10.1177/1096348005276496JOURNAL OF HOSPITALITY & TOURISM RESEARCHHu et al. / E-RELATIONAL CHARACTERISTICS

RESEARCH NOTE

E-RELATIONAL CHARACTERISTICSON HOSPITALITY AND TOURISM

PROGRAM WEB SITES

Clark HuYunxuan (Tess) Han

Temple University

SooCheong (Shawn) JangPurdue University

Billy BaiUniversity of Nevada, Las Vegas

The increasingly competitive market of recruiting prospective students stimulates aca-demic program administrators to adopt relationship marketing (RM) principles and to le-verage the capacity of the Internet technologies in facilitating RM practices. The authorsdevelop an operational framework to evaluate electronic RM (e-RM) features on the Websites of selected postsecondary hospitality and tourism programs in the United States. As aresult of the two-step cluster analysis procedure, the sampled programs are first groupedinto two distinctive categories based on designed measures of preidentified e-RM featuresadopted on their Web sites. A logistic regression analysis then reveals that the program sizeand the level of the program (undergraduate vs. graduate) are significant indicators of thelevel of e-RM Web feature adoption.

KEYWORDS: electronic relationship marketing; hospitality and tourism educationalprograms; Internet technology; program Web sites; Web site features

The boom of the hospitality and tourism business has brought about anincrease in the number of educational programs and has stirred up the competitionbetween these programs. Strategically, the programs vying for recruiting studentshave tried to pay attention to their Web sites to provide necessary information andto better present themselves as appealing to students. We believe that hospitalityand tourism programs can be managed and promoted more efficiently and effec-tively by integrating the use of relationship marketing (RM) theories withinnovative Web site features.

It has been recognized that the traditional marketing mixes are not as effectiveas relationship-based marketing practices in capturing the changing behavior of

Journal of Hospitality & Tourism Research, Vol. 29, No. 4, November 2005, 508-522DOI: 10.1177/1096348005276496© 2005 International Council on Hotel, Restaurant and Institutional Education

508

today’s competitive marketplace (Grönroos, 1990, 1997). The concept of RM hasbeen appealing to marketers because firms can benefit financially from repeatedpurchases by loyal customers and reduced high costs incurred in new customeracquisition (Shoemaker & Lewis, 1999). Meanwhile, Internet technologies haveprovided an exceptional opportunity for improving relationship building withstrong interactivity and personalizability. In the academic context, school andprogram Web sites enable both current and prospective students to access desiredinformation anytime and anywhere in the world. As the youngsters become moretechnologically savvy, future students are expected to interact more frequentlywith educational programs in an online environment for their education deci-sions. In this respect, program Web sites can be considered an essential marketingtool for student recruitment and retention, as commercial Web sites are for theircustomers (Bai, Jang, & Hu, 2003; Gilbert, Powell-Perry, & Widijoso, 1999).

Despite the importance of the program Web sites, little attention has been paidto understanding the contemporary RM concept in developing program Websites. We attempted to fill this research gap by (a) developing an operationalframework for evaluating the use of the RM features on program Web sites, (b)clustering the hospitality and tourism programs based on the level of the RM fea-ture adoption on their Web sites, and, finally, (c) identifying influential programand school characteristics that tend to indicate greater RM efforts on programWeb sites.

LITERATURE REVIEW

Although Berry (1983) first discussed the primary RM ideas only 2 decadesago, the concept of RM, characterized by value enhancement and long-termreciprocal relationships between buyers and sellers, has dominated managerialand academic discussions of the 1990s (Tuominen, Rajala, & Möller, 2000).Among all the efforts to define RM, Grönroos’s (1996) definition is considered tobe the best based on its comprehensive coverage of the underlying conceptualiza-tions of RM and its acceptability throughout the RM community:

Relationship marketing is to identify and establish, maintain and enhance andwhen necessary also to terminate relationships with customers and other stake-holders, at a profit, so that the objectives of all parties are met, and that this isdone by a mutual exchange and fulfillment of promises. (p. 7)

Meanwhile, Internet technology as a practical instrument has had a significantimpact on building, maintaining, and enhancing relationships (Domegan, 1996;Donaldson & O’Toole, 2002; Sisodia & Wolfe, 2000). Gilbert et al. (1999) ana-lyzed 143 hotel Web sites and studied management attitudes toward the use of theInternet as an RM tool. They reported that the majority of the hotels had used theWeb as an information center and a reservation medium, whereas only a smallportion of them viewed the Internet as an RM tool. Arnott and Bridgewater’s(2002) study of 78 goods and services companies also showed that the majority ofstudied firms adopted the Internet for informational purposes, whereas relation-

Hu et al. / E-RELATIONAL CHARACTERISTICS 509

ship building and transactional uses of the Internet were found mainly in larger orinternational firms. In addition, a few researchers examined the characteristics ofsome businesses, which indicated superior online customer relationship buildingand the impact of such electronic RM (e-RM) on performance. Feinberg, Kadam,Hokama, and Kim (2002) comparatively examined the Web sites of the top 100specialty stores, standard retail stores, and Internet retailers and found thatInternet retailers were more likely to have e-customer relationship attributes ontheir Web sites. They argued that some features, such as the company profile,mailing address, links, and search engines, were highly associated with customersatisfaction. Based on the evidence drawn from retail Web sites, Feinberg andKadam (2002) also discovered a positive relationship between the extent ofadopting an electronic customer RM (e-CRM) feature on a Web site and customersatisfaction with that Web site, even though not all e-CRM attributes were equallyimportant in generating customer satisfaction.

The issue of Web site effectiveness has also attracted some attention from edu-cational researchers. A study conducted by Lipman Hearne and Fastweb.comindicated that Web sites were considered the most important information sourceduring high school students’ college search process and one of the important fac-tors in making their final college choice (Stoner, 2001). Nevertheless, academicinquiries into Web-based relationship building with prospective students havebeen scarce and fragmented. Most of the industry reports were based on one oftwo schools that made significant investment in Web site construction andreceived satisfying results (Parmar, 2004). With limited theoretical or empiricalevidence, some scholars suggested interactive and individualized service featuresas the most effective mechanisms attracting students, whereas some others inves-tigated Web page elements that motivate, inhibit, or increase the likelihood ofprospects’ applications (Poock & Lefond, 2001; Stoner, 2001).

Developing an Operational Framework for the Current Study

With attention toward the supplier’s initiative to communicate with customers,Kotler, Bowen, and Makens (2003, p. 391) discussed a model that illustrated fiveprogressive levels of relationships: basic, reactive, accountable, proactive, andpartnership. The communication-focused model entails significant implicationsfor the Internet-facilitated relationship management because Internet technologyhas resulted in a revolutionary transformation of traditional communication. Spe-cifically, the Internet Web site provides abundant alternative communication toolsthat enable the expansion and diversification of business-customercommunication.

In a recent study on the effectiveness of relationship building on academic Websites, Klassen (2002) used this five-level model to evaluate top- and lower rankeduniversities’ Web sites and concluded that few university Web sites had reachedthe “partnership level” of RM and suggested that top-ranked schools enjoyed “an‘interactive edge’ by offering Web sites suitable to creating and sustaining effec-tive relationships with their students” (Klassen, 2002, p. 84).

The present study attempted to advance the inquiry by developing a more com-prehensive framework for benchmarking the program Web sites. Based on previ-

510 JOURNAL OF HOSPITALITY & TOURISM RESEARCH

ous work by Kittle and Ciba (1998) and Klassen and Sitzman (2000), Klassen(2002) developed three information areas offered by university and college Websites (applications, faculty, and tours) in relation to the five relationship levelsmentioned by Kotler (1992). Drawing on the aforementioned past studies oninformation-seeking behaviors of prospective students and our observations fromthe selected program Web sites, we built on Klassen’s framework and strength-ened it by including one additional important dimension, program information(nonexistent in Klassen’s study), and expanded the Klassen’s “tours” dimensionto “on-campus facilities and community resource information” (p. 83) to reflectenhanced information sources available on the Web. Therefore, the current studyidentified four dimensions of academic program marketing—academic applica-tion and financial services, program information, faculty and current studentinformation, and on-campus facilities and community resource information—and created a 5 × 4 matrix based on these four dimensions and the five relationshiplevels (Table 1). Some earlier studies revealed that the application and financialaid services are important for the prospective students at both the undergraduateand graduate levels (Dean & Trainor, 2002; Poock & Lefond, 2003). The financialassistance services on program Web sites allows prospective students to explorethe various types of available aids, contact the institution with questions, and evenapply for aid online. Program information, such as curriculum design, coursedescription, and career opportunities, is desirable to prospective students in gen-eral (Dean & Trainor, 2002). Faculty and current student information has differ-ent appeals to prospective students: Graduate students want to find more informa-tion about faculty members and their research interests, whereas undergraduatestudents are more interested in the student organizations and their activities (Dean& Trainor, 2002). The on-campus facilities and community resources dimensionprovides information about both academic and nonacademic (entertainment andrecreational) facilities on campus, sites of interest in the neighborhood,instructions for campus visits, and housing issues.

The original model and definitions by Kotler et al. (2003) provided the concep-tual foundations for identifying the appropriate Web site features at each gridwithin the matrix. At the basic level, there is no communication between theschool and the prospects, except the information provision from the school pro-gram’s end. The reactive level is featured by the school’s provision of contactinformation to the prospects, who can subsequently initiate the communication.Some representative Web site features included e-mail addresses and onlineinformation request forms, which also serve as an information collection tool.The program striving for the accountable level of relationship with its prospectsdemonstrates its intention and effort to create trust with prospective students.Some program Web sites listed the responsible staff or faculty members’ namesand job titles in addition to phone numbers (personal contact information) tomake individual contacts as easily and trustily as possible. The proactive level isfeatured by the supplier’s effort to provide extra information or services. Manyprogram Web sites provided more than one application option and a variety ofcommunication channels for the students’convenience. The interactivity and per-sonalization of the Internet greatly enhance the program marketer’s ability to

Hu et al. / E-RELATIONAL CHARACTERISTICS 511

512

Tab

le 1

Op

erat

ion

al E

-RM

Mat

rix

for

Pro

gra

m W

eb s

ites

App

licat

ion

Pro

cedu

re a

ndFa

culty

and

Cur

rent

Stu

dent

Pro

gram

Info

rmat

ion

On-

Cam

pus

Faci

litie

s an

dF

inan

cial

Aid

Ser

vice

(D

imen

sion

1)

Info

rmat

ion

(Dim

ensi

on 2

)(D

imen

sion

3)

Com

mun

ity R

esou

rces

(D

imen

sion

4)

Bas

ic (Lev

el 1

)1.

Gen

eral

app

licat

ion

and

adm

issi

on in

form

atio

n at

prog

ram

Web

site

2.G

ener

al fi

nanc

ial a

idin

form

atio

n at

pro

gram

Web

site

3.A

pplic

atio

n or

fina

ncia

l aid

info

rmat

ion

in th

e fo

rm o

f FA

Qat

pro

gram

or

univ

ersi

ty W

ebsi

te

1.Li

st o

f fac

ulty

mem

bers

2.Li

st o

f stu

dent

org

aniz

atio

nsat

uni

vers

ity W

eb s

ite

1.G

ener

al p

rogr

am in

form

atio

n2.

Gen

eral

pro

gram

info

rmat

ion

(exc

ludi

ng a

pplic

atio

n or

finan

cial

aid

) in

the

form

of F

AQ

1.G

ener

al c

ampu

s fa

cilit

ies

and

reso

urce

s in

form

atio

n at

univ

ersi

ty W

eb s

ite2.

Gen

eral

cam

pus

map

s or

dire

ctio

ns3.

Pro

fess

iona

l fac

ilitie

s an

dre

sour

ces

info

rmat

ion

at p

ro-

gram

Web

site

(e.

g., r

esea

rch

cent

er, c

aree

r de

velo

pmen

tce

nter

, lab

kitc

hen,

etc

.)R

eact

ive

(Lev

el 2

)1.

Gen

eral

con

tact

info

rmat

ion

for

appl

icat

ion

at p

rogr

am W

ebsi

te2.

Gen

eral

con

tact

info

rmat

ion

for

finan

cial

aid

at p

rogr

am W

ebsi

te (

link

to u

nive

rsity

Web

site

will

suf

fice)

3.O

nlin

e in

form

atio

n re

ques

t for

mat

pro

gram

or

univ

ersi

ty W

ebsi

te

1.Fa

culty

dire

ctor

y (li

st w

ithco

ntac

t inf

orm

atio

n)2.

Des

crip

tive

intr

oduc

tion

(tex

tan

d/or

gra

ph)

of s

tude

ntor

gani

zatio

ns3.

Gen

eral

e-m

ails

(or

link

s) to

stud

ent o

rgan

izat

ions

at

univ

ersi

ty W

eb s

ite

1.G

ener

al c

onta

ct in

form

atio

nab

out p

rogr

am in

form

atio

nat

pro

gram

Web

site

2.O

nlin

e in

form

atio

n re

ques

tfo

rm a

t pro

gram

Web

site

1.A

ny c

onta

ct in

form

atio

n to

requ

est c

ampu

s in

form

atio

n or

to a

rran

ge c

ampu

s vi

sit a

nd s

ofo

rth

2.O

nlin

e in

form

atio

n re

ques

t for

m3.

Onl

ine

regi

stra

tion

(sig

n-up

)fo

rm (

for

cam

pus

tour

s an

d so

fort

h)

513

Acc

ount

able

(Lev

el 3

)1.

Per

sona

lized

con

tact

info

rmat

ion

for

appl

icat

ion

atpr

ogra

m W

eb s

ite2.

Per

sona

lized

con

tact

info

rmat

ion

for

finan

cial

aid

at p

rogr

am W

eb s

ite3.

Dow

nloa

dabl

e ac

adem

icap

plic

atio

n fo

rms

4.D

ownl

oada

ble

finan

cial

aid

appl

icat

ion

form

s

1.Fa

culty

per

sona

l Web

pag

esco

ntai

ning

bas

ic in

form

atio

n(e

.g.,

cont

act,

phot

o, d

egre

es,

and

cour

ses

taug

ht)

2.S

tude

nt p

rofe

ssio

nal

orga

niza

tions

at p

rogr

am o

run

iver

sity

Web

site

3.P

erso

naliz

ed c

onta

ct in

form

a -tio

n of

the

stud

ent p

rofe

ssio

nal

orga

niza

tion

repr

esen

tativ

es

1.P

erso

naliz

ed c

onta

ct in

form

a -tio

n at

pro

gram

Web

site

2.E

xten

sive

pro

gram

info

rmat

ion

with

link

s (e

.g.,

links

to a

dmis

-si

on r

equi

rem

ents

, cur

ricul

um,

depa

rtm

enta

l fac

ulty

and

sta

ff,de

part

men

t fac

ilitie

s, e

tc.)

at

prog

ram

Web

site

1.Li

nks

to lo

dgin

g an

d ot

her

visi

tor

serv

ice

busi

ness

esin

the

com

mun

ity2.

Link

s to

Web

site

s of

loca

lin

tere

sts

3.C

ampu

s sa

fety

info

rmat

ion

4.H

ousi

ng in

form

atio

n

Pro

activ

e(L

evel

4)

1.O

nlin

e pr

ogra

m a

pplic

atio

n2.

Onl

ine

finan

cial

ass

ista

nce

appl

icat

ion

3.In

form

atio

n on

off-

cam

pus

fund

ing

sour

ces

(ban

ks,

finan

cial

inst

itute

s, e

tc.)

1.Fa

culty

per

sona

l Web

pag

esco

ntai

ning

det

aile

d in

form

atio

nor

link

s to

CV

(e.

g., e

duca

tion,

indu

stry

exp

erie

nce,

res

earc

hin

tere

sts,

and

pub

licat

ions

)2.

Exa

mpl

es o

f cur

rent

stu

dent

test

imon

y

1.G

ener

al in

form

atio

n re

late

dto

inte

rnsh

ip p

rogr

am o

rca

reer

opt

ions

2.S

tate

men

ts o

f or

links

toco

urse

des

crip

tions

1.Li

nks

to in

tera

ctiv

e m

aps

ordi

rect

ions

req

uest

s2.

Cam

pus

tour

—pi

ctor

ial

3.S

earc

h en

gine

with

in u

nive

rsity

Web

site

s4.

Sea

rch

engi

ne e

xten

ded

to th

eIn

tern

et5.

Onl

ine

hous

ing

appl

icat

ion

form

Par

tner

ship

(Lev

el 5

)1.

Onl

ine

appl

icat

ion

trac

king

thro

ugh

pers

onal

acc

ount

(fo

rei

ther

aca

dem

ic o

r fin

anci

al a

idap

plic

atio

ns)

2.Li

nks

to o

ff-ca

mpu

s fu

ndin

gso

urce

s (W

eb s

ites

of b

anks

,fin

anci

al in

stitu

tes,

etc

.)

1.Fa

culty

per

sona

l Web

site

s co

n-ta

inin

g ex

tens

ive

info

rmat

ion

(e.g

., co

urse

syl

labi

, res

earc

hso

urce

s lin

ks, o

r in

dust

ry li

nks)

2.D

epar

tmen

tal n

ewsl

ette

r3.

Stu

dent

onl

ine

supp

ort c

omm

u-ni

ties

(new

sgro

ups)

1.O

nlin

e in

tern

ship

or

job

post

ings

2.Li

nks

to in

tera

ctiv

e jo

b se

arch

engi

nes

3.A

cces

s to

onl

ine

clas

sroo

ms

1.E

-mai

l lis

tser

v op

tion

to r

ecei

veca

mpu

s ne

ws

and

upda

tes

2.O

nlin

e qu

ery

sear

ch fo

r ho

usin

gin

form

atio

n3.

Cam

pus

tour

—vi

deo

stre

amin

g4.

Onl

ine

chat

roo

m

Not

e:e-

RM

= e

lect

roni

c re

latio

nshi

p m

arke

ting;

FAQ

= fr

eque

ntly

ask

ed q

uest

ions

;CV

= c

urric

ulum

vita

e.

develop a partnership-level relationship. For example, the Intranet, an onlineforum, and other communication portals create virtual communities for theirprospects (Stoner, 2001). The aforementioned operational model provides afoundation for a quantitative assessment of the relationship-oriented features onthe studied program Web sites.

RESEARCH METHODOLOGY

Sample and Data Collection

Based on the Guide to College Programs in Hospitality, Tourism & CulinaryArts published by the International Council on Hotel, Restaurant, and Institu-tional Education (CHRIE, 2002), the sample of this study only included the 4-year undergraduate and graduate programs in the United States. The URLs of theprograms’Web sites provided in the guide were verified and, whenever necessary,updated using major search engines, such as google.com. The final sample com-prised the proprietary Web sites of 91 hospitality and tourism programs (22 grad-uate and 69 undergraduate programs). After completing a pilot test using the 24randomly selected program Web sites, we extensively discussed all questionsregarding discrepancies in collected data, reached consensus in feature defini-tions, and subsequently modified the operational framework (Table 1) for thefinal data collection. Data from the final sample was collected during a 3-weekperiod in August 2003. Each e-RM feature for an observation was binary coded,with 1 recorded for the existence of a feature and 0 otherwise. Each of the fourauthors examined one program marketing dimension (i.e., the columns in thematrix) of all the selected Web sites to ensure coding consistency and to increaseoperational efficiency of the procedure. The statistical analysis used the totalscores along the relationship levels (rows), which was obtained by summing thecell ratios (cell ratio = number of observed features divided by number of identi-fied features) along the level. By doing this, the assessments of the four evaluatorswere equally weighted into the analysis. Also obtained from the InternationalCHRIE (2002) guide was additional information on the program and schoolattributes, including the program’s academic level (undergraduate vs. graduate),the program size in terms of the self-reported enrollment number, the emphasesoffered by the program, the type of school funding, the annual tuition in dollaramount, and the geographical location of the school.

Statistical Analyses

The building block used to construct a series of complex scores for data analy-sis was the cell ratio (a ratio between the numbers of observed and proposed Website features within the cell at each grid of the matrix). Although the assessmentwas split between us along the columns, the analysis was conducted on the scoresalong the rows (the five relationship levels) and across the whole matrix (all fiverelationship levels and four dimensions of program marketing for each sampledWeb site). For example, to quantify the e-RM Web site feature application statusat the basic level, the cell ratios of the four dimensions at this level were summed,

514 JOURNAL OF HOSPITALITY & TOURISM RESEARCH

meaning that the assessment results of the four evaluators were summed. Eventhough there are more features in Dimension 1 than in Dimension 2, the use of cellratio eliminated this difference and ensured that each evaluator’s opinion weighedone fourth in the final score, which was close to a pooled result across a panel. Inaddition, the subjective bias was minimized by the fact that each of the fourauthors was responsible for data collection of each of the four dimensions only,which was intended to maintain the consistency in evaluations during the datacollection process.

To assess the adoption of e-RM Web features quantitatively, we developed aset of scores based on the raw data: e-RM cell ratio (Rijk), e-RM level score (LSik),and overall e-RM score (OSk). E-RM cell ratio (Rijk) indicates the percentage of theobserved Web features of all the proposed Web features at the ith relationship leveland jth program marketing dimension at the kth program Web site (Equation 1):

RM

Nijkijk

ijk

= (minimum = 0, maximum = 1),(1)

where Mijk = number of observed Web features at ith relationship level and jth pro-gram marketing dimension for the kth observation, Nijk = number of proposed Webfeatures at ith level and jth dimension for the kth program Web site, i = 1 . . . 5, j = 1 . . .4, and k = 1 . . . 91.

E-RM level score (LSik) indicates the summed proportion of the observed Webfeatures of all the proposed Web features at the ith relationship level for the kth pro-gram Web site, showing the level of Web feature adoption at each level (Equation2):

LS Rik ijkj

==∑

1

4

(minimum = 0, maximum = 4)(2)

Overall e-RM score (OSk) indicates the summed proportion of all the observedWeb features of all the proposed Web features in the matrix for the kth programWeb site, showing the level of Web feature adoption of each program (Equation3):

OS Rk ijkji

===∑∑

1

4

1

5

(minimum = 0, maximum = 20)(3)

We conducted a two-step cluster analysis on the LSik scores to group programsbased on their level patterns of e-RM Web feature adoption. The two-step clusteranalysis is a unique clustering tool recently made available in Statistical Packagefor Social Sciences (SPSS) for Windows. This clustering method is characterizedby its capability to handle large data sets, deal with both continuous and categori-cal variables, and generate the optimal number of clusters automatically (SPSS,

Hu et al. / E-RELATIONAL CHARACTERISTICS 515

2001). Based on the membership information produced by the cluster analysis, alogistic regression analysis was conducted to identify the program and school at-tributes that might predict the level of the e-RM feature application.

RESULTS AND DISCUSSION

In general, the selected educational programs performed well in maintaininglower level communication with their prospects but failed to explore the higherlevel online relationship-building approaches (Table 2). In particular, partnership-level communication features were extremely underused. It suggested that therewere some major deterrents in adopting sophisticated Web site features facilitat-ing the development of partnership communications. One example is the onlineapplication status tracking system that uses the Internet’s interactivity and person-alization capacity. It allows the applicants to access a personal account protectedby a user name and a password for all updates on their applications. However,maintaining a tracking system may be deemed challenging both technologicallyand administratively as it requires a seamless communication of informationacross a variety of offices and departments, which could only be achieved throughcommitting all the faculty and staff involved in the application process. The appli-cation of Frequently Asked Questions (FAQ) stresses the importance of a morefundamental conceptual shift. FAQ is a user-friendly format of information provi-sion. Even though it hardly posed any technological or financial challenges, usingFAQs to present application information and program information onlyaccounted for 47% and 12% of the Web sites, respectively, in the study. Webelieve that program marketers have to overcome some cognitive obstacle inapplying FAQs on their Web sites.

The two-step cluster analysis in SPSS is an automatic clustering process thatrequires only one data pass in its hierarchical clustering procedure. In general, itprovides the capability to automatically find the optimal number of clusters whenBayesian Information Criterion (BIC, also known as Schwarz’s Bayesian Crite-rion) is minimized. In the first step, the BIC for each number of clusters (i.e., eachsolution) within a specified range is calculated and used to find the initial estimatefor the number of clusters. In the second step, the algorithm refines the initial esti-mate by finding the greatest change in distance between the two closest clusters ineach hierarchical clustering stage (SPSS, 2001). The general guideline for select-ing the optimal number of clusters suggests that BIC differences of less than 2 cor-respond to weak evidence, differences between 2 and 6 to positive evidence, dif-ferences between 6 and 10 to strong evidence, and differences greater than 10 tovery strong evidence in favor of the model solution with the smallest BIC (Fraley& Raftery, 1998; Kass & Raftery, 1995). Using the two-step cluster analysis pro-cedure, the study resulted in two distinct clusters of program Web sites. The find-ings indicated an increasing trend in BIC changes from –21.42 (two-clustermodel solution with BIC = 1,504.86) to 31.89 (three-cluster model solution withBIC = 1,536.75) to 51.25 (four-cluster model solution with BIC = 1,589) and soforth. Thus, there was very strong evidence to support the optimal two-clustersolution. The 66 program Web sites in Cluster 1 had a higher average e-RM level

516 JOURNAL OF HOSPITALITY & TOURISM RESEARCH

Hu et al. / E-RELATIONAL CHARACTERISTICS 517

Table 2The Cluster Profiles

Cluster 1 (N = 66) Cluster 2 (N = 25)

M SD Frequency % M SD Frequency %

Level 1 3.07 0.38 2.28 0.43Level 2 3.20 0.45 2.17 0.44Level 3 2.78 0.54 1.80 0.56Level 4 2.39 0.60 1.69 0.66Level 5 1.07 0.53 0.53 0.44Program size 311.2 520.5 126.7 105.9Annual tuition

(in dollars) 11,016.40 3,612.70 9,262.70 5,017.80Number of

programemphases 3.0 1.2 3.0 1.3

Academic levelGraduate 20 30.3 2 8.0Undergraduate 46 69.7 23 92.0Total 66 100.0 25 100.0

Geographic locationEast north central 12 18.2 2 8.0East south central 3 4.5 2 8.0Middle Atlantic 10 15.2 2 8.0Mountain 5 7.6 0 0.0New England 3 4.5 3 12.0Pacific 4 6.1 3 12.0South Atlantic 13 19.7 8 32.0West north central 7 10.6 4 16.0West south central 9 13.6 1 4.0Total 66 100.0 25 100.0

Types of fundingPublic 52 78.8 14 56.0Private 11 16.7 7 28.0Church affiliated 3 4.5 4 16.0Total 66 100.0 25 100.0

Program emphasisa

Hotel and lodgingmanagement 62 25

Restaurant andfood servicemanagement 51 18

Tourism and travelmanagement 27 15

Meeting, convention,or event management 16 4

Club management 12 4Business administration 11 0Leisure, sport, or

recreationmanagement 6 1

Resort and golfmanagement 6 6

Hospitality education 4 0Global tourism and

hospitality 2 0

a. One program could have more than one emphasis.

score (LSik) than the 25 programs in Cluster 2, indicating the distinction betweenbetter performers that used more e-RM Web features and improvement-neededperformers that adopted fewer features (Table 2). A cluster profile revealed thatthe programs in Cluster 1 were larger in average size, charged a slightly higherannual tuition, and were mainly public schools and graduate programs.

Given a group of characteristic variables, the stepwise logistic regression pro-cedure generated a model including two explanatory variables: the academic level(undergraduate vs. graduate) and the program size (in student enrollment). Theircoefficients implied that, holding other terms constant, larger programs are morelikely to be better performers in using e-RM features and graduate programs aremore likely to be identified as better performers. The odds ratio related to aca-demic level (graduate vs. undergraduate) was found to be 10.793, which signifies

518 JOURNAL OF HOSPITALITY & TOURISM RESEARCH

Table 3The Logistic Regressiona Results

ExponentialParameters M SE Wald Statistic df p Coefficient

Constant –0.453 .459 0.971 1 .324 0.636Academic Level 1 2.379 .836 8.104 1 .004** 10.793Program size 0.006 .002 5.983 1 .014* 1.006

Note: Academic Level (1) = graduate; program size = number of self-reported enrollmentsspecified in the International Council on Hotel, Restaurant, and Institutional Education guide.For the Hosmer-Lemeshow test, χ2 = 2.358, p = .968.The model fits the data well given the in-significant result of the Hosmer-Lemeshow lack-of-fit test. The model chi-square is17.663***, indicating that the model is significant, that is, the value of the dependent variable(or the chance for an observation to fall into Cluster 1) could be predicted by the independentvariables kept in the function. Cox-Snell R2 = .178, implying that the model explained about17.8% of the variance observed in the value of the dependent variables. The rate of correctclassification was 73.3%.Compared to the rate of correct classification by chance (50%), themodel’s ability to predict the membership of the observations was considered satisfactory.a. As the cluster analysis identified two groups that vary in the level of the electronic relation-ship marketing Web feature application, a binomial variable was created for each observationto represent its membership of either group.Logistic regression took the binomial variable asthe dependent variable and identified from a host of independent variables (i.e., the programand school attribute variables) the variables that appear to be strong indicators of the chancethat an observation would fall into Cluster 1. Based on the coefficients produced through theStatistical Package for Social Sciences stepwise logistic regression procedure, the regres-sion function was as follows:

logit (the chance of being a Cluster 1 member) = –.453 + 2.379 • ACADEMIC Level (1)+ .006 • PROGRAM SIZE,

where logit = lnthe chance of being cluster 1 member

(1 the chance− of being cluster 1 member).

The increase in the chance of a case being a Cluster 1 member | 10 additional enrollment =[Exp(.006 • 10) – 1] • 100%.

The chance of a graduate program being a Cluster 1 memberover that of an undergraduate program = Exp(2.397).

*p < .05. **p < .01. ***p < .001.

that the graduate program is associated with more than a 10-fold increase in theodds of becoming a good performer (Table 3).

The results also indicate that larger programs tend to apply more e-RM fea-tures to their Web sites than their smaller counterparts. This finding was consis-tent with previous research results that the size of commercial businesses was akey factor affecting Internet use (Ellinger, Lynch, & Hansen, 2003). A post hocexplanation might be that programs with large enrollment are likely to offer com-prehensive emphases targeting a diverse population. To attract a larger and morediverse audience, schools are probably more willing to invest in and leverageInternet technologies to promote their programs. It is also speculated that largeprograms can apply sophisticated Web features because of their advantages asso-ciated with the program size, such as superior financial resources, more techno-logical expertise, and meticulous management strategies. The financial capabilityis particularly important in that despite the cost reduction as a result of recent tech-nological advancements, constructing and maintaining an efficiently functioningWeb site remains costly (Klassen, 2002).

The academic level also emerged as an important factor in predicting the appli-cation level of e-RM Web features. A subsequent ANOVA analysis revealed thatthe graduate programs’ Web sites achieved a significantly higher overall e-RMindex than their undergraduate counterparts (F = 5.18, p = .025). Previous studieshave discovered that prospective graduate students have distinctive demands andtend to follow more sophisticated decision-making procedures in selecting theappropriate academic programs (Bitler, Rankin, & Schrass, 2000; Poock &Lefond, 2001, 2003). The wide recognition of the graduate prospects’preferencesand/or information-seeking behaviors might have led the hospitality and tourismprograms to different efforts in constructing undergraduate and graduate programWeb sites. Another ANOVA test showed that graduate program Web sites usedsignificantly more partnership-level e-RM features than undergraduate programWeb sites did (F = 6.07, p = .016). For example, a higher percentage of the gradu-ate program Web sites applied the online internship and job posting or interactivesearch engine functions to facilitate the job-searching process for their students. Itis anticipated that prospective students visiting the program Web sites would beimpressed by such student-oriented functions and accordingly develop favorableperceptions of the programs.

CONCLUSION

The results of the study indicated that the application of e-RM Web featureswas uneven across the hospitality and tourism programs, most likely because ofboth financial reasons and administrative inefficiency. Challenged by limitedfinancial capacity, small programs need to seek innovative ways of making theirWeb sites more user centered to best serve the purpose of program marketing.They should pursue a research-driven path by spending the money on the mostefficient technologies. The inadequate application of the e-RM features in under-graduate programs might be attributed to administrative reasons other than finan-cial or technological incapabilities. Marketers and administrators should use

Hu et al. / E-RELATIONAL CHARACTERISTICS 519

research findings on prospects’ interests and preferences to improve their Websites. For example, career testimonials by program alums are a powerful way ofconvincing potential students.

Some limitations in the methodology and overall research design should benoted and improved in future studies. In conducting the online content analysis,subjective bias may still exist even after special arrangements were designed tominimize the bias. Future studies can further reduce such bias by engaging alarger panel of experts to arrive at a pooled opinion.

The logistic regression analysis achieved an overall correct prediction rate of73.3% by including two important variables (academic level and program size) inthe model. Even though the two cases were not perfectly predicted, the overallcorrect prediction rate was much better than chance (i.e., 50% for the dichoto-mous cases in this study). Nevertheless, the model correctly predicted the mostfrequent category (Cluster 1) 72.2% of the time. We recognize that there is stillroom for improving the overall prediction rate by the logistic regression methodand that future research can investigate other characteristics of educational pro-grams that may contribute to the adoption of e-RM features on the educationalprograms’ Web sites.

The outcome of program marketing as a result of the online relationship-buildingeffort also needs to be investigated. Subsequent studies concerning Web siteeffectiveness and the impact of e-RM Web features on program marketing ishighly desirable. However, researchers should be cautious as well as creative inmeasuring Web site effectiveness and program marketing outcome as they aresimultaneously affected by a wide range of factors in addition to the adoption of e-RM Web features. We speculate that the growth in the conversion of Web visitor toactual applicants may be a more direct measure for the performance outcome oreffectiveness of the online relationship-building functions. However, obtainingdata for this measure appears highly challenging, and it requires a reliable onlinedesign, operation, and methodology to warrant such an approach in the future.

REFERENCES

Arnott, D. C., & Bridgewater, S. (2002). Internet, interaction and implications for market-ing. Marketing Intelligence & Planning, 20(2), 86-95.

Bai, B., Jang, S. S., & Hu, C. (2003, August). E-relationship marketing: An application tohotel website development. Paper presented at the I-CHRIE (International Council onHotel, Restaurant, and Institutional Education) Conference, Palm Springs, CA.

Berry, L. L. (1983). Relationship marketing. In L. L. Berry, G. L. Shostack, & G. D. Upah(Eds.), Emerging perspectives of service marketing (pp. 25-38). Chicago: AmericanMarketing Association.

Bitler, D. A., Rankin, W. P., & Schrass, J. M. (2000). Academic affairs online: A survey ofinformation available on websites in higher education. College Student Journal, 34(3),325-333.

Dean, C., & Trainor, P. (2002). How graduate/professional students use the Internet toselect a degree program, Part I. Retrieved September 28, 2003, from http://www.aallnet.org/sis/cssis/newsletter/jun2002/Connecting06_02.pdf

520 JOURNAL OF HOSPITALITY & TOURISM RESEARCH

Domegan, C. T. (1996). The adoption of information technology in customer service.European Journal of Marketing, 30(6), 52-69.

Donaldson, B., & O’Toole, T. (2002). Chapter 9: E-relationships. In B. Donaldson & T.O’Toole (Eds.), Strategic market relationships: From strategy to implementation (pp.167-184). West Sussex, UK: Wiley.

Ellinger, A. E., Lynch, D. F., & Hansen, J. D. (2003). Firm size, Web site content, and finan-cial performance in the transportation industry. Industrial Marketing Management,32(3), 177-185.

Feinberg, R. A., & Kadam, R. (2002). E-CRM Web service attributes as determinants ofcustomer satisfaction with retail Web sites. International Journal of Service IndustryManagement, 13(5), 432-451.

Feinberg, R. A., Kadam, R., Hokama, L., & Kim, I. (2002). The state of electronic cus-tomer relationship management in retailing. International Journal of Retail & Distri-bution Management, 30(10), 470-481.

Fraley, C., & Raftery, A. E. (1998). How many clusters? Which clustering method?Answers via model-based cluster analysis. The Computer Journal, 41(8), 578-588.

Gilbert, D. C., Powell-Perry, J. A., & Widijoso, S. (1999). Approaches by hotels to the useof the Internet as relationship marketing tool. Journal of Marketing Practice—AppliedMarketing Science, 5(1), 21-38.

Grönroos, C. (1990). Relationship approach to marketing in service contexts: The market-ing and organizational behavior interface. Journal of Business Research, 20(1), 3-11.

Grönroos, C. (1996). Relationship marketing: Strategic and tactical implications. Manage-ment Decision, 34(3), 5-14.

Grönroos, C. (1997). From marketing mix to relationship marketing—Towards a paradigmshift in marketing. Management Decision, 35(3, 4), 322-339.

International Council on Hotel, Restaurant, and Institutional Education. (2002). Guide tocollege programs in hospitality, tourism & culinary arts (7th ed.). Richmond, VA:Author.

Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American StatisticalAssociation, 90(430), 773-795.

Kittle, B., & Ciba, D. (1998, December). Relationship marketing in higher education viathe World Wide Web: A follow-up study of home pages examining student recruitmentstrategies. Paper presented at the 1998 Symposium for the Marketing of Higher Educa-tion, American Marketing Association, Chicago.

Klassen, M. L. (2002). Relationship marketing on the Internet: The case of top- and lower-ranked U.S. universities and colleges. Journal of Retailing and Consumer Services,9(2), 81-85.

Klassen, M. L., & Sitzman, E. (2000). At the intersection of demand creation and demandfulfillment: Which schools are getting there first and why? Journal of Marketing forHigher Education, 10(1), 43-53.

Kotler, P. (1992). Marketing’s new paradigm: What’s really happening out there. PlanningReview, 20(5), 50-52.

Kotler, P., Bowen, J. T., & Makens, J. C. (2003). Chapter 11: Building customer loyaltythrough quality. In P. Kotler, J. T. Bowen, & J. C. Makens (Eds.), Marketing for hospi-tality and tourism (3rd ed., pp. 380-440). Upper Saddle River, NJ: Prentice Hall.

Parmar, A. (2004). Student e-union: Colleges write textbook on Internet marketing. Mar-keting News, 38(6), 13-14.

Hu et al. / E-RELATIONAL CHARACTERISTICS 521

Poock, M. C., & Lefond, D. (2001). How college-bound prospects perceive university Websites: Findings, implications, and turning browsers into applicants. College and Univer-sity, 77(1), 15-22.

Poock, M. C., & Lefond, D. (2003). Characteristics of effective graduate school Web sites:Implications for the recruitment of graduate students. College and University, 78(3),15-19.

Shoemaker, S., & Lewis, R. C. (1999). Customer loyalty in hotels: The future of hospitalitymarketing. International Journal of Hospitality Management, 18(4), 345-370.

Sisodia, R. S., & Wolfe, D. B. (2000). Chapter 20—Information technology: Its role inbuilding, maintaining and enhancing relationships. In J. N. Sheth & A. Parvatiyar(Eds.), Handbook of relationship marketing (pp. 525-563). Thousand Oaks, CA: Sage.

Statistical Package for Social Sciences. (2001). The SPSS two-step cluster component(White Paper No. TSCWP-0101). Chicago: Author.

Stoner, M. (2001). What has the Web wrought? College admission in the digital age. Col-lege Board Review, 193, 32-37.

Tuominen, M., Rajala, A., & Möller, K. (2000). Intraorganizational relationships and oper-ational performance. Journal of Strategic Marketing, 8(2), 139-160.

Submitted May 3, 2004First Revision Submitted October 7, 2004Final Revision Submitted February 7, 2005Accepted February 8, 2005Refereed Anonymously

Clark Hu, Ph.D. (e-mail: clark.hu@temple.edu), is an assistant professor in the NationalLaboratory for Tourism & eCommerce of the School of Tourism and Hospitality Manage-ment at Temple University. Yunxuan (Tess) Han (e-mail: tesshan@temple.edu) is a Ph.D.student in the Department of General and Strategic Management of the Fox School of Busi-ness and Management at Temple University. SooCheong (Shawn) Jang, Ph.D. (e-mail:jangs@purdue.edu), is an assistant professor in the Department of Hospitality and TourismManagement at Purdue University. Billy Bai, Ph.D. (e-mail: billy.bai@ccmail.nevada.edu), is an assistant professor in the Department of Tourism and Convention Administra-tion of the William F. Harrah College of Hotel Administration at the University of Nevada,Las Vegas.

522 JOURNAL OF HOSPITALITY & TOURISM RESEARCH