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Tourism is a worthy catalyst for development in emerging economies and supports growth in most developing countries. Consequently, countries continuously strive to enhance tourism competitiveness to magnetize worldwide tourists. However, due to significant environmental changes and intense competition, tourism organizations are compelled to adopt new strategies. A literature review demonstrates a broad spectrum of possible and available strategies for countries to choose from, necessitating the prioritization of strategies appropriate to the host country's situation and conditions. In this study, we implemented the Antifragility Analysis Algorithm (AAA) to address the research problem (identifying and prioritizing tourism strategies in the western region of Mazandaran province, Iran). We also collected experts' verbal judgments using Neutrosophic Sets (NSs), which can effectively address ambiguity and uncertainty. Initially, with the help of experts in the field, we identified eleven available strategies. Then, we identified five influential environmental indicators and possible states for each, defining thirteen alternative scenarios (one current scenario and twelve future scenarios). Subsequently, the performance of strategies in each indicator state was estimated using NSs. Then, considering the future scenarios, the antifragility scores of the strategies were determined. The results indicate all listed strategies are antifragile, meaning that adopting and implementing each could yield more significant benefits than the current situation. According to the findings, market research, infrastructure development, community engagement, diversification, and monitoring strategies should be implemented in the initial stage. Following them, destination branding, halal tourism, and crisis management strategies should be implemented in the next stage, and the remaining strategies can be executed in the final stage. [ABSTRACT FROM AUTHOR]

Strategic decision-making is often complex and uncertain, especially in turbulent environments. A large number of frequently conflicting indicators, rapid and unpredictable environmental changes, and the long-term consequences of making a decision have revealed the need for managers to use more efficient tools. Today, for tomorrow, conventional tools such as Quantitative Strategic Planning Matrix (QSPM) and MADM help us make the best decision based on yesterday's information. In some scenario planning approaches, the number of scenarios is so few that it cannot represent future uncertainty. In MADM methods, the problem of calculation complexity arises due to the increase in the number of elements. To address these issues, this research proposed an alternative approach for choosing the best strategy in which the alternative strategies are defined by SWOT analysis, future scenarios are determined by applying a matrix approach, irrational scenarios are eliminated by using interpretive structural modelling, and strategies are assessed by implementing Robustness Analysis (RA). The proposed method involves a case study related to a distributing centre for a food and beverage company located west of Mazandaran province, Iran. Nine alternative strategies' performances were evaluated in twenty-two scenarios based on six significant indicators shaping the future environment, and the best strategy was selected. Finally, some directions for future studies were presented. This study provides managerial implications by showing that despite the classic strategy selection approach being appropriate for austere environments and the MADM models being reasonable for complex environments, RA produces more reliable results when dealing with turbulent environments. [ABSTRACT FROM AUTHOR]

Purpose: In a competitive environment where business competition has become a fundamental challenge for managers, various ways to maintain, survive, or grow the organization are conceivable. Among these, marketing experts believe that customer loyalty is one of the most effective tools in facing this challenge. To achieve customer loyalty, various and diverse strategies have been mentioned in the literature by researchers and experts, which organizations can use, depending on the conditions, one or a combination of them. In such circumstances, managers usually have several strategies and must choose the most appropriate one(s). The present study aims to provide a combined approach for prioritizing customer loyalty strategies. Methodology: This research uses a matrix-based approach to robustness analysis, which can deal with complexity and uncertainty. The proposed algorithm combines it with strategic planning tools (strategies derived from strategic objectives and SWOT analysis) for prioritizing and selecting strategies. The proposed approach was implemented in a case study on prioritizing customer loyalty strategies for a women's clothing boutique in Ramsar City. Available strategies, influential environmental variables, definitions of future scenarios, and the performance of strategies in different environmental conditions were determined based on the judgments of the problem owner. Findings: The results showed that considering influential environmental variables (national currency value, market access and raw materials, lifestyle changes, investment security, government-private sector relations, and the speed of technological change), supplier selection, contractor selection, and attracting a sponsor have the highest priority strategies. Afterwards, environmental advertising, collaborative production, and customer relationship management were placed in subsequent rankings. The outputs of the proposed approach indicate that considering the country's foreseeable future conditions, higher-priority strategies minimize environmental risks and their impact on the business. Originality/Value: The problem of choosing a customer loyalty strategy is a decision-making problem that can be addressed with the help of decision-making knowledge. However, the literature suggests that classical strategic planning approaches Quantitative Strategic Planning Matrix (QSPM) or multi-criteria Decision-Making Approaches (MCDM) are used in most cases of such decision-making. Despite their capabilities and features, these approaches face challenges in dealing with variable and evolving conditions (future uncertainty). An alternative approach, robustness analysis, can consider alternative futures but cannot define available strategies. Based on this, combining the matrix approach to robustness analysis with classical strategic planning approaches will be a response to the above problem. [ABSTRACT FROM AUTHOR]

Purpose: Creative and innovative human resources have a tremendous impact on the survival and improvement of the organization. Management has to pave the way for creativity and innovation. The current research seeks to explain one of the main reasons for the problem of creativity and innovation in some Iran's big-size state-owned enterprises with the help of a metaphor (here, Procrustes' bed metaphor). Methodology: This research has been done descriptively. A questionnaire containing six questions with a Likert scale was designed to investigate the current situation using the help of human resources experts and the Delphi method. Questionnaires were distributed among experts and experts in charge of five big-size state-owned companies, and finally, 124 questionnaires were analyzed descriptively. Findings: The research findings show that the Procrustes bed has become a company desk in the examined enterprises. The freedom of action to present creative and innovative ideas, the possibility of implementing innovative ideas, welcoming new ideas, supportive management, offering rewards to new ideas, and overall satisfaction with the current state are in an unfavorable situation. Originality/Value: Following the social sciences and especially structuring and problem-solving approaches, we believe that knowing the problem is the first and most crucial step in solving a problem. Based on this, we introduced Procrustes' bed metaphor in this research to understand the existing problem. [ABSTRACT FROM AUTHOR]