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Tourism Management ] (]]]]) ]]]–]]]

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Web-based permission marketing: Segmentation for thelodging industry

Eric T. Breya,�, Siu-Ian (Amy) Sob, Dae-Young Kimc, Alastair M. Morrisond

aKemmons Wilson School of Hospitality and Resort Management, University of Memphis, 3700 Central Ave., Suite 140G, Memphis, TN 38152, USAbInstitute For Tourism Studies, Macao, China

cDepartment of Hotel & Restaurant Management, University of Missouri-Columbia, MO 65211, USAdHospitality and Tourism Management, College of Consumer and Family Sciences, Purdue University, West Lafayette, IN 47907, USA

Received 26 December 2005; accepted 10 January 2007

Abstract

Permission marketing is becoming an important tool in maintaining relationships with travelers via the Internet. Its growing

importance can be seen in tourism marketing, specifically in the lodging industry. With an increase in industry use, the effectiveness of

this technique needs assessment. This paper initiates this process by examining current methods used to collect contact information for

the purpose of permission marketing. Three segments of the market are identified and compared based upon their willingness to supply

contact information. Significant differences were found in socio-demographics, online habits, trip characteristics, and website design

preferences. Implications for lodging marketers are presented and future research topics are discussed.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Consumer comparison; Lodging marketing; Permission marketing; Relationship marketing; Web marketing

1. Introduction

Marketing to lodging consumers has gone throughsignificant changes in recent history due to Internet growth.Web-based techniques have become integral to successfulhotel sales and marketing (Gregory, Kline, & Breiter,2005). This channel offers major advantages over othercommunication forms by enhancing overall marketability(Gilbert & Powell-Perry, 2002). Furthermore, lodgingwebsites can accelerate marketing, establish brand names,and expand current markets (Jeong & Choi, 2004). Giventhese benefits, developing an effective website is importantto lodging facilities regardless of size and other classifica-tion differences (Ham, 2004).

But just having a website does not guarantee thatpotential or current guests will be attracted to the site(Kasavana, 2002). Within the lodging sector, proprietary-owned websites are realizing the need to further capitalizeon the Internet (Miller, 2004), brought on by increased

e front matter r 2007 Elsevier Ltd. All rights reserved.

urman.2007.01.002

ing author. Tel.: +1901 678 4584.

ess: [email protected] (E.T. Brey).

s article as: Brey, E. T., et al. Web-based permission marketin

tourman.2007.01.002

sales and marketing that these sites afford (Gregory et al.,2005). To benefit from these technological advancements,marketing methods not available for widespread use in thepre-Internet era have flourished. One such method, perm-ission marketing, is based upon garnering initial customerconsent to receive information about a product or servicesfrom a company (Marketing Terms.com, 2004). The techni-que, considered an excellent venue for successfully reachingand maintaining consumers, has not garnered much attentionin lodging-based research.The need for research is compounded as the misuse of

this method carries detrimental impacts to marketingeffectiveness. Spam, or unsolicited commercial e-mail(UCE), occurs when consumers perceive e-mail commu-nications as unwanted or unnecessary (Hodges, 2004).Through misuse, permission that was granted can berevoked and potential business lost. Oppositely, effective e-mail can lead to viral marketing, the electronic equivalentof traditional word-of-mouth (MacPherson, 2001). Muchlike its traditional counterpart, encouraging online referralsfrom potential clients is both effective and economical. Butthere exists a thin line between successfully encouraging

g: Segmentation for the lodging industry. Tourism Management (2007),

www.elsevier.com/locate/tourman

dx.doi.org/10.1016/j.tourman.2007.01.002

mailto:[email protected]


ARTICLE IN PRESSE.T. Brey et al. / Tourism Management ] (]]]]) ]]]–]]]2

potential clients to forward a marketing message and beingseen as invasive (Nussey, 2004). Without research investi-gating the effective use of permission marketing and a needto examine e-behavior within the lodging industry (Miller,2004), a significant knowledge gap exists. This researchbegins to resolve this by providing a deeper understandingof permission marketing. By analyzing data concerningconsumers’ willingness to supply information online, thisstudy’s two research objectives were to:

O1: Segment groups of consumers based upon the levelsof permission they supply to websites.O2: Examine group differences including socio-demo-graphics, trip characteristics, online habits and beha-viors, and website design preferences.

While the first objective represents an initial effort tocategorize consumers based upon permission granted, thesecond examines important differences between thesegroups. Current evidence exists that consumer preferencesonline can be influenced by socio-demographics. Gender,household income, and situational factors such as trippurpose or travel party significantly impact which informa-tion sources are used (Luo, Feng, & Cai, 2005). Further-more, direct correlation has been found between travelbehavior and online activities. In the examination of repeatvisitors, So and Morrison (2004) found that informationsearch behavior significantly affected repeat travel beha-vior (2004). As for website design preferences, the need forunderstanding is fundamental. Often the most difficultaspect of permission marketing is obtaining qualified leads(Lewis, 2002). In addition, additional research is needed toexamine how usability of hotel websites impacts onlinemarketing initiative (Essawy, 2006). By examining differ-ences in website preferences, additional insight into designdynamics in permission marketing can be examined.

Based upon these objectives, the outcomes of this studywill provide an understanding of consumers’ willingness tosupply contact information, the methods of collecting datafor permission marketing purposes that are most successful,and a comparison of the characteristics of the consumersegments. These findings are then applied in a lodgingcontext and applications for marketing professionals arediscussed along with future research opportunities.

2. Permission marketing

Most mass media venues do not allow marketers totarget consumers with a high degree of precision, eventhough targeting and segmenting are arguably marketingcenterpieces (Krishnamurthy, 2001). With this difficultyexisting, one of the most recent direct marketing ap-proaches focuses on consumers’ preferences and develop-ing a meaningful interactive dialogue (Kent & Brandal,2003). One such proposed technique is permission market-ing (Godin, 1999), which seeks permission in advance fromconsumers to send marketing communications. Consumers

Please cite this article as: Brey, E. T., et al. Web-based permission marketin

doi:10.1016/j.tourman.2007.01.002

provide interested marketers with information about thetypes of advertising messages they would like to receive.The marketers then use this information to target adver-tisements and promotions. The aim is to initiate, sustainand develop a dialogue with customers, building trust andover time lifting the levels of permission, making it a morevaluable asset (Kent & Brandal, 2003). Godin (1999) states:

Consumers are now willing to pay handsomely to savetime, while marketers are eager to pay bundles to getattentiony The alternative is permission marketing,which offers the consumer an opportunity to volunteerto be marketed to. By talking only to volunteers,permission marketing guarantees that consumers paymore attention to the marketing message (pp. 42–43).

This technique is seen as reducing clutter and loweringsearch costs for the consumer while increasing the targetingprecision of marketers (Godin, 1999; Krishnamurthy, 2001;Marinova, Murphy, & Massey, 2002). This is accomplishedby obtaining trust and building a two-way relationshipwith consumers.Permission marketing has three specific characteristics

that set it apart from traditional direct marketing (Godin,1999). First, customers who permit their names to beincluded on direct-mail lists can anticipate receivingcommercial messages (anticipated). Second, the sendingcompany can personalize those messages (personal). Third,the messages will be more relevant to the customers’ needs(relevant). These characteristics are what allow marketersto cut through the clutter and speak to prospects as friends,not as strangers. This personalized, anticipated, frequent,and relevant communication has a greater impact than arandom message displayed in a random place at a randommoment. Five levels of permission can be won fromcustomers targeted by a permission-marketing campaign(Godin). These levels include:

L1: ‘‘Situation’’ permission is a one-time or limited-timepermission, which is the least potent of the five levels.This is given when consumers agree to receive sales orpromotional messages from a company for a specifiedtime.L2: ‘‘Brand trust’’ is the most common way marketerspractice their craft. With this permission, consumershave developed confidence in a product or service thatcarries a particular or well-known brand name.L3: ‘‘Personal relationship’’ uses individual relationshipsbetween the consumer and marketer to temporarilyrefocus the attention or modify the consumer’s beha-vior. This approach is the best technique to sellcustomized or highly involving products.L4: In the level of ‘‘points permission,’’ points are aformalized, scalable approach to attracting and keepingthe prospect’s attention. This involves consumersallowing the company to collect personal data and tomarket its products and services to them on a points-based loyalty scheme.



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L5: ‘‘Intravenous’’ refers to the highest level of permis-sion to be won from consumers. This involves customerstrusting the marketer to make buying decisions forthem.

According to MacPherson (2001), one of the theoriesbehind permission marketing is that, presumably, acustomer who has given permission to receive promotionsis a better, more loyal and profitable customer. It entails ashift in power from the marketer to the consumer.Consumers’ permission has to be sought, which allowsconsumers to realize the power in the data they can provide(Kent & Brandal, 2003).

Krishnamurthy (2001) also points out that the conceptof permission marketing is closely related to two conceptsthat have been discussed within marketing literature—relationship marketing (Han, Hu, Bal, & Jang, 2005) andone-to-one marketing (Simonson, 2005). Relationshipmarketing proposes that marketers focus on long-termrelationships with customers rather than single transac-tions. The main idea of one-to-one marketing is thatmarketers must think of a segment as one person andcustomize the marketing mix to each customer. Krishna-murthy (2001) also adds that while ‘‘permission market-ing’’ was coined by Godin, the generalized concept ofcustomer permission in direct marketing had been pre-viously discussed in the context of privacy issues.

2.1. Permission marketing and the Internet

According to Farris (2001), permission marketing hasmainly been adopted by organizations practicing Internetand e-mail marketing. E-mail in particular can be inte-grated into a one-on-one medium, with a more interactiveand multi-layered communication process. MacPherson(2001) describes permission based e-mail marketing asbeing the future of direct marketing with such benefits as:direct communication with prospective and existing custo-mers, interactivity, lower costs, and targeting of qualifiedleads.

In the early stages of Internet marketing, banneradvertising and sponsorships were theorized as havingpotential to provide consumers with relevant information.Despite early promise detailed in pioneering research(Hoffman & Novak, 1996), click-through rates have notimproved, averaging approximately 0.5 percent. Moreover,eye-tracking research indicates that Internet users mayavoid looking at banner ads during online activities (Dreze& Hussherr, 2003). In this sense, it may reasonably beassumed that placing banners on websites is ineffective indelivering the message.

Given these current issues of marketing to the onlineaudience, researchers argue that permission marketingoffers improved targeting by helping consumers interfacewith marketers most likely to provide relevant promotionalmessages (Chittenden & Rettie, 2003; Godin, 1999;Kavassalis et al., 2003; Krishnamurthy, 2001). Although


doi:10.1016/j.tourman.2007.01.002

permission marketing can be implemented in any directmedium, its use has increased with the advent of theInternet and e-commerce. The emergence of permissionmarketing via the Internet is based upon the low cost ofmarketer-to-consumer communication (Hoffman & Novak,1996; Shiman, 1996). The development of the Internet hasalso enabled rapid feedback mechanisms allowing instanta-neous two-way communication (Hoffman & Novak).An additional reason for executing permission marketing

via the Web has been the failure of the direct mail approachof sending unsolicited promotional messages. The primaryexample of this is UCE or ‘‘spam’’ as it is widely known(Cranor & LaMacchia, 1998). Despite the enormousamount of spam disseminated on the Web, this methoddoes not represent a legitimate form of marketing commu-nication (Shiman, 1996). It leads to an excessive messagevolume for consumers, weakening of brand reputation anda slowing of the entire network. In this sense, permissionmarketing can be seen as a feasible alternative for Internetmarketing.

2.2. Demographic influence

Demographics and situational variables could alsosignificantly impact permission marketing activities. Ex-tensive literature exists supporting the close relationshipbetween demographic characteristics and informationsearch behavior (for example Dodd, 1998; Eby, Molnar,& Cai, 1999; Luo et al., 2005; Prideaux, Wei, & Ruys,2001). This body of literature has indicated that respondentcharacteristics influence information sources and indivi-duals’ attitudes toward different online channels. Demo-graphic characteristics such as gender, education, income,race, and vocation are considered to be influential factorsfor individuals’ Internet usage and search behavior.Specifically, for example, Internet users are more likelyCaucasian males with higher education and householdincome (Bonn, Furr, & Susskind, 1998; McDonald &Adam, 2003).Another important determinant of search behavior and

channel usage is an individual’s experience. Familiarity andexpertise, relating to both websites and the destination ofinterest, have been posited as impacting information searchbehavior (Gursoy & McCleary, 2004). Gender alsoproduces differences as males’ technology usage is dictatedby perceptions of usefulness while women are influenced byperceptions of ease (Venkatesh & Morris, 2000). Priorexperience or familiarity with a product also impactsconsumer choices. As familiarity with a product increasesthe time required, depth of information gathered, andoverall confidence change (Park & Lessig, 1981). Further-more, as people gain experience, their knowledge accumu-lates due to the integration of information. This increasedexperience with a product increases reliance upon storedproduct information in-lieu of collected information(Park, Mothersbaugh, & Feick, 1994). In this sense,trip experience and characteristics potentially impact




information search behavior and the success of permissionmarketing.

Accompanying the expansion of literature on Web-basedpermission marketing has been the exponential growthwithin industry, specifically within the lodging sector.While this direct method of marketing to consumers ispurported to be successful in comparison to other Web-marketing techniques, relatively little is known about itsapplication. In addition, a dearth of information concern-ing how demographics and experience influence tourist’sonline information search behavior exists. Based uponthese information gaps, this research will establish a betterunderstanding of impacts through comparison of consu-mer segments.

3. Methodology

The data utilized in this study were from the 2001Internet Travel Survey conducted for the Canadian Tour-ism Commission (CTC). While collected in 2001, the dataare valid to investigate permission marketing in the currentenvironment for several reasons. First, the nature of thisresearch is explorative and investigates an area withminimal coverage. Use of this data provides an introduc-tion of permission marketing within the lodging industryand provides impetus for research into this increasinglyeffective marketing method (Dufrene, Engelland, Lehman,& Pearson, 2005). Second, previous studies dealing withtourism consumers have effectively used mature data insegmentation-based studies (Carmichael & Smith, 2004).While recently collected primary data would be mostadvantageous, this survey provides satisfactory informa-tion for the study’s objectives. Third, even thoughconsumer’s perceptions of e-mail have evolved (Hoffman,Novak, & Venkatesh, 2004), methods of building an e-maillist for permission marketing have remained fundamentallyunchanged. This study not only provides an introductoryanalysis of the topic but serves as a foundation from whichfuture comparisons can be made.

The focus of the Internet Travel Survey was to evaluateonline travel behaviors by collecting information relatingto trip planning information sources, number and types ofonline sources, time spent online specifically for tripplanning, information search timelines, influence of in-formation search in decision making, online travel bookingbehaviors, and socio-demographic information of NorthAmerican travelers with Internet access (iTravellers).Collection of data was completed via two methods:telephone interview and Internet survey. The Internet data,collected between November 8th and December 18th, 2001,was made available for this research. The sampling frame,consisting of e-mail addresses collected from participatingdestination management organizations within Canada,were sent an e-mail requesting participation along with alink to the survey. A total of 2470 respondents completedthe online survey, with those indicating the use of the


doi:10.1016/j.tourman.2007.01.002

Internet to decide on accommodations in the last 12months selected for analysis (n ¼ 1066).Questions that sought responses as to when respondents

provided their name or e-mail were used for the analysis.These measures were: ‘‘given name/e-mail to personalize asite’’, ‘‘given name/e-mail to obtain a login or password’’,‘‘given name/e-mail to subscribe to a newsletter’’, ‘‘givenname/e-mail to receive notification of discounts’’, ‘‘givenname/e-mail to enter a contest’’, ‘‘given name/e-mail topurchase online’’, and ‘‘given name/e-mail to request atravel brochure’’. The responses were either yes or no.These questions were selected based upon the assumptionthat a company would receive access to a consumer forpermission marketing activities.Several statistical techniques were applied to analyze the

data. Cluster analysis, which allowed the researchers togroup consumers based upon similar characteristics, wasused to identify clusters or like groups of respondents(Hair, Anderson, Tatham, & Black, 1998). Discriminantanalysis was then used to validate the clusters. After theclusters were identified, each segment was profiled basedupon the type of channel to which respondents were willingto provide names/e-mails, and the total number of channelsused by respondents in each cluster. Chi-square analysesand one-way ANOVAs were conducted to determine if theclusters (segments) were significantly different. Multino-mial logistic regression, which analyzes the relationshipbetween independent and dependent variables (Hair et al.,1998), was then used to compare socio-demographics,online habits, trip characteristics, and website designpreferences for the three clusters.The socio-demographic characteristics included age and

income. The statements that measured online habits were‘‘How many hours, in total, do you personally surf/browsethe Internet for work or personal reasons in an averageweek?’’ and ‘‘How long have you been using the Internet tosurf/browse the World Wide Web?’’ The trip characteristicswere ‘‘How many vacation, leisure or get-away trips haveyou taken in the past 12 months (since September 2000)?’’,‘‘Number of nights away from home?’’, ‘‘How many hoursin total, did you spend on the Internet planning/research-ing your trip?’’, ‘‘Did you have any destination in mindwhen you started planning on the trip?’’, ‘‘How much didyou spend’’, and ‘‘Where was the destination?’’ As thisstudy examined destinations within North America, theresponses for ‘‘Where was the destination’’ were recoded toeither ‘‘within state/province’’ or ‘‘out of state/province butwithin North America.’’To measure website design preferences, principal com-

ponents factor analysis with varimax rotation was used toreduce the original 10 questions into fewer dimensions. The10 questions used were ‘accommodations should bepresented using video’, ‘accommodations should be pre-sented using virtual tours’, ‘accommodations should bepresented using flash’, ‘accommodations should be pre-sented including ratings’, ‘accommodations should bepresented including price’, ‘accommodations should be




presented using objective information’, ‘accommodationsshould be presented using testimonials’, ‘accommodationsshould be presented using text’, and ‘accommodationsshould be presented using pictures’. A minimum eigenvalueof one was used in order to control the number of factorsextracted. All the items had factor loadings between 0.542and 0.833. The results of the principal components analysisindicated four factors, which were ‘‘accommodation shouldbe presented in video/virtual tour/flash’’, ‘‘accommodationshould be presented in price/ratings’’, ‘‘accommodationshould be presented in objective information’’, and‘‘accommodation should be presented in text pictures’’.These factors explained a total of 59.11 percent variance.

3.1. Sample characteristics

Approximately 57.7 percent of the respondents in thesample were female (Table 1). Around 59 percent of therespondents were between 35 and 54 years old with themajority between 45 and 54 years. Half of the respondents(52.3 percent) had annual household incomes of more than$60,000. About 21.4 percent had household incomes of$40,000–$59,999. The majority (39.4 percent) graduatedfrom university/college/technical school, and approxi-mately 20 percent had postgraduate education.

4. Results and discussion

4.1. Cluster analysis results

The results of the cluster analysis identified threerespondent groups. The three-cluster solution was vali-dated with a more stringent canonical discriminantanalysis, which showed significant differences among thethree clusters in all seven variables with po0.000. In a

Table 1

Descriptive profile of the sample

Type Percentage Type Percentage

Gender Annual household income

Male 42.3 Less than $15,000 3.5

Female 57.7 $15,000–$24,999 7.1

Age $25,000–$34,999 15.8

18–24 2.6 $40,000–$59,999 21.4

25–29 7.5 $60,000–$79,999 18.5

30–34 10.4 $80,000–$99,999 12.7

35–39 13.5 $100,000–$149,999 12.3

40–44 14.4 $150,000–$199,999 4.7

45–49 15.8 $200,000 or more 4.1

50–54 15.3 Education

55–59 10.4 Less than high school 0.4

60–64 6.0 Some high school 2.5

65–70 2.7 Graduated high school 11.5

71 or

older

1.3 Some university or college 25.8

Graduated university or

college

39.4

Postgraduate 20.4


doi:10.1016/j.tourman.2007.01.002

multiple discriminant analysis, if there is K groups, K�1discriminant functions will be estimated. Since this studyidentified three clusters, two functions were estimated. Theoverall Wilks’ lambda statistics (0.054; 0.289) for bothfunctions were statistically significant (po.0001). A classi-fication procedure within discriminant analysis had 97.4percent accuracy in predicting membership of the threegroups.The three segments identified from the cluster analysis

were labeled as the recurrent (RG), typical (TG), andoccasional (OG) groups. These groupings and namesreflected the propensity to supply names/e-mail addresseswhile visiting websites. RG members provided their contactinformation the most while the OG provided their’s theleast. The TG represented middling responses compared toRG and OG. The ANOVA results demonstrated that thethree clusters were significantly different in terms of thetotal number of the channels to which they were willing toprovide their names/e-mail addresses while visiting a site(Table 3). The Chi-square results confirmed significantdifferences among the channels used by the three segments(Table 2).For the RG, the average number of channels that

respondents were willing to give names/e-mails was thehighest with an average of 5.93 channels compared to theTG’s 4.15 channels, and the OG’s 2.87 channels. All RGrespondents were willing to give their names/e-mails on theInternet to request a travel brochure. Besides requesting atravel brochure, the primary channels that RG respondentswere willing to provide their names/e-mails were to obtaina login or password, to enter a contest, to subscribe to anewsletter, and to receive notifications of discounts.The two primary purposes for the TG to supply their

names/e-mails were to obtain a login or password and toenter a contest. None of the TG respondents were willingto provide their names/e-mails to request a travel brochure.For the OG, the primary channels to which respondentswere willing to provide their names/e-mails were to requesta travel brochure and to obtain a login or password.Comparisons of the three segments’ socio-demographics

uncovered several statistically significant differences(Table 3). Approximately 51.8 percent of the RG wasbetween 30 and 49 years old, compared to 56.3 percent ofthe OG, and 58.4 percent of the TG. Gender compositionalso differed as 54.5 percent of the OG was male and only37.4 percent of the TG was male. The incomes of thesegments varied as 53.7 percent of the RG earned $60,000or more, compared to 54.8 percent of the OG, and only46.2 percent of the TG. Employment status also differed as14.7 percent of the OG compared to 10.9 percent of the TGwas self-employed. Homemakers were about 11 percent ofthe RG group and only 3.6 percent of the OG. More than 3percent (3.4) of the TG were students while less than 1percent (0.9) of the OG was students. About 5 percent ofthe TG and 0.8 percent of the OG were unemployed.Trip characteristics were also analyzed with only the city

trip variable indicating significant difference between



ARTICLE IN PRESS

Table 2

Profiles of the three-cluster solution

Variables Recurrent Occasional Typical Total F/Chi-square�

n ¼ 604 n ¼ 224 n ¼ 238

No. of channels respondents willing to give name/e-mail 5.9288 2.8705 4.1471 4.8884 944.240

Given name/e-mail to personalize a site 182 31 40 253 32.087

Given name/e-mail to obtain a login or password 578 137 214 929 176.093

Given name/e-mail to subscribe to a newsletter 562 90 170 822 264.361

Given name/e-mail to receive notification of discounts 554 52 189 786 396.542

Given name/e-mail to enter a contest 571 50 200 821 489.932

Given name/e-mail to purchase online 530 113 183 826 130.391

Given name/e-mail to request a travel brochure 604 170 0 774 NAa

�p-values ¼ 0.000.aThe expected cell of the typical group do not satisfy the Chi-square tests assumption in which any expected cell size should be at least 5, Chi-square test

is not applicable in this case.

Table 3

Three-cluster significant differences

RG (%) OG (%) TG (%) Total (%) Chi-square

n ¼ 604 n ¼ 224 n ¼ 238

Socio-demographic

Age

18–24 2.65 1.79 3.36 2.63 34.36*

25–29 8.11 6.25 7.14 7.50

30–34 10.10 12.05 9.66 10.41

35–39 13.58 10.71 15.97 13.51

40–44 11.59 17.86 18.49 14.45

45–49 16.39 15.63 14.29 15.76

50–54 16.23 11.61 16.39 15.29

55–5 9 10.93 9.82 9.66 10.41

60–64 6.79 6.70 3.36 6.00

65–70 2.81 4.02 1.26 2.72

71 or older 0.83 3.57 0.42 1.31

Gender

Male 39.74 54.46 37.39 42.31 17.56**

Female 60.26 45.54 62.61 57.69

Annual income

Less than $15,000 3.38 5.53 1.71 3.45 26.98*

$15,000–$24,999 6.42 7.37 8.55 7.09

$25,000–$39,999 16.39 11.06 18.80 15.82

$40,000–$59,999 20.10 21.20 24.79 21.38

$60,000–$79,999 19.09 17.51 17.95 18.50

$80,000–$99,999 14.36 11.98 8.97 12.66

$100,000–$149,999 12.67 14.75 8.97 12.27

$150,000–$199,999 4.39 3.69 6.41 4.70

$200,000 or more 3.21 6.91 3.85 4.12

Employment status

Self- employed 11.98 14.73 10.92 12.32 27.47**

Employed full-time 55.07 62.05 61.34 57.95

Employed part-time 5.16 5.36 4.20 4.99

Homemaker 10.98 3.57 7.56 8.65

Student 3.00 0.89 3.36 2.63

Retired 10.82 12.50 7.56 10.44

Unemployed 3.00 0.89 5.04 3.01

Trip purpose and pattern

Trip pattern

City trip 29.22 16.52 21.52 24.81 34.27**

Outdoor focus 23.71 29.02 29.54 26.13

Touring trip 21.70 34.38 18.57 23.68

Visiting friends/family 25.38 20.09 30.38 25.38

n ¼ 1022, *po0.10, **po0.05.

E.T. Brey et al. / Tourism Management ] (]]]]) ]]]–]]]6

Please cite this article as: Brey, E. T., et al. Web-based permission marketing: Segmentation for the lodging industry. Tourism Management (2007),

doi:10.1016/j.tourman.2007.01.002



groups. About 29.2 percent of the RG indicated theirrecent trip was more of a city trip as compared with only16.5 percent of the OG.

4.2. Multinomial logistic regression results

The results of the multinomial logistic regressionindicated significant differences among the three groups(RG, TG, and OG) based upon their socio-demographics,online habits, trip characteristics, and website designpreferences. The results showed a statistically significantdifference among the three groups with a Chi-square ¼ 167.626 and a p-value of less than 0.001 (Table 4).

For the RG, the odds of the number of hours online perweek among these respondents were 1.134 times greaterwhen compared to the TG (Table 4). The odds that thenumber of vacation trips in the last year among those in theRG were 1.245 times more than for the TG. The oddsconcerning number of hours spent online research/plan-ning for the last recent vacation trip were 1.3 times greaterfor the TG. Generally, the RGs were more likely to spendadditional hours online per week, have taken morevacation trips in the previous year, and spend more hoursonline to research and plan vacation trips.

In terms of web design preferences, those in the RG were1.38 times more likely to prefer video/virtual tours andflash, 1.149 times more likely to prefer price/ratings onwebsites, and 1.180 times more likely to prefer objective

Table 4

Multinomial logistic regression results

Predictors I-Reference gro

Recurrent (RG)

Socio-demographic

Age 1.063

Annual income 1.023

Online habit

Hours spend online per week 1.134**

Time began online 1.053

Trip characteristics

Number of vacation trips in the last year 1.245***

Number of nights away on recent trip 0.998

Hours spent online research/planning last vacation trip 1.300***

Destination in mind when you started planned on the

last vacation trip

1.096

Money spent on most recent trip 0.961

Destination of the last vacation trip 0.924

Website design preferences

Accommodation should be presented in video/virtual

tour/flash

1.380***

Accommodation should be presented in price/ratings 1.149*

Accommodation should be presented in objective

information

1.180*

Accommodation should be presented in text pictures 1.067

LR Chi-square 167.626***

n ¼ 1022, *po0.10, **po0.05, ***po0.001.


doi:10.1016/j.tourman.2007.01.002

information on websites when compared to those in theOG (Table 4). RG members were also more likely to preferaccommodation sites to be presented in video/virtual tour/flash, price/ratings to be included on the site, and objectiveaccommodation information when compared to the TG.When compared to the OG, the RG tended to spend morehours online per week and devote more hours to onlineresearching/planning for vacation trips. However, theywere less likely to spend a longer amount of time on theactual vacation and less likely to vacation within theirhome state or province.The OG tended to spend fewer hours online per week but

took more vacation trips in the last year when compared tothe TG (Table 4). Additionally, the OG was more likely tospend more time at a destination and stay online longerwhen compared to those in the TG. The OG was morelikely to have taken their last vacation within their homestate or province than those in the TG.

4.3. Implications

This study brings to light important implications thatlodging marketers can use to address the need to capitalizeon the Internet and improve their websites. The first isidentification of distinct online consumer groupings:recurrent, typical, and occasional. These group labelsreflect the degree of potential access that marketers have toconsumers to collect contact information. These groupings

up typical (TG) II-Reference group occasional (OG)

Occasional (OG) Recurrent (RG) Typical (TG)

1.066 0.997 0.938

1.007 1.016 0.993

0.720*** 1.574*** 1.388***

1.154* 0.912 0.867*

1.188** 1.047 0.841**

1.067** 0.935*** 0.937**

1.041 1.248** 0.961

1.254 0.874 0.797

0.998 0.962 1.002

1.416* 0.652** 0.706*

1.166 1.183* 0.857

1.031 1.114 0.970

1.013 1.166* 0.987

0.978 1.091 1.023

Log likelihood 1848.465




supply an initial context in which further exploration ofpermission marketing can be conducted in tourism andhospitality. While the current study does not provide an in-depth analysis of possible differences based upon levels ofpermission, the findings indicate meaningful differencesamong them.

The second finding is the variation in willingness tosupply information for specific information purposes.There was only one channel to which more than 60 percentof all three groups were willing to supply their information;to obtain a login or password. The RG were willing toprovide their names/e-mails to five of the six otherchannels. The TG was generally less willing to providetheir details than the OG, but a majority would do so forfour of the six other channels. The OG was the mostreluctant to supply their details, with a majority willing todo so for just two other channels. This indicates thatlodging properties should develop their websites with threealternative strategies to collect permission marketinginformation: primarily rely upon the login/passwordmethod, advancement of current methods, or developnew avenues. The first strategy would be to increase theeffectiveness of the most popular method for catchingconsumer’s information, the login/password system. Thiscould be accomplished by providing additional incentivesfor logging in or simplifying the procedure (i.e., collectingonly the consumer’s name and e-mail address). The secondand third strategies would be to address the lack ofconsistency in effectively collecting consumer informationacross the groupings. A resort website should supply avariety of channels to effectively collect information fromconsumers, either that presented in this study or throughdevelopment of new methods.

The third finding directly relates to the fact that informa-tion can only be collected successfully from all consumergroups through one channel, the login/password method.This channel is best represented by the first level ofpermission, the situational level. Although marketerspossess a direct link to the consumer, lodging companiesneed to explore avenues in which communication at a higherlevel can be established. The goal should be the intravenouslevel, where they can make the decision for the consumer.But as the feasibility of this option is uncertain, the point’slevel, which is second highest, may be a more realistic goal.This is where personal information can be collected to makespecific suggestions to the consumer. Upon attaining thislevel of trust between the marketer and consumers, market-ers would have greater influence to ‘‘suggest’’ those productsin which they want consumers to actively participate.

Of specific interest for lodging marketers are thedifferences between each consumer group. The RG is morelikely to be traveling to an urban location regardless of trippurpose. This presents specific opportunities and chal-lenges for lodging properties in these locales. An almosteffortless opportunity to collect contact information existsas the RG have a greater propensity to supply contactinformation through most existing channels. The challenge


doi:10.1016/j.tourman.2007.01.002

is for urban properties’ marketers to supply stimulatingand technologically advanced web content with objectiveand pertinent information. Despite the high technologyaptitudes of this consumer group, everyone requested aprinted brochure via postal mail. This suggests thatalthough the Web is a powerful source of information,traditional print materials are still required by Internet-savvy consumers.Despite greater difficulties in collecting contact informa-

tion from the OG, they are still an attractive segment. Theytypically have a higher number of vacation nights and areapt to travel more than the TG and only slightly less thanthe RG. Of particular interest is the location and how theOG prefers to travel. They are more likely to travel in atour group closer to home than the RG and TG. Therefore,accommodation providers looking to attract the OGshould develop web-based permission marketing thatcaters to older, regional group travelers and concentrateon the most successful methods to collect information.Given these differences among the three consumer groups,permission marketing can and should be tailored to eachgroup based upon the individual lodging property.

4.4. Future research

This research study analyzed seven channels used tocollect information to pursue a permission marketingagenda. Three primary groupings of consumers wereidentified and compared. While this study provides under-standing regarding specific elements of permission market-ing, additional areas of study should be pursued. First, thisstudy did not examine nonresponse as secondary data wereused and that information was unavailable. Future studiesmay examine the nonresponses and overall generalizabilityto a wider group of tourism customers. Second, subsequentstudies should concentrate on directly analyzing lodgingconsumers. Using the Internet Travel Survey allowed forexploration of the permission marketing concept, butrespondents were not specifically targeted for their lodgingexperiences. By examining lodging-only consumers, com-parisons between lodging types and perceptual differencescan be explored. Third, special consideration should begiven to the security features of websites (Kesh &Ramanujan, 2004). While current data allowed forexploration, additional studies should take into accountthe complexity of data security relating to lodging websites.Fourth, the permission marketing function should befurther investigated to support hospitality and tourismoperators. As numerous components have been identifiedas impacting effectiveness, personalization and previousrelationships, for example, should be examined (Tezinde,Smith, & Murphy, 2002). Similarly, research focusing onfactors affecting response rate success such as subject linecontent, e-mail length, incentives and image use should beexamined (Chittenden & Rettie, 2003). By examining theseand other relevant topics, a substantial contribution tohospitality and tourism marketing can be made.




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