Change Management in Aviation Organizations: A Multi-Method Theoretical Framework for External Environmental Uncertainty

Abstract

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making.

1. Introduction

Aviation organizations today face a multitude of complex challenges that necessitate agile, forward-looking, and well-informed change management practices supporting sustainability through resilience and adaptability. Sustainability refers to maintaining operations without degrading quality and resilience represents an organization’s ability to anticipate, adapt to, and recover from disruptions, many of which can be mitigated through sustainable principles, thereby reinforcing long-term adaptability in change management decision-making and transforming challenges into opportunities that foster inclusive growth [1]. These challenges emerge from various sources, including regulatory pressures [2], technological advancements [3], environmental concerns [4,5], and the impacts of global events [6,7]. Rapid technological advancement and innovative digital tools management [8,9,10,11], geopolitical tensions, environmental pressures [12], regulatory shifts [13,14], and social and economic disruptions [15,16] have intensified the need for aviation service providers—particularly airports—to develop structured approaches for managing change under uncertainty. A changing environment forces organizations to effectively adapt to external challenges, seeking to improve existing management systems and implement innovative business projects, aligning with the organization’s goals [17]. Traditional change management models often fall short in addressing the multifaceted and unpredictable nature of the external environment. This limitation is primarily due to their structured and static nature, which does not align well with the dynamic and complex external conditions organizations face today [18,19,20].

The core problem with existing change management models is their limited capacity to explicitly integrate uncertainty into the decision-making process. Traditional models often overlook how uncertainty affects implementation, leading to resistance among employees, especially when communication is poor and participation in decisions is lacking [21,22]. The aviation sector is increasingly adopting proactive and predictive frameworks to manage change, enhance safety, and improve operational efficiency—these frameworks leverage advanced technologies, collaborative approaches, and continuous monitoring seeking to adapt to disruptions effectively [23,24,25,26]. Existing aviation modelling practices, employ probabilistic and statistical methods for operational and safety purposes [27,28], but often rely on linear assumptions that are insufficient for capturing system behaviour in volatile environments. These models tend to overlook complex interdependencies, particularly those arising from external disruptions. While the importance of quantifying interdependencies to support risk-informed decision-making has been recognized [29], a significant gap remains in capturing the dependence between external environmental impact and change management decision making in uncertain conditions. In the aviation sector in particular, where even minor disruptions can have significant consequences, there is a critical need for frameworks that not only react to change but also adapt to it proactively.

To fill this scientific and practical gap, the research aims to develop a theoretical change management model oriented towards aviation service organizations operating in uncertain environments.

The primary task of this research is to construct and validate a multi-method theoretical model that supports decision-makers in managing change under varying uncertainty conditions. The proposed model integrates three main analytical approaches: Bayesian decision theory to capture probabilistic relationships and identify influential external factors under uncertainty (normative analysis); scenario planning theory to explore alternative futures and develop adaptive pathways (prescriptive analysis) and fuzzy comprehensive evaluation to assess the quality of change management decisions under vagueness and complexity (descriptive analysis). The proposed model is structured around a four-stage framework: observation, analysis, evaluation, and response, which enables the continuous monitoring of the external environment, the development of future-oriented scenarios, and data-driven change management decision-making. The model supports both operational flexibility and adaptability, equipping aviation organizations with a proactive and sustainable approach to navigating change.

2. Theoretical Background

2.1. Normative Analysis Using Bayesian Decision Theory

To analyze and improve decision-making processes, especially in cases where the data structure and decision conditions are complex and uncertain, the normative analysis research process, applying Bayesian decision theory (BDT), involves defining the decision problem, forming assumptions, updating these assumptions based on new data, and making decisions based on posterior probabilities. This iterative process can analyze uncertainty and available data, making it suitable where decision-making conditions are complex and often uncertain [30,31].

In order to model change management decision-making processes integrating the aspect of uncertainty, it is necessary to apply methods that allow one to assess the interdependencies of variables and their influence on decision outcomes. Bayesian network development and analysis supports a systematic approach to the examination of these relationships, the process consists of three main stages (Figure 1):

• Application of the Spearman’s rank correlation matrix, which is used to test data relationships by identifying associations between variables in complex models [32,33]. The results of this analysis are useful for assessing the strength and direction of the relationship between variables, thereby determining the initial structures of the Bayesian network [34,35].
• Additionally, the visualization of the directed acyclic graph (DAG) of Bayesian networks graphically presents dependencies, revealing causal relationships in the network [36,37].
• Integrating sensitivity analysis into the research process helps to assess how changes in the initial parameters of variables may affect the final probabilities, allowing the identification of key aspects and ensuring the resilience of change management decision-making [38].

Figure 1. Normative analysis using various methods (source: compiled by the authors).

Figure 1. Normative analysis using various methods (source: compiled by the authors).

The normative analysis process, which includes Spearman’s rank correlation, Bayesian networks and sensitivity analysis, provides an accurate data-driven basis for analyzing and forecasting change management decision-making when data are characterized by uncertainty and complexity. This allows one to not only assess and visualize the dependencies of variables and their impact on the final results, but also contributes to the validation of decision-making by highlighting critical factors and possible sources of variation. The results of the normative analysis process are extremely important in the context of change management, as they allow one to not only better understand the nature of the uncertainty of the external organizational environment, but also to model potential change scenarios based on probabilistic conclusions. Change management requires a clear understanding of how various factors may interact and what impact they will have on future situations; therefore, the application of normative analysis helps in the anticipation of possible external environment challenges, the prediction of their impact and the application of the most appropriate strategies. In addition, such analysis gives organizations the opportunity to not only react to changes, but also to actively shape future change management decisions focused on flexibility and adaptation to various conditions of uncertainty. Based on the results of normative analysis, it is possible to more accurately formulate scenarios that are not only hypothetical, but also empirically based. Accordingly, change management decision-making becomes data-driven and less dependent on subjective assumptions, and the proposed change management solutions are adapted to real conditions and possible future trends. In this way, the normative analysis process not only strengthens the reliability of scenario planning, but also ensures that change management decisions are based on a comprehensive and structured analysis.

2.2. Prescriptive Planning Using Scenario Approach

A scenario is a plausible description of future events based on different assumptions about fundamental uncertainties. Scenarios are alternative future narratives that help organizations visualize possible paths for their organization’s development without predicting exact outcomes [39,40]. Scenario theory contributes to change management by enabling organizations to navigate uncertainty, anticipate the unpredictable dynamics of the organization’s external environment, and develop adaptive change management solutions for possible future scenarios [41]. In addition, this theory is based on theoretical principles of change management and heuristic methods, which form the basis for effective change management decisions and promote organizational learning [42]. Scenario theory can be applied to change management as an organizational intervention tool, as scenario planning helps influence organizational operating routines and promotes change [43]. In change management, scenario theory includes scenario planning and analysis methods that allow to prepare for possible future scenarios [44,45,46].

Scenario planning helps organizations navigate uncertainty by assessing various possible futures. This method is used in organizations to create situations to improve decision-making under uncertain conditions and prepare for various possible future scenarios [47]. By developing and analyzing possible future scenarios based on assumptions about fundamental uncertainties, decision-makers can identify risks and opportunities, while strengthening the organization’s resilience and strategic flexibility by properly preparing for possible outcomes and thus increasing its ability to adapt to unforeseen environmental changes [48,49,50]. Scenario planning reveals the benefits of systematic change management, allowing decision-makers to consider known variables and remain alert to new uncertainties [51]. Healey and Hodgkinson (2024) [45] emphasize that scenario-based interventions can help managers manage change by adapting to new challenges and ensure flexibility in response to changing circumstances.

In conditions of constantly changing uncertainty, static projections can mislead managers and lead to serious consequences [52]. Uncertainties in management arise from a number of factors, including unclear objectives and limited knowledge about the systems being managed [53]. Uncertainty can be of four main levels: low, medium, deep uncertainty, and acknowledged ignorance. Scenario planning complements change management decision-making in low-uncertainty environments by focusing on resource optimization and the current situation, testing hypotheses and experimenting to reduce uncertainty in management actions, complementing the evaluation of alternatives [54]. In the context of moderate uncertainty, scenario planning helps to clearly understand the dynamics of the environment, including drivers of change and data-driven decisions [55]. In conditions of deep uncertainty, traditional tools often become insufficient—scenario planning is useful in contexts of deep uncertainty because it allows organizations to assess how different environmental changes may affect their operations [56,57,58]. The scenario approach, which encompasses various possible future options, enables decision-makers to consider the dynamic external environment of the organization, identifying potential various outcomes and their consequences, and evaluating alternatives in order to make informed decisions even with insufficient information. The three main principles of scenario development are as follows:

• A long-term perspective, which requires looking beyond immediate needs and choosing a sufficiently long-time frame to identify new opportunities;
• outside-in thinking, because, while attention is often focused on internal changes, it is important to consider external factors that may be more significant in the long run;
• inclusion of multiple perspectives, because, in order to achieve a comprehensive understanding of the future, it is necessary to include various possible assumptions, including those that may not coincide with the scenario developers’ views, as this can negatively affect the quality of the final results [59].

The scenario planning process, which allows organizations to systematically prepare for managing uncertainties and envision possible future development directions, includes 10 key stages (Figure 2) [39,47,59].

Scenario planning is a method that could help organizations cope with uncertainty and make change management decisions in dynamic environments. The adaptability of scenario planning to different levels of uncertainty allows organizations to develop proactive change management solutions that enhance organizational resilience, long-term viability, and adaptability, making it a useful tool in a variety of fields and situations [48,49,50]. Through various stages—from setting goals and identifying uncertainties to sensitivity analysis and evaluating alternatives—scenario planning enables organizations to adopt flexible and resilient change management solutions that help prepare for a variety of possible future events. Such a structured approach not only enables more effective change management and faster response to challenges posed by the external environment, but also promotes the long-term viability of the organization. Finally, by integrating scenario insights into change management, an organization can prepare to not only overcome challenges, but also to exploit new opportunities that emerge in a dynamic business environment.

2.3. Descriptive Analysis Using Fuzzy Set Theory

Descriptive analysis using fuzzy set theory and fuzzy comprehensive evaluation (FCE) supports scenario assessment in the context of uncertainty and complexity in change management. FCE allows for a comprehensive assessment of the created scenarios, applying both qualitative and quantitative indicators. FCE as a method is useful in assessing change under uncertainty, as it helps organizations make informed decisions based on a variety of, often uncertain, factors. Integrated with scenario planning, FCE can further refine change management decisions, especially in complex and uncertain environments, as this method provides a systematic way to evaluate multiple criteria and alternatives. In conjunction with scenario planning, FCE allows for the evaluation of the effectiveness of different change management solutions in various scenarios [60].

In change management, FCE can be used to assess an organization’s readiness to implement change, taking into account a variety of internal and external factors that can affect the success of the initiative. Due to its adaptability and flexibility, FCE is particularly useful in dynamic environments where traditional models may be limited [32,61]. In this way, organizational decision-makers can implement data-driven improvements to change management, ensuring that each step aligns with the organization’s goals.

Another significant application of FCE in change management is stakeholder involvement and performance analysis. FCE provides an approach that allows for the inclusion of multiple stakeholder perspectives, which is particularly important when change requires support from multiple groups. For example, FCE can help assess the readiness of different departments or teams to support a change initiative, ensuring alignment with organizational goals [32]. Using fuzzy sets, consensus and differences can be identified, allowing for more effective decision targeting [62]. In the context of change management, this method helps organizations select stakeholders who are best prepared to support the change process, thereby enhancing the implementation of changes and reducing resistance. FCE provides the ability to evaluate performance against multiple criteria, which is particularly useful for changes aimed at improving processes or implementing new protocols—the structured framework of FCE allows organizations to visualize performance indicators, highlighting both strengths and areas for improvement, which helps organizations flexibly adapt change strategies to meet changing needs [61].

The structure of the FCE process in change management can be composed of several essential stages to ensure systematic and accurate scenario assessment under conditions of uncertainty [63,64,65], as follows:

• Setting evaluation criteria: definition of evaluation criteria that are consistent with the organization’s goals and change management objectives. The criteria may relate to alignment of strategic goals, stakeholder satisfaction, resource availability, or operational efficiency—this ensures that the FCE analysis is focused on specific aspects of change evaluation.
• Determining the weight of criteria based on expert assessments: assessment by the expert group is based on the importance of each criterion in the context of change management using a predefined scale. The collected expert assessments are processed and normalized to obtain a weight for each criterion that reflects its relative importance in the overall assessment process. This step allows for the experts to draw on their insights, taking into account their experience and knowledge of the aspects of changes under consideration. In this way, the weights of the criteria become reasonable and reflect the different opinions of the experts on the factors of the change process.
• Data collection: collection of the necessary information about each of the selected criteria sets. The data can be both quantitative (e.g., statistical performance indicators) and qualitative (e.g., expert opinions, survey results, or subjective assessments).
• Fuzzy evaluation: collected data are transformed into fuzzy sets. This allows one to model the uncertainty and subjectivity that often occurs in change management, especially when assessments are partially accurate or based on subjective opinion.
• Aggregation of results: individual criterion assessments, which are then combined into a common scenario assessment indicator. This indicator provides decision-makers with a comprehensive and summarized understanding of the suitability, strengths and weaknesses of each scenario.
• Interpretation of results and feedback mechanism: the strengths and weaknesses of each scenario are assessed and possible corrective measures are identified. A continuous feedback system is also implemented, allowing decisions to be adapted based on new insights, thus increasing the likelihood of successful change implementation.

This approach, when applied to change management scenarios, provides the flexibility needed for organizations to operate in dynamic environments. The adaptability of FCE helps manage uncertainty and incorporates both quantitative and qualitative factors, allowing organizations to better understand the potential impact of change and prepare for various scenarios. FCE’s ability to manage uncertainty and incorporate both quantitative and qualitative factors allow organizations to better understand the potential impact of change initiatives. FCE supports decision-makers in the implementation of data-driven change management decisions, thereby increasing change effectiveness and organizational resilience in a dynamic environment.

3. Proposed Theoretical Model of Change Management

Based on the insights of the theoretical part, a change management model has been developed that could be practically applied by aviation service organizations—especially airports. The proposed model would support the existing practical change management decision-making processes and supplement them with the aspect of uncertainty. The theoretical concepts and methodologies analyzed during the research revealed that change management depends on the ability to systematically monitor and analyze the external environment, assessing its impact on the organization and appropriately selecting change management solutions. The proposed change management model is based on the theoretical perspective of change management under uncertainty, which emphasize the importance of systematic analysis of the external environment, the development of effective scenarios and the evaluation of complex solutions. The theoretical analysis and the proposed change management model provide the framework for change management solutions that would practically contribute to the ability of organizations providing aviation services to adapt to uncertain environmental conditions. This includes not only the responses to environmental changes, but also the preparations for unexpected and unpredictable external environmental challenges.

To ensure compliance with the minimum requirements set out in civil aviation safety regulations, airports are required to establish a safety management system [66], which incorporates the components defined by the ICAO Safety Management Manual [67], including change management as an element of safety assurance, as follows:

• Safety policy and objectives: management commitment, safety accountability and responsibility, designation of key safety personnel, coordination of emergency response planning, and safety management system (SMS) documentation.
• Safety risk management: hazard identification, safety risk assessment and mitigation.
• Safety assurance: monitoring and measuring safety performance, change management, continuous improvement of the SMS.
• Safety promotion: training and education, safety communication.

In order to manage change effectively, reduce risk and ensure aviation safety, the ICAO (2018) [67] change management process consists of six main stages: understanding and defining the change, identifying the impact of the change, identifying the hazards associated with the change and assessing the safety risk, developing an action plan, approving the change, and ensuring the assurance plan. The proposed theoretical change management model aligns well with the existing real-world regulatory requirements for change implementation in airports.

The proposed change management model includes four main stages: observation, analysis, evaluation and response, which combine normative, prescriptive and descriptive analysis approaches (Figure 3).

Observation stage: the ability of organizations to recognize potentially impactful changes in their external environments at an early stage is a key factor in ensuring operational adaptability. This stage aims to identify major external environmental factors and their interactions using correlation and causal analysis methods. This helps in understanding how political, economic, social, technological, environmental, and legal factors shape the external environment of an aviation organization.

Analysis stage: aviation service organizations need to not only identify change factors but also to assess their significance, so this stage employs probabilistic Bayesian networks to determine the dependencies of external factors and their influence. The prioritization of key uncertainties and sensitivity analysis of external environmental factors are conducted.

Evaluation stage: at this stage, potential alternative scenarios for change management decision-making are formulated. First, scenario frameworks are developed based on the most sensitive and significant variables, followed by detailed narratives for each scenario. This enables a clearer interpretation of the scenario structure and applicability in the context of change management.

Response stage: this stage combines alternative assessment and their integration into change management, ensuring organizational operational flexibility and adaptability to a changing environment.

Additionally, the model could act as a change management decision support tool, allowing one to assess and select the most appropriate actions based on the current conditions of uncertainty. Since this model would be an additional tool supporting existing practices and methods, its practical integration into existing systems would be standard without additional resources or limitations. The main users of the model could be middle and top managers, as airport management teams are pivotal in decision-making processes, especially in the context of operational aspect [68,69].

The model could help organizations operating in the aviation sector respond faster and more effectively to critical challenges, such as pandemics, geopolitical conflicts, the impact of climate change or technological innovations. The proposed change management model would promote not only rapid response in change management but also long-term sustainability in aviation service organizations by incorporating external environmental factors into change management decisions. This may contribute to strengthening organizational resilience in the context of global challenges.

4. Methodology

To validate the theoretical change management model, a semi structured questionnaire was sent to six selected experts. The semi-structured questionnaire focuses on structure and flexibility, allowing for in-depth exploration of complex phenomena, including professional experiences and contextual nuances that are critical in the aviation sector [70,71,72]. This method improves the validity and comprehensiveness of responses, particularly in capturing elements that purely quantitative tools may overlook [72]. Furthermore, the adaptability of this format allows one to delve deeper into emerging topics during the questionnaire process, resulting in richer and more insightful data [71,72].

While the expert panel consisted of only six individuals, the selection of this number of experts is often seen as optimal for gathering diverse perspectives without compromising manageability, it enables effective organization, streamlined analysis, and constructive consensus building, avoiding the complexity that larger experts might introduce [71,73]. The primary aim is to gather informed, domain-specific expert opinions, not to generalize findings to a larger population. This size of expert group, in this instance six, can provide high-quality detailed feedback while ensuring that the scope of the data remains focused and analytically tractable [71,73]. Additionally, selecting experienced individuals ensures valid and reliable insights grounded in domain-specific knowledge [74,75]. Including experts from companies of various sizes and countries enhances content validity and cross-cultural applicability, while involving professionals from different job positions contributes role-specific perspectives essential for a comprehensive assessment [76,77]. Consequently, for this research the selection criteria for experts are as follows: a country of the European Union, a managerial level position in an international airport, and a minimum five years of experience in the field of safety management (

Table 1. Data of industry experts in research (source: compiled by the authors).

Expert Index	Represented Country	Position	Years in the Relevant Field	Education Level
E1	Hungary	Safety manager	9	Master or equiv.
E2	Spain	Head of directorate	7	Master or equiv.
E3	Denmark	Safety and compliance manager	6	Bachelor or equiv.
E4	Lithuania	Safety manager	14	Master or equiv.
E5	Austria	Safety manager	25	Master or equiv.
E6	Czechia	Director safety, quality and process management	26	Ph.D.

). The responses from experts were gathered during the period from 25 May 2025 to 12 June 2025.

Expert evaluations of the change management model covered 26 aspects related to the suitability, clarity, logical structure, applicability, and visual presentation of the model and its components. Ratings were provided on a five-point Likert scale (1 = very low, 5 = very high). To evaluate both consistency and priority of expert assessments, the results were analyzed using descriptive statistics (mean and standard deviation) and Kendall’s W coefficient with mean rank analysis. While the overall Kendall’s W indicated limited statistical consensus (W = 0.215; p = 0.149), results were interpreted in the context of the small panel size and the exploratory nature of the study. Given the limited statistical power, the effect size remains informative, a more detailed analysis at the individual factor level revealed meaningful patterns of agreement and prioritization.

5. Results

5.1. Quantitative Expert Opinion Analysis

The main results of the quantitative expert opinion analysis, which are the mean ranks derived from Kendall’s W analysis, support the findings from descriptive statistics (Figure 4). Aspects such as the clarity and comprehensibility of stage 1 (mean rank = 18.42; mean = 4.50) and stage 2 (mean rank = 17.25; mean = 4.33), as well as both visual presentation indicators—the usefulness of the model’s visual representation of the change management model (mean rank = 17.58; mean = 4.33) and the clarity of the presented information (mean rank = 17.08; mean = 4.17)—were not only rated highly but also consistently ranked among the top priorities by experts. Several other factors also demonstrated strong evaluations and high prioritization. These include the suitability of stage 1, stage 2, and stage 3 for managing change in aviation organizations (mean ranks = 15.17, 16.58, and 15.08; means = 4.00, 4.17, 4.00, respectively), and the suitability of stage 10 (mean rank = 16.83; mean = 4.17). Moreover, both aspects related to the logical sequence of the model: its internal structural consistency (mean rank = 17.00; mean = 4.33) and its applicability for assessing external conditions and supporting decisions (mean rank = 17.75; mean = 4.33) were among the most favorably ranked. These consistently high scores and ranks indicate that experts viewed these components as the most effective, relevant, and clearly articulated elements of the model.

Between the clearly strong and weak aspects, a group of moderately ranked components emerged, reflecting neutral to moderately positive expert assessments. These included the overall suitability of the model for representing change management in aviation organizations (mean rank = 12.83; mean = 3.67), as well as the clarity of stages 3 (mean rank = 12.58; mean = 3.83) and 5 through 9 (mean ranks = between 11.50 and 13.08; mean scores ranging from 3.67 to 3.83) and the suitability of stages 4 and 5 (mean rank = 12.83; mean = 3.67). While not ranked among the most critical elements, these aspects were evaluated with moderate consistency and generally favorable assessments. This suggests that these components are functionally acceptable but may benefit from minor adjustments to enhance clarity, contextual adaptability, or alignment with stakeholder expectations in applied settings.

In contrast, the lowest-ranked aspect was the model’s applicability in the context of aviation service organizations (mean rank = 6.50; mean = 3.00). Despite moderate variation in responses (SD = 0.89), this consistently low prioritization reflects a shared critical perspective regarding the model’s practical implementation across diverse operational environments. Similarly, the clarity and comprehensibility of stage 10 (mean rank = 9.42; mean = 3.33) was rated significantly lower than earlier stages, suggesting that the final integration phase of the model may be poorly understood or insufficiently defined. Other lower-ranked and critically evaluated aspects include the clarity of stage 4 (mean rank = 10.67; mean = 3.50), as well as the suitability of stages 6, 7, 8 and 9 for managing change in aviation organizations (mean ranks = 10.58–11.33; means = 3.33–3.50). These were consistently rated lower and exhibited higher standard deviations, reflecting both limited agreement and perceived lack of clarity or feasibility. This trend suggests that the middle and implementation-related stages of the model require further refinement in terms of conceptual definition, practical integration, and contextual adaptability across airports of different scales and capacities.

In summary, although the overall level of agreement among expert evaluations was not statistically significant, the detailed analysis allowed for the identification of those model components that were evaluated consistently and uniformly, and those that raised divergent interpretations or uncertainties. These results not only contribute to the assessment of the model’s overall suitability but also help identify specific areas where adjustments to wording or additional explanatory detail may be required.

To further explore expert evaluation patterns, a hierarchical cluster analysis using the Ward linkage method was conducted. The resulting dendrogram revealed four medium-sized clusters, which were subsequently merged into two broader clusters. These groupings reflect not only the conceptual similarities perceived by experts but also the varying strength and consistency of their evaluations across model components (Figure 5).

The first cluster consisted of six factors encompassing the suitability of later-stage model components and the overall applicability of the model within aviation organizations. These aspects received the lowest average ratings in the dataset, as it can be noted that the lowest-rated item across the entire assessment was included in this group. This cluster indicates considerable expert disagreement and points to perceived challenges regarding the suitability and practical feasibility of later implementation stages. These components may require substantial revision, simplification, or further clarification to ensure applicability across different aviation organizations.

The second cluster comprised eight factors related to the clarity and comprehensibility of model stages 4 through 10. While the mean scores in this cluster ranged from 3.33 to 3.83, several items—such as stage 4 and stage 10—showed high levels of variability among expert responses. This suggests that, while the model’s progression beyond the initial stages is conceptually understood, the wording, structure, and internal logic of these steps may not be consistently clear. The cluster reflects a need for targeted refinement in the articulation of middle and later stages, potentially through rephrased descriptions, visual support, or improved alignment with practical application.

The third cluster included five items evaluating the suitability of implementation stages 1 through 5 and stage 10. These components received above-average mean scores and generally low standard deviations, reflecting a consistent and positive perception among experts. These findings suggest that the early-to-mid implementation stages are viewed as well-aligned with actual change management practice and can serve as the functional core of the model with only minimal adjustment.

The fourth and most highly rated cluster comprised seven components addressing the model’s visual structure, clarity of early stages, and logical coherence. These items were consistently rated at the top of the scale—clarity of stage 1 received the highest rating in the entire dataset, followed closely by the model’s visual usefulness and logical consistency. The strong coherence within this cluster suggests that experts widely agree on the effectiveness of the model’s foundational structure, visual representation, and initial phase descriptions.

At a broader level, the four medium-sized clusters formed two distinct higher-order groupings. The first, comprising the two lower-rated clusters, indicates areas of concern related to the model’s practical applicability and the clarity of later stages. The second, comprising the two more favorably evaluated clusters, includes those components related to the model’s structural clarity, early-stage formulation, and visual representation. This division highlights a consistent pattern across expert feedback: while the model is structurally sound and clearly presented in its early phases, it becomes less precise and more operationally ambiguous in its later stages, suggesting a need for improvement in those areas to enhance overall model robustness and usability.

5.2. Qualitative Expert Opinion Analysis

The results of the qualitative content analysis, based on data collected through semi-structured interviews, reveal key aspects of change management practices and experiences within the airport sector.

The analyzed aspects of the suggested model’s suitability in terms of missing or removable components revealed that, while the model covers essential aspects, experts suggest that its practical relevance and robustness could be strengthened by incorporating elements such as change readiness assessments, structured stakeholder engagement, and post-implementation monitoring mechanisms. Although there was general agreement on the necessity of the model’s components, several experts have emphasized that simplification and contextual adaptation may be necessary, particularly for smaller airports or specialized functional areas with limited operational capacity (

Table 2. Expert assessment of the suitability of models’ components (source: compiled by the authors).

Description	Experts’ Comments
Evaluation of whether any essential components are missing from the model, with identification and justification for their inclusion.	Expert responses revealed several aspects that could be incorporated into the model’s structure. While E1 and E5 found no missing components, E1 noted that “this model seems overly and unnecessarily complicated,” and E5 affirmed that “it contains all necessary details”—others highlighted areas for improvement. E2 observed that the model’s first two stages align with standard practices, though later stages differ in execution. E3 acknowledged the model’s ideal structure but questioned its feasibility, stating, “sometimes the workload for a change is huge compared to what impact it has.” E6 provided a comprehensive critique, recommending the inclusion of several key elements: stakeholder engagement and communication strategy, change readiness assessment, and post-implementation feedback loops. As noted, “The process would benefit from a closed-loop learning: PDCA cycle.” These additions emphasize the importance of organizational context, stakeholder dynamics, and long-term monitoring. E4 also pointed to the need for regulatory alignment, referencing EU Regulation 139/2014 and relevant EASA guidance.
Evaluation of whether any components could be omitted, including identification and justification for their exclusion.	Expert perspectives on whether any components of the model could be omitted were largely aligned, though with varying degrees of nuance. E5 and E6 gave a clear response of “No,” indicating that all components appear necessary. However, several experts expressed concerns about the model’s complexity or contextual relevance. E1 argued that “very few changes come from external factors—the majority of changes are from internal sources,” suggesting that the model’s emphasis on evaluating scenarios and alternatives may be unnecessary in some contexts. E2 noted that the evaluation and response stage is overly generic, stating that “in reality this depends on the specific airport area we are assessing,” and highlighting variation in timing and external frameworks between domains such as operations and infrastructure. E3 raised a practical concern related to resource constraints: “for my airport only one person do both safety and compliance, it does seem excessive.” Similarly, E4 suggested simplifying the model.

).

Throughout the assessment of the statements’ clarity, experts also highlighted the need for clearer definitions of several model stages, along with practical guidance that aligns more closely with real-world conditions. The importance of avoiding overly abstract language and ensuring consistency with both operational practices and regulatory requirements was repeatedly stressed. While the overall sequence of the model was considered logical, it was noted that understanding may vary depending on users’ familiarity with regulatory documentation, and one stage in particular was identified as requiring clarification. When considering practical applicability, experts pointed to challenges such as the model’s perceived complexity, limited time and resources, and the need for prioritization mechanisms to distinguish between routine and significant changes. The implementation, therefore, would benefit from a more streamlined approach, allowing for integration into daily operations without becoming burdensome, especially in under-resourced settings (

Table 3. Expert assessment of the clarity, logical sequence of the model’s components and the potential applicability of the model (source: compiled by the authors).

Description	Experts’ Comments
Identification of any stages that require more precise formulation, with justification for proposed refinements.	Experts identified several stages in need of clearer formulation. E1 listed stages 4, 5, 6, 7, 9, and 10 as requiring clarification. E6 elaborated on this, recommending that stage 4 include specific sensitivity analysis methods, stage 7 clarify what “testing” entails (e.g., simulations or expert judgment), and stage 10 be revised due to conceptual ambiguity: “How the ‘change management’ can be integrated into ‘change management’?” E2 emphasized that different airport areas operate under distinct regulatory frameworks, making uniform application of the model challenging. E3 highlighted the need for practical language, noting that the model appears abstract and inaccessible to operational staff. E4 advised aligning the model with EASA guidance (GM1 ADR.OR.B.040(f)). E5 found no need for changes.
Review of the model’s structure to determine whether the sequence of stages is logically coherent, noting any unclear transitions.	Most experts found the model’s sequence of stages to be logically coherent. E1, E4, and E5 responded with a clear “No” to any issues, and E2 noted it was not applicable. E3 affirmed that the sequence is “very clear,” but added an important remark: “I am also very used to read regulatory things and have a lot of pre-existing knowledge. For others in my organisation, not so much” indicating that clarity may depend on the user’s background. Only E6 identified a specific concern, pointing to stage 10, integration into change management, as potentially unclear in its placement or formulation.
Identification and explanation of potential challenges in applying the model in real-world context.	Experts broadly acknowledged the challenges of applying the model in real-world airport environments. Several pointed to the model’s complexity as a key limitation. E1 remarked that it is “far too complex” for normal operations, where time constraints often prevent scenario modelling and assessments rely more on professional judgment than formal processes. E4 suggested that “the model shall be more simple.” E2 emphasized that, while a generic structure is acceptable, “applying it in every case is not practical” due to varying legal, financial, and operational contexts. E3 highlighted the difficulty of translating theory into practice, particularly for frontline staff, noting: “we need to do change management on a lot of stuff… but nobody seems to grasp the importance of it.” They advocated for practical, time-efficient approaches embedded in daily operations. E5 identified stakeholder engagement as a central challenge, particularly in the observation phase, where “cooperation and the bring-in of all changes” may be difficult to ensure. E6 pointed to the need for thresholds of significance, stating that “thousands of changes cannot all be dealt with in such a complex bureaucratic process,” especially when time and resources are limited.

).

Visual aspects of the model were also addressed in detail. Experts recommended improvements to layout clarity and suggested the inclusion of practical examples to enhance user comprehension. Some visual issues were attributed to design limitations rather than conceptual flaws. Furthermore, experts proposed more substantive visual and structural enhancements, such as incorporating stakeholder identification and engagement plans, introducing organizational readiness assessments, specifying applicable tools and methods, and ensuring that model outputs lead to clearly assigned responsibilities and corrective actions (

Table 4. Expert assessment of the visual representation of the model (source: compiled by the authors).

Description	Experts’ Comments
Suggestions for improving the overall visual clarity and effectiveness of the model’s representation.	Experts’ feedback highlighted a few areas for improving the model’s visual clarity, though several experts found no major issues. E1 suggested the inclusion of concrete examples to reduce perceived complexity, stating that “as it is shown above it seems unrealistically complex.” E3 pointed out inconsistency in arrow direction between stages 5–8, noting that “it might be confusing, if you quickly glance over it,” even though it may stem from layout constraints. E2 and E5 responded with “no” concerns, while E4 referred to previous comments without elaboration, and E6 did not provide feedback on this point.
Identification and justification of specific, high-impact visual improvements to enhance the model’s presentation.	Experts proposed several enhancements to improve the model’s clarity, relevance, and usability. Expert 1 noted that while the model may be helpful in specific cases, “in the majority of the cases it appears to be too cumbersome.” Expert 2 recommended going “deeper into details,” and expert 3 reiterated the need for “practical examples of both minor and major changes,” aligning with earlier comments about operational relevance. Expert 4 emphasized simplicity, stating that “simple model is the best model,” particularly from an airport operations perspective. Expert 5 raised a critical question: “How can it be ensured that top management is aware of all changes which are going on in the company or which are planned?”—highlighting a gap in the model’s visibility mechanisms. Expert 6 provided a structured list of key enhancements, including: stakeholder involvement through an internal/external engagement plan, change readiness assessment prior to implementation, tool and method specification, post-implementation monitoring and learning loops, and actionability, by ensuring outputs lead to assigned responsibilities and corrective actions.

).

Based on the assessed expert opinions it could be stated that, while the model presents a sound framework, it could be significantly improved through greater clarity, simplification, contextual flexibility, and practical alignment with the operational and organizational realities of diverse airport environments.

6. Conclusions

Uncertainty is not just an external environment’s condition, it is an aspect that must be explicitly embedded in change management processes within the aviation sector, seeking to foster sustainable principles supporting resilience and adaptability. The validated theoretical model could enable organizations to move from reactive to proactive change management decisions by combining structured environmental observation, probabilistic analysis, and scenario-based foresight.

Expert input from six European airports highlighted the model’s logical structure and comprehensive scope but emphasized the need for simplification to improve its practical applicability, particularly in smaller airports with limited resources. While no essential components were missing, several stages were viewed as overly complex or abstract. Key recommendations included adding stakeholder engagement, change readiness assessments, post-implementation feedback loops, and ensuring alignment with EASA regulations to enhance relevance. Experts also highlighted the importance of clearer terminology, practical examples, and distinguishing between major and minor changes to avoid excessive bureaucracy. Visually, the model would benefit from improved flow and clarity, along with mechanisms to ensure top management visibility. Overall, while the methodologically is robust, the model requires refinement to strengthen its usability in real-world aviation settings. Additionally, it can be said that high uncertainty does not require radical transformation but needs targeted, adaptable responses based on scenario-specific evaluations. It could be stated that the most resilient scenarios are those that balance feasibility with responsiveness, not necessarily the most ambitious. Understanding the uncertainty as an opportunity rather than a barrier offers aviation organizations a structured, practical tool for designing adaptive and informed change processes.

A key limitation of this research can be identified in its partial partiality, based on the response perspectives of different experts, though this could also be a strength if the model’s applicability encompasses the region overall, rather than the specific country. Additionally, change management aspects and models are often confidential in aviation organizations, so the gathered responses could be considered slightly generalized for research matters, which should not affect the research results, as the model is not prepared for any specific organization.

The main theoretical contribution of this research is the integration of Bayesian decision theory, scenario thinking, and fuzzy evaluation into a unified change management model with a focus on uncertainty. Practically, it offers a methodological tool for airport managers and aviation decision-makers to enhance preparedness, resilience, and sustainability in a rapidly evolving sector. Future research should focus on improving the model’s clarity and simplification. Additionally, it is highly recommended to develop a rigorous methodology for the model’s practical application. The practical model’s potential application regionwide should be explored, incorporating empirical testing, and providing a more comprehensive understanding of potential applicability, seeking to validate if theoretical aspects work in real-world environments.