Visitor relationship orientation of destination marketing organizations

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Pike, Steven D., Murdy, Samantha, & Lings, Ian (2011) Visitor relation-ship orientation of destination marketing organisations. Journal of TravelResearch, 50(4), pp. 443-453.

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Visitor relationship orientation of destination marketing organisations

Pike, S., Murdy, S. & Lings, I. (2011). Visitor relationship orientation of destination marketing organisations. Journal of Travel Research. 50(4): 443-453.

Abstract

The proposition underpinning this study is engaging in meaningful dialogue with

previous visitors represents an efficient and effective use of resources for a

destination marketing organization (DMO), compared to above the line advertising in

broadcast media. However there has been a lack of attention in the tourism literature

relating to destination switching, loyalty and customer relationship management

(CRM) to test such a proposition. This paper reports an investigation of visitor

relationship marketing (VRM) orientation among DMOs. A model of CRM

orientation, which was developed from the wider marketing literature and a prior

qualitative study, was used to develop a scale to operationalise DMO visitor

relationship orientation. Due to a small sample, the Partial Least Squares (PLS)

method of structural equation modelling was used to analyse the data. Although the

sample limits the ability to generalise, the results indicated the DMOs’ visitor

orientation is generally responsive and reactive rather than proactive.

Key words

Tourism marketing, CRM, destination marketing, repeat visitors, destination loyalty,

visitor relationship marketing (VRM)

2

Introduction

In today’s competitive tourism markets the consumer-traveller is spoilt by choice of

available destinations, and so it is likely many places are substitutable in decision

making (Pike, 2008). In response to increasing competition, destinations have

progressively become involved in place branding development since the 1990s

(Morgan, Pritchard & Pride, 2002). Successfully differentiating a destination brand in

markets crowded with places offering the same benefits, at consumer decision time, is

arguably the greatest challenge now faced by destination marketing organisations

(DMOs). Since the emergence of the destination branding literature in the late 1990s

(see Dosen, Vransevic & Prebezac 1998, Pritchard & Morgan 1998), there has been

little reported about issues related to visitor loyalty and destination switching.

Customer relationship management (CRM) has emerged as an important branding

tool and strategy within the marketing field (Gronroos, 1994). There are two types of

consumer-traveller of interest to DMOs; those who have not previously visited the

destination, and those who have. Arguably, most above the line destination

advertising appears to fail to distinguish between the two groups. The proposition

guiding this paper is that establishing dialogue with selected visitors, in the pursuit of

repeat visitation and destination loyalty, is a more efficient and effective use of

resources than traditional above the line advertising that targets new customers. CRM

is underpinned by the philosophy that stimulating long term relationships with certain

customers will be more profitable over time than a never ending series of one-off

sales transactions. The cost of reaching new customers by far outweighs the cost of

maintaining contact with existing clientele (Kincaid, 2003).

3

A recent exploratory study undertaken by one of the authors found there was a general

recognition of the potential for visitor relationship management (VRM) by regional

tourism organisations in Queensland, Australia (Pike, 2007a). However, none of the

RTOs had been able to develop a formal approach towards engagement with previous

visitors. The study concluded more research was needed to guide destination

marketers about how to initiate meaningful dialogue, at the right time, with the

hundreds of thousands of potential repeat visitors to their destination, with whom

they do not come into direct contact. Four themes emerged, with the first being the

inability of RTOs to track repeat visitation. Following this point it was not surprising

therefore that little if any direct communication was undertaken with previous

visitors. With the exception of the development of an opt-in online consumer

database, RTOs were not explicitly attempting to stay in touch with previous visitors.

Third, there was a general assumption that some accommodation operators would be

engaging in VRM, even though participants acknowledged VRM had not been topic

of discussion in meetings with local industry. Additionally, in a separate survey of

consumers in Queensland (Pike, 2006) only 13% of participants could recall receiving

promotional material in the previous year from short break destinations they had

previously visited. A significant barrier for the RTOs is that visitors’ contact details

are only captured by accommodation operators. Thus, there are distinct and

substantial differences between the application of CRM by individual businesses and

by DMOs, which usually have no direct contact with the visitors they assist in

attracting to their destination. It is for this reason the term visitor relationship

management (VRM) is considered a more appropriate term for destination marketers

than CRM. The aim of the current project was to develop a scale to operationalise

visitor relationship orientation of DMOs.

4

Literature review

The relatively recent shift in thinking towards destinations as brands, particularly

since the 1990s, requires a brand management approach focusing on developing

relationships with visitors rather than simply focusing on generating sales. The

purpose of branding is to evoke emotions and prompt repeat consumer behaviour by

way of a promise to the consumer. Recognition of this has led to the development of

the concept of consumer-based brand equity (CBBE) as a brand performance measure

since the early 1990s (see Aaker 1991, Keller 2003), which offers the potential to

provide a link between past marketing efforts and future performance. CBBE has

been conceptualised as comprising brand awareness, perceived quality, brand

associations and brand loyalty. While there have been many studies of destination

awareness, quality and associations in the literature (see for example reviews of the

destination image literature by Chon 1990, Echtner and Ritchie 1991, Pike 2002,

2007b, Gallarza, Saura & Garcia 2002), there has been a lack of attention towards

destination loyalty (Oppermann 2000, Ritchie & Crouch 2003), which is at the

pinnacle of the CBBE hierarchy.

Every successful brand strategy requires effective communication. Not long after the

arrival of Aaker’s (1991) seminal branding text was the first integrated marketing

communication (IMC) text (see Schultz, Tannenbaum, & Lauterborn, 1993). While

there has been little published on the topic in the destination marketing literature IMC

has been incorporated in tourism marketing texts (see for example Morrison 2002,

Kotler, Bowen & Makens 1999). IMC has been defined as:

5

…a cross functional process from creating and nourishing profitable

relationships with customers and other stakeholders by strategically

controlling or influencing all messages sent to these groups and

encouraging data-driven, purposeful dialogue with them (Duncan,

2002).

There are five key fundamentals in this definition, all of which are likely to present

significant challenges for destinations. The first is stimulating profitable customer

relationships. The second is strengthening stakeholder relationships to enhance

understanding of the destination’s brand objectives. The third is creating a cross-

functional process between stakeholders, to ensure brand communication buy-in. The

fourth is generating message synergy across different types of media. The fifth is

stimulating purposeful dialogue with customers. ‘Purposeful dialogue’ is also at the

heart of CRM, about which the first texts also appeared in the early 1990s (see

McKenna 1991, Sheth & Parvatiyar 1993).

It is proposed there are two main reasons for DMOs to engage in VRM. The first is

the increasing potential for repeat visitation in some market segments. For example,

the state tourism organisation for Victoria in Australia reported strong repeat

visitation from some of the state’s key markets (see Harris, Jago & King, 2005), such

as New Zealand (over 90% repeaters), Singapore (60%) and Japan (10%). Likewise,

Tourism Queensland (2006) reported that 93% of New Zealand arrivals were repeat

visitors. There are differences in perceptions of destinations between people who have

visited a destination before and those who have not visited (Fakeye & Crompton

1991, Milman & Pizam 1995). While many travellers seek new places and new

6

experiences, there is evidence to suggest in domestic short break markets, travellers

take multiple trips each year and do have favourite destinations (Pike, 2006). The

second reason is the extent to which destinations are likely to be substitutable in the

face of the sheer number of competing places, particularly for low involvement

decisions.

Oppermann (1996, 1997) claimed little had been reported in the tourism literature

about repeat visitation. However, in the time since, there has been increasing interest

by tourism academics in examining the differences between repeat and first time

visitors, particularly relating to: re-visit intentions (see Yuksel 2000, Chen & Gursoy

2001, Niinen, Szivas & Riley 2004, Yoon & Uysal 2005, Alegre & Clader 2006,

Huang & Chiu, 2006, Um, Chon & Ro 2006), spending patterns (Alegre & Juaneda,

2006), and trip characteristics (Gitelson & Crompton 1984, Gyte & Phelps 1989, Pyo,

Song & Chang 1998, Oppermann 1996, 2000, Lau & McKercher 2004, McKercher &

Wong 2004). Research suggests destinations ought to determine whether repeat

visitors offer advantages in terms of increased satisfaction, loyalty and therefore yield.

Positive indicators would lead to the recognition of the need to engage in dialogue

with these visitors.

CRM has been given many different definitions, (see for example Nicholls 2000,

Binggelt et al. 2002, Bose 2002, Chang et al. 2002, Chen & Popovich 2003, Boulding

et al. 2005, Kamakura et al. 2005, Sigala 2005, Ozgener & Iraz 2006, Stockdale

2007), which generally fit into three distinct categories: i) a business strategy to

understand and anticipate consumer needs and wants, ii) a tool to gather customer

data over a period of time, and iii) a software program. One of the most commonly

7

cited definitions is that proposed by Gronroos (1994, p.9), who suggested the purpose

is to “identify and establish, maintain and enhance… relationships with customers…at

a profit…by mutual exchange and fulfilment of promises”. It is important to note that

in marketing the term ‘relationship’ is a metaphor that is literally false but offers

creative possibilities (O’Malley & Tynan, 1999). Marketing relationships are built on

knowledge, and when a customer shares information, the marketer should customise

the offer (Peppers & Rogers, 2000). CRM is concerned with using customer

information and technology to enhance quality experiences in order to maximise

loyalty (Kincaid, 2003). It is proposed a VRM orientation involves explicit efforts to

develop a long term bond with selected visitors to the destination, in order to

stimulate repeat visitation. The aim of this research was to test the development of a

scale to operationalize visitor relationship orientation of DMOs.

8

Method

Since no DMO VRM orientation scale has been developed, a model of CRM

orientation was developed from an extensive review of the wider marketing literature,

as well as interpretation of a previous qualitative investigation. Initially a total of 42

CRM critical success factors were identified from nine studies (see Bose 2002,

Wilson, Daniel & McDonald 2002, Bielski 2003, Beaujean, Davidson & Madge 2006,

King & Burgess 2006, Lin, Lin, Huang & Kuo 2006, Ozgener & Iraz 2006, Marchand

2006, Raman, Wittmann & Rauseo 2006). These were then compared to a scale

developed to measure CRM orientation in the financial sector (see Sin et al., 2005),

which along with a review of the interpretation of a prior qualitative investigation

(Pike, 2007a), resulted in the development of a proposed model of DMO VRM

orientation. As shown in Figure 1, the model contains 23 items in five dimensions.

(FIGURE 1 ABOUT HERE)

A database of 1435 DMOs was developed as the sample frame. The DMOs comprised

CVBs, RTOs, RTBs and STOs in Australia, New Zealand, North America and the

United Kingdom, as well as NTOs from around the world. Where possible, specific e-

mail contacts were obtained for the marketing/ tourism/ communication management,

or chief executive officer/ director positions, otherwise a general enquiries e-mail was

obtained. Unfortunately many DMOs provide only general enquiry forms with no

contact e-mail addresses, which necessitated the request to participate had to be

inserted in this form.

9

During August 2008 an invitation to participate in an online survey, hosted by the

faculty, was emailed to the 1435 DMOs. One follow-up e-mail was sent in an attempt

to increase the response rate, following Sue and Ritter (2007, p. 90). The

questionnaire contained two sections. The first asked participants to rate the

importance of the 23 CRM items, using a seven point scale anchored at ‘not

important’ (1) and ‘very important’ (7). To test the scale properties, the second

section asked participants to rate their organisation’s performance across the same 23

items, using a seven point scale. Other questions related to issues of location,

structure, and legal entity. An incentive prize draw of 10 copies of a destination

marketing text was offered to participants.

10

Results

A total of 65 completed questionnaires were received, while a further 174 e-mails

(12.1%) were returned without reaching the intended participant, for a variety of

reasons, such as out of date e-mail addresses and SPAM filters (see Sue & Ritter,

2007). Although the 5.2% usable response rate is considered low, surveys targeting

businesses tend to have a lower response than consumer surveys (Sheehan &

McMillan, 1999, p. 46, Frazer and Lawley, 2000, p. 74). For example, Boo, Busser

and Baloglu’s (2008) online survey of consumers’ perceptions of gambling

destinations attracted a 5% response. Additionally, e-mail response rates are typically

lower than that for mail surveys due to an increasingly high rate of undeliverable e-

mails (Schaefer & Dillman, 1998, Weible & Wallace, 1998, p. 21). The literature

review highlighted the problematic nature of generating large samples of DMOs. For

example, Blain, Levy and Ritchie (2005) achieved a sample of 99 DMOs from around

the world. Park and Petrick (2006) received only eight responses. They suggested the

reasons that DMOs chose not to participate, included nondisclosure policies and

uncertainties about how their organisation would progress with the topic being

surveyed. These suggestions may help explain the low response rate for this type of

study.

Another potential reason for non-response identified in this study was a perception of

VRM within DMOs as something not conducted by them, but by visitor centres. For

example, a non-participating DMO voiced this concern about the study:

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I have had a look at your survey. In Western Australia it is not the role of

DMOs to deal with visitors – it is the role of LTOs local tourist organisations ie

visitor centres. In WA no DMOs run VCs so the questionnaire is largely irrelevant.

Another opinion was based around the funding of one New Zealand RTO and the

concerns that they faced building and maintaining visitor relationships:

…it impossible to respond to the questions on the basis that as a Regional

Marketing Organisation we have neither the mandate nor the funding to be able to

even consider undertaking the kind of activities in your questionnaire… The fact is

that as an RTO we struggle to maintain our existing funding let alone to secure any

increase in funding (we have had the same funding levels for 11 years). Our funders

are one Regional Council Community Trust and three District Councils and as we

have 3 new Mayors at District Council level none of them really understands why they

fund tourism at all. This means that aside from talking to and lobbying Councils, our

marketing efforts must be targeted to achieve the best result we possibly can with very

limited funds - and the bottom line is visitor nights.

I understand there may be some argument for an RTO to endeavor to maintain visitor

relationships; however we have to see this as the responsibility of the tourism

operator who is in fact better placed to do so.Perhaps your survey will be easier for a

larger and better funded RTO to respond to (our funding is currently around

$520,000 pa).

12

The sample characteristics are shown in Table 1, where it can be seen that the

majority of participants were from North America (51%) and the United Kingdom

(28%). Unfortunately, Asia, Africa and South America yielded no responses. In terms

of the type of DMO, CVBs were the largest group (46%), followed by RTOs (20%)

and TIC/ VICs (20%). Almost half of the participants represented DMOs with staff

numbers of 10 employees of less (48%). Only three DMOs had more than 150 staff.

TABLE 1 HERE

A cross-tabulation of employment of a CRM specialist, DMO hierarchy and the

number of staff employed, as shown in Table 2 highlights the majority of participants

(74%), which were not either an NTO or STO, did not hire a CRM specialist. In

comparison, only one of the six NTOs and STOs did not hire a CRM specialist. The

grand mean for the importance attribute items is 5.95, which indicates the validity of

the items from the perspective of these practitioners. The Cronbach alpha for 23

attribute importance items was .94, which indicates strong reliability for the scale.

As shown in Table 3, the means for the 23 attribute performance items range from 6.3

to 4.0. The performance results of the top four attributes indicate the DMOs have a

strong visitor orientation. However, the two lowest performance items suggest DMOs

struggle to engage in dialogue with previous visitors. As shown in Table 4, the

majority (89%) recognised the potential for CRM within their organisation, while

almost two thirds indicate communicating with previous visitors. However, less than

half are able to track repeat visitation. These results indicate the DMOs’ visitor

orientation is generally responsive and reactive rather than proactive.

13

(TABLE 2 ABOUT HERE)

(TABLE 3 ABOUT HERE)

(TABLE 4 ABOUT HERE)

Partial least squares (PLS) was used to analyse the data and to test a measurement

model of relationship orientation as a second order construct. PLS optimises the

explained variance of relationship orientation and supports variance analysis ( R 2 )

and prediction. This is useful for exploratory studies characterised by relatively weak

a priori theory and where the primary focus is theory development. Another

advantage of PLS is that it supports model types with both reflective and formative

constructs and can be used with small samples as is the case with this study (Chin,

1998a, b; 2000; Chin and Gopal, 1995). For PLS analysis, the recommended sample

size should be equal ten times either the number of indicators of the most complex

formative latent variable or the largest number of independent variables impacting a

dependent variable; whichever is greater (Barclay et al. , 1995). Relationship

Orientation has five first-order reflective dimensions (i.e. Relationship Commitment,

Resources management/allocation, CRM culture, Information generation,

Responsiveness) with the most complex dimension, Resources

management/allocation, being reflected by seven items. Thus, the dependent variable

with the largest number of independent variables impacting it is CRM Organisation.

Accordingly, the recommended sample size is would be 70. At 65, the sample size

available to us approaches the minimum recommended for PLS analysis.

A two stage approach to developing a measure of relationship orientation as a higher

order construct was adopted. First, a lower order measurement model of the five

dimensions of relationship orientation was estimated. This was followed by the

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estimation of a higher order model of relationship orientation, using aggregated data

for each of the first order constructs (factor scores). The reliability and validity of the

measures at both levels of abstraction were assessed using a variety of heuristics

(factor loadings, average variance extracted, composite reliability).

First order model

The average variance extracted, composite reliability, R2 and Cronbach’s α for the

five subscales of relationship orientation are reported in Table 5. All of these values

exceed their generally accepted recommended minima, and with moderately high R2

values, this suggest that the items reflect the underlying constructs well.

Several tests of discriminant validity were undertaken, these are discussed next and

evidence to support discriminant validity is presented. Following this, evidence from

three approaches to support the convergent validity of the scale is presented.

(TABLE 5 ABOUT HERE)

Discriminant validity

Evidence of discriminant validity is provided by a low to moderate correlation among

measures that are designed to measure conceptually different but related constructs.

For example, a phi coefficient significantly less than one offers support for

discriminant validity between constructs (Anderson & Gerbing, 1988). The inter-

15

factor correlations (φ) are reported in Table 6 and indicate that the scale items

discriminate between the dimensions of relationship orientation that they represent.

Additional evidence of discriminant validity is provided if the average variance

explained by a construct's items is greater than the construct's shared variance with

every other construct (i.e. the square of the inter-factor correlations between any two

constructs (φ2), Fornell and Larcker 1981). Analysis of the data provides strong

evidence of discriminant validity, with the average variance of each Relationship

Orientation dimension being greater than its shared variance with any other

dimension. It is therefore reasonable to assume all of the first order dimensions of the

Relationship Orientation scale to be unidimensional. The inter-factor correlations (φ),

squares of the inter-factor correlations (φ2), and average variances extracted are

reported in Table 6 also.

(TABLE 6 ABOUT HERE)

Composite reliability

Having established that each of the sub-scales measuring various dimensions of

relationship orientation do indeed discriminate between these factors, the next stage in

the analysis was to examine composite reliabilities of each of the sub-scales (Gerbing

& Anderson 1988, Hair et al. 1998, p. 611) exceed the recommended standards of

both Bagozzi and Yi (1991) and Hair et al. (1998), and provide evidence of the

internal consistency of the construct indicators. This suggests that the scale items do

indeed measure the latent constructs that they purport to.

16

Convergent validity

Fornell and Larcker (1981) suggest that variance extracted is a stringent test of

internal stability and convergent validity. Anderson and Gerbing (1988) offer an

alternative heuristic; that significant t-values support the convergent validity of scale

items. Both approaches were used to test the convergent validity of the relationship

orientation scales. Examining the variances extracted for each of the relationship

orientation dimensions, indicates that the scales explain more than 50% of the

variance in the data for each of the dimensions and so meet the stringent test of

convergent validity set by Fornell and Larcker (1981).

Finally, all 23 items load well onto the underlying dimension that they reflect (λ) and

have significant t-values >1.96 (Table 7) exceeding Anderson’s and Gerbing’s (1988)

heuristic, and suggesting that the scale items adequately represent the dimension that

they purport to measure. Overall these tests indicate that the scales measuring the

relationship orientation dimensions possess sufficient internal stability and convergent

validity to be considered for aggregation to test a higher order model of relationship

orientation.

(TABLE 7 ABOUT HERE)

Second order model

In order to measure the more abstract construct of relationship orientation, the data

from each of the scales measuring the dimensions of relationship orientation were

aggregated by calculating their factor scores. These aggregate measures were

modelled as items reflecting the higher order ‘relationship orientation’ construct.

17

In order to test the adequacy of the higher order measurement model a variety of

heuristics were examined: item loadings (λ), average variance extracted, composite

reliability. The item loadings are reported in Table 8 and, as can be seen in the table,

all are high and significant. Other fit statistics also exceed the recommended minima

(AVE = 0.71, Composite Reliability = 0.92 and Coefficient α = 0.90) suggesting that

aggregating the data to measure the higher order construct is appropriate.

(TABLE 8 ABOUT HERE)

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Conclusion

Customer relationship management (CRM) is a business strategy used by

organisations to enhance relationships with customers, increase brand loyalty and

stimulate repeat purchases. The rationale for CRM is that engaging in dialogue with

existing customers can represent a more efficient use of resources than above the line

advertising to attract a continual stream of non-customers. This study examines the

CRM orientation of destination marketing organisations (DMOs), since there has been

a lack of published research in this area. Motivating this research was an exploratory

study of 11 Queensland RTOs , which found that while management recognised the

potential of CRM, implementation was problematic. The aim of the study was to test a

scale to operationalise visitor relationship orientation of DMOs.

A model of visitor relationship orientation, comprising 23 items in five dimensions

was developed through a literature review as well as interpretation of a prior

qualitative investigation. The items were similar to a model of CRM orientation in the

financial sector developed by Sin, Tse and Yim (2005). An online questionnaire was

sent to a sample frame of 1,435 DMOs around the world, consisting of national

tourism organisations, state tourism organisations and regional tourism organisations.

A usable response rate of 5.2% (65 participants) was obtained. Due to the small

sample size the PLS method of structural equation modelling was used to examine the

data. The data fit the proposed model well.

One of the key issues to emerge from the study was the small response rate from a

sample frame of 1435 DMOs. Even though incentives were offered, and a follow up

request to participate was made, the study failed to attract a large and representative

19

sample. As discussed the problem is not unique to this project, with previous studies

also failing to attract a sample of DMOs that would enable generalisation of findings.

Given that i) the majority of tourism activities take place at destinations, ii)

destinations are the biggest brands in tourism, and iii) there is a lack of published

research about visitor relationship management by DMOs, there is clearly a need for a

different approach to sampling. At a global level, perhaps the solution lies with a

coordinated approach in conjunction with the UNWTO, and at national and state

levels through NTOs and STOs.

While the sample characteristics do not permit generalising, the findings did support

the interpretation of a previous qualitative investigation of visitor relationship

orientation of RTOs in Queensland, Australia. The results of this study indicate the

DMOs’ visitor orientation is generally responsive and reactive rather than proactive.

That is, DMOs are in general able to respond to visitor enquiries in an efficient and

effective manner, but fail to be able to proactively engage in dialogue with previous

visitors. Comments from a New Zealand RTO suggest that DMOs remain focussed on

attracting visitors rather than encouraging repeat visits. The RTO stated: our

marketing efforts must be targeted to achieve the best result we possibly can with very

limited funds - and the bottom line is visitor nights. This appears to suggest that the

lack of funding and the focus on recruiting new visitors hinders the development of a

relationship orientation and that tactics remain focused on a more transactional

approach to marketing. DMOs rarely come into direct contact with the visitors they

have been successful in attracting to their destination. The DMO doesn’t therefore

have access to the contact details of these consumers, who stay in commercial

accommodation or with friends and relatives. Without access to such contact details,

20

the development of a database is problematic for the DMO. Such a database underpins

any form of continued contact with previous visitors. Therefore more research is

required to identify i) how DMOs are able to develop a database of previous visitors,

and then ii) how DMOs can engage in meaningful dialogue. The latter involves

engaging with previous visitors at an individual or segment level, where offers are

tailored to specific needs at relevant times. This is clearly more than an e-newsletter

emailed to all database contacts.

21

References

Aaker, D. A. (1991). Managing Brand Equity. New York: Free Press.

Alegre, J. & Cladera, M. (2006). Repeat Visitation in Mature Sun and Sand Holiday

Destinations. Journal of Travel Research, 44(1) 288.

Anderson, James. C. and Gerbing, David. W. (1988), “Structural Equation Modelling

in Practice: A Review and Recommended Two-Step Approach”,

Psychological Bulletin, Vol. 103, No. 3, pp. 411-423.

Anton,. & Petouhoff. (2001). Customer Relationship Management. Sydney: Pearson

Education Australia.

Bagozzi, R. P., Yi, Y. and Phillips, L. W., (1991), “Assessing construct validity in

organizational research”, Administrative Science Quarterly, 36, 421-458

Barclay, D., C. Higgins and R. Thompson, 1995. The partial least squares (PLS)

approach to causal modeling: Personal computer adoption and use as an

illustration. Technology Studies, 2 (2): 285–309.

Beaujean, M., Davidson, J., & Madge, S. (2006). The 'moment of truth' in customer

service. McKinsey Quarterly, 2006(1).

Blain, C., Levy, S. & Ritchie, J. (2005). Destination Branding: Insights and Practices

from Destination Management Organizations. Journal of Travel Research,

43(4) 328-338.

Binggelt, U., Gupta, S. and de Pommes, C. (2002). CRM in the air. McKinsey

Quarterly, 36(2).

Bose, R. (2002). Customer relationship management: key components for IT success.

Industrial Management + Data Systems, 102(1/2) 89-97.

22

Boulding, W., Staelin, R., Ehret, M. & Johnston, W. (2005). A Customer Relationship

Management Roadmap: What is known, Potential Pitfalls, and Where to Go.

Journal of Marketing, 69(2005) 155-166.

Chang, J., Yen, D., Ku, C.-Y., & Young, D. (2002). Critical issues in CRM adoption

and implementation. International Journal of Services and Technology

Management, 3(3), 311-324.

Chen, J.S., & Gursoy, D. (2001). An investigation of tourists’ destination loyalty and

preferences. International Journal of Contemporary Hospitality Management.

13(2): 79-85.

Chen, I., & Popovich, K. (2003). Understanding customer relationship management

(CRM): People, process and technology. Process Management Journal, 9(5),

672-688.

Chin Wynne. W (1998), “Issues and Opinion on Structural Equation Modeling”, MIS

Quarterly, Vol. 22, No. 1, pp. vii-xvi.

Chin, W. W. (1998), "The partial least squares approach for structural equation

modeling", in Marcoulides, G. A. (Ed.), Modern Methods for Business

Research, Lawrence Erlbaum Associates, Mawah, NJ, pp. 295-336).

Chin W.W and Gopal A (1995) Adoption Intentions in GSS: Relative Importance of

Beliefs, Database for Advanced Information Systems 26(2 & 3) 42-64

Chon, K. (1990). The role of destination image in tourism: a review and discussion.

The Tourist Review. 45(2):2-9.

Dosen, D.O., Vranesevic, T., Prebezac, D. (1998). The importance of branding in the

development of marketing strategy of Croatia as tourist destination. Acta

Turistica. 10(2): 93-182.

23

Duncan, T. (2002).IMC: Using Advertising and Promotion to Build Brands.

McGraw-Hill.

Echtner, C.M. and Ritchie, J.R.B. (1991). The meaning and measurement of

destination image. The Journal of Tourism Studies. 2(2): 2-12.

Fakeye, P. C., & Crompton, J. L. (1991). Image differences between prospective, first

time, and repeat visitors to the Lower Rio Grande Valley. Journal of Travel

Research. 30: 10-16.

Fornell, Claes and Larcker, David (1981), “Structural Equation Models with

Unobservable Variables and Measurement Error: Algebra and Statistics”,

Journal Of Marketing Research, Vol. 18, No. 3, pp. 382-388

Frazer, L. & Lawley, M. (2000). Questionnaire design & administration: a practical

guide. Brisbane: John Wiley & Sons Australia, Ltd.

Gallarza, M. G., Saura, I. G., & Garcia, H. C. (2002). Destination image: toward a

conceptual framework. Annals of Tourism Research. 29(1): 56-78.

Gardner, B. B., & Levy, S. J. (1955). The product and the brand. Harvard Business

Review. March-April: 33-39.

Gerbing, David W. and Anderson, James. C. (1988), “An Updated Paradigm for Scale

Development Incorporating Unidimensionality and its Assessment’, Journal of

Marketing Research, Vol. 25, No. 2, pp. 361-370.

Gitelson, R., & Crompton, J. (1984). Insights into the Repeat Vacation Phenomenon.

Annals of Tourism Research, 11(1) 199-217.

Grönroos, C. (1994). From marketing mix to relationship marketing: towards a

paradigm shift in marketing. Management Decision. 32(2): 4-20.

Gyte, D.M., & Phelps, A. (1989). Patterns of destination repeat business: British

24

tourists in Mallorca, Spain. Journal of Travel Research. Summer: 24-28.

Hair, Joesph. F. Jr., Anderson, Ronald. E., Tatham, Rolph. L. and Black, William. C.

(1998), Multivariate Data Analysis, 5th edition, Prentice Hall, New Jersey,

USA.

Harris, R., Jago, L., & King, B. (2005). Case Studies in Tourism & Hospitality

Marketing. Frenchs Forest, NSW: Pearson.

Huang, H.H., & Chiu, C.K. (2006). Exploring customer satisfaction, trust and

destination loyalty in tourism. Journal of American Academy of Business.

10(1): 156-159.

Kamakura, W., Mela, C., Ansari, A., Bodapati, A., Fader, P., Iyengar, R., et al.

(2005). Choice Models and Customer Relationship Management. Marketing

Letters, 16(3/4), 279-291.

Keller, K.L. (2003). Strategic Brand Management. Upper Saddle River, NJ: Prentice

Hall.

Kincaid, J.W. (2003). Customer Relationship Management – Getting it Right. Upper

Saddle River, NJ: Prentice Hall PTR.

King, S., & Burgess, T. (2006). Beyond critical success factors: A dynamic model of

enterprise system innovation. International Journal of Information

Management, 26(1), 59-69.

Kotler, P., Bowen, J., & Makens, J. (1999). Marketing for Hospitality and Tourism.

(2nd Ed). Upper Saddle River, N.J: Prentice-Hall.

Lau, A.L.S., & McKercher, B. (2004). Exploration versus acquisition: a comparison

of first-time and repeat visitors. Journal of Travel Research. 42(February):

279-285.

25

Marchand, D. (2006). Customer Relationship Management Challenging the Myth:

Focus on People not the Technology. Perspectives for Managers, 131(1), 1.

McKercher, B., & Wong, Y.Y. (2004). Understanding tourism behaviour: examining

the combined effects of prior visitation history and destination status. Journal

of Travel Research. 43(November): 171-179.

Milman, A., & Pizam, A. (1995). The role of awareness and familiarity with a

destination: The central Florida case. Journal of Travel Research. 33 (3): 21-

27.

Morgan, N., Pritchard, A., & Pride, R. (2002). Destination Branding – Creating the

Unique Destination Proposition. Oxford: Butterworth-Heinemann.

Morrison, A. (2002). Hospitality and Travel Marketing. (3rd Ed). Albany, NY:

Delmar.

Nicholls, C. (2000). CRM systems provide excellent source for research. Marketing

News, 34(19), 28-29.

Niininen, O., Szivas, E., & Riley, M. (2004). Destination loyalty and repeat

behaviour: an application of optimum stimulation measurement. International

Journal of Tourism Research. 6: 439-447.

O’Malley, L., & Tynan, C. (1999). The utility of the relationship metaphor in

consumer markets: a critical evaluation. Journal of Marketing Management.

15: 587-602.

Oppermann, M. (1996). Visitation of tourism attractions and tourist expenditure

patterns - repeat versus first-time visitors. Asia Pacific Journal of Tourism

Research. 1 (1): 61-68.

Oppermann, M. (1997). First-time and repeat visitors to New Zealand. Tourism

Management. 18(3): 177-181.

26

Oppermann, M. (1999). Where psychology and geography interface in tourism

research and theory. In Woodside, A. G., Crouch, G.I, Mazanec, J. A.,

Oppermann., & Sakai, M. Y. (Eds). ConsumerPsychology of Tourism,

Hospitality and Leisure. Wallingford: CABI.

Oppermann, M. (2000). Tourism destination loyalty. Journal of Travel Research.

39(August): 78-84.

Ozgener, S., & Iraz, R. (2006). Customer relationship management in small-medium

enterprises: The case of Turkish tourism industry. Tourism Management,

27(6), 1356-1363.

Peppers, D., & Rogers, M. (2000). Build a one-to-one learning relationship with your

customers. Interactive Marketing. 1(3): 243-250.

Park, S. & Petrick, J. (2006). Destinations’ Perspectiveas of Branding. Annals of

Tourism Research, 33(1) 262-265.

Pike, S. (2002). Destination Image Analysis: A Review of 142 Papers from 1973-

2000. Tourism Management. 23(5): 541-549.

Pike, S. (2006). Destination decision sets: A longitudinal comparison of stated

Destination preferences and actual travel. Journal of Vacation Marketing.

12(4): 319-328.

Pike, S. (2007a). Repeat visitors – an exploratory investigation of RTO responses.

Journal of Travel & Tourism Research. Spring: 1-13.

Pike, S. (2007b). Destination image literature: 2001 – 2007. Acta Turistica. 19(2):

107-125.

Pike, S. (2008). Destination Marketing. Burlington, MA: Butterworth-Heinemann.

Pritchard, A., & Morgan, N. (1998). Mood marketing - the new destination branding

strategy: a case of Wales the brand. Journal of Vacation Marketing. 4(3): 215-

27

29.

Pyo, S., Song, J., & Chang, H. (1998). Implications of repeat visitor patterns: the

Cheju Island case. Tourism Analysis. 3: 181-187.

Raman, P., Wittmann, C., & Rauseo, N. (2006). Leveraging CRM for sales: the role

of organizational capabilities in successful CRM implementation. Journal of

Personal Selling and Sales Management, 26(1), 39-53.

Ritchie, J.R.B., & Crouch, G. (2003). The Competitive Destination – A Sustainable

Tourism Perspective. Oxon: CABI Publishing.

Schaefer, D. & Dillman, D. (1998). Development of a standard e-mail methodology.

Public Opinion Quarterly, 62(3), 378-397.

Schultz, D., Tannenbaum, S., & Lauterborn, R. (1993). Integrated Marketing

Communications. Lincolnwood, IL: NTC Publishing.

Sigala, M. (2005). Integrating customer relationship management in hotel operations:

managerial and operational implications. Hospitality Management, 24(2005),

391-413.

Sin, L., Tse, A., & Yim, F. (2005). CRM: conceptualization and scale development.

European Journal of Marketing, 39(11/12), 1264-1392.

Sheehan, K. & McMillan, S. (1999). Response variation in E-mail surveys: an

exploration. Journal of Advertising Research, 1999(July/ August).

Stockdale, R. (2007). Managing customer relationships in the self-service

environment of e-tourism. Journal of Vacation Marketing, 13(3), 205-219.

Sue, V. & Ritter, L. (2007). Conducting Online Surveys. Sage Publications: London.

Weible, R. & Wallace, J. (1998). Cyber research: the impact of the internet on data

collection. Marketing Research, 1998(Fall), 19-31.

28

Wilson, H., Daniel, E., & McDonald. (2002). Factors for Success in Customer

Relationship Management (CRM) Systems. Journal of Marketing

Management, 18(1/2), 193-219.

Yuksel, A. (2000). Managing customer retention: a case of tourism destinations,

Turkey. In Robinson, M., Evans, N., Long., Sharpley, R., & Swarbrooke, J.

(Eds). Management, Marketing and the Political Economy of Travel and

Tourism. Sunderland: Business Education Publishers.

29

Figure 1 – Proposed model of DMO VRM orientation

CRM culture

Having effective interdepartmental communication.

An organisational structure that is designed around visitors.

Top management supporting the acquisition and deepening of visitor relationships.

Assessing whether visitors feel services should be modified.

Information generation

Providing ongoing, two-way communication with key visitors.

Making an effort to find out what key visitor needs are.

Providing customised services to key visitors.

Understanding the needs of key visitors.

Maintaining a comprehensive database of visitors.

Resources management

Employee training to help acquire and deepen visitor relationships.

The right personnel for the technology used to build visitor relationships.

Having the right hardware to serve visitors.

Having the right software to serve visitors.

The sales and marketing expertise and resources to manage visitor relationships.

Establishing and monitoring performance standards relative to visitors.

Working with key visitors to customise the destination’s offerings.

Responsiveness

Visitor relationship orientation

CRM culture

Information

generation

Relationship

commitment

Resources

management

Responsiveness

30

Responding to visitor requests promptly.

Prompt service from employees of the organisation.

Employees being willing to help visitors in a responsive manner.

Treating all key visitors with great care.

Relationship commitment

Having clear goals for visitor acquisition and retention

Committing time and resources to managing relationships with visitors.

Having individual visitor information available.

Table 1 – Sample characteristics

n Valid Percent

Organisation Location North America 33 50.8

United Kingdom 18 27.7

New Zealand 8 12.3

Europe 3 4.6

Australia 2 3.1

Asia 0 0

Africa 0 0

Central/ South America 0 0

Other 1 1.5

Total 65 100

Type of DMO CVB 30 46.2

RTO 13 20.0

TIC/ VIC 13 20.0

NTO 5 7.7

STO 1 1.5

31

Other 3 4.6

Total 65 100

Number of Staff Less than 10 31 48.4

11-20 16 25.0

21-30 6 9.4

31-40 4 6.2

51-100 2 3.1

41-50 1 1.6

101-150 1 1.6

More than 150 3 4.7

Total (One missing) 64 100

CRM Specialist No CRM specialist 48 23.1

Full-time CRM specialist 15 3.1

Part-time CRM specialist 2 73.8

Total 65 100

32

Table 2 - Cross Tabulation of DMO characteristics

Hierarchy

(NTOs/STOs)

Hierarchy

(All others)

No. of Staff

(<10)

No. of

Staff (10

or more)

CRM Specialist

(Employed)

5 11 8 8

CRM Specialist

(Not Employed)

1 48 23 25

Missing Values 1

33

Table 3 – Attribute performance

Attribute Mean

Employees being willing to help visitors in a responsive manner. 6.3

Responding to visitor requests promptly. 6.1

Prompt service from employees of the organisation. 6.0

Treating all key visitors with great care. 5.9

Making an effort to find out what key visitor needs are. 5.7

Providing customised services to key visitors. 5.4

Having individual visitor information available. 5.3

Understanding the needs of key visitors. 5.2

Committing time and resources to managing relationships with visitors. 5.2

Top management supporting the acquisition and deepening of visitor

relationships.

5.1

Having effective interdepartmental communication. 5.0

Providing ongoing, two-way communication with key visitors. 4.9

The sales and marketing expertise and resources to manage visitor

relationships.

4.9

Assessing whether visitors feel services should be modified. 4.8

The right personnel for the technology used to build visitor relationships. 4.8

Working with key visitors to customise the destination’s offerings. 4.8

Establishing and monitoring performance standards relative to visitors. 4.8

Employee training to help acquire and deepen visitor relationships. 4.7

Having the right software to serve visitors. 4.5

Having the right hardware to serve visitors. 4.5

An organisation structure that is designed around visitors. 4.4

Maintaining a comprehensive database of visitors. 4.3

Having clear goals for visitor acquisition and retention. 4.0

Grand Mean 5.1

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Table 4 - Impediment Questions

Impediment question Yes No

Is your organisation able to track repeat visitation? 42% 58%

Does your organisation communicate with previous visitors? 63% 37%

Does your organisation encourage local operators to stimulate repeat

visitation?

83% 17%

Does your organisation recognise the potential for customer

relationship management?

89% 11%

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Table 5 – First order model reliability

AVE CR R2 Cronbach’s α

CRM culture 0.70 0.91 0.78 0.86

Information generation 0.69 0.92 0.77 0.89

Resources management/allocation 0.63 0.92 0.79 0.90

Responsiveness 0.80 0.94 0.64 0.91

Relationship Commitment 0.68 0.86 0.60 0.76

36

Table 6 – Inter factor correlations

CRM

culture

Information

generation

Resources

management/allocationResponsiveness

Relationship

Commitment

CRM culture

Information generation φ=0.69

(φ2=0.48)

Resources

management/allocation

φ=0.79

(φ2=0.62)

φ=0.67

(φ2=0.45)

Responsiveness φ=0.59

(φ2=0.35)

φ=0.72

(φ2=0.52)

φ=0.56

(φ2=0.31)

Relationship

Commitment

φ=0.65

(φ2=0.42)

φ=0.62

(φ2=0.38)

φ=0.63

(φ2=0.40)

φ=0.54

(φ2=0.29)

37

Table 7

Item Dimension λ T Statistics

IdentifyingCustomisationP_1

CULTURE

0.868296 29.538292

InterdepartmentalP_1 0.798969 14.306140

StructureP_1 0.857201 16.994713

TopManagementP_1 0.831785 13.788277

DatabaseP_1

INFO GEN

0.628830 6.063308

EffortNeedsP_1 0.886565 28.789880

ProvidingCustomisationP_1 0.911894 46.633826

TwoWayP_1 0.835680 15.763912

UnderstandingP_1 0.868585 16.386037

ExpertiseP_1

RES MGMT

0.766922 12.611620

HardwareP_1 0.830284 14.770839

ModificationP_1 0.784923 11.392503

PerformanceP_1 0.715042 8.737552

PersonnelP_1 0.801086 15.602162

SoftwareP_1 0.821619 14.142469

TrainingP_1 0.838968 21.424484

PromptP_1

RESPONSIVENESS

0.865471 10.875748

RequestsP_1 0.909945 33.475255

ResponsiveP_1 0.917187 30.256512

VisitorCareP_1 0.873758 12.457790

TimeP_1 rel commit 0.897235 33.090193

38

IndividualP_1 0.776374 8.333310

GoalsP_1 0.793348 8.644823

39

Table 8

Item/dimension λ t-value

crm_cult 0.88 18.113850

info_gen 0.87 18.491765

rel_commit 0.80 11.899272

res_mgmt 0.86 20.156231

Resp 0.79 11.623715