Requirements for destination management organizations in destination governance: Understanding DMO success

Tourism Management 41 (2014) 64e75

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Requirements for destination management organizations in destination governance: Understanding DMO success Michael Volgger*, Harald Pechlaner 1 European Academy of Bozen-Bolzano (EURAC Research), Drususallee 1/VialeDruso 1, 39100 Bozen/Bolzano, Italy

h i g h l i g h t s
We analyze determinants of DMO success and establish a link with destination success.
We examine the role played by networking capability in detail.
Networking capability indirectly affects DMO success via increased DMO authority.
We apply an innovative approach in mediation analysis.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 20 August 2012 Accepted 2 September 2013

In current conceptualizations of destination management, destination management organizations (DMOs) are required to act as network managers. Previous research claims that DMOs capable of actively fostering collaboration between destination stakeholders are key to ensuring a destination’s competitiveness. Yet, empirical investigations concerning the role of DMO success in establishing the competitiveness of destinations are rare. Even less is known about determinants of DMO success. Therefore, the main objective of this study is to better understand DMO and destination success by investigating the role played by networking capability. One key insight is that the effect of networking capability works through increased DMO authority, i.e. enhanced power and acceptance of the DMO within the destination network. Besides the advances in terms of content, our research also offers a relevant methodological contribution by introducing a recently developed approach in mediation analysis, which has so far received little attention in the tourism literature. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Destination governance Networking capability Destination management organizations DMO success Destination success Destination competitiveness Mediation analysis

1. Introduction Tourist destinations or tourist districts, understood in the sense of geographically embedded meeting points of supply and demand (Dredge, 1999; Sainaghi, 2006), face the challenge of bundling a fragmented supply into a consistent tourism product. In particular, in several traditional tourist destinations with their variety of independent operators and scattered patterns of ownership, hierarchical steering (Powell, 1990; Williamson, 1991) characterized by direct top-down management, strong administrative control and clear lines of authority as it is usual in intraorganizational contexts is difficult to implement (d’Angella & Go, 2009; Laws, Agrusa, Scott, & Richins, 2011). In the context of such

* Corresponding author. Tel.: þ39 0471 055325; fax: þ39 0471 055429. E-mail addresses: [email protected] (M. Volgger), harald.pechlaner@ eurac.edu (H. Pechlaner). 1 Tel.: þ39 0471 055420; fax: þ39 0471 055429. 0261-5177/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tourman.2013.09.001

community type of destinations, strategic leadership and the task of ensuring collective agency toward shared goals typically lie with the DMO and must be based on stakeholder oriented management approaches (Flagestad & Hope, 2001; Sainaghi, 2006). In fact, DMOs play a role merely as initiators and mediators: they can bring about a flexible interface management system and promote selfresponsibility, self-organization, and self-regulation of the destination network. Recent attention to destination governance has largely been the product of a growing interest in the potential benefits and drawbacks of managing tourist destinations in the form of networks. Several authors have suggested that networking firms and industries, with DMOs managing these networks, are essential elements for the sustainable and competitive development of tourist destinations (Dredge, 2006; Moscardo, 2011; Nordin & Svensson, 2007; Raich, 2006; Ritchie & Crouch, 2003). Exchange of information, use of synergies and coordination of action are supposed to positively affect destination development and are considered to be the building blocks for innovation and a versatile competitive base.

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However, while the assumptions are numerous, empirical evidence is still thin. As Bornhorst, Ritchie, and Sheehan (2010) observe, studies analyzing success in tourism from a demand perspective are abundant (e.g. Archer & Fletcher, 1996; Kozak, 2002), whereas less attention has been devoted to destination success relative to supply or from a resource-based view. In particular and most astonishingly, few have explicitly investigated the relationship between DMO success and destination success, and even fewer have scrutinized the major success determinants of effective DMOs. Here, we advance the discussion of how a DMO can contribute to the competitiveness of a destination. The mechanisms behind DMO success are of special interest in this regard. More specifically, the purpose of this study is to advance the qualitative analysis of DMO success carried out by Bornhorst et al. (2010) and to investigate quantitatively the contributors to DMO success, and in turn their potential contribution to destination success. First, we tested the model of DMO success proposed by Bornhorst et al. (2010) in a regression analysis, and second, further explored the mechanism by which networking capability affects DMO success. The questions can be expressed as: Does a networking approach to destination management contribute to increase the success of DMOs (and destinations), and how does this mechanism work? Scott, Laws, Agrusa, and Richins (2011) doubt the necessity of ideology to answer questions regarding the role of networking and democracy in destination governance. In line with their notion, our study approaches this crucial issue from an empirical and evidencebased point of view. We claim that networking capability tends to exert a positive effect on DMO success by increasing the authority of the DMO within the destination network, and that this effect can be empirically shown.

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can be observed using the efficient and sustainable use of resources as indicators (Inskeep, 1991; Mihalic, 2000). Some influential research has tried to approach destination success more indirectly via the construct of destination competitiveness. Ritchie and Crouch (2000, 2003) and Crouch (2011) offer probably the most widely cited framework within this line of research. It identifies five main determinants that affect destination competitiveness, namely destination policy, planning and development, destination management, core resources and attractors, and supporting factors and resources. In a similarly comprehensive attempt, Dwyer and Kim (2003) propose that resources, demand factors, destination management and situational conditions relating to the socio-cultural and economic environment are key determinants of destination success. Criticizing the representation by Ritchie and Crouch (2000, 2003), Mazanec, Wöber, and Zins (2007) tried to disentangle the concept of destination competitiveness, to transform it into an empirically verifiable and falsifiable explanatory model. Based on previous work of Gooroochurn and Sugiyarto (2005), they present destination competitiveness as both a latent construct comprising three dimensions (cultural heritage, economic wealth, and education), and an antecedent of destination success. In their model, destination success itself is measured in terms of growth in tourism demand and market share. The existing studies on destination success and/or competitiveness mainly differ with respect to their strategies for putting them into operation. While some try to work with ‘objective’ indicators of destination success (e.g. Mazanec et al., 2007), others, such as Crouch (2011), Dwyer and Kim (2003), Dwyer, Mellor, Livaic, Edwards, and Kim (2004) or Enright and Newton (2004) measure success and competitiveness using ‘subjective’ stakeholder perceptions.

2. Theory, model and hypotheses To better understand the role of networking capability in ensuring DMO and destination success in a comprehensive way, the results of the analysis need to be embedded in the ongoing debate about tourism success. Several such models have been put forward in the tourism literature; some of the most influential are presented below. 2.1. Previous studies on destination success Overall, research on performance can be subdivided into three broad groups (Venkatraman & Ramanujam, 1986): in the first group, a narrow conception of performance focusing exclusively on financial indicators (e.g. profitability) is predominant; the second group adopts a somewhat broader notion of performance and considers also non-financial operational indicators (e.g. product quality) (Eccles, 1991); finally, group three is characterized by the broadest approach, which analyzes organizational effectiveness including conflicting goals and diverging stakeholder views (see Kaplan & Norton, 1992). As far as research on hotel businesses is concerned, the broader conceptualization of performance have been predominant, including also subjective variables and internal performance determinants such as strategy, production, marketing, organization and IT/ICT (Sainaghi, 2010). Similarly, a variety of indicators have been proposed to measure tourist destination success. On the demand side, for instance, Archer and Fletcher (1996) and Kozak and Rimmington (1999) propose ‘hard’ data such as visitor numbers and expenditure, whereas Fuchs and Weiermair (2004), Kozak (2002), Kozak and Rimmington (1999) and Ritchie and Crouch (2003) also suggest the suitability of the ‘soft’ measure of guest satisfaction. On the supply side, it is sometimes recommended that destination success

2.2. Previous studies on DMO success Exemplifying the approach taken by the majority of empirical studies on DMO success, Blain, Levy, and Ritchie (2005), Faulkner (1997) and Gretzel, Yuan, and Fesenmaier (2000) focus primarily on investigating the impact of marketing activities. This bias may be due to the historical view of DMOs as destination marketing organizations. However, it is increasingly recognized that the role of a DMO goes beyond marketing and includes other management activities. This development was accompanied by a shift in terminology so that today the ‘M’ in DMO mainly refers to management (Presenza, Sheehan, & Ritchie, 2005). Several, mostly conceptual, papers are dedicated to exploring a DMO’s roles and tasks. For instance, based on Ritchie and Crouch (2003), Presenza et al. (2005) identify two core competencies of successful DMOs: the first, marketing, is related to external performance, and the second, coordinating destination stakeholders, is related to the internal performance of DMOs. As an indicator of DMO success they propose “the quality of visitor experience”. Heath and Wall (1992) suggest that DMOs fulfill four tasks, namely formulating strategies, representing stakeholders’ interests, developing products, and marketing. Fulfilling these functions accompanied with sustainable resource planning (Gill & Williams, 1994; Inskeep, 1991) and a focus on evaluation as well as on monitoring of quality (Kozak, 2002; Müller & Berger, 2012) should put the DMO in a position to handle the destination life cycle (Butler, 1980). Notably, leadership capabilities are an important boundary condition to act in the network context of destinations (Gretzel, Fesenmaier, Formica, & O’Leary, 2006; Harrill, 2009). Sainaghi (2006) classifies a DMO’s activities and tasks into primary processes (operative processes such as the management of

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resources, product development, communication) and support processes (internal marketing, training, research). Bieger, Beritelli, and Laesser (2009) and Getz, Anderson, and Sheehan (1998) highlight the importance of funding and resources, which operate as constraints on fulfilling these tasks. Additionally, Bieger et al. (2009) make a plea for considering the size (geographical scope) of DMOs in judging their effectiveness. However, especially in recent papers, the most consistently and strongly sought DMO capabilities refer to intermediation and networking (e.g. d’Angella & Go, 2009; Dwyer & Kim, 2003; Jamal & Getz, 1995; Sheehan & Ritchie, 2005). Given the focus of this paper and the number of these contributions, they will be dealt with in detail below. Although these contributions have advanced our understanding of how DMOs function, empirical investigations into the factors responsible for the overall effectiveness of DMOs are lacking. Given the practical relevance of this question for tourism, and especially destination managers, there is room for further analytical work. 2.3. Integrating both perspectives of destination and DMO success Although some of the above-mentioned studies include destination management in their conceptions of destination competitiveness (e.g. Crouch, 2011; Dwyer & Kim, 2003; Ritchie & Crouch, 2003), explicit investigations into the relationship between DMO success and destination success are largely missing from the literature. To close this gap, Bornhorst et al. (2010) embraced an integrative perspective, and as a result of an exploratory qualitative study, presented a model of perceived DMO and destination success. First, they hypothesize that perceived DMO success and perceived destination success overlap in part, meaning that both units of analysis should correlate positively. Due to the fact that the success of a destination (conceived as a network) rests on many actors and parameters, however, also discrepancies between DMO and destination success might be conceivable. Second, they allege that a DMO is evaluated successfully by its stakeholders if: (i) it demonstrates networking capability (i.e. the ability to interact and collaborate effectively with stakeholders in the destination, which includes developing and sustaining inter-organizational relationships) and promotes internal stakeholder relations; (ii) it is able to perform the operational and strategic activities (marketing, service provision, product development, management etc.) in a professional manner; (iii) personnel and funding are sufficient; and (iv) it provides transparent evidence of performance. Third, they also identify some variables specific to destination success (such as product and service offerings, location/accessibility, and the quality of visitor experience), which will be omitted here as being beyond

the scope of this paper. An adapted model integrating the first two insights is presented in Fig. 1. Given the ambition to embed the specific findings in a contemporarily comprehensive and parsimonious framework, the model from Bornhorst et al. (2010) was deemed suitable from a conceptual perspective. Thus, the first part of this study is devoted to evaluating its tenability on an empirical basis by verifying the implied assumptions in a statistical analysis: H1. A positive correlation exists between perceived DMO success and perceived destination success. H2. Perceived DMO success depends on the variables of (perceived) networking capability, provision of transparent evidence of performance, resource endowment and professionalism in operational work. 2.4. Understanding the role of networking in DMO success The second part of the analysis investigates an extension of the above model: We highlight the role played by authority, power and acceptance in linking networking capability to DMO success. In general, our paper puts special emphasis on networking because e as d’Angella and Go (2009: 429) assert e “there appears to be a dearth in the tourism literature on the relationship between collaborative tourism marketing and the effects of social relations on DMO performance”. This focus on networking is in accordance with a number of recent studies on destination management and governance (Beritelli, 2011; Nordin & Svensson, 2007; Pechlaner, Raich, & Beritelli, 2010; Sautter & Leisen, 1999; Sheehan & Ritchie, 2005). The concept of destination governance rests on recognizing that tourist destinations can hardly be hierarchically controlled in the same way as companies, and thus traditional management approaches may prove ineffective (Laws et al., 2011; Nordin & Svensson, 2007). Therefore, contributions to destination governance often advocate a form of self-governance that rests essentially on cooperation between stakeholders, and aims to develop joint strategies and collective action (d’Angella & Go, 2009; Jamal & Getz, 1995; Palmer & Bejou, 1995; Raich, 2006). In principle, however, one cannot assume that a-priori a destination’s actors are willing to work together. Transaction costs (Williamson, 1979) and the existence e or non-existence e of social ties need to be taken into consideration (Presenza & Cipollina, 2010; Zehrer & Raich, 2010). Therefore, actors are needed who are able to identify and articulate collective interests, establish links as well as coordinate negotiations. This crucial task of enhancing stakeholder collaboration is normally assigned to DMOs, and consequently, networking

Fig. 1. Determinants of DMO success and their relationship to destination success (model based on Bornhorst et al., 2010).

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Fig. 2. Extended model of the determinants of DMO success and their relationship to destination success.

capability is considered a primary prerequisite for evaluating their performance in a positive manner (d’Angella & Go, 2009; Halme, 2001). However, while previous research in destination management and governance argues for the importance of networking capabilities in effective management of tourist destinations, an empirical analysis investigating whether and how the degree of cooperation and stakeholder involvement affects DMO success is yet to be attempted. We propose that the concepts of authority, power and acceptance may help to better explain the influence of networking capability on DMO success. Following Weber (1976), power is defined as the ability of actor A to influence the behavior of actor B. Several papers apply the concept of power to tourism issues (see e.g. Cheong & Miller, 2000; Elliott, 1983; Nyaupane & Timothy, 2010). Previous work has also more specifically addressed the role of power within destination management, governance and planning, as well as within tourism networks. For instance, Reed (1997) and Nunkoo and Ramkissoon (2012) argue that power disparities and concentration may inhibit positive effects of, and satisfaction with cooperation. The idea of balancing power in order to limit its negative effects is also put forward by Dredge (2006). Often, authors have been reluctant to differentiate between different bases of power, and thus generally considered that power differences have negative effects on the effectiveness of destination planning and management. By contrast, Bramwell and Meyer (2007) and Jamal and Getz (1995) suggest ways to manage power issues in tourism networks by including legitimate stakeholders. Beritelli and Laesser (2011) investigate a set of potential sources of power in tourist destinations and find knowledge control over processes to be particularly relevant. In a similar attempt, Ford, Wang, and Vestal (2012) identify reputation, allies and position in the network as well as in the hierarchy as most influential means to influence power distributions in tourism networks. Thus, they implicitly suggest a positive relationship between networking capabilities and power. As becomes clear from these contributions, power as a concept does not distinguish between its various possible sources. Full recognition of this fact leads to the conclusion that “power is not always bad” (Robbins, 2005: 390). An actor’s power can be socially accepted, thereby becoming authority and providing a strong basis for action (Giddens, 1997; Robbins, 2005). Thus, acceptance seems crucial to differentiate between potential consequences of power accumulation.

Although some papers include the concept of acceptance in their analyses (see Bachleitner & Zins, 1999; Ministry of Tourism, 1992; Pearce, 1980), its potential in understanding perceived DMO success in a destination network has not yet been fully appreciated. Particularly, its potential to turn destructive elements of power into constructive ones, has not received adequate coverage in the tourism literature. Based on this widely held assumption in social science, it is claimed here that the networking capability of DMOs has a positive effect on their perceived success precisely because it contributes to increasing their authority, i.e. both their power and their acceptance within the destination network (see Fig. 2). H3. Perceived networking capabilities of a DMO indirectly influence perceived DMO success through DMO authority, i.e. DMO power and DMO acceptance. Or, alternatively: DMO authority, i.e. DMO power and DMO acceptance, work as mediators of the effect of perceived networking capability on perceived DMO success. 3. Method 3.1. Sample and measures The data used to estimate the model and perform the mentioned tests were collected within a survey among tourist destination managers in the Alpine regions of Switzerland, Austria and South Tyrol, addressing the role of DMOs in the governance of destinations. During the summer period of 2008, an online questionnaire was distributed to the 47 members of the South Tyrolean Board of Tourism Managers (response rate 55%), the 250 members of the Federation of Austrian Tourism Managers (BÖTM) (response rate 22%) and the 233 active members of the Association of Swiss Tourism Managers (VSTM) (response rate 20%). These organizations comprise the vast majority of tourist destination managers in these three Alpine regions, and thus constitute a consistent subset of Alpine destination managers in general. A total of 127 questionnaires were gathered, of which n ¼ 80 provided information on all relevant items within the purpose of this study. The 80 questionnaires e distributed to 29 Swiss, 33 Austrian and 18 South Tyrolean (Italian) DMO managers e represent a sample of about 15% of the universe of the Swiss, Austrian and South Tyrolean destination managers enrolled in the above associations. Tourism is a relevant

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Table 1 Extracted items from the questionnaire. How would you evaluate your DMO regarding.

Very bad

Very good

Professionalism Networking capability Acceptance within the destination Power and influence within the destination Resource endowment (with regard to current tasks) Transparency in dealing with its members Overall evaluation

1 1 1 1 1

through through through through through

5 5 5 5 5

1 1

through through

5 5

How would you evaluate your destination regarding. Overall evaluation

Very bad 1

through

Very good 5

this situation-specificity. One practical and repeatedly practiced way to accomplish this is to make use of more subjective successindicators such as manager assessments (Bornhorst et al., 2010; Crouch, 2011; Enright & Newton, 2004; Pearce, 1992). Such assessments also have the advantage of being more capable of capturing the future and potential aspects included in the ideas of destination and DMO competitiveness (see Pechlaner, 1999). Among the impression-based measures of DMO and destination success, it is useful to discriminate between supply-based and demand-based approaches. Within the scope of the present study, an internal or supply perspective of DMO success was preferred over a guest perspective, because of the impossibility for a guest to evaluate the role of a tourist organization in a comprehensive manner. 3.2. Analytical model and statistical procedure: mediation analysis

or highly relevant sector in a vast majority of the destinations in the sample. Some 55% of the managers considered tourism economically very important in their regions and another 21% regarded it as important. The size of the surveyed DMOs ranges from 3 to more than 7000 members, with the latter being an outlier. More than 90% of the responding DMOs has less than 1000 members and about one third has less than 70 members. Overall, the average DMO size in the sample amounts to 465 members (median value) with tourism, retail, handcraft, various services, and agriculture being the most strongly represented sectors (in this order). More than 61% of managers considered the climate between the different actors in their respective destinations as collaborative, 39% interpreted it as competitive. Following Beritelli, Bieger, and Laesser (2007) as well as d’Angella and Go (2009), we can read this reported level of collaborative climate within destinations as an indicator of a strong presence of community-based structures. This is underscored by a low number of destination managers who perceived the general attitude in their destinations to be characterized by indifference towards tourism development (20%). A very low number of answering managers denied the presence of active network structures in their destinations; less than 5% completely lack destination networks that include actors from different sectors. Some 51% of DMO managers argued that they have regular contact and exchange with the local population, and almost 80% indicated having similar regular exchange with political representatives. Following Pearce (1992) and Crouch (2011), an impressionbased measurement of the variables was applied: The group of tourism managers themselves had to evaluate the success of their DMOs and their destinations both in the respective sub-domains and overall (Table 1). The respondents were asked to judge the various performance-dimensions on a five-point Likert scale, with 1 being very unsatisfactory and 5 being very satisfactory. To a large extent, the definitions of destination success and DMO success were left open to the respondents, by asking generally for an “overall evaluation” of the DMO and the destination. Given the perception-based measurement, we subsequently refer to these results as “perceptions”. Taking perceptions as indicators of performance instead of ‘objective’ measurements, as advocated for instance by Mazanec et al. (2007), can be justified by the fact that the issue of what criterion is appropriate for measuring performance and competitiveness has not yet been answered satisfactorily for tourist destinations, let alone for DMO success. Even Mazanec et al. (2007) admit that different objective performance indicators, ranging from growth rates to relative or absolute market shares, may be required for different destinations in differing situations. They recommend considering indicators capable of taking into account

The basic model, captured by H1 and H2, was tested using standard correlation and multiple regression analysis. However, given the specific focus on H3, the major aim of this analysis was not only to establish the existence of a relationship but rather to extend this basic model by understanding particularly how networking capability influences DMO success. Statistical mediation analysis was deemed a methodological approach suited to this aim, because it is an established statistical tool to look behind the scenes of hypothesized causal relations. In cases when we are not only interested in the relation between the input stimulus and the output, but want to cast a glance into the black box and better grasp the “generative mechanism”, i.e. the process, mediation analysis is the usual standard method to be used in statistics (Baron & Kenny, 1986; Shrout & Bolger, 2002). A series of approaches have been developed to estimate the different paths in the mediation model and perform inferential tests (for an overview see MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002), most importantly the causal step approach (Baron & Kenny, 1986), the product-of-coefficients approach (Sobel, 1982, 1986), the distribution of products approach (MacKinnon et al., 2002), and the resampling or bootstrapping approaches (Bollen & Stine, 1990; MacKinnon, Lockwood, & Williams, 2004; Preacher & Hayes, 2008). Recently, an increasing number of methodologists have suggested that resampling methods such as bootstrapping provide more accurate, but still easily manageable tests of indirect effects, especially for small to medium sample sizes and for non-normal distributions of the indirect effect (Hayes, 2009; MacKinnon et al., 2004; Preacher & Hayes, 2008; Shrout & Bolger, 2002). Due to its independence from strong and restrictive parametric assumptions (such as normality), in mediation analyses with moderate sample sizes of 50e100 cases (others speak of 20e80 cases) bootstrapping is considered a viable option (except in cases with heavy tailed and highly skewed distributions) (Chernick, 2007; Chernick & LaBudde, 2010; Efron & Tibshirani, 1993; Shrout & Bolger, 2002; Stoffer & Wall, 1991). Standard bootstrapping resamples for a predefined number of times from the actual data set and estimates for each of these replaced samples the indirect effect. In our case results are based on 5000 resampling processes, a number considered sufficient in the statistics literature (Chernick, 2007; Preacher & Hayes, 2008). Through the process of resampling the sampling distribution of the indirect effect is approximated and described by confidence intervals. If the calculated interval does not comprise zero, the indirect effect can be considered significant (Hayes & Preacher, in press). However, these recent methodological developments have not yet been fully received in tourism literature. Indeed, the largely criticized causal step method continues to dominate in both OLS

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(see Hayes & Preacher, in press; Kaplan, 2009). This simple model can be extended to control for covariates (Wj) and account for multiple mediator variables (Mi). Based on the notation used by Hayes and Preacher (in press), in the relevant case of two intervening variables and three covariates the model can be written as

M1 ¼ iM1 þ a1 X þ M2 ¼ iM2 þ a2 X þ

3  P j¼1 3  P j¼1

 d1j Wj þ eM1  d2j Wj þ eM2

Y ¼ iY þ c0X þ b1 M1 þ b2 M2 þ

Fig. 3. Basic mediation model (based on MacKinnon et al., 2004).

and SEM approaches to mediation analysis in the tourism literature (Back, 2005; Baker & Crompton, 2000; Chi & Qu, 2008; He & Song, 2009; Horng, Liu, Chiu, & Tsai, 2012; Jeong, Oh, & Gregoire, 2003; Oh, 2001; Tsaur & Lin, 2004). A rare exception is provided by Yuksel, Yuksel, and Bilim (2010), who also calculate confidence intervals on a bootstrap-basis. Thus, by taking into account the recent methodological developments in mediation analysis, the present paper also methodologically contributes to advancing tourism literature. The basic idea of mediation analysis is that a total effect c from X on Y (see Fig. 3, part I) can be arranged into two parts, a direct effect c0 and an indirect effect via the path ab, which exerts its influence passing through a mediator variable M (see Fig. 3, part II). Mathematically, the total effect c is the sum of the direct (c0 ) and the indirect effects (ab), that is c ¼ c0 þ ab. With M, Y and X handled as continuous variables and all effects considered to be linear, the paths in the model can be estimated with parallel ordinary least squares regressions (OLS) or alternatively, following a structural equation modeling (SEM) approach

3   P d3j Wj

j¼1

In such an extended multiple mediator model each path aibi represents a specific indirect effect, which is carried solely by the single mediator Mi, given the presence of the other hypothesized mediators and covariates in the model. The total indirect effect of X on Y can be obtained by summing the specific indirect effects aibi (Preacher & Hayes, 2008). To test H3, such a mediation analysis was performed, with networking capability as independent variable X, DMO success as dependent variable Y, and power and acceptance as mediators M1 and M2, while controlling for the effects of the other three hypothesized determinants of DMO success, i.e. transparency, resources and professionalism (W1eW3) (see Fig. 4). The authors made use of two macros for SPSS provided by Preacher and Hayes (2008) and Hayes and Preacher (in press), INDIRECT and MEDIATE, which allow for an easy handling of multiple mediation analysis and are able to perform different bootstrapping techniques to calculate confidence intervals for specific and total indirect effects (to download freely from http://www.afhayes.com/ spss-sas-and-mplus-macros-and-code.html). Additional key figures were calculated with the econometric analysis software EViews (Econometric Views). 4. Results First, we tested H1 by analyzing the correlation between DMO success and destination success; second, we used regression analysis to test the previously explained model of DMO success as proposed by Bornhorst et al. (2010) and captured in H2; and third,

Fig. 4. Tested mediation model.

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M. Volgger, H. Pechlaner / Tourism Management 41 (2014) 64e75 Table 2 Regression model explaining DMO success (total effects model).a Est. Independent variables Constant Networking Transparency Resources Professionalism Model Summary F-value Adjusted R2 White test # VIF > 9 Determinant Jarque-Bera

1.3600 0.1173 0.1548 0.0951 0.2553 13.8145 0.3935 0.7863 0 0.4725 3.6767

SE

t-value

p-value

0.3425 0.0821 0.0738 0.0476 0.0816

3.9704 1.4286 2.0986 1.9989 3.1277

0.0002 0.1573 0.0392 0.0492 0.0025 0.0000 0.6799

0.1591

a

Note: Bold p-values indicate that their corresponding variables are significant at p < 0.05. Fig. 5. Scatter plot showing the relationship between destination success and DMO success.

we put H3, which claims a role for power and acceptance in accounting for the effect of a DMO’s networking capability on its success, under scrutiny in a mediation analysis. Descriptive statistics of all included variables are provided in the Appendix. Given the absence of scale differences in the measurement of the different variables, all estimates and coefficients reported in this section are unstandardized. This approach conforms with the general practice in mediation analysis. 4.1. The relationship between DMO success and destination success Both the optical interpretation of the scatter plot in Fig. 5 and the Pearson correlation coefficient, r ¼ 0.6804, p < 0.01 (Spearman’s rho is r ¼ 0.675, p < 0.01), indicate a pronounced and significant linear relationship between the two variables destination success and DMO success. Thus, as hypothesized above (H1) and claimed by several scholars, destination success and DMO success can be regarded as positively correlated. 4.2. A total effect regression model to explain DMO success As mentioned above, Bornhorst et al. (2010) offer a model that goes beyond the relationship between DMO success and destination success and also proposes an explanation for DMO success (see Fig. 1). The multiple regression analysis performed to test this explanatory model indicates that the four proposed regressors (networking capability, transparency, resource endowment and professionalism) account for almost 40% of the variance in the response variable DMO success (see Table 2). Thus, based on the adjusted R2 the overall goodness of fit can be judged as modest. A set of statistics strengthen interpretation of the obtained tand F-values (Table 2). The results of the White-test indicate that the assumption of homoscedasticity is satisfied and both the VIFs and the determinant of the correlation matrices show low multicollinearity. As shown by the not significant Jarque-Bera-value, residuals also conform to the requirement of normality. On the level of the single independent variables, Table 2 illustrates that professionalism in strategic and operational management, resource endowment, and providing transparent information about performance all significantly influence the success of DMOs. Among these variables, professionalism in strategic and operational management shows the strongest effect on DMO success, with resource endowment having the least effect.

By contrast, a significant total effect of networking capability on DMO success cannot be shown. However, as has recently been underscored by several statisticians, the absence of a total effect (i.e. an effect to be mediated) does not exclude the existence of a significant indirect effect (see Hayes, 2009; MacKinnon, Krull, & Lockwood, 2000; Preacher & Hayes, 2008; Shrout & Bolger, 2002). They argue that any total effect can be interpreted as the sum of numerous indirect and direct effects, only a few of which are normally included in the model that was actually tested. For instance, some of these (not included) paths could operate in opposite directions, thus canceling each other out (MacKinnon et al., 2000). Therefore it is suggested that a mediation analysis should never stop at the total effects model. This holds all the more since power of the test concerning the total effect of networking capability is relatively low. 4.3. Mediation model: power and acceptance mediating the effect of networking capability on DMO success Although the total effect of networking capability on DMO success (c ¼ 0.1173) is not statistically significant with p < 0.05, significant indirect effects originating in the network capability of the DMO and passing by the mediators of DMO acceptance and DMO power can nevertheless be found (see Fig. 6 and Table 3). The coefficients of the single paths are depicted in a mediation analysis involving networking capability as an independent variable, DMO success as a dependent variable, and the two mediators DMO power and DMO acceptance, while controlling for the other three above regressors (transparency, resources, professionalism) (Fig. 6). The direct, unmediated effect of networking capability on DMO success (c0 ¼ 0.004) is very low and insignificant (also having a negative sign). However, networking capability significantly enhances both the power (a1 ¼ 0.354, p < 0.05) and the acceptance (a2 ¼ 0.349, p < 0.05) of a DMO within a destination. Respondents evaluated a DMO with a perceived high networking capability as having more power and influence and being better accepted within the tourist destination. In turn, the two mediators DMO acceptance (b2 ¼ 0.214, p < 0.05) and e to a lesser extent e DMO power (b1 ¼ 0.132, p < 0.1) predict DMO success. The results of the mediation analysis with respect to the indirect effects are of particular interest for this paper (Table 3). The total indirect effect of perceived networking capability on perceived DMO success through both mediators taken together is shown to be a1b1 þ a2b2 ¼ 0.1212. To determine the significance of this effect, we calculated a bootstrap 95% confidence interval, obtaining [0.0288, 0.2535], which led us to rejecting the null hypothesis that the total indirect effect is zero (p < 0.05). This means that the degree of

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71

Fig. 6. Mediation of the effect of perceived networking capability on perceived DMO success through DMO power and DMO acceptance. (Note: Values marked with * are significant at p < 0.1, values with ** at p < 0.05; paths covered by significant values are bold).

perceived networking capability of a DMO indirectly increases its perceived success by strengthening the DMO’s perceived acceptance and/or power within the destination. In a subsequent step, the total indirect effect was separated into its two specific indirect effects in order to establish the unique ability of each single mediator to account for the effects of networking capability on DMO success. Both a bootstrap 95% confidence interval [0.0087, 0.1766 ] and a Monte Carlo 95% confidence interval [0.0125, 0.1567] indicate that the indirect effect through the mediator acceptance (a2b2 ¼ 0.0746) is significantly higher than zero. With respect to the other proposed mediator, power (a1b1 ¼ 0.0466), the results are more ambiguous: While a Monte Carlo 95% confidence interval [0.0141, 0.1574] suggests a significant positive indirect effect, a bootstrap 95% confidence interval [0.0014, 0.1320] does not. However, when comparing the two specific indirect pathways, that is each mediator’s unique contribution to explaining the effect of networking capability on DMO success, independently of any effects of the other mediator, somewhat paradoxically (but possible when conducting multiple tests), we detected no significant difference between acceptance and power. These partially contrasting results concerning the specific role of power as an intervening variable suggest a cautious interpretation of this part of the

Table 3 Mediation of the effect of perceived networking capability on perceived DMO success through DMO power and DMO acceptance.a Point estimate

Indirect effects Power Acceptance TOTAL Contrast Acceptance vs. Power

SE

Bootstrapping

Monte Carlo

95% Confidence intervals

95% Confidence intervals

Lower

Upper

Lower

Upper

0.0141 0.0125

0.1574 0.1567

0.0466 0.0746 0.1212

0.0346 0.0434 0.0573

0.0014 0.0087 0.0288

0.1320 0.1766 0.2535

0.0279

0.0536

0.0735

0.1423

a Note: 5000 bootstrap samples; bold intervals do not contain zero (indicate a significant indirect effect through their corresponding variable at p < 0.05).

analysis. Furthermore, given the consistent correlation between the two proposed mediators, the specific analyses capture only a relatively small portion of the mediators’ (total) indirect effects, and so tend to potentially underestimate their individual roles as intervening variables (see Hayes & Preacher, in press; Preacher & Hayes, 2008). Overall, the 95% confidence interval for the total indirect effect, and three of four yielded 95% confidence intervals for the two specific indirect effects do not comprise zero. On this basis, acceptance and power can be interpreted as significantly mediating together and e with some limitations with respect to power e also individually the influence of networking capability on DMO success (while controlling for the transparency, professionalism and resource factors). 5. Discussion Our study supports the developed model, which is based on the literature review and represented in Fig. 2. In the first part, correlation and multiple regression analyses were employed to quantitatively test the model of destination and DMO success, which Bornhorst et al. (2010) had presented on the basis of qualitative data. One central notion contained in this model, but also implicitly held by other tourism researchers (e.g. Dwyer & Kim, 2003; Ritchie & Crouch, 2003) is that destination success and DMO success are positively correlated. Our results are in line with this claim, thus supporting Hypothesis 1. Bornhorst et al. (2010) also hypothesized about the determinants of DMO success, which are covered by Hypothesis 2. The multiple regression analysis lends support to three of the four suggested determinants. Perceived transparent provision of evidence of performance, resource endowment, and operational professionalism were found to significantly influence the success of a DMO in the hypothesized (positive) direction. In contrast, contrary to our hypothesis, the significant influence of perceived networking capability on DMO success could not be shown. However, both the relatively low power to detect the total effect of networking capability and the subsequently performed mediation analysis call for prudence about too hastily interpreting this result. Indeed, while no total effect of networking capability was detected, an indirect one was found. In line with recent thinking by methodologists, the

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insignificance of the total effect may reasonably be interpreted as being the result of several (partly undiscovered) indirect effects canceling each other out. In this context, the present study not only validated the model proposed by Bornhorst et al. (2010) but also added to the literature by further expanding it. The findings suggest that networking capability of a DMO is influential on DMO success via increasing its acceptance and power within a destination. The results provide some evidence that higher networking capability significantly enhances both power and acceptance of a DMO among its stakeholders. In turn, higher DMO acceptance led to significantly higher perceived DMO success. With respect to DMO power, the influence on DMO success was less clear, but in any case significant at p < 0.1. Whereas the direct (i.e. unmediated) effect of the networking capability of a DMO on its success was not significantly different from zero, the total indirect influence via the mediators DMO acceptance and DMO power was significant. The significant and positive total indirect effect shows that the two proposed mediators taken together carry a consistent part of the positive effect of perceived networking capability on perceived DMO success. In short, we could say that the influence of networking capability on DMO success is to a significant extent mediated by power and acceptance. Thus, it seems tenable to argue that if networking capability enhances DMO success, it does so through increasing the DMO’s authority (i.e. its power and acceptance) in the destination. When comparing the two suggested mediators, DMO acceptance seems to play a clearer and stronger role in carrying the effects of networking capability than the DMO’s power within a destination. Specifically, the relationship between a powerful DMO and a successful DMO remains unclear to some extent. An examination of the specific indirect effects on the basis of 95% confidence intervals indicated DMO acceptance as a mediator that carried a significant proportion of the total effect, but produced ambiguous results with respect to DMO power. 6. Conclusions, implications and limitations The study suggests that at least in the surveyed Alpine regions the management of DMOs is related to effective destination governance and ultimately to the success of (community-based) tourist destinations. While not being completely congruent, destination success and the success of DMOs appear to be closely linked to each other. This finding lends further support to previous studies on the topic (Bornhorst et al., 2010; Dwyer & Kim, 2003; Ritchie & Crouch, 2003). Therefore, in order to understand how to increase the competitiveness of a destination, it seems to be crucial to scrutinize what determinates a DMO’s success. It has been argued that (perceived) networking capability, providing transparent evidence of performance, resource endowment and operational professionalism are the major influencing factors of DMO success (Bornhorst et al., 2010). The substantive contribution of our research is validating and refining this model by putting special emphasis on the aspect of networking capability. Surprisingly, a networking approach to destination management could not be unambiguously shown directly and totally to strengthen a DMO’s success. However, we provide some evidence of indirect influence that helps us better understand the effective mechanisms behind the networking approach. In this study, the impact of a DMO’s better networking capability on a DMO’s perceived success was largely accounted for by changes in the DMO’s acceptance and power, i.e. its authority. For instance, on the basis of the results obtained it may be claimed that networking capability of a DMO increases its power and acceptance as an actor within the destination, which in turn may increase a DMO’s success.

A major implication of these findings is that power imbalance is neither absent nor per se a problem in cooperative and networking conceptions of destination governance. As our study suggests, it might rather be a prerequisite or by-product of successful DMOs; and differences in the actors’ networking capabilities may well contribute to enhancing the unequal distribution of power, resulting in more capable actors in terms of networking becoming more powerful than others. Conceptually, we thus suggest considering networking capability as a source of power that has a strong impact on the allocation of specific means of governance within a destination. This interpretation contributes to a better understanding of the relationships between the various means of governance as proposed by Raich (2006), which include money, trust, knowledge and cooperation (capability). However, our findings also indicate that a DMO’s power needs to be socially accepted and thus transformed into authority in order to become able to significantly enhance its perceived success. The results lend support to the claim that networking capability is an effective means to increase a DMO’s acceptance and thus facilitate the transformation of power into authority. In line with the assertion recently put forward by Scott et al. (2011), the evidence collected in our research suggests that the roles of democracy and networking in destination governance need not necessarily be treated normatively or ideologically but can, at least partly, be analyzed empirically. In this regard, the findings of this study indicate that to a certain extent networking capability and democracy may be inversely related. Therefore, based on our results we argue that a higher networking capability of single actors does not lead to a more balanced distribution of power, or even ‘democracy’ in tourist destinations. Quite the opposite might be true. What a higher networking capability might enhance, are an unequal power distribution and acceptance of this unequal power distribution. It might further affect the effectiveness and success of the destination governance as a whole. In sum, while we do not argue against an inclusive and participatory approach, the assumption that completely equal and evenly distributed governance of tourist destinations is associated with the perceived success of DMOs (and a related competitiveness of destinations) could not be supported by this research. Rather, our study lends some evidence to the idea that community models of destination governance (Beritelli et al., 2007; Flagestad & Hope, 2001; Gill & Williams, 2011; Jamal & Getz, 1995), which necessarily rely on the high networking capability of their actors, always carry the intrinsic seeds of hierarchy. As suggested above, networking capability tends to increase power, and power and hierarchy reinforce each other, at least until the complexity of the situation requires radically new solutions (see Raich, 2006). In any case, such an idea paves the way for a very dynamic or maybe even cyclical conception of destination governance, oscillating between the two extremes of hierarchy (corporate model) and heterarchy (community model). Our study has a number of implications for DMO managers. The main implication for managers willing to increase the perceived acceptance and success of the DMO refers to the utmost importance of improving networking capability. However, DMO managers should be aware that building and exploiting networking capabilities might e somewhat paradoxically e gradually undermine the community structure of the destinations. As discussed, findings suggest a positive relationship between networking capability (of DMOs) and the concentration of power in destination networks (in the hands of DMOs). While awaiting further studies focusing on detailed analysis of turning points, thresholds and peaks in this relationship, the present study calls for carefully monitoring the potential and dynamic trade-off between the networking capability of a DMO and a destination’s community orientation. In situations where it is

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strategically paramount to maintain an equal power distribution at any cost, it might be necessary to avoid significant differences in networking capabilities. In such contexts, DMO managers need to be prudent in their initiating, mediating and orchestrating efforts.

73

Acknowledgments The authors would like to thank Frieda Raich for her contribution in data collection and David Airey for his helpful comments on an earlier draft of this paper.

6.1. Limitations and future research Appendix One limitation is linked to the causal interpretation of the proposed model. To interpret the model causally, it is necessary to assume that X (networking capability) causes the intermediary variables Mj (acceptance and power) and that Mj in turn cause Y (DMO success) (see Hayes & Preacher, in press). The direction of the causal effect itself cannot be proved from the statistical model, and can only be justified logically and theoretically. In any case, it remains an assumption, and it is ultimately up to the reader to determine its tenability. However, the validity of the model was increased by controlling for a set of variables (transparency, resource endowment and professionalism) that could cause problems related to multicollinearity. Further possible limitations might be related to the impression-based measurement of variables. Another limitation concerns sample size. Although we reached coverage of 15% of the universe of Swiss, Austrian and South Tyrolean destination managers enrolled in their professional associations and followed the robust bootstrap approach in mediation analysis in order to reduce impact of the moderate sample size, the actual sample size may have adversely affected the sensitivity of detecting the total effect of networking capability on DMO success. Therefore, this particular result should be considered with caution all the more since the indirect effects via the mediators DMO power and DMO acceptance were significant. In future studies it would be desirable to build upon the model shown in Fig. 2 and to further investigate some issues. First, better measurement of the variables via a specifically developed instrument would be worthwhile. Here, working with latent constructs may be productive. Furthermore, alternative research designs may help avoid potentially biasing common methods variance (Podsakoff, MacKenzie, & Podsakoff, 2003; Spector, 2006). Second, the relationship between the determinants of DMO success and their mediators on the one hand, and destination success on the other deserve to be scrutinized in a more detailed manner. Again, a mediation analysis using DMO success, not as a response variable but rather as a mediator, could be a fruitful methodological option. Third, structural equation modeling (SEM) has the potential to provide a more comprehensive picture of the relationships between the different variables employed in our analysis. A SEM-study may also facilitate working with latent constructs and indirectly measuring these constructs. For example, the present research might be effectively extended by summarizing some of the indicators used by Bornhorst et al. (2010) to form, for instance, the latent variable of corporate governance, thereby establishing an explicit connection to this growing research stream (e.g. Beritelli et al., 2007; Pechlaner, Volgger, & Herntrei, 2012). Fourth, given the consistent but not overwhelmingly high goodness of fit of the tested model, it may be fruitful to perform a stepwise regression based on a broad data set to obtain a model with a higher R-squared. Fifth, it seems worth repeating the analyses presented in this paper, conceptualizing DMO acceptance and DMO power not as mediators of networking capability on DMO success, but rather as moderators of this effect. As a further extension of the present paper, future studies could examine the relationship between DMO success and destination success in a more detailed manner. Whereas the present study paid particular attention to determinants of DMO success, a causal model that considers diverse moderators and mediators in the interplay between DMO success and destination success could generate additional value.

Descriptive statistics of all included variables.a Variable

Mean

s.d.

Skew

Kurtosis

Jarque-Bera

1. Networking capability 2. Transparency in dealing with its members 3. Resource endowment 4. Professionalism 5. Power 6. Acceptance 7. DMO success [overall evaluation of DMO] 8. Destination success [overall evaluation of destination]

3.93 4.34

0.91 0.81

0.56 0.97

3.01 3.07

4.18 12.61**

2.56 3.93 3.68 3.80 3.74

1.15 0.87 0.92 0.91 0.61

0.10 1.26 0.76 0.63 0.13

1.92 5.20 3.48 3.64 2.94

4.04 37.23** 8.55* 6.59* 0.24

3.49

0.89

0.57

3.26

4.50

a

n ¼ 80; *p  0.05; **p  0.01.

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Michael Volgger is researcher at the Institute for Regional Development and Location Management at the European Academy of Bozen-Bolzano (EURAC research), Italy, and doctoral student at the Catholic University of EichstaettIngolstadt (Germany). His main fields of research are destination governance, location management, cooperation and innovation in tourism.

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Harald Pechlaner holds a Chair in Tourism at the Catholic University of Eichstaett-Ingolstadt (Germany) and is scientific director of the Institute for Regional Development and Location Management at the European Academy of BozenBolzano (EURAC research) (Italy). He earned a Doctorate in Social and Economics Sciences at the University of Innsbruck. He was President of the German Association of Tourism Research (DGT) and is a board member of the Association Internationale d’Experts Scientifiques du Tourisme (AIEST).