Distributed leadership typologies in destination management organisations

Tourism Management Perspectives 28 (2018) 126–143

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Distributed leadership typologies in destination management organisations Dean Hristov a b

a,⁎,1

, Noel Scott

b,2

, Sonal Minocha

T

a

Bournemouth University, Talbot Campus, Fern Barrow, Poole BH12 5BB, UK Griffith Institute for Tourism, Griffith University, Gold Coast, QLD 4222, Business 2 (G27) Room 3.12, Australia

A R T I C LE I N FO

A B S T R A C T

Keywords: Distributed leadership Destination management organisations Social network analysis Destinations Organisational change

This paper investigates processes and practices related to the enactment of Distributed Leadership (DL) within Destination Management Organisations (DMOs) through a cross-disciplinary approach by adapting a framework developed by Hoppe and Reinelt (2010) for evaluating leadership development in networks in situ and the visual strand of Social Network Analysis (SNA). The paper unfolds the case of Milton Keynes – an emerging destination in England and its local destination management structure – Destination Milton Keynes. Six leadership typologies within a network of DMO member organisations are identified, which demonstrate different, yet complementary DL behaviours. The study contributes to an understanding of how traditional DMOs shift their predominant organisational models through the development of different leadership behaviours of their member organisations in line with changes in their operational environment. The identification of different leadership behaviours serves as the basis of the development of DL typologies to support DMO policy and practice.

1. Introduction Destination Management Organisations (DMOs) face an increasingly networked environment and significant changes in their funding and governance (Coles, Dinan, & Hutchison, 2014; Hristov & Petrova, 2015; Reinhold, Beritelli, & Grünig, 2018). Such disruptions to the operational environment for DMOs are evident in a number of countries, such as such as Switzerland (Beritelli, Bieger, & Laesser, 2014), Australia (Pforr, Pechlaner, Volgger, & Thompson, 2014), China (Wang & Ap, 2013) and the UK (Hristov & Zehrer, 2017). Financially constrained DMOs face considerable challenges in delivering value to their destinations, visitors and member organisations (Hristov & Zehrer, 2015; Reinhold, Laesser, & Beritelli, 2015). Distributed Leadership (DL) is a recent paradigm used in destination research as a response to these challenges as it provides a mechanism for pooling knowledge and resources, and hence an opportunity to ensure the long-term sustainability of DMOs (Kozak, Volgger, & Pechlaner, 2014; Pechlaner, Kozak, & Volgger, 2014). DL provides a framework for collective responsibility and leadership of dispersed DMO resources advocated by recent government policy (Penrose, 2011; Reinhold et al., 2015). Implementing DL requires champions from the various destination stakeholder groups with developmental resources and strategic vision on the DMO board

(Hristov & Zehrer, 2015). Such network champions can play an important linking function within DMOs (Beritelli, Buffa, & Martini, 2015). Buchanan, Addicott, Fitzgerald, Ferlie, and Baeza (2007) suggest that network champions and the interplay between them is important for the enactment and promotion of DL across networks and organisations. A number of recent academic contributions in the domains of destinations and destination organisations suggest the importance of considering alternative approaches to DMO and destination governance practices within a new funding and governance landscape (Laesser & Beritelli, 2013; Pikkemaat, Peters, & Chan, 2018; Reinhold et al., 2015) and highlight the opportunities provided by shared forms of leadership, such as DL (Hristov & Zehrer, 2015; Kennedy & Augustyn, 2014; Kozak et al., 2014; Valente, Dredge, & Lohmann, 2015). Whilst the extant literature on DMOs and destinations has incorporated network theory and SNA (see Baggio and Cooper, 2010; Gajdošík, Gajdošíková, Maráková, & Flagestad, 2017; Scott, Baggio, & Cooper, 2008), evidence from academic contributions on the adoption of DL in the DMO and destination context is thin (Hristov & Zehrer, 2017; Pechlaner et al., 2014). Arguably, the extant literature on DMOs and destinations has not provided investigations into how DL is enacted and practiced by a multitude of leaders on board DMOs and their

⁎

Corresponding author. E-mail addresses: [email protected] (D. Hristov), noel.scott@griffith.edu.au (N. Scott). Postal address: Bournemouth University, Talbot Campus, Fern Barrow, Poole BH12 5BB, United Kingdom Tel: +44 7526 326,021; Alternative e-mail: [email protected] 2 Postal address: Griffith University, Griffith Institute for Tourism, Gold Coast, QLD 4222, Business 2 (G27) Room 3.12, Tel: +61 7 55,528,586; E-mail: noel.scott@ griffith.edu.au 1

https://doi.org/10.1016/j.tmp.2018.08.003 Received 16 April 2018; Received in revised form 31 July 2018; Accepted 10 August 2018 2211-9736/ © 2018 Elsevier Ltd. All rights reserved.

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et al. (2007) contended that the empowerment of network champions and nurturing interaction between them, leads to the development of DL across networks of organisations, such as DMOs. Beritelli et al. (2015) provided evidence that such network champions play an important linking function within DMOs.

networks of member organisations using a network approach guided by an established framework for leadership development emanating from the organisational leadership literature (Hoppe & Reinelt, 2010). Reinhold et al. (2015) and more recently Hristov and Zehrer (2015, 2017) called for empirical evidence into how DL is put into practice in the domain of destinations and DMOs. Similar calls for further study are also found in the wider organisational leadership literature (Cullen & Yammarino, 2014). Within this context, the overarching purpose of this study is twofold:

2.1. From collaboration to distribution of leadership DL builds on the concepts of cooperation and collaboration (see Scott & Marzano, 2015), which hold a prominent role in the changing funding and governance landscape, where DMOs in England are expected to adopt a strategic leadership role (Hristov & Petrova, 2015). DL builds on stakeholder collaboration in destinations through interdependence as its defining feature and indeed a condition for the enactment and practice of DL (Harris, 2005; Spillane, 2006). Inter-dependence in the context of DMOs implies limited resources, and response to this is framed in a networked fashion as opposed to on a bilateral basis, which is often the case with traditional stakeholder collaboration (Mason, 2015). DL therefore requires collective decisionmaking roles and responsibilities in the context of inter-dependency, where the latter is gaining more prominence in light of recent developments in the funding and governance landscape for DMOs. DL requires collaboration in the form of communication and resource exchange across a multitude of leaders to provide access to much needed developmental resources and oversee strategic destination decisionmaking in destinations. The last two decades have seen major shifts in paradigms of the concept of leadership discussed across the mainstream leadership literature (see Cullen & Yammarino, 2014; Fitzsimons, James, & Denyer, 2011; Harris, 2008; Martin, Currie, & Finn, 2009; Spillane, 2006). Cullen and Yammarino (2014) describe a transition from orthodox and ‘heroic’ leadership towards collective forms of leadership as ‘a paradigm shift’ within the field. These authors suggest that ‘teams, organisations, coalitions, communities, networks, systems, and other collectives carry out leadership functions through a collective social process’ (Cullen & Yammarino, 2014, p.1). The term ‘distributed leadership’ was first introduced by Gibb (1954) in the mainstream leadership literature in his investigation of dynamics in influence processes taking place in both formal and informal groups and organisations. After Gibb (1954) little attention was placed on the concept until its rediscovery by Brown and Hosking (1986). Harris (2008) contends that DL emerges within organisations as a consequence of major shifts and subsequent complexities in an attempt to respond to them and cannot be prescribed in advance as it is the case of ‘heroic’ leadership. Traditional theories of leadership emanating from the mainstream leadership literature tend to discuss characteristics, values and attitudes held by individuals, i.e. leaders (Bass, 1985; Bass & Steidlmeier, 1999), which is aligned with the notion of ‘heroic’ leadership. DL, in contrast, is enacted by multiple individuals within an organisation or across organisations (Fitzsimons et al., 2011) and therefore occurs in a variety of group and organisation settings (Thorpe, Gold, & Lawler, 2011). A DL perspective then ‘recognises the inclusive and collaborative nature of the leadership process’ (Oborn, Barrett, & Dawson, 2013, p.254). DL, according to Fitzsimons et al. (2011), is inherently inclusive as the concept captures whole organisations as units of analysis and importantly, takes into account their organisational environments. The focus of DL is on the study of leadership at an organisational level and across organisations. The practice of DL is founded on and thus heavily shaped by interactions within the organisation and its operational environment (Fitzsimons et al., 2011). DL is thus defined as leadership that is not concentrated in just a few individuals but distributed across a network. DL also goes beyond merely the interdependence of individual actors to capture other defining features such as interactions rather than actions and the sharing of developmental resources and communication (e.g. see Fitzsimons et al., 2011; Harris & Spillane, 2008). It is important to note that

(i) To investigate how DL is enacted in a DMO through the identification of different leadership behaviours of DMO member organisations; and. (ii) To develop a DL functional typology of DMO leaders by building on findings related to the identification of different leadership behaviours of DMO member organisations. This paper thus discusses distributed leadership as an alternative perspective to traditional ‘heroic’ leadership approaches to orchestration of DMOs and their network of public, private and non-profit member organisations within a dynamic organisational context fuelled by shifts on a global to local scale (Milne & Ateljevic, 2001). Although this study is grounded in a specific DMO context, i.e. England, UK characterised with a shifting funding and governance landscape (see Coles et al., 2014; Hristov & Zehrer, 2017), these challenges faced by DMOs are not exclusive to this specific context (Reinhold et al., 2015; Scott & Marzano, 2015). This makes this investigation relevant to other DMOs and destinations operating under similar context to the one in England, UK. The remainder of this paper firstly provides an overview of prominent leadership contributions in the domain of DL and its interplay with destination and DMO research. It then discusses the DMO and destination that is the focus of this study, the guiding methodological framework based on network analysis, and data collection and sampling considerations. Hoppe and Reinelt (2010) recommend network analysis to study the interaction of network actors and resources and hence to provide insights into the enactment of DL at a DMO level. As a result six types of leadership behaviours are identified and implications for DL within DMOs discussed. 2. Literature review Strategic cooperation and collaboration on a DMO level has long been perceived as an important catalyst of strategic destination decision-making for DMOs and other key destination stakeholders (Bramwell & Lane, 2000; Scott, 2011; Scott & Marzano, 2015). Indeed, successful DMOs excel in establishing partnerships and collaborative networks (Harrill, 2009; Pikkemaat et al., 2018). Whilst partnerships between DMOs and other key destination stakeholders can be productive and functional, they may also be problematic and highly political (Sheehan, Brent Ritchie, & Hudson, 2007) particularly in times of shifting governance and funding arrangements for DMOs and destinations introducing a degree of uncertainty and complexity. Recent policy developments involving a new funding landscape are set to challenge the traditional paradigms and governance models discussed in the extant literature of DMOs and destinations (Reinhold et al., 2015). This complexity in the operational environment together with the rapid development of tourism as a multifaceted phenomenon creates new challenges to both destination practitioners and academics attempting to predict global industry shifts (Kozak & Baloglu, 2011; Laesser & Beritelli, 2013; Urry & Larsen, 2011). These trends, coupled with the rapid globalisation processes and increased competition, require destination organisations to become more effective and efficient (Scott, Baggio, & Cooper, 2008). To respond, DMOs need a network of champions to provide the leadership and strategic vision required to support more effective operation (Hristov & Zehrer, 2015). Buchanan 127

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destination context. While there were a few previous contributions that discuss leadership in the context of DMOs and destinations (Benson & Blackman, 2011; Haven-Tang & Jones, 2012; Wray, 2009), the works by Kozak et al. (2014) and Pechlaner et al. (2014) are the first consolidated effort to both recognise DL in DMO and destination research and this section discusses some of these contributions. In his discussion, Valente, Dredge, and Lohmann (2014) examined leadership practice in two Brazilian Regional Tourism Organisations (RTOs) by interviewing RTO executives and destination stakeholders while Beritelli and Bieger (2014) developed a leadership research framework based on discussions with influential actors from four destinations in Switzerland, Austria, and Italy. Blichfeldt, Hird, and Kvistgaard (2014) investigate the relationship between leadership and power in DMOs and other destination actors by employing a non-conventional vignettes approach. Zmyślony (2014) proposed a method of identifying and evaluating leadership potential of stakeholders in emerging destinations through employing an in-depth analysis of stakeholders representing the public, private and non-profit sectors. Pröbstl-Haider, Melzer, and Jiricka (2014) investigated leadership in rural destinations undertaking an analysis of case studies and case study-based literature. Despite the above contributions discussed in this two-part special issue, no studies have looked at distributed leadership behaviours and a typology of leadership and its distributed dimension on a strategic organisational level i.e. within a complete network of DMO member organisations.

although this paper discusses leadership in a networked context, the practising of leadership is a property of individuals who represent organisations being part of this network of DMO member organisations. Hairon and Goh (2015) argued that processes related to the enactment of DL practice can be attributed to recent governmental reforms calling upon the need to adopt a more ‘joined up’ and ‘networked’ approach to governance. This is the case with reshaped DMOs in England that have undergone a public-to-private transition in their existing leadership model (Hristov & Naumov, 2015). Indeed, as formerly public-led bodies, DMOs in England were responsible for the provision of the bulk of funding for destinations (Coles et al., 2014). Such processes implied management and leadership functions exercised by individuals behind predominantly local government organisations and other public sector bodies. However, recent developments in the organisational environment imposed as a result of new political ideologies (Cameron, 2010; Hristov & Naumov, 2015) and introduction of new models involving a public-to-private shift in funding for destinations and destination organisations (Coles et al., 2014; Penrose, 2011), call for the recognition that resources are now located in the diversity of DMO member organisations representing various stakeholder groups, such as businesses, along with government and non-profit organisations. This collective and distributed provision of leadership in meeting strategic organisational and destination objectives implies greater appreciation of the interdependence of individual DMO members and calls for, and ultimately supports the enactment of DL in place of traditional public sector leadership. Distributed leadership is founded on interactions, rather than actions (Harris, 2005; Harris & Spillane, 2008). As such, resources are central to the enactment of distributed leadership at an organisational level (Chreim, 2014; Tian, Risku, & Collin, 2015). Distributed leadership hence provides opportunity for reshaped DMOs across England to shape a response to changes in their operational environment (Currie & Lockett, 2011). DL supports organisations in their efforts to ‘benefit from diversity of thought in decision-making’ (Evaggelia & Vitta, 2012, p.3). Equally, distributed leadership recognises that diverse resources and the “varieties of expertise are distributed across the many, not the few” (Bennett, Wise, Woods, & Harvey, 2003, p. 7) as again is the case of reshaped business-led DMOs in England. However, ‘there remains a poor understanding of how and why collaborative styles are enacted’ (Oborn et al., 2013, p.255) in leadership practice. Equally, there is a lack of evidence on the ‘practice’ of DL in organisations (Bennett et al., 2003; Tian et al., 2015). Indeed, the discourse on DL to date has been predominantly on DL as an alternative to ‘heroic’ leadership with an emphasis on the fact that leadership roles and tasks, along with knowledge and resources have been distributed across teams and networked organisations (e.g. see Cullen & Yammarino, 2014). This study examines how distribution of leadership works in practice. As Harris and Spillane (2008) contend, current empirical evidence into how leadership is distributed is a rather uncharted territory.

2.3. Using DL and SNA research to study DMOs and destinations The network literature has grown exponentially in the past two decades across a wide range of fields, in this case business and management (Borgatti, Everett, & Freeman, 2002; Borgatti, Everett, & Johnson, 2013). SNA is a key method used to study networks (Ahuja & Carley, 1999; Cattani & Ferriani, 2008; Cross, Parker, & Borgatti, 2002) and the emergence of powerful network visualisation tools has fuelled the use of SNA techniques by both academia and business consultants and managers alike (Conway, 2014). Examining networks is not a new approach in the domains of destination management and development (Scott, Baggio, & Cooper, 2008). However, the majority of works in the field have rather used the network concept as a metaphor for connectedness as opposed to integrating more precise mathematical measures (Ahmed, 2012). The latter usually involves SNA methods for network data collection and analysis (Gajdošík et al., 2017). In addition, a number of studies have pursued investigations into network collaboration and knowledgesharing practices, within and across organisations in destinations through studying the network of actors in a locality, or specific public, private or mixed network clusters within geographic boundaries (Baggio & Cooper, 2010; Beritelli, 2011b; Cooper et al., 2006; Del Chiappa & Presenza, 2013; Gajdošík et al., 2017; Krakover & Wang, 2008; Yabuta and Scott, 2011; Zach & Racherla, 2011; Longjit and Pearce, 2013; Pearce, 2014). In other words, the extant literature in the domain of DMOs and destinations has given considerable attention to the conceptualisation of destinations as networks (Bregoli & Del Chiappa, 2013; Cooper, Scott, & Baggio, 2009; Gajdošík et al., 2017; Pavlovich, 2003; Pechlaner, Volgger, & Herntrei, 2012; Pforr, 2006; Scott, Cooper, & Baggio, 2008; Shih, 2006; Timur & Getz, 2008). Little or no network research has, however, been carried out on strategic organisational level, exploring the DMO network of organisations involved in destination management (see Del Chiappa & Presenza, 2013; Williams & Hristov, 2018), such as businesses, local government and non-profit organisations (Hristov, 2015). Recognition of the role of such networks in orchestrating the majority of key destination management and development-interested groups (Ness, Aarstad, Haugland and Grønseth, 2014; Volgger & Pechlaner, 2014) across today's predominantly market-driven DMOs has also been somewhat overlooked (OECD, 2014).

2.2. DL research in DMOs and destinations A context for DMOs and destinations, which can be defined with a collective responsibility and leadership, assumes that vital expertise and destination resources are now distributed among public, private and non-profit organisations (Valente et al., 2015). Amid decreasing public sector leadership and funding in England, this wealth of resources provides a considerable scope for the enactment and practice of DL on board DMOs within a changing funding and governance landscape for DMOs and destinations in England. There is nevertheless little research into the leadership paradigm and its distributed dimension in DMOs and destinations (Hristov & Ramkissoon, 2016a). Benson and Blackman (2011), arguably the first scholars to adopt DL in a destination context, debated the relevance and practicalities of DL in a 128

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high BC also play an important role in spreading information, knowledge and resources across networks (Hanneman & Riddle, 2005).

The extant literature on DMOs and destinations has incorporated network theory and SNA (see Baggio & Cooper, 2010; Scott, Baggio, & Cooper, 2008), albeit primarily on a destination level. Pavlovich (2001) examined the management and coordination of a destination stakeholder group through the adoption of network theory and its qualitative dimension on to explore informal coordination mechanisms that structure networks. Pforr (2006) adopted a policy network approach to study the dynamics of the tourism policy domain on a destination level in Australian context. By employing a quantitative network study, Shih (2006) investigated the network characteristics of drive tourism destinations in Taiwan. It is evident that network theory and SNA have been applied extensively on a destination and on a DMO level, albeit to a lesser extent. This however has not been the case with DL in a DMO context (Hristov & Ramkissoon, 2016b; Pechlaner et al., 2014). Hristov and Zehrer (2015) argued that no studies to date have investigated how DL is enacted and practiced by a multitude of leaders on board DMOs and their networks of member organisations using a social network approach. One reason for the lack of such studies might be due to the complexity in framing appropriate network measures to identify and study the enactment of DL in a DMO context. The extant organisational leadership literature provides evidence of the appropriateness of centrality network measures in the identification of DL (Balkundi & Kilduff, 2006). Equally, a number of influential works have both investigated and provided evidence of the correlation between centrality measures and the identification, enactment and practice of distributed leadership (Balkundi & Kilduff, 2006; De Lima, 2008; Johnson, Safadi, & Faraj, 2015; Sutanto, Tan, Battistini, & Phang, 2011). Surfacing DL through the adoption of a set of network centrality measures has been carried out in the context of education (De Lima, 2008; Spillane, 2005); leadership and management teams (Balkundi & Kilduff, 2006); virtual collaboration settings (Sutanto et al., 2011); online community leadership (Johnson et al., 2015) among other contexts. However, such studies have not been carried out in the context of DMOs with the view to identify different leadership behaviours in these organisations and indeed surface DL typologies. Building on this correlation between centrality measures and the identification, enactment and practice of distributed leadership, a range of network centrality measures, according to Hoppe and Reinelt (2010) from the organisational leadership literature may be adopted for the study of DL development in organisations and networks. Key network measures suitable for the identification of leadership, which is distributed in nature, and as proposed by Hoppe and Reinelt (2010), include betweenness centrality, closeness centrality, eigenvector centrality, and indegree centrality.

• Closeness centrality (CC) is another important network centrality

measure, which similarly to BC, proves to be practical in identifying salient actors, who are able to link disparate actors within the complete network. However, closeness centrality is focused on the distance from each network member to all other members (Hanneman & Riddle, 2005). The latter enables surfacing the level of closeness of individual DMO member organisations to the rest of the network. In other words, these are network actors, who are highly connected to others within their own network communities and sub-networks. Closeness centrality champions have a number of direct links with others within their own network communities or sub-networks (Cherven, 2015). Where betweenness centrality was interested in DMO member organisations bridging otherwise distant network communities, closeness centrality has its focus on champions demonstrating the same function within network communities. Closeness centrality and betweenness centrality champions may therefore complement each other so long that they provide a reflection of various sectors represented in DMOs. Further, closeness centrality allows for surfacing DMO network members who act as gatekeepers, i.e. have the highest number of direct links within their own network communities and are thus able to facilitate distribution of resources that may otherwise be difficult to access. Whilst network actors with high closeness centrality are not necessarily central to the overall network, they play an important role within their own communities or sub-networks. These actors are seen as agents of DL practice within their own communities, which may or may not be tied to a particular sector or membership status in DMOs. Central DMO member organisations within (e.g. as in the case of closeness centrality) and across (e.g. as in the case of betweenness centrality) network communities play an active brokerage role. Brokers within DMOs are central to the spread of communication and resource flows (Beritelli et al. 2015b), both of which provide evidence of the enactment and practice of DL.

• Eigenvector centrality (EC), as Prell (2012) concluded, builds on

basic degree centrality together with closeness and betweenness centrality as it captures the sum of an actor's connections to other actors, weighted by their degree centrality. As such, eigenvector centrality is seen as a refined version of the basic degree centrality (see Borgatti, 2005). Eigenvector centrality provides a closer look at the local network of actors that are immediately adjacent to the focal actor (Bonacich, 2007; Bonacich and Lloyd, 2001). As EC is the sum of a network member's connections to other actors, weighted by these actors' degree centrality (Prell 2012), this network measure implies that EC champions are reliant upon other members' ties to establish themselves as highly influential leaders. Eigenvector centrality thus provides opportunities for wider influence of well-connected individual actors across different communities within networks, as followers of EC champions are also well-connected network actors (Cherven, 2015). High eigenvector centrality network members then tend to be leaders in the network who are surrounded by other well-connected actors (Borgatti et al., 2002). The ideas, resources and influence of EC champions can thus reach a large number of individual network actors, network communities and sub-networks. When actors with high eigenvector centrality reflect different sectors in DMOs, this then provides wider opportunities to embedding DL practice by these highly influential leaders across individual member organisations, which represent different sectors.

• Betweenness centrality (BC) takes into consideration the rest of the

network in its attempt to establish a score to surface the status of each member of the studied network. However, betweenness centrality does not look at numbers as in the case of degree centrality, outdegree centrality and indegree centrality, but is interested in the location of an actor within the complete network, i.e. its location among others in the network (Hanneman & Riddle, 2005). Network members with high betweenness centrality can act as network bridges (Hoppe & Reinelt, 2010). They connect network members and link network communities, which will not otherwise be connected (Stienmetz & Fesenmaier, 2015). In other words, betweenness centrality champions facilitate the most direct path between otherwise disconnected communities and sub-groups within a network by playing an active brokerage role (Cherven, 2015). High betweenness centrality then indicates bridging (Hoppe & Reinelt, 2010). Network actors with high betweenness centrality are vital to promoting DL practice. They are therefore seen as agents of DL and can provide DMO member organisations from various sectors of the economy with the opportunity to shape strategic leadership decisions, influence destination decision-making. Network actors with

• Indegree Centrality (IC), on the other hand, surfaces the number of

links, received by investigated network node from a range of other

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(Timmermans & Tavory, 2012, p.180). Milton Keynes is an emerging and relatively unexplored destination, at the heart of the South East Midlands region at the beginning of its destination lifecycle (Butler, 1980). Within a globalised visitor economy and market, emerging destinations are the ones under pressure (Halkier, Kozak, & Svensson, 2014) to deliver value. In early stages of destination development, and in less-mature tourism and visitor destinations, partnerships emerge and tend to work more effectively (Fyall, Fletcher, & Spyriadis, 2009). As such, the case of Milton Keynes may offer a pertinent insight into how less established destinations navigate through contextual changes for DMOs and destinations in the UK. DMK was established in 2006 with an initial membership base of 13 founding organisations, representing local authorities, businesses, sustainability trusts and community organisations. The on-going funding of DMK was guaranteed by Milton Keynes Council, which provided significant support to the DMO prior to the new funding regime introduced in 2010 (Hristov & Petrova, 2015). This was complemented by other sources of funding, such as membership fees. The organisation was established as the official tourist information service provider for Milton Keynes, and exercised predominantly marketing functions. Within the new political and economic context (Coles et al., 2014; Hristov & Naumov, 2015), DMK was expected to take on board a wider array of responsibilities for the destination in times when the public MK Council funding was declining. Today, DMK functions as an independent, non-profit organisation and its funding structure include a mixture of membership fees, some grants from Milton Keynes Council and commissions from its members. DMK is an official DMO consisting of a network of key destination businesses, council and other public bodies, along with a diverse mix of non-profit and community organisations. Having a well-defined geography, the network of DMK covers over 70 member organisations representing nine stakeholder groups (Attractions & Activities, Conferences & Events, Evening Economy, Higher Education, Hospitality, Local Government, Non-Profit, Retail & Services and Transportation) and two membership tiers (corporate i.e. founding and non-corporate) located in central Milton Keynes and the surrounding market towns and villages The core objectives of DMK are to promote Milton Keynes as a visitor destination, and to explore opportunities in developing further business, leisure, heritage and other types of both urban and rural tourism products (DMK, 2014). This is expected to be achieved by involving key interested destination actors who serve businesses, local government and non-profit organisations.

nodes in the network (Cherven, 2015). Whilst outdegree centrality is far more likely to imply power, i.e. network actors with high outdegree are seen as power actors (Ang, 2011), indegree centrality in contrast, is well positioned to evaluate emergent and already established leaders in the network (Balkundi, Barsness, & Michael, 2009; Scott, 2012) as this SNA measure indicates the existence of leadership practice across actors within a network. Indegree centrality is a measure, which enables the identification of organisations, which are a source of leadership in the network (Contractor, DeChurch, Carson, Carter, & Keegan, 2012). The identification of the number of follower IC links in a network is aimed at surfacing both already established and emergent leaders. Indegree centrality also helps uncover perceived influence (Cherven, 2015; Freeman 1979) as a result of leadership development initiatives (Hoppe & Reinelt, 2010). Influence is one of the key traits of demonstrating leadership. Indegree centrality is thus well-positioned to surface already established leaders in DMO networks, who are often linked to large corporate and public sector members. Emergent leaders, in contrast, are more likely to be tied to non-corporate members (Hristov & Zehrer, 2015). Indegree centrality with the use of valued network data may also serve to uncover key recipients of developmental resources across stakeholder groups on board DMK. These resource-empowered DMO member organisations also provide an evidence of the enactment of DL. Despite current progress and opportunities for the adoption of network centrality measures to yield evidence of the DL enactment and practice (Balkundi & Kilduff, 2006; De Lima, 2008; Johnson et al., 2015; Sutanto et al., 2011), evidence of conceptualising and investigating DMOs through the perspective of both DL and SNA in an attempt to yield network data-driven DL insights is scarce (Hristov & Zehrer, 2015, 2017). The wider organisational leadership literature also called for more empirical evidence into fusing both paradigms in surfacing DL (Cullen & Yammarino, 2014). Cullen and Yammarino (2014) proposed a multitude of topical areas for further enquiry, some of which are relevant to the case in focus. Carter and Dechurch (2012, p.412) also emphasised the importance of future investigations into fusing the concepts of DL and networks, where they believed that “taking a network perspective provides a tool that can facilitate future empirical research on “we” leadership.” This study attempts to address this by using DL and network theory in the context of DMOs. 3. Methods

3.2. Methodological framework 3.1. Case study This study is guided by the Hoppe and Reinelt (2010) framework, which is a set of both generic and specific organisational network questions for evaluating leadership development initiatives in networks embedded in formal organisations. Understanding the process of leadership development implies understanding of the development of social interactions within that process (Day, Fleenor, Atwater, Sturm, & McKee, 2014) which in light of this research, it is addressed by adopting a social network approach (Scott & Cooper, 2007). The DMO network under study is conceptualised as a DL network and subsequently studied as one developing and exercising leadership functions. Previous empirical research has provided evidence of the enactment and practice of DL on a DMO level, albeit on a small scale (Hristov & Zehrer, 2017). Hoppe and Reinelt (2010) introduced their framework to demonstrate the importance of SNA in the evaluation of leadership development initiatives in networks embedded across diverse organisations, in addition to business and civic communities. Their framework for evaluating leadership development practice mirrors both generic and specific questions, i.e. mainstream leadership network development questions and more specific organisational leadership network development questions aimed at networks embedded in formal organisations, such as DMOs. The approach taken by this study adapts Hoppe

Data were collected from a case study of Destination Milton Keynes (DMK). Milton Keynes was formally designated as a new town in 1967 (The London Gazette, 1967) and it is one of the fastest growing in the UK (Hopkins, 2013). The town boasts a strong local economy. It is projected as one of the forerunning places in England to lead the country out of recession (Centre for Cities, 2012; DMK, 2014). Unlike other prominent English destinations and their local lead organisations (e.g., Marketing Manchester, City of London, Visit Brighton), Destination Milton Keynes presents a case that is well placed to capture the challenges and opportunities that less-prominent, yet important (as per the 2010 coalition's localism agenda for England - see Penrose, 2011) local urban and rural destinations face within the new political and economic context. Milton Keynes is an emerging destination providing a range of core tourism products to suit business, leisure and heritage visitors. Lessprominent destinations are well placed to provide novel insights into DL practice at a DMO level as they face greater challenges within a shifting context. This is not the case with already established destinations boasting diversified funding models. Indeed, there is ‘little methodological value in gathering confirming cases’ of existing phenomena 130

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Fig. 1. Adaptation of Hoppe and Reinelt (2010) Framework: The Route.

vision such as that embedded in a destination management plan as an important manifestation of emergent DL. Further, Hoppe and Reinelt (2010) concluded that an organisational leadership Type 2 network requires the ability to plan, organise, implement and evaluate projects. The DMP developed and implemented by DMK is an expression of such ability and is evidence of the enactment of DL within DMK. Hoppe and Reinelt's (2010) framework is based on analysis of the networks embedded in organisations, e.g. systems of multiple organisations working together towards a common goal, but it does not provide comprehensive guidelines on structural and relational network measures. Instead, Hoppe and Reinelt (2010) refer to “potential questions for evaluation” (Hoppe & Reinelt, 2010, p.605), which could be answered by mainstream network analysis measures, such as centrality ones. The chosen Type 2 network route of the framework then provided a number of questions with focus on both network actors and network flows, which in turn informed the selection of a set of network measures and visualisation approaches (see route Fig. 1). Hence, in adapting Hoppe and Reinelt's (2010) framework for evaluating leadership practice in networks embedded in organisations, this study makes use of a series of network centrality measures, which mirror structural and relational network properties. Further, the underpinning study attempts to build on a recent DL contribution in the context of DMOs, the DMO Leadership Cycle (see Hristov & Zehrer, 2015), which discusses how DMOs can serve as leadership networks in destinations. The DMO Leadership Cycle, which builds on three dominant organisational paradigms, namely management, governance and leadership, is seen as a framework for the enactment of DL on a DMO level. Attempts to conceptualise the three

and Reinelt's (2010) specific organisational leadership network development questions to the context of investigation as depicted in Fig. 1 below. Fig. 1. Adaptation of Hoppe & Reinelt, 2010 Framework: The Route. Leadership networks, e.g. social networks among destination leaders, as contended by Hoppe and Reinelt (2010) can be classified under four types: (1) Peer leadership networks, relying on personal trust and providing access to resources; (2) Organisational leadership networks, emerging in the shadow of formal structures and focused on increasing network performance and impact; (3) Field-policy leadership networks charged with shaping the environment; and (4) Local, bottom-up collective leadership networks emerging on a self-organising basis. DMOs are Organisational leadership (Type 2) networks as according to Hoppe and Reinelt (2010, p. 607) because they consist of: “…the informal relationships that exist alongside the formal structure within an organisation… Organisational leadership networks refer to systems of multiple organisations that work together to more efficiently deliver services or produce a product”. Further, ‘leadership networks support organisations with shared interests to produce a product or deliver a service more efficiently’ (Hoppe & Reinelt, 2010, p.601). This is the case for DMK and its member network, which is expected to lead collectively and follow a coherent strategy and vision (i.e. the DMP Plan – see Hristov & Petrova, 2015) driven by a common interest to promote Milton Keynes. Indeed, leadership within organisations requires strength of collective action, aligning resources and inspiring others to participate (LeMay & Ellis, 2007), which is consistent with the new vision for DMK unveiled in the recently launched DMP. Pearce (2004) sees the creation of a shared 131

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across academics (Ahmet, 2012; Conway, 2014; Hristov, 2015) and practitioners (Hoppe & Reinelt, 2010). The response rate for the complete network of DMK member organisations (n = 70) was 57% whereby the majority of the core organisations of the DMO network have been included. In a recent DMO contribution, Beritelli et al. (2015) argued that an achieved sample of 50% can provide trustworthy and representative results as long as the network boundaries are specified (as in the case of this research). Similarly, an in-depth network data validity analysis undertaken by Costenbader and Valente (2003) made similar claims. Costenbader and Valente (2003) applied 11 centrality measures to their enquiry of 59 networks (network size ranging from n = 34 through to n = 169) where the response rate ranged from 51% to 100%. Centrality measures included indegree centrality, outdegree centrality, Eigenvector centrality, betweenness centrality, closeness centrality among others. The average correlation for the 11 both actual and sampled centrality measures computed for their 59 networks demonstrated that credible outcomes were achieved with 50% of the network data missing providing that the boundaries of the network are clear. Thus network data provides credible outcomes with a 50% response rate, which is less than the response rate achieved in this study. Nevertheless, undertaking an SNA study in organisations is a challenging task and a response rate below 100% is likely to omit important network data (Conway, 2014). The achieved response rate (i.e. 57%), despite providing credible outcomes (see Costenbader & Valente, 2003), covered 40 out of a total of 70 DMK member organisations on board DMK. The study has therefore been unable to explore the enactment and practice of DL from the perspective of the remaining 43%, which did not respond to the survey questionnaire. The achieved response rate of 57% is therefore still considered as a limitation.

organisational paradigms within the context of DMOs were initially discussed in a contribution by Hristov and Zehrer (2015). 3.3. Data collection and sampling Network data were collected over six months (between July 2014 and January 2015) by means of network survey questionnaires with senior personnel of DMK member organisations. Chief Executive Officers (CEOs) and Managing Directors (MDs), forming 85% of all respondents, acted as representatives of their organisations. Within the context of this study, leadership is seen as a feature, which is attributed to senior individuals that represent DMK member organisations but the discussion of findings is instead focused on organisations that individuals represent in light of matters of anonymity and confidentiality. The survey was conducted by employing a sophisticated organisational network analysis web platform (ONA Surveys, 2015). Survey questionnaires with various stakeholder groups were used as part of the data collection process. The first two questions of the survey questionnaire sought to establish the profile of studied organisations and individuals (question one) and collect binary data to demonstrate the presence of relationships (question two). Building on them, questions three and four were aimed at the collection of valued network data, which sought to explore the nature of established relationships, their value and intensity. The survey questionnaires were distributed across senior leadership individuals, in this case CEOs, Mayors and GMs, who represent each of the 70 DMK member organisations. Responses to the questions that were used as part of the network data collection process served as the basis for creating both binary and valued matrices, which reflect relations between individuals representing organisations across the nine stakeholder groups. Both network data matrices served as an input into Gephi (see Cherven, 2015). A range of network centrality measures (Fig. 1) linked to specific questions from the survey questionnaire informed the methodological approach adopted in this study. Their relevance is discussed below. The organisational leadership literature points to a correlation between centrality measures and the identification, enactment and practice of distributed leadership (Balkundi & Kilduff, 2006; De Lima, 2008; Johnson et al., 2015; Sutanto et al., 2011). Collectively, the centrality measures depicted on Fig. 1 sought to provide a response to the objectives set out in the outset of this paper. The Gephi SNA software package was employed for analysis of the organisational network data. Gephi has a number of network and actor level functions that can be used to measure the structural and relational properties of networks (Bastian, Heymann, & Jacomy, 2009). Gephi also provides a range of network layout algorithms, which are used for transforming network data into readable and insightful network depictions. One of the strength of SNA lies in network depictions (Cherven, 2015; Stienmetz & Fesenmaier, 2015). The methodological approach adopted in this investigation is in line with Cullen and Yammarino (2014) recent call for introducing advances in visualising and measuring the enactment and practice of DL networks.

4. Findings The first objective of this study was to investigate how DL is enacted in a DMO through the identification of different leadership behaviours of DMO member organisations. This section of the paper investigates the influence of the changing organisational context over DMOs network behaviour including evidence into the enactment of DL using Type 2 network in Hoppe and Reinelt's (2010) framework for evaluating leadership development in networks embedded in organisations (Fig. 1). The SNA measures that have been selected on the basis of their relevance to the first leadership network-specific question (Fig. 1) include betweenness centrality, closeness centrality, eigenvector centrality and indegree centrality. Centrality measures are key to surfacing emergent leadership in networks (Estrada and Vargas-Estrada 2013). Contractor et al. (2012) also proposed the use of centrality measures in surfacing DL practice in networks. 4.1. Betweenness centrality champions Betweenness centrality was identified through relational network data collected through questions one and two of the adopted survey questionnaire (see Appendix 1) and visualised using a Radial Axis Layout algorithm (Groeninger, 2012) via Gephi (Table 1). BC enabled the identification of agents of DL practice, which are able to provide distant network communities (e.g. across different stakeholder groups and membership tiers) with opportunities to shape

3.4. Network data limitations The challenges of obtaining network data have been well recognised Table 1 BC network data and layout algorithm characteristics. Layout algorithm

Radial Axis Layout (Groeninger, 2012)

On Spotlight Network Data Data Key

Betweenness Centrality (by Membership, network constructed by Sector) Directed, Binary Minimum value 0, Maximum value 543,3. The bigger a node in Fig. 2, the higher its betweenness centrality. The closer a node to the network core, the lower its betweenness centrality. DMO member organisations with high betweenness centrality are considered as boundary spanners. The latter facilitate communication and resource flows across loosely connected sub-networks and communities; promote DL practice across the complete network.

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Fig. 2. Network cross-community leaders identified through betweenness centrality.

leadership decisions and facilitate access to vital network resources. A community of 15 BCE champions in DMK was identified through BC and the Radial Axis Layout (Groeninger, 2012) algorithm. These champions demonstrated BC values between 60.5 and 543.3, which is provided in the BC distribution table below (Table 1). The remaining 55 DMO member organisations had BC ranging from 0 to 50. Network actors with high betweenness centrality, such as the ones depicted on Fig. 2, can be seen as agents of DL and can provide distant actors and communities with the opportunity to shape strategic leadership decisions, influence destination decision-making and facilitate wider representation of peripheral network actors and loosely embedded network communities. Hence BC champions have been called network cross-community leaders (Fig. 2). BC champions traditionally act as bridges across network communities on board DMK i.e. facilitators of DL practice across network communities. The results demonstrated that the very few BC champions were not representative of all nine stakeholder groups in DMK. BC champions represented fewer corporate and non-corporate member organisations from both the Higher Education and Non-Profit groups, which had the highest BC rank among all stakeholder groups on board DMK. This is evident on Fig. 2, where corporate members are depicted in red and non-corporate ones in green. This non-representation of all stakeholder groups leaves major private sector-led sectors, namely Hospitality (22.86%) and Conferences & Events (18.57%) without BC champions, which introduces challenges to the enactment of DL across all sectors represented in DMK. Fig. 2. Network cross-community leaders identified through betweenness centrality.

Table 2 CC network data and layout algorithm characteristics. Layout algorithm

Radial Axis Layout (Groeninger, 2012)

On Spotlight Network Data Data Key

Closeness Centrality (by Sector, by Membership) Directed, Binary Minimum value 0, Maximum value 3,74. Higher values of closeness indicate higher centrality of certain actors in communities or sub-networks within a DMO, where 0 indicates the absence of centrality and 3,74 is the highest closeness centrality, where nodes with high closeness centrality are closer to all other actors in the network.

closer to all other member organisations within the network. Hence, CC champions in contrast to BC ones, act as bridgers within their own network communities in DMK. A cohort of 32 CC champions in DMK that exhibit similar leadership behaviour was identified through CC and the Radial Axis Layout (Groeninger, 2012) layout algorithm. Those champions demonstrated CC values between 1.95 and 3.74, which is provided in the CC distribution. The remaining 38 DMO member organisations had BC ranging from 0 to 1.95. Network actors with high closeness centrality, such as the ones depicted on Fig. 3, can be seen as catalysts of DL within their own sector on board DMK. CC champions have been called network in-community leaders (Fig. 3), where corporate CC champions are in depicted in red and non-corporate ones in green. The results provided evidence that unlike BC, DMK members championing CC were present across all nine stakeholder groups as members from all these groups had high CC rank, i.e. at least one DMK member of each sector has closeness centrality value of at least 1,95. The latter suggests that within all sectors on board DMK, at least one representative of these sectors can champion embedding DL practice, as champions are well-placed member organisations within their own network communities and sub-networks. Importantly, a cross-comparison of DMK's BC and CC network suggested that the network depends on different stakeholder groups to facilitate the practice of DL both within and across network communities i.e. some groups were best placed as BC champions, whilst others

4.2. Closeness centrality champions Closeness centrality was identified through relational network data derived from questions one and two of the adopted survey questionnaire (see Appendix 1) and visualised using a Radial Axis Layout algorithm (Groeninger, 2011) via Gephi (Table 2). CC identified a number of prominent DMK members, who were well placed within their own communities and sub-networks, as well as 133

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Fig. 3. Network in-community leaders identified through closeness centrality.

spread across DMK as they have the potential to empower other DMO members. Hence EC champions have been called highly influential network leaders (Fig. 4), where corporate EC champions are in depicted in red and non-corporate ones in green. Findings related to eigenvector centrality distribution across all DMK member organisations, also highlighted that DMK members with EC≥ 0.5 corresponded to only 13% of the complete network. The remaining 87% of DMK, which demonstrated EC = 0.5 or below, suggest a case, where inherent power create barriers for other DMO members to be recognised as leaders. This calls for a wider inclusion, where the importance of involving the diversity of DMO member organisations as they often collectively shape destination identity. Further, the results suggested that the top nine EC champions in DMK represent only five out of the nine stakeholder groups in the DMO where Local Government and Non-Profit bodies had the highest EC rank as in the case of betweenness centrality. Yet, the network computation through Levallois (2013) provided evidence that there is a balance between corporate and non-corporate EC champions with some emerging private sector-led member organisations being among these champions (Fig. 4). The latter points to an emerging DL practice, which at present is championed by organisations with different membership tiers. A total of 10 DMK member organisations demonstrated EC = 0. The latter suggests that 14% of all members on board DMK can be considered as peripheral actors, who are not seen as influencers, nor are

demonstrated high CC. This suggests that diversity of stakeholder groups breaks down barriers to wider distribution of leadership including vital resources across the complete network. Such balance among BC and CC champions within DMK is an important facilitator of embedding DL practice within and across network communities on board DMK and as such, they complement each other. Fig. 3. Network in-community leaders identified through closeness centrality. 4.3. Eigenvector centrality champions Eigenvector centrality was identified through relational network data collected through question three of the adopted survey questionnaire (see Appendix 1) and visualised using a Force Atlas 3D Layout algorithm (Levallois, 2013) (Table 3). EC was adopted to identify individual DMK member organisations seen as network leaders, who are followed by or connected to other leaders in the network. A cohort of nine champions in DMK that provide evidence of similar leadership behaviour was identified through EC and the Force Atlas 3D (Levallois, 2013) layout algorithm. Those champions demonstrated EC values between 0.5 and 1, which is provided in the EC distribution. The remaining 61 DMO member organisations had BC ranging from 0 to 0.49. Network actors with high Eigenvector centrality, such as the ones depicted on Figs. 4 and 7, can be seen as important enables to DL and its Table 3 EC network data and layout algorithm characteristics. Layout algorithm

Force Atlas 3D (Levallois, 2013)

On Spotlight Network Data Data Key

Eigenvector Centrality (by Membership); Surfacing highly influential leaders in the network Directed, Binary Minimum value 0, Maximum value 1. Eigenvector centrality is a real number between 0 and 1, where 1 is an indicator for a well-connected DMO member organisation, who have established links with other well-connected member organisations. The bigger a node, the higher the EC, i.e. the leadership capacity to shape influential leaders of that node.

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Fig. 4. Highly influential leaders identified using eigenvector centrality.

through wider recognition of individual DMK member organisations, empowering and providing a voice in destination decision-making. The proportion of DMK member organisations with IC value of IC ≥ 1 was over 84% This figures leave only 16% of the DMK network with IC = 0, i.e. no followers. DL advocates broad empowerment and engagement (Martin et al. 2015) and the above figures provide evidence of DL practice where 84% of DMK's member organisations are recipients of information, knowledge and resource flows and are followed by at least one other member of DMK thus allowing for their voice to be heard. This scenario presents a case whereby traditional followers become co-producers of leadership through their interactions with established leaders (Harris, 2005), as it becomes evident further down where emergent leaders are surfaced within DMK. Further, the IC distribution suggested that 48% of all DMK members were already established or emergent leaders (13% established leaders with IC: 15–30, 35% emerging leaders with IC: 5–15), as they had high IC rank. Hence a cohort of eight established and further 23 leaders exhibiting similar leadership behaviour were identified. These figures

they connected to or following any influencers in the network thus limiting the opportunities of these actors for shaping strategic destination development initiatives and developing DL practice. Fig. 4. Highly influential leaders identified using eigenvector centrality. 4.4. Indegree centrality champions Indegree centrality was identified through relational network data derived through question three of the adopted survey questionnaire (see Appendix 1) and visualised using a Fruchterman Reingold Layout algorithm (Fruchterman & Reingold, 1991) via Gephi (Table 4). IC was employed alongside the Fruchterman Reingold (Fruchterman & Reingold, 1991) layout algorithm with the view to identify emergent and already established leaders within DMK (Fig. 5), as well as the proportion of member organisations, who are followed by and being able to acquire resources from others in the network. Outcomes of the employed IC analysis also provide evidence of emerging DL practice Table 4 IC network data and layout algorithm characteristics. Layout algorithm

Fruchterman Reingold (Fruchterman & Reingold, 1991)

On Spotlight Network Data Data Key

Weighted Indegree Centrality (by Membership); Surfacing established and emergent leaders in the network Directed, Valued Minimum value 0, Maximum value 29. The indegree centrality captures a number between 0 and 29, where 0 indicates the lowest possible indegree centrality and 29 indicates the highest possible indegree centrality in the investigated network. The higher the indegree centrality of an actor, the higher the number of followers, level of empowerment and acquired resources of this actor across the DMO network, which demonstrates evidence of DL practice. Further, the higher the proportion of network actors with indegree centrality, which is different than 0, the more opportunities for embedding DL practice across the complete network through both established and emergent leaders. Unlike outdegree centrality where arrow colour mirrors the source of power, the arrow colour in the case of indegree centrality mirrors the target node, where follower links are depicted.

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Fig. 5. Established and emergent leaders identified through in-degree centrality.

facets or principles of DL.

can be seen as an important indicator of involvement in DL practice of nearly half of the network and further promoting empowerment and facilitating DL across the complete network. Fig. 5. Established and emergent leaders identified through in-degree centrality. Hence IC champions have been called both established and emergent leaders, where corporate IC champions are depicted in red and non-corporate ones in green. Among the defining features or facets of DL, as contended by Harris (2008), is the presence of a more broadbased leadership, which involves both formal and informal leaders at multiple levels. The above IC-surfaced leaders in situ include both formal (corporate) and emergent (non-corporate) leaders on board the DMO (Fig. 5). This serves as evidence of DL practice on board DMK. The balance between corporate and non-corporate members (evident in the already established and emerging leaders in Fig. 5) provide evidence that DMK member organisations assuming leadership functions now go beyond the traditional government and DMO corporate member affiliation. Evidence of leadership, which encompasses both formal and informal leaders as in the case of the IC-identified established and emergent leaders, as argued by Harris (2008), is also among the core

4.5. Focus on network flows The second leadership network-specific question proposed by Hoppe and Reinelt (2010) has its focus on network flows (Fig. 1). This question is interested in whether information, resources and knowledge flow seamlessly through the network so that they are accessible to network members when they need it (Hoppe & Reinelt, 2010). DL is founded on interactions, rather than actions (Harris, 2005; Harris & Spillane, 2008) and knowledge and resource exchange are fundamental ingredients of nurturing and practicing DL (Tian et al., 2015). DL recognises that leadership is constructed and ultimately founded on shared action and interaction (Harris, 2005) – as in the case of the first leadership network-specific question proposed by Hoppe and Reinelt (2010), as well as distribution of resources, knowledge and expertise (Spillane, 2006). Developmental resource flows along with frequent communication and knowledge exchange are also building blocks of DL practice (Harris, 2005) and as such, they deserve further attention. They have

Table 5 BC network data and layout algorithm characteristics. Layout algorithm

Circular (Groeninger, 2012)

On Spotlight

Developmental Resource Flows (by Sector) – Indegree Logics Used; Surfacing evidence of empowerment, providing a voice in strategic destination decisionmaking and recognition of individual DMK member organisations Undirected, Valued 5-point Impact Likert (Transformative, Highly Supportive, Moderate Support, Some Support, Marginal to none), where ‘Transformative’ mirrors the highest and ‘Marginal to none’ the lowest impact of acquiring developmental resources. Edge (resource flows) corresponds to the colour of target, i.e. identifying key resource holders and recipients. The thicker the link, the more impactful the process of acquiring developmental resources for the target node, i.e. a DMK member organisation. The bigger the node, the higher the impact of acquiring developmental resources for that node.

Network Data Impact Scale Data Key

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Fig. 6. Resource-empowered leaders identified through valued in-degree centrality.

Resources are instrumental to the enactment and practice of DL at an organisational level (Chreim, 2014; Tian et al., 2015) and Fig. 7 depicting the distribution of developmental resources across the complete network and empowering individual member organisations serves as evidence of the practice of DL.

been identified through question four of the adopted survey questionnaire (see Appendix 1) and by adopting a Circular Layout algorithm (Groeninger, 2012) (Table 5). Aimed at deconstructing DMK's developmental resource network, findings provide important insights into the impact of flows of vital resources across the 70 member organisations and thus highlight DL practice through resources across the complete network. As such, indegree centrality aims to identify resource-empowered DMO member organisations (Fig. 6), where different colours reflect the nine stakeholder groups on board DMK. Fig. 6. Resource-empowered leaders identified through valued indegree centrality. These figures indicate that just over 7% of all network transactions mirroring patterns of developmental resource sharing have demonstrated a marginal or less impact over DMK member organisations, whereas 53.01% of the complete network's resource transactions provided some support for members on board DMK (Fig. 7). The latter figures demonstrate that processes of acquiring developmental resources in the case of over half of the network's links prove to have provided some support to individual DMK member organisations. Over 40% of the developmental resource flows in the complete network prove to have provided moderate through to high support or even transformative impact over individual DMK member organisations (Fig. 7). Fig. 7. Deconstructing DMK's Developmental Resources Network. The above figures point to evidence of empowerment, voice in strategic destination decision-making and recognition of individual DMK member organisations going beyond the traditional leadership network community linked to corporate members. This builds upon IC insights where it became evident that 84% of all member organisations have been resource-empowered and followed by at least one other member of DMK. The above figures provide evidence of empowerment, facilitating a voice in strategic destination decisionmaking and recognition of individual DMK member organisations going beyond the traditional leadership network community linked to corporate members. Muijs and Harris (2003) identify empowerment as an important dimension of DL.

5. Dl typologies across DMO member organisations Building on findings, which stem from the first objective, the second objective of this study was to develop a DL functional typology of DMO leaders by building on findings related to the identification of different leadership behaviours across DMO member organisations. This DL typology emerged from the application of the set of network measures and visualisation approaches (see Fig. 1), where a collective of leaders with different leadership behaviours were identified for each of the six types of leaders. Building on this initial contribution discussed in Hristov and Zehrer (2015) and evidence of different leadership behaviours of DMO member organisations, this paper attempts to further conceptualise the leadership dimension of the Cycle aimed at collective leadership, by introducing a revisited version of it. This is achieved through expanding on the initial leadership dimension by putting forward six leader types on board DMOs (Fig. 8). The leader types identified are named after the function they serve in the network of DMO member organisations. This function is defined by the set of network measures, each of which serves to define particular leadership functions of individual network members as discussed in the previous section of the paper. Despite exhibiting distinctive features, it is important to acknowledge that different types of leaders exhibit multiple leadership roles within the identified six DL typologies in a DMO context and this was evident primarily across larger and more established DMK member organisations. The six leader types, which include leadership communities, were identified and their functions are discussed below. Fig. 8. The DMO leadership cycle revisited. Based on the above findings six types of leadership behaviour (Fig. 5), below we provide/propose a DL typology in DMK, providing 137

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Fig. 7. Deconstructing DMK's Developmental Resources Network.

• Highly influential leaders (identified through EC); • Established leaders (identified through IC); • Emerging leaders (identified through IC); • Resource-empowered leaders (developmental resources; identified

evidence of the enactment and practice of DL on a DMO level, namely:

• Network in-community leaders (identified through CC); • Network cross-community leaders (identified through BC); 138

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Fig. 8. The DMO L-ship Cycle Revisited.

communication of the vision and distribution of developmental resources due to their high reach and connectivity to other leaders across the network. Highly influential leaders can serve as agents of DL on board DMOs as long as they represent a healthy mix of organisations with corporate and non-corporate membership and represent different stakeholder groups within the network of DMO member organisations. Established leaders are member organisations with high influence, which are regarded as important gatekeepers, who have the potential to empower and enable others to participate in leadership and as such, they support the enactment of DL further across the network of DMO member organisations (Fig. 5). Established leaders were identified as such through IC and the Fruchterman Reingold (Fruchterman & Reingold, 1991) layout algorithm. They had IC values IC = 5–15. They are often dominated by corporate (founding) DMO members. When located, established leaders can promote the distribution of leadership across other corporate members with less influence on board DMOs. As such, they enable more opportunities for penetration of distributed leadership across less-influential corporate members on board DMOs. Emergent leaders are member organisations with moderate influence, which are regarded as important gatekeepers, who have the potential to empower and enable other, often non-corporate member organisations to participate in leadership and as such, they support the enactment of DL further across the network of DMO member organisations (Fig. 5). Emergent leaders were identified as such through IC and the Fruchterman Reingold (Fruchterman & Reingold, 1991) layout algorithm. They had IC values IC = 15–30. When located, emergent leaders can promote empowerment and facilitate DL across the complete network and beyond the network of established leaders. As such, they support the enactment of DL further across the network as the presence of emergent leaders is an indication of a more broad-based leadership, which involves both formal and informal leaders. Resource-empowered leaders are member organisations, which are often seen as recipients of strategic developmental resources from other member organisations on board DMOs, which provides evidence of

through valued IC). Network in-community leaders are member organisations in DMOs, which are well-placed within their own communities and sub-networks (Fig. 3). Network in-community leaders were identified as such through CC and the Radial Axis Layout (Groeninger, 2012) layout algorithm. They demonstrated CC values between 1.95 and 3.74. When located, network in-community leaders can act as bridgers within their immediate network communities by connecting members of their community with other communities on board DMOs. As such, they enable the distribution of developmental resources, the provision of a voice and the communication of the vision within their immediate network communities in DMOs. Network cross-community leaders are member organisations, which are well-placed across often distant network communities and subnetworks in DMOs (Fig. 2). Network cross-community leaders were identified as such through BC and the Radial Axis Layout (Groeninger, 2012) layout algorithm. These DMK members demonstrated BC values between 60.5 and 543.3. When located, network cross-community leaders can act as a bridge between network communities by connecting members of one community on board DMOs with members of another, which may not be connected otherwise. As such, they enable the distribution of developmental resources, the provision of a voice and the communication of the vision across often distant network communities on-board DMOs. Highly influential leaders are member organisations seen as network leaders as they are well-connected to other, also well-connected leaders in the complete network on board DMOs (Fig. 4). Highly influential leaders were identified as such through EC and the Force Atlas 3D (Levallois, 2013) layout algorithm. Those champions demonstrated EC values between 0.5 and 1. Highly influential leaders can act as bridgers across leaders of network communities by connecting leaders of one community on board DMOs with leaders from another, which may not be connected otherwise. As such, they have strong influence over the 139

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experience system.” Reinhold et al. (2015, p.4) argue that contemporary DMOs “will require less of a lone leader that personifies and tries to direct the entire destination like a corporate CEO” and the advancement of the Leadership dimension of the DMO Leadership Cycle (Fig. 5) demonstrates how leadership can multiply within and across a network of DMO member organisations. This diversity in leader types found in DMK provides evidence of the enactment of DL, and supports calls for further embedding DL practice and recognising the role of different leader types in resource-constrained DMOs. Such leaders may come from outside government and corporate membership-associated member organisations and have an important role to play in strategic destination decision-making. However, Chreim (2014) argues that the fundamental premises of DL, namely widening participation, cooperation, pooling knowledge and resources among others, often do not materialise to their fullest extent i.e. there is always a scope for further improvement of DL practice in networks. Equally, DL may not necessarily provide more advantages to DMOs than traditional leadership and governance models and this often depends on the network structure, its connectivity, cohesiveness and other structural and relational factors. Harris et al. (2007) concluded that DL cannot be seen as a panacea for organisations undergoing change. Building on this, Leavitt (2005) emphasised that there is often a need for hierarchical leadership in contemporary organisations. Building on this, it is worth highlighting that no empirical evidence, which links DL network structure to destination outcomes and performance is available to support the notion that DL is better located to support the work of contemporary DMOs than traditional leadership and governance models. Yet, identifying and mobilising different leader types and behaviours as per the identified six leadership types is an opportunity for DMOs to promote value-driven collaboration, inclusivity and distribution of leadership across their networks of member organisations. The identification of different leader types within DMK provides an opportunity to shift from the notion of ‘power’, in DMOs and destination management (either DMO board or destination elite-driven) and ‘empower’ individual member organisations instead, and to consider the opportunities that distributed forms of leadership can bring to DMOs and destinations. This shift from predominantly orthodox or ‘heroic’ to shared or even distributed forms of leadership then challenges existing perceptions that strategic destination decision-making carried out by DMOs should only be a property of the privileged, particularly in times when resources and expertise are located in multiple DMO member organisations.

empowerment (Fig. 6). Resource-empowered leaders were identified as such through IC using valued data and the Circular (Groeninger, 2012) layout algorithm. When located, resource-empowered leaders can facilitate access of other member organisations to vital developmental resources, which may not otherwise have access to these resources. As such, they are both a sign of and can also further support the empowerment, providing a voice in strategic destination decision-making and recognition of peripheral member organisations, particularly in resource-constrained DMOs. 6. Conclusion This paper has identified different leadership behaviours of DMO member organisations and developed a DL functional typology of DMO leaders by building on findings related to the identification of different leadership behaviours of DMO member organisations. The investigation was based on a network study guided by an established leadership development framework from the organisational leadership literature (Hoppe & Reinelt, 2010). The study examined Milton Keynes – an emerging destination in England and its local destination management structure, namely Destination Milton Keynes (DMK). As a result the study identified six leader types with different leadership behaviours that complement one another. The contribution that this paper makes is grounded in the adoption of a cross-disciplinary approach to the identification of the enactment and practice of DL in DMOs. This cross-disciplinarity is embedded in how the network analysis has been conducted, which was guided by an established framework for the identification of leadership development in networks drawn from the organisational leadership literature (Hoppe & Reinelt, 2010). The study provides some direction for how traditional DMOs shift their predominant organisational models in line with new developments in their funding and governance landscape. This will require a transition from a traditional top-down governance model of DMOs towards a flatter and more fluid, perhaps network-shaped organisations. That is the transition from demonstrating power in decision-making and heroic leadership towards a more-collective, DL practiced by a collective of member organisations. If the former model is founded on power relations and public sector-led leadership, the latter one values the wider opportunities to participate in leadership decisions, distribution of knowledge, expertise and essential developmental resources across the network within a changing organisational context for DMOs. As Valente et al. (2015) concludes, DMO practitioners should embrace the idea that expertise and destination resources are distributed among public, private and non-profit organisations and as such, they provide a considerable scope for embedding DL practice across stakeholder groups and different membership tiers in DMOs. Insights from the network investigation in situ build upon an earlier conceptual contribution, by Hristov and Zehrer (2015) of a DMO Leadership Cycle, which provides a theoretical framework on how reshaped DMOs assume leadership functions in destinations and serve as leadership networks. Findings from the current study provided evidence that leadership can come in all sizes and shapes and can also be embedded in diversity of stakeholder groups and membership tiers of DMO member organisations. This present study provides empirical evidence into the sharing of leadership roles within a leadership network (Cullen & Yammarino, 2015), as well as a typology of six leader types demonstrating DL behaviours. This multitude of leader types and leadership functions supports recent recommendations by the 2nd Biennial Destination Management Forum held in St Gallen, Switzerland (Reinhold et al., 2015, p. 4), which concluded that: “… it is questionable whether and to what extent a sole individual is able to pave the way to a consensus in decision-making when resources, expertise, leadership influence, and skills reside in diverse destination actors who contribute in different ways to various parts of the

6.1. Implications for theory and practice Discussing the concept of leadership and its distributed dimension is not a novice development in the literature of DMOs and destinations (see Benson & Blackman, 2011; Hristov & Williams, 2018; Kozak et al., 2015). However, the current discourse in the DL literature has focussed mainly on the ‘what's’ as opposed to the ‘how's’ of the concept where DL is projected as an alternative to ‘heroic’ leadership (see Cullen & Yammarino, 2014 and Hristov & Ramkissoon, 2016b) without delving into how leadership is distributed. This study, builds on previous contributions by providing insights into how this distribution of the leadership roles is enacted within the context of both network communication and developmental resource exchange – both being among the defining features of DL (see Harris, 2005; Spillane, 2006). Two of the pioneers in the field of leadership (see Cullen & Yammarino, 2014; Cullen-Lester and Yammarino 2016) called for fusing the concepts of DL and SNA. That is the introduction of crossdisciplinary network approaches to investigate the enactment and practice of leadership, which is networked, distributed among entities and grounded in interactions. Study findings build on this call to further 140

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Cullen and Yammarino (2014) call for methodological developments in studying leadership and its networked or distributed dimension. This study used a visually-driven framework, however a much more in-depth response is needed to turn complex scientific numbers into depictions which are understandable and address the world of practice. This presents an exciting but challenging avenue for further research.

our understanding of distributed leadership practice located at the merged nexus of the concepts of DL and SNA and in so doing builds on the practice based cross-disciplinary literature in this domain. From a practitioner's perspective, the methodology adopted by this paper introduces a refreshed approach to the identification of both distribution of leadership across all members in the networks and leadership properties of individual DMO member organisations. Visualisations, providing that ethical matters have been taken into account (see Borgatti & Molina, 2003), can facilitate future tourism network planning and strategising for DMO leaders and other destination decision-makers. This is supported by findings related to the six leader types and leadership behaviours, which provide valuable insights on how leadership in DMOs has been distributed in situ. The identification of different leader types on board DMOs highlight to destination management and leadership practitioners the importance of identifying and utilising these leaders to strengthen the productivity and capacity in the network. Findings from this study highlight to practitioners that skills, expertise, influence and other destination resources are distributed among a network of public, private and non-profit organisations that feature both established and emergent leaders. Hence the importance of recognising these in delivering the very vision, mission and purpose of DMOs in destinations is of key importance to destination decision-makers. This multitude of expertise and resources provide considerable scope for DL across sectors and membership tiers on board DMOs, particularly in times when the public purse is tight for DMOs and destinations (Coles et al., 2014; Hristov & Zehrer, 2017).

Acknowledgements The authors would like to acknowledge the invaluable support of Jackie Inskipp (Founding CEO, DMK), Steven Gordon-Wilson (CEO, DMK) and the vital contribution of all public, private and not-for-profit DMK member organisations in facilitating this insightful investigation. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.tmp.2018.08.003. References Ahmed, E. (2012). Network analysis. In L. Dwyer, A. Gill, & N. Seetaram (Eds.). Handbook of Research Methods in Tourism: Quantitative and Qualitative Approaches (pp. 472–491). Cheltenham: Edward Elgar. Ahuja, M., & Carley, K. (1999). Network structure in virtual organizations. Organization Science, 10, 741–757. Ang, C. S. (2011). Interaction networks and patterns of guild community in massively multiplayer online games. Social Network Analysis and Mining, 1(4), 341. Baggio, R., & Cooper, C. (2010). Knowledge transfer in a tourism destination: tThe effects of a network structure. The Service Industries Journal, 30(10), 1–15. Balkundi, P., Barsness, Z., & Michael, J. H. (2009). Unlocking the influence of leadership network structures on team conflict and viability. Small Group Research, 40(3), 301–322. Balkundi, P., & Kilduff, M. (2006). The ties that lead: A social network approach to leadership. The Leadership Quarterly, 17(4), 419–439. Bass, B. M. (1985). Leadership and performance beyond expectations. New York: Free Press. Bass, B. M., & Steidlmeier, P. (1999). Ethics, character and authentic transformational leadership behaviour. The Leadership Quarterly, 10(2), 181–217. Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: An open source software for exploring and manipulating networks. ICWSM, 8, 361–362. Bennett, N., Wise, C., Woods, P. A., & Harvey, J. A. (2003). Distributed Leadership. Nottingham: National College of School Leadership. Benson, A. M., & Blackman, D. (2011). To distribute leadership or not? A lesson from the islands. Tourism Management, 32(5), 1141–1149. Beritelli, P. (2011a). Do actors really agree on strategic issues? Applying consensus analysis of stakeholder perceptions in tourist destination communities. Tourism Analysis, 16(3), 219–241. Beritelli, P. (2011b). Cooperation among prominent actors in a tourist destination. Annals of Tourism Research, 38(2), 607–629. Beritelli, P., & Bieger, T. (2014). From destination governance to destination leadership – Defining and exploring the significance with the help of a systemic perspective. Tourism Review, 69(1), 25–46. Beritelli, P., Bieger, T., & Laesser, C. (2014). The new frontiers of destination management: Applying variable geometry as a function-based approach. Journal of Travel Research, 53(4), 403–417. Beritelli, P., Buffa, F., & Martini, U. (2015). The coordinating DMO or coordinators in the DMO?–an alternative perspective with the help of network analysis. Tourism Review, 70(1), 24–42. Blichfeldt, B. S., Hird, J., & Kvistgaard, P. (2014). Destination leadership and the issue of power. Tourism Review, 69(1), 74–86. Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71. Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2002). Ucinet for Windows: Software for Social Network Analysis. Harvard, MA: Analytic Technologies. Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. London: SAGE. Borgatti, S. P., & Molina, J. L. (2003). Ethical and strategic issues in organizational social network analysis. The Journal of Applied Behavioral Science, 39(3), 337–349. Bramwell, B., & Lane, B. (2000). Tourism collaboration and partnerships: Politics, practice and sustainability. Vol. 2. Channel View Publications. Bregoli, I., & Del Chiappa, G. (2013). Coordinating relationships among destination stakeholders: Evidence from Edinburgh (UK). Tourism Analysis, 18(2), 145–155. Bonacich, P. (2007). Some unique properties of eigenvector centrality. Social networks, 29(4), 555–564. Bonacich, P., & Lloyd, P. (2001). Eigenvector-like measures of centrality for asymmetric relations. Social Networks, 23(3), 191–201. Brown, M. H., & Hosking, D. M. (1986). Distributed leadership and skilled performance as successful organization in social movements. Human Relations, 39, 65–79. Buchanan, D. A., Addicott, R., Fitzgerald, L., Ferlie, E., & Baeza, J. I. (2007). Nobody in charge: Distributed change agency in healthcare. Human Relations, 60, 1065–1090.

6.2. Avenues for future research A number of areas remain for further study. Firstly a dominant principles of DL is its fluid and interchangeable nature (Harris, 2008) whereby rules may change over time. The current study adopted a quantitative, cross-sectional design and future researchers may wish to adopt a longitudinal and mixed methods approach to examine the complete network involving all DMO member organisations in greater detail. This is in line with calls for longitudinal methodologies in the study of strategic destination decision-making in DMOs and destinations (see Beritelli, 2011a; Pavlovich, 2003; Pavlovich, 2014). By adding a qualitative dimension to the study of DL in a DMO context and indeed a ‘Phase 2’ data collection building on the network data through the provision of reflective, in-depth accounts by DMO organisation representatives, who may have already taken part in a ‘Phase 1’ quantitative network study, further investigations would have the opportunity to enrich DL accounts of enactment and practice. A second area for further research is to conduct cross-case comparison of DMOs adopting a DL approach such as the collection of similar data guided by the adopted methodological framework in other destinations and DMO contexts than the one used in this study. This may also be enhanced by the adoption of cluster analysis in the identification of DL typologies in a DMO context. This may reveal how DL is enacted and practiced in different DMO contexts and across geographies, and may reveal other potential leader types and network leadership behaviours beyond the six types of leaders identified in this study. Indeed, Small and Rentsch (2010) called for further research into the distribution of different leadership behaviours and operationalising DL and there is scope for research in this direction. A cross-case comparison may also shed light on different DMO approaches to restructuring their organisations as a response to government expectations to adopt a more inclusive leadership role, which may or may not necessarily be linked to DL. A third area for further research is to develop new visualising processes and practices related to DL and its relationship with the issue of power is, for example, one avenue for future research in this regard. 141

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Dean Hristov is Global Talent Research Analyst at Bournemouth University, United Kingdom. He has been engaged in the co-delivery of global HE projects and provides research outputs related to graduate employability, internationalisation, higher-level skills development and global talent. He has published over 20 peer-reviewed journal articles and book chapters in leading outlets including Tourism Management and Annals of Tourism Research. Dean is also a regular reviewer for the International Journal of Management Education and Current Issues in Tourism.

Noel Scott is Professor in the Griffith Institute for Tourism at Griffith University, Gold Coast, Australia. His research interests include the study of tourism experiences, destination management and marketing, and stakeholder organisation. He is a frequent speaker at international academic and industry conferences. He has over 210 academic articles published including 11 books. He has supervised 18 doctoral students to successful completion of their theses. He is on the Editorial Board of five journals and a member of the International Association of Scientific Experts in Tourism (AIEST). Prior to his academic career, Noel worked as a senior manager in a variety of leading businesses including as Manager Research and Strategic Services at Tourism and Events Queensland.

Dr. Sonal Minocha is Pro Vice-Chancellor at Bournemouth University, UK and has over 15 years of senior academic leadership experience in international Higher Education in both the public and private sectors. Among the key areas of work that Sonal has led in her present role include the embedding of employability and internationalisation into the core instituional proposition through the pioneering Global Talent Programme. As a widely published author, Sonals research interests are interdisciplinary and span the areas of International Higher Education, talent management, strategic change management and organisational learning. Her key external appointments include External Examiner at the University of Edinburgh, Governor at Poole Hospitals NHS Trust, Advisory Board of Study Portals, UKCISA Board of Trustees, International Academic Advisory Council for QS Conferences.

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