Improving the Strategic Management of UNESCO Creative Cities of Gastronomy: Integrating Sensitivity Analysis and Tourism Destination Image Based on Analytic Hierarchy Process

Abstract

The globalization of tourism poses complex challenges for destination planning and management, requiring the involvement of various stakeholders and strategic decision-making at various scales. Gastronomic tourism, a key factor in tourist satisfaction and loyalty, has been widely studied for its impact on tourism destination image (TDI). Despite advances in methodologies such as the Analytical Hierarchy Process (AHP), which hierarchies the factors that influence TDI, there are still gaps in the use of sensitivity analysis to improve strategic planning, in particular to leverage TDI as a management tool. Specifically in UNESCO Creative Cities of Gastronomy (UCCG), it is crucial to understand how sensitivity analysis can improve the robustness of AHP models. To address this gap, this study investigates how sensitivity analysis can refine decision-making processes for effective tourism management in Brazil’s UCCG. Using AHP as a decision-making methodology, it integrates sensitivity analysis to assess the reliability of six dimensions in the Analytic Hierarchy Process Image—UNESCO Creative Cities Gastronomy (AHPI-UCCG) model, introducing a dynamic approach to dealing with the complexities of destination planning. The results are valuable for policy-makers and private players in the tourism, creativity, and gastronomy sectors. They offer practical perspectives for improving destination promotion and encouraging coopetition. The study also demonstrates the adaptability of this approach based on sensitivity analysis, suggesting its applicability beyond Brazil to other UCCG networks, contributing to better tourism planning and management at a global level.

1. Introduction

Global tourism demands strategic planning across diverse stakeholders and scales, which involves complex decision-making [1]. This necessity becomes even more apparent when considering the influence of economic, technological, social, and environmental factors on destination choices, underscoring the importance of strategic planning in these contexts [2]. A robust tourism destination image (TDI) significantly influences traveler satisfaction, loyalty, and destination success, which is a determining factor in shaping tourists’ expectations and experiences and the likelihood that they will recommend or revisit a tourism destination [3,4]. In this context, gastronomic experiences have a significant impact on tourist satisfaction, loyalty, and destination image [5]. Research confirms that gastronomic experiences enhance TDI, fostering loyalty and elevating a destination’s global appeal [6,7,8]. This highlights the broader role of gastronomy in tourism, where brand experience within UNESCO Creative Cities of Gastronomy (UCCG) further enhances its impact. Moreover, gastronomic satisfaction increases loyalty, with overall destination satisfaction remaining a critical factor [9].

About the Creative Cities, the United Nations Educational, Scientific, and Cultural Organization (UNESCO) provides the Chancel of Creativity Cities from seven thematic fields, called by UNESCO Creative Cities Network (UCCN), and they are Crafts and Folk Art, Design, Film, Gastronomy, Literature, Media Arts, and Music [10].

This study focuses on the field of gastronomy, and some authors explain the value of gastronomy’s image to the global travel market [8,11]. There are some common studies, such as how UCCG leverage their brands for economic and social benefits [12], while Gathen et al. [13] examined how the UCCN enhances city branding through knowledge sharing and international recognition. Dimitrova et al. [14] identified educational and environmental barriers as key obstacles to gastronomy tourism, with economic barriers having less impact. Ploll and Stern [15] highlighted several motivational factors driving food tourism. However, despite the clear impact of gastronomic experiences on destination image and loyalty [6], there is a research gap regarding the role of destination image as a strategic planning tool in tourism management. Recognizing the importance of destination image in strategic planning could strengthen tourism strategies and enhance a destination’s competitive advantage.

The Analytic Hierarchy Process (AHP) is a valuable tool for studying this gap, as it offers a systematic and structured approach to evaluate and prioritize the factors that influence destination image, helping tourism planners make informed decisions based on multiple criteria and complex interdependencies [16]. Furthermore, the AHP helps destinations make informed strategic decisions to improve their image and competitiveness [17]. Several AHP models have been developed in tourism studies [14,18,19,20], demonstrating their versatility and effectiveness in evaluating and prioritizing factors that influence a destination’s image. The AHP has also been successfully applied in the gastronomy industry [15], further reinforcing its potential as a valuable tool for strategic planning in tourism management. The combination between the AHP and other techniques/methods can add value in some cases of real-world problems using scientific knowledge [14,20], and it is essential to combine theoretical and practical perspectives to advance methodological approaches. Saaty [16] demonstrated the robustness of the AHP as a methodology by solving decision-making problems in various fields using pairwise comparison scales. However, the growing complexity of contemporary challenges [21,22] has led to the incorporation of uncertainties as a key category in analyzing decision-making [23]. In this context, sensitivity analysis can play a crucial role in understanding future scenarios in the context of variable perspectives [20,24]. For example, sensitivity analysis has already been applied to address the complex issue of tourism environmental impact assessment (EIA), and its role in the decision-making process is increasingly being recognized [25].

Based on the previous discussion of the strategic role of TDI and its impact on traveller satisfaction, loyalty, and competitiveness, TDI is a critical success factor in global competitiveness within the destination selection process [3,26]. As such, it is essential to understand TDI as a planning and management tool. Sensitivity analysis, when viewed from an AHP perspective, provides a valuable approach to dealing with the complexities of decision-making and improving the strategic use of TDI in tourism management [27]. In this context, the win–win perspective requires collaboration to compete together, as mentioned, when the new concept is coopetition. In practice, the old rivals can become new partners to compete [28]. In addition, in the XXI century, planners and managers should require scientific evidence to make decisions based on data. The impact of gastronomic tourism as a crucial component of the tourist experience has been increasingly studied, highlighting the positive contribution to tourist satisfaction levels and their intention to revisit destinations when they appreciate the local cuisine [29]. However, within the framework of the international initiative program, developed in 2004 and called UNESCO Creative Cities Network (UCCN), the members can request tools to deliver new solutions to plan and manage their tourism destination image from several scientific approaches including the role of infrastructure and services, image promotion, education as part of TDI, among others [12].

In this sense, Moreira et al. [30] searched for TDI categories in the UCCG on a scientific basis (such as Web of Science, Scopus, etc.) through a systematic literature review (SLR). Therefore, Moreira [31] used this SLR and validated six key dimensions with the help of a group of experts: (1) “Infrastructure and Services”; (2) “Image Destination” (as a destination image promotion dimension); (3) “Governance and Sustainability”; (4) “Gastronomic Experiences”; (5) “Education”; and (6) “Cultural Attractions”. These dimensions were used to create and test a new model, called AHPI-UCCG (Analytic Hierarchy Process Image—UNESCO Creative Cities Gastronomy), to measure TDI in the context of UCCG. Nevertheless, sensitivity analysis is crucial to determine the robustness of AHP solutions and to understand how changes in criteria weights affect the results [32,33,34]. Thus, in light of the above, and considering the validated key dimensions for measuring TDI in UNESCO Creative Cities of Gastronomy through the AHPI-UCCG model and the importance of sensitivity analysis in assessing the robustness of AHP solutions [16], the following research question arises: How can sensitivity analysis improve the robustness and effectiveness of the AHPI-UCCG model in assessing tourism destination image for strategic management and planning in UNESCO Creative Cities of Gastronomy in Brazil?

This research builds upon the AHPI-UCCG model proposed by Moreira [31] by introducing two key advancements: (1) a theoretical–conceptual contribution to the understanding of coopetition, an increasingly relevant approach in today’s complex world and (2) a methodological enhancement through the application of sensitivity analysis in an uncertain environment, which transforms the AHPI-UCCG model into a dynamic tool. These advances not only deepen and justify the model’s applicability but also serve to further align it with the ongoing academic discourse, promoting a meaningful link between theory, practice, and the market in general.

Based on these advances, the next step is to apply the knowledge acquired to a specific context. To address this, the main objective of this paper is to conduct a sensitivity analysis on the role of tourism destination image as a planning and management tool for UNESCO Creative Cities of Gastronomy in Brazil based on a multi-criteria decision model to improve coopetition. In addition, the secondary aim is to verify the TDI as a planning and management tool in the context of UNESCO Creative Cities Networking—Gastronomy.

This study uses the Analytic Hierarchy Process (AHP) as a decision-making tool and integrates sensitivity analysis to assess the reliability of results within the six dimensions of the AHPI-UCCG model [16]. As exploratory and descriptive research, it combines both qualitative and quantitative methods. By applying sensitivity analysis to the static AHPI-UCCG model, the study aims to enhance its dynamic capabilities, adjusting the weight of each dimension from 0.02% to 190% [20]. This approach provides insights into different contexts within the Brazilian UNESCO Creative Cities of Gastronomy (Belém, Belo Horizonte, Florianópolis, and Paraty) and offers potential for testing similar models in other international UNESCO networks.

The results of the sensitivity analysis reveal significant insights into how variations in dimension weights affect the evaluation of the TDI for UNESCO Creative Cities of Gastronomy (UCCG). The practical implications of this research include helping to prioritize resources to enhance coopetition and improve rankings and developing targeted strategies to optimize TDI positions within UCCG.

The paper is divided into several sections: Introduction, Literature Review, Materials and Methods, Results and Discussion, and Conclusions and Recommendations

2. Literature Review

2.1. Tourism Destination Image as Tool of Planning and Management

A tourism destination image (TDI) [35] is recognized as a relevant factor that influences decision-making [3,36] and is defined as the set of beliefs, ideas, and impressions that a person has about a destination. Taking into account the specificity of the destination, some authors extend the concept as a mental framework formed by a set of attributes that define the destination in its different dimensions [7,36,37]. TDI is a complex construct that includes various components, including attribute-based images, holistic impressions, and functional, psychological, unique, and common characteristics [7,36]. Hallmann et al. [38] summarized the concept as a collection of impressions and ideas that people have about the tourism destination, the concept resulting in a “global representation of the destination in the tourist’s mind” [39]. It is important to emphasize that there should be a differentiation in the treatment of the TDI and its ramifications [14], which are minor issues that can be understood to fall within this broader area. Agreeing with this affirmation, some researchers argue that the destination image is composed of three elements mainly: global image, destination brand, and, derived from the latter, brand personality [40]. However, the importance of the TDI lies in how a destination is perceived by tourists; it is also a critical success factor in global competitiveness in the destination selection process, regardless of whether these representations are true depictions of what that place has to offer [37].

Understanding TDI is crucial for planners and managers [26], as it can guide destination promotion campaigns in new markets, or for first-time visitors in existing markets, and can capitalize on the psychological aspects that make up TDI [36]. In this sense, TDI serves as a strategic tool for planning and managing destination attributes, as argued by academics in the field [41,42].

Building on this basic understanding, the work of Görür and Güzeller [11] developed a scale that measures both the cognitive and affective components of TDI, allowing the model to be tested in order to outline future destination strategies. The complexity of these model dimensions lies in regarding how destination imagery (DY) is processed in the individual’s memory upon receiving verbal stimuli related to the destination [7].

Furthermore, gastronomic satisfaction alone is not sufficient for full tourist loyalty; other elements inherent to the destination itself are necessary to achieve such an advantage [9]. Based on these challenges reported in the scientific literature, it is concluded that TDI as a planning and management tool depends on deepening the understanding of multiple dimensions, attributes, and indicators. This is especially true when it comes to analyzing them in the context of specific designations such as those from UNESCO regarding Creative Cities Networking—Gastronomy Field.

2.2. UNESCO Creative Cities Networking—Gastronomy Field

The UNESCO Creative Cities Networking (UCCN) program aims to bring together creative cities with different levels of income, capacity, and population from different regions [10]. Within the network, seven fields of creativity are presented, including gastronomy [10]. To become a member of the UCCN program in the field of gastronomy, the nominated gastronomic destination must go through a process and possess certain characteristics, of which gastronomic culture is one of the most important [43]. In addition, the protection and promotion of this culture are important, where participating communities are expected to pass on their knowledge to future generations [43,44].

The use of regionalism, whether through the preparation of local dishes or the provision of local food markets [45], is also one of the requirements for obtaining this endorsement [43]. Local cuisine serves as a key element of a destination’s tourism resources and plays a central role in its marketing and promotion [45]. Food also functions as a strategic brand asset that destinations can cultivate for development [46]. All of this aims to fulfill UNESCO’s objectives of making this network a paradigm for further valuing active intangible heritage and promoting regional development [47] as well as encouraging cooperation among participating cities [44].

In terms of sustainability, it is relevant to highlight that one of the macro-objectives of the UCCN program is to contribute to the implementation of the 2030 Agenda for Sustainable Development proposed by the United Nations (UN) [10] through creativity and cultural activities as development plans at the local level and cooperation at the global level [10,12]. It is essential to emphasize the critical need for destination planners and managers to enhance local gastronomic experiences for tourists, with the aim of increasing competitiveness and promoting loyalty [4,29]. Currently (November, 2024), there are 56 UCCG [48], four of which are located in Brazil [39]: Florianópolis (SC), Belém (PA), Belo Horizonte (MG), and Paraty (RJ), which are the first three capitals of their respective states.

There is a lack of studies on TDI in the context of the UCCN—Gastronomy Field, and those that do exist seem to be predominantly qualitative in approach [8]. This highlights the need to broaden the discussion using other methodologies, including quantitative approaches. For example, although Guo and Hsu [8] focus on UCCG, they did not address the research problem using the AHP technique, as is conducted in the present work. Instead, the authors prioritized collecting data from participants who visited the UCCG (515 tourists) both online and offline to provide a comprehensive understanding of how brand experience positively affects identification and attachment. Thus, the referenced study enriches the literature by providing empirical evidence and insights on the marketing and branding of these cities, which can be useful for developing an AHP decision tree, especially in the context of coopetition.

2.3. The Coopetition: From AHP to Sensitivity Analysis

The world has become more complex and uncertain [21]. Complexity is part of the geographical perspective, for instance, Milton Santos, the famous Brazilian geographer, added the role of globalization in order to step towards internationalization of capital [22]. The processes of fragmentation and compartmentalization include the argumentation from some perspectives [22], such as (a) time (past, present, and future); (b) through the change in velocity (speed, fluidity, and fragmentation itself); and (c) competitiveness versus solidarity. Thus, the win–win perspective requires collaboration to compete together in multiple scenarios. To innovate, it is relevant to understand cooperation as differential [28]. Coopetition is a neologism that juxtaposes two different ideas—collaboration and competition—in order to create a new concept more adapted to the challenge of the XXI century [28,49]. Therefore, the synergy between academic, market, and government with the intention of advancing on a methodology can contribute to solving problems in this noisy context [50]. This relationship is quite clear in scientific articles dealing with tourism planning and management problems, for example, “The multi-criteria method Analytical Hierarchy Process (AHP) is applied to examine the prioritization of the components of the marketing mix of services, and the classification is made in relation to the reason behind the choice of a particular rural destination” [51].

Amidst the complexity of contemporary challenges, methodologies such as the Analytic Hierarchy Process (AHP) offer valuable perspectives for navigating complex scenarios [52]. Although the competition from the AHP perspective is rare in tourism studies, the AHP as a methodological approach is more common in tourism studies. The common use of the AHP to solve problems in various tourism thematic areas can be found, for instance, in (1) community-based tourism (CBT) to establish a blue economy in the Tulungagung community, Indonesia [18]; (2) evaluating the potential of community-based ecotourism development in the Tua Chua Karst Plateau, Vietnam. Regarding the AHP, the research was conducted in in-depth interviews with 25 experts, and the evaluation system included eight criteria that classified tourism resources. These criteria were divided into two groups: the internal potential group and the external potential group [19]; (3) demonstrating the relevance of the AHP for studies on wine tourism, identifying the barriers (i.e., criteria) that hinder circularity in the Bulgarian wine industry, and proposing a preliminary circular economy (CE) index to rank these barriers by order of importance, providing an adequate and replicable model [14]; and (4) although the AHP had limited and not obvious use regarding tourism in research by Ploll and Stern [15], it is noteworthy that comparing the motives and calculating the forces related to the studied topic, namely veganism, highlight a way to understand the AHP in gastronomy, considering cause and effect. This approach can be useful for models that include the relationship between gastronomy and tourism.

However, the usefulness of the AHP goes further, in assessing the factors that influence the effectiveness of tourism promotion and destination choice, respectively [7]. Studies collectively underscore the value of the AHP in understanding and enhancing tourism destination image; when the target is to contribute to UCCN by gastronomic field, the AHPI-UCCG model can help us to understand the image as tools of planning and management [53,54]. From the systematic review of the literature (SRL) and document review literature [30], the six dimensions were validated and weighted by a group of experts [31], as highlighted in the introduction section. Nevertheless, the final priorities of the alternatives depend generally on the weights assigned to the main criteria. Thus, changes in the relative weights resulted in large changes in the final classification. For example, in the fuzzy AHP-Mairca model for the evaluation of overtourism, the sensitivity analysis provides the aspect of the change in the weighting coefficients of the criteria and, consequently, the change in the ranking order of the alternatives [55].

Sensitivity analysis allows us to understand different scenarios and reflect the evolution of future outcomes [23,56]. Sensitivity analysis was used to understand the sustainability dimensions of railway tourism in Brazil, for instance, when one dimension (social, cultural, economic, among others) was more important than others, the hierarchy of tourist trains changed and required more attention from managers [33]. Similarly, a sensitivity analysis of nature-based tourism used geographic information systems (GIS) and the analytic hierarchy process to conduct their research [20]. In the present study, sensitivity analysis can provide an overview of the original static model by Moreira [31].

3. Materials and Methods

To address the research question—How can sensitivity analysis improve the robustness and effectiveness of the AHPI-UCCG model in assessing tourism destination image (TDI) for strategic management and planning in UNESCO Creative Cities of Gastronomy (UCCG) in Brazil—this study adopts the Analytic Hierarchy Process (AHP) [16] as a decision-making methodology mainly by transforming subjective criteria into something concrete, as was conducted with TDI [25,51,57], and applies sensitivity analysis [58], using scenarios [24], to evaluate the reliability of the results in six dimensions of the AHPI-UCCG model.

Building on the contributions of Moreira [31] in order to fill a gap in the complexity of the decision-making process in a market environment where coopetition is increasingly necessary [49,59], this analysis presents two significant advances that deserve emphasis, fulfilling the bidirectional path between the feedback that scientific knowledge provides and its practical application to solve the following problems: (1) theoretical–conceptual, in terms of understanding coopetition as a value for stakeholders working with UCCG in Brazil; and (2) methodological, in terms of incorporating sensitivity analysis for decision-making in a complex and uncertain scenario, in which tourist destinations are increasingly immersed.

First of all,

Table 1. Area of study—UCCG Brazilian.

UCCG (Brazilian Region)	Gastronomy	Area	GDP * **	Population
Belém (North)	Fusion culture: colonizers, original people, and Africans	1059.466 km2	33,427,126	1,303,403
Belo Horizonte (Southeast)	Mineira gastronomy originates from simplicity; influenced by colonizers (Portuguese), original people, and Africans	9472.6 km2	105,829,675	2,315,560
Florianópolis (South)	A combination of influences by colonizers (Portuguese) and original people	674.844 km2	23,555,034	537,211
Paraty (Southeast)	Typically caiçara, it uses elements of original people, Portuguese, and African origin	924.296 km2	1,905,303	45,243

* Gross Domestic Product; ** values expressed in thousands. Source: [31,60,61,62,63].

shows the area of study. After, the AHP and sensitivity analysis demonstrate the techniques and overview all of the steps of the research.

3.1. The AHP and Sensitivity Analysis as Techniques

In the realm of decision methodology, the Analytic Hierarchy Process (AHP) is a structured technique designed to organize and analyze complex decisions involving multiple criteria or alternatives [16,20,27]. Originally developed by Thomas Saaty in the 1970s, the AHP provides a systematic approach for decision-makers to prioritize and make informed choices by decomposing complex problems into a hierarchical structure of criteria and alternatives [16]. To accomplish this, the AHP uses pairwise comparisons and mathematical calculations to derive weights and rankings, thereby increasing the rigor and precision of the decision-making process [16,20].

A series of studies explored the application of the AHP in tourism [18,19,20,51]. For instance, the AHP can be illustrated in the assessment of destination competitiveness [64]. On the other hand, practical application of the AHP in tourism planning assessment or country ranking, respectively, involves key steps in order to facilitate decision-making [65,66], as shown in Figure 1.

Sensitivity analysis plays a central role in assessing the robustness of decision outcomes to variations in judgments or criteria weights [20,56]. The process allows us to evaluate the stability of the decision model with respect to future scenarios [56]. Accordingly, the integration of the AHP with sensitivity analysis involves a series of structured steps [16,67]: (a) hierarchy formation; (b) pairwise comparison—using Saaty’s scale, ranging from 1 to 9—incorporating perspective as outlined in

Table 2. Numerical and verbal scales.

Numerical Scale	Verbal Scale
1.0	Equally important
3.0	Moderately more important
5.0	More important
7.0	Much more important
9.0	Extremely more important
−3.0, −5.0, −7.0, −9.0	Inversely proportional scale, simply replace the word ‘more’ with ‘less’.

Source: Adapted from [68].

; (c) calculation weighted priorities: using Super Decisions software (version 2.10), in line with methodologies adopted in other studies [25,51]; (d) consistency check: ensuring a consistency ratio of 0.1; and (e) aggregation: once the priorities are calculated for all elements (criteria and alternatives), the results are aggregated to determine the priorities or rankings of the alternatives in two ways: local and global.

Sensitivity analysis within the AHP is used to refine decision-making processes by providing insights into how potential changes or uncertainties might affect decision models [23]. By evaluating the impact of variations in judgments and criteria on final outcomes, this approach enables decision-makers to make more informed and confident decisions [23]. In this research, the application of sensitivity analysis followed the procedural steps described in

Table 3. Sensitivity analysis.

Steps	Brief Description
1. Identify Parameters for Analysis	Determine which parameters (pairwise comparison judgments, criteria weights) will be varied during sensitivity analysis;
2. Perform Scenario Analysis	Explore different scenarios by adjusting these parameters. For example, increase or decrease pairwise comparison values or modify criteria weights to observe their impact on the final decision outcomes;
3. Evaluate Results	Examine the results of the sensitivity analysis to comprehend the impact of parameter variations on the priorities or rankings of alternatives. Determine which parameters exert the most substantial influence on the decision outcome and evaluate the robustness of the decision model;
4. Decision Adjustment	Based on the findings from the sensitivity analysis, decision-makers can reassess their judgments, criteria weights, or decision criteria to enhance the robustness and reliability of the decision-making process.

.

As mentioned above, sensitivity analysis has been used to understand the sustainability dimensions for new scenarios of railway tourism in Brazil [33] and in an engineering context [56]. Both used Expert Choice software (https://www.expertchoice.com/), however, this software is expensive; therefore, like other studies [25,51], it was decided to use the free software SuperDecisions (version 2.10). It can also provide sensitivity analysis in the same mode. The next section shows us more details of all steps of research.

3.2. An Overview from All Steps of Research

This exploratory and descriptive research has been performed step by step, in which the AHP and sensitivity analysis, as techniques, are the main keys.

Table 4. Steps of research.

Steps	Brief Description
Narrative Review	We conducted a narrative review about three perspectives: (a) TDI as tool of planning and management; (b) UNESCO Creative Cities Networking—Gastronomy Field; and (c) from the AHP to sensitivity analysis and uncertainties in complex world.
Adaptation of AHPI-UCCG model	According to theoretical perspective on TDI as tool for planning and management [2], we made two adjustments to AHPI-UCCG model: (a) adapted the root of decision tree referred to as TDI UCCG and (b) redefined main goals of dimension called “Destination Image” to “Destination Promotion” to provide a clearer understanding for a layperson. These adjustments are detailed in Figure 2.

shows us the steps of research.

From Table 4, we detach these results and discussions in the next section; although, they should not be read and interpreted in a way that is detached from reality. In this sense, the sources used to collect data on the alternatives (Brazilian UCCG cities) must always be highlighted, even in a sensitivity analysis, therefore we based it on Moreira’s [31] similar sources. In this way, to perform a sensitivity analysis on the role of TDI as a planning and management tool for UCCG in Brazil, we created the next figure. Figure 2 illustrates the adjustment to the AHPI-UCCG model to perform the sensitivity analysis from a normative scenario (100%). The term normative was used to indicate the weighting by the judgment of specialists.

It is emphasized that each dimension, attribute, and indicator is the result of the literature and document review [21], validated by experts, and then weighted by experts and decision-makers using Saaty’s scale, as shown in Table 2. The “weight per cluster” (Figure 2) was the result of this weighting process.

As mentioned in Table 4, the third step is sensitivity analysis. Therefore, to perform the sensitivity analysis, the new rule previews the range from 0.02% to 190% by each dimension. It is important that the decision tree in your root, i.e., the target, shown in Figure 2, is 100% of total weight, even if the scenarios change to less (0.02%) or more (190%). This is all to understand the future behavior (of each alternative—UCCG Brazilian) in a complex and uncertain scenery. Therefore, based on scientific evidence, planners and managers can make more realistic decisions to solve problems of image tourism in UCCG contexts. In contrast, 0.02% scenery has a low valuation and 190% scenery has a high valuation by each dimension. Nevertheless, Figure 2 proves the range from 0.02% to 190%, and accordingly it is possible to understand the dynamic perspectives in two main sceneries.

4. Results and Discussions

To make decisions in the XXI century, it is necessary to observe the complex and uncertain scenarios [21] and to study globalization from the challenges in order to connect the fragmentation and compartmentalization of time (past, present, and future), different speeds, and competition versus solidarity ways [22]. The concept of coopetition, in which competitors become partners to competition, is relevant. The sensitivity analysis allows us to understand several future scenarios [56]. As a matter of fact, coopetition can improve the performance of alternatives if they can understand the concept.

4.1. Sensitivity Analyses of TDI UCCG

Considering this, the role of sensitivity analysis can provide future benchmarking involving multiple stakeholders from the government and market [51] to improve the tourism destination through the creative gastronomy field. However, the promotion of benchmarking must consider the context of the scientific evidence, for instance, in scenarios in which one criterion was given half of the total weight of the decision tree, while the remaining weight was equally distributed among the other criteria [33]. Hence, the benchmarking of railway tourism for coopetition needs to be discussed in this direction. Two of the most important aspects of sensitivity analysis are criteria weights and scores [20].

Furthermore, the application of sensitivity analysis to the AHPI-UCCG adaptation model facilitated the development of Figure 3, which is based on data from the Brazilian UCCG. This analytical approach not only enriches the discussion on coopetition but also contributes significantly to the achievement of the main objectives of this research.

One of the secondary aims is verified in Figure 3, when the TDI dimensions are highlighted as planning and management tools in the UNESCO Creative Cities Gastronomy (UCCG). In summary, Figure 3 shows us the normative scenery (i.e., 100%) through the square point from each of six dimensions, as shown in Figure 2. However, to read the sensitivity analysis in Figure 3, the two main future scenarios are (a) high valuation (i.e., 190%) is on the left side of each square and (b) low valuation (i.e., 0.02%) is on the right side of each square. From normative scenery, the two main future perspectives (high and low) are discussed to improve the Brazilian UCCG in the direction of possibilities of coopetition among them.

Based on Figure 3, several analyses were possible. First, we highlight the high scenario (190%) for each of the dimensions: (1) “Infrastructure and Services” (IS)—Florianópolis would rank near the top; (2) “Destination Promotion” (DP)—this would imply greater competitiveness for Paraty; (3) “Governance and Sustainability” (GS)—Belém and Paraty’s positions would improve; (4) “Gastronomic Experiences’‘ (GE)—Belo Horizonte would remain at the top of the ranking and would stand out even more compared to other alternatives; (5) “Education” (Ed)—this dimension would further expose Paraty’s weaknesses; and (6) “Cultural Attractions’‘ (CA)—Paraty’s strength in this dimension would be evident.

Meanwhile, in the low scenario (0.02%), the following can be emphasized: (1) “Infrastructure and Services” (IS)—little change among the alternatives with only a slight distance between Belém and Florianópolis; (2) “Destination Promotion” (DP)—increased competitiveness among the alternatives that are capital cities with Paraty regressing; (3) “Governance and Sustainability” (GS)—Florianópolis moves to second place in the overall ranking; (4) “Gastronomic Experiences” (GE)—the capital city alternatives move closer to Belo Horizonte in the overall ranking, while Paraty loses points; (5) “Education” (Ed)—Paraty moves closer to the other alternatives; and (6) “Cultural Attractions” (CA)—Paraty falls even further behind the other alternatives.

4.2. Sensitivity Analyses of TDI UCCG as a Tool of Planning and Management Destinations

It is important to note that variations in the weights assigned to each dimension of the adapted AHPI-UCCG model in the present work affect, in some way, the reading of the future scenario, whether it is of low or high valuation [23,56]. The interpretation of the sensitivity analysis defies simple cause-and-effect due to inherent complexity and uncertainty. For example, an increase in the value of dimension “X” (representing one of the six dimensions in the model) does not necessarily result in improved positioning of all alternatives—in this case, the Brazilian UCCG being tested. Instead, the interpretation must be contextual, an increase in the valuation of dimension “X” will only strengthen the positioning for those alternatives that possess “attributes” evaluated by “indicators” associated with that “dimension” (see Figure 3).

The reverse is also true, i.e., a lower valuation does not imply a worse positioning since it is necessary to assess the existence of attributes and their respective valuation by the indicators of this dimension. In this way, sensitivity analysis allows for the tangibility of complexities and uncertainties, bringing dynamism to Moreira’s originally static model [31]. Up next, sensitivity analysis of TDI UCCG as a tool of planning and management destinations presents the two main scenarios called high and low evaluations.

Contrary to the static model proposed by Moreira [31], the new perspective (Figure 3 and Figure 4) illustrates the dynamic tool of destination planning and management. This approach allows better decision-making in an uncertain world, as dynamic views of high or low valuation become more apparent. Thus, decision-makers from different stakeholders (such as government, market, and others within the interfaces of tourism, creative, and gastronomy sectors) now have new scientific evidence to improve possible benchmarking in the Brazilian UCCG. Although in all scenarios Belo Horizonte is the most suitable destination for providing a future benchmarking considering the overall, it is important to note that in the high scenario, the discrepancy is greater than in the low scenario. Therefore, these destinations have more to learn from each other in a low scenario than in a high scenario. It is believed that the research can also guide actions not only for public managers but also for the private sector due to the dimensions and attributes present in the AHPI-UCCG model. In practical terms, as a planning and management tool, these methods can help to (a) improve decisions on the allocation of resources to develop the coopetition perspectives and (b) design strategies in order to achieve the best positions of TDI in UCCG. Furthermore, UNESCO itself, through a critical view of its network, can use this research to support other destinations within the UCCN.

5. Conclusions

Fulfilling the problem of this research, which is to improve the robustness and effectiveness of the AHPI-UCCG model in assessing tourism destination image for strategic management and planning in UNESCO Creative Cities Gastronomy (UCCG) in Brazil, this study overcame the static limitation of the AHPI-UCCG model [31] and made it possible to achieve two significant advances. This is based on a conceptual theoretical deepening of coopetition and the methodological undertaking of sensitivity analysis, making the model dynamic and contextualized to the challenges of tourism destination image (TDI) in the XXI century.

TDI is a crucial component in determining global competitiveness, as it has a significant impact on how destinations are perceived and ranked in the international market. From a theoretical point of view, this research discusses two links, the first being the dynamic nature of TDI, which is related to its complexity and involves a set of holistic attributes and perceptions that are fundamental to understanding the global rankings of destinations. Sensitivity analysis is incorporated into the AHPI-UCCG model, according to low valuation, 0.02%, and high valuation, 190%. This study highlights how fluctuations in the importance of the different dimensions of the TDI can dynamically influence destination ratings. This finding underscores the need for adaptive and nuanced strategic planning approaches in tourism management.

The concept of coopetition, which focuses on fostering win–win relationships between traditional rivals or new partners in the global travel market, is relatively new. In this context, the second theoretical contribution highlights advances in coopetition and methodology. By integrating the Analytic Hierarchy Process (AHP) and sensitivity analysis within the UNESCO Creative Cities of Gastronomy framework, this research introduces innovative methodologies for managing both competitive and cooperative dynamics. It deepens the theoretical understanding of coopetition by illustrating how structured, quantitative approaches can enable strategic collaboration between competing destinations. The dynamic nature of the improved AHPI-UCCG model provides a robust tool for optimizing a tourism destination’s image in a complex and evolving global scenario. The study’s findings highlight that coopetition—a blend of competition and cooperation—is crucial for optimizing performance among rival destinations. Sensitivity analysis shows that high valuation scenarios reveal varying strengths and weaknesses among Brazilian UCCG, while low valuation scenarios present opportunities for mutual learning and cooperation.

This dynamic approach enables stakeholders to make more informed decisions about resource allocation and strategic planning, fostering a more nuanced understanding of coopetition. Future research should build on these insights by exploring additional contexts within the UNESCO network and beyond. This could involve developing benchmarking strategies that incorporate coopetition perspectives, enhancing both theoretical and practical applications of TDI management. The enhanced AHPI-UCCG model, with its dynamic capabilities, offers a valuable framework for navigating the complexities of tourism destination management in a rapidly evolving global market.

From a practical perspective, the study reveals practical implications: (1) In the strategic resource allocation, the integration of sensitivity analysis with the AHP model provides decision-makers with robust tools for strategically allocating resources.

By understanding how changes in specific dimensions affect tourist destination rankings, stakeholders can prioritize investments in areas such as infrastructure, destination promotion, and sustainability to maximize the impact on the UCCG. Other implications include (2) benchmarking for coopetition—this research enables the development of effective benchmarking strategies by identifying areas where UCCG cities can learn from each other, particularly in low valuation scenarios. By fostering cooperation between competing cities, destinations can improve their overall performance and promote mutual growth in the global tourism market; (3) dynamic destination management—the study shows that by applying sensitivity analysis, cities can adjust their management strategies in response to changes in key indicators. This dynamic approach supports better decision-making in uncertain environments, allowing cities to adapt to evolving challenges and optimize their tourism destination image; (4) replication to other UNESCO networks—the model developed in this study can be replicated beyond the Brazilian UCCG to other international UCCN. This offers global applicability, providing a scalable tool for tourism planning and management that can be adapted to different contexts; and (5) collaboration between sectors: the conclusions encourage greater collaboration between the public and private sectors, using the findings of the sensitivity analysis to develop specific strategies that improve the competitiveness of cities while promoting coopetition. This synergy can lead to better tourist experiences and more sustainable urban development [50,51].

Finally, a limitation of this study is its reliance on available data specific to the Brazilian UCCG, which may not fully capture the diversity of contexts or challenges faced by other cities around the world. In addition, the static nature of some model components may limit the real-time adaptability needed to deal with rapid changes in external factors, such as economic shifts, political instability, or technological advances. Future research should explore the integration of more dynamic, real-time data sources and test the model in a wider range of international destinations to improve generalizability and robustness.