Comparison of the Impact Pathways of Small Destination Entries on Different Crisis Events--Analysis based on Singapore Case

Comparison of the Impact Pathways of Small Destination Entries on Different Crisis Events--Analysis based on Singapore Case | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Comparison of the Impact Pathways of Small Destination Entries on Different Crisis Events--Analysis based on Singapore Case Ruibo Zha, Jiayi Wu, Zhuoyi Zheng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9227743/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Economic and epidemic crises severely impact global tourism, yet their effects on small-scale destinations remain understudied. Focusing on Singapore—a tourism-dependent small economy—this study examines its vulnerability and resilience using an integrated "Type-Scale" framework and the Soft-Hard Power Relationship Influence Model (SHPRIF). Qualitative Comparative Analysis (QCA) of Singapore’s inbound tourism data (1995–2024) reveals three pathways: (1) high net-worth tourists respond to economic/visa factors, while non-high net-worth groups rely on cultural/institutional stability; (2) epidemic crises involve harder power disruptions, whereas economic crises are mediated by soft power; (3) regional crises depend on proximity/adaptability, while global crises require compensatory measures like visa liberalization. Findings show resilience stems from dynamic hard-soft power synergy, suggesting small economies can enhance crisis strategy and market resilience through differentiated policy. Social science/Development studies Business and commerce/Economics Social science/Economics Scientific community and society/Geography Social science/Geography Social science/Politics and international relations Social science/Social policy Social science/Sociology Small Destinations Crisis Event Comparison Inbound Tourism QCA Impact Path Singapore Case Figures Figure 1 Figure 2 1. Introduction Small-scale tourism destinations are defined by their reliance on unique resources to offer niche experiences, setting them apart from mass tourism. Characterized by high external dependency, limited buffer capacity, and openness, these destinations are particularly vulnerable to systemic collapse during crises, a vulnerability exacerbated when their economic structures are deeply integrated into global systems. In an era of intensified globalization, economic and epidemic crises have emerged as the predominant disruptions to global inbound tourism. These crises follow distinct causal pathways: economic crises undermine demand by reducing source-market purchasing power, while epidemic crises suppress it through health risks and travel restrictions. The impact on small economies further varies depending on whether a crisis is regional or global, dictating whether the shock is geographically concentrated or globally synchronized. Singapore epitomizes such a small-scale, high-dependency destination. Its pronounced crisis awareness stems from inherent vulnerabilities as a trade-reliant city-state(Long, 2012 ), a model that accelerates the transmission of external shocks into domestic cascading crises. Since the 1990s, Singapore's tourism has endured four major disruptions: the 1997 Asian Financial Crisis, the 2003 SARS outbreak, the 2008 Global Financial Crisis, and the 2020 COVID-19 pandemic. These events, differing in type and scale, have each uniquely impacted its tourism sector, making Singapore an instructive case for examining how heterogeneous crises affect small destinations. Inbound tourism flow denotes the dynamic process through which groups of international tourists enter a host country, disperse or congregate across its tourism sites, and eventually depart. Extant literature on inbound tourism flows has primarily addressed seasonal variations (Grossi and Mussini, 2021 ; Wang et al., 2022 ; Yao et al., 2016 ; Ye et al., 2023 ;Ma, and Deng, 2021 ) driving mechanisms (Balli et al., 2016 ; Liu et al., 2015 ; Dou, 2019 ), regional economic interactions (Wang and Li, 2021 ), as well as market segmentation and its determinants (Ma et al., 2024 ). Research themes are broadly categorized into spatiotemporal dynamics, impact mechanisms, tourist behavioral characteristics, and on-site management practices (Zhang et al., 2013 ). Concurrently, scholars have increasingly adopted Qualitative Comparative Analysis (QCA), facilitating its evolution into a mainstream method with considerable potential for further utilization in tourism research (Zhang and Bao, 2021 ; Wang, 2023 ). Crisis events in tourism are typically defined as unexpected external shocks that exert significant negative impacts on tourism activities (Sun, 1998 ). Research on tourism crisis events in China has predominantly examined the differential impacts and recovery patterns associated with various crisis types (Li, 2009 ). In terms of methodological approaches, Sun Gennian (1998) pioneered the background trend line model to isolate the effects of crises, while Li Feng (2009) employed a baseline model to compare the differential impacts of four distinct crisis types on inbound tourism. Luo Miaoxuan and other scholars (Luo and Wu, 2018 ; Li et al., 2020 ) developed a combined TBTL-IA model to refine and supplement prior research. While research has extensively examined the impact of crisis events as a general phenomenon, studies that specifically analyze these impacts through the dual lenses of crisis scale and type remain relatively scarce. Furthermore, existing research on Singapore within China's tourism academia has primarily concentrated on themes including cultural identity construction (Li et al., 2020 ; Xia, 2008 ), sustainable tourism economic policies (Zha et al., 2018 ; Rong and Bu, 2018 ), digital technology empowerment (Wang and He, 2024 ), crisis response models (Yu, 2020 ), and regional cultural linkages (Gu and Yin, 2013 ) At present, relatively little research has focused on how crisis events affect inbound tourism specifically in Singapore as a small-scale destination. In summary, the existing literature exhibits three primary limitations. First, regarding research scope, there is a lack of integrative frameworks that simultaneously consider crisis type and scale to dissect their differential impacts on inbound tourism in small-scale destinations. Second, in terms of methodology, prevailing approaches are predominantly confined to basic data description and correlational analysis, lacking analytical tools—such as QCA—capable of revealing the configurational effects and causal pathways through which crises exert influence. Third, concerning case selection, research has disproportionately focused on medium and large destinations, resulting in a significant underrepresentation of small-scale, high-dependency cases like Singapore.In view of these gaps, this study adopts Singapore as a critical case. It employs QCA to examine the asymmetric causal pathways through which inbound tourism responds to crises. Furthermore, utilizing an integrated "Type-Scale" analytical framework, the research systematically compares and elucidates the differential response mechanisms of small-scale destinations under varying crisis conditions. Ultimately, the study aims to provide both a theoretical foundation and practical insights for optimizing the sustainable development of inbound tourism in such destinations. 2. Theoretical framework and assumptions 2.1 1.1 Tourism crisis"type-scale" framework Tourism crises are unexpected events that undermine tourist confidence, disrupt market functioning, and lead to industry volatility. Scholars categorize crises by their nature and scale. From a typological perspective, Li Jiuquan et al. classified crises based on cause and spatial scope, distinguishing between human-induced and natural, as well as international and domestic events, noting their distinct negative effects (Li et al., 2003 ). Similarly, Sun Gennian highlighted how different crisis types shape tourist risk perception and recovery patterns, such as the “U-shaped” cycle of financial crises versus the “W-shaped” cycle of epidemics (Sun, 2022 ). Consequently, crisis type determines the differential impact and distinct underlying mechanisms on tourism flows. From a scale perspective, crises vary in temporal and spatial reach, forming national and global scales, with impacts generally correlating positively with scale magnitude (Sun, 2022 ). Sun Gennian’s analysis (2010) of U.S. tourism confirmed that global crises exert bidirectional, persistent impacts with long recovery, whereas national events primarily suppress inbound flows, linking scale to impact symmetry and duration. Thus, the varying spatial impact range of crises across scales influences tourism market equilibrium. Building on this, this study constructs a “Type-Scale” framework to examine inbound tourism crisis response mechanisms in small destinations, analyzing both the intrinsic nature and spatial scale of crisis events. 2.2 The"soft-hard power" relationship model of inbound tourism The determinants of inbound tourism are multifaceted, encompassing factors such as visa policies (Yin et al., 2015 ), sister-city ties (Wang et al., 2019 ), transportation infrastructure (Ma and Sun, 2019 ), and cultural dynamics (Tang et al., 2022 ). Geographical distance reduces visitation rates (Wu et al., 1997 ), while source-country exchange rates and GDP act synergistically (Cheng et al., 2014 ), and population size positively correlates with tourism trade (Yin et al., 2015 ). Cultural distance also nonlinearly affects destination choice (Zhou and Bi, 2017 ). To integrate these complex factors, this study constructs the “Soft-Hard Power Relationship Influence Framework (SHPRIF)” by synthesizing tourism relationship theory (Zha et al., 2018 ; Zha et al., 2016 ) with insights from interpersonal and international relations (Zhang and Bao, 2021 ; Zha et al., 2022 ). SHPRIF examines six dimensions—geographical, economic, and population (hard relationships) and cultural, institutional, and visa (soft relationships)—and is further analyzed within a “Type-Scale” structure that accounts for crisis variations (Fig. 1).Empirically, tourism drivers interact dynamically, generating synergistic effects and explaining differences in high and non-high net-worth tourist composition across contexts. Different crisis types moderate these factor configurations distinctly (Lai et al., 2021 ), and spatial variation in crises creates heterogeneous impact mechanisms. Whereas regional crises are geographically contained, global crises exert wider influence, leading to divergent effects on inbound tourism (Sun et al., 2010 ). Based on this, the study proposes three hypotheses regarding SHPRIF’s pathways under economic and epidemic crises: H1: The configuration paths of high and non-high net-worth inbound tourists in Singapore are asymmetric across crisis types. H2: SHPRIF’s impact pathways on small destinations differ between economic and epidemic crises. H3: The impact pathways of regional and global crises on small destinations exhibit distinct characteristics. 3. Research Methods and Data Sources 3.1 Selection of variables and data sources 3.1.1number of inbound tourists The dependent variable data on inbound tourism relationships originate from 52 major source countries/regions provided by the Singapore Department of Statistics( https://www.singstat.gov.sg ). After excluding cases with missing or discontinued statistics, 34 consistent and accurate cases were retained as outcome variables. Additional data were sourced from the World Bank and UNWTO. The processed dataset maintains authenticity and meets QCA case-study requirements (Du and Jia, 2017 ). Monthly averaging was applied to relevant indicators across event periods to ensure comparability 3.1.2 Index of influencing factors of "soft-hard" strength relationship Data for the independent variables, comprising hard-power factors and soft-power factors, were obtained as follows: Hard-Power Factor (HP):① Geographic Dimension (GD): Distcap data ( http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp ), representing the spherical distance between Singapore and the capital or political center of each relevant country or region.② Economic Dimension (ED): measured by the bilateral trade index, calculated as the ratio of GDP between each country/region and Singapore (source: https://databank.worldbank.org/home.aspx) .③ Population Dimension (PD): measured by the United Nations Human Development Index (source: https://hdr.undp.org/ ). Soft-Power Factor (SP):④ Cultural Dimension (CD): Measured using six cultural indicators from Hofstede Insights ( https://www.hofstede-insights.com/ ), including power distance, individualism, achievement motivation, uncertainty avoidance, long-term orientation, and indulgence. These indicators were integrated into a composite measure via the KSI index method (Kogut and Singh, 1988 ), calculated as follows: $$\:{CD}_{ij}=\frac{1}{6}{\sum\:}_{k=1}^{6}{\left({C}_{kti}-{C}_{ktj}\right)}^{2}/{V}_{kt}$$ 1 where CDij represents the cultural distance between Singapore and country j, Ckti and Cktj denote the scores of Singapore and country j on the k-th cultural dimension, respectively, and Vkt is the variance of the scores across all sample countries on the k-th dimension. ⑤Institutional dimension(ID): The institutional dimension draws upon six indicators from the Worldwide Governance Indicators system ( http://info.worldbank.org/governance/wgi ), covering: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, and Government Effectiveness. Regulatory Quality, Rule of Law, and Control of Corruption are also synthesized using the KSI index method (Kogut and Singh, 1988 ). ⑥ Visa Dimension (VD): Bilateral visa policy data between Singapore and each case country/region were obtained from the official website of the Singapore Immigration & Checkpoints Authority (ICA; https://www.ica.gov.sg ). These policies were qualitatively quantified using an ordinal scale: prohibition = 0, visa application required = 1, visa on arrival or electronic visa = 2, visa exemption for up to 30 days = 3, visa exemption for 31–90 days = 4, and visa exemption for over 90 days = 5. Missing data were interpolated based on actual contextual circumstances. 3.1.3 Time period division Data visualization shows Singapore’s inbound tourism grew consistently from 1995 to 2019, with arrivals rising from 7.14 million to 19.1 million (a 167% increase). However, during four episodes of economic and epidemic crises, growth turned negative and tourist volume contracted, confirming Singapore’s high susceptibility as a small-scale destination to global shocks (Fig. 2). To examine potential differences in impact pathways, this study selects these four crises and categorizes them by spatial scope:Event 1 (ECO-R): Asian Financial Crisis (Jul 1997–Dec 1998), regional economic crisis.Event 2 (PAN-R): SARS outbreak (Nov 2002–Jul 2003), regional epidemic crisis.Event 3 (ECO-G): Global Financial Crisis (Aug 2007–Mar 2009), global economic crisis.Event 4 (PAN-G): COVID-19 pandemic (Jan 2020–Dec 2021), global epidemic crisis. 3.2 Qualitative Comparative Analysis (QCA) 3.2.1 A Review of QCA Methods Qualitative Comparative Analysis (QCA), introduced by Ragin in the late 1980s, is a configurational method that integrates qualitative and quantitative approaches. It examines combinations of causal conditions and asymmetric effects, making it well-suited for complex social phenomena and applicable across different sample sizes (Du and Jia, 2017 ). Common variants include crisp-set, fuzzy-set, and multi-value QCA. Given the continuous variables (e.g., crisis severity, policy intensity) and limited sample size in Singapore’s inbound tourism data, the fuzzy-set approach was employed to enhance robustness, implemented via fsQCA 3.0 software. 2.2.2 fsQCA method steps Following Ragin’s methodological guidelines, this study implements fsQCA through six sequential steps: ①Case Selection: Sample size was determined based on the research question and practical constraints, ensuring comparability by setting a population-heterogeneity threshold via multi-dimensional indicators. For example, 34 inbound source countries reported by the Singapore Tourism Board were selected, aligning with fsQCA’s suitability for small-sample robustness, given Singapore’s status as a small open economy with limited data availability. ②Selection of Antecedent Conditions: Based on theoretical rationale, priority was given to key antecedent variables causally related to the outcome. To maintain concise and meaningful conclusions, variables were streamlined to focus on core relationships, mitigating “limited diversity” through differentiated combinations. Six core inbound-tourism-related variables were selected, consistent with recommendations that small-to-medium-sized samples include 4–7 antecedents (Ragin, 2008 ). ③Calibration of Case Data: The direct calibration method commonly used in academic practice was employed. Each variable was treated as a set, with raw values transformed into membership scores in [0,1], enabling fsQCA software to assess the degree of membership in “high” or “low” sets. ④Analysis of Condition Sufficiency: Software procedures were used to calculate consistency and coverage of calibrated data, verifying configuration adequacy. Consistency measures the necessity of a condition by assessing how far the outcome set is a subset of the condition set; coverage evaluates sufficiency by indicating the proportion of the outcome explained by the condition among cases passing the consistency threshold. Following Schneider et al. (2009), the consistency threshold for necessary conditions was set at 0.9. ⑤Analysis of Configuration Results: After necessity analysis, the truth-table algorithm systematically enumerated all logically possible condition configurations and their associations with the outcome. Given a natural cutoff in raw consistency values, the consistency threshold was set at 0.8. As the sample size (N = 34) is small-to-medium, the case-frequency threshold was set to 1. The PRI (Proportional Reduction in Inconsistency) threshold was set at 0.7, with configurations having PRI ≥ 0.7 retained as sufficient—a criterion also used by Du Yunzhou et al. The intermediate solution was selected for interpretation because its moderate complexity aligns with theoretical expectations and retains necessary conditions. Core, complementary, and peripheral conditions were distinguished by comparing intermediate and parsimonious solutions. ⑥Robustness Testing of Configurations: Core parameters (calibration, frequency, and consistency thresholds) were systematically adjusted to observe variation in configuration results across different parameter combinations. Theoretical explanations under different settings were compared; configurations were considered robust if results remained stable, otherwise deemed non-robust. Moreover, QCA treats high net-worth and non-high net-worth as relative, not absolute, constructs in configuration analysis. 4. Analysis of inbound tourism configuration path results 4.1 variable calibration Given the non‑homogeneous nature of the selected “soft–hard” power indicators, direct comparison was not feasible. Therefore, aligning with data distribution characteristics, the direct calibration method was applied to both outcome and antecedent variables. Following the approach of Du Yunzhou et al., the 75th percentile, median (50th percentile), and 25th percentile were set as anchor points for full membership, crossover, and full non‑membership, respectively. Values above the 75th percentile were calibrated as fully affiliated, below the 25th as fully non‑affiliated, and the median as the crossover point. This process transformed raw data into comparable fuzzy‑set membership scores, meeting fsQCA input requirements (Table 1). 4.2 Configuration variable consistency analysis This study examines causal relationships using necessity and sufficiency criteria. Following Schneider et al. (2010), sufficiency thresholds are 0.85 (Ragin, 2008) or 0.80 (Fiss, 2011), while necessity requires consistency ≥0.9. All individual antecedent conditions show consistency below 0.9 (Table 2), indicating no single condition is necessary for Singapore’s outcome. Instead, outcomes arise from combined factors, highlighting the multi‑causal nature of complex social systems and justifying the use of fsQCA to analyze configurational effects. 4.3 Configuration path difference analysis 4 .3.1Net worth perspective: "high-not high" inbound tourism relationship path comparison Consistency in Singapore's configuration models for high and non‑high net‑worth inbound tourism exceeds 0.8, indicating statistical significance and strong reliability. ①For high inbound tourism, 15 pathways were identified (Table 3) : 4 ECO‑R, 3 PAN‑R, 3 ECO‑G, and 5 PAN‑G. The configuration~GD~CDEDPDVD shows the absence of geographical and cultural dimensions, while economic, population, and visa dimensions remain stable. Visa dimension recurs across stages, suggesting high configurational involvement. Over time, GD is typically absent as a core condition, whereas ED shifts from core to absent in some paths, reflecting temporal variation in influence. Some pathways (e.g., T2R2, T3R2) repeat across stages, indicating structural stability. Soft‑power factors have broad influence but appear in only 6 pathways. The economic dimension significantly shapes tourism relationships, underscoring the need for economic foundations. ~CD shows frequent core‑condition absence, implying cultural factors are not central. Case sites include both neighboring East/Southeast Asian regions and distant European/American countries, illustrating that high inbound tourism relies not only on proximity but also on economic, visa, and other multidimensional drivers across spatial scales. ②Non‑high inbound tourism (Table 4): 11 pathways were identified (3 ECO‑R, 3 PAN‑R, 2 ECO‑G, 3 PAN‑G). The configuration~GD~VDCD*ID shows geographical and visa dimensions absent as core conditions, while CD remains stable as core/complementary across most stages—indicating culture’s consistent role. In contrast, ID varies significantly in core‑condition status. R1 recurs across stages, while R2 and R3 differ in performance, reflecting stage‑specific combinations. Soft‑power factors exert broad influence but appear in only 2 of 11 pathways, showing their impact can be present or absent. CD is key in constructing non‑high inbound tourism relationships. PD and VD act only as complementary conditions in certain stages, implying limited influence. Thus, complementary conditions often need to collaborate with core conditions to shape specific pathways. Case sites with fixed combinations appear consistently across stages 1‑4, indicating stability in dimensions such as GD and CD over time. ③Comparison reveals distinct dimensional influences across tourism types. In high inbound tourism, the economic dimension frequently serves as a core condition with significant influence, whereas its role is often absent or weaker in non‑high inbound tourism. Similarly, the visa dimension commonly acts as a core condition in high inbound contexts, reflecting visa convenience’s strong facilitating effect, but is typically absent as a core condition and exhibits limited influence in non‑high inbound tourism. Under the soft‑hard power framework, high inbound configurations are primarily dominated by economic and visa dimensions (reflecting co‑dominance of hard and soft power), while non‑high inbound configurations are mainly led by the cultural dimension, with other dimensions playing complementary roles. Overall, different inbound tourism relationships depend distinctly on soft‑ and hard‑power dimensions. 4 .3.2 Type perspective: comparison of "economy-epidemic" crisis paths Pathway analysis reveals clear distinctions between economic and epidemic crises. Epidemic crises generate more pathways than economic crises for both high and non‑high inbound tourism, with structures that are more complex in high tourism cases and comparatively simpler in non‑high tourism cases. Specifically, economic crises yield 7 high and 4 non‑high pathways, while epidemic crises produce 8 and 6 pathways respectively. This pattern suggests that epidemic crises involve richer combinations of conditions and more extensive correlation mechanisms. Structurally, economic crises position the economic dimension as core for high tourism and the cultural dimension as core for non‑high tourism. In contrast, epidemic crises shift the emphasis to the visa and population dimensions for high tourism, and to the institutional and population dimensions for non‑high tourism. Beyond the common geographic factor, all other influencing factors exhibit varied and complex causal roles across different crises. 4 .3.3 Scale perspective: comparison of "regional-global" crisis paths Under the global‑regional scale dimension, pathways for regional crises are limited in number and diversity, especially in Stage 1. In contrast, global crises produce more pathways (e.g., expanding to R1‑R5 in Stage 4), demonstrating greater complexity and diversity. Path performance shows that regional crises involve simpler dimensional combinations with stable core/complementary conditions, while global crises combine multiple dimensions (e.g., geography, economy, culture, institutions) with more frequent shifts in condition roles. The influence of regional crises remains spatially concentrated, with cases mostly within the affected region. Global crises, however, span continents—including Asia, Europe, and the Americas—and exhibit an expanding impact across more countries and tourism‑relationship types. 5. Conclusions and discussion 5.1 Conclusion Drawing on the integrated “type‑scale” and “soft‑hard” relational influence (SHPRIF) frameworks, this study examines how economic and epidemic crises configurationally affect Singapore’s inbound tourism. The results support the three proposed hypotheses (H1, H2, H3) and can be summarized as follows: Configuration paths differ by net‑worth level. High inbound tourism has more numerous and structurally more complex pathways than non‑high inbound tourism, which shows relatively uniform configurations. High tourism is predominantly shaped by economic and visa dimensions, while non‑high tourism relies more on cultural and institutional dimensions. The visa factor is core for high tourism; cultural and institutional factors are core for non‑high tourism. Soft‑power relationships exert more extensive influence than other hard‑power factors on both tourism types. These findings confirm H1. Configuration pathways also vary by crisis type. Epidemic crises generate more numerous, more complex, and richer pathway combinations than economic crises for both high and non‑high tourism. During economic crises, high tourism centers on the economic dimension, whereas non‑high tourism centers on the cultural dimension. During epidemic crises, high tourism is dominated by visa and population dimensions, while non‑high tourism is centered on institutional and population dimensions. Furthermore, epidemic crises affect a broader set of source‑market countries, indicating a wider spatial impact. These differences confirm H2. Finally, configuration pathways differ by crisis scale. In regional economic crises, high inbound tourism is strongly influenced by economic activity; in global economic crises, it is constrained by economic contraction, requiring compensation via non‑economic factors such as visa convenience and institutional stability. For epidemic crises, global outbreaks trigger a surge in high‑tourism‑covered countries, whereas regional outbreaks rely on an “institutional + geographical proximity” configuration. Global epidemics, due to institutional deficiencies, shift toward visa‑convenience alternatives and reconstruct networks through visa and population dimensions. These scale‑based distinctions confirm H3. 5.2 Discussion and recommendations This study employs a “Type‑Scale” and “Soft‑Hard” power framework to examine Singapore’s crisis responses and inbound tourism configuration pathways via qualitative comparative analysis, exploring causal asymmetry. The findings largely support the hypotheses and offer explanatory insights. Analysis under the “Type‑Scale” framework shows that soft‑ and hard‑power factors critically influence both high and non‑high inbound tourism markets in Singapore. Economic crises directly affect economic conditions, while epidemic crises rely more heavily on visa policies. The interplay of soft‑ and hard‑power factors (e.g., mobility restrictions) triggers multidimensional chain effects. Global crises exert broader and more complex impacts than regional crises, confirming that larger‑scale crises demand longer tourism‑system recovery cycles. Cultural factors are crucial in non‑high inbound markets, requiring attention to cultural adaptability and institutional optimization. In high net‑worth markets, economic foundations and visa convenience are central; flexible visa policies can mitigate systemic risks during sudden crises such as epidemics. Based on these conclusions, the following recommendations are proposed for small‑scale destinations: 1.Coordinate soft and hard power: High net‑worth tourists emphasize hard power (infrastructure, services), hile non‑high net‑worth tourists are drawn to soft power (culture, institutions). Economic bases attract the former; cultural appeal attracts the latter. Avoid one‑size‑fits‑all approaches; flexibly adjust strategies based on target segments and market dynamics. Optimize visa procedures to enhance hard‑power attributes, and deepen local cultural exploration through inclusive activities and transparent governance to attract diverse tourists. 2.Differentiate crisis responses: Epidemic crises impact inbound tourism more severely than economic crises. Small open economies should formulate tailored plans. For epidemic crises, establish cooperation with high‑yield neighboring markets and implement transparent health‑safety policies to reduce risks and build resilience. For economic crises, adopt dynamic pricing, focus on short‑term markets, and engage nearby customers to secure baseline revenue. 3.Establish scale‑based response mechanisms: Small‑scale destinations have limited hinterlands but high openness, rendering them highly sensitive to crises of all scales. Differentiated contingency plans should be developed according to crisis scale, supported by multi‑stakeholder scenario drills and adequate daily‑supply reserves to minimize impact when crises occur. Declarations Author Contribution Zha conceived the study and designed the theoretical framework. Wu conducted the data analysis and Zheng wrote the main manuscript text. Wu and Zheng collected and processed the data, performed the calibration and QCA analysis, and prepared the figures and tables. Zha and Wu contributed to the interpretation of results and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript. Acknowledgement This work was supported by the National Natural Science Foundation of China (Award Number 42201267), the China Scholarship Council (Award Number 202108350051), and the Fujian Provincial Department of Science and Technology (Award Number 2023R0031). Data Availability The dependent variable data on inbound tourism relationships were obtained from the Singapore Department of Statistics (https://www.singstat.gov.sg), which covers 52 major source countries or regions. After excluding cases with missing statistics and those where data collection had been discontinued, 34 groups of source countries or regions with consistent and accurate data were selected as the outcome variable for the case study. Additional data used for calculations were sourced from the World Bank and the UNWTO Statistical Yearbook. Ethical approval: This article does not contain any studies with human participants performed by any of the authors. 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Singapore's Experience in Natural Resources Management and Its Enlightenment to the Construction of Hainan Free Trade Zone and International Tourism Island. Land & Resources Information, (12), 10-15+9. Wang, Y., & He, M. M. (2024). The Spatial Pattern and Practical Characteristics of Singapore's Educational Digital Transformation. Comparative Education Review, 46(12), 89-97. https://doi.org/10.20013/j.cnki.ICE.2024.12.10 Yu, W. X. (2020). Sudden Crisis Events and Organizational Learning: Insights from Singapore's COVID-19 Response Strategies. Urban Governance Studies, 5(01), 76-96+4-5. Gu, Z. Q., & Yin, J. D. (2013). A Preliminary Exploration of the Composite Linkage between Culture and Business in Contemporary Urban Cultural Architecture—Taking the Esplanade - Theatres on the Bay and the Singapore Civic Centre as Examples. Architecture & Culture, (07), 66-67. Li, J. Q., Li, K. Y., & Zhang, Y. F. (2003). Crisis Accidents and Its Management in Tourism. Human Geography, (06), 35-39. Sun, G. N. (2022). Crisis Events and Tourism Recovery. Tourism Tribune, 37(07), 1-4. https://doi.org/10.19765/j.cnki.1002-5006.2022.07.001 Sun, G. N., Shu, J. J., Ma, L. J., & Wang, J. J. (2010). Influences of Five Crises on US Inbound and Outbound Tourism: Analysis Based on the Tourism Background Trend Line in High Time Resolution. Progress in Geography, 29(08), 987-996. Yin, J., Zheng, X. M., & Dong, B. B. (2015). The 21st Century Maritime Silk Road Tourism Trade：Potential, Efficiency and Its Influencing Factors. Southeast Asian Affairs, (11), 8-14. Wang, Y. H., Quan, H., & Wang, Y. L. (2019). Effect of International Sister City on Inbound Tourism and Its Spatiotemporal Heterogeneity. Progress in Geography, 38(12), 1903-1916. Ma, H. H., & Sun, G. N. (2019). An Empirical Research on the Interaction of Transportation, International Tourism and Trade (3T) in Hong Kong. Journal of Chongqing University of Arts and Sciences (Social Sciences Edition), 38(04), 35-44. https://doi.org/10.19493/j.cnki.issn1673-8004.2019.04.005 Tang, P., He, J. M., & Feng, X. G. (2022). Impact of Cultural Conflict on Chinese Inbound Tourism Demand. Scientia Geographica Sinica, 42(04), 711-719. https://doi.org/10.13249/j.cnki.sgs.2022.04.016 Wu, B. H., Tang, J. Y., Huang, A. M, et al. (1997). A Study of Tourist Destination Selection Behavior of Chinese Urban Residents. Acta Geographica Sinica, (02), 3-9. Cheng, Y. W., Fan, R. T., & Zhang, H. (2014). Research on the Impact of RMB Exchange Rate Changes on China’s Inbound and Outbound Tourism Market: Analysis Based on Panel Data of Major Source and Destination Countries. Exploration of Economic Issues, (06), 93-101. Zhou, L. Q., & Bi, J. (2017). The Influence of Cultural Distance on International Tourism Destination Choice: A Case Study of Chinese Inbound Tourism Market. Journal of Zhejiang University (Humanities and Social Sciences), 47(04), 130-142. Zha, R. B., Sun, G. N., & Dong, Z. B. (2016). Changes of the Hong Kong Inbound Tourism Relationship Circle and Its Indications Since 1976. Acta Geographica Sinica, 71(10), 1801-1814. Zha, R. B., Huang, Y., Du, S. Y, et al. (2022). The Response Path of Inbound and Outbound Tourism of Urban Destinations under Inter-Regional Relationship Influence Framework: Configuration Analysis Based on the Data of Hong Kong, China, from 1997 to 2019. Scientia Geographica Sinica, 42(10), 1788-1798. https://doi.org/10.13249/j.cnki.sgs.2022.10.011 Lai, F. F., Xie, C. W., & Huang, R. (2021). Research on the Impact of Multidimensional Distance Factors on China’s Outbound Tourism under Two-Stage Scenarios. Geography and Geo-Information Science, 37(04), 128-136. Du, Y. Z., & Jia, L. D. (2017). Configurational Perspective and Qualitative Comparative Analysis (QCA): A New Path for Management Research. Management World, (06), 155-167.https://doi.org/10.19744/j.cnki.11-1235/f.2017.06.012. Kogut, B., & Singh, H. (1988). The Effect of National Culture on the Choice of Entry Mode. Journal of International Business Studies, 19(3), 411-432. Ragin, C. C. (2008). Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago Press. Carsten Q. Schneider & Claudius Wagemann.(2010).Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets.Comparative Sociology,9(3),418-418. https://doi.org/10.1163/156913210X12493538729793. Fiss, P. C. (2011). Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Academy of Management Journal, 54(2), 393-420.https://doi.org/10.5465/amj.2011.59637329 Tables Tables 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9227743","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":631435260,"identity":"e3e42668-e3f7-4fb7-9687-d28fca6f225a","order_by":0,"name":"Ruibo Zha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACCQY2BoYKOWYwh4d4LWeMSdXC2GbMQLwW89ntzx78nGfAbi6RwPjgbRuDvDkhLTJ3DqQb9m4zYLackcBsOLeNwXBnAyF3SSQck+Dd9ofZ4EYCmzRvG0OCwQGCWhLbJP/OMQBpYf9NpJZkoOENYC1szMRpkTnGJi1zDKjlzMNmyTnnJAw3ENQi3f5M8k2NQbLB8eSDH96U2cgTtAUGkhkYGBtARhCpHgjsiFc6CkbBKBgFIw4AAHZdN/HVuJ36AAAAAElFTkSuQmCC","orcid":"","institution":"Fujian Normal University","correspondingAuthor":true,"prefix":"","firstName":"Ruibo","middleName":"","lastName":"Zha","suffix":""},{"id":631435261,"identity":"dc2a25b8-d815-453d-b90a-fb1ac6a0ab75","order_by":1,"name":"Jiayi Wu","email":"","orcid":"","institution":"Fujian Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiayi","middleName":"","lastName":"Wu","suffix":""},{"id":631435263,"identity":"9fc3709d-531d-4d01-a4cd-3b803e7d8baf","order_by":2,"name":"Zhuoyi Zheng","email":"","orcid":"","institution":"Fujian Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhuoyi","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2026-03-26 00:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9227743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9227743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108821319,"identity":"0f00f61e-6c75-45e8-84b6-8a8340380355","added_by":"auto","created_at":"2026-05-08 16:45:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":271797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eModel of the SHPRIF Response Path of Inbound Tourism in Singapore under the \"Type-Scale\" crisis Framework\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9227743/v1/48eb37cd5327c7df9774e3fa.png"},{"id":108821313,"identity":"318607ee-297d-4ffb-8ef7-5db68add7dba","added_by":"auto","created_at":"2026-05-08 16:45:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":259098,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMonthly change of total inbound tourist arrivals in Singapore from 1995 to 2024\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9227743/v1/9c547d83a5945abbd0fbf422.png"},{"id":108822902,"identity":"cb9eaf00-0455-4b82-ac00-5ce40e395c05","added_by":"auto","created_at":"2026-05-08 16:51:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":779672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9227743/v1/58c90b9b-36ae-4a18-abe1-897ebe5a7969.pdf"},{"id":108820956,"identity":"5511458b-b0b1-460b-b145-846ee686cf13","added_by":"auto","created_at":"2026-05-08 16:44:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":232118,"visible":true,"origin":"","legend":"","description":"","filename":"rawdatasets.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9227743/v1/89691a8bd95a03a09a726704.xlsx"},{"id":108821318,"identity":"8f267c72-322d-4253-abb7-5ed55e7926b6","added_by":"auto","created_at":"2026-05-08 16:45:16","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":39676,"visible":true,"origin":"","legend":"","description":"","filename":"Tables1234.docx","url":"https://assets-eu.researchsquare.com/files/rs-9227743/v1/0c931644df43d0f01e3b60bb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of the Impact Pathways of Small Destination Entries on Different Crisis Events--Analysis based on Singapore Case","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSmall-scale tourism destinations are defined by their reliance on unique resources to offer niche experiences, setting them apart from mass tourism. Characterized by high external dependency, limited buffer capacity, and openness, these destinations are particularly vulnerable to systemic collapse during crises, a vulnerability exacerbated when their economic structures are deeply integrated into global systems. In an era of intensified globalization, economic and epidemic crises have emerged as the predominant disruptions to global inbound tourism. These crises follow distinct causal pathways: economic crises undermine demand by reducing source-market purchasing power, while epidemic crises suppress it through health risks and travel restrictions. The impact on small economies further varies depending on whether a crisis is regional or global, dictating whether the shock is geographically concentrated or globally synchronized.\u003c/p\u003e \u003cp\u003eSingapore epitomizes such a small-scale, high-dependency destination. Its pronounced crisis awareness stems from inherent vulnerabilities as a trade-reliant city-state(Long, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), a model that accelerates the transmission of external shocks into domestic cascading crises. Since the 1990s, Singapore's tourism has endured four major disruptions: the 1997 Asian Financial Crisis, the 2003 SARS outbreak, the 2008 Global Financial Crisis, and the 2020 COVID-19 pandemic. These events, differing in type and scale, have each uniquely impacted its tourism sector, making Singapore an instructive case for examining how heterogeneous crises affect small destinations.\u003c/p\u003e \u003cp\u003eInbound tourism flow denotes the dynamic process through which groups of international tourists enter a host country, disperse or congregate across its tourism sites, and eventually depart. Extant literature on inbound tourism flows has primarily addressed seasonal variations (Grossi and Mussini, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ye et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e;Ma, and Deng, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) driving mechanisms (Balli et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dou, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), regional economic interactions (Wang and Li, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), as well as market segmentation and its determinants (Ma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Research themes are broadly categorized into spatiotemporal dynamics, impact mechanisms, tourist behavioral characteristics, and on-site management practices (Zhang et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Concurrently, scholars have increasingly adopted Qualitative Comparative Analysis (QCA), facilitating its evolution into a mainstream method with considerable potential for further utilization in tourism research (Zhang and Bao, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCrisis events in tourism are typically defined as unexpected external shocks that exert significant negative impacts on tourism activities (Sun, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Research on tourism crisis events in China has predominantly examined the differential impacts and recovery patterns associated with various crisis types (Li, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In terms of methodological approaches, Sun Gennian (1998) pioneered the background trend line model to isolate the effects of crises, while Li Feng (2009) employed a baseline model to compare the differential impacts of four distinct crisis types on inbound tourism. Luo Miaoxuan and other scholars (Luo and Wu, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) developed a combined TBTL-IA model to refine and supplement prior research. While research has extensively examined the impact of crisis events as a general phenomenon, studies that specifically analyze these impacts through the dual lenses of crisis scale and type remain relatively scarce. Furthermore, existing research on Singapore within China's tourism academia has primarily concentrated on themes including cultural identity construction (Li et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xia, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), sustainable tourism economic policies (Zha et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rong and Bu, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), digital technology empowerment (Wang and He, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), crisis response models (Yu, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and regional cultural linkages (Gu and Yin, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) At present, relatively little research has focused on how crisis events affect inbound tourism specifically in Singapore as a small-scale destination.\u003c/p\u003e \u003cp\u003eIn summary, the existing literature exhibits three primary limitations. First, regarding research scope, there is a lack of integrative frameworks that simultaneously consider crisis type and scale to dissect their differential impacts on inbound tourism in small-scale destinations. Second, in terms of methodology, prevailing approaches are predominantly confined to basic data description and correlational analysis, lacking analytical tools\u0026mdash;such as QCA\u0026mdash;capable of revealing the configurational effects and causal pathways through which crises exert influence. Third, concerning case selection, research has disproportionately focused on medium and large destinations, resulting in a significant underrepresentation of small-scale, high-dependency cases like Singapore.In view of these gaps, this study adopts Singapore as a critical case. It employs QCA to examine the asymmetric causal pathways through which inbound tourism responds to crises. Furthermore, utilizing an integrated \"Type-Scale\" analytical framework, the research systematically compares and elucidates the differential response mechanisms of small-scale destinations under varying crisis conditions. Ultimately, the study aims to provide both a theoretical foundation and practical insights for optimizing the sustainable development of inbound tourism in such destinations.\u003c/p\u003e"},{"header":"2. Theoretical framework and assumptions","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 1.1 Tourism crisis\"type-scale\" framework\u003c/h2\u003e \u003cp\u003eTourism crises are unexpected events that undermine tourist confidence, disrupt market functioning, and lead to industry volatility. Scholars categorize crises by their nature and scale. From a typological perspective, Li Jiuquan et al. classified crises based on cause and spatial scope, distinguishing between human-induced and natural, as well as international and domestic events, noting their distinct negative effects (Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Similarly, Sun Gennian highlighted how different crisis types shape tourist risk perception and recovery patterns, such as the \u0026ldquo;U-shaped\u0026rdquo; cycle of financial crises versus the \u0026ldquo;W-shaped\u0026rdquo; cycle of epidemics (Sun, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Consequently, crisis type determines the differential impact and distinct underlying mechanisms on tourism flows. From a scale perspective, crises vary in temporal and spatial reach, forming national and global scales, with impacts generally correlating positively with scale magnitude (Sun, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sun Gennian\u0026rsquo;s analysis (2010) of U.S. tourism confirmed that global crises exert bidirectional, persistent impacts with long recovery, whereas national events primarily suppress inbound flows, linking scale to impact symmetry and duration. Thus, the varying spatial impact range of crises across scales influences tourism market equilibrium. Building on this, this study constructs a \u0026ldquo;Type-Scale\u0026rdquo; framework to examine inbound tourism crisis response mechanisms in small destinations, analyzing both the intrinsic nature and spatial scale of crisis events.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The\"soft-hard power\" relationship model of inbound tourism\u003c/h2\u003e \u003cp\u003eThe determinants of inbound tourism are multifaceted, encompassing factors such as visa policies (Yin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), sister-city ties (Wang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), transportation infrastructure (Ma and Sun, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and cultural dynamics (Tang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Geographical distance reduces visitation rates (Wu et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), while source-country exchange rates and GDP act synergistically (Cheng et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), and population size positively correlates with tourism trade (Yin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Cultural distance also nonlinearly affects destination choice (Zhou and Bi, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To integrate these complex factors, this study constructs the \u0026ldquo;Soft-Hard Power Relationship Influence Framework (SHPRIF)\u0026rdquo; by synthesizing tourism relationship theory (Zha et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zha et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) with insights from interpersonal and international relations (Zhang and Bao, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zha et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). SHPRIF examines six dimensions\u0026mdash;geographical, economic, and population (hard relationships) and cultural, institutional, and visa (soft relationships)\u0026mdash;and is further analyzed within a \u0026ldquo;Type-Scale\u0026rdquo; structure that accounts for crisis variations (Fig.\u0026nbsp;1).Empirically, tourism drivers interact dynamically, generating synergistic effects and explaining differences in high and non-high net-worth tourist composition across contexts. Different crisis types moderate these factor configurations distinctly (Lai et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and spatial variation in crises creates heterogeneous impact mechanisms. Whereas regional crises are geographically contained, global crises exert wider influence, leading to divergent effects on inbound tourism (Sun et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Based on this, the study proposes three hypotheses regarding SHPRIF\u0026rsquo;s pathways under economic and epidemic crises:\u003c/p\u003e \u003cp\u003eH1: The configuration paths of high and non-high net-worth inbound tourists in Singapore are asymmetric across crisis types.\u003c/p\u003e \u003cp\u003eH2: SHPRIF\u0026rsquo;s impact pathways on small destinations differ between economic and epidemic crises.\u003c/p\u003e \u003cp\u003eH3: The impact pathways of regional and global crises on small destinations exhibit distinct characteristics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Research Methods and Data Sources","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Selection of variables and data sources\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1number of inbound tourists\u003c/h2\u003e \u003cp\u003eThe dependent variable data on inbound tourism relationships originate from 52 major source countries/regions provided by the Singapore Department of Statistics(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.singstat.gov.sg\u003c/span\u003e\u003cspan address=\"https://www.singstat.gov.sg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). After excluding cases with missing or discontinued statistics, 34 consistent and accurate cases were retained as outcome variables. Additional data were sourced from the World Bank and UNWTO. The processed dataset maintains authenticity and meets QCA case-study requirements (Du and Jia, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Monthly averaging was applied to relevant indicators across event periods to ensure comparability\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Index of influencing factors of \"soft-hard\" strength relationship\u003c/h2\u003e \u003cp\u003eData for the independent variables, comprising hard-power factors and soft-power factors, were obtained as follows:\u003c/p\u003e \u003cp\u003eHard-Power Factor (HP):① Geographic Dimension (GD): Distcap data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cepii.fr/cepii/en/bdd_modele/bdd.asp\u003c/span\u003e\u003cspan address=\"http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), representing the spherical distance between Singapore and the capital or political center of each relevant country or region.② Economic Dimension (ED): measured by the bilateral trade index, calculated as the ratio of GDP between each country/region and Singapore (source: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://databank.worldbank.org/home.aspx)\u003c/span\u003e\u003cspan address=\"https://databank.worldbank.org/home.aspx)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.③ Population Dimension (PD): measured by the United Nations Human Development Index (source: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hdr.undp.org/\u003c/span\u003e\u003cspan address=\"https://hdr.undp.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoft-Power Factor (SP):④ Cultural Dimension (CD): Measured using six cultural indicators from Hofstede Insights (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.hofstede-insights.com/\u003c/span\u003e\u003cspan address=\"https://www.hofstede-insights.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), including power distance, individualism, achievement motivation, uncertainty avoidance, long-term orientation, and indulgence. These indicators were integrated into a composite measure via the KSI index method (Kogut and Singh, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), calculated as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{CD}_{ij}=\\frac{1}{6}{\\sum\\:}_{k=1}^{6}{\\left({C}_{kti}-{C}_{ktj}\\right)}^{2}/{V}_{kt}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere CDij represents the cultural distance between Singapore and country j, Ckti and Cktj denote the scores of Singapore and country j on the k-th cultural dimension, respectively, and Vkt is the variance of the scores across all sample countries on the k-th dimension. ⑤Institutional dimension(ID): The institutional dimension draws upon six indicators from the Worldwide Governance Indicators system (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://info.worldbank.org/governance/wgi\u003c/span\u003e\u003cspan address=\"http://info.worldbank.org/governance/wgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), covering: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, and Government Effectiveness. Regulatory Quality, Rule of Law, and Control of Corruption are also synthesized using the KSI index method (Kogut and Singh, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). ⑥ Visa Dimension (VD): Bilateral visa policy data between Singapore and each case country/region were obtained from the official website of the Singapore Immigration \u0026amp; Checkpoints Authority (ICA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ica.gov.sg\u003c/span\u003e\u003cspan address=\"https://www.ica.gov.sg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These policies were qualitatively quantified using an ordinal scale: prohibition\u0026thinsp;=\u0026thinsp;0, visa application required\u0026thinsp;=\u0026thinsp;1, visa on arrival or electronic visa\u0026thinsp;=\u0026thinsp;2, visa exemption for up to 30 days\u0026thinsp;=\u0026thinsp;3, visa exemption for 31\u0026ndash;90 days\u0026thinsp;=\u0026thinsp;4, and visa exemption for over 90 days\u0026thinsp;=\u0026thinsp;5. Missing data were interpolated based on actual contextual circumstances.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Time period division\u003c/h2\u003e \u003cp\u003eData visualization shows Singapore\u0026rsquo;s inbound tourism grew consistently from 1995 to 2019, with arrivals rising from 7.14\u0026nbsp;million to 19.1\u0026nbsp;million (a 167% increase). However, during four episodes of economic and epidemic crises, growth turned negative and tourist volume contracted, confirming Singapore\u0026rsquo;s high susceptibility as a small-scale destination to global shocks (Fig.\u0026nbsp;2). To examine potential differences in impact pathways, this study selects these four crises and categorizes them by spatial scope:Event 1 (ECO-R): Asian Financial Crisis (Jul 1997\u0026ndash;Dec 1998), regional economic crisis.Event 2 (PAN-R): SARS outbreak (Nov 2002\u0026ndash;Jul 2003), regional epidemic crisis.Event 3 (ECO-G): Global Financial Crisis (Aug 2007\u0026ndash;Mar 2009), global economic crisis.Event 4 (PAN-G): COVID-19 pandemic (Jan 2020\u0026ndash;Dec 2021), global epidemic crisis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Qualitative Comparative Analysis (QCA)\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 A Review of QCA Methods\u003c/h2\u003e \u003cp\u003eQualitative Comparative Analysis (QCA), introduced by Ragin in the late 1980s, is a configurational method that integrates qualitative and quantitative approaches. It examines combinations of causal conditions and asymmetric effects, making it well-suited for complex social phenomena and applicable across different sample sizes (Du and Jia, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Common variants include crisp-set, fuzzy-set, and multi-value QCA. Given the continuous variables (e.g., crisis severity, policy intensity) and limited sample size in Singapore\u0026rsquo;s inbound tourism data, the fuzzy-set approach was employed to enhance robustness, implemented via fsQCA 3.0 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 fsQCA method steps\u003c/h2\u003e \u003cp\u003eFollowing Ragin\u0026rsquo;s methodological guidelines, this study implements fsQCA through six sequential steps:\u003c/p\u003e \u003cp\u003e①Case Selection: Sample size was determined based on the research question and practical constraints, ensuring comparability by setting a population-heterogeneity threshold via multi-dimensional indicators. For example, 34 inbound source countries reported by the Singapore Tourism Board were selected, aligning with fsQCA\u0026rsquo;s suitability for small-sample robustness, given Singapore\u0026rsquo;s status as a small open economy with limited data availability.\u003c/p\u003e \u003cp\u003e②Selection of Antecedent Conditions: Based on theoretical rationale, priority was given to key antecedent variables causally related to the outcome. To maintain concise and meaningful conclusions, variables were streamlined to focus on core relationships, mitigating \u0026ldquo;limited diversity\u0026rdquo; through differentiated combinations. Six core inbound-tourism-related variables were selected, consistent with recommendations that small-to-medium-sized samples include 4\u0026ndash;7 antecedents (Ragin, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e③Calibration of Case Data: The direct calibration method commonly used in academic practice was employed. Each variable was treated as a set, with raw values transformed into membership scores in [0,1], enabling fsQCA software to assess the degree of membership in \u0026ldquo;high\u0026rdquo; or \u0026ldquo;low\u0026rdquo; sets.\u003c/p\u003e \u003cp\u003e④Analysis of Condition Sufficiency: Software procedures were used to calculate consistency and coverage of calibrated data, verifying configuration adequacy. Consistency measures the necessity of a condition by assessing how far the outcome set is a subset of the condition set; coverage evaluates sufficiency by indicating the proportion of the outcome explained by the condition among cases passing the consistency threshold. Following Schneider et al. (2009), the consistency threshold for necessary conditions was set at 0.9.\u003c/p\u003e \u003cp\u003e⑤Analysis of Configuration Results: After necessity analysis, the truth-table algorithm systematically enumerated all logically possible condition configurations and their associations with the outcome. Given a natural cutoff in raw consistency values, the consistency threshold was set at 0.8. As the sample size (N\u0026thinsp;=\u0026thinsp;34) is small-to-medium, the case-frequency threshold was set to 1. The PRI (Proportional Reduction in Inconsistency) threshold was set at 0.7, with configurations having PRI\u0026thinsp;\u0026ge;\u0026thinsp;0.7 retained as sufficient\u0026mdash;a criterion also used by Du Yunzhou et al. The intermediate solution was selected for interpretation because its moderate complexity aligns with theoretical expectations and retains necessary conditions. Core, complementary, and peripheral conditions were distinguished by comparing intermediate and parsimonious solutions.\u003c/p\u003e \u003cp\u003e⑥Robustness Testing of Configurations: Core parameters (calibration, frequency, and consistency thresholds) were systematically adjusted to observe variation in configuration results across different parameter combinations. Theoretical explanations under different settings were compared; configurations were considered robust if results remained stable, otherwise deemed non-robust.\u003c/p\u003e \u003cp\u003eMoreover, QCA treats high net-worth and non-high net-worth as relative, not absolute, constructs in configuration analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Analysis of inbound tourism configuration path results","content":"\u003ch2\u003e\u003cem\u003e4.1 variable calibration\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eGiven the non‑homogeneous nature of the selected \u0026ldquo;soft\u0026ndash;hard\u0026rdquo; power indicators, direct comparison was not feasible. Therefore, aligning with data distribution characteristics, the direct calibration method was applied to both outcome and antecedent variables. Following the approach of Du Yunzhou et al., the 75th percentile, median (50th percentile), and 25th percentile were set as anchor points for full membership, crossover, and full non‑membership, respectively. Values above the 75th percentile were calibrated as fully affiliated, below the 25th as fully non‑affiliated, and the median as the crossover point. This process transformed raw data into comparable fuzzy‑set membership scores, meeting fsQCA input requirements (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003ch2\u003e\u003cem\u003e4.2 Configuration variable consistency analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study examines causal relationships using necessity and sufficiency criteria. Following Schneider et al. (2010), sufficiency thresholds are 0.85 (Ragin, 2008) or 0.80 (Fiss, 2011), while necessity requires consistency \u0026ge;0.9. All individual antecedent conditions show consistency below 0.9 (Table 2), indicating no single condition is necessary for Singapore\u0026rsquo;s outcome. Instead, outcomes arise from combined factors, highlighting the multi‑causal nature of complex social systems and justifying the use of fsQCA to analyze configurational effects.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e4.3 Configuration path difference analysis\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003cem\u003e.3.1Net worth perspective: \u0026quot;high-not high\u0026quot; inbound tourism relationship path comparison\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConsistency in Singapore\u0026apos;s configuration models for high and non‑high net‑worth inbound tourism exceeds 0.8, indicating statistical significance and strong reliability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e①For high inbound tourism, 15 pathways were identified (Table 3) : 4 ECO‑R, 3 PAN‑R, 3 ECO‑G, and 5 PAN‑G. The configuration~GD~CDEDPDVD shows the absence of geographical and cultural dimensions, while economic, population, and visa dimensions remain stable. Visa dimension recurs across stages, suggesting high configurational involvement. Over time, GD is typically absent as a core condition, whereas ED shifts from core to absent in some paths, reflecting temporal variation in influence. Some pathways (e.g., T2R2, T3R2) repeat across stages, indicating structural stability. Soft‑power factors have broad influence but appear in only 6 pathways. The economic dimension significantly shapes tourism relationships, underscoring the need for economic foundations. ~CD shows frequent core‑condition absence, implying cultural factors are not central. Case sites include both neighboring East/Southeast Asian regions and distant European/American countries, illustrating that high inbound tourism relies not only on proximity but also on economic, visa, and other multidimensional drivers across spatial scales.\u003c/p\u003e\n\u003cp\u003e②Non‑high inbound tourism (Table 4): 11 pathways were identified (3 ECO‑R, 3 PAN‑R, 2 ECO‑G, 3 PAN‑G). The configuration~GD~VDCD*ID shows geographical and visa dimensions absent as core conditions, while CD remains stable as core/complementary across most stages\u0026mdash;indicating culture\u0026rsquo;s consistent role. In contrast, ID varies significantly in core‑condition status. R1 recurs across stages, while R2 and R3 differ in performance, reflecting stage‑specific combinations.\u003c/p\u003e\n\u003cp\u003eSoft‑power factors exert broad influence but appear in only 2 of 11 pathways, showing their impact can be present or absent. CD is key in constructing non‑high inbound tourism relationships. PD and VD act only as complementary conditions in certain stages, implying limited influence. Thus, complementary conditions often need to collaborate with core conditions to shape specific pathways.\u003c/p\u003e\n\u003cp\u003eCase sites with fixed combinations appear consistently across stages 1‑4, indicating stability in dimensions such as GD and CD over time.\u003c/p\u003e\n\u003cp\u003e③Comparison reveals distinct dimensional influences across tourism types. In high inbound tourism, the economic dimension frequently serves as a core condition with significant influence, whereas its role is often absent or weaker in non‑high inbound tourism. Similarly, the visa dimension commonly acts as a core condition in high inbound contexts, reflecting visa convenience\u0026rsquo;s strong facilitating effect, but is typically absent as a core condition and exhibits limited influence in non‑high inbound tourism.\u003c/p\u003e\n\u003cp\u003eUnder the soft‑hard power framework, high inbound configurations are primarily dominated by economic and visa dimensions (reflecting co‑dominance of hard and soft power), while non‑high inbound configurations are mainly led by the cultural dimension, with other dimensions playing complementary roles. Overall, different inbound tourism relationships depend distinctly on soft‑ and hard‑power dimensions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003cem\u003e.3.2 Type perspective: comparison of \u0026quot;economy-epidemic\u0026quot; crisis paths\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePathway analysis reveals clear distinctions between economic and epidemic crises. Epidemic crises generate more pathways than economic crises for both high and non‑high inbound tourism, with structures that are more complex in high tourism cases and comparatively simpler in non‑high tourism cases. Specifically, economic crises yield 7 high and 4 non‑high pathways, while epidemic crises produce 8 and 6 pathways respectively. This pattern suggests that epidemic crises involve richer combinations of conditions and more extensive correlation mechanisms. Structurally, economic crises position the economic dimension as core for high tourism and the cultural dimension as core for non‑high tourism. In contrast, epidemic crises shift the emphasis to the visa and population dimensions for high tourism, and to the institutional and population dimensions for non‑high tourism. Beyond the common geographic factor, all other influencing factors exhibit varied and complex causal roles across different crises.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003cem\u003e.3.3 Scale perspective: comparison of \u0026quot;regional-global\u0026quot; crisis paths\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUnder the global‑regional scale dimension, pathways for regional crises are limited in number and diversity, especially in Stage 1. In contrast, global crises produce more pathways (e.g., expanding to R1‑R5 in Stage 4), demonstrating greater complexity and diversity. Path performance shows that regional crises involve simpler dimensional combinations with stable core/complementary conditions, while global crises combine multiple dimensions (e.g., geography, economy, culture, institutions) with more frequent shifts in condition roles. The influence of regional crises remains spatially concentrated, with cases mostly within the affected region. Global crises, however, span continents\u0026mdash;including Asia, Europe, and the Americas\u0026mdash;and exhibit an expanding impact across more countries and tourism‑relationship types.\u003c/p\u003e"},{"header":"5. Conclusions and discussion","content":"\u003ch2\u003e\u003cem\u003e5.1 Conclusion\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eDrawing on the integrated \u0026ldquo;type‑scale\u0026rdquo; and \u0026ldquo;soft‑hard\u0026rdquo; relational influence (SHPRIF) frameworks, this study examines how economic and epidemic crises configurationally affect Singapore\u0026rsquo;s inbound tourism. The results support the three proposed hypotheses (H1, H2, H3) and can be summarized as follows:\u003c/p\u003e\n\u003cp\u003eConfiguration paths differ by net‑worth level. High inbound tourism has more numerous and structurally more complex pathways than non‑high inbound tourism, which shows relatively uniform configurations. High tourism is predominantly shaped by economic and visa dimensions, while non‑high tourism relies more on cultural and institutional dimensions. The visa factor is core for high tourism; cultural and institutional factors are core for non‑high tourism. Soft‑power relationships exert more extensive influence than other hard‑power factors on both tourism types. These findings confirm H1.\u003c/p\u003e\n\u003cp\u003eConfiguration pathways also vary by crisis type. Epidemic crises generate more numerous, more complex, and richer pathway combinations than economic crises for both high and non‑high tourism. During economic crises, high tourism centers on the economic dimension, whereas non‑high tourism centers on the cultural dimension. During epidemic crises, high tourism is dominated by visa and population dimensions, while non‑high tourism is centered on institutional and population dimensions. Furthermore, epidemic crises affect a broader set of source‑market countries, indicating a wider spatial impact. These differences confirm H2.\u003c/p\u003e\n\u003cp\u003eFinally, configuration pathways differ by crisis scale. In regional economic crises, high inbound tourism is strongly influenced by economic activity; in global economic crises, it is constrained by economic contraction, requiring compensation via non‑economic factors such as visa convenience and institutional stability. For epidemic crises, global outbreaks trigger a surge in high‑tourism‑covered countries, whereas regional outbreaks rely on an \u0026ldquo;institutional + geographical proximity\u0026rdquo; configuration. Global epidemics, due to institutional deficiencies, shift toward visa‑convenience alternatives and reconstruct networks through visa and population dimensions. These scale‑based distinctions confirm H3.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e5.2 Discussion and recommendations\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study employs a \u0026ldquo;Type‑Scale\u0026rdquo; and \u0026ldquo;Soft‑Hard\u0026rdquo; power framework to examine Singapore\u0026rsquo;s crisis responses and inbound tourism configuration pathways via qualitative comparative analysis, exploring causal asymmetry. The findings largely support the hypotheses and offer explanatory insights.\u003c/p\u003e\n\u003cp\u003eAnalysis under the \u0026ldquo;Type‑Scale\u0026rdquo; framework shows that soft‑ and hard‑power factors critically influence both high and non‑high inbound tourism markets in Singapore. Economic crises directly affect economic conditions, while epidemic crises rely more heavily on visa policies. The interplay of soft‑ and hard‑power factors (e.g., mobility restrictions) triggers multidimensional chain effects. Global crises exert broader and more complex impacts than regional crises, confirming that larger‑scale crises demand longer tourism‑system recovery cycles. Cultural factors are crucial in non‑high inbound markets, requiring attention to cultural adaptability and institutional optimization. In high net‑worth markets, economic foundations and visa convenience are central; flexible visa policies can mitigate systemic risks during sudden crises such as epidemics.\u003c/p\u003e\n\u003cp\u003eBased on these conclusions, the following recommendations are proposed for small‑scale destinations:\u003c/p\u003e\n\u003cp\u003e1.Coordinate soft and hard power: High net‑worth tourists emphasize hard power (infrastructure, services), hile non‑high net‑worth tourists are drawn to soft power (culture, institutions). Economic bases attract the former; cultural appeal attracts the latter. Avoid one‑size‑fits‑all approaches; flexibly adjust strategies based on target segments and market dynamics. Optimize visa procedures to enhance hard‑power attributes, and deepen local cultural exploration through inclusive activities and transparent governance to attract diverse tourists.\u003c/p\u003e\n\u003cp\u003e2.Differentiate crisis responses: Epidemic crises impact inbound tourism more severely than economic crises. Small open economies should formulate tailored plans. For epidemic crises, establish cooperation with high‑yield neighboring markets and implement transparent health‑safety policies to reduce risks and build resilience. For economic crises, adopt dynamic pricing, focus on short‑term markets, and engage nearby customers to secure baseline revenue.\u003c/p\u003e\n\u003cp\u003e3.Establish scale‑based response mechanisms: Small‑scale destinations have limited hinterlands but high openness, rendering them highly sensitive to crises of all scales. Differentiated contingency plans should be developed according to crisis scale, supported by multi‑stakeholder scenario drills and adequate daily‑supply reserves to minimize impact when crises occur.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZha conceived the study and designed the theoretical framework. Wu conducted the data analysis and Zheng wrote the main manuscript text. Wu and Zheng collected and processed the data, performed the calibration and QCA analysis, and prepared the figures and tables. Zha and Wu contributed to the interpretation of results and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (Award Number 42201267), the China Scholarship Council (Award Number 202108350051), and the Fujian Provincial Department of Science and Technology (Award Number 2023R0031).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dependent variable data on inbound tourism relationships were obtained from the Singapore Department of Statistics (https://www.singstat.gov.sg), which covers 52 major source countries or regions. After excluding cases with missing statistics and those where data collection had been discontinued, 34 groups of source countries or regions with consistent and accurate data were selected as the outcome variable for the case study. Additional data used for calculations were sourced from the World Bank and the UNWTO Statistical Yearbook.\u003c/p\u003e\u003ch2\u003eEthical approval:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003ch2\u003eInformed consent:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLong, D. X. (2012). Dilemma and Transcendence: Deconstructing Singapore\u0026apos;s ASEAN Strategy under the Awareness of a Small Country\u0026apos;s Crisis. Southeast Asian Studies, (04), 27-32+38. https://doi.org/10.19561/j.cnki.sas.2012.04.005 \u003c/li\u003e\n\u003cli\u003eGrossi, L., \u0026amp; Mussini, M. (2021). Seasonality in tourist flows: Decomposing and testing changes in seasonal concentration. 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Schneider \u0026amp; Claudius Wagemann.(2010).Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets.Comparative Sociology,9(3),418-418. https://doi.org/10.1163/156913210X12493538729793.\u003c/li\u003e\n\u003cli\u003eFiss, P. C. (2011). Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Academy of Management Journal, 54(2), 393-420.https://doi.org/10.5465/amj.2011.59637329\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Small Destinations, Crisis Event Comparison, Inbound Tourism, QCA Impact Path, Singapore Case","lastPublishedDoi":"10.21203/rs.3.rs-9227743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9227743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEconomic and epidemic crises severely impact global tourism, yet their effects on small-scale destinations remain understudied. Focusing on Singapore\u0026mdash;a tourism-dependent small economy\u0026mdash;this study examines its vulnerability and resilience using an integrated \"Type-Scale\" framework and the Soft-Hard Power Relationship Influence Model (SHPRIF). Qualitative Comparative Analysis (QCA) of Singapore\u0026rsquo;s inbound tourism data (1995\u0026ndash;2024) reveals three pathways: (1) high net-worth tourists respond to economic/visa factors, while non-high net-worth groups rely on cultural/institutional stability; (2) epidemic crises involve harder power disruptions, whereas economic crises are mediated by soft power; (3) regional crises depend on proximity/adaptability, while global crises require compensatory measures like visa liberalization. Findings show resilience stems from dynamic hard-soft power synergy, suggesting small economies can enhance crisis strategy and market resilience through differentiated policy.\u003c/p\u003e","manuscriptTitle":"Comparison of the Impact Pathways of Small Destination Entries on Different Crisis Events--Analysis based on Singapore Case","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 16:25:39","doi":"10.21203/rs.3.rs-9227743/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"322947546293896594543432828299388099417","date":"2026-04-29T03:48:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36986309357430622696911691655831621131","date":"2026-04-28T16:52:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"233604341029553681495981237313285547509","date":"2026-04-28T09:14:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-24T03:45:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-23T11:42:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-14T13:24:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-11T09:23:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-04-11T09:19:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bd954f91-7a3b-41d9-bf32-7e5a5b6fbc80","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67205908,"name":"Social science/Development studies"},{"id":67205909,"name":"Business and commerce/Economics"},{"id":67205910,"name":"Social science/Economics"},{"id":67205911,"name":"Scientific community and society/Geography"},{"id":67205912,"name":"Social science/Geography"},{"id":67205913,"name":"Social science/Politics and international relations"},{"id":67205914,"name":"Social science/Social policy"},{"id":67205915,"name":"Social science/Sociology"}],"tags":[],"updatedAt":"2026-05-08T16:25:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 16:25:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9227743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9227743","identity":"rs-9227743","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}