Keywords

DMO Business Model, Destination Marketing Organisations, DMO role, Destination leadership, Effectuation

Introduction

In order to enhance and sustain the competitiveness, growth and profitability of tourism destinations and travel brands, in recent decades, places and governments, establish and fund Destination Marketing Organisations (DMOs), among others (Pike, 2004; Pike & Page, 2014; Pike, 2016). Since the first DMO was established in 1901 in New Zealand, the DMOs have grown to be a regular and prominent feature in destinations systems (UNWTO, 2011; Pike, 2004; Pike & Page, 2014; Pike, 2016). In the case of Zambia, the DMO is the Zambia Tourism Agency (ZTA), which is operating as a quasi-government entity at national level (The Tourism and Hospitality Act Number 13 of 2015, 2015).

Traditionally, DMOs from their early establishment were oriented to destination promotion (Pike & Page, 2014; Pike, 2016). Eventually they evolved into full-fledged marketing entities focusing on marketing. This became the germane of the current destination marketing paradigm that is distinct from its antecedent, tourism marketing (Wang & Pizam, 2011; Pike & Page, 2014; Pike, 2016).

However, in both practice and literature, the purpose and legitimacy of this traditional monolithic and universal DMO archetype focused on marketing is now being questioned, and both practice and academia are now calling for DMOs’ shift towards organisations that lead the way in destination ‘experiential systems’ (Reinhold, Laesser & Beritelli, 2015; UNWTO, 2015; Pike, 2016). The World Tourism Organisation (UNWTO) observes that “though DMOs have typically undertaken marketing activities, their remit is becoming far broader, to become a strategic leader in destination development” (UNWTO, 2015). According to UNWTO (2019), the contemporary definition of a DMO is “the leading organizational entity which may encompass the various authorities, stakeholders and professionals and facilitates partnerships towards a collective destination vision.” This is a departure from its earlier view of a DMO fixated on marketing and management, defined as “those organisations which are responsible for management and / or marketing of individual tourist destinations” (UNWTO, 2004). Similarly, several other authors such as Hristov, Ramkissoon & Naumov (2020) note that the contemporary role of DMOs is expanding beyond the traditional destination marketing “towards assuming greater management and even leadership role”. In tourism policy discourses, it is argued that there are shifts in the “function, responsibilities and structures of DMOs” (Hristov and Petrova, 2015), accelerated by: continuous turbulence in political and economic environments (Preston, 2012; Coles et al., 2014); changes in dominant political ideologies; rapid developments of the tourism product and its mass globalization (Buhalis et al., 2014); and the changing conceptualization of the tourism product and other environmental developments taking place. Policy is compelled to reconsider not only exiting approaches to policy development in destinations but also the role of the DMO in the strategic agenda of policy (Hristov & Petrova, 2015).

Scholars argue that going forward there is need to recognise the evolutions taking place in destinations, organisation of destinations and role of DMOs (Hristov, Ramkissoon & Naumov, 2020). Therefore, the contemporary and future strategic agenda for academia, practice and policy regarding the afore-mentioned should include recognising and responding to the challenges, that include:

DMOs “will soon no longer exist in their current form” … “so now is an opportune time for tourism academics to engage in innovative thinking about the future of these entities” (Pike, 2016).

“Both academia and practice beyond the case of England are also signalling this major shift into the modus operandi of DMOs (Hristov & Zehrer, 2019).

“This shifting landscape questions the long-term sustainability of reshaped, yet financially- constrained DMOs facing severe challenges to deliver value to destinations, visitors and member organisations” (Hristov & Zehrer, 2019).

“Shifts in DMO governance and funding provide opportunities for the introduction of new models with a focus beyond traditional management and marketing to enable DMOs to flourish within this new landscape” (Hristov, Ramkissoon & Naumov, 2020).

However, despite the above-mentioned calls and evolutions, literature notes that the general structure and core activities of many DMOs across the world have by and large remained the same for decades now, including Zambia (Zambia Tourism Policy, 2015; Pike, 2016).

Secondly, literature has also touted various and at times enigmatic views on the factors causing these changes in the role and / or expected role of the DMO and other components in the destination system (Pike & Page, 2014; Hristov & Petrova, 2015; Pike, 2016; Dredge, 2016; Hristov, Ramkissoon & Naumov, 2020). Third, “the extant literature in the tourism domain appears to provide little insight on how to deal with tourism-specific contingencies when developing new or managing existing business models” (Reinhold, Zach & Krizaj, 2017). Fourth, literature that has argued for the shift in the role of the DMO has been enigmatic on the both the direction and what the new role of the DMO is or will become. Others have argued for retention of the marketing rationale; others a radical change to a new paradigm, such as destination management; and others for a combination but increased role towards a new paradigm, such as destination leadership (Wang & Pizam, 2011; Morrison, 2013; Pike & Page, 2014; Reinhold, Laesser & Beritelli, 2015; Hristov & Petrova, 2015; Pike, 2016; Dredge, 2016; Hristov & Zehrer, 2019; Hristov, Ramkissoon & Naumov, 2020).

Combined, the above four remain open questions in both literature and practice, and have become the four-fold problem in recent years for policy and practice in several destinations, including Zambia (Reinhold, Laesser & Beritelli, 2015; Hristov & Petrova, 2015; Pike, 2016; Dredge, 2016; Hristov & Zehrer, 2019; Ministry of Tourism and Arts, 2019; Hristov, Ramkissoon & Naumov, 2020).

The aim of the study was to develop a technical account explaining the shifting expected role of the Destination Marketing Organisation (DMO) among the various tourism industry stakeholders, and that of the structure and core activities of the DMO remaining the same in policy despite the open questions in Zambia; and to use the account to develop a framework for a DMO business model. The objectives were to (i) describe the nature of shifting expected role of the DMO and that of other components in the destination system among the various tourism industry stakeholders in Zambia; (ii) explain the shifting expected role of the DMO and that of other components among the various tourism industry stakeholders in the tourism destination of Zambia; (iii) explain the unchanging general structure and core activities of the DMO in policy despite its expected role shifting among the various stakeholders in Zambia; and (iv) develop a framework for DMO business model(s) from the technical accounts of the shifting expected role of the DMO and that of unshifting policy of the DMO in Zambia.

The above objectives were an attempt to answer the following questions:

1. What has shifted the expected role of the DMO and that of other components in the tourism destination system among the various tourism industry stakeholders in Zambia?

2. Why is the expected role of the DMO and that of other components in the tourism destination system shifting among the various tourism stakeholders in Zambia?

3. Why is the general structure and core activities of the DMO not changing in Zambia despite the expected role and the nature of destination leadership shifting among the various tourism stakeholders?

4. How can the technical accounts of the shifting expected role of the DMO and that of unshifting policy of the DMO in Zambia be used to develop a framework for DMO business model(s)?

Discourses on Causality of the Shifting Expected Role of Destination Marketing Organisations (DMOS)

Although no specific studies have been undertaken on the underlying causal mechanisms and structures on the shifting role of the DMO and other components in destinations, literature has variously discussed this subject, at times normatively or arising in the course of discussing the role of DMOs generally (Wang & Pizam, 2011; Morrison, 2012; Pike & Page, 2014; Hristov & Naumov, 2015; Hristov & Zehrer, 2015; Reinhold, Laesser & Beritelli, 2015; Dredge, 2016; Pike, 2016). What appears apparent from the various strands of literature is that academic discourses have accounted for and attributed the changes, disruptions and reorganisations that are taking place at various levels in tourism destinations in different ways. Consequently, the propositions of resolving the problems facing the tourism industry, DMOs, destination components on the organisation of destinations have also been varied, and at sometimes even contradictory (Pike, 2016; Dredge, 2016). Even where there has been consensus, the blends of propositions on the future of DMOs in destinations have been multifarious (Wang & Pizam, 2011; Morrison, 2013; Pike and Page, 2014; Reinhold, Laesser and Beritelli, 2015; Hristov & Petrova, 2015; Pike, 2016; Dredge, 2016; Hristov & Zehrer, 2019; Hristov, Ramkissoon & Naumov, 2020).

The above notwithstanding, it appears uncontested in both academic and professional practice discourses that there are shifts taking place in the tourism industry in general and destinations in specific that are challenging the traditionally held views on the role of the DMO, organisation of destinations, roles of components and the required policy environment (World Tourism Organisation, 2011; Wang & Pizam, 2011; Morrison, 2012; Pike & Page, 2014; Hristov & Petrova, 2015; (Reinhold, Laesser and Beritelli, 2015; Pike, 2016; Dredge, 2016; Hristov, Ramkissoon & Naumov, 2020).

Secondly, it is also widely acknowledged in discourses that DMOs and the definition of their roles have over the years evolved substantially in policy, practice and theory from the traditional role and the nature of leadership they provided in earlier destination modelling to the current varied approaches suggested in some literature discussions (Pike & Page, 2014; Hristov & Naumov, 2015; Dredge, 2016).

Thirdly, it is also generally acknowledged that the tourism industry in general and specifically the destinations are somewhat reorganizing in terms of its structure, governance and leadership somehow (Gyr, 2010). What has been witnessed in the case of Zambia is that at times these have appeared through polycentricism and ahead of policy. In some case they have forced public policy enablement; tended to be continuously evolving also; and even compete with the DMO for resources and politics of DMO decision-making (Pike, 2016; Tourism Council of Zambia, 2020).

Those who have commented or discussed the subject of causality in academics have predominantly associated these changes to be mainly falling under two domains, namely: (a) changing tourism operating environment and resulting structural changes and social transformations (Dredge, 2016); (b) changes in the environment and conceptualization of destinations as complex social systems (Baggio, 2013; Jakulin, 2016). The main argument by the former can be inferred from literature as that of the interactions of the environment, structural changes and social transformation in a late modernity or reflexive era translating into “changes in character and behaviour of organisations and in public expectations about the role and responsibility within society” (Saffic et al., 2010). The latter cluster of literature base the arguments on the growing literature which acknowledges the changes in the conceptualisation of tourism destinations from being viewed as amalgam of products as once conceptualised by Buhalis (2000), to complex systems as espoused by growing number of recent literature (Reinhold et al., 2015; Baggio, 2013; Jakulin, 2016). These changes are considered to have had ramifications on the organisation of tourism in destinations in general and also on the role of the components in these system.

Destination Systems \=][P;L.,] ‘/As Complex Social Systems : Rising Theoretical Framework for Destination Phenomena

A growing body of recent literature acknowledges that destinations comprise a multiplicity of interconnected product and service providers, and is open to high levels of external influences from both political and societal dynamics (Quinlan et al., 2015). In addition, literature acknowledges that destinations and problems faced are complex in nature due to their inherent characteristics, and therefore require holistic and systemic approach to understand and resolve them (Wang & Pizam, 2011; Pike & Page, 2014; Baggio, 2013; Jakulin, 2016).

The focal argument in these discourses is that a tourism destination is “a complex system, which deals with many subsystems and softly defined problems (Jakulin, 2016) and that “the only way to fully understand why a problem or element occurs and persists is to understand the part in relation to the whole” (Capra & Lusi, 2014; Jakulin, 2016).

In regard to changes taking place in destinations in general and role of DMOs in specific, some argue that this lies in unveiling the changes in the conceptualization of destinations and the problems they face (Beritelli et al., 2014; Pike and Page, 2014; Hristov & Zehrer, 2015; Reinhold et al., 2015). As the 2014 St Gallen Consensus on Destination Management argue that, going forward, a tourism destination must be viewed as “a market-oriented productive system” where tourists activate this productive system and “together, tourists and supply-side actors co-produce an experience – from origin to destination(s) and back” (Reinhold, Laesser & Beritelli, 2015). As already indicated, this view is a departure from the amalgam conceptualization of destinations as suggested by Buhalis (2000); it rather moves destinations to be conceptually viewed as multifaceted systems and spaces of flow as in a number of recent literature (Castells, 2004; Beritelli et al., 2014; Baggio, 2013; Jakulin, 2016).

The above shift in the conceptualisation of destinations presents complicatedness, especially when considered in terms of understanding the DMO, its stakeholders and problematisation of destination of their challenges. First, this shift implies increase in the role of stakeholders in any destination modelling and also brings the challenge of realigning how the DMO and the various stakeholders coordinate tasks in the ‘production entities’, from the practice and policy perspective. Second, it alters the long-held theory on location of the problems of destinations from discrete components as in analytic thinking to the systems or ‘whole’, and yet there is a gap in tourism literature and practice because of “a profound lack of research on the productivity of destinations” (Reinhold et al., 2015). Third, while it reifies recent literature that views tourism and its problems from the system complexity views, this view has not taken root in destination and tourism research, especially that of DMOs. Most importantly for the last two points is that there are still limited theoretical positions and tools within tourism and destination literature at the disposal of researchers.

Generally destinations characteristically display complexity arising from, among others: (a) tourism destinations have many elements of a variety of people, institutions and organizations that include government and non-government actors with varied interests; (b) these elements are interconnected and influence each other; (c) destination elements have a common history and enjoy feedback information within the system; (d) destinations are open systems, interacting with so many other systems; and (e) their problem situations are multifarious and far from being linear.

The Gap

Literature from both the system complexity and social transformation strands have not identified the causally efficaciousness of the cited plausible factors and the actual generating mechanism and structures, and to possibly also eliminate the competing factors that have no causality efficacy in destination systems. Second, despite literature acknowledging destinations as complex social systems and problems they face as complex from complexity of destinations (Capra & Lusi, 2014; Jakulin, 2016), there has been limited attempts to understand and explain the shifting role of DMOs from their complexity and complexity theory. Reductionist approaches still dominant in tourism studies. Third, as already alluded to, “the extant literature in the tourism domain appears to provide little insight on how to deal with tourism-specific contingencies when developing new or managing existing business models” (Reinhold, Zach & Krizaj, 2017).

Theoretical Framework

The study was approached from a theoretical frame of reference of complexity theory, with ‘mechanisms of emergence’ as the explanatory frame of reference. The approach was to use complexity theory to retro duct causal mechanisms and develop model explanation of the phenomena from complexity emergentism.

The doctrine of emergence as proposed by Mihata (1997) could be summarized as follows: “The concept of emergence is most often used today to refer to the process by which patterns or global-level structures arise from interactive local-level processes. This ‘structure’ or ‘pattern’ cannot be understood or predicted from the behavior or properties of the component unit alone…. In the doctrine of emergence, the combination of elements with one another brings with it something that was not there before”. Bringing ‘something that was not there before’ is ‘emergentism’ and the structure of the emergent entity or entities to infer how it came about is a ‘causal mechanism’ in this case.

Emergent systems are “complex dynamical systems that display behavior that cannot be predicted from a full and complete description of the component units of the system” (Sawyer, 2001). Similarly, Lichtenstein (2014), summarizes emergence as follows: “the creation of order, the formation of new properties and structures in complex systems” and that “when emergence happens, something new and unexpected arises, with aspects that can’t be predicted even from knowing everything about the parts of the system”. The central question in any study under the complexity view is that of causality – what causes the destination organization to display patterns of stability when they do; what causes them to change when they change; and what makes them take the form they take (Stacey et al., 2000).

Complexity theory provides a new framing for inquiries involving change and complex social phenomena by, among others, offering new ways of re-thinking old assumptions of equilibrium in favor of far-from-equilibrium and rectifying the relationship between a system and its environment (Walby, 2003). In regard to change, two pathways have sprung out of complexity theory, namely: (a) the conceptualisation of change from the point of co-evolution of complexity adaptive systems; and (b) the notion of novelty, sudden, critical turning points where complex systems give rise to “bifurcation and new paths of development that are self-sustaining” (Walby, 2003). These two and the respective frames are briefly discussed below.

Co-Evolution of Complex Adaptive System

In summary, the behavior of complex adaptive systems characteristically is defined by two fundamental elements as follows: (a) the system’s behavior is both patterned and unpredictable; and (b) adaptive behavior indicates that such systems influence and are open to be influenced by its environment. Others include (a) large number of possible states; (b) components are dispersed and free to interact locally within a hierarchical structure; and (c) emergent behavior with pockets of unpredictability (Battram, 1998; Tosey, 2002).

Using the above notion, complexity theory re-frames accounts of social change because the concept of co-evolution presupposes that the system takes other systems to be its environment (Walby, 2003). As systems interact with each other, they do so through coupling and assist in the reproduction of each other. Coupling of the systems can potentially lead to the creation or generation of another system unit in a different domain “from that in which the coupled entities maintain their identities” (Walby, 2003). In the case of tourism destinations, this could be in the form of a polycentric entity. This would amount to a display of emergence behaviour at the empirical level if we are to use the Bhaskarian stratified ontology.

The scope of emergence in complexity causality as approached in this study could comprise of any or a combination of the following elements:

a) Emergence of ‘new level’ of reality in the destination, such as emergence of polycentric organisations at messo or macro levels (Mihata (1997);

b) Emergence of patterns or macro-level structures of interactions and communications (Gavetti & Levinthal, 2000; Ganco & Agarawal, 2009) – the greater the structural patterns, the greater the adaptative of the system is;

c) Emergence of overall novel and coherent structures, patterns and properties or system-wide shift that transforms the destination system (Lichtenstein et al., 2009).

Emergence is at the centre of complexity theory and therefore sits at the core of research in complex systems, especially those leaning on the assumption of re-organization (Lichtenstein, 2014). In theory, the centrality of emergence has been used by management scholars at both micro and macro levels. For example, at micro levels for decision-making (Minniti, 2004), leadership (Plowman et al., 2007), organizational design (Brown and Eisenhardt, 1997) and processes of organisational adaptation and strategic change (MacIntosh & MacLean, 1999) studies; and at macro level for governance processes (O’Mahony & Ferraro, 2007), emergence of new markets (Sarasvathy & Dew, 2005), creation of new industry forms (Perretti et al., 2008) and agglomeration studies (Chiles et al., 2004). The capacity to be useable at both micro and macro levels without losing ‘holism’ of phenomena is one of the reasons that make the complexity theory approaches compete with sociological emergentism as explanatory frameworks.

As alluded to above, complexity theory re-frames the debate about the micro-macro relationship in causality and is assumed to be linked through emergence in complexity theory (Walby, 2003). In sociological views the challenge has been how different levels of patterns are linked, and the variations of response has included ‘macro-micro’ linkage, ‘individual and society’, and structure, action and structuration (Giddens, 1984). Complexity theory assumes that “each level contains the objects that are present in the other levels” but these can be analysed differently, and emergence can be studied from either the bottom-up or bottom-down approaches (Walby, 2003).

Another important view within complex adaptive system is the notion of edge-of-chaos behaviour. Complex adaptive systems display behaviour between states of stability and that of chaos. Stability is considered to be a temporal state due to regular perturbations and constant chaos catalysts. Edge-of-chaos arises from the fact that complex systems display dynamism, with stable equilibrium and random chaos on the extremes. In between these distinctive dynamics, they may be at the edge-of-chaos, which is a state of readiness for radical change (Stacey et al., 2000; Russell, 2006). Contemporary literature describes destination stability as ‘transitional’ and change is considered to be imminent at any time because of the heightened sensitivity to disruptive internal and external influences and the constant barrage of a multiplicity of chaos catalysts (Burns, 2006; Russell, 2006; Pike, 2016). This essentially is a display of edge-of-chaos characteristic.

Important to note is that complex systems are open to their environment and because of this permeability they import inputs, export outputs and interpret the feedback they receive from the environment. Consequently, they are affected by the environment and its changes. Perturbations cause the system to go into self-amplification. Self-amplification of the fluctuations generated within the system from the perturbations is taken to be the self-organisation of the system (Yolles, 1999).

Going by the edge-of-chaos assumptions in destination as complex adaptive systems, edge-of-chaos is to be expected and could actually be desirable because it drives a destination system and its components towards more competitiveness, resilient and robustness if the system is harnessed to be a learning tourism destination. A learning destination system is achieved by a DMO, government agencies and tourism industry associations acting as “intermediaries, to facilitate the generation, sharing, storage and processing of a myriad of interconnected businesses which exhibit the characteristics associated with creativity and robustness” (Senge, 1990; Speakman, 2017). This is important to keep in policy propositions. Complexity theory approaches under self-organization are suited for investigating phenomena that does not fall in the extremes of the spectrum of order-disorder but rather those on the edge-of-chaos (Langton, 1990).

Figure 1 is a conceptual view of complex adaption through self-organization. At stage one, the destination is displaying temporal stability and soon perturbations trigger the system to go into self-organization at stage two. Three rules governing behavior at stage two that can be studied are: (a) interconnections of the components; (b) self-referential behaviour of components; and (c) emergent behavior of the components at their level. At stage three, the system as a whole displays emergent behavior and is in temporal equilibrium again.

Path Dependency and Bifurcation

The other way of analysing change in complexity theory apart from complex adaptive systems is the display of novelty or sudden changes, which can lead to different paths of development rather than one universal route (Walby, 2003). This is somewhat different from the concept of co-evolution, which implies gradualism. In studies, the point of interest under path dependency assumptions is the point at which paths diverge, comparable to crystallization and taken to be the critical turning point or ‘bifurcation’.

Bifurcation is taken to be the point at which the system changes. Crises in destinations are considered to be points of these changes, and could send the destination system into disarray or complete destruction (Speakman, 2017). Using this argument, the shifts in the expected role of the DMO and its components, and the loss of efficacy of the archetype traditional DMO as recorded in literature (Reinhold et al., 2015) could be ascribed to system bifurcation if it arises from the ‘butterfly effect’. ‘Butter fly effect’ as discussed in the last chapter is a notion of small changes that have large effects on unstable systems. At this stage of bifurcation, policy should provide for avoidance of cosmology. In destinations, cosmology is considered to be the panic of components, visible in the system’s agents. A destination able to cultivate an inherent ability to respond to crises is more “adept at adapting to bifurcation and minimizing episodes of cosmology” (Speakman, 2017).

Bringing the above together, the mechanisms by which change and emergence take place in complexity theory could be approached from three perspectives. These are based on the versions of complexity theory from the two schools discussed above, of either change stimulus lying in the endogenous learning and developments or the sudden transformation of the environment (Walby, 2003). The three are: (a) co-evolution and complex adaptive system based on the Santa Fe Institute version; (b) sudden transformation or chaos theory version influenced by Prigogine; and (c) Eldredge’s punctuated equilibria theory, where periods of little change are followed by periods of rapid change (Eldredge, 1985; 1986; Walby, 2003).

Complex Systems as Nested Systems

Complexity emergentism gives a system property of a nested system, where a system is considered to enclose and simultaneously enclosed by other systems (Walloth, 2015). Enclosing is systems emerge diachronically from the systems they enclose. In terms causality, the basic argument is that enclosed system’s activities or frequencies of change will always be faster than those of enclosing systems. According to this doctrine, eventually enclosing systems emerge from the generative activities of the system they enclose. Walloth (2015) found that enclosed system’s activities or frequencies are always faster than those of enclosing systems. Secondly, because of emergent behaviour, there is no way of predicting the quality of emergence until it is there. This study views the destination system to be a nested system.

Methodology

The study followed a qualitative single case study design and was conducted in three sites cross-sectionally in the southern tourism circuit of Zambia (Yin, 2003; Denzin & Lincoln, 2000). The three sites were (a) Livingstone; (b) Kafue Ecosystem; and (c) Lusaka, Siavonga and Lower Zambezi tourism area. Zambia’s tourism product is segmented closely with the geographical spatial areas, and each of these offers a unique product offering and stakeholder constitution in the destination. These sites offer advantage of providing a variety of informant views and comparisons in the construction of theory under the grounded theory approach.

Livingstone is the tourist capital of Zambia, and has some of the major tourism assets and products. It is home to the Victoria falls and is part of the Kavango-Zambezi Trans-frontier Conservation Area (KAZA), which comprises of joint conservation areas in Zambia, Zimbabwe, Botswana, Namibia and Angola. Livingstone is also one of the oldest destinations with developed tourism products that include adventure activities and seasoned tourism industry operators in Zambia.

The Kafue ecosystem tourism area has the largest national park in Zambia, the Kafue National Park and like Livingstone has developed products and variety of stakeholders that include tour operators, safari accommodation facilities and a host of non-government actors active in conservation. The combination of Lusaka, Siavonga and Lower Zambezi National Park offers a somewhat different product line to Kafue National Park and Livingstone. Lusaka is the national headquarters for government and a number of quasi-government organisations in the tourism sector. Additionally, it is promoted as a meeting, incentive, events and conferencing (MICE) destination just like Siavonga. Lower Zambezi National Park is a safari destination with a number of international brands of tourism products.

The research problem was philosophically viewed from critical realism, and used the abduction, retroduction and pragmatism logics of scientific reasoning (Bhaskar, 1978; Orlikowski & Baroudi, 1991;1993; 1998; Porpora, 1998; Carter & New, 2004; Mingers, 2011; Wynn & Williams, 2012; Wynn & Williams, 2012; Bygstad, et al., 2016; Hoddy, 2018).

The study sample was thirty (30) informants chosen through non-random theoretical sampling method from among licensed private sector operators based on their in-depth knowledge of the tourism industry from business organisations with not less than ten years in Zambia and also from among the government and its main agencies in the tourism sector. Data was collected through face-to-face semi structured in-depth interviews, archival unobstructed data and review of documents, and was analysed from grounded theory approach and thematic content analysis. This methodological approach was found valuable for in-depth and adequate explanation of a complex social and not-empirically-clear contemporary real-life context and phenomena.

Table 1 Sample Size and Respondents Categories
Respondents interviewed	Livingstone	Kafue Ecosystem	Lusaka	Total
Travel trade / activity companies	04	-	01	05
Accommodation providers	03	01	02	06
Tourism industry associations and other industry structures	03	-		03
Quasi-government institutions	03	01	03	07
Government (department of tourism)	01			01
Government (wildlife and national parks department)	-	01	01	02
Conservation organisations (non-government actors)	-	03	01	04
Others – funding partners	-	-	01	01
Others – events organisers	-	-	01	01
Total	14	06	10	30

The sampling frame was based on the stakeholders drawn from the supply-side of the destination system. Zambia has twelve category of tourism businesses, according to the licensing regime. Tourism business stakeholders will be picked from each of these categories of businesses. However, weight has been given to tour operators because of comprising of both wholesalers and retailers in the sector, and they market products in some of the sub destinations within Zambia, such as North and South Luangwa not included in the case study. The businesses are: (a) accommodation businesses; (b) tour operators; (c) travel agencies; (d) car hire businesses; (e) adventure / activity businesses; (f) restaurant businesses; (g) night club businesses; (h) night club businesses; (i) air charters; (j) events management businesses; (k) hunting safari operators; (l) convention and conference businesses; and (m) theme parks.

Results ULTS

Research Question One: Complexity Theory Model Description of Phenomena

The study found that under the complexity theoretical frame of reference, the shifting expected role of the DMO and the unchanging structure and core activities could be described using four domains, as follows: (i) general behaviour exhibiting a combination of four elements, namely, interactions, feedback and emergent order; (ii) the interplay of the macro-level superordinate enclosing systems and the enclosed tourism destination system; (iii) active regenerative influences at micro and messo levels; and (iv) general characteristics of displayed behaviour.

General Behaviour

The destination system was found to be complex, primarily because of interactions, feedback and emergent order within the system components and those exogenous to it. Therefore, the critical underlying causally factor was taken to be the complexity of the destination itself. This can also be inferred to have also been the underlying challenge in policy propositions and accounts of the structure and core activities of the DMO remaining the same for years, despite shift in the expected role of the DMO.

In Zambia the range and inter relationships among components include those that emanate across the geopolitical boundary borders, away from where the Zambia Tourism Agency (ZTA) and Government has no formal jurisdiction. These complex systems in adjacent destinations still nonetheless influence policy and practice on the Zambian side, because of, among others, the shared tourism resources and historical links of operators. The various policy and government direction decisions and the direction of markets and the travel trade also increased the complexity. Highlights of the interactions, feedback, emergent and nested behaviour are as below.

Interactions: The destination system was found to have numerous interactions of nested and highly active heterogenous components that exhibit variations, and each of these are ‘systems’ on their own with influences.

The tourism destination system has numerous interactions of nested and highly active heterogenous components that exhibit variations, and each of these are ‘systems’ on their own with influences, as shown below. The destination system comprises of and are influenced by other complex systems that equally have numerous nested and heterogeneous system elements that exhibit considerable variation, with each element being a system in their own right. The interactions of the destination as a whole and the DMO comprised of multiple entities, organisations, agencies and sectors at local, regional, state, national and international levels, all of which vary in terms of their structure, function and interests. Each component in the destination system has a unique relationship to and influence on the destination system.

Feedback: The destination, typical of complex systems, is displaying dynamic behaviour and has the ability to change, adapt and reorganise in response to its environment.

The destination, typical of complex systems, is displaying dynamic behaviour and has the ability to change, adapt and reorganise in response to its environment. It has feedback loops of interconnections. The displayed behaviour of the destination is largely as a result of the cumulative effect of positive (reinforcing or self-enhancing) and negative (balancing or goal seeking) feedback mechanisms.

What increases complexity is that the feedback loops in the destination include those connected to regional destinations and the international travel trade.

Emergent order: The model description could be that the multifarious interactions and feedback mechanisms in the destination are the ones that produce emergent order or properties displayed by the whole destination, as witnessed in the case of Zambia. The shifting role of the DMO cannot therefore be inferred by the study of individual system elements or variables but rather through the study of relationships in the whole system.

Nested System with Enclosed and Enclosing Systems: The destination system was found to show itself as nested system that has properties of enclosing other systems and is simultaneously enclosed by other systems.

Efficacious Influences of Emergence in the Destination from Other Systems

This study found that the main exogenous influences in the destination to be around six clusters or themes, namely (a) government and public policy directions; (b) regional structures, collaborations and shared products with adjacent destinations within the region; (c) international markets and travel trade directions; (d) culture and history of the destination; (e) international membership organisations; and (f) others, such as crises. In addition to these, there are also still possibilities of some unknown-unknows that emanate from the internal behaviour of the destination itself.

In the explanation model, the six cluster of influences act as superordinate systems influencing the destination.

Cluster influence one: Government and Public Policy Directions: The study found the role of government policy and its direction has had a telling effect in shifting the expected role of the DMO among the various tourism industry stakeholders. Some of the decisions in the immediate past that has affected how the DMO is viewed and expectations from stakeholders are budget and funding models to the DMO; destination strategy and vision; and political and policy environment.

Cluster Influence Two: Regional Structures, Collaborations and Shared Product Directions: Zambia as a destination has highly active entities and mechanisms arising from proximity and shared products in major tourism areas, especially in the southern circuit. Zambia is part of the Kavango-Zambezi Transfrontier Conservation Area (KAZA TFCA). Second, it shares one of the major tourism attractions, the Victoria Falls, with Zimbabwe. Within the region there are both opportunities and threats of competition according to respondents.

Respondents viewed that the DMO had limitations in making decisions regarding regional competition because of the sluggishness of following centralized decision-making processes. Respondents from the private sector viewed that the DMO should play a role of coordinating with other regional equivalents so as Zambia to benefit from the transfrontier. However, it was found that the private sector has started their own initiatives as already indicated above.

An interesting finding was that while Livingstone town on the Zambian side and Victoria Falls town on the Zimbabwean side at policy and government level each operated on rules and regulations unique to each side, private sector operators on the two sides had made their own localized boundaries and regulations for areas and activities viewed to be of common interest to tourism businesses. These included in some cases collaborating across geopolitical boundaries. However, ZTA as a public entity is driven by government policy and positions, and could not enter into such localised arrangements in transfrontier tourism areas. This has caused private sector operators on both sides of the Victoria falls to create an informal structure of collaboration among themselves. This has also included undertaking destination promotion activities in long-haul markets as opposed to seeing themselves as competitors. This is different with what is found in most of the literature and government narratives.

Cluster Influence Three: Markets and Travel Trade in Source Markets: A number of private sector operators interviewed viewed that there is heightened competition for international markets, and the DMO was not moving in tandem with the private desire to remain competitive and profitable in these markets.

Secondly, a more prominent view in Livingstone is the generally declining regional market for tourism in the Kavango-Zambezi Trans-frontier Area (KAZA) during the last few years. Private sector operators viewed the need for immediate action to regain and grow the regional market. This is also attributed as one of the reasons for the formation of a regional marketing association to promote the area.

Additionally, a number of respondents, including from quasi-government entities viewed that there had been a lot of changes in the major source markets that called for various changes in the DMO role. These changes include destination promotion channels and product preferences. The private sector respondents viewed that they were ahead of the DMO in responding to eco-friendly products, which the DMO should lead in championing. These entailed, among others, for the DMO to support private sector in the delivery of the brand promise based updated market requirements. The view of respondents was that the DMO was sluggish in responding to changes pull-factors in the market place, and there was need to make it a bit more versatile in responding to markets. This has made the private sector to take up their own initiatives.

Tied to the two above, respondents viewed that the absence of destination marketing in major source markets was a major concern because of competition from regional competition which other destinations dominated. DMO was viewed as being absent in some of the potential growth markets for Zambia, such as India, and not promoting the destination in readiness for post-covid 19 era or indeed to continue with market visibility worsened it.

Cluster Influence Four: Culture and History: The destination exhibits a high level of diverse historical backgrounds and ties among individual investments, circuits and markets; and a high heterogeneity, and diverse interests, displaying, among others, the following: (i) competition and rivalry within the Industry and Sub Destinations; and (ii) southern circuit and northern circuit product development rivalries

Cluster influence five: International Organisations’ influences - World Tourism Organisation (UNWTO) and Other international membership associations of Zambia: Zambia is a member of the international membership organisations, such as World Tourism Organisation (UNWTO), United Nations Educational, Scientific and Cultural Organisation (UNESCO) and a party to the Conventional on International Trade in Endangered Species of Wild Fauna and Flora (CITES). These influenced were found to influence the destination and its organisation.

Other Cluster of Influences: Responses to Crisis: Respondents from both the private and public sector viewed that the destination through various government entities was slow to respond to crises. An example of response to the covid-19 pandemic was referred to. It was observed that despite some of the accommodation providers taking measures that assure international tourists, the DMO and government not increased budgetary allocation to target markets looking for safer destinations.

Active Regenerative Influences at Micro and Messo levels: The active regenerative influences in the destination were to found to be: (i) rivalries within destination, mainly based on spatial areas and product types: (ii) heterogeneity of operators and their interests; (iii) historicity at micro and messo levels; and (iv) levels and sizes of operations and ownerships.

General Characteristics – Displayed Behaviour: The general characteristics of the destination included: (i) a display of emergence behaviour in general; (ii) various non-linear combinatorics; (iii) linear combinatorial productivity; (iv) opportunity tension; and (v) policy resistance – unexpected responses to policy creating worse reality.

Research Questions Two and Three: Explanation of Shifting Role of the DMO and Unchanging DMO Core Structure and Activities in Destination Policy

From the above description of causally behaviour, the model of causality under complex theoretical assumptions can be explained from three intertwined behaviour of the destination system, namely: (i) the superordinate enclosing systems enclosing the destination system and the regeneration activities of the destination system as a whole; (ii) multiple regeneration activities of the various destination components to multiple rule-settings by the various superordinate systems; and (iii) multiple enclosing and enclosed relationships within the destination system. This explains and answers the two why-questions in this study.

Superordinate Enclosing Systems and Enclosed Destination System

As found by the study, the most active superordinate systems in the destination are the government and public policy systems; regional structures and shared products system with near destinations; international tourism systems; market and travel trade systems; and culture and history system. In addition to these, there is also a possibility of the unknown unknowns existing in the destination.

Causally behaviour of the superordinate systems arises from their enclosing, rule-setting movements that enclose the destination and in return the destination system as a whole or its most active actors embark on regeneration activities. The regeneration activities are faster than the rule-setting emergence. In the end, the destination system as a whole displays the visible emergence behaviour. As shown in figure 3 the macro-level elements act as enclosing systems that are setting the rules for emergence behaviour. These elements or factors are systems on their own.

Multiple Rule-Settings Emergences by Superordinate Systems and Multiple Regeneration Activities by Destination Actors

While the systems may seem to be taken as a ‘wholes’ in the destination, there are multiple enclosing systems and enclosed systems simultaneously responsible for the ultimate causally behaviour. Each of the superordinate systems has enclosing influence of each of the components in the systems and at the same time each of the enclosed destination system may engage in different regeneration activities. Depending on the most imposing enclosing system characteristics and the fastest reacting destination components, the display of emergence may also vary. In studies of urban settlements, among cultural, political, economic and technological influences, the slowest was found to be cultural influences and fastest political ones (Walloth, 2015).

Destination System Enclosing and Enclosed by Each Other

In addition to the influences from superordinate systems mentioned above, within the destination systems there are simultaneously multiple enclosing-and-enclosed relationships among the components. The activities include rejection of some of the enclosing rules. Depending on the rule-setting components (systems) and the regeneration activities of enclosed of the components, within the systems the expected role of the DMO could shift or viewed differently among the components.

Combining the three domains provides explanations of why the expected role of the DMO is shifting among the various tourism industry stakeholder; and at the same time policy is not changing in tandem with this shifting. The explanations as follows:

Shifting Role of the DMO

Retroductively, the most plausible explanation for the shifting expected DMO among the stakeholders is that the destination system as the enclosed system generates activities faster than enclosing system’s inward rules and guidance from the superordinate influences; and depending on the most imposing enclosing system, it emerges from the activities of enclosed system, diachronically. Some of what has been cited or implied in literature as causal factors are not causal factors but superordinate influences.

Failing Public Policy Propositions

Again, through retroduction, the most reliable account of why policy propositions have been failing is that in nested systems, enclosed system’s activities or frequencies of change are always faster than those of enclosing system (Walloth, 2015). First, policy consultations with stakeholders done from generative activities of enclosed systems which change faster are not likely to stand for long, depending on the enclosing systems at play in the destination.

Second, the destination was found to have too many unaccounted-for influences, some acting as ‘black boxes’ such as influences from adjacent destinations of Zimbabwe and their actors and the international brands exerting influences from their countries of origin. This study found that the tourism industry components displayed several black-boxes in its relationship with the DMO. So, it is plausible for policy to miss critical destination regeneration activities, and even destination actors themselves.

Third, it is also plausible that some critical views are not usually captured during stakeholder policy consultations because even among destination actors or stakeholders there are slower and faster actors within the destination, for example, some spatial areas or tourism sub sector could be slower and enclosing or faster and enclosed. So, depending on how stakeholder views are captured for policy propositions, it is possible to rely on the faster actors and their outward influences in comparison to the rest.

Fourth, in complex systems, there is always a ‘delay’ and possible policy resistance between a policy intervention and reaction of the destination. Assessing the effect of policy interventions done during this period could be misleading. It follows that decisions or changes made from this assessment is likely to be unstable. Therefore, policy propositions should be preceded with a thorough understanding based on the destination complexity, including accounting for likely ‘delays’ and / or ‘resistance’ emanating from complexity of the destination system. The policy formulation and consultative process as found by the study have been on an assumption of linearity of destination relationships and actions. This assumption is flawed.

Frameowrk For Developing DMO Business Models Proposition

The findings of this study have demonstrated that there is no way of predicting the quality of emergence as has been discussed in literature (Walloth, 2015) before it is there because of three principal factors. First, systems are dynamic and while enclosing are simultaneously being enclosed by other systems. Second, there may also be unknow unknows that show up, as the case in this study where likely unknow-unknows (Walloth, 2015) or unaccounted for behavior in the destination could remain. It is likely that there will be some black boxes to policy, practice and DMOs. The latter argument can also be extended to include those effects that studies may not capture during the course of policy formulation.

Because of the unknown-unknows, high dynamism of destinations systems and lack of predictability of the properties of emergence until it is displayed, a framework based on causation becomes a challenge. The alternative is to mirror causation by using what Walloth (2015) suggests, namely, the effectuation concept, traced to Sarasvathy (2001). This becomes the source of plausible principles to use in decision-related choices for complexity systems that display nesting behaviour. This study adopts this view, but goes beyond by emphasizing on the feedback loops as key even within the effectuation concept application.

According to Walloth (2015), the probable options for influencing in such a problem situation would be:

(a) Changing the generative activities of the destination system components in a way that make them to no longer comply with existing rules of the inward influences from the superordinate systems. The expected end result is changing the enclosing guiding quality

(b) Undertaking activities in the destination system that are in compliance with existing rules by disturbing the established and stable whole; and

(c) Inwardly influencing the guiding rules from the enclosing systems so as to change the activities of the destination system and its components.

In view of the findings of the study, which show high level inward influences and some of the major influences being possible to be identified with rigorous work, the proposal is to use inward the latter option, namely, influence of the guiding rules from the through various tools. So, the key theoretical principles, concepts and theory that the proposed framework is based on are as follows:

Concept of Effectuating: The concept of effectuation shifts the choice of tools from causation to effectuation, which Sarasvathy (2001), distinguishes as follows: “Causation processes take a particular effect as given and focus on selecting between means to create that effect. Effectuation processes take a set of means as given and focus on selecting between possible effects that can be created with that set of means”.

Although Sarasvathy (2001) argues from entrepreneurship standpoint, effectuation was used in the study for policy formulation because of the likely black-boxes and a number of unknown-unknowns. The useful and applicable principles of effectuation as originally suggested by Sarasvathy (2001) in comparison to causation are as in table 2. The context of focus was more ubiquitous in human action and useful under assumptions of dynamic, nonlinear, and ecological environments. On the other hand, causation focuses on more ubiquitous in nature and becomes more useful in static, linear and independent environments.

Table 2 Contrasting Causation and Effectuation Principles
Categories of Differentiation	Causation Processes	Effectuation Processes
Givens	Effect is given	Only some means or tools are given
Decision-making selection criteria	Help choose between means to achieve the given effect Selection criteria based on expected return Effect dependent: Choice of means is driven by characteristics of the effect the decision maker wants to create and his or her knowledge of possible means	Help choose between possible effects that can be created with given means Selection criteria based on affordable loss or acceptable risk Actor dependent: Given specific means, choice of effect is driven by characteristics of the actor and his or her ability to discover and use contingencies
Competencies employed	Excellent at exploiting knowledge	Excellent at exploiting contingencies
Context of relevance	More ubiquitous in nature More useful in static, linear, and independent environments	More ubiquitous in human action Explicit assumption of dynamic, nonlinear, and ecological environments
Nature of unknowns	Focus on the predictable aspects of an uncertain future	Focus on the controllable aspects of an unpredictable future
Underlying logic	To the extent we can predict future, we can control it	To the extent we can control future, we do not need to predict it
Outcomes	Market share in existent markets through competitive strategies	New markets created through alliances and other cooperative strategies

In effectuating and as adopted in this study, decision choice criteria focuses on helping choose between possible effects that can be created with given means; selection criteria is based on affordable loss or acceptable risk; and is actor dependent, given specific means, choice of effect is driven by characteristics of the actor and his or her ability to discover (Sarasvathy, 2001). The latter makes policy formulation process to make more of scientific based process than merely as political decision-makers assign role of DMO as implied in some discourses (Dredge, 2016).

Complexity Feedback: As highlighted in the previous chapter, one of the findings is a typical complex system network of feedbacks within and from outside the system. For, example the findings in this study were that the operators viewed that both the overall tourism master plan and the DMO strategy positions were not shared with them. The findings also indicate policy resistance and delays in the system. These are all factors of feedbacks in the system. In this regard, the feedback loops are important in the success of the DMO business model. The formulation process should therefore include a deliberate monitoring of feedback, and if need be adjusted the policy propositions to influence the system to behaviour accordingly.

Nested Behavior: Underlying the framework is the nested behaviour of the system, as already discussed. This study views that the destination system is a complex nested system, and therefore any proposal for DMO business model should be based on the understanding of the destination form this perspective. This again, entails a more elaborate process of arriving at policy propositions.

The study proposes a seven-step framework that could be used to develop a business model for the DMO. The framework is based on two main theoretical principles that also appeared highly prevalent in the case of the current destination system, namely (a) reinforcing feedback; and (b) influencing the destination system. The overarching concept followed is that of effectuating. However, the framework is such that it allows for use of several tools from other disciplines, such as strategic management, marketing, tourism and others. The outline of activities on each of the steps is as follows:

Step One: Understanding and Characterising the Destination

This is the exploratory stage that seeks to understand and characterise the destination system and its environment. The outcome of this stage is drawing of boundaries for the destination; and determining the nested relationships of likely outward and inward influences. Tools at this stage could include the various environmental scanning tools from strategic management. Among the questions to be answered at this stage are:

(a) What are the boundaries of the destination and characteristics of the nested relationships – both the outward and inward influences, including identification of main destination actors.

(b) What are the guiding rules of inward and outward influence by enclosing systems?

(c) What are the activities likely to trigger change in the destination?

(d) What activities of the destination actors are likely to manipulate the rules of the enclosing system(s)?

(e) What activities of destination components / actors are likely to work within given rules?

Step Two: Ranking likely Influencing Superordinate Enclosing Systems

Step two involves ranking the superordinate influences that have enclosing properties on the destination system and their likely influences. For example, the ranking could be as follows:

Rank 1: Main Enclosing System: Slowest Superordinate System (s) – High influence

Rank 2: Mid Enclosing System (s): Slow Superordinate systems – Mid influences

Rank 3: Enclosed System (s): Fast Superordinate systems – Low influences on Destination system but possible high activities on other systems

Step Three: Rank Main Actors in the Destination System

Step three involves identifying and ranking the main destination actors. These are the fast - enclosed destination system components with outward influence. In the case of destination like Zambia which has high regional influences, it would an idea to explore these likely influences on the destination and policy propositions. The ranking could be based on the spatial, sub destinations, or sub-sector in industry. For example:

Rank 1: Slowest Actors (Intra destination system enclosing – Low activities

Rank 2: Slow Actors (Intra destination system enclosing – Mid activities

Rank 3: Fast Actor (Intra destination system enclosed) – High activities

Step Four: Determine Choice Links

Step four involves two main activities, as follows:

(a) Deducing potentially effective “enslavement” interventions targeted at other superordinate fast systems by the main slow enclosing superordinate system(s) among the macro-level influences, it is likely that some of these systems may influence others, so at this stage it is important to recognise these macro-to-macro influences. For example, political influences may move faster than cultural influences. If need be, interventions should be devised that dampens or amplifies the undesirable and desirable influences, respectively.

(b) Deduce potentially effective “Enslavement” interventions target at the destination system by the main slow enclosing system, where possible through the fastest destination system actors. (for example, sub-destinations / sub-sector/ groups).

Step Five: Deduce Alternatives

During this step, the potentially effective “enslavement” interventions of destination system are considered. Critical is also to anticipate unintended distant or long-term consequences of proposed current actions and build in braking or balancing processes and feedback to detect unintended effects and reduce the likelihood of runaway effects of the destination.

Step Six: Develop DMO Business Model

At this stage it is possible to develop the DMO model, together with the overall goal for policy.

Step Seven: Share or Evaluate or Reframe Policy Goal

Once the DMO policy goal has been formulated, unknown-unknows or indeed what has been missed in the steps or unanticipated consequences may surface, this step provides an opportunity to review policy propositions by revisiting step four. Where necessary, gently reduce input of energy or push on process through step-four. Where the policy proposition is acceptable, then the policy frame for the DMO business model is shared with the key stakeholders.

These steps are presented in figure 6.

Discussion and Conclusion

The addition of this study to the discussion is useful at this juncture because policy, practice and even academia are all looking for ways of responding to the shift that have occurred and become more prominent in recent years to tourism destinations, accelerated by the various challenges already alluded to in the study that have been traced to the rapidly changing operating environment in as far as the role of the DMO and organization of tourism destinations is concerned. The contemporary discussion of DMO role and its survival going forward as viewed from both the complexity theory and social critical realism theory in this study adds a discussion upon which future studies could build on. The study made a number of interesting findings, such as efficacious mechanisms of the observed events, that be subjected to further studies using either one or both of the theoretical views.

Second, this study has also suggested a useful DMO business model framework. For practice and policy, this development of a DMO business model proposition is useful to decision making and serves as a starting point in the overall conceptualization, organization and modelling of destination systems under the contemporary and future environment. Hitherto, the frameworks used to develop DMO business models have remained open questions in literature, policy and practices.

The study developed a relatively reliable and adequate account for policy proposition of DMO role. The study found that with the complexity theoretical frame of reference, the shifting expected role of the DMO and the unchanging policy could be described and explained using a mix of two principal domains. First, with a combination of three elements, namely interactions, feedback and emergent order; and secondly, the interplay of the superordinate enclosing systems and the enclosed destination system. The model of causality under complex theoretical assumptions can be explained from three intertwined behaviour of the destination system, namely: (i) the superordinate enclosing systems enclosing the destination system and the regeneration activities of the destination system as a whole; (ii) multiple regeneration activities of the various destination components to multiple rule-settings by the various superordinate systems; and (iii) multiple enclosing and enclosed relationships within the destination system.

Going forward, the policy propositions ought to focus on influencing the active slower inward macro level influences by changing their guiding rules so as to change the faster generative activities of micro level destination actors. In this case, if the policy goal is to use the DMO as an industrial policy tool by the policy decision-makers, its main role becomes that of leading destination governance and destination leadership.

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Received: 01-Apr-2022, Manuscript No. JLERI-22-11494; Editor assigned: 04-Apr-2022; PreQC No. JLERI-22-11494 (PQ); Reviewed: 16-Apr-2022, QC No. JLERI-22-11494; Revised: 25-Apr-2022, Manuscript No. JLERI-22-11494 (R); Published: 17-May-2022.