How can destination competitiveness play an essential role in small island sports tourism development? Integrated ISM-MICMAC modelling of key factors

Abstract

PurposeThis paper aims to identify factors that affect the sports tourism destination's competitiveness on a small island. Hence, this study looks at and evaluates these factors. The study then comes up with a model that clarifies the interrelationships between these factors.Design/methodology/approachThe authors broke down the data analysis process into three steps. The first step was to conduct a literature review and use industry and academia experts' help to determine the essential aspects (fuzzy Delphi method). Then, a hierarchical model was developed, and the factors were categorised using the interpretive structural modelling (ISM) approach. Factors' driving and dependency power were also determined using MICMAC analysis.FindingsThis work has identified 13 key factors related to the sports tourism destination's competitiveness on a small island. For a small island like Kish Island, the two independent variables (government support and destination political stability) that define the institutional framework for the destination are most important. Building corresponding competitive and support strategies to address these two independent variables is thus beneficial.Research limitations/implicationsThe research's results provide decision-makers, practitioners, and researchers with new insights into the hierarchical model of determinants. The study will fill the existing gap between theory and practice.Practical implicationsSports tourism destination managers on small islands may benefit from the proposed model since the model will enable them to organise the managers' priorities better to enhance the managers' destinations' competitiveness and provide tourists with a more accurate depiction of the destination.Originality/valueAccording to the authors' knowledge, the research design presented in this article has provided the first attempt to hierarchical analyse these factors and develop a model for sports tourism destination competitiveness on small islands and destinations with less-developed economies. This study fills the gap in the destination competitiveness and sports tourism literature by not only identifying the key influencing factors but also examining the interactions between these factors and providing empirical evidence supporting their relationships.

Mapping of Journal of Hospitality and Tourism Insights themes: a retrospective overview

“…Cluster 4 (Blue) – The documents included in the collection related to destination image (Jeannie Hahm and Tasci, 2020; Valduga et al , 2020; Karri and Dogra, 2022), tourism destination (McLeod, 2020; Moradi et al , 2022b) and affective image (Ferreira and Giraldi, 2020; Uner et al , 2022).…”

Destination competitiveness since 2010: research themes, approaches, and agenda

“…Most frequently, the reviewed papers did not adopt a specific theory. The limited theories used included cluster theory (Chin et al , 2017), chaos theory (Altinay and Kozak, 2021), grounded theory (Moradi et al , 2022a, 2022b), stakeholder theory (Zehrer and Hallmann, 2015), forgotten effects theory (Perez-Romero et al , 2021) and resource-based theory (Zhu et al , 2021). This promising trend first emerged in 2017.…”

“…If destination competitiveness is regarded as an outcome, or tourists’ perceptions of the destinations, several of its antecedents can influence either supply or demand. On the supply side, representative antecedents were found to include destination branding (Miličević et al , 2017), the business environment (Ivanov and Ivanova, 2016), sustainability (Cucculelli and Goffi, 2016; Dodds and Holmes, 2020) and environmental value (Esparon et al , 2015), quality of government and support (Lee, 2015; Moradi et al , 2022a, 2022b), education and training (Andrades and Dimanche, 2019) and innovative technologies (Cimbaljević et al , 2019; Milićević et al , 2020a, 2020b). Other antecedents were less evident, including consumer protection legislation (Petrovic and Milicevic, 2017), crisis management (Xu et al , 2021) and corruption (negative, Kubickova and Smith, 2019).…”

Assessing the challenges to medical waste management during the COVID-19 pandemic: Implications for the environmental sustainability in the emerging economies

“…Again, since ISM modeling develops a level-by-level fundamental understanding of complicated interactions among the factors in complex situations in both graphical and textual forms, it is more effective at analyzing the interrelationships among the challenges [ 55 ], while MICMAC analysis is appropriate for grouping the factors into clusters that will clearly define the factors that are driven and those that are driven by Ref. [ 38 ]. Thereby, ISM-MICMAC has been utilized to analyze the interrelationships among the challenges in this study.…”

“… Electric vehicle industry Vehicle performance and reliability were identified to be the most significant driver Principal Component Analysis (PCA), ISM-MICMAC [ 37 ] Simulating and evaluating Green Lean Six Sigma success elements for the Indian healthcare facilities. Healthcare industry Management's commitment and financial availability were the most critical success factors ISM-MICMAC [ 38 ] Figuring out what makes an island nation a competitive sports tourist location Tourism industry Government support and Destination political stability were found to be the major factors Fuzzy Delphi, ISM-MICMAC [ 39 ] Modeling the drivers of solar energy development in an emerging Economy The solar and renewable energy sector The favorable geographical location in terms of solar Irradiation was identified to be the most important driver BWM, ISM-MICMAC [ 40 ] Evaluating the factors for sustainable supplier selection in the steel industry Steel industry Reputation and the supplier position was found to be the most crucial factor BWM, TOPSIS, ISM-MICMAC [ 41 ] Analyzing operational Hazards in heavy fuel oil-based power plants Heavy Fuel Oil-based power plants Standard Operating Procedure (SOP) and Training were identified as the two most critical hazards FAHP, FTOPSIS, ISM-MICMAC [ 42 ] Analyzing barriers to the sustainable supply chain in the apparel & textile sector Apparel and textiles sector Poor supplier performance was determined as the most significant barrier ISM, Fuzzy MICMAC, DEMATEL [ 43 ] Mapping interactions among green innovations barriers in the manufacturing industry Manufacturing industry …”