Drivers of Efficient Destination Management in Times of Transition: Key Findings for Destination Development Management and Marketing Organisations (DDMMOs)

Abstract

This paper reflects on the results of a survey and aims to illuminate the operations of Destination Development, Management and Marketing Organisations (DDMMOs) by identifying different Key Performance Areas (KPAs), the indicators connected to them, and examining how they influence each other. Various linkages were explored between Enablers and Results performance areas, both within and across these categories. The use of multivariate statistical techniques such as Structural Equation Modelling (SEM), along with Analysis of Variance (ANOVA), chi-square tests, Pearson correlation, and other descriptive statistical methods yielded several insightful findings. The authors developed a research model which operated at an observation level and measured all the latent variables and tested all the hypothetical dependencies. The model investigates causal relationships among variables and understands how each contributes to overall performance. Researchers created a questionnaire using the EFQM framework, which consisted of seven constructs and 72 indicators rated on a Likert scale (1–5). Out of the 141 questionnaires distributed, 128 were considered valid and formed the sample for this research. All respondents were experienced employees/managers of DDMMOs in various roles. The results revealed that Leadership is one of the most valuable functions that DDMMOs can provide, and that when stakeholders trust the DDMMO, they become more efficient. The optimal size and ownership structure should be tailored to the specific needs of the destination, which can also influence how it manages its response. Furthermore, this paper revealed the link between sustainability and performance. The effectiveness of DDMMOs will largely determine the impact on the local economy and society. The research model developed together with the insights revealed is a testament of the practical relevance of this paper.

1. Introduction

The Destination Development Management and Marketing Organisation (DDMMO) “…is the leading organisational entity that may include various authorities, stakeholders, and professionals, and it facilitates tourism sector partnerships to achieve a collective destination vision” (United Nations World Tourism Organisation, n.d.). DDMMOs have a history of over 100 years, and due to cuts in government spending, the private sector has become more involved in such entities recently. They undertake various roles such as marketing and management, promotion, crisis management, stakeholder engagement, implementation of tourism policies, strategic planning, and product development at a destination level. They are regarded as critical players within the destination. Destinations increasingly need to utilise their resources more effectively, and this type of organisation appears to be a better solution. Public authorities’ participation in such collaborations is seen as a balancing act towards a more inclusive approach that benefits not only short-term profit but also the community’s well-being.

The ability of DDMMOs to manage destination procedures effectively is now critically scrutinised, especially with increasing challenges like climate change impacting all destinations. Urban destinations must address overtourism and balance housing rights amidst the pressure of the visitor economy on neighbourhoods (Koens et al., 2021). The digital transition and the growing influence of Artificial Intelligence (AI) on service delivery create new demands and shifts that are not easily adaptable. Alternative approaches are emphasised as solutions to the green and digital transition that destinations must face; Matteucci et al. (2021) explain that even the broader philosophical foundation of tourism is being challenged as we move towards a zero-waste, de-growth future.

Recent research highlights the need for an entity that serves as a ‘network orchestrator’ to effectively manage the economic impacts of tourism at destinations (Stienmetz & Fesenmaier, 2019). DDMMOs can take on this role. Many studies confirm that the success of any destination depends on the alignment between products and services (the processes) and organisational structure (Stienmetz & Fesenmaier, 2019). Other research also emphasises the importance of process, which describes the dynamic relationship between information and the structure of relationships within the system (Baggio, 2017). The importance of DDMMO for the overall success of the destination remains an issue requiring further investigation, as the current literature reveals gaps in understanding the influence of DDMMOs on the destination’s fate (Bornhorst et al., 2010; Negrușa & Coroș, 2016). Researchers emphasise their ability to efficiently manage market forces by ensuring stakeholder cohesion through strong, consistent leadership (Cochrane, 2010; Matteucci et al., 2021). The key drivers of success for DDMMO are arguably those that can address heterogeneity, complex social relations, networks, and change processes. Therefore, DDMMOs need to enhance their efficiency and effectiveness by focusing on resilience and rapid responses to change (Vlassi et al., 2024).

A few studies have explored the performance variables and models of DDMMOs (Ritchie & Crouch, 2003; Spyriadis, 2014). Bornhorst et al. (2010) identified the factors influencing tourism success for DDMMOs and destinations, while Ritchie and Crouch (2003) proposed a model of destination competitiveness, suggesting that the evaluation of tourism and destination success should include both input and output variables. Volgger and Pechlaner (2014) report that the literature on destination and DDMMOs performance can be categorised into three groups: (a) those emphasising financial indicators (e.g., profitability), (b) those focusing on non-financial operational indicators (e.g., product quality), and (c) those examining organisational effectiveness, including conflicting goals and diverging stakeholder perspectives. Very little attention has been given to a holistic approach that integrates these different streams of research (Volgger & Pechlaner, 2014). The targeted literature review conducted by the authors indicates that a holistic approach to destination management is lacking. This approach accounts not only for financial indicators influencing DDMMO performance but also for non-financial indicators such as product quality. There is increasing pressure on DDMMOs to report the financial impact of their operations, demonstrating their importance and remaining relevant and valuable to tourism stakeholders in the region. DDMMOs utilise Key Performance Indicators (KPIs) such as room tax revenue, lodging metrics, occupancy rates, number of trips, and visitor spending, among others.

These metrics are essential because DDMMOs can demonstrate the tangible economic impact of their activities, allow various stakeholders to hold them accountable, and utilise collected data for strategic planning and resource allocation. The non-financial indicators include metrics such as strategic planning and management, the destination’s overall competitiveness and performance, long-term sustainability, stakeholder engagement, community alignment, and attractive product offerings. These indicators are crucial because they provide a comprehensive view of the destination’s health, as they can track social, cultural, and environmental goals in addition to financial ones.

To achieve the overall aim of the research, several objectives have been established. More specifically, this research aims to:

• Identify and analyse the cause-and-effect relationships outlined by the European Foundation for Quality Management (EFQM) model, emphasising how Enablers influence Results and how interrelationships exist both within and between these categories.
• Highlight and evaluate the key factors and performance indicators that directly or indirectly affect the efficiency and overall performance of Destination Management/Marketing Organisations (DDMMOs).
• Design and propose a comprehensive research model that can be applied across various contexts to measure and compare the efficiency of different DDMMOs systematically.
• Develop and present a set of evidence-based recommendations and best practices that current and future DDMMOs can adopt to enhance their organisational efficiency and performance outcomes.

2. Literature Review

In the past, destination organisations mainly focused on promoting destinations through marketing and advertising efforts aimed at attracting as many visitors as possible. The organisation’s performance was measured by metrics such as the number of visitors and the revenue generated (see Figure 1). Although many destination organisations still operate this way, the modern business environment and various internal and external factors require a more systemic approach. There is a growing debate among academics and professionals on integrating financial and non-financial indicators (Crotts et al., 2022).

There is increasing pressure for a more comprehensive and inclusive strategy to enhance visitors’ experiences and boost the economic impact of tourism (Pike & Page, 2014). Some destinations mainly aim to increase visitor numbers, which benefits local communities economically and socially, while others struggle to manage tourism growth and visitor flows (OECD, 2025). Additionally, there is rising concern among academic circles and tourism authorities about how to achieve a better balance between tourism development and the well-being of all stakeholders (Godovykh et al., 2025). Ruggieri et al. (2022) highlight the growing need for brokering—an approach that signifies a paradigm shift. Over the past fifteen years, it has become evident at the destination and local levels that “tourism stakeholders have gradually shifted focus from traditional marketing and promotion functions to a more coordinated strategic approach to destination management” (Shkira & Qirici, 2013). This led the authors to propose the acronym DDMMO, which stands for Destination Development, Management, and Marketing Organisation.

The key consideration for destinations is how to utilise their resources for maximum benefit and the criteria that influence their performance. For individual businesses, the task is more straightforward because they understand their resources and largely control them. However, destinations are complex systems and highly unpredictable. They involve many stakeholders with diverse interests and goals. Pike (2008, p. 29) states that “the critical role of a DDMMO is better to connect the supply and demand aspects of tourism to maximise the use of destination resources.” Destination management should be viewed as an activity aimed at balancing the interests of operators with those of the community, managing formal and informal relationships, and aligning public and private objectives (Fyall & Garrod, 2020). In the long term, DDMMOs should focus on optimizing destination development to improve local quality of life, protect ecological and cultural heritage, and create a supportive framework.

DDMMOs, which have a century-long history, have recently seen increased involvement of the private sector in various forms. Several underlying reasons, including declining public funding and shrinking traditional revenue streams, drive the shift towards Public–Private Partnerships (PPPs). This type of partnership has proven effective in helping destinations recover after the COVID-19 pandemic (Wan et al., 2022). These factors have encouraged destinations to utilise their resources more efficiently, and this organisational approach seems to be a more viable solution. Public authorities’ participation in such collaborations is regarded as a balancing act towards a more inclusive approach that benefits not only short-term profit but also the community’s well-being.

DDMMOs are considered key players within the destination, and achieving success is essential. The United Nations World Tourism Organisation (UNWTO) was among the first to establish criteria and indicators for DDMMOs’ effectiveness in key performance areas. Strategic Leadership was associated with seven success criteria and 21 indicators; Effective Execution with eleven success criteria and 27 indicators; and Efficient Governance with five success criteria and 16 indicators. These are further discussed in Appendix A.1.

Another stream of research on DDMMO efficiency and performance has focused on empirical studies that offer insights into indicators and mechanisms that can enhance its positive impact. Interviews were employed to identify variables and develop a model supporting DDMMOs’ success in community relations, marketing, and economic indicators (Bornhorst et al., 2010). This research revealed that critical elements for successful organisations include supplier relations, effective management, strategic planning, organisational focus and drive, adequate funding, and high-quality personnel. More recent studies using a mixed-method approach have suggested that DDMMOs must adopt data-driven decision-making practices (Novotny et al., 2024). Additionally, some scholars propose introducing Digital Destination Branding (DDB) as a key practice, demonstrated through empirical implementation in top European city destinations. They emphasise the need to incorporate additional criteria and new frameworks to help DDMMOs fulfil their complex roles in the digital economy.

To address the objectives of this research, the authors integrate the concepts of systems theory, stakeholder theory, business ecosystem theory, and business excellence. This study does not aim to subdivide the various destination stakeholders into smaller groups for independent analysis, as other researchers have previously employed this approach. The novelty of this study lies in the attempt to examine DDMMOs as an interconnected system, considering the relationships between different stakeholders and the processes occurring within the operational environment. The study aims to identify inefficiencies in the performance of DDMMOs and improve decision-making in real-world destination management.

The EFQM excellence model is a suitable theoretical framework for research. EFQM enables a comprehensive analysis of processes and activities within an organisation at all levels, involving all employees, and helps identify areas requiring improvement (Rajnoha et al., 2019). When appropriately implemented, the EFQM business excellence model helps organisations achieve sustained superior results across various dimensions (Edgeman, 2018). It offers a credible reconstruction of complex business realities. EFQM can be utilised as a diagnostic tool to pinpoint a firm’s strengths and weaknesses (EFQM.org, 2020). Exploring causal links between the model’s elements can further reveal the causes of performance deficiencies (Paraschi et al., 2019). Additionally, weighing different criteria helps determine the Critical Success Factors (CSFs) for any industry. The combination of these qualities supports EFQM as an effective decision-making tool (Rusjan, 2005).

Furthermore, the EFQM model was selected as the ideal framework for this research because it can illuminate the software components of the business (Leadership, Management, People, Partnerships) and the process ‘black box’ that has so far been overlooked by destination performance literature. This feature addresses the core of what the authors aim to achieve: the procedures that convert inputs into outputs. It is a framework repeatedly applied in hospitality/tourism and destination management, proving its value (Zhang et al., 2021; Dania et al., 2019; Sozuer, 2011; Politis et al., 2009). In addition to the EFQM theoretical framework, which was used to develop the measurement tool for this research, other theoretical frameworks have also informed and influenced this study.

3. Materials and Methods

3.1. Questionnaire and Variables Design

The questionnaire, developed using the EFQM framework, comprised seven constructs and 72 indicators, each assessed on a Likert scale (1–5). Of the 141 questionnaires distributed, 128 were deemed valid and served as the sample for this study. All respondents were experienced employees or managers of DDMMOs with diverse roles. The questionnaire also included an introduction and a section gathering general information about the DDMMOs. Additionally, respondents were asked to rate the importance of each construct on a scale from 0 to 100.

Table 1. The DDMMO Business Excellence Model, Interrelations & Hypothesis.

DDMMO Business Excellence Model
Enablers					Results
E1.	Strategy				R1.	Stakeholder Perceptions
E2.	Leadership				R2.	Strategic & Operational Performance
E3.	Engaging Stakeholders
E4.	Sustainable Value
E5.	Performance & Transformation
Research Hypotheses
1. Interrelations between Enabler Criteria
H1. E1 → E2		H5. E2 → E3	H9. E3 → E5
H2. E1 → E3		H6. E2→ E4	H10. E4 → E5
H3. E1 → E4		H7. E2 → E5
H4. E1 → E5		H8. E3 → E4
2. Interrelations between Result Criteria
H11. R1 →R2
3. Relations between Enabler and Result Criteria
H12. E1 → R1		H15. E2 → R2	H18. E4 → R1	H21. E5 → R2
H13. E1 → R2		H16. E3 → R1	H19. E4 → R2
H14. E2 → R1		H17. E3 → R2	H20. E5 → R1

Source: own analysis.

below presents the relationships between the constructs and the hypotheses to be tested. Within the EFQM framework, enablers represent the organisation’s efforts to achieve outstanding results, while the results reflect the key performance outcomes of these efforts. Before finalising the questionnaire, it was pre-tested with two experts working within the DDMMO sector. This pre-test was conducted in May and June 2021, and two industry specialists reviewed it for errors. One expert is an active manager at “This is Athens” DDMMO, and the other is a former manager of a similar organisation in Thessaloniki’s municipality, now owning a consultancy specialising in Destination Marketing. Furthermore, to reflect the dynamic nature of these organisations, respondents were asked to consider the outcomes over the past three years. The questionnaire consisted of two parts: Part A, which collected general information and details about the DDMMOs, and Part B, comprising Likert scale questions divided into Enabler and Result sections. Figure 2 below presents an overview of the components of our research model. A detailed summary of all the items used to measure the different constructs is given in Appendix A.2.

3.2. Research Strategy

To meet the research objectives and address the research questions, the authors developed a research model comprising a measurement model and a structural model. The Structural Equation Model (SEM), which encompasses both the measurement and structural models, was employed (McDonald & Ho, 2002). The measurement model operates at an observation level and is responsible for measuring all the latent variables, while the structural part of SEM tests all the hypothetical dependencies. SEM allows us to investigate causal relationships among variables and understand how each contributes to overall performance. It also enables us to observe latent variables and their influence on overall performance. It is ideal for investigating complex research models such as the one we are examining (Richter et al., 2016).

The DDMMO assessment instrument includes seven criteria divided into Enablers: Strategy (E1), Leadership (E2), Stakeholders (E3), Creating Value (E4), and Performance & Transformation (E5), along with two Results: Stakeholders (R1) and Operational Performance (R2). These seven constructs are further subdivided into 72 sub-criteria/indicators, which have been adapted and enhanced to meet the specific needs of this research. The authors employed a reflective (top-down) approach, indicating that specific indicators drive each of the constructs.

The measurement model must be evaluated for reliability and validity (Bagozzi & Yi, 2012) to ensure it accurately reflects the constructs. Since there is no single goodness-of-fit criterion for PLS-SEM methodology, the assessment of the measurement model is conducted using several non-parametric criteria (indicator reliability, composite reliability, convergent validity, and discriminant validity), employing bootstrapping and blindfolding techniques (J. F. Hair et al., 2011). The measurement model was refined three times. The authors followed a systematic approach to evaluate the results of the structural model proposed by J. F. J. Hair et al. (2021).

3.3. Sampling

Sampling transparency involves stating the sample size in advance and illustrating how saturation has been reached (O’Reilly & Parker, 2013). Furthermore, J. F. Hair et al. (2011) suggest that a sample size of 100 to 200 can be considered adequate. Iacobucci (2010) proposed that a sample size of 50 is acceptable, and more recently, Bagozzi and Yi (2012) argued that sample size is not the most crucial factor in surveys. It is essential to focus on estimation methods, model complexity, and distributional properties. Therefore, the primary inquiries concerning sampling selection in this research are as follows: (a) What criteria should be employed for the selection of DDMMO (sample)? (b) What is the appropriate number of DDMMOs (sample size) to be approached for primary data collection?

To address the aforementioned challenges, this study adhered to Veal’s (2017) recommendation to employ a variety of methods when selecting samples in quantitative research. Specifically, three principal sampling decision challenges were identified: (1) determining the relevant region(s) at the regional level; (2) selecting the appropriate sub-regional DDMMOs at the sub-regional level; and (3) choosing the DDMMO managers at the organizational level. The authors adopted a quantitative, non-random, purposive, and snowball sampling approach. The latter offers advantages of efficiency, effectiveness, and maximum variation for in-depth analysis. This approach saves time and targets participants who can provide the most valuable insights into the research.

The DDMMO research on efficiency received 141 responses, of which 128 were fully completed and valid. Therefore, the final sample consists of N = 128 DDMMOs. The sample was collected using SurveyMonkey (4.1.1) software through numerous emails sent to managers working in DDMMOs. These professionals were asked to complete the questionnaire considering the last three years of operations. This was done to enhance the research, improving the validity and reliability of the data collected. The managers who completed the survey hold various positions, including marketing, sales, exhibitions, administration, front of house, and product development.

An additional breakthrough of this research is that the collected sample is the most diverse in the existing DDMMO performance literature, spanning 33 countries and including various regions within the same country. Moreover, the sample comprises respondents from different roles within DDMMOs. Additionally, the sample includes organisations of various sizes (medium, large, and small) and locations (urban, island, rural-urban). All participants were assured that their responses would be treated as confidential, remain anonymous, and be used solely for this research. All respondents participated willingly, and no coercion was exercised. A pretest of the questionnaire took place between May and June 2021, involving two experienced DDMMO managers who worked for the Athens and Thessaloniki Destination Organisations. The sample collection occurred between September 2021 and April 2022.

Table 2. Description of the Sample.

Number of Respondents	Ownership	Position in the Organization	Size of Organizations (Number of Employees)	Types of Organizations	Location
128	17 private	3 Innovation	28 micro (≤10 employees)	33 City	70 Urban
	93 public	54 Management	28 small (11–50 employees)	52 National	10 Rural
	18 (public–private)	33 Marketing & Advertising	62 medium (51–249 employees)	35 Regional	11 Rural-Urban
		38 Operations	10 large (≥250 employees)	8 State	37 Island

Source: own data.

below summarises the features of the research sample.

3.4. Data Analysis

The statistical approach combines elements of Factor Analysis and Path Analysis. PLS-SEM has been chosen as the preferred statistical methodology because it can handle data with smaller samples and non-normal distributions. This is a requirement that the CB-SEM (Covariance-Based) approach does not fulfil. In summary, the PLS-SEM methodology allows the researchers to estimate the complex model of the DDMMO without imposing distributional assumptions on the data. The latter would limit the scope of the research.

Smart PLS software version 4 was utilised to analyse the results. The choice of this software was based on the type of research conducted. Since non-normal categorical ordinal scales with complex models, numerous variables, and indicators were employed, this software was considered the most suitable. Coding was executed using a combination of numbers and letters (e.g., E2_mv). This coding was necessary to prevent additional space requirements in tables and graphics when displaying variable names.

To test the Structural Equation Models, the following steps were recognised and implemented:

• Definition of the individual constructs: First, the constructs to be used were defined, drawing on both structural and measurement theories.
• Preparation for Confirmatory Factor Analysis: The measurement model must be specified, and a path diagram should be developed.
• Conducting Confirmatory Factor Analysis: This assesses the validity and reliability of the DDMMO model to ensure that the measures meet the specified cut-off criteria.
• Structural Modelling Undertaking: Test and establish the relationships between the constructs, identify linkages, and evaluate the model for validity and fit.
• Findings Report: Report and interpret the results after executing the measurement model.

To ensure the validity and reliability of the SEM model, various indices were employed, including composite reliability, discriminant validity, indicator loadings, Cronbach’s alpha, and Average Variance Extracted (AVE), among others. For each measurement, there is a cut-off value that the model must meet. At this final stage, the researcher can eliminate some indicators or modify the model if they do not meet the specified criteria (Shaheen et al., 2017). The goal is to develop a model that is both reliable and effective.

3.5. Validity and Reliability

One of the main concerns for every researcher is to ensure the validity and reliability of their data and findings to uphold the high quality of their paper. If a paper “lacks these two measures, then the model produced might be biased, leading researchers to overlook relationships that could be significant” (J. F. J. Hair et al., 2021, p. 424). This is an issue that the authors addressed by employing well-known measures of reliability such as convergent and discriminant validity, Average Variance Extracted (AVE), Cronbach’s Alpha, Heterotrait-Monotrait Ratio (HTMT), and Variance Inflation Factor (VIF). During the stage of establishing and analysing the measurement model, other measures such as R², F², and Q² are used to validate and interpret the DDMMO measurement model using SEM technique. The values of these measures were within acceptable ranges even though there are no universally accepted cut-off values (Bagozzi & Yi, 2012).

The loading values of the manifest variables on the constructs reflect indicator reliability. Outer loadings should ideally be higher than 0.708 (or around 0.7) and above 0.4 (J. F. Hair et al., 2011). While the traditional criterion for internal consistency is Cronbach’s alpha, PLS-SEM employs a different measure deemed more suitable. Composite reliability (pc) accounts for the different outer loadings of indicator variables. It should be above the 0.7 threshold; however, values over 0.95 are considered undesirable because they suggest that all Key Performance Indicators (KPIs) measure the same phenomenon. To assess convergent validity, the Average Variance Extracted (AVE) for each latent variable is used. AVE values of 0.50 or higher show that a factor explains more than half of the variance in its indicators, while AVE values below 0.50 indicate that more measurement error remains in the items than the variance explained by the construct.

The Fornell–Larcker criterion and cross-loadings are primary methods for assessing discriminant validity in variance-based SEM, like partial least squares. However, Henseler, Ringle, and Sarstedt argue these do not reliably detect a lack of discriminant validity in common research scenarios and suggest the Heterotrait-Monotrait (HTMT) ratio of correlations as an alternative (Henseler et al., 2015). If HTMT is below 0.90, the two reflective constructs exhibit discriminant validity. The HTMT values for the measurement model are shown in

Table 3. Discriminant Validity based on the Heterotrait-Monotrait Ratio (HTMT).

	E1	E2	E3	E4	E5	R1	R2
E1
E2	0.712
E3	0.594	0.789
E4	0.641	0.799	0.745
E5	0.634	0.81	0.878	0.874
R1	0.601	0.658	0.658	0.641	0.624
R2	0.508	0.726	0.797	0.625	0.755	0.855

Source: Own Analysis with SmartPLS4.

; all values fall below the 0.9 threshold.

In

Table 4. Evaluation indices for the measurement model.

	Cronbach’s Alpha	Composite Reliability (rho_a)	Composite Reliability (rho_c)	Average Variance Extracted (AVE)
E1	0.802	0.813	0.863	0.559
E2	0.865	0.87	0.894	0.515
E3	0.762	0.765	0.841	0.515
E4	0.775	0.783	0.847	0.527
E5	0.836	0.839	0.877	0.507
R1	0.883	0.889	0.909	0.591
R2	0.865	0.871	0.897	0.555

Source: Own Analysis with SmartPLS4.

below, the third and final measurement model is presented, which includes Composite reliability values exceeding the 0.7 threshold and AVE values above the 0.5 threshold.

Regarding the Fornell–Larcker criterion, in

Table 5. Fornell–Larcker criterion for the third measurement model.

	E1	E2	E3	E4	E5	R1	R2
E1	0.748
E2	0.601	0.718
E3	0.466	0.641	0.717
E4	0.514	0.66	0.582	0.726
E5	0.521	0.692	0.700	0.707	0.712
R1	−0.522	−0.584	−0.552	−0.538	−0.547	0.769
R2	−0.438	−0.641	−0.653	−0.531	−0.646	0.757	0.745

Source: Own Analysis with SmartPLS4.

, the top number (which is the square root of AVE) in each factor column is shown to be higher than the numbers (correlations) below it; thus, there is discriminant validity.

Finally, the analysis of cross-loadings has demonstrated that each indicator shows the strongest relationship with its own factor, and notably, the intended loadings exceed 0.7 in all cases. Consequently, the assessment of the final measurement model has yielded satisfactory results, and it will therefore be regarded as the final measurement model (see Figure 3).

4. Results

The next step in applying the SEM model is to present the structural model, which shows the relationships among the constructs (Dash & Paul, 2021). Several fit indices are used in SEM to assess the quality of the developed model (Nunkoo & Ramkissoon, 2012). One such index is Chi-square, but it is sensitive to sample size and data normality (Byrne, 2001). Other fit indices employed in our research include the Variance Inflation Factor (VIF) and R² and f² values.

Evaluation of the structural model involves estimating the overall model’s predictive capabilities and examining the causal relationships among the constructs defined by the research hypotheses. The primary criteria for assessing the structural model in PLS-SEM are the R² values and their associated f-square effect sizes for the exogenous factors, the predictive relevance (Q²), and the significance of the path coefficients. Before applying the criteria above, it is essential to assess collinearity among the constructs. A standard metric for evaluating collinearity is the Variance Inflation Factor (VIF), defined as the reciprocal of the tolerance (J. F. Hair et al., 2011). The collinearity statistics presented in

Table 6. VIF values of the structural model latent structures.

	E1	E2	E3	E4	E5	R1	R2
E1		1.000	1.566	1.593	1.639	1.650	1.726
E2			1.566	2.119	2.449	2.588	2.652
E3				1.729	1.851	2.180	2.257
E4					1.973	2.300	2.336
E5						2.902	2.909
R1							1.797
R2

Source: Own Analysis with Smart PLS4.

indicate that all VIF values are below the threshold of 5.00, as recommended in prior literature, thereby confirming the lack of multicollinearity among the latent constructs of the structural model.

The analysis of the structural model begins with the estimation of R² and f² values. The coefficient of determination (R² value) measures the model’s predictive accuracy, reflecting the combined effects of exogenous variables on a specific endogenous variable. R² values above 0.25 are acceptable, but values above 0.5 are preferred. The f² values, which represent the f-squared effect size measure, are another term for the R-squared change effect. According to Cohen et al. (2009), 0.02 indicates a “small” f² effect size, 0.15 a “medium” effect, and 0.35 a ‘large’ effect size. In

Table 7. Estimation values of the model—f².

	E1	E2	E3	E4	E5	R1	R2
E1		0.566	0.018	0.029	0.006	0.046	0.02
E2			0.353	0.156	0.057	0.025	0.038
E3				0.07	0.178	0.036	0.055
E4					0.166	0.016	0.008
E5						0.002	0.046
R1							0.497
R2

Source: Own Analysis with Smart PLS4.

, the f² values are shown.

Q², on the other hand, measures the model’s predictive relevance, indicating how well the path model can predict the observed initial values. Q² values are calculated using the blindfolding procedure and should be above zero to denote acceptable predictive relevance. Q² can range from 0 to 1.

In

Table 8. Estimation values of the model—R².

	R-Square	R-Square Adjusted
E2	0.361	0.356
E3	0.422	0.412
E4	0.493	0.481
E5	0.655	0.644
R1	0.444	0.421
R2	0.688	0.673

Source: Own Analysis with Smart PLS4.

below, all R² values are presented. Notably, the R² values are 0.361, 0.422, 0.493, 0.655, 0.444, 0.688 for E2, E3, E4, E5, R1, R2, respectively, which are reasonably satisfactory based on the recommended thresholds. Table 7 and Table 8 demonstrate how significant the effect of each structure is on the remaining structures; for example, E2 has a substantial effect on E3 (0.353), whereas E1 has a much smaller impact on E3 (0.018).

Subsequently, the path coefficient and its significance were estimated using the bootstrapping technique, as shown in

Table 9. Structural model bootstrapping results—Direct effects.

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	2.50%	97.50%	T Statistics (|O/STDEV|)	p Values
E1 → E2	0.601	0.609	0.048	0.512	0.701	12.454	<0.001
E1 → E3	0.126	0.132	0.083	−0.032	0.293	1.528	0.127
E1 → E4	0.153	0.156	0.077	0.006	0.306	1.979	0.048
E1 → E5	0.060	0.056	0.065	−0.075	0.181	0.919	0.358
E1 → R1	−0.206	−0.206	0.109	−0.418	0.001	1.884	0.060
E1 → R2	0.104	0.103	0.078	−0.049	0.259	1.329	0.184
E2 → E3	0.566	0.567	0.073	0.418	0.704	7.723	<0.001
E2 → E4	0.409	0.406	0.102	0.207	0.607	4.015	<0.001
E2 → E5	0.219	0.213	0.099	0.012	0.397	2.211	0.027
E2 → R1	−0.189	−0.182	0.123	−0.431	0.056	1.534	0.125
E2 → R2	−0.178	−0.176	0.086	−0.354	−0.013	2.065	0.039
E3 → E4	0.248	0.251	0.087	0.075	0.415	2.864	0.004
E3 → E5	0.337	0.344	0.080	0.192	0.502	4.199	<0.001
E3 → R1	−0.208	−0.223	0.111	−0.445	−0.010	1.872	0.061
E3 → R2	−0.197	−0.192	0.078	−0.343	−0.038	2.528	0.011
E4 → E5	0.336	0.338	0.068	0.203	0.468	4.908	<0.001
E4 → R1	−0.142	−0.146	0.121	−0.376	0.092	1.178	0.239
E4 → R2	0.076	0.085	0.094	−0.094	0.275	0.808	0.419
E5 → R1	−0.063	−0.050	0.130	−0.296	0.205	0.486	0.627
E5 → R2	−0.204	−0.216	0.093	−0.406	−0.038	2.200	0.028
R1 → R2	0.527	0.532	0.078	0.373	0.678	6.742	<0.001

Source: own analysis.

and

Table 10. Structural model bootstrapping results—Indirect effect.

	Original Sample (O)	Sample Mean (M)	Standard Deviation (STDEV)	2.5%	97.5%	T Statistics (|O/STDEV|)	p Values
E1 → E3	0.340	0.345	0.053	0.246	0.456	6.364	<0.001
E1 → E4	0.362	0.367	0.055	0.266	0.478	6.526	<0.001
E1 → E5	0.461	0.471	0.062	0.351	0.597	7.453	<0.001
E1 → R1	−0.317	−0.319	0.069	−0.456	0.188	4.584	<0.001
E1 → R2	−0.542	−0.547	0.084	−0.716	−0.385	6.459	<0.001
E2 → E4	0.141	0.142	0.052	0.043	0.247	2.707	0.007
E2 → E5	0.375	0.380	0.067	0.260	0.525	5.623	<0.001
E2 → R1	−0.233	−0.239	0.085	−0.409	−0.080	2.762	0.006
E2 → R2	−0.414	−0.414	0.074	−0.570	−0.277	5.559	<0.001
E3 → E5	0.083	0.086	0.037	0.021	0.164	2.268	0.023
E3 → R1	−0.062	−0.058	0.054	−0.173	0.039	1.153	0.249
E3 → R2	−0.209	−0.220	0.063	−0.351	−0.102	3.331	0.001
E4 → R1	−0.021	−0.014	0.045	−0.098	0.081	0.477	0.633
E4 → R2	−0.155	−0.158	0.070	−0.303	−0.028	2.217	0.027
E5 → R2	−0.033	−0.025	0.070	−0.157	0.120	0.478	0.633

Source: Own Analysis with SmartPLS4.

, at the 5% level of significance.

Next, based on bootstrapping, all the statistically significant direct path coefficients were presented in a separate table, revealing interesting insights about the internal structure of the PLS-SEM Model. The corresponding paths, along with the coefficients in descending order, are shown in

Table 11. Structural Model—Direct Effects in Descending Order.

	Path Coefficients
E1 → E2	0.601
E2 → E3	0.566
R1 → R2	0.527
E2 → E4	0.409
E3 → E5	0.337
E4 → E5	0.336
E3 → E4	0.248
E2 → E5	0.219
E1 → E4	0.153
E2 → R2	0.178
E3 → R2	−0.197
E5 → R2	−0.204

Source: own analysis with SmartPLS4.

below.

Based on Table 11, it is essential to discuss the path coefficients among the Enabler criteria of the EFQM model that were used to build the DDMMO model. Examining the above relationships, it is evident that all of them have a positive coefficient, with the most substantial effect being 0.601 (Leadership → Vision & Strategy), and the weakest being 0.06 (Leadership → Performance & Transformation). The Leadership-Vision relationship reveals a moderate correlation between organisational culture and leadership, and the Purpose, Vision, and Strategy of the organisation. This suggests that the first criterion has a significant impact on the second. Furthermore, the p-value is very close to 0 (0.001), which signals a statistically significant relationship. The other effects of organisational culture on stakeholders, sustainable value, performance, and transformation (Leadership → Stakeholders, Leadership → Sustainable Value & Leadership → Performance & Transformation) show little, if any, correlation, with values less than 0.3. This suggests that organisational culture and leadership have a minimal impact on the other elements of the Enabling factors. Furthermore, examining the p-values reveals that all (0.127, 0.048, 0.358) are above the threshold of 0.05, except for 0.048, which also suggests that these results are not statistically significant.

Moreover, Table 11 shows values as low as 0.126, 0.153, and 0.06. Other notable relationships are found between Purpose, Vision & Strategy and Engaging Stakeholders (E2 → E3), as well as between Vision and Sustainable Value (E2 → E4). The link between Sustainable Value and Performance and Transformation (E4 → E5) is less significant. Additionally, there is a low correlation between Engaging Stakeholders and Performance and Transformation (E3 → E5), with a value of 0.337, which is under 0.3, suggesting little to no relationship between Engaging Stakeholders and Sustainable Value (E3 → E4). The p-values for all these relationships are below 0.05 (0.001, 0.001, 0.001, 0.004). Lastly, the research identified another significant correlation between Engaging Stakeholders and Driving Performance and Transformation (E3 → E5). The p-value is very low (0.001), reinforcing the validity of this correlation, as the p-value indicates a high level of confidence that the result is not due to chance.

With the above in mind, the results from the previous tables are now summarised visually in Figure 4.

5. Discussion

The above findings show that culture influences the vision and strategy of companies and organisations, and this is well documented. High-performing DDMMOs must first build a strong cultural identity supported by inspiring leadership. This will foster a positive response to the organisation’s vision and strategy. Culture’s importance lies in the fact that it does not need enforcement or supervision to be effective. It is widespread, lasting, shared, and implicit. These qualities are vital in organisations like DDMMOs, where individuals with diverse skills and backgrounds work together to create intangible products and services that depend heavily on stakeholder acceptance of the destination. Previous research supports this finding and emphasises the importance of organisational culture by offering learning and training to enhance marketing communications (Gato et al., 2022).

Leadership plays a vital role in a DDMMO, and current research underscores its impact on Strategy and Vision. The importance of leadership is further supported by earlier studies indicating that DDMMOs must deliver effective leadership to guide stakeholders, which is a key success factor for any destination marketing organisation (Baker & Cameron, 2008). Empirical evidence has also shown that destinations need strong leadership to successfully implement smart destination strategies (Sorokina et al., 2022).

This significant correlation between Engaging Stakeholders and Driving performance and transformation highlights the vital role stakeholders play in improving DDMMOs’ performance. Stakeholders can share their expertise, local knowledge, and understanding of their customer base to advise DDMMOs on better destination resource management and performance enhancement. This supports previous research conducted in various sectors (Twaissi & Aldehayyat, 2021). Moreover, active engagement with destination stakeholders is advocated by other researchers who anticipate the future of DDMMOs in an era dominated by data analysis, machine learning, and AI (Huang et al., 2022). DDMMOs can also learn valuable lessons from the private sector, and by engaging with them, they can enhance their efficiency and effectiveness.

The theoretical contribution of this study is important because it introduces a framework and research model that include constructs and indicators suitable for various destinations to evaluate the efficiency and effectiveness of the DDMMOs. It is the first of its kind to be applied within a tourism destination setting. The research also advances organisational culture theory by demonstrating its function as a self-regulating mechanism that affects strategic alignment and performance within DDMMOs. This highlights the need to incorporate culture as a fundamental element in any destination management model to foster coherence and trust among stakeholders.

Another theoretical contribution lies in leadership and the theoretical support for the idea that leadership acts as an intermediary between culture with vision and strategic outcomes. This enhances transformational leadership theory and emphasises the importance of leaders in inspiring and fostering shared purpose. This study broadens our understanding of how leadership functions within multi-stakeholder, service-oriented contexts. This paper has strengthened the stakeholder theory, as the strong correlation between stakeholder engagement and performance indicates that stakeholders are active participants in strategy development, supporting contemporary views of co-creation and open innovation theories.

6. Conclusions

Theoretical and Practical Implications

Most representatives of DDMMOs appear to be aligned regarding their organisations’ priorities. The significant correlations found were among the Organizational Culture & Leadership (E1), Purpose, Vision & Strategy (E2), Engaging Stakeholders (E3), Creating Sustainable Value (E4), Driving Performance & Transformation (E5), and Strategic and Operational Performance (R2). This suggests that enhancing the organisation’s culture and Leadership (E1) can also influence strategy (E2) and operational performance (R2) of the DDMMO. Several initiatives with potential practical benefits on this issue are proposed.

DDMMOs need to become leaders within their local communities and work collaboratively to create a framework that promotes their prosperity. They must establish a standard of success against which organisations will be evaluated. Research also shows a correlation between Leadership (E1) and Stakeholder engagement (E3). Stakeholders will engage if they can trust the DDMMO and believe they can benefit from its leadership. DDMMOs also need to develop their organisational culture to reflect the local reality and tell a story about the destination. This culture then guides the vision and strategy (E2) that the organisation will follow. When planned for the long term, leadership and organisational culture can positively impact strategy and operational outcomes. Building this culture is very challenging for an organisation. Additionally, DDMMOs should expand their role from DMOs to DDMMOs, providing leadership and resources to develop new products and manage the destination. This requires DDMMOs to adopt a more innovative and creative approach at a cultural level.

The culture and leadership of the organisation need to shift from a centrally controlled approach to a more open, transparent, and accountable one that is visible and inspiring. DDMMOs need to transform into network-driven platform organisations that will reach and complement the stakeholders operating within the designated geographical area. This aligns with the findings of scholars such as Baggio (2017), who emphasise the importance of agency and stakeholder management.

The Stakeholder perceptions–Strategic & Operational Performance linkage shows a moderate correlation, and the p-value is very low (0.001), confirming the significance of the relationship. The Stakeholder perceptions criterion is linked to results based on feedback from Key Stakeholders about their personal experiences of engaging with the DDMMO (Gonçalves et al., 2022). Stakeholder perceptions are also correlated with Strategic and Operational Performance (R1 & R2). Managing the perceptions of various stakeholders is essential, as their beliefs about the DDMMO can shape their reality and influence their actions.

Findings also indicate a significant correlation between Strategy (E2), Engaging Stakeholders (E3), and the Creation of Sustainable Value (E4). This suggests that they progress in the same direction. The organisation needs a clear strategy grounded in a well-defined vision of what the destination represents to consumers. Both Engaging Stakeholders (E3) and Creating Sustainable Value (E4) are linked to improving performance and driving transformation (E5). This implies that enhancing stakeholder engagement will also boost the other two enablers. An outstanding DDMMO in this area demonstrates its ability to build and maintain sustainable relationships with customers, staff, society, partners, and suppliers. Another key correlation identified by our research is between Creating Sustainable Value (E4) and Strategic and Operational Performance (R2). If a DDMMO can enhance and deliver the value that customers seek, it can also improve organisational and destination performance.

Moreover, the ANOVA test has shown that the size of the DDMMO significantly affects Organisational Culture and Leadership, as well as driving organisational performance and transformation. Furthermore, the ownership of the DDMMO—whether public, private, or Public–Private (PP)—also plays a role in Organisational Culture and Leadership. Ownership additionally influences the performance and transformation of the organisation. All other factors do not impact the Enablers or the Results.

In conclusion, it is crucial to emphasise the significance and capability of DDMMOs to manage moments of crisis for destinations. Whether a crisis arises from an unexpected external event (e.g., war), natural disasters (floods), shifting consumer preferences, economic downturns, or any other occurrence, their role is indispensable. Leadership is one of the most valuable functions DDMMOs can provide, and this research supports the importance it has on the vision and perceptions of stakeholders. A strong and efficient DDMMO can respond effectively to a crisis, offer guidance to stakeholders, and present a vision that everyone can relate to. When stakeholders trust the DDMMO, the response to any external or internal unexpected event will be more effective. The optimal size and ownership structure should be tailored to the specific needs of the destination, which can also influence how it manages its response. Furthermore, this paper emphasises the link between sustainability and performance. Destinations that integrate sustainable practices into their governance are more likely to recover from crises swiftly and with minimal negative impacts. Resilience will be vital for the future of tourism destinations. We are navigating a period where crises, whether small or large, are becoming the new normal for tourism destinations. The effectiveness of DDMMOs in responding will largely determine the impact on the local economy and society.

Due to the nature of DDMMOs, we were unable to use more objective data, such as the number of arrivals and expenditures per tourist per day, despite our efforts. It proved impossible to obtain data that was directly comparable and easy to use. Our approach was to combine this data with results from the data gathered through the DDMMO business model and triangulate our research. It is very difficult, for example, to distinguish arrivals directed towards a specific area of a country overseen by a DDMMO from those visiting a nearby area as well. Perhaps a new set of measures could address these issues. Additionally, if we had access to financial data, such as balance sheets, it would aid in providing a more objective evaluation of these organisations’ performance. Another limitation of the research is the subjectivity inherent in the self-assessment DDMMO survey, which was completed by DDMMO executives and employees. In many cases, however, multiple questionnaires from the same organisation were completed.

Future research could explore new tools and techniques such as the Fuzzy Set Qualitative Comparative Analysis (FsQCA), a method that offers certain advantages for research. FsQCA can be combined with variance-based methods (e.g., SEM) like in our study, enabling existing research to be expanded and complemented through its application. Lastly, for future reference, it would be beneficial to direct questionnaires to other stakeholders (such as tourists, regulatory bodies, suppliers, close partners, and the local community) operating within the same area as the specific DDMMOs to achieve a more holistic understanding of their performance.