Predictive decision support for underutilization risk in public sector tourism: Evidence mapping and a design science roadmap
DOI:
https://doi.org/10.66472/cybernet.v1i2.440Keywords:
Underutilization, Public Sector Tourism, Predictive Decision Support, Explainable AI, Design Science ResearchAbstract
Publicly funded tourism assets can become stranded when utilization persistently falls below a reasonable level relative to capacity or policy-defined potential. Yet tourism analytics research largely forecasts demand or composite performance and seldom formalizes underutilization as a governance outcome, nor evaluates decision quality within planning and budgeting workflows. This study (i) maps recent evidence and research gaps and (ii) proposes a conceptual artefact in the form of a policy-ready methodology and roadmap for developing a predictive decision support system (DSS) to mitigate underutilization risk. An evidence-mapping review of 117 Scopus-indexed studies (2021–2026) reveals a critical gap: 0% of the analyzed studies explicitly formalize "underutilization" as a policy outcome in their titles. Furthermore, evaluation procedures remain opaque, with 79.5% of studies failing to clearly specify their methodologies. In response, we outline a design-science roadmap for an auditable predictive DSS that operationalizes underutilization through two complementary metrics: the Underutilization Gap (UG) and the Utilization Ratio (UR). The proposed architecture integrates heterogeneous tourism, spatial, and socio economic data while providing traceable audit trails via Explainable AI (XAI) to ensure scores are logically defensible in public budgeting. Crucially, the framework introduces a two-layer evaluation that couples technical predictive performance (E1) with decision-utility metrics (E2), such as rank agreement and allocation efficiency. This methodology equips local governments with a practical, theoretically grounded instrument to justify prioritization, optimize resource allocation, and reduce the likelihood of underutilization-related policy failure.
References
[1] W. Wu, “Digital tourism and smart development: state-of-the-art and research agenda,” Sustainability, vol. 16, no. 23, p. 10382, 2024, doi: 10.3390/su162310382.
[2] Y. Zhang, “Tourism demand forecasting based on a hybrid temporal BiLSTM–Transformer model,” Sustainability, vol. 17, no. 5, p. 2210, 2025, doi: 10.3390/su17052210.
[3] A. Louati, “Machine learning and artificial intelligence for a sustainable tourism sector: predicting tourist spending and classifying spending behaviors during the COVID-19 pandemic,” Information, vol. 15, no. 9, p. 516, 2024, doi: 10.3390/info15090516.
[4] J. Huang, “Daily tourism demand forecasting based on iTransformer and weather factors,” Sustainability, vol. 16, no. 23, p. 10678, 2024, doi: 10.3390/su162310678.
[5] F. Krügel, “An online multicriteria–spatial decision support system for planning: bridging web-based GIS and MCDA,” Applied Sciences, vol. 14, no. 4, p. 1526, 2024, doi: 10.3390/app14041526.
[6] P. Manna, “A geospatial decision support system for land degradation neutrality: a web-based tool for SDG 15.3.1,” Land (Basel)., vol. 13, no. 1, p. 89, 2024, doi: 10.3390/land13010089.
[7] J. Bravo, R. Alarcón, C. Valdivia, and O. Serquén, “Application of machine learning to predict visitors to tourist attractions: the Moche route in Peru,” Sustainability, vol. 15, no. 11, p. 8967, 2023, doi: 10.3390/su15118967.
[8] L. Capuano, “Explainable artificial intelligence in cybersecurity: a survey,” IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3204171.
[9] A. M. Salih et al., “A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME,” Advanced Intelligent Systems, vol. 7, no. 1, Jan. 2025, doi: 10.1002/aisy.202400304.
[10] L. Li, “Comparing SHAP and LIME in explainable AI: a theoretical and practical analysis,” Electronics (Basel)., vol. 14, no. 23, p. 4766, 2025, doi: 10.3390/electronics14234766.
[11] J. Donia, “Algorithmic accountabilities and health systems: A review and sociomaterial approach,” Big Data Soc., vol. 12, no. 2, Jun. 2025, doi: 10.1177/20539517251334099.
[12] L. S. Goecks, M. de Souza, T. P. Librelato, and L. R. Trento, “Design Science Research in practice: review of applications in Industrial Engineering,” Gestão & Produção, vol. 28, no. 4, 2021, doi: 10.1590/1806-9649-2021v28e5811.
[13] M. A. Fathullah, A. Subbarao, A. Ahmad, and S. Muthaiyah, “Merging Qualitative Design Methods With Design Science,” Int. J. Qual. Methods, vol. 24, Apr. 2025, doi: 10.1177/16094069251337585.
[14] M. Ji, “An evaluation framework for successful AI-enabled clinical decision support systems,” JMIR Med. Inform., vol. 9, no. 1, p. e25929, 2021, doi: 10.2196/25929.
[15] M. Saarela and V. Podgorelec, “Recent Applications of Explainable AI (XAI): A Systematic Literature Review,” Applied Sciences, vol. 14, no. 19, p. 8884, Oct. 2024, doi: 10.3390/app14198884.
[16] A. M. Alghamdi, “Social media analysis and knowledge management in tourism and hospitality: a systematic literature review,” Sustainability, vol. 15, no. 13, p. 9957, 2023, doi: 10.3390/su15139957.
[17] G. Heydari, “A sustainability-oriented spatial multi-criteria decision-making model for ecotourism development: a case study,” Sustainability, vol. 17, no. 2, p. 731, 2025, doi: 10.3390/su17020731.
[18] H. Lin, “Intention to visit smart tourism destinations based on the theory of planned behavior,” Sustainability, vol. 16, no. 23, p. 10484, 2024, doi: 10.3390/su162310484.
[19] Z.-A. Torabi, M. Pourtaheri, C. M. Hall, A. Sharifi, and F. Javidi, “Smart tourism technologies, revisit intention and word-of-mouth in emerging and smart rural destinations,” Sustainability, vol. 15, no. 14, p. 10911, 2023, doi: 10.3390/su151410911.
[20] E. Archi, “Smart tourism destinations: a systematic literature review analysing smart tourism destinations,” Sustainability, vol. 15, no. 6, p. 5086, 2023, doi: 10.3390/su15065086.
[21] N. L. Tran and W. Rudolf, “Social media and destination branding in tourism: a systematic literature review,” Sustainability, vol. 14, no. 20, p. 13528, 2022, doi: 10.3390/su142013528.
[22] F. Campbell et al., “Mapping reviews, scoping reviews, and evidence and gap maps (EGMs): the same but different— the ‘Big Picture’ review family,” Syst. Rev., vol. 12, no. 1, p. 45, Mar. 2023, doi: 10.1186/s13643-023-02178-5.
[23] M. J. Page, “The PRISMA 2020 statement: an updated guideline for reporting systematic reviews,” BMJ, vol. 372, p. n71, 2021, doi: 10.1136/bmj.n71.
[24] N. Alkharafi, “The role of women in driving national innovation and entrepreneurship through entrepreneurial ecosystems,” Journal of Innovation & Knowledge, vol. 9, no. 4, p. 100550, Oct. 2024, doi: 10.1016/j.jik.2024.100550.
[25] B. W. Wirtz, “Open government data, algorithms and artificial intelligence in the public sector: a systematic literature review,” Electronic Markets, vol. 32, pp. 1801–1820, 2022, doi: 10.1007/s12525-022-00553-2.
[26] V. Nicholson, “A FAIR perspective on data quality frameworks,” Data (Basel)., vol. 10, no. 9, p. 136, 2025, doi: 10.3390/data10090136.
[27] Fischer, “Approaches and tools for user-driven provenance and data quality in geospatial data,” Int. J. Digit. Earth, vol. 16, no. 1, pp. 1–24, 2023, doi: 10.1080/17538947.2023.2198778.
[28] J. Fan, W. Lu, S. B. Abduraimovna, J. Cheng, and H. Fan, “Graph-Guided Neural Network for Tourism Demand Forecasting,” IEEE Access, vol. 11, pp. 134259–134268, 2023, doi: 10.1109/ACCESS.2023.3336702.
[29] A. Stepin, “A survey of contrastive and counterfactual explanation generation methods for explainable AI,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3051315.
[30] H. Wu, “Research on interpretable tourism volume forecasting based on deep learning models,” Neural Comput. Appl., vol. 35, pp. 8653–8667, 2022, doi: 10.1007/s00521-022-07967-y.
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