Strategic Adaptation in Travel Agencies: Integrating MARA with SWOT for Uncertainty Navigation

In the current global landscape, organizations and social institutions confront a complex milieu, both within their internal structures and in the external environment. It has been observed that the business environment has undergone significant transformations, necessitating the evolution and adaptation of organizations [1]. The capacity of these entities to adapt effectively to environmental changes has become a critical factor for their survival and prosperity. Embracing change as an integral component of the modern business paradigm is essential for organizational success [2].

Effective management of organizations now requires a holistic and systemic approach. This approach must encompass a comprehensive understanding of the factors influencing organizational performance, enabling strategies that address the impacts of both internal and external elements. Such a strategic approach is vital for exerting influence and control over these factors [3].

In contrast to previous eras, where organizational decisions were predominantly driven by customer needs and shareholder profitability, the contemporary competitive landscape demands a focus on satisfying a broader spectrum of stakeholder groups. This shift is crucial for organizational growth and survival [4]. The balancing act between satisfying the immediate and long-term demands of both internal and external stakeholders has become a cornerstone for management in ensuring organizational survival [5]. Sustainable survival and growth, especially in the face of challenges such as declining demand, reduced investment activity, currency fluctuations, and other unpredictable economic conditions, are paramount for economic success across industries [6].

However, during economic recessions, sustainable growth may impede a company's development, potentially leading to reduced shareholder income and, frequently, bankruptcy [7]. The aviation industry, a critical sector within the tourism and transportation domain, exemplifies these challenges. Operating in an intensely competitive environment, airlines must engage in prudent decision-making to ensure their sustainability and continued existence [8].

Conventional strategic decision-making in organizations has often relied on the assumption that future events can be predicted accurately, thus guiding their strategic direction [9]. This approach, however, has demonstrated limitations, particularly in its tendency to overlook or underestimate the inherent uncertainties of the business environment [10]. The premise that future events are predictable with a high degree of certainty, based on the efficacy of an organization's strategic analysis, has been increasingly challenged. In reality, business environment changes are frequently unpredictable, leading to potential missed opportunities or unanticipated challenges [11].

The global market for products and services experiences continuous fluctuations, with organizations gaining a competitive advantage by detecting market imbalances before their competitors. This advantage enables them to introduce new offerings, adjust their value propositions, and capitalize on emerging opportunities [12]. Traditional strategic analysis methods have shown diminished effectiveness in such dynamic market conditions. Analysts are now required to adopt novel approaches for assessing market conditions and environmental uncertainties [13]. This involves recognizing and evaluating the impact of external factors, including market trends, competitors, and technological advancements, to inform decision-making processes.

To navigate this uncertainty, various methods and models have been developed, both within strategic and non-strategic domains [14]. Some scholars have focused on enhancing classical strategy selection models, such as Quantitative Strategic Planning Matrix (QSPM) and SWOT analysis, under conditions of uncertainty [15]. Another category of models includes the multi-criteria decision-making approach, which offers a more nuanced evaluation [16].

SWOT analysis, in particular, serves as a fundamental tool for organizations to assess their internal and external environments amidst uncertainties. It systematically evaluates an organization’s strengths, weaknesses, opportunities, and threats, aiding in the identification of both internal and external factors that can influence success [17]. Opportunities often refer to favorable external factors and the potential to address gaps or launch new initiatives, while threats encompass external challenges to organizational success. Importantly, opportunities and threats may also arise internally. Strengths are internal attributes that facilitate goal achievement, whereas weaknesses are internal elements that could limit or impede growth. Thus, SWOT analysis provides a comprehensive framework for organizations to evaluate their internal and external landscapes.

SWOT analysis, recognized extensively in strategic planning literature, has been widely employed to assess an organization's position and strategize accordingly [18]. Despite its extensive application across diverse fields, the literature has often been too generalized or overly field-specific, thereby lacking a comprehensive perspective on its utilization [10]. Another tool frequently used in strategic decision-making is the QSPM, which assists in evaluating strategic options and their relative appeal. The QSPM is designed to assess the feasibility and sustainability of strategies within the context of organizational and environmental conditions [19]. Effective role planning across various managerial levels is imperative for organizational success.

A significant challenge in decision-making, particularly in uncertain environments, is the difficulty in accurately forecasting future conditions [20]. This challenge is addressed by evaluating internal preferences for future options while maintaining the flexibility to select them. It involves assessing different potential outcomes for each decision, ensuring that choices remain viable over time. It is widely acknowledged that classical strategic planning approaches are insufficient in addressing uncertainties [21].

Robustness analysis, particularly valuable in highly uncertain scenarios, falls under the Interpretive paradigm and is a part of soft operations research. This method is pertinent in addressing management and organizational issues, especially where human factors are significant [22]. It is employed to evaluate the long-term implications of decisions made under uncertain conditions. The process necessitates implementing and assessing successive decisions to confirm their durability. This analysis involves the development of future scenarios, a profound understanding of the problem, and the factors influencing outcomes [23]. It includes creating multiple potential future scenarios and evaluating their impact on the organization or management issue. Techniques such as scenario writing and Futurology are instrumental in identifying possible futures and their probable outcomes based on current situations. Robustness analysis then assists in identifying stable factors across scenarios and understanding their primary influences, thereby facilitating decision-making under uncertainty [24].

In this study, the integration of SWOT analysis and robustness analysis is explored for strategy selection. Initially, potential strategies are identified using SWOT analysis, which are then prioritized through robustness analysis. The subsequent sections of this study demonstrate the application of this integrated approach in a case study.

Arulmozhi et al. [25] explored the factors influencing the growth of medical tourism in India through a SWOT analysis. Primary data were collected from 372 medical tourists, predominantly from countries such as Bangladesh, Bhutan, Nepal, Maldives, Afghanistan, Pakistan, and Sri Lanka. Respondents were randomly selected and surveyed using a seven-point scaling technique to gather their opinions and feedback. The study underscored the significance of analyzing hospital performance and other determinants for medical tourists. It posited that India's leadership in global medical tourism hinges on enhancing foreign policies related to medical visas and treatment processes.

In another study, Tanriverdi and Kucukyilmaz [26] investigated the experiences of airlines with cooperative strategies. Interviews with five senior managers using a semi-structured approach revealed the crucial role of cooperative strategies for airlines' survival in the industry. The study suggested that competition boards should offer guidance on methods that are advantageous for airlines.

Mustika and Aditya [27] focused on the tourism village of Kampong Tajor, aiming to identify its SWOT and to develop strategies for tourism development. Their empirical analysis highlighted the vulnerability of rural areas to the influx of tourists and emphasized the necessity for policies to enhance their advantages, minimize weaknesses, address threats, and capitalize on opportunities. The study recommended leveraging comparative advantages to bolster the tourism sector in the region.

Barati et al. [28] conducted a study to devise a composite method for strategizing and selecting developmental approaches for rural cooperatives. This research integrated SWOT analysis, the TOWS matrix, and the Analytical Network Process (ANP). Input was solicited from experts, including 10 CEOs of rural cooperatives and senior personnel from the central organization of rural cooperatives, using a brainstorming technique to analyze the external and internal environments of these entities. Subsequent to identifying key SWOT factors, a TOWS matrix was constructed to generate strategic alternatives, which were later prioritized using the ANP. The study unearthed 19 crucial strategic factors, such as deficiencies in management knowledge and the ability to enhance value and supply chains. Furthermore, 11 strategic alternatives were identified, including public policy implementation, technical and financial service provision, and the facilitation of input procurement, supply, and value chain development. The hybrid approach was posited as beneficial for decision-makers and managers in adopting optimal strategies and alternatives for the development of rural cooperatives.

In their research, Dimić et al. [29] introduced a strategic transportation management model. Employing SWOT analysis, the study evaluated impacting factors to formulate a sustainable transportation strategy. The strategic options were developed through SWOT analysis, and the optimal selection was achieved using ANP based on the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The Serbian Oil Industry (NIS) implemented this model in the production and transportation of oil derivatives. The findings demonstrated the model's successful application in profit-oriented organizations and its utility in strategy formulation based on scientific principles, thereby creating conditions conducive to the successful execution of sustainable strategies.

Yang's study [30] focused on developing an appropriate logistics strategy for Hangzhou Airport. The research highlighted the airport's logistical weaknesses and threats, suggesting the need to capitalize on opportunities to enhance airport logistics, including accelerating infrastructure construction, strengthening the information system, and forming strategic alliances. These findings offer practical guidance for the logistics development of Hangzhou Airport.

The methodology of this research commences with the assembly of a matrix encapsulating internal strengths and weaknesses, along with external opportunities and threats, as perceived by research experts. It is imperative to focus on the most critical factors in this matrix to ensure that the derived strategies adequately reflect the influence of each factor. The MARA facilitates this process, as it does not impose any constraints on the computational aspect, allowing for an expansion in the number of strategies without hindrance [31].

The second stage involves the alignment of internal strengths with external opportunities and threats, leading to the formulation of SO_i and ST_i strategies (where i=1,2,...,n). Similarly, the matching of internal weaknesses with external opportunities and threats results in the identification of WO_i and WT_i strategies. This stage is characterized by the absence of limitations on the number of strategies that can be formulated.

In the third stage, the most salient indicators related to environmental factors, encompassing political, economic, social, technological, environmental, and legal aspects, are listed. These indicators, which significantly impact the business and the implementation of organizational strategies and decisions, are prioritized based on their importance, with decision-makers selecting the most crucial indicators from each category.

The fourth stage requires the problem owners to ascertain the possible states for all selected indicators from the preceding stage. For each indicator, a minimum of two states should be envisaged. The number of future scenarios is determined by the product of the different states of these indicators. For instance, if an indicator such as political relations has two states (peace and war), and another indicator like national currency value has three states (increase, robustness, and decrease), the total number of future scenarios would be calculated accordingly (18 = 2 × 3 × 3).

In the fifth stage, future scenarios undergo a refinement process, where implausible scenarios are eliminated. This refinement is conducted from two perspectives: firstly, by excluding states of indicators deemed unlikely to occur, and secondly, by removing scenarios with contradictory states of indicators. For instance, if it is established that an increase in the national currency value is improbable during the planning period, this state is excluded, reducing the number of future scenarios (12 = 2 × 3 × 2).

$\mathrm{S}=\left(\begin{array}{llllllllllll}1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 \\ 2 & 2 & 2 & 3 & 3 & 3 & 2 & 2 & 2 & 3 & 3 & 3 \\ 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3\end{array}\right)$

In the methodology, rows of the matrix represent indicators, while columns depict future scenarios. For instance, the first column (Scenario 1) illustrates a future characterized by peace, stability in national currency value, and high access to global markets. Scenarios combining mutually exclusive states, such as political conditions of war with high access to global markets, are identified as implausible and thus excluded. Consequently, after eliminating scenarios 7 and 10, twelve scenarios are retained for analysis.

The sixth step involves delineating the conditions under which each strategy performs effectively, represented in a performance matrix (P).

$\mathrm{P}=\left(\begin{array}{llll}1 & 2 & 2 & 2 \\ 2 & 2 & 3 & 3 \\ 1 & 2 & 1 & 3\end{array}\right)$

In this matrix, columns are allocated to strategies, and rows to different states of indicators. For example, the first column (Strategy 1) indicates optimal performance under conditions of peace, stable national currency value, and high access to global markets.

The final phase of this methodology involves a critical alignment of the Scenario (S) and Performance (P) matrices to ascertain the robustness score for each strategy. The process entails a meticulous comparison of each strategy's respective column in matrix P against the columns of matrix S. In this context, every instance where a strategy aligns with a scenario is awarded a positive point. For illustrative purposes, the robustness of the first strategy is evaluated by juxtaposing its column in matrix P with each column in matrix S. For example, in the first scenario, if all criteria match, the performance score for strategy 1 is determined as 3. Conversely, in scenarios where there are fewer matches, as in the second scenario, the performance score may be lower. This assessment is systematically conducted across all scenarios.

Upon completion of these comparisons, the robustness score for each strategy is calculated as the sum of its performance across the spectrum of scenarios. This robustness score is a crucial metric, reflecting the strategy's overall effectiveness and adaptability in various hypothetical situations. Subsequently, strategies are prioritized based on their robustness scores, presented in a descending order, illustrating their relative strength and feasibility in diverse future conditions.

The initial phase of implementing the proposed approach involved establishing a team to participate in the strategy selection process. This team included the CEO and technical director of Q-Parvaz Travel Agency. An introduction to the approach was provided, followed by a systematic progression through the specified steps.

The primary stage of this approach entailed the identification of potential strategies for the organization. Given the absence of a formal strategic document at Q-Parvaz Travel Agency, semi-structured interviews were conducted with the agency's owners. These interviews facilitated the identification of the agency’s strengths and weaknesses, as documented in Table 1, and the external threats and opportunities, as noted in Table 2. The SWOT matrix was then derived from these identified elements in Table 3, leading to the formulation of twelve potential strategies for the travel agency, outlined in Table 4.

In the subsequent phase of scenario development, the agency's owners identified critical indicators impacting the business environment. For this purpose, environmental factors were assessed using an expert scoring method according to Table 5, where indicators were assigned scores based on their impact: ‘no impact' (0 point), ‘low impact' (1 point), and ‘high impact' (2 points).

In this study, experts identified the most critical environmental indicators impacting Q-Parvaz Travel Agency: diplomatic relations and political robustness (political index), inflation rate and exchange rate fluctuations (economic index), market size potential (social index), and government regulations (legal index). Following this identification, Table 6 delineates various states of each selected indicator as determined by the owners.

Given these states, a total of 144 future scenarios (2×2×3×3×2×2) were initially considered. To design more feasible future scenarios, the agency's owners conducted a refinement process over a five-year perspective. This process involved two steps: 1) Elimination of states deemed unlikely to occur, and 2) Removal of scenarios with contradictory states. Expert analysis facilitated the refinement of possible scenario states, as outlined in Table 7.

Following the initial scenario development, implausible scenarios were systematically eliminated, resulting in a reduction of the total scenarios to 32 (1×2×2×2×2×2×2), forming the preliminary matrix S.

$S=\left[\begin{array}{llllllllllllllllllllllllllllllll}1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\ 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\ 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 \\ 1 & 2 & 2 & 2 & 1 & 1 & 2 & 2 & 1 & 1 & 2 & 2 & 1 & 1 & 2 & 2 & 1 & 1 & 2 & 2 & 1 & 1 & 2 & 2 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 \\ 1 & 1 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 \\ 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\end{array}\right]$.

An exemplar scenario from the first column of matrix S envisages a future where diplomatic relations with other countries are not only maintained but also improved. In this scenario, political robustness within the country is sustained, inflation rates remain stable, exchange rate fluctuations are minimal, market size experiences growth, and a facilitation in regulatory frameworks is observed. Through a rigorous refinement process, scenarios deemed infeasible by the agency's owners were excluded, culminating in the extraction of matrix S comprising 19 distinct scenarios.

$S=\left[\begin{array}{lllllllllllllllllll}1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\ 1 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 \\ 1 & 1 & 1 & 1 & 2 & 2 & 2 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 1 & 1 & 2 & 2 \\ 1 & 1 & 2 & 2 & 1 & 1 & 2 & 1 & 1 & 2 & 1 & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 \\ 1 & 2 & 1 & 2 & 1 & 2 & 2 & 1 & 2 & 2 & 1 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\ 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1\end{array}\right]$.

Subsequently, the agency's owners were requested to evaluate each of the 12 strategies against these scenarios within a performance matrix (denoted as matrix P). Each strategy's effectiveness was assessed in the context of the defined scenarios.

$P=\left[\begin{array}{llllllllllll}1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & - & 2 \\ 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 \\ 1 & 2 & 1 & 1 & 1 & 2 & 1 & 2 & 2 & 2 & 1 & 2 \\ 2 & 2 & 1 & 1 & 1 & 2 & 1 & 2 & 2 & 2 & 1 & 2 \\ 2 & 2 & 1 & 1 & 2 & 1 & 2 & 1 & 2 & 2 & 2 & 2 \\ 1 & 1 & 1 & 1 & 1 & 1 & 2 & 1 & 2 & 2 & 2 & 2\end{array}\right]$.

The final stage involved a comparative analysis between matrices S and P to calculate the robustness scores of the strategies. To ascertain the robustness of each strategy, a corresponding column in matrix P was matched against columns in matrix S (scenarios). Positive scores were allocated for each strategy under specific conditions within this matching process. This scoring was conducted in two distinct modes: rigorous and lenient. The outcomes of this analysis are presented in Table 8.

In the rigorous mode of analysis, Table 8 illustrates the matching of the scenario matrix (S) with the performance matrix (P) for Q-Parvaz Travel Agency. Each column in Table 8 corresponds to a specific scenario, while each row represents the performance of a strategy within that scenario. The strategies were assessed for their robustness across various environmental conditions, with each match between the scenario and the strategy's performance earning a positive score.

This rigorous scoring approach provided a detailed and stringent evaluation of each strategy's effectiveness in different potential future states. The strategies were then ranked based on their cumulative scores across all scenarios, offering a comprehensive view of their viability in varying conditions.

In the rigorous evaluation mode, the final scores of each strategy across different scenarios were color-coded in red at the end of each strategy row. In this mode, scores ranging from 0 to 3 were assigned a value of 0, and scores between 4 and 6 were assigned a value of 1. This led to the formulation of Table 9, which presents the final scores of strategies in various scenarios under rigorous conditions.

Following this, the robustness scores of the 12 strategies in rigorous mode were calculated based on the 19 scenarios. These scores were visualized in Figure 1.

Based on the robustness balance depicted in Figure 1, the strategies were prioritized, leading to the development of Table 10. This table presents the prioritization of strategies based on their robustness balance in rigorous mode.

In the lenient mode of the analysis, Table 11 displays the matching of the scenario matrix (S) with the performance matrix (P) for the strategies of Q-Parvaz Travel Agency.