derived from a particular transac-
tion, while service quality is thought of as a generalattitude of service excellence (Parasuraman et al.,
1988). To that end, satisfaction is an antecedent of
service quality, and therefore, it is employed to mea-sure the quality of service (Oliver, 1981). In a univer-
sity environment, satisfaction is a good indicator to
measure the quality of education and training pro-vided by the university.
Intelligence generation at universities
Intelligence generation is a process whereby the in-
formation about customer needs and wants is identi-
fied, collected, and assessed. In this process, criticalinformation on competitors and their activities isalso gathered, enabling an organization to have bet-
ter insights to the customers and the market. As a re-
sult, intelligence generation is viewed as a stimulusthat leads to the organization ’s development of en-
tire business systems (Jaworski & Kohli, 1993). This
requires cooperation from multiple departments inthe organization, because each has a typical market
perspective (Jaworski & Kohli, 1993; Narver &
Slater, 1990). An organization ’s ability to collect
and process information results in the organization ’s
better ability to predict their capability, embrace ad-
aptation, and create value for customers (Pelham &Wilson, 1996).
The consequence is severe if an organization pro-
duces products or services that customers do not
want because of a lack of knowledge about theircustomers. Similar to a business entity, an academic
institution should be aware of and appreciate the
importance of students ’expectations and should at-
tempt to meet or exceed those expectations. Conse-
quentially, the onus is on the responsible personnel
of the university to collect information about stu-dents ’needs, wants, and aspirations in an attempt
to satisfy them. At the same time, university adminis-
trators would pursue corrective necessary actions,
should the service provided not meet students ’
expectations.
Intelligence dissemination at universities
Intelligence dissemination refers to the formal andinformal processes associated with the market infor-mation exchange within the firm. The degree to
which information sharing takes place inter-
departmentally and intra-departmentally within anorganization is critical in MO strategy (Narver &Slater, 1990; Jaworski & Kohli, 1993). An organiza-
tion’s success in implementing MO depends on the
degree to which the collected information is sharedacross departments or colleges at a university. Infor-
mation exchange enhances coordination at both
intra-departmental and inter-departmental levelswhich results in a better operational performance.Market orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

(Han, Kim, & Srivastava, 1998). If information is
shared successfully across departments, it provides
the university leaders or administrators with an op-portunity to understand ongoing situations and
problems, which, in turn, enables them to make bet-
ter decisions (Quinn, 1992; Glazer, 1991). To facili-tate the process of information dissemination, it is
required of an organization ’s management to create
a forum for information to be transferred or shared.Forum discussions can be implemented through on-
line or face-to-face methods. In an academic institu-
tion, when communication is open acrossdepartments or colleges, information about students ’
needs and expectations is collected effectively,
which enables the university to formulate a more ef-
ficient solution to address those needs (Siu & Wilson,1998). Therefore, information dissemination is a key
driver leading to the success of the MO strategy at a
university.
Responsiveness at universities
Actions taken in response to the market intelligence
generated and disseminated across departments or
functions of an organization are termed “responsive-
ness.”A firm is more likely to be responsive to an
event taking place in the market when the event is
important to the firm (Jaworski & Kohli, 1993;
Narver & Slater, 1990). The extent to which a firmtakes actions to respond to customers ’requirements
or to competitors ’initiatives determines the degree
of implementation of MO strategy (Kohli & Jaworski,1990; Narver & Slater, 1990). Education quality in an
educational institution is generally evaluated on the
basis of the institution ’s ability to respond to stu-
dents ’needs. Based on the flow of information gener-
ated and disseminated, the university, like other
business organizations, drafts a plan to take actions.
Because universities also compete to attract more
students, a quick action to students ’needs are imper-
ative, helping a school stay competitive. To illustrate,
if students ’demands for computer access, Internet
connection, and other educational facilities arequickly taken care of, they likely feel that their voices
are heard, and they will be more satisfied with their
decision to pursue a program in that university. Inthis study, the three components constituting MO
are correlated with the second-order MO construct,
which then is expected to positively relate to studentsatisfaction. The theoretical framework is presented
inFigure 1.
Methodology
Data collection
The data were collected from 233 university stu-
dents from the University of North Texas (UNT), inDenton, Texas. UNT is one of the nation ’s largest
public universities and the most comprehensive in
the Dallas-Fort Worth Metroplex. UNT enrolls
36 000 students and offers 99 bachelor ’s, 83
master ’s, and 36 doctoral degree programs. In order
to increase the number of respondents, a self-
completion survey was created in both online and“paper-and-pen ”versions. The self-completion sur-
vey was based on a one-wave cross-sectional ap-
proach. Missing data were handled with pairwiseprocedure using SPSS. Out of the total of 233
Figure 1. Theoretical framework. MIG, modified intelligence
generation; MID, modified intelligence dissemination; MRE,
modified responsiveness; TMO, total second-order market orien-tation; TS, total satisfaction.Trang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

responses, 166 responses came from online survey
and the rest from the paper-and-pen survey. More fe-
male respondents (71%) completed the survey thanmale respondents (29%). In terms of ethnicity, non-
Hispanic White was dominant (58.5%), followed by
African American (19.7%), and Hispanic or Latino(12%). The overwhelming majority (95.7%) of the
sample in this study were undergraduate students,
and only 3.4% were graduate students.
Measurement Scales
Measurement scales were adapted from existing lit-erature (Wright & Kearns, 1998; Jacoby, 1978). Spe-cifically, MO scale was obtained from Matsuno et al.
(2000), while satisfaction came from Oliver (1981).
The former has three components with 22 items,among which eight items measure intelligence gen-
eration, six items measure intelligence dissemina-
tion, and the remaining eight items measureresponsiveness. The latter has six items. To test the
scales in a university setting, revisions in the form
of basic re-description of the items were made fol-lowing consultations with an academic expert anda pilot test among 20 undergraduate students. Five-
point Likert scales anchored from 1 ( strongly dis-
agree )t o5( strongly agree ) characterized the mea-
surement of the two constructs: MO and student
satisfaction.
Statistical procedures
Statistical procedures were performed following
Pallant and Bailey ’s (2005) suggestions. Cronbach ’s
αcoefficients were utilized to determine the reli-
ability of three sub-dimensions of MO. This was
followed by two sequential steps: exploratory
factor analysis and confirmatory factor analysisdesigned to capture the underlying structure of
MO construct in measurement model. Finally,
structural model was created to test the predictiveability of MO.Reliability
The Cronbach ’sαvalues for MIG, MID, and MRE
were 0.75, 0.76, and 0.74, respectively. A capital let-ter“M”written in front of each subscale stands for
“modified, ”which implies that these scales are
different from those adapted from the originalliterature. All the values were greater than the ac-ceptable level of 0.70, proving adequate internal
consistency. MIG represents modified intelligence
generation, MID refers to modified intelligence dis-semination, and MRE reflects modified responsive-
ness (Appendix A).
Exploratory factor analysis
The sample ’s suitability for factor analysis was ex-
amined first by employing two criteria: the
Kaiser –Meyer –Olkin test and Bartlett ’s test of sphe-
ricity. The fact that the Kaiser –Meyer –Olkin mea-
sure of sampling adequacy value was 0.85 and
Bartlett ’s test of sphericity was significant
(p<0.001) allowed factor analysis to proceed. Five
eigenvalues derived from principal component
analysis were greater than 1. However, only thefirst three factors exceeded the criterion valuefrom the parallel analysis (Horn, 1965). Addition-
ally, scree-plot results supported a three-factor so-
lution. Therefore, the findings supported thethree-factor solution, and hence, the three-factor
solution was selected for further analysis. The re-
sults of the factor analysis using the three factorsare shown in Table 1.
The pattern matrix showed a three-factor solu-
tion that appeared to be consistent to the MOscale of Matsuno et al. (2000), with only a few ex-
ceptions. MRE4 and MRE5, for instance, loaded
strongly but inappropriately on MID factor (0.672
and 0.630, respectively) and loaded barely(/C00.930 and 0.168, respectively) on MRE factor —
the factor that those items (MRE4 and MRE5) were
supposed to load. Similarly, MIG8R had the sameissue, loading strongly on MRE (0.404) but barelyMarket orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

on MIG (0.269). For MRE7, although it loaded ap-
propriately on MRE, the loading was not strong
(0.365 less than the cutoff value of 0.40).
Although the results apparently supported the
unidimensionality of three subscales of MO, the
factor content did not seem to entirely supportthe initial instrument scale suggested by Matsuno
et al. (2000). The problem was the inclination
that three items (MRE4, MRE5, and MIG8R)showed more substantial loading on different fac-tors to which they were not supposed. The load-
i n go fM R E 7o nt h eM R Ef a c t o rw a sa l s ov e r yl o w .
Therefore, these four items were removed, leav-ing the MO scale at 18 items (hereafter called
MO-18) in which MIG included seven items,MID included six items, and MRE included five
items. This revised scale was used for the next
step of data analysis.
After removal, the same procedure was repeated
with MO-18. The pattern matrix ( Table 2) illustrated
that all items loaded properly on their respective fac-tors: MID, MIG, and MRE with the lowest loading of
0.46 (MIG2 on MIG). Reliability values of three fac-
tors were 0.79, 0.76, and 0.76, respectively, indicat-ing good internal consistency. These numbers weremore acceptable than those before the removal of
cross-loading or low-loading items. More impor-
tantly, the MO-18 represented a simple structure inwhich all the items loaded appropriately on their re-
spective factors.Table 1. Pattern and structure matrix for PCA of the three-factor solution with all items
MO itemsIntelligence dissemination Intelligence generation Responsiveness
Communalities Pattern Structure Pattern Structure Pattern Structure
MID5 0.738 0 .726 /C00.026 /C00.369 /C00.119 0.037 0.541
MID4 0.719 0 .703 0.011 /C00.330 /C00.047 0.101 0.497
MRE4 0.672 0 .643 0.019 /C00.295 /C00.093 0.044 0.422
MID3 0.654 0 .670 /C00.023 /C00.341 0.026 0.164 0.450
MID6 0.638 0 .643 0.081 /C00.249 0.213 0.337 0.461
MRE5 0.630 0 .683 /C00.037 /C00.358 0.168 0.303 0.495
MID1 0.559 0 .634 /C00.170 /C00.436 /C00.031 0.103 0.425
MID2 0.416 0 .475 /C00.033 /C00.255 0.206 0.296 0.267
MIG6 /C00.002 0.360 /C00.753 /C00.752 /C00.004 0.073 0.566
MIG4 /C00.004 0.369 /C00.751 /C00.754 0.056 0.133 0.572
MIG5 0.012 0.401 /C00.722 /C00.748 0.199 0.276 0.599
MIG3 0.128 0.473 /C00.661 /C00.736 0.129 0.224 0.576
MIG1 0.023 0.304 /C00.576 /C00.588 0.017 0.081 0.347
MIG7 0.347 0.496 /C00.424 /C00.564 /C00.265 /C00.149 0.450
MIG2 0.260 0.403 /C00.321 /C00.440 /C00.056 0.032 0.244
MRE6R 0.051 0.151 0.121 0.018 0.763 0 .761 0.590
MRE3R /C00.052 0.170 /C00.138 /C00.190 0.750 0 .754 0.583
MRE1R 0.011 0.244 /C00.206 /C00.278 0.647 0 .670 0.493
MRE2R /C00.093 0.132 /C00.190 /C00.212 0.641 0 .642 0.440
MRE8R 0.134 0.243 0.007 /C00.114 0.546 0 .573 0.345
MIG8R 0.016 /C00.031 0.269 0.220 0.404 0 .379 0.212
MRE7 0.188 0.325 /C00.125 /C00.254 0.365 0 .417 0.245
Note: Items with a letter “R”at the end (i.e., MIG8R) are reverse coded.
The bold is used to emphasize high factor loadings of items on each factor.
PCA, principal component analysis; MO, market orientation; MID, modified intelligence dissemination; MRE, modified responsive-
ness; MIG, modified intelligence generation.Trang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

Confirmatory factory analysis
Measurement model was assessed through confir-
matory factor analysis with maximum likelihoodestimation procedure. This was conducted on ameasurement model of the MO-18 scale, as con-
firmed in the exploratory factor analysis with
three factors correlated ( Figure 2). In this
model, all factor loadings were statistically signif-
icant. Although the chi-squared test was signifi-
cant ( χ
2(132) = 218.87, p<0.001), other fit
indices produced good fit (normed fit index
(NFI) = 0.83, comparative fit index (CIF) = 0.92,
and root mean square error of approximation(RMSEA) = 0.05) (Hair et al., 2006). Then, a
second-order model was tested. Fit statistics for
the second-order MO model were the same asthose of the first-order model ( Table 3). How-
ever, of particular interest at this point was to
s e ew h e t h e rt h ep a t hc o e f f i c i e n t sb e t w e e nt h esecond-order MO factor (hereafter called TMO
with the capital letter “T”implying total or overall)
and three first-order factors (MID, MIG, and MRE)
were significant. The results showed that all the pathestimates between the second-order factor and thefirst-order factors were significant ( p<0.001) and so
were all the estimates between the indicators
and their respective first-order factors ( p<0.001)
(Table 3). The completely standardized solution for
the second-order measurement model of TMO is
presented in Table 3.
It was of importance to examine the factorial
structure of TMO not only through the hierarchi-
cal factorial relationship between three first-orderfactors and the second-order factor but also
within each first-order factor. In general, most of
the fit statistics for three factors were acceptable(for MIG, χ
2= 30.83 at degrees of freedom (d.f.)
= 14, NFI = 0.93, CFI = 0.96, RMSEA = 0.07; for
MID, χ2= 28.78 at d.f. = 9, NFI = 0.91, CFI = 0.93,Table 2. Pattern and structure matrix for PCA of MO-18 (after item deletion)
MO itemsIntelligence dissemination Intelligence generation Responsiveness
Communalities Pattern Structure Pattern Structure Pattern Structure
MID4 0.784 0 .761 /C00.017 0.354 /C00.072 0.089 0.585
MID3 0.730 0 .727 /C00.019 0.341 0.032 0.181 0.530
MID5 0.666 0 .696 0.115 0.419 /C00.122 0.035 0.507
MID1 0.629 0 .687 0.146 0.442 /C00.064 0.090 0.491
MID6 0.623 0 .646 /C00.035 0.298 0.190 0.314 0.452
MID2 0.466 0 .504 /C00.019 0.243 0.223 0.317 0.301
MIG4 /C00.035 0.348 0.760 0 .753 0.058 0.171 0.570
MIG6 0.012 0.368 0.738 0 .741 /C00.015 0.104 0.550
MIG5 0.005 0.389 0.712 0 .743 0.183 0.296 0.585
MIG3 0.126 0.474 0.664 0 .744 0.118 0.249 0.583
MIG1 /C00.079 0.239 0.641 0 .607 0.026 0.110 0.373
MIG7 0.284 0.467 0.485 0 .583 /C00.255 /C00.119 0.446
MIG2 0.084 0.300 0.460 0 .495 /C00.037 0.053 0.251
MRE3R /C00.021 0.173 0.066 0.178 0.775 0 .781 0.613
MRE6R 0.118 0.181 /C00.193 /C00.017 0.754 0 .748 0.589
MRE2R /C00.009 0.166 0.065 0.169 0.687 0 .696 0.488
MRE1R 0.030 0.247 0.163 0.282 0.662 0 .694 0.513
MRE8R 0.007 0.163 0.078 0.171 0.565 0 .579 0.342
Note: PCA, principal component analysis; MO, market orientation; MID, modified intelligence dissemination; MIG, modified intelli-
gence generation; MRE, modified responsiveness.Market orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

RMSEA = 0.10; and for MRE, χ2=9 . 4 2 a t d .f .=5 ,
NFI = 0.96, CFI = 0.98, RMSEA = 0.06) (Hair et al.,
2006). Among those indices, all comparativeindices for three factors were good (NIF and CIF
were greater than 0.90); RMSEA values for MIG
and MRE were adequate, although RMSEA forMID was barely adequate (0.1). Lastly, the chi-
squared values for MID and MIG were significant,
which did not provide a good fit, while the chi-squared value for MRE were nonsignificant, imply-
ing a good fit. However, as previously explained,
the chi-squared test values are impacted by otherfactors; therefore, when evaluating the model fit,
additional fit statistics were taken into account.
Overall, these figures sh owed unidimensionality
of all three first-order dimensions of the TMOscale. Additionally, the re liability coefficients
(Cronbach ’sα) for each dimension were
MIG = 0.79, MID = 0.76, and MRE = 0.76, whilethe reliability for the whole 18-item scale was
0.84.
Structural model
After measurement model analysis, the structural
equation model was examined to evaluate thepredictive ability of the MO scale. Practically, it
was completed by fitting the second-order MO
factor (TMO) as the independent variable for stu-dent satisfaction (TS —the capital letter “T”im-
plying total or overall) as a dependent variable.
The structural parameter was estimated byAMOS, and the overall model fit was estimated
by fit indices. The fit statistics of the structural
model was adequate ( χ
2= 478.56, d.f. = 248,
NFI = 0.81, CFI = 0.90, RMSEA = 0.06) (Hair et al.,
2006). The results (see Table 4a n d Figure 3)
showed that the TMO was significantly and
positively associated with student satisfaction(β= 0.57, p<0.001). Therefore, through this
empirical analysis, the revised scale effectively
represents an adequate antecedent of studentsatisfaction. Furthermore, the revised and refined
MO-18 items can be considered a good MO
construct appropriately used in an academicenvironment.
Figure 2. Measurement model. MIG, modified intelligence
generation; MID, modified intelligence dissemination; MRE,
modified responsiveness.Trang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

Table 3. Completely standardized solution of the second-order MO model
Fit statistics and reliability Path estimate
TMO (18 items, second-order model) χ2218.87 MIG </C0/C0/C0 TMO 0.73 ***
d.f. 132 MID </C0/C0/C0 TMO 0.95 ***
NFI 0.83 MRE </C0/C0/C0 TMO 0.43 ***
CIF 0.92RMSEA 0.05
ECV 1.44
α 0.84
MIG (seven items) χ230.83 MIG1 </C0/C0/C0 MIG 0.49 ***
d.f. 14 MIG2 </C0/C0/C0 MIG 0.42 ***
NFI 0.93 MIG3 </C0/C0/C0 MIG 0.73 ***
CIF 0.96 MIG4 </C0/C0/C0 MIG 0.68 ***
RMSEA 0.07 MIG5 </C0/C0/C0 MIG 0.71 (fixed item)
ECVI 0.31 MIG6 </C0/C0/C0 MIG 0.67 ***
α 0.79 MIG7 </C0/C0/C0 MIG 0.52 ***
MID (six items) χ228.78
d.f. 9 MID1 </C0/C0/C0 MID 0.63 ***
NFI 0.91 MID2 </C0/C0/C0 MID 0.47 ***
CIF 0.93 MID3 </C0/C0/C0 MID 0.66 ***
RMSEA 0.10 MID4 </C0/C0/C0 MID 0.65 (fixed item)
ECVI 0.28 MID5 </C0/C0/C0 MID 0.61 ***
α 0.76 MID6 </C0/C0/C0 MID 0.58 ***
MRE (five items) χ2 9.42
d.f. 5 MRE1R </C0/C0/C0 MRE 0.63 (fixed item)
NFI 0.96 MRE2R </C0/C0/C0 MRE 0.64 ***
CIF 0.98 MRE3R </C0/C0/C0 MRE 0.73 ***
RMSEA 0.06 MRE6R </C0/C0/C0 MRE 0.63 ***
ECVI 0.17 MRE8R </C0/C0/C0 MRE 0.47 ***
α 0.76
Note: All estimates are completely standardized. A letter “M”before each factor means “modified, ”implying that this scale has been
modified from the original scale.
The asterisk (*) signs are used to refer to significant level.
MO, market orientation; TMO, overall second-order market orientation; MIG, modified intelligence generation; MID, modified intel-
ligence dissemination; MRE, modified responsiveness; d.f., degrees of freedom; NFI, normed fit index; CIF , comparative fit index;
RMSEA, root mean square error of approximation; ECVI, expected cross-validation index.
Table 4. Predictive validity of MO scale
Model fit statistics Path estimate
χ2 478.56 MIG </C0/C0/C0 TMO 0.73 ***
d.f. 248 MID </C0/C0/C0 TMO 0.95 ***
NFI 0.81 MRE </C0/C0/C0 TMO 0.43 ***
CIF 0.90 TS </C0/C0/C0 TMO 0.57 ***
RMSEA 0.06
ECVI 2.72
Note: The asterisk (*) signs are used to refer to significant level. MO, market orientation; TMO, overall second-
order market orientation; MIG, modified intelligence generation; MID, modified intelligence dissemination;
MRE, modified responsiveness; TS, total satisfaction; d.f., degrees of freedom; NFI, normed fit index; CIF , com-
parative fit index; RMSEA, root mean square error of approximation; ECVI, expected cross-validation index.Market orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

Figure 3. Structural model. MIG, modified intelligence generation; MID, modified intelligence dissemination; MRE, modified re-
sponsiveness; TMO, total second-order market orientation.Trang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

Summary
A structured process of scale refinement and valida-
tion has produced a revised 18 item MO scale
which shows better fit than the original scale devel-oped by Matsuno et al. (2000). The difference of
chi-squared values between two scales is statisti-
cally significant ( χ
2(74)= 262.091, p<0.001), prov-
ing that the revised scale is superior to theoriginal scale and therefore better represents the
MO construct in the university context. This study
succeeds in applying the MO construct in a profes-sional service domain (i.e., university) where vari-
ous market-oriented activities (i.e., intelligence
generation, intelligence dissemination, and respon-siveness) are incorporated to address different as-
pects of MO in the academic environment.
Content validity has a better representation of themeaning of items specifically designed to accom-modate the nature of the relationship between stu-
dents and the university.
This proposed scale consists of three distinct first-
order subscales as components of the broad second-
order MO construct. Empirical results indicate that
the three first-order subscales covary with thesecond-order factor, and the covariances are repre-
sented by statistically significant paths. This higher-
order model produces acceptable fit indices. Withregard to the structural model, the new scale is sta-tistically and positively related with student satisfac-
tion, indicating that MO is an important antecedent
of student satisfaction. In other words, if universitiesapply MO strategy effectively, students become
more satisfied with the decision they make when
selecting a university for their higher education.Through the findings in measurement and
structural model, it is suggested that the revised
scale can be used appropriately in the academic en-vironment as an antecedent to student satisfaction.
Conclusions and implications
The findings of the current study indicate a signifi-cant positive correlation between MO and studentsatisfaction, which is consistent with the findings
from previous literature concerning the increasing
importance of marketing in schools (Stokes, 2002;Bock, Poole, & Joseph, 2014), the relationship be-
tween branding strategies and student recruitment
(Bock et al., 2014; Chapleo, 2015), the effect of uni-
versity heritage and reputation on prospective stu-
dents ’attitudes (Merchant et al., 2015), and the
pivotal role of marketing in corporate environment(Sanzo, Santos, Vázquez, & Álvarez, 2003; Narver
& Slater, 1990). It seems reasonable to assume a pos-
itive relationship between MO and student satisfac-tion because, similar to a business, the universityattempts to implement marketing strategies in order
to fulfill students ’needs, wants, and aspirations,
thereby improving student satisfaction.
The findings, to some extent, can also be general-
ized into a broader context in social institutions,
such as community service, health care, or politicalinstitutions (for example, Fischer, 2014). There will
always be “customers ”of such institutions whose
needs, wants, and aspirations are to be satisfied.However, on the other hand, the idea of higher edu-cation itself as a social institution has shifted into a
semblance of “higher education industry ”or
“higher education business. ”This phenomenon is
taking place as more colleges and universities are
motivated to adapt management and marketing
frameworks from the business environment(Birnbaum, 2000; Stokes, 2002).
Indeed, MO is an option for universities to
adopt. The existence and development of eacheducational institution rely on its key resource: stu-
dents. Being able to make students satisfied with
educational programs and related services isconsidered one of the key goals in the educationalsetting. To translate this goal into reality, it is
important for university leaders and staff to
embrace the role of MO through a combinationof three components: intelligence generation, intel-
ligence dissemination, and responsiveness. Like
businesses, many universities now are operatingin a competitive environment, where they must
find ways to attract more students and seek moreMarket orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

financial resources. For state institutions that rely
in large part on state and federal funding, the num-
ber of enrolled students is a critical factor that de-termines the funding sponsored by states.
Students ’decisions as to whether they will study
at one university or switch to another universityresults in a considerable increase or decrease in
revenue through tuition fees. Only by paying ade-
quate attention to the significance of students ascustomers with specific needs and expectations
can universities overcome funding constraints and
increase their competitiveness.
Although a positive correlation between MO and
an organization ’s performance has been empirically
tested in previous literatures (Ledwith & O ’Dwyer,
2009; Morgan et al., 2009), the results of this study
have reflected a unique relationship between the
degree of MO developed in the education environ-
ment and the level of satisfaction perceived by stu-dents. One of the most significant implications
of the current research study is the extent to
which a university is market oriented determinesthe extent to which students feel positively abouttheir educational experience. It also further sug-
gests that MO may be correlated with the
university ’s performance because student satisfac-
tion is one of the measures of that performance.
Other indications of university ’sp e r f o r m a n c e
could be student retention and university reputa-tion—measures that are beyond the scope of the
current study.
In an academic setting, if MO enhances student
satisfaction, then strategically developing a higher
level of MO could contribute to a higher degree of
student satisfaction. These may include, but arenot limited to, cross-functional or inter-departmentalmeetings among colleges and departments, the
provision of opportunities to discuss students ’
needs, taking appropriate actions from a strategicmarketing plan perspective, creation of new
courses if appropriate, and the accommodation
of changes in education delivery. In brief, toadapt MO culture within universities, university
administrators, college deans, and departmentchairs must provide pertinent market-oriented
guidance and ensure that information is commu-
nicated and disseminated well within the univer-sity, its colleges, and its departments, and
actions are taken to respond to any needed
changes.
Another important academic or practical implica-
tion is related to the required changes in managing
universities. As indicated in the current research,university survival depends on the level of students ’
satisfaction, which, in turn, depends largely on how
information about students is managed. In order toachieve such goals, universities need significantchanges in mindset, system, and processes. Apart
from the mindset related to student –teacher rela-
tionships, students can be viewed as customersfrom whom valuable information may be derived.
Rigid and insensitive bureaucracy related to the
processing of information in universities must beeliminated. Finally, administrators in universities
should act as the agents of change, focusing on
leveraging the units, departments, and functionalareas in the university to be more responsive withthe most updated needs, wants, and aspirations of
students.
Limitations and future research
Similar to all exploratory studies, the results of
this research are not without limitations. First,
because we collected data from a convenience
sample and solely from one state university inthe United States, the issue of generalizability is
questionable. In addition, the data are not repre-
sentative of the entire university student body,rendering the results problematic for generaliz-ability. Notwithstanding this limitation, it is im-
portant to reiterate that this study is
exploratory, and its purpose is to operationalizea literature-derived MO construct (i.e., MARKOR)
in the higher education domain. In addition, de-
spite the directions from experts, a limitation ofthis study is that the scale is adapted from theTrang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

original MO construct that was created to mea-
sure managerial point of view, not customers ’
point of view. While Jacoby (1978) and Wrightand Kearns (1998) encourage marketing scholars
to adapt marketing constructs in different envi-
ronments and contexts, there is still the needfor future researchers to carve out a study com-
paring the views of students and those of univer-
sity administrators with the view of fine-tuningthe current model. In reality, students are not al-
ways in the best position to determine what
they need to succeed in their educational experi-ence; they need guidance, and therefore, theirsatisfaction level, while important, must be mea-
sured against the knowledge of the faculty mem-
bers who are aware of the necessary skill set forthe students ’success in universities.
The current research focuses on the applica-
tion of MO models in the higher education set-ting, a service domain. An emerging notion
bordering on the MO concept is service-
dominant logic (Vargo & Lusch, 2004). Such anotion involves the view of customers as co-producers of service. For future research, it
would be interesting to investigate how service-
dominant logic and co-production in universitieswill impact students ’satisfaction. Finally, future
studies replicating this research in several states
in the United States and/or other overseas envi-ronments (Stokes, 2002) will enhance generaliz-
ability of the current model. Continued
refinement of the model through longitudinalquantitative and qualitat ive studies will be an in-
teresting avenue for future research.
Biographical notes
Trang P . Tran (PhD, University of North Texas) is As-
sistant Professor of Marketing at the State University
of New York at Oneonta. His research interests in-clude international marketing, customer behavior,and service marketing. His research has been
accepted or published in the Journal ofMacromarketing, Computers in Human Behavior ,
International Journal of Nonprofit and Voluntary
Sector Marketing ,Services Marketing Quarterly ,
andJournal of Marketing Development and Com-
petitiveness . Also, his name appeared in several pro-
ceedings of the American Marketing Association,Academy of Marketing Science, Society of Marketing
Advances, Association of Marketing Theory and
Practice, and Decision Science Institute.
Charles Blankson (PhD, Kingston University) is
Associate Professor at the University of North Texas.
He received his post-doctoral research in small busi-ness management at the Kingston University SmallBusiness Research Center prior to commencing uni-
versity teaching career. His research articles have
been published or are forthcoming in the Journal
of Advertising Research ,Journal of Business Re-
search ,European Journal of Marketing ,Industrial
Marketing Management ,Psychology & Marketing ,
Journal of Marketing Theory & Practice ,Journal of
Marketing Management ,Journal of Current Issues
and Research in Advertising ,International Jour-
nal of Advertising ,Journal of Services Marketing ,
Journal of Business and Industrial Marketing ,
Journal of Product & Brand Management ,Thun-
derbird International Business Review ,Journal of
Global Business ,Journal of Strategic Marketing
and others. He is on the editorial review boards of
Industrial Marketing Management and the Journalof International Marketing and is ad-hoc reviewer
for several marketing journals including the Interna-
tional Marketing Review, Journal Business Research,Journal of Marketing Management, Thunderbird In-
ternational Business Review and Journal of Product
& Brand Management.
Widyarso Roswinanto (PhD, University of North
Texas) is Professor at the PPM School of Manage-
ment, in Indonesia. He has most recently been a Re-
search and Teaching Fellow at the University ofNorth Texas. His primary areas of research interest
are in brand management, advertising, and cognitive
research. He also writes and engages in research andconsulting in the areas of marketing strategy and
strategic brand management.Market orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

References
Aiello Jr, A, Czepiel JA, Rosenberg LJ. 1977. Scaling the
heights of consumer satisfaction: An evaluation of alterna-
tive measures. Consumer Satisfaction ,Dissatisfaction ,
and Complaint Behavior (Indiana University School
of Business, Bloomington, IN): 43 –50.
Atuahene-Gima K. 1996. Market Orientation and Innova-
tion. Journal of Business Research 35(2): 93 –103.
Baker WE, Sinkula JM. 2009. The Complementary Effects
of Market Orientation and Entrepreneurial Orientation
on Profitability in Small Businesses. Journal of Small
Business Management 47(4): 443 –464.
Birnbaum R. 2000. Management Fads in Higher Educa-
tion: Where They Come from, What They Do, WhyThey Fail . Jossey-Bass: San Francisco.
Bock DE, Poole SM, Joseph M. 2014. Does Branding
Impact Student Recruitment: A Critical Evaluation. Jour-
nal of Marketing for Higher Education 24(1): 11 –21.
Boshoff C. 1999. RECOVSAT: An Instrument to Measure
Satisfaction with Transaction-specific Service Recovery.Journal of Service Research 1(3) (February): 236 –249.
Boyd D, Grossman P , Ing M, Lankford H, Loeb S, Wyckoff
J. 2011. The Influence of School Administrators onTeacher Retention Decisions. American Educational
Research Journal 48(2): 303 –333.
Brookes M. 2003. Higher Education: Marketing in a
Quasi-commercial Service Industry. International
Journal of Nonprofit and Voluntary Sector Market-ing8(2): 134 –142.
Brown SW , Swartz TA. 1989. A Gap Analysis of Professional
Service Quality. Journal of Marketing 53(2): 92 –98.
Caplan L. 1997. Contemporary calculus. U.S. News and
World Report (September 1), 80 –82.
Caruana A, Ramaseshan B, Ewing MT. 1998. Do Universi-
ties That Are More Market Orientated Perform Better?
International Journal of Public Sector Management11(1): 55 –70.
Chapleo C. 2007. Barriers to Brand Building in UK Univer-
sities? International Journal of Nonprofit and Volun-
tary Sector Marketing 12(1): 23 –32.
Chapleo C. 2015. An Exploration of Branding Approaches
in UK Universities. International Journal of Nonprofit
and Voluntary Sector Marketing 20:1–11.Clark B. 1996. Education via Internet. Chartered Accoun-
tants Journal of New Zealand 75(2): 6 –
8.
Drucker PF . 1954. The Practice of Management , 1st edn.
Harper: New York.
Esteban Á, Millán Á, Molina A, Martín-Consuegra D. 2002.
Market Orientation in Service: A Review and Analysis.European Journal of Marketing 36(9/10): 1003 –1021.
Felton AP . 1959. Making the Marketing Concept Work.
Harvard Business Review 37(4): 55 –65.
Fischer S. 2014. Hospital Positioning and Integrated
Hospital Marketing Communications: State-of-the-art
Review, Conceptual Framework, and ResearchAgenda. Journal of Nonprofit & Public Sector Mar-
keting 26(1): 1 –34.
Fredriksson A. 2009. On the Consequences of the
Marketisation of Public Education in Sweden: For-profit Charter Schools and the Emergence of the
“Market-oriented Teacher ”.European Educational Re-
search Journal 8(2): 299 –310.
G a r v i nD A .1 9 8 7 .C o m p e t i n go nt h eE i g h tD i m e n –
sions of Quality. Harvard Business Review 65(6):
101–109.
Glazer R. 1991. Marketing in an Information-intensive En-
vironment: Strategic Implications of Knowledge as an
Asset. Journal of Marketing 55(4): 1 –19.
Hair JF , Black WC, Babin BJ, Anderson RE, Tatham RL.
2006. Multivariate Data Analysis , Vol. 6. Pearson
Prentice Hall: Upper Saddle River, NJ.
Hammond KL, Webster RL, Harmon HA. 2006. Market
Orientation, Top Management Emphasis, and Perfor-
mance Within University Schools of Business: Implica-
tions for Universities. Journal of Marketing Theory
and Practice 14(1): 69 –85.
Han JK, Kim N, Srivastava RK. 1998. Market Orientation
and Organizational Performance: Is Innovation a Miss-ing Link? Journal of Marketing 62(4): 30 –45.
Harris LC. 2001. Market Orientation and Performance:
Objective and Subjective Empirical Evidence from UK
Companies. Journal of Management Studies 38(1):
17–43.
Hemsley-Brown J, Oplatka I. 2010. Market Orientation in
Universities: A Comparative Study of Two NationalHigher Education Systems. International Journal of
Educational Management 24(3): 204 –220.Trang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

Horn JL. 1965. A Rationale and Test for the Number of Fac-
tors in Factor Analysis. Psychometrika 30:1 7 9 –185.
Houston FS. 1986. The Marketing Concept: What It Is and
What It Is Not. Journal of Marketing 50(2): 81 –87.
Jacoby J. 1978. Consumer Research: How Valid and Use-
ful Are All Our Consumer Behavior Research Findings?A State of the Art Review. Journal of Marketing 42:
87–96.
Jaworski BJ, Kohli AK. 1993. Market Orientation: Ante-
cedents and Consequences. Journal of Marketing
57(3): 53.
Kirca AH, Jayachandran S, Bearden WO. 2005. Market
Orientation: A Meta-analytic Review and Assessmentof Its Antecedents and Impact on Performance. Jour-
nal of Marketing 69(2): 24 –41.
Kohli AK, Jaworski BJ. 1990. Market Orientation: The
Construct, Research Propositions, and Managerial Im-
plications. Journal of Marketing 54(2): 1 –18.
Kotler P , Levy SJ. 1969. Broadening the Concept of Mar-
keting. Journal of Marketing 33(1): 10 –15.
Krepapa A, Berthon P , Webb D, Pitt L. 2003. Mind the
Gap: An Analysis of Service Provider Versus CustomerPerceptions of Market Orientation and the Impact onSatisfaction. European Journal of Marketing 37(1/2):
197–218.
Krieger J. 1996. Virtual Office Hours Project Links Stu-
dents and Professors at UCLA. Chemical and Engineer-
ing News 74(11): 37 –38.
Lavidge RJ. 1970. The Growing Responsibilities of Mar-
keting. Journal of Marketing 34(1): 25 –28.
Ledwith A, O ’Dwyer M. 2009. Market Orientation, NPD
Performance, and Organizational Performance in SmallFirms. Journal of Product Innovation Management
26(6): 652 –661.
Levitt T. 2008. Marketing Myopia. Harvard Business
School Press Books 1.
Lin CH, Peng CH, Kao DT. 2008. The Innovativeness Ef-
fect of Market Orientation and Learning Orientation
on Business Performance. International Journal of
Manpower 29(8): 752 –772.
Lundkvist A, Yakhlef A. 2004. Customer Involvement
in New Service Development: A ConversationalApproach. Managing Service Quality 14(2/3):
249–257.Matsuno K, Mentzer JT, Rentz JO. 2000. A Refinement and
Validation of the MARKOR Scale. Journal of the Acad-
emy of Marketing Science 28(4): 527.
McNamara CP . 1972. The Present Status of the Marketing
Concept. Journal of Marketing 36(1): 50 –57.
Merchant A, Rose GM, Moody G, Mathews L. 2015. Effect
of University Heritage and Reputation on Attitudes of
Prospective Students. International Journal of Non-
profit and Voluntary Sector Marketing 20:2 5–37.
Morgan N, Vorhies D, Mason C. 2009. Market Orientation,
Marketing Capabilities, and Firm Performance. Strate-
gic Management Journal 30(8): 909 –920.
Narver JC, Slater SF . 1990. The Effect of a Market Orien-
tation on Business Profitability. Journal of Marketing
54(4): 20 –35.
Noble CH, Sinha RK, Kumar A. 2002. Market Orientation
and Alternative Strategic Orientations: A Longitudinal
Assessment of Performance Implications. Journal of
Marketing 66(4): 25 –39.
Oliver RL. 1981. Measurement and Evaluation of Satisfac-
tion Processes in Retail Settings. Journal of Retailing
57(3): 25.
Pallant JF , Bailey CM. 2005. Assessment of the Structure of
the Hospital Anxiety and Depression Scale in Musculo-
skeletal Patients. Health and Quality of Life Outcomes
3(1): 82.
Paul KE. 1994. Market Orientation, Quality, and Cus-
tomer Satisfaction in an Industrial Services Market .
Doctoral dissertation: University of Memphis.
Parasuraman A, Zeithaml VA, Berry LL. 1988. SERVQUAL:
A Multiple-item Scale for Measuring Consumer
Perceptions of Service Quality. Journal of Retailing
64(1): 12 –40.
Pelham AM, Wilson DT. 1996. A Longitudinal Study of the
Impact of Market Structure, Firm Structure, Strategy,and Market Orientation Culture on Dimensions ofSmall-firm Performance. Journal of the Academy of
Marketing Science 24(1): 27 –43.
Poprzeczny J. 1996. Unis pushed to crossroad. The Sun-
day Times 21 July, 8.
Quinn JB. 1992. Intelligent Enterprise . The Free Press:
New York, NY .
Sanzo MJ, Santos ML, Vázquez R, Álvarez LI. 2003. The Ef-
fect of Market Orientation on Buyer –SellerMarket orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

Relationship Satisfaction. Industrial Marketing Man-
agement 32(4): 327 –345.
Sargeant A. 2008. Marketing for nonprofit organizations.
The Marketing Book: 526.
Siu NYM, Wilson RMS. 1998. Modelling Market Orienta-
tion: An Application in the Education Sector. Journal
of Marketing Management 14(4): 293 –323.
Stokes D. 2002. Entrepreneurial Marketing in the Public
Sector: The Lessons of Headteachers as Entrepreneurs.Journal of Marketing Management 18: 397 –414.
Storey R. 1996. VC ’s discuss Curtin, Murdoch merger. The
Australian 17 July, 37.
Vargo SL, Lusch RF . 2004. Evolving to a New Dominant Logic
for Marketing. Journal of Marketing 68(1): 1 –17.
Voon BH. 2006. Linking a Service-driven Market Orientation to
Service Quality. Managing Service Quality 16(6): 595 –619.
Webb D, Webster C, Krepapa A. 2000. An Exploration of the
Meaning and Outcomes of a Customer-defined Market Ori-
entation. Journal of Business Research 48(2): 101 –112.
Wright M, Kearns Z. 1998. Progress in Marketing Knowl-
edge. Journal of Empirical Generalizations in Mar-
keting Science 3:1–21.
Zhou KZ, Brown JR, Dev CS. 2009. Market Orientation,
Competitive Advantage, and Performance: A Demand-
based Perspective. Journal of Business Research
62(11): 1063 –1070.
Appendix A. Measurement scales
Intelligence generation
MIG1. My University/College polls students at
least once a year to assess the quality of education.
MIG2. In my University/College, intelligence on
rival universities/colleges is generated indepen-dently by several departments.
MIG3. My University/College periodically re-
views the likely effect of changes in education envi-
ronment on students.
MIG4. My University/College frequently collects
and evaluates general educational information.
MIG5. My University/College maintains contacts
with student bodies/associations in order to collect
and evaluate pertinent information.MIG6. My University/College collects and evalu-
ates information concerning general social trends
that might affect our education.
MIG7. My University ’s Leader/College ’s Dean
spends time with the faculty to learn more about
various aspects of their education.
MIG8R*. In my University/College, only a few
people are collecting rival university ’s/college ’s
information.
Intelligence dissemination
MID1. Academic staff in my University/College
spends time discussing students ’future needs with
other staff in the university/college.
MID2. My University/College periodically circu-
lates documents (e.g., newspapers, newsletters)that provide information on students.
MID3. My University/College has cross-functional
meetings very often to discuss student educationand student developments.
MID4. My University/College has inter-
departmental meetings to update the knowledge ofregulatory requirements.
MID5. Technical people in my College spend a
lot of time sharing information about technologywith other colleges in the university.
MID6. Education information spreads quickly
through all levels in my University/College.
Responsiveness
MRE1R. For one reason or another, my University/
College tends to ignore changes in students ’course
or service.
MRE2R. The courses my University/College provides
depend more on internal politics than real students ’needs.
MRE3R. My University/College is slow to start
new courses even though my University/College
thinks they are better than existing ones.
MRE4*. If a rival university were to launch an inten-
sive campaign targeted at students, my University/
College would implement a response immediately.Trang Tran et al.
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm

MRE5*. The activities of the different departments
in my University/College are well coordinated.
MRE6R. Even if my University/College came up
with a great marketing plan, my University/College
probably would not be able to implement it in a
timely fashion.
MRE7*. If a special interest group were to
publicly accuse my University/College of old curric-
ulum, my University/College would respond to thecriticism immediately.
MRE8R. My University/College tends to take lon-
ger than rival universities to respond to a changein education policy.
Student satisfaction
S1. I am satisfied with my decision to attend my
University/CollegeS2R. If I had to do it all over again, I would not en-
roll in my University/College
S3. My choice to enroll in my University/College
was a wise one
S4R. I feel bad about my decision to enroll in my
University/College
S5. I think I did the right thing when I decided to
enroll in my University/College
S6R. I am not happy that I enrolled in my
University/College
Note: Items with letter “R”at the end are reverse
coded. Items with asterisk “*”are deleted after
confirmatory factor analysis.Market orientation: an option for universities
Copyright © 2015 John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., 2015
DOI: 10.1002/nvsm
View publication statsView publication stats