Deconstructing the formation of ski tourism destination experience quality: a configurational framework based on online reviews

Deconstructing the formation of ski tourism destination experience quality: a configurational framework based on online reviews | 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 Deconstructing the formation of ski tourism destination experience quality: a configurational framework based on online reviews Zong Kexin, Xu Maowei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9182910/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Understanding user experiences in complex service ecosystems has long relied on traditional symmetric, net-effect methodologies. Drawing on complexity theory, this study examines the non-linear and asymmetric formation of experience quality (EQ) in ski tourism destinations by integrating BERTopic, Necessary Condition Analysis (NCA), and fuzzy-set Qualitative Comparative Analysis (fsQCA). Based on large-scale multi-platform online reviews, BERTopic was first used to identify seven core dimensions of ski tourism experience from the bottom up. The NCA results show that ticketing and on-site processes constitute the strongest bottleneck condition, while slope and snow quality and rental and equipment also impose substantial threshold constraints. The fsQCA results reveal three equifinal configurations associated with high EQ, indicating that core skiing resources, rental support, and price fairness/value jointly form a shared baseline for superior tourist evaluations. In contrast, low EQ is associated with four asymmetric configurational paths, revealing a non-mirror-image logic of deterioration. Some low-EQ paths are characterised by overlapping absences of key conditions, whereas others indicate that isolated strengths in selected dimensions cannot compensate for critical weaknesses elsewhere. These findings extend the application of complexity theory in tourism research and provide a scalable analytical framework for transforming unstructured online reviews into configurational evidence. Practically, the study suggests that ski tourism destinations should first remove critical bottlenecks and then adopt differentiated improvement strategies aligned with their specific resource endowments. Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography Physical sciences/Mathematics and computing Complexity theory Natural Language Processing (NLP) Fuzzy-set Qualitative Comparative Analysis (fsQCA) Necessary Condition Analysis (NCA) User-generated content (UGC) Customer experience Figures Figure 1 Figure 2 Figure 3 1 Introduction With the rapid expansion of China’s ice and snow tourism market, competition among ski tourism destinations has intensified considerably (Chi et al., 2025 ). Ski tourism is a multifaceted activity that involves multiple service stages, including transport access, ticketing and entry, equipment rental, on-slope experience and instructional support. Accordingly, tourists’ overall evaluations of ski tourism destinations are shaped by the combined performance of these interrelated dimensions (Weiermair and Fuchs, 1999 ). Traditional tourism experience research has largely relied on multiple regression or structural equation modelling to identify the independent net effects of individual factors on overall evaluation. However, tourist evaluations are often produced by interactions among multiple conditions rather than by isolated factors alone (Perdomo-Verdecia et al., 2024 , Bi et al., 2019 ). Complexity theory suggests that improvement in a single attribute does not necessarily enhance overall evaluation (Eastman et al., 2024 ). Instead, tourist satisfaction is shaped by specific combinations of multiple dimensions, and high evaluations may be achieved through multiple alternative pathways (Xu et al., 2025). Accordingly, this study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how different configurations of experience elements jointly generate high levels of tourist evaluation. Furthermore, conventional measures of tourism experience rely heavily on researcher-designed questionnaire surveys (Wang et al., 2024 ). In the skiing context, which is highly sensitive to environmental conditions and characterised by an extended service chain, fixed-format questionnaires may fail to capture the concerns that tourists actually experience during their visits. By contrast, online reviews, as a form of user-generated content (UGC), provide more spontaneous and fine-grained accounts of tourists’ perceptions across different service stages (Guo et al., 2017 ). Therefore, applying text mining techniques to large-scale review data makes it possible to identify the experience elements that matter most to tourists and to reconstruct the operational strengths and weaknesses of ski tourism destinations more accurately (Wu et al., 2024 ). Against this background, the study addresses the following research questions: RQ1: What core dimensions of the ski tourism experience can be identified from user-generated content? RQ2: Which experience dimensions constitute necessary threshold conditions for high experience quality ( EQ )? RQ3: Which configurational pathways lead to high EQ , and how do they differ from those associated with low EQ ? This study makes three main contributions. Methodologically, it converts unstructured online reviews into quantifiable indicators of experience quality. Theoretically, by integrating NCA and fsQCA, the study identifies both the baseline constraints and the equifinal pathways associated with high EQ , thereby extending the application of complexity theory in tourism research. Practically, it offers targeted managerial implications by suggesting that ski destinations should first remove critical bottlenecks and then select service configurations that align with their specific resource endowments. 2 Literature Review 2.1 Measuring Ski Tourism Experience via UGC Although traditional structured scales (e.g. SERVQUAL) have long been used to measure ski tourism experiences (Alexandris et al., 2006 , Baker and Crompton, 2000 ), their predefined dimensions often fail to capture the dynamic and context-specific concerns inherent in complex, nature-dependent outdoor activities (Steiger and Scott, 2020 ). Consequently, user-generated content (UGC) has emerged as an important alternative, offering fine-grained and relatively unobtrusive data on tourists’ lived experiences without relying on a priori researcher-defined categories (Zhang et al., 2021 ). To extract meaningful themes from unstructured UGC, early tourism research relied heavily on Latent Dirichlet Allocation (LDA) (Guo et al., 2017 ). However, because it is constrained by a bag-of-words assumption, LDA overlooks contextual semantics and may generate noisy or weakly interpretable topics when applied to fragmented and colloquial online reviews (Jelodar et al., 2019 ). To address these limitations, recent studies have increasingly adopted the deep-learning-based BERTopic model (Grootendorst, 2022 ). By combining contextual embeddings with density-based clustering, BERTopic can better filter textual outliers and improve both topic coherence and boundary clarity (Liu et al., 2023). Accordingly, integrating BERTopic-derived themes with sentiment analysis offers a robust and data-driven approach to constructing comprehensive indicators of ski tourism experience, thereby reducing reliance on the theoretical constraints embedded in traditional predefined scales. 2.2 Complexity Theory and Ski Tourism Experience Traditional empirical studies of tourism experience are largely grounded in assumptions of symmetry and net effects. Such studies typically employ multiple regression analysis (MRA) or structural equation modelling (SEM) to estimate the independent net effect of specific attributes on overall evaluations (Rasoolimanesh et al., 2025 , Geremew et al., 2024 ). However, tourism consumption decisions are inherently holistic. Complexity theory suggests that consumer evaluations are not linear aggregations of attribute performance but rather the result of complex and non-linear interactions among multiple elements (Woodside, 2014 ). Complexity theory provides three key principles for explaining the evaluation mechanisms of ski destinations (Qin et al., 2025 ). First, equifinality indicates that high tourist evaluations may be achieved through multiple alternative configurations, meaning that destinations with different resource endowments can reach similarly favourable outcomes through different combinations of advantageous elements. Second, causal asymmetry suggests that the configurations associated with high evaluations are not simply the inverse of those associated with low evaluations. Third, configurational effects imply that weaknesses in one element may, under certain conditions, be compensated for by strengths in others. Therefore, to explain the formation of ski destination evaluations more accurately, research needs to move beyond unifocal net-effect models and examine how multiple experience dimensions combine to shape outcomes. 2.3 Integrating NCA and fsQCA To address the non-linear and equifinal mechanisms highlighted by complexity theory, recent tourism research has increasingly adopted fuzzy-set Qualitative Comparative Analysis (fsQCA) (Fiss, 2011 , Geremew et al., 2024 ). However, although fsQCA is particularly effective for identifying sufficient configurations associated with high performance, it is less well suited to identifying precise threshold levels for necessary conditions, which may lead to ambiguity in the interpretation of necessity (Dul, 2022 ). To address this limitation, Necessary Condition Analysis (NCA) can be used to assess necessity in terms of minimum threshold levels by estimating a ceiling line. More specifically, NCA employs a bottleneck table to identify the minimum level of antecedent conditions required for different levels of the outcome (Dul et al., 2023 ). Accordingly, recent methodological studies increasingly recommend combining NCA and fsQCA: NCA is first used to establish the baseline threshold levels of key conditions, and fsQCA is then applied to identify the alternative configurations associated with high performance once these baseline requirements are satisfied (Rasoolimanesh, 2026 ). Building on the above review, two gaps remain in the existing literature. First, although UGC-based studies have enriched the measurement of tourism experience, research on ski tourism has paid limited attention to transforming unstructured review data into multidimensional indicators of destination experience quality. Second, while complexity theory highlights equifinality, causal asymmetry and configurational effects, existing studies have rarely combined necessary-condition logic with configurational analysis to explain how different experience dimensions jointly shape ski tourism destination evaluations. To address these gaps, this study develops an integrated analytical logic in which BERTopic is used to identify the core dimensions of ski tourism experience from UGC, NCA is applied to assess the minimum threshold levels of key conditions, and fsQCA is then employed to uncover the multiple configurations associated with high and low experience quality. 3 Data Sources and Sample Construction 3.1 Destination Definition and Sample Selection To examine the formation mechanisms of ski tourism destination experience quality, this study focuses on ski tourism destinations rather than single-facility ski venues. A tourism destination can be understood as a combination of core attractions, accessibility, facilities and ancillary services, in which the tourist experience arises from the joint operation of multiple elements rather than from a single facility alone (Buhalis, 2000 , Bichler and Pikkemaat, 2021 , Hallmann et al., 2012 , Konu et al., 2011 ). Accordingly, the target sample comprises ski tourism destinations centred on skiing activities, supported by relatively complete tourism service portfolios and capable of providing an integrated consumption experience. Operationally, an initial pool of 936 ski-related points of interest (POIs) across China was collected through Python-based web scraping. To align the empirical sample with the theoretical definition of a tourism destination, a two-stage screening procedure was applied based on the platforms’ native metadata (i.e. industry type). First, for reasons of theoretical consistency, POIs classified into non-destination categories, such as indoor ski resorts, ice and snow parks and city parks, were excluded because these venues typically provide single-facility entertainment rather than comprehensive tourism experiences. Second, for reasons of operational validity, destinations that had permanently closed, been suspended for extended periods or lacked stable operations during the previous year were removed. This filtering process yielded a final analytical sample of 91 ski tourism destinations, which formed the basis of the subsequent NCA and fsQCA analyses. In this study, the term “ski tourism destinations” refers to destination units centred on skiing activities and supported by relatively complete tourism service portfolios. For brevity, these units are occasionally referred to as ski destinations in the remainder of the paper. 3.2 Data Collection and Preprocessing To capture tourist feedback more comprehensively and mitigate the potential demographic and algorithmic biases associated with a single platform (Xiang et al., 2017 ), data were collected from four major Chinese platforms: Meituan, Dianping, Ctrip and Mafengwo. These platforms occupy important positions in China’s online consumption and travel booking markets and represent diverse review ecosystems, ranging from local lifestyle services and online travel agency (OTA) transactions to UGC travel communities, thereby improving the breadth and heterogeneity of the data source (Guo et al., 2017 , Guo et al., 2021). To reflect the characteristics of the post-Beijing Winter Olympics ski market, the data collection window was defined as 1 March 2022 to 28 February 2025. For the 91 selected destinations, textual reviews and supplementary information, such as posting dates and helpfulness votes, were extracted, yielding nearly 150,000 raw reviews. To reduce noise and improve the suitability of the text for topic modelling, systematic preprocessing was conducted (Qin et al., 2021). First, the data were normalised and deduplicated by standardising text encoding, removing formatting symbols and eliminating obvious duplicates (Lv et al., 2024 ). Second, platform-generated platform-generated default positive comments and overly short reviews containing fewer than 10 characters were removed because they lacked sufficient semantic content (Chen et al., 2024). Third, lengthy reviews were segmented into semantically manageable sentence units using a transparent rule-based sentence segmentation procedure, thereby facilitating the identification of more fine-grained evaluative information. After the removal of platform-generated default positive comments and other low-information entries, 85,941 valid reviews were retained and subsequently segmented into review units for text processing, topic modelling and sentiment analysis. 4 Unstructured Text Processing 4.1 BERTopic Modeling Configuration To balance semantic representation and computational efficiency, this study employed the all-MiniLM-L6-v2 Sentence-BERT model to encode large volumes of fragmented ski reviews into 384-dimensional dense vectors, thereby preserving contextual semantic information at the sentence level (Chen and Xu, 2026 , Reimers and Gurevych, 2019 ). As shown in Fig. 1, the BERTopic workflow then combined UMAP for dimensionality reduction, HDBSCAN for density-based clustering and noise filtering, and c-TF-IDF for keyword extraction (McInnes et al., 2018 ). [Insert Fig. 1 here] 4.2 Topic Coherence and Robustness Validation To determine the appropriate number of topics (K), topic coherence was used as the main quantitative criterion (Röder et al., 2015 ). As shown in Fig. 2, coherence scores were calculated across multiple parameter settings for different K values. The results indicate a clear peak at K = 7, where the coherence score reached 0.458. Taking this statistical result together with the interpretability of the topics in the ski tourism context, seven core themes were retained. To reduce the influence of randomness in unsupervised clustering, an additional validation procedure was implemented to assess the robustness of the topic structure (Chen and Xu, 2026 ). Specifically, two independent researchers conducted a blind semantic review of the keywords and representative texts generated under different parameter settings. Highly overlapping topics were merged, whereas semantically ambiguous clusters were either treated as noise or reassigned to the most relevant dominant topic. This validation process suggests that the seven-topic solution offered an appropriate balance among information retention, semantic distinctiveness and managerial interpretability. [Insert Fig. 2 here] 4.3 Theme Identification and Definition Through algorithmic extraction followed by semantic refinement, seven core dimensions of the ski tourism experience were identified. Unlike traditional scales based on predefined theoretical categories, BERTopic supports a bottom-up identification of tourist concerns through c-TF-IDF-based keyword weighting (Grootendorst, 2022 , Yang and Kim, 2025 ). To enhance interpretive clarity, Table 1 summarises the high-probability keywords, variable interpretations and representative review excerpts associated with each theme. The table reports the most distinctive terms according to c-TF-IDF weight, defines the substantive meaning of each dimension and provides illustrative quotations from the review corpus. These themes provide the analytical basis for the subsequent configurational analysis of ski tourism destination experience quality. Table 1 Overview of topics, representative keywords, and variable interpretations. Theme Top keywords (c-TF-IDF Weight) Variable interpretation Representative Quote Slope and Snow Quality ( SSQ ) Professional (0.072); Children (0.065); Beginners (0.058); Environment (0.049); Slope gradient (0.042); Beginner trails (0.035); Intermediate trails (0.028); Vertical drop (0.021); Uncrowded (0.020); Practice (0.015) Captures the quality of core skiing resources, including slope difficulty, snow conditions and related infrastructure. "The intermediate trails are well-groomed, and the vertical drop is thrilling. It is highly professional and suitable for both beginners and advanced skiers." Rental and Equipment ( REQ ) Ski boards (0.068); Queueing (0.061); Boots (0.053); Customer service (0.045); Front desk (0.038); Snowboards (0.032); Helmets (0.026); Friendly (0.020); Service counter (0.017); Clean (0.012) Reflects the adequacy and efficiency of rental services and equipment provision. "The rental counter is spacious and the snowboards and helmets are quite new. However, queueing for boots takes a bit long on weekends." Accessibility and Transport ( AT ) Distance (0.075); Nearest (0.067); Parking (0.059); Expressway (0.051); Kilometres (0.044); Urban area (0.037); Surroundings (0.031); Self-drive (0.025); High-speed rail (0.019); Hours (0.016) Represents travel convenience and access costs associated with reaching the destination. "It’s very close to the urban area, right off the expressway. Self-driving here for a weekend trip is extremely convenient." Natural Scenery and Environment ( NSE ) Return visit (0.069); Satisfaction (0.062); Beauty (0.058); Will return (0.048); Interesting (0.041); Scenery (0.034); Ultimate (0.027); Highly recommend (0.022); Destination (0.018); Entertainment options (0.015) Describes the scenic and environmental atmosphere perceived by visitors. "The scenery at the summit is absolutely breathtaking, offering the ultimate romantic vibe. Highly recommend, I will definitely return next season!" Ticketing and On-site Process ( TOP ) Refund (0.066); Ticket return (0.059); Ticket purchase (0.052); Merchant (0.045); Queueing (0.038); Admission ticket (0.031); Deposit (0.025); Convenience (0.019); Customer service (0.016); Front desk (0.013) Reflects the efficiency and transparency of ticketing and on-site operational processes. "Ticket redemption at the self-service machine is convenient, avoiding long queues. But the deposit refund process at the front desk could be more transparent." Service Encounter and Instruction ( SEI ) Tour leader (0.070); Responsibility (0.063); Enthusiasm (0.056); Explanation (0.049); Professionalism (0.042); Instructor (0.035); Attention to detail (0.028); Teaching (0.022); Staff (0.017); Communication (0.014) Captures the professionalism and responsiveness of staff and instructors. "Our instructor was incredibly patient and professional. They explained the details clearly, making the teaching process easy and safe." Price Fairness and Value ( PFV ) Affordable (0.071); Good value (0.064); Cost-effective (0.056); Too expensive (0.049); Discounts (0.042); Weekdays (0.035); Ticket price (0.029); Double price (0.023); Value for money (0.018); Slightly pricey (0.014) Reflects visitors’ assessment of price fairness and value for money. "The weekday ticket with online discounts is very cost-effective. Considering the overall facilities, it's definitely good value for money." [Insert Table 1 here] 5 Results of configurational analysis 5.1 Variable construction and calibration After topic identification and aspect-level sentiment scoring, the review-level unstructured data were aggregated to the ski-destination level to construct the variables used in the NCA and fsQCA analyses. 5.1.1 Antecedent conditions. Based on the seven core experience dimensions identified in Table 1 , this study used Slope and Snow Quality ( SSQ ), Rental and Equipment ( REQ ), Accessibility and Transport ( AT ), Natural Scenery and Environment ( NSE ), Ticketing and On-site Process ( TOP ), Service Encounter and Instruction (SEI), and Price Fairness and Value ( PFV ) as antecedent conditions. Consistent with prior research, topic modelling and aspect-level sentiment analysis can be combined to capture both salient experience dimensions and their evaluative direction in online reviews (Kirilenko et al., 2021 , Grootendorst, 2022 , Li et al., 2023 ). For each ski destination, the score of a given condition was calculated as the average sentiment score of all review segments assigned to the corresponding topic. To ensure comparability across conditions, sentiment scores were rescaled to the [0,1] interval, where values closer to 1 indicate more positive evaluations and values closer to 0 indicate more negative evaluations. The condition score for destination i on topic j was defined as follows: $$\:\begin{array}{c}{C}_{ij}=\frac{1}{{n}_{ij}}\sum\:_{k=1}^{{n}_{ij}}{s}_{ijk}\#\left(1\right)\end{array}$$ Where \(\:{n}_{ij}\) denotes the number of reviews for resort \(\:i\) under topic \(\:j\) , and \(\:{s}_{ijk}\) ​ is the sentiment score of review \(\:k\) . 5.1.2 Outcome variable. The outcome variable, overall ski tourism destination experience quality ( EQ ), was constructed at the ski destination level by combining topic salience with topic-specific sentiment. Following prior studies, the weight of each topic was defined as its proportion among all valid reviews for a given destination (Qin et al., 2025 ). Specifically, the topic weight was calculated as follows: $$\:\begin{array}{c}{W}_{ij}=\frac{{n}_{ij}}{{n}_{i}}\#\left(2\right)\end{array}$$ where \(\:{n}_{i}\) is the total number of valid reviews for resort \(\:i\) . The overall experience quality index was then calculated as: $$\:\begin{array}{c}E{Q}_{i}=\sum\:_{j=1}^{J}{W}_{ij}\times\:{C}_{ij}\#\left(3\right)\end{array}$$ Where \(\:J\) is the number of core experience topics. Since \(\:\sum\:_{j=1}^{J}{W}_{ij}=1\) , a higher \(\:E{Q}_{i}\) ​ indicates more positive tourist perceptions across the major experience dimensions. \(\:E{Q}_{i}\) ​ was therefore used as the outcome variable in the subsequent NCA and fsQCA analyses. 5.1.3 Fuzzy-set calibration. Because fsQCA requires set-membership scores, the outcome variable and all seven antecedent conditions were calibrated into fuzzy sets using Ragin’s direct method (Ragin, 2009 ). As no widely accepted external thresholds were available for these continuous variables, the calibration anchors were derived from the sample distribution. Specifically, following common practice in fsQCA research, the 75th percentile was used as the threshold for full membership, the median as the crossover point, and the 25th percentile as the threshold for full non-membership (Fiss, 2011 ). The calibration anchors and descriptive statistics are reported in Table 2 . Table 2 Calibration anchors and descriptive statistics. Variable Calibration values Descriptive analysis Full membership Crossover point Full non-membership Mean SD Max Min EQ 0.7423 0.7037 0.6477 0.7013 0.0627 0.8451 0.5831 SSQ 0.7525 0.6770 0.5825 0.6620 0.1299 0.9040 0.3520 REQ 0.7510 0.6820 0.6220 0.6739 0.1111 0.9100 0.3530 AT 0.7910 0.7210 0.5535 0.6822 0.1508 0.9800 0.2700 NSE 0.7835 0.7120 0.6185 0.7034 0.1146 0.9910 0.3690 TOP 0.7515 0.7210 0.6820 0.7097 0.0881 0.9160 0.2330 SEI 0.7905 0.7520 0.6855 0.7362 0.1098 0.9980 0.3210 PFV 0.8060 0.7220 0.6520 0.7437 0.1131 0.9880 0.5120 [Insert Table 2 here] 5.2 Necessary condition and bottleneck analysis Before examining sufficient configurations, this study first assessed whether any single experience dimension constituted a necessary condition for high overall experience quality ( EQ ). To do so, fsQCA and NCA were used in a complementary manner. Whereas fsQCA evaluates necessity in set-theoretic terms, NCA identifies the minimum level of a condition required to attain a given outcome level (Dul, 2016 , Vis and Dul, 2018 ). Combining the two approaches allows a more complete assessment of baseline constraints before the sufficiency analysis, and this combined strategy has also been recommended in recent tourism methodology research (Rasoolimanesh, 2026 ) 5.2.1 Necessity analysis using fsQCA. The first step was to test whether any individual experience dimension, or its absence, was necessary for high EQ. Following standard practice in fsQCA, a consistency threshold of 0.90 was adopted to identify necessary conditions (Fiss, 2011 ). The results are reported in Table 3 . All conditions and their negations fell below the 0.90 threshold. This indicates that no single experience dimension can be regarded as a necessary condition for high EQ in the strict fsQCA sense. In other words, highly positive tourist evaluations are not associated with excellence in any single dimension alone, but with the joint presence of multiple conditions. Table 3 Necessity analysis for high EQ using fsQCA. Antecedent High EQ POCONS POCOV SSQ 0.880 0.867 ~SSQ 0.318 0.316 REQ 0.823 0.823 ~REQ 0.345 0.337 AT 0.543 0.531 ~AT 0.631 0.632 NSE 0.867 0.851 ~NSE 0.343 0.341 TOP 0.604 0.607 ~TOP 0.559 0.544 SEI 0.726 0.706 ~SEI 0.460 0.463 PFV 0.846 0.808 ~PFV 0.340 0.349 Notes : ~ indicates the absence of the antecedent condition. POCONS = pooled necessity consistency; POCOV = pooled necessity coverage. [Insert Table 3 here] 5.2.2 Threshold necessity analysis using NCA. Although fsQCA did not identify any necessary condition in the strict set-theoretic sense, high EQ may still be subject to threshold-type constraints. NCA was therefore used to estimate the effect size (d) of each antecedent condition and to identify potential bottleneck conditions for achieving high EQ (Dul, 2016 , Vis and Dul, 2018 ). Figure 3 presents the ceiling-line patterns of the seven antecedent conditions, and Table 4 reports the corresponding effect sizes and significance levels. TOP showed the largest effect size and thus emerged as the most salient bottleneck condition. SSQ and REQ also exhibited substantial threshold effects, whereas PFV and NSE showed moderate effects. By contrast, AT and SEI showed relatively small and statistically non-significant effects, suggesting limited baseline constraint. Overall, the results suggest that high EQ is more strongly constrained by process quality and core skiing resources than by accessibility or service encounter factors alone. Table 4 NCA results for bottleneck-type necessity. Condition Effect size \(\:d\) (CE-FDH) p-value Interpretation TOP 0.502 0.009 Very strong SSQ 0.420 < 0.001 Strong REQ 0.323 < 0.001 Strong PFV 0.255 < 0.001 Medium NSE 0.237 0.024 Medium AT 0.216 0.083 Weak / ns SEI 0.208 0.435 Weak / ns Notes: d = NCA effect size (CE-FDH). p-values from permutation test. 'ns' = not significant. [Insert Table 4 here] [Insert Fig. 3 here] 5.3 Sufficiency analysis 5.3.1 Analytical settings. After ruling out any single necessary condition, fsQCA was used to identify the configurations associated with high overall experience quality. A fuzzy-set truth table was constructed on the basis of the seven antecedent conditions ( SSQ, REQ, AT, NSE, TOP, SEI and PFV ), yielding \(\:{2}^{7}=128\) logically possible configurations. Following established fsQCA practice, the consistency threshold for sufficiency was set at 0.80, the PRI threshold at 0.70 and the frequency cutoff at 2 (Fiss, 2011 ). For the high-EQ analysis, no strong theoretical basis was available to specify directional expectations ex ante. For the low-EQ analysis, directional expectations were specified when deriving the intermediate solution, so that core and peripheral conditions could be identified by comparing the intermediate and parsimonious solutions. 5.3.2 Overall configurations for high EQ enhancement. As shown in Table 5 , three configurations were associated with high EQ, with a solution consistency of 1.000 and a solution coverage of 0.628. Across all three configurations, SSQ , REQ and PFV consistently appeared as core conditions, suggesting that core skiing resources, rental support and value perception jointly form the baseline for high EQ . Beyond this shared foundation, the results reveal clear equifinality, as different combinations of process quality, service encounter, accessibility and scenery were associated with high EQ . Table 5 Configurations associated with high EQ. Antecedent S1 S2 S3 SSQ ⬤ ⬤ ⬤ REQ ⬤ ⬤ ⬤ AT ○ ● NSE ● ● ○ TOP ⬤ ⬤ SEI ⬤ ⊗ PFV ⬤ ⬤ ⬤ Consistency 0.999 1.000 1.000 Coverage 0.299 0.555 0.171 Unique coverage 0.028 0.283 0.042 Solution consistency 1.000 Solution coverage 0.628 Notes: ⬤=core presence; ●=peripheral presence; ⊗=core absence; ○=peripheral absence; blank = do not care. [Insert Table 5 here] S1: Core Resource and Process Synergy Type. High EQ was associated with the core presence of SSQ , REQ , TOP and PFV , together with the peripheral presence of NSE and the peripheral absence of AT . This configuration suggests that strong on-site processes and favourable value may offset weaker accessibility when core skiing resources remain strong. S2: Service-Augmented Value Type. With the largest raw coverage (0.555), S2 represented the most empirically prevalent pathway. In this configuration, the core triad was reinforced by SEI as a core condition and NSE as a peripheral condition, suggesting that service encounter and instruction may further strengthen tourists’ evaluations when core resources are already favourable. S3: Accessibility-Driven Pragmatic Type. This configuration combined the core presence of SSQ , REQ , TOP and PFV with the peripheral presence of AT , while SEI was absent as a core condition and NSE was peripherally absent. This suggests that, for some tourists, convenient access and efficient on-site processes may compensate for weaker scenic appeal or instructional support, provided that core resources and perceived value remain strong. 5.3.3 Causal Asymmetry and Low EQ. To examine causal asymmetry, the sufficiency analysis was repeated with low experience quality as the outcome. Table 6 reports four configurations associated with low EQ, with a solution consistency of 0.972 and a solution coverage of 0.635. Table 6 Configurations associated with low EQ (~ EQ). Antecedent S1 S2 S3 S4 SSQ ⊗ ⬤ ○ ○ REQ ⊗ ○ ⊗ ⊗ AT ⬤ ○ ⊗ NSE ⊗ ⊗ ⊗ ⬤ TOP ○ ⊗ ⊗ SEI ⬤ ○ ○ PFV ⊗ ● ⊗ ● Consistency 0.984 0.933 0.996 0.997 Coverage 0.458 0.143 0.335 0.157 Unique coverage 0.248 0.028 0.118 0.034 Solution consistency 0.972 Solution coverage 0.635 Notes : ⬤ = core presence; ● = peripheral presence; ⊗ = core absence; ○ = peripheral absence; blank = do not care. The low-EQ results do not represent a simple mirror image of the high-EQ pathways. S1 combines the core absence of SSQ, REQ, NSE and PFV with the core presence of AT, suggesting that convenient access alone cannot prevent low EQ when core skiing resources, scenic appeal and perceived value remain weak. S2 reveals a fragmented-strength pattern in which SSQ and SEI are present as core conditions, while NSE is absent as a core condition and REQ, AT and TOP are absent as peripheral conditions. This indicates that isolated strengths in skiing quality or staff interaction cannot offset weaknesses in scenery, accessibility and on-site processes. S3 reflects a cumulative-deficiency pattern characterised by the core absence of REQ, NSE, TOP and PFV, together with the peripheral absence of SSQ and SEI. S4 shows that even when NSE is present as a core condition and PFV is present peripherally, low EQ may still arise when REQ, AT and TOP are absent as core conditions and SSQ and SEI remain weak. Overall, these findings further support causal asymmetry by showing that deterioration in experience quality follows configurational logics that differ from those associated with high EQ. [Insert Table 6 here] 5.4 Robustness Checks To assess the robustness of the findings, additional tests were conducted on the configurations associated with both high EQ and low EQ(Skaaning, 2011 ). Following standard QCA procedures(Oana and Schneider, 2024 ), stricter truth-table thresholds were applied by increasing the minimum case frequency to 3, the consistency threshold to 0.85, and the PRI consistency threshold to 0.70. As reported in Table 7 , all three high-EQ configurations remained stable when more conservative consistency and frequency criteria were imposed. However, the results were more sensitive to PRI thresholds: only one configuration passed the PRI threshold of 0.70, and none passed the more stringent threshold of 0.75. Overall, these findings support the robustness of the main high-EQ solution structure, while also indicating that some individual configurations are sensitive to stricter PRI requirements. Table 7 Robustness checks for configurations associated with high EQ. Configuration n (X ≥ 0.5) Consistency PRI consistency Coverage Robustness summary SSQ*REQ*NSE*SEI*PFV 21 1.000 0.708 0.555 Passed stricter consistency, PRI ≥ 0.70, and frequency thresholds; failed PRI ≥ 0.75 SSQ*REQ*~AT*NSE*TOP*PFV 9 0.999 0.540 0.299 Passed stricter consistency and frequency thresholds; failed stricter PRI thresholds SSQ*REQ*AT*~NSE*TOP*~SEI*PFV 3 1.000 0.253 0.171 Passed stricter consistency and frequency thresholds; failed stricter PRI thresholds Notes: n counts cases with configuration membership ≥ 0.5. PRI consistency follows Schneider and Wagemann (2012). The robustness of the low-EQ configurations was also examined under the stricter criteria. As reported in Table 8 , two low-EQ configurations remained stable under the more conservative thresholds, whereas the other two were sensitive to case-frequency and PRI requirements. Overall, the robustness checks support the stability of the main configurational structure while indicating that low-EQ paths are comparatively less stable than the dominant high-EQ configurations. Table 8 Robustness checks for configurations associated with low EQ (~ EQ). Configuration n (X ≥ 0.5) Consistency PRI consistency Coverage Robustness summary ~SSQ~REQAT ~ NSE~PFV 20 0.984 0.973 0.458 Passed stricter consistency, PRI ≥ 0.70, PRI ≥ 0.75, and frequency thresholds SSQ ~ REQ~AT ~ NSE~TOPSEIPFV 2 0.933 -0.206 0.143 Failed frequency and PRI thresholds; passed stricter consistency threshold ~SSQ ~ REQ~NSE ~ TOP~SEI*~PFV 11 0.996 0.992 0.335 Passed stricter consistency, PRI ≥ 0.70, PRI ≥ 0.75, and frequency thresholds ~SSQ ~ REQ~ATNSE ~ TOP~SEIPFV 2 0.997 0.978 0.157 Failed frequency threshold; passed stricter consistency and PRI thresholds Notes: n counts cases with configuration membership ≥ 0.5. PRI consistency follows Schneider and Wagemann (2012). 6 Discussion Drawing on complexity theory, this study combines unstructured text mining (BERTopic), threshold analysis (NCA) and configurational analysis (fsQCA) to explain how overall ski tourism destination experience quality is formed. The main findings and implications are discussed below. 6.1 Responses to the research questions RQ1. What are the core dimensions of the ski tourism experience derived from online reviews? Using bottom-up mining of multi-platform online reviews, this study identified seven core dimensions of ski tourism experience: Slope and Snow Quality, Rental and Equipment, Accessibility and Transport, Natural Scenery and Environment, Ticketing and On-site Process, Service Encounter and Instruction, and Price Fairness and Value. This suggests that ski tourism destination experience is shaped not by a single resource attribute, but by a multidimensional structure involving core skiing resources, supporting facilities, operational processes, service support and value perception. Tourists’ concerns have also expanded from the traditional focus on slopes and facilities to the broader service chain, including ticketing, entry, rental and instruction. This finding is consistent with prior research on the multidimensional nature of tourism service evaluation (Weiermair and Fuchs, 1999 , Alexandris et al., 2006 , Konu et al., 2011 , Chi et al., 2025 ). At the same time, by combining BERTopic with UGC analysis, this study extends earlier work on extracting tourism experience attributes from online reviews (Guo et al., 2017 ). RQ2. What minimum threshold levels of these dimensions are necessary to achieve high EQ? The results show that no single dimension constitutes a necessary condition for high experience quality in the strict set-theoretic sense. However, clear differences emerged in the strength of threshold constraints across dimensions. Ticketing and on-site processes showed the strongest bottleneck effect, while slope and snow quality, together with rental and equipment, also imposed substantial constraints. By contrast, accessibility and transport and service encounter and instruction displayed relatively weak threshold effects. In practical terms, this means that ski tourism destinations must first ensure smooth on-site processes, adequate core skiing resources and usable rental systems before other dimensions can further enhance experience quality. This finding supports the view that necessity is often expressed in threshold form rather than as an absolute yes-or-no condition (Dul, 2016 , Vis and Dul, 2018 , Dul, 2022 ). It also extends recent work on the combined use of NCA and fsQCA by highlighting the pivotal baseline role of ticketing and on-site processes in the ski tourism context (Rasoolimanesh, 2026 ). RQ3. What equifinal configurations drive high EQ, and is there causal asymmetry between high and low EQ? The findings reveal three equifinal configurations associated with high experience quality. The first is a core resource and process synergy path, in which strong slope and snow quality, adequate rental and equipment provision, efficient ticketing and on-site processes, and favourable price fairness and value jointly underpin high evaluations. The second is a core resource and service reinforcement path, in which service encounter and instruction further enhance tourists’ evaluations when core skiing resources are already favourable. The third is a contextual compensation path, in which accessibility and transport advantages, together with efficient on-site processes and favourable value perception, may compensate for weaker scenery or instructional support. By contrast, the low-EQ results reveal a non-mirror-image causal structure. Some low-EQ paths are characterised by overlapping absences of key conditions, whereas others show that relatively strong performance in selected dimensions, such as skiing quality, scenery or staff interaction, cannot offset decisive weaknesses elsewhere. This indicates that high and low experience quality do not follow mirror-image causal logics, but instead reflect asymmetric configurational structures. The finding is consistent with prior research on equifinality and causal asymmetry in complex service settings (Fiss, 2011 , Woodside, 2014 , Qin et al., 2025 , Bi et al., 2020, Perdomo-Verdecia et al., 2024 ). 6.2 Managerial implications First, ski tourism destinations should protect the experiential baseline by removing micro-process bottlenecks that may trigger disproportionately negative evaluations. The NCA results suggest that tourists’ experience quality is constrained less by scenery or accessibility than by often-overlooked operational details such as ticketing, entry and equipment rental. Even destinations with excellent snow conditions and attractive landscapes may fail to achieve high EQ if visitors encounter congestion or friction at these touchpoints. Managers should therefore prioritise process redesign, such as timed reservations, pre-arrival rental booking, fast-track channels for self-equipped skiers and clearer separation between beginner and advanced service flows. Second, managers should adopt differentiated improvement strategies rather than pursuing a single best-practice model. The fsQCA results show that high EQ can be achieved through multiple pathways. For urban or suburban ski tourism destinations with limited natural scenery, high EQ may still be achieved by strengthening slope maintenance, process efficiency and perceived value. By contrast, larger destination resorts with stronger resource endowments may gain more by integrating service delivery and instructional support into the core ski experience. In this sense, the managerial value of fsQCA lies in showing that ski tourism destinations should align improvement strategies with their own endowments rather than imitate industry leaders indiscriminately. Third, ski tourism destinations should establish asymmetric intervention mechanisms to prevent marked deterioration in experience quality. The low-EQ configurations suggest that negative evaluations do not arise through a single uniform route. In some cases, deterioration is driven by the overlap of multiple weaknesses, whereas in others, limited strengths in selected dimensions fail to offset critical weaknesses elsewhere. Managers should therefore monitor combinations of risks rather than isolated indicators, especially during periods of poor snowmaking conditions or holiday overcrowding, and respond quickly with targeted compensation or flow-control measures before dissatisfaction accumulates into broader negative evaluations. 6.3 Contributions, limitations, and future research This study makes three main contributions. First, it extends the measurement of ski tourism experience by moving beyond predefined survey scales and using BERTopic to uncover latent but important dimensions, such as ticketing and rental processes. Second, by introducing NCA, it adds a threshold perspective to tourism experience research and shifts attention from average net effects to baseline constraints. Third, through fsQCA, it demonstrates both equifinality in the formation of high EQ and causal asymmetry between high and low EQ , thereby deepening the application of complexity theory to ski tourism. The study also has several limitations. First, the data were drawn from four major Chinese platforms and may underrepresent some groups, such as older or international tourists. Future studies could incorporate multilingual reviews or interview data for cross-cultural validation. Second, due to the anonymity of UGC, individual heterogeneity, such as skill level, could not be incorporated. Future research could combine structured surveys with unstructured reviews to examine whether beginners and advanced skiers follow different experiential logics. Third, the present analysis is essentially cross-sectional. Future studies could use temporal QCA or longitudinal data to explore how high EQ configurations evolve over time and across seasonal or destination life-cycle stages. Declarations Ethical Approval This article does not contain any studies with human participants performed by any of the authors. Informed Consent This article does not contain any studies with human participants performed by any of the authors. Author Contribution M.X.: Conceptualization, Supervision, Validation, Writing – Review & Editing. K.Z.: Data curation, Investigation, Methodology, Formal analysis, Writing – Original Draft. All authors read and approved the final manuscript. Data Availability Statement The data files supporting the findings of this study are provided as supplementary materials for editorial assessment and confidential peer review. The supplementary package includes: (1) the initial ski-related POI list with geographic coordinates used for sample screening; (2) the screened final sample list of ski tourism destinations retained for analysis; (3) the underlying online review corpus used for text preprocessing, topic modeling, sentiment scoring, and destination-level variable construction; (4) the destination-level analytical dataset before fuzzy-set calibration; (5) the calibrated analytical dataset used for the NCA and fsQCA analyses; and (6) a summary results workbook containing calibration anchors, NCA results, fsQCA necessity analysis, truth tables, solution summaries, case-membership information, and robustness checks. Because part of the underlying review texts was collected from third-party online platforms and may be subject to platform terms of use and redistribution restrictions, the review corpus is provided for confidential editorial and peer-review purposes only and is not intended for unrestricted public dissemination. The authors are willing to cooperate with the editors regarding any further reasonable requests for verification materials during the review process. References ALEXANDRIS K, KOUTHOURIS C, MELIGDIS A (2006) Increasing customers' loyalty in a skiing resort: The contribution of place attachment and service quality. Int J Contemp hospitality Manage 18:414–425 BAKER DA, CROMPTON JL (2000) Quality, satisfaction and behavioral intentions. Annals tourism Res 27:785–804 BI J-W, LIU Y, FAN, Z.-P., CAMBRIA E (2019) Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. Int J Prod Res 57:7068–7088 BI J-W, LIU, Y., FAN, Z.-P., ZHANG J (2020) Exploring asymmetric effects of attribute performance on customer satisfaction in the hotel industry. Tour Manag 77:104006 BICHLER B, PIKKEMAAT B (2021) Winter sports tourism to urban destinations: Identifying potential and comparing motivational differences across skier groups. J Outdoor Recreation Tourism BUHALIS DD (2000) Marketing the competitive destination of the future. Tourism Tribune 21:97–116 CHEN K, ZHENG, J., JIN J (2024) Ranking products through online opinions: A text analysis and regret theory-based approach. Appl Soft Comput 158:111571 CHEN Y, XU J (2026) Exploring interdisciplinary boundaries: A BERTopic modeling approach to analyze four interdisciplinary fields at the abstract and sentence levels. Engl Specif Purp 81:56–72 CHI X, ZHANG B, TANG C, ZHOU H, HAN H (2025) Ski tourism and destination attractiveness: Two separate importance-performance analyses before and after the pandemic in Korean and Chinese markets. Tourism Manage Perspect 57:101360 DUL J (2016) Identifying single necessary conditions with NCA and fsQCA. J Bus Res 69:1516–1523 DUL J (2022) Problematic applications of Necessary Condition Analysis (NCA) in tourism and hospitality research. Tour Manag 93:104616 DUL J, HAUFF S, BOUNCKEN RB (2023) Necessary condition analysis (NCA): review of research topics and guidelines for good practice: J. Dul et al. RMS 17:683–714 EASTMAN JK, SHIN H, JAIN V, WANG X (2024) Determinants of positive consumer affect toward luxury brands’ social media posts on sustainability: A fsQCA approach. J Bus Res 183:114871 FISS PC (2011) Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Acad Manag J 54:393–420 GEREMEW YM, HUANG W-J, HUNG K (2024) Fuzzy-set qualitative comparative analysis as a mixed-method and analysis technique: a comprehensive systematic review. J Travel Res 63:3–26 GROOTENDORST M (2022) BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794 GUO X, PESONEN, J., KOMPPULA R (2021) Comparing online travel review platforms as destination image information agents. Inform Technol Tourism 23:159–187 GUO Y, BARNES SJ, JIA Q (2017) Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tour Manag 59:467–483 HALLMANN K, MüLLER S, FEILER S, BREUER C, ROTH R (2012) Suppliers' perception of destination competitiveness in a winter sport resort. Tourism Rev 67:13–21 JELODAR H, WANG Y, YUAN C, FENG X, JIANG X, LI Y, ZHAO L (2019) Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia tools Appl 78:15169–15211 KIRILENKO AP, STEPCHENKOVA SO, DAI X (2021) Automated topic modeling of tourist reviews: Does the Anna Karenina principle apply? Tour Manag 83:104241 KONU H, LAUKKANEN T, KOMPPULA R (2011) Using ski destination choice criteria to segment Finnish ski resort customers. Tour Manag 32:1096–1105 LI H, LI BRUCEX, G., GAO H (2023) Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tour Manag 96:104707 LV S, QIN XIAOA, XU Y, Z., WANG X (2024) A decision framework for improving the service quality of charging stations based on online reviews and evolutionary game theory. Transp Res Part A: Policy Pract 187:104168 MCINNES L, HEALY J, MELVILLE J (2018) Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 OANA I-E, SCHNEIDER CQ (2024) A robustness test protocol for applied QCA: theory and R software application. Sociol Methods Res 53:57–88 PERDOMO-VERDECIA V, GARRIDO-VEGA P, SACRISTáN-DíAZ M (2024) An fsQCA analysis of service quality for hotel customer satisfaction. Int J Hospitality Manage 122:11 QIN Y, LUO C, NGAI EW (2025) Deconstructing customer satisfaction recipes: A dynamic configurational framework leveraging the power of online reviews in tourism contexts. Tour Manag 110:105181 RAGIN CC (2009) Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press RASOOLIMANESH SM (2026) Applying fsQCA, NCA and NConfA in tourism research. Annals Tourism Res 116:104069 RASOOLIMANESH SM, GANNON M, TAHERI B, MIKULIć J (2025) Assessing a model of customer rage: does customer entitlement moderate relationships between rage emotions, expressions and behaviors? J Travel Res 64:462–484 REIMERS N, GUREVYCH I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), 3982–3992 RöDER M, BOTH A, HINNEBURG A (2015) Exploring the space of topic coherence measures. Proceedings of the eighth ACM international conference on Web search and data mining, 399–408 SKAANING S-E (2011) Assessing the robustness of crisp-set and fuzzy-set QCA results. Sociol Methods Res 40:391–408 STEIGER R, SCOTT D (2020) Ski tourism in a warmer world: Increased adaptation and regional economic impacts in Austria. Tour Manag 77:104032 VIS B, DUL J (2018) Analyzing relationships of necessity not just in kind but also in degree: Complementing fsQCA with NCA. Sociol methods Res 47:872–899 WANG R, WU C, WANG X, XU F, YUAN Q (2024) E-Tourism information literacy and its role in driving tourist satisfaction with online travel information: A qualitative comparative analysis. J Travel Res 63:904–922 WEIERMAIR K, FUCHS M (1999) Measuring tourist judgment on service quality. Annals tourism Res 26:1004–1021 WOODSIDE AG (2014) Embrace• perform• model: Complexity theory, contrarian case analysis, and multiple realities. J Bus Res 67:2495–2503 WU J, ZHAO N, YANG T (2024) Wisdom of crowds: SWOT analysis based on hybrid text mining methods using online reviews. J Bus Res 171:114378 XIANG Z, MA DUQ, Y., FAN W (2017) A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tour Manag 58:51–65 XU J, SHI, P.-H., CHEN X (2025) Curators or creators: Role configurations of digital innovation strategy in museum tourism destination and the principles underlying their attractiveness. Tour Manag 106:105024 YANG C, KIM Y (2025) Enhancing topic coherence and diversity in document embeddings using LLMs: A focus on BERTopic. Expert Syst Appl 281:127517 ZHANG C, XU Z, GOU X, CHEN S (2021) An online reviews-driven method for the prioritization of improvements in hotel services. Tour Manag, 87 Additional Declarations No competing interests reported. Supplementary Files SupplementaryData.zip Data3.Onlinereviewsof91skitourismdestinations.zip Data1.SkirelatedPOIsandgeographiccoordinates.xlsx Data2.Finalsampleof91skitourismdestinations.xlsx Data4.Analyticaldatasetbeforecalibration.xlsx READMEforSupplementaryDataFiles.txt Data6.NCAandfsQCAresults.xlsx Data5.Calibratedanalyticaldataset.xlsx Code1BERTopictopicmodelingpipeline.py Code4Destinationlevelvariableconstruction.py READMEforCodeFiles.txt Code5Fuzzysetcalibration.py Code2Reviewpreprocessingandsentencesegmentation.py Code6NCAanalysis.py Code7fsQCAanalysisandrobustness.r Code3Topicsentimentscoring.py Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 19 May, 2026 Reviews received at journal 18 May, 2026 Reviews received at journal 12 May, 2026 Reviews received at journal 02 May, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers invited by journal 17 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 08 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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07:56:46","extension":"py","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":8734,"visible":true,"origin":"","legend":"","description":"","filename":"Code6NCAanalysis.py","url":"https://assets-eu.researchsquare.com/files/rs-9182910/v1/d94d0d6f1d68c18aa72b704a.py"},{"id":107872104,"identity":"d4ea17d4-16f4-41a6-b7dd-43dc10012dc2","added_by":"auto","created_at":"2026-04-27 07:55:34","extension":"r","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":6088,"visible":true,"origin":"","legend":"","description":"","filename":"Code7fsQCAanalysisandrobustness.r","url":"https://assets-eu.researchsquare.com/files/rs-9182910/v1/7df9d13e8fe0e6037ab30a87.r"},{"id":107871965,"identity":"830fc360-4888-40da-9de0-e2a9f10a05cf","added_by":"auto","created_at":"2026-04-27 07:54:42","extension":"py","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":8056,"visible":true,"origin":"","legend":"","description":"","filename":"Code3Topicsentimentscoring.py","url":"https://assets-eu.researchsquare.com/files/rs-9182910/v1/c4f4e4a4213cadc099975309.py"}],"financialInterests":"No competing interests reported.","formattedTitle":"Deconstructing the formation of ski tourism destination experience quality: a configurational framework based on online reviews","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the rapid expansion of China\u0026rsquo;s ice and snow tourism market, competition among ski tourism destinations has intensified considerably (Chi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Ski tourism is a multifaceted activity that involves multiple service stages, including transport access, ticketing and entry, equipment rental, on-slope experience and instructional support. Accordingly, tourists\u0026rsquo; overall evaluations of ski tourism destinations are shaped by the combined performance of these interrelated dimensions (Weiermair and Fuchs, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditional tourism experience research has largely relied on multiple regression or structural equation modelling to identify the independent net effects of individual factors on overall evaluation. However, tourist evaluations are often produced by interactions among multiple conditions rather than by isolated factors alone (Perdomo-Verdecia et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Bi et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Complexity theory suggests that improvement in a single attribute does not necessarily enhance overall evaluation (Eastman et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Instead, tourist satisfaction is shaped by specific combinations of multiple dimensions, and high evaluations may be achieved through multiple alternative pathways (Xu et al., 2025). Accordingly, this study employs fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how different configurations of experience elements jointly generate high levels of tourist evaluation.\u003c/p\u003e \u003cp\u003eFurthermore, conventional measures of tourism experience rely heavily on researcher-designed questionnaire surveys (Wang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the skiing context, which is highly sensitive to environmental conditions and characterised by an extended service chain, fixed-format questionnaires may fail to capture the concerns that tourists actually experience during their visits. By contrast, online reviews, as a form of user-generated content (UGC), provide more spontaneous and fine-grained accounts of tourists\u0026rsquo; perceptions across different service stages (Guo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, applying text mining techniques to large-scale review data makes it possible to identify the experience elements that matter most to tourists and to reconstruct the operational strengths and weaknesses of ski tourism destinations more accurately (Wu et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Against this background, the study addresses the following research questions:\u003c/p\u003e \u003cp\u003eRQ1: What core dimensions of the ski tourism experience can be identified from user-generated content?\u003c/p\u003e \u003cp\u003eRQ2: Which experience dimensions constitute necessary threshold conditions for high experience quality (\u003cem\u003eEQ\u003c/em\u003e)?\u003c/p\u003e \u003cp\u003eRQ3: Which configurational pathways lead to high \u003cem\u003eEQ\u003c/em\u003e, and how do they differ from those associated with low \u003cem\u003eEQ\u003c/em\u003e?\u003c/p\u003e \u003cp\u003eThis study makes three main contributions. Methodologically, it converts unstructured online reviews into quantifiable indicators of experience quality. Theoretically, by integrating NCA and fsQCA, the study identifies both the baseline constraints and the equifinal pathways associated with high \u003cem\u003eEQ\u003c/em\u003e, thereby extending the application of complexity theory in tourism research. Practically, it offers targeted managerial implications by suggesting that ski destinations should first remove critical bottlenecks and then select service configurations that align with their specific resource endowments.\u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Measuring Ski Tourism Experience via UGC\u003c/h2\u003e \u003cp\u003eAlthough traditional structured scales (e.g. SERVQUAL) have long been used to measure ski tourism experiences (Alexandris et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Baker and Crompton, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), their predefined dimensions often fail to capture the dynamic and context-specific concerns inherent in complex, nature-dependent outdoor activities (Steiger and Scott, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, user-generated content (UGC) has emerged as an important alternative, offering fine-grained and relatively unobtrusive data on tourists\u0026rsquo; lived experiences without relying on a priori researcher-defined categories (Zhang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo extract meaningful themes from unstructured UGC, early tourism research relied heavily on Latent Dirichlet Allocation (LDA) (Guo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, because it is constrained by a bag-of-words assumption, LDA overlooks contextual semantics and may generate noisy or weakly interpretable topics when applied to fragmented and colloquial online reviews (Jelodar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To address these limitations, recent studies have increasingly adopted the deep-learning-based BERTopic model (Grootendorst, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). By combining contextual embeddings with density-based clustering, BERTopic can better filter textual outliers and improve both topic coherence and boundary clarity (Liu et al., 2023). Accordingly, integrating BERTopic-derived themes with sentiment analysis offers a robust and data-driven approach to constructing comprehensive indicators of ski tourism experience, thereby reducing reliance on the theoretical constraints embedded in traditional predefined scales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Complexity Theory and Ski Tourism Experience\u003c/h2\u003e \u003cp\u003eTraditional empirical studies of tourism experience are largely grounded in assumptions of symmetry and net effects. Such studies typically employ multiple regression analysis (MRA) or structural equation modelling (SEM) to estimate the independent net effect of specific attributes on overall evaluations (Rasoolimanesh et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Geremew et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, tourism consumption decisions are inherently holistic. Complexity theory suggests that consumer evaluations are not linear aggregations of attribute performance but rather the result of complex and non-linear interactions among multiple elements (Woodside, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComplexity theory provides three key principles for explaining the evaluation mechanisms of ski destinations (Qin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). First, equifinality indicates that high tourist evaluations may be achieved through multiple alternative configurations, meaning that destinations with different resource endowments can reach similarly favourable outcomes through different combinations of advantageous elements. Second, causal asymmetry suggests that the configurations associated with high evaluations are not simply the inverse of those associated with low evaluations. Third, configurational effects imply that weaknesses in one element may, under certain conditions, be compensated for by strengths in others. Therefore, to explain the formation of ski destination evaluations more accurately, research needs to move beyond unifocal net-effect models and examine how multiple experience dimensions combine to shape outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Integrating NCA and fsQCA\u003c/h2\u003e \u003cp\u003eTo address the non-linear and equifinal mechanisms highlighted by complexity theory, recent tourism research has increasingly adopted fuzzy-set Qualitative Comparative Analysis (fsQCA) (Fiss, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Geremew et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, although fsQCA is particularly effective for identifying sufficient configurations associated with high performance, it is less well suited to identifying precise threshold levels for necessary conditions, which may lead to ambiguity in the interpretation of necessity (Dul, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo address this limitation, Necessary Condition Analysis (NCA) can be used to assess necessity in terms of minimum threshold levels by estimating a ceiling line. More specifically, NCA employs a bottleneck table to identify the minimum level of antecedent conditions required for different levels of the outcome (Dul et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accordingly, recent methodological studies increasingly recommend combining NCA and fsQCA: NCA is first used to establish the baseline threshold levels of key conditions, and fsQCA is then applied to identify the alternative configurations associated with high performance once these baseline requirements are satisfied (Rasoolimanesh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding on the above review, two gaps remain in the existing literature. First, although UGC-based studies have enriched the measurement of tourism experience, research on ski tourism has paid limited attention to transforming unstructured review data into multidimensional indicators of destination experience quality. Second, while complexity theory highlights equifinality, causal asymmetry and configurational effects, existing studies have rarely combined necessary-condition logic with configurational analysis to explain how different experience dimensions jointly shape ski tourism destination evaluations. To address these gaps, this study develops an integrated analytical logic in which BERTopic is used to identify the core dimensions of ski tourism experience from UGC, NCA is applied to assess the minimum threshold levels of key conditions, and fsQCA is then employed to uncover the multiple configurations associated with high and low experience quality.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Data Sources and Sample Construction","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Destination Definition and Sample Selection\u003c/h2\u003e \u003cp\u003eTo examine the formation mechanisms of ski tourism destination experience quality, this study focuses on ski tourism destinations rather than single-facility ski venues. A tourism destination can be understood as a combination of core attractions, accessibility, facilities and ancillary services, in which the tourist experience arises from the joint operation of multiple elements rather than from a single facility alone (Buhalis, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e, Bichler and Pikkemaat, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Hallmann et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Konu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Accordingly, the target sample comprises ski tourism destinations centred on skiing activities, supported by relatively complete tourism service portfolios and capable of providing an integrated consumption experience.\u003c/p\u003e \u003cp\u003eOperationally, an initial pool of 936 ski-related points of interest (POIs) across China was collected through Python-based web scraping. To align the empirical sample with the theoretical definition of a tourism destination, a two-stage screening procedure was applied based on the platforms\u0026rsquo; native metadata (i.e. industry type). First, for reasons of theoretical consistency, POIs classified into non-destination categories, such as indoor ski resorts, ice and snow parks and city parks, were excluded because these venues typically provide single-facility entertainment rather than comprehensive tourism experiences. Second, for reasons of operational validity, destinations that had permanently closed, been suspended for extended periods or lacked stable operations during the previous year were removed. This filtering process yielded a final analytical sample of 91 ski tourism destinations, which formed the basis of the subsequent NCA and fsQCA analyses. In this study, the term \u0026ldquo;ski tourism destinations\u0026rdquo; refers to destination units centred on skiing activities and supported by relatively complete tourism service portfolios. For brevity, these units are occasionally referred to as ski destinations in the remainder of the paper.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Data Collection and Preprocessing\u003c/h2\u003e \u003cp\u003eTo capture tourist feedback more comprehensively and mitigate the potential demographic and algorithmic biases associated with a single platform (Xiang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), data were collected from four major Chinese platforms: Meituan, Dianping, Ctrip and Mafengwo. These platforms occupy important positions in China\u0026rsquo;s online consumption and travel booking markets and represent diverse review ecosystems, ranging from local lifestyle services and online travel agency (OTA) transactions to UGC travel communities, thereby improving the breadth and heterogeneity of the data source (Guo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Guo et al., 2021).\u003c/p\u003e \u003cp\u003eTo reflect the characteristics of the post-Beijing Winter Olympics ski market, the data collection window was defined as 1 March 2022 to 28 February 2025. For the 91 selected destinations, textual reviews and supplementary information, such as posting dates and helpfulness votes, were extracted, yielding nearly 150,000 raw reviews.\u003c/p\u003e \u003cp\u003eTo reduce noise and improve the suitability of the text for topic modelling, systematic preprocessing was conducted (Qin et al., 2021). First, the data were normalised and deduplicated by standardising text encoding, removing formatting symbols and eliminating obvious duplicates (Lv et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Second, platform-generated platform-generated default positive comments and overly short reviews containing fewer than 10 characters were removed because they lacked sufficient semantic content (Chen et al., 2024). Third, lengthy reviews were segmented into semantically manageable sentence units using a transparent rule-based sentence segmentation procedure, thereby facilitating the identification of more fine-grained evaluative information. After the removal of platform-generated default positive comments and other low-information entries, 85,941 valid reviews were retained and subsequently segmented into review units for text processing, topic modelling and sentiment analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Unstructured Text Processing","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 BERTopic Modeling Configuration\u003c/h2\u003e \u003cp\u003eTo balance semantic representation and computational efficiency, this study employed the all-MiniLM-L6-v2 Sentence-BERT model to encode large volumes of fragmented ski reviews into 384-dimensional dense vectors, thereby preserving contextual semantic information at the sentence level (Chen and Xu, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2026\u003c/span\u003e, Reimers and Gurevych, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As shown in Fig.\u0026nbsp;1, the BERTopic workflow then combined UMAP for dimensionality reduction, HDBSCAN for density-based clustering and noise filtering, and c-TF-IDF for keyword extraction (McInnes et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;1 here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Topic Coherence and Robustness Validation\u003c/h2\u003e \u003cp\u003eTo determine the appropriate number of topics (K), topic coherence was used as the main quantitative criterion (R\u0026ouml;der et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As shown in Fig.\u0026nbsp;2, coherence scores were calculated across multiple parameter settings for different K values. The results indicate a clear peak at K\u0026thinsp;=\u0026thinsp;7, where the coherence score reached 0.458. Taking this statistical result together with the interpretability of the topics in the ski tourism context, seven core themes were retained. To reduce the influence of randomness in unsupervised clustering, an additional validation procedure was implemented to assess the robustness of the topic structure (Chen and Xu, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Specifically, two independent researchers conducted a blind semantic review of the keywords and representative texts generated under different parameter settings. Highly overlapping topics were merged, whereas semantically ambiguous clusters were either treated as noise or reassigned to the most relevant dominant topic. This validation process suggests that the seven-topic solution offered an appropriate balance among information retention, semantic distinctiveness and managerial interpretability.\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;2 here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Theme Identification and Definition\u003c/h2\u003e \u003cp\u003eThrough algorithmic extraction followed by semantic refinement, seven core dimensions of the ski tourism experience were identified. Unlike traditional scales based on predefined theoretical categories, BERTopic supports a bottom-up identification of tourist concerns through c-TF-IDF-based keyword weighting (Grootendorst, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Yang and Kim, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). To enhance interpretive clarity, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarises the high-probability keywords, variable interpretations and representative review excerpts associated with each theme. The table reports the most distinctive terms according to c-TF-IDF weight, defines the substantive meaning of each dimension and provides illustrative quotations from the review corpus. These themes provide the analytical basis for the subsequent configurational analysis of ski tourism destination experience quality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of topics, representative keywords, and variable interpretations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTop keywords (c-TF-IDF Weight)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable interpretation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRepresentative Quote\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope and Snow Quality (\u003cem\u003eSSQ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfessional (0.072); Children (0.065); Beginners (0.058); Environment (0.049); Slope gradient (0.042); Beginner trails (0.035); Intermediate trails (0.028); Vertical drop (0.021); Uncrowded (0.020); Practice (0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaptures the quality of core skiing resources, including slope difficulty, snow conditions and related infrastructure.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"The intermediate trails are well-groomed, and the vertical drop is thrilling. It is highly professional and suitable for both beginners and advanced skiers.\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRental and Equipment (\u003cem\u003eREQ\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSki boards (0.068); Queueing (0.061); Boots (0.053); Customer service (0.045); Front desk (0.038); Snowboards (0.032); Helmets (0.026); Friendly (0.020); Service counter (0.017); Clean (0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReflects the adequacy and efficiency of rental services and equipment provision.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"The rental counter is spacious and the snowboards and helmets are quite new. However, queueing for boots takes a bit long on weekends.\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility and Transport (\u003cem\u003eAT\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance (0.075); Nearest (0.067); Parking (0.059); Expressway (0.051); Kilometres (0.044); Urban area (0.037); Surroundings (0.031); Self-drive (0.025); High-speed rail (0.019); Hours (0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRepresents travel convenience and access costs associated with reaching the destination.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"It\u0026rsquo;s very close to the urban area, right off the expressway. Self-driving here for a weekend trip is extremely convenient.\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural Scenery and Environment (\u003cem\u003eNSE\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReturn visit (0.069); Satisfaction (0.062); Beauty (0.058); Will return (0.048); Interesting (0.041); Scenery (0.034); Ultimate (0.027); Highly recommend (0.022); Destination (0.018); Entertainment options (0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescribes the scenic and environmental atmosphere perceived by visitors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"The scenery at the summit is absolutely breathtaking, offering the ultimate romantic vibe. Highly recommend, I will definitely return next season!\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTicketing and On-site Process (\u003cem\u003eTOP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRefund (0.066); Ticket return (0.059); Ticket purchase (0.052); Merchant (0.045); Queueing (0.038); Admission ticket (0.031); Deposit (0.025); Convenience (0.019); Customer service (0.016); Front desk (0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReflects the efficiency and transparency of ticketing and on-site operational processes.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Ticket redemption at the self-service machine is convenient, avoiding long queues. But the deposit refund process at the front desk could be more transparent.\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eService Encounter and Instruction (\u003cem\u003eSEI\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTour leader (0.070); Responsibility (0.063); Enthusiasm (0.056); Explanation (0.049); Professionalism (0.042); Instructor (0.035); Attention to detail (0.028); Teaching (0.022); Staff (0.017); Communication (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaptures the professionalism and responsiveness of staff and instructors.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"Our instructor was incredibly patient and professional. They explained the details clearly, making the teaching process easy and safe.\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrice Fairness and Value (\u003cem\u003ePFV\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffordable (0.071); Good value (0.064); Cost-effective (0.056); Too expensive (0.049); Discounts (0.042); Weekdays (0.035); Ticket price (0.029); Double price (0.023); Value for money (0.018); Slightly pricey (0.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReflects visitors\u0026rsquo; assessment of price fairness and value for money.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\"The weekday ticket with online discounts is very cost-effective. Considering the overall facilities, it's definitely good value for money.\"\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Results of configurational analysis","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Variable construction and calibration\u003c/h2\u003e \u003cp\u003eAfter topic identification and aspect-level sentiment scoring, the review-level unstructured data were aggregated to the ski-destination level to construct the variables used in the NCA and fsQCA analyses.\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.1.1 Antecedent conditions.\u003c/em\u003e Based on the seven core experience dimensions identified in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, this study used Slope and Snow Quality (\u003cem\u003eSSQ\u003c/em\u003e), Rental and Equipment (\u003cem\u003eREQ\u003c/em\u003e), Accessibility and Transport (\u003cem\u003eAT\u003c/em\u003e), Natural Scenery and Environment (\u003cem\u003eNSE\u003c/em\u003e), Ticketing and On-site Process (\u003cem\u003eTOP\u003c/em\u003e), Service Encounter and Instruction (SEI), and Price Fairness and Value (\u003cem\u003ePFV\u003c/em\u003e) as antecedent conditions. Consistent with prior research, topic modelling and aspect-level sentiment analysis can be combined to capture both salient experience dimensions and their evaluative direction in online reviews (Kirilenko et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Grootendorst, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor each ski destination, the score of a given condition was calculated as the average sentiment score of all review segments assigned to the corresponding topic. To ensure comparability across conditions, sentiment scores were rescaled to the [0,1] interval, where values closer to 1 indicate more positive evaluations and values closer to 0 indicate more negative evaluations. The condition score for destination \u003cem\u003ei\u003c/em\u003e on topic \u003cem\u003ej\u003c/em\u003e was defined as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{C}_{ij}=\\frac{1}{{n}_{ij}}\\sum\\:_{k=1}^{{n}_{ij}}{s}_{ijk}\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{ij}\\)\u003c/span\u003e\u003c/span\u003e denotes the number of reviews for resort \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e under topic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{s}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e​ is the sentiment score of review \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.1.2 Outcome variable.\u003c/em\u003e The outcome variable, overall ski tourism destination experience quality (\u003cem\u003eEQ\u003c/em\u003e), was constructed at the ski destination level by combining topic salience with topic-specific sentiment. Following prior studies, the weight of each topic was defined as its proportion among all valid reviews for a given destination (Qin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Specifically, the topic weight was calculated as follows:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}{W}_{ij}=\\frac{{n}_{ij}}{{n}_{i}}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{n}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the total number of valid reviews for resort \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e. The overall experience quality index was then calculated as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\begin{array}{c}E{Q}_{i}=\\sum\\:_{j=1}^{J}{W}_{ij}\\times\\:{C}_{ij}\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:J\\)\u003c/span\u003e\u003c/span\u003e is the number of core experience topics. Since \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{j=1}^{J}{W}_{ij}=1\\)\u003c/span\u003e\u003c/span\u003e, a higher \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E{Q}_{i}\\)\u003c/span\u003e\u003c/span\u003e​ indicates more positive tourist perceptions across the major experience dimensions. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E{Q}_{i}\\)\u003c/span\u003e\u003c/span\u003e​ was therefore used as the outcome variable in the subsequent NCA and fsQCA analyses.\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.1.3 Fuzzy-set calibration.\u003c/em\u003e Because fsQCA requires set-membership scores, the outcome variable and all seven antecedent conditions were calibrated into fuzzy sets using Ragin\u0026rsquo;s direct method (Ragin, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). As no widely accepted external thresholds were available for these continuous variables, the calibration anchors were derived from the sample distribution. Specifically, following common practice in fsQCA research, the 75th percentile was used as the threshold for full membership, the median as the crossover point, and the 25th percentile as the threshold for full non-membership (Fiss, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The calibration anchors and descriptive statistics are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalibration anchors and descriptive statistics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eCalibration values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eDescriptive analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull membership\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrossover point\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFull non-membership\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.8451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSSQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2700\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.2330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSEI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.3210\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePFV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.9880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.5120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Necessary condition and bottleneck analysis\u003c/h2\u003e \u003cp\u003eBefore examining sufficient configurations, this study first assessed whether any single experience dimension constituted a necessary condition for high overall experience quality (\u003cem\u003eEQ\u003c/em\u003e). To do so, fsQCA and NCA were used in a complementary manner. Whereas fsQCA evaluates necessity in set-theoretic terms, NCA identifies the minimum level of a condition required to attain a given outcome level (Dul, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Vis and Dul, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Combining the two approaches allows a more complete assessment of baseline constraints before the sufficiency analysis, and this combined strategy has also been recommended in recent tourism methodology research (Rasoolimanesh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2026\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.2.1 Necessity analysis using fsQCA.\u003c/em\u003e The first step was to test whether any individual experience dimension, or its absence, was necessary for high EQ. Following standard practice in fsQCA, a consistency threshold of 0.90 was adopted to identify necessary conditions (Fiss, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The results are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. All conditions and their negations fell below the 0.90 threshold. This indicates that no single experience dimension can be regarded as a necessary condition for high EQ in the strict fsQCA sense. In other words, highly positive tourist evaluations are not associated with excellence in any single dimension alone, but with the joint presence of multiple conditions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNecessity analysis for high EQ using fsQCA.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntecedent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eHigh \u003cem\u003eEQ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOCONS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePOCOV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSSQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e~SSQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e~REQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e~AT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.851\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e~NSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e~TOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSEI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e~SEI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePFV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e~PFV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNotes\u003c/b\u003e: \u003cem\u003e~ indicates the absence of the antecedent condition. POCONS\u0026thinsp;=\u0026thinsp;pooled necessity consistency; POCOV\u0026thinsp;=\u0026thinsp;pooled necessity coverage.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.2.2 Threshold necessity analysis using NCA.\u003c/em\u003e Although fsQCA did not identify any necessary condition in the strict set-theoretic sense, high \u003cem\u003eEQ\u003c/em\u003e may still be subject to threshold-type constraints. NCA was therefore used to estimate the effect size (d) of each antecedent condition and to identify potential bottleneck conditions for achieving high EQ (Dul, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Vis and Dul, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFigure 3 presents the ceiling-line patterns of the seven antecedent conditions, and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the corresponding effect sizes and significance levels. \u003cem\u003eTOP\u003c/em\u003e showed the largest effect size and thus emerged as the most salient bottleneck condition. \u003cem\u003eSSQ\u003c/em\u003e and \u003cem\u003eREQ\u003c/em\u003e also exhibited substantial threshold effects, whereas PFV and NSE showed moderate effects. By contrast, \u003cem\u003eAT\u003c/em\u003e and SEI showed relatively small and statistically non-significant effects, suggesting limited baseline constraint. Overall, the results suggest that high \u003cem\u003eEQ\u003c/em\u003e is more strongly constrained by process quality and core skiing resources than by accessibility or service encounter factors alone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNCA results for bottleneck-type necessity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCondition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect size \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:d\\)\u003c/span\u003e\u003c/span\u003e (CE-FDH)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVery strong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSSQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePFV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak / ns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSEI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeak / ns\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes: d\u0026thinsp;=\u0026thinsp;NCA effect size (CE-FDH). p-values from permutation test. 'ns' = not significant.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003e[Insert Fig.\u0026nbsp;3 here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Sufficiency analysis\u003c/h2\u003e \u003cp\u003e \u003cem\u003e5.3.1 Analytical settings.\u003c/em\u003e After ruling out any single necessary condition, fsQCA was used to identify the configurations associated with high overall experience quality. A fuzzy-set truth table was constructed on the basis of the seven antecedent conditions (\u003cem\u003eSSQ, REQ, AT, NSE, TOP, SEI\u003c/em\u003e and \u003cem\u003ePFV\u003c/em\u003e), yielding \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{2}^{7}=128\\)\u003c/span\u003e\u003c/span\u003e logically possible configurations. Following established fsQCA practice, the consistency threshold for sufficiency was set at 0.80, the PRI threshold at 0.70 and the frequency cutoff at 2 (Fiss, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For the high-EQ analysis, no strong theoretical basis was available to specify directional expectations ex ante. For the low-EQ analysis, directional expectations were specified when deriving the intermediate solution, so that core and peripheral conditions could be identified by comparing the intermediate and parsimonious solutions.\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.3.2 Overall configurations for high EQ enhancement.\u003c/em\u003e As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, three configurations were associated with high EQ, with a solution consistency of 1.000 and a solution coverage of 0.628. Across all three configurations, \u003cem\u003eSSQ\u003c/em\u003e, \u003cem\u003eREQ\u003c/em\u003e and \u003cem\u003ePFV\u003c/em\u003e consistently appeared as core conditions, suggesting that core skiing resources, rental support and value perception jointly form the baseline for high \u003cem\u003eEQ\u003c/em\u003e. Beyond this shared foundation, the results reveal clear equifinality, as different combinations of process quality, service encounter, accessibility and scenery were associated with high \u003cem\u003eEQ\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfigurations associated with high EQ.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntecedent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSSQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eREQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAT\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTOP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSEI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePFV\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes: ⬤=core presence; ●=peripheral presence; \u0026otimes;=core absence; ○=peripheral absence; blank\u0026thinsp;=\u0026thinsp;do not care.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e here]\u003c/p\u003e \u003cp\u003eS1: Core Resource and Process Synergy Type. High EQ was associated with the core presence of \u003cem\u003eSSQ\u003c/em\u003e, \u003cem\u003eREQ\u003c/em\u003e, \u003cem\u003eTOP\u003c/em\u003e and \u003cem\u003ePFV\u003c/em\u003e, together with the peripheral presence of \u003cem\u003eNSE\u003c/em\u003e and the peripheral absence of \u003cem\u003eAT\u003c/em\u003e. This configuration suggests that strong on-site processes and favourable value may offset weaker accessibility when core skiing resources remain strong.\u003c/p\u003e \u003cp\u003eS2: Service-Augmented Value Type. With the largest raw coverage (0.555), S2 represented the most empirically prevalent pathway. In this configuration, the core triad was reinforced by SEI as a core condition and NSE as a peripheral condition, suggesting that service encounter and instruction may further strengthen tourists\u0026rsquo; evaluations when core resources are already favourable.\u003c/p\u003e \u003cp\u003eS3: Accessibility-Driven Pragmatic Type. This configuration combined the core presence of \u003cem\u003eSSQ\u003c/em\u003e, \u003cem\u003eREQ\u003c/em\u003e, \u003cem\u003eTOP\u003c/em\u003e and \u003cem\u003ePFV\u003c/em\u003e with the peripheral presence of \u003cem\u003eAT\u003c/em\u003e, while \u003cem\u003eSEI\u003c/em\u003e was absent as a core condition and \u003cem\u003eNSE\u003c/em\u003e was peripherally absent. This suggests that, for some tourists, convenient access and efficient on-site processes may compensate for weaker scenic appeal or instructional support, provided that core resources and perceived value remain strong.\u003c/p\u003e \u003cp\u003e \u003cem\u003e5.3.3 Causal Asymmetry and Low EQ.\u003c/em\u003e To examine causal asymmetry, the sufficiency analysis was repeated with low experience quality as the outcome. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports four configurations associated with low EQ, with a solution consistency of 0.972 and a solution coverage of 0.635.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfigurations associated with low EQ (~\u0026thinsp;EQ).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntecedent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTOP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e⬤\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e○\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026otimes;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e●\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnique coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution consistency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolution coverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNotes\u003c/b\u003e: \u003cem\u003e⬤ = core presence; ● = peripheral presence; \u0026otimes; = core absence; ○ = peripheral absence; blank\u0026thinsp;=\u0026thinsp;do not care.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe low-EQ results do not represent a simple mirror image of the high-EQ pathways. S1 combines the core absence of SSQ, REQ, NSE and PFV with the core presence of AT, suggesting that convenient access alone cannot prevent low EQ when core skiing resources, scenic appeal and perceived value remain weak. S2 reveals a fragmented-strength pattern in which SSQ and SEI are present as core conditions, while NSE is absent as a core condition and REQ, AT and TOP are absent as peripheral conditions. This indicates that isolated strengths in skiing quality or staff interaction cannot offset weaknesses in scenery, accessibility and on-site processes. S3 reflects a cumulative-deficiency pattern characterised by the core absence of REQ, NSE, TOP and PFV, together with the peripheral absence of SSQ and SEI. S4 shows that even when NSE is present as a core condition and PFV is present peripherally, low EQ may still arise when REQ, AT and TOP are absent as core conditions and SSQ and SEI remain weak. Overall, these findings further support causal asymmetry by showing that deterioration in experience quality follows configurational logics that differ from those associated with high EQ.\u003c/p\u003e \u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e here]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Robustness Checks\u003c/h2\u003e \u003cp\u003eTo assess the robustness of the findings, additional tests were conducted on the configurations associated with both high EQ and low EQ(Skaaning, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Following standard QCA procedures(Oana and Schneider, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), stricter truth-table thresholds were applied by increasing the minimum case frequency to 3, the consistency threshold to 0.85, and the PRI consistency threshold to 0.70.\u003c/p\u003e \u003cp\u003eAs reported in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, all three high-EQ configurations remained stable when more conservative consistency and frequency criteria were imposed. However, the results were more sensitive to PRI thresholds: only one configuration passed the PRI threshold of 0.70, and none passed the more stringent threshold of 0.75. Overall, these findings support the robustness of the main high-EQ solution structure, while also indicating that some individual configurations are sensitive to stricter PRI requirements.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness checks for configurations associated with high EQ.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfiguration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (X\u0026thinsp;\u0026ge;\u0026thinsp;0.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePRI consistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRobustness summary\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSQ*REQ*NSE*SEI*PFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePassed stricter consistency, PRI\u0026thinsp;\u0026ge;\u0026thinsp;0.70, and frequency thresholds; failed PRI\u0026thinsp;\u0026ge;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSQ*REQ*~AT*NSE*TOP*PFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePassed stricter consistency and frequency thresholds; failed stricter PRI thresholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSQ*REQ*AT*~NSE*TOP*~SEI*PFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePassed stricter consistency and frequency thresholds; failed stricter PRI thresholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes: n counts cases with configuration membership\u0026thinsp;\u0026ge;\u0026thinsp;0.5. PRI consistency follows Schneider and Wagemann (2012).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe robustness of the low-EQ configurations was also examined under the stricter criteria. As reported in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, two low-EQ configurations remained stable under the more conservative thresholds, whereas the other two were sensitive to case-frequency and PRI requirements. Overall, the robustness checks support the stability of the main configurational structure while indicating that low-EQ paths are comparatively less stable than the dominant high-EQ configurations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRobustness checks for configurations associated with low EQ (~\u0026thinsp;EQ).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfiguration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (X\u0026thinsp;\u0026ge;\u0026thinsp;0.5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConsistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePRI consistency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoverage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRobustness summary\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~SSQ~REQAT\u0026thinsp;~\u0026thinsp;NSE~PFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePassed stricter consistency, PRI\u0026thinsp;\u0026ge;\u0026thinsp;0.70, PRI\u0026thinsp;\u0026ge;\u0026thinsp;0.75, and frequency thresholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSQ\u0026thinsp;~\u0026thinsp;REQ~AT\u0026thinsp;~\u0026thinsp;NSE~TOPSEIPFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFailed frequency and PRI thresholds; passed stricter consistency threshold\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~SSQ\u0026thinsp;~\u0026thinsp;REQ~NSE\u0026thinsp;~\u0026thinsp;TOP~SEI*~PFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePassed stricter consistency, PRI\u0026thinsp;\u0026ge;\u0026thinsp;0.70, PRI\u0026thinsp;\u0026ge;\u0026thinsp;0.75, and frequency thresholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e~SSQ\u0026thinsp;~\u0026thinsp;REQ~ATNSE\u0026thinsp;~\u0026thinsp;TOP~SEIPFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFailed frequency threshold; passed stricter consistency and PRI thresholds\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes: n counts cases with configuration membership\u0026thinsp;\u0026ge;\u0026thinsp;0.5. PRI consistency follows Schneider and Wagemann (2012).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6 Discussion","content":"\u003cp\u003eDrawing on complexity theory, this study combines unstructured text mining (BERTopic), threshold analysis (NCA) and configurational analysis (fsQCA) to explain how overall ski tourism destination experience quality is formed. The main findings and implications are discussed below.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Responses to the research questions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eRQ1. What are the core dimensions of the ski tourism experience derived from online reviews?\u003c/em\u003e Using bottom-up mining of multi-platform online reviews, this study identified seven core dimensions of ski tourism experience: Slope and Snow Quality, Rental and Equipment, Accessibility and Transport, Natural Scenery and Environment, Ticketing and On-site Process, Service Encounter and Instruction, and Price Fairness and Value. This suggests that ski tourism destination experience is shaped not by a single resource attribute, but by a multidimensional structure involving core skiing resources, supporting facilities, operational processes, service support and value perception. Tourists\u0026rsquo; concerns have also expanded from the traditional focus on slopes and facilities to the broader service chain, including ticketing, entry, rental and instruction. This finding is consistent with prior research on the multidimensional nature of tourism service evaluation (Weiermair and Fuchs, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Alexandris et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Konu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Chi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). At the same time, by combining BERTopic with UGC analysis, this study extends earlier work on extracting tourism experience attributes from online reviews (Guo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ2. What minimum threshold levels of these dimensions are necessary to achieve high EQ?\u003c/em\u003e The results show that no single dimension constitutes a necessary condition for high experience quality in the strict set-theoretic sense. However, clear differences emerged in the strength of threshold constraints across dimensions. Ticketing and on-site processes showed the strongest bottleneck effect, while slope and snow quality, together with rental and equipment, also imposed substantial constraints. By contrast, accessibility and transport and service encounter and instruction displayed relatively weak threshold effects. In practical terms, this means that ski tourism destinations must first ensure smooth on-site processes, adequate core skiing resources and usable rental systems before other dimensions can further enhance experience quality. This finding supports the view that necessity is often expressed in threshold form rather than as an absolute yes-or-no condition (Dul, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Vis and Dul, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Dul, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It also extends recent work on the combined use of NCA and fsQCA by highlighting the pivotal baseline role of ticketing and on-site processes in the ski tourism context (Rasoolimanesh, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ3. What equifinal configurations drive high EQ, and is there causal asymmetry between high and low EQ?\u003c/em\u003e The findings reveal three equifinal configurations associated with high experience quality. The first is a core resource and process synergy path, in which strong slope and snow quality, adequate rental and equipment provision, efficient ticketing and on-site processes, and favourable price fairness and value jointly underpin high evaluations. The second is a core resource and service reinforcement path, in which service encounter and instruction further enhance tourists\u0026rsquo; evaluations when core skiing resources are already favourable. The third is a contextual compensation path, in which accessibility and transport advantages, together with efficient on-site processes and favourable value perception, may compensate for weaker scenery or instructional support. By contrast, the low-EQ results reveal a non-mirror-image causal structure. Some low-EQ paths are characterised by overlapping absences of key conditions, whereas others show that relatively strong performance in selected dimensions, such as skiing quality, scenery or staff interaction, cannot offset decisive weaknesses elsewhere. This indicates that high and low experience quality do not follow mirror-image causal logics, but instead reflect asymmetric configurational structures. The finding is consistent with prior research on equifinality and causal asymmetry in complex service settings (Fiss, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Woodside, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Qin et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e, Bi et al., 2020, Perdomo-Verdecia et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Managerial implications\u003c/h2\u003e \u003cp\u003eFirst, ski tourism destinations should protect the experiential baseline by removing micro-process bottlenecks that may trigger disproportionately negative evaluations. The NCA results suggest that tourists\u0026rsquo; experience quality is constrained less by scenery or accessibility than by often-overlooked operational details such as ticketing, entry and equipment rental. Even destinations with excellent snow conditions and attractive landscapes may fail to achieve high \u003cem\u003eEQ\u003c/em\u003e if visitors encounter congestion or friction at these touchpoints. Managers should therefore prioritise process redesign, such as timed reservations, pre-arrival rental booking, fast-track channels for self-equipped skiers and clearer separation between beginner and advanced service flows.\u003c/p\u003e \u003cp\u003eSecond, managers should adopt differentiated improvement strategies rather than pursuing a single best-practice model. The fsQCA results show that high \u003cem\u003eEQ\u003c/em\u003e can be achieved through multiple pathways. For urban or suburban ski tourism destinations with limited natural scenery, high \u003cem\u003eEQ\u003c/em\u003e may still be achieved by strengthening slope maintenance, process efficiency and perceived value. By contrast, larger destination resorts with stronger resource endowments may gain more by integrating service delivery and instructional support into the core ski experience. In this sense, the managerial value of fsQCA lies in showing that ski tourism destinations should align improvement strategies with their own endowments rather than imitate industry leaders indiscriminately.\u003c/p\u003e \u003cp\u003eThird, ski tourism destinations should establish asymmetric intervention mechanisms to prevent marked deterioration in experience quality. The low-EQ configurations suggest that negative evaluations do not arise through a single uniform route. In some cases, deterioration is driven by the overlap of multiple weaknesses, whereas in others, limited strengths in selected dimensions fail to offset critical weaknesses elsewhere. Managers should therefore monitor combinations of risks rather than isolated indicators, especially during periods of poor snowmaking conditions or holiday overcrowding, and respond quickly with targeted compensation or flow-control measures before dissatisfaction accumulates into broader negative evaluations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Contributions, limitations, and future research\u003c/h2\u003e \u003cp\u003eThis study makes three main contributions. First, it extends the measurement of ski tourism experience by moving beyond predefined survey scales and using BERTopic to uncover latent but important dimensions, such as ticketing and rental processes. Second, by introducing NCA, it adds a threshold perspective to tourism experience research and shifts attention from average net effects to baseline constraints. Third, through fsQCA, it demonstrates both equifinality in the formation of high \u003cem\u003eEQ\u003c/em\u003e and causal asymmetry between high and low \u003cem\u003eEQ\u003c/em\u003e, thereby deepening the application of complexity theory to ski tourism.\u003c/p\u003e \u003cp\u003eThe study also has several limitations. First, the data were drawn from four major Chinese platforms and may underrepresent some groups, such as older or international tourists. Future studies could incorporate multilingual reviews or interview data for cross-cultural validation. Second, due to the anonymity of UGC, individual heterogeneity, such as skill level, could not be incorporated. Future research could combine structured surveys with unstructured reviews to examine whether beginners and advanced skiers follow different experiential logics. Third, the present analysis is essentially cross-sectional. Future studies could use temporal QCA or longitudinal data to explore how high \u003cem\u003eEQ\u003c/em\u003e configurations evolve over time and across seasonal or destination life-cycle stages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\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\u003c/h2\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.X.: Conceptualization, Supervision, Validation, Writing \u0026ndash; Review \u0026amp; Editing. K.Z.: Data curation, Investigation, Methodology, Formal analysis, Writing \u0026ndash; Original Draft. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability Statement\u003c/h2\u003e\n\u003cp\u003eThe data files supporting the findings of this study are provided as supplementary materials for editorial assessment and confidential peer review.\u003c/p\u003e\n\u003cp\u003eThe supplementary package includes: (1) the initial ski-related POI list with geographic coordinates used for sample screening; (2) the screened final sample list of ski tourism destinations retained for analysis; (3) the underlying online review corpus used for text preprocessing, topic modeling, sentiment scoring, and destination-level variable construction; (4) the destination-level analytical dataset before fuzzy-set calibration; (5) the calibrated analytical dataset used for the NCA and fsQCA analyses; and (6) a summary results workbook containing calibration anchors, NCA results, fsQCA necessity analysis, truth tables, solution summaries, case-membership information, and robustness checks.\u003c/p\u003e\n\u003cp\u003eBecause part of the underlying review texts was collected from third-party online platforms and may be subject to platform terms of use and redistribution restrictions, the review corpus is provided for confidential editorial and peer-review purposes only and is not intended for unrestricted public dissemination. The authors are willing to cooperate with the editors regarding any further reasonable requests for verification materials during the review process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eALEXANDRIS K, KOUTHOURIS C, MELIGDIS A (2006) Increasing customers' loyalty in a skiing resort: The contribution of place attachment and service quality. Int J Contemp hospitality Manage 18:414\u0026ndash;425\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBAKER DA, CROMPTON JL (2000) Quality, satisfaction and behavioral intentions. Annals tourism Res 27:785\u0026ndash;804\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBI J-W, LIU Y, FAN, Z.-P., CAMBRIA E (2019) Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. Int J Prod Res 57:7068\u0026ndash;7088\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBI J-W, LIU, Y., FAN, Z.-P., ZHANG J (2020) Exploring asymmetric effects of attribute performance on customer satisfaction in the hotel industry. Tour Manag 77:104006\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBICHLER B, PIKKEMAAT B (2021) Winter sports tourism to urban destinations: Identifying potential and comparing motivational differences across skier groups. J Outdoor Recreation Tourism\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBUHALIS DD (2000) Marketing the competitive destination of the future. Tourism Tribune 21:97\u0026ndash;116\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN K, ZHENG, J., JIN J (2024) Ranking products through online opinions: A text analysis and regret theory-based approach. Appl Soft Comput 158:111571\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHEN Y, XU J (2026) Exploring interdisciplinary boundaries: A BERTopic modeling approach to analyze four interdisciplinary fields at the abstract and sentence levels. Engl Specif Purp 81:56\u0026ndash;72\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCHI X, ZHANG B, TANG C, ZHOU H, HAN H (2025) Ski tourism and destination attractiveness: Two separate importance-performance analyses before and after the pandemic in Korean and Chinese markets. Tourism Manage Perspect 57:101360\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDUL J (2016) Identifying single necessary conditions with NCA and fsQCA. J Bus Res 69:1516\u0026ndash;1523\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDUL J (2022) Problematic applications of Necessary Condition Analysis (NCA) in tourism and hospitality research. Tour Manag 93:104616\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDUL J, HAUFF S, BOUNCKEN RB (2023) Necessary condition analysis (NCA): review of research topics and guidelines for good practice: J. Dul et al. RMS 17:683\u0026ndash;714\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEASTMAN JK, SHIN H, JAIN V, WANG X (2024) Determinants of positive consumer affect toward luxury brands\u0026rsquo; social media posts on sustainability: A fsQCA approach. J Bus Res 183:114871\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFISS PC (2011) Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Acad Manag J 54:393\u0026ndash;420\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGEREMEW YM, HUANG W-J, HUNG K (2024) Fuzzy-set qualitative comparative analysis as a mixed-method and analysis technique: a comprehensive systematic review. J Travel Res 63:3\u0026ndash;26\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGROOTENDORST M (2022) BERTopic: Neural topic modeling with a class-based TF-IDF procedure. \u003cem\u003earXiv preprint arXiv:2203.05794\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUO X, PESONEN, J., KOMPPULA R (2021) Comparing online travel review platforms as destination image information agents. Inform Technol Tourism 23:159\u0026ndash;187\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGUO Y, BARNES SJ, JIA Q (2017) Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation. Tour Manag 59:467\u0026ndash;483\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHALLMANN K, M\u0026uuml;LLER S, FEILER S, BREUER C, ROTH R (2012) Suppliers' perception of destination competitiveness in a winter sport resort. Tourism Rev 67:13\u0026ndash;21\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJELODAR H, WANG Y, YUAN C, FENG X, JIANG X, LI Y, ZHAO L (2019) Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia tools Appl 78:15169\u0026ndash;15211\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKIRILENKO AP, STEPCHENKOVA SO, DAI X (2021) Automated topic modeling of tourist reviews: Does the Anna Karenina principle apply? Tour Manag 83:104241\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKONU H, LAUKKANEN T, KOMPPULA R (2011) Using ski destination choice criteria to segment Finnish ski resort customers. Tour Manag 32:1096\u0026ndash;1105\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI H, LI BRUCEX, G., GAO H (2023) Restaurant survival prediction using customer-generated content: An aspect-based sentiment analysis of online reviews. Tour Manag 96:104707\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLV S, QIN XIAOA, XU Y, Z., WANG X (2024) A decision framework for improving the service quality of charging stations based on online reviews and evolutionary game theory. Transp Res Part A: Policy Pract 187:104168\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMCINNES L, HEALY J, MELVILLE J (2018) Umap: Uniform manifold approximation and projection for dimension reduction. \u003cem\u003earXiv preprint arXiv:1802.03426\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOANA I-E, SCHNEIDER CQ (2024) A robustness test protocol for applied QCA: theory and R software application. Sociol Methods Res 53:57\u0026ndash;88\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePERDOMO-VERDECIA V, GARRIDO-VEGA P, SACRIST\u0026aacute;N-D\u0026iacute;AZ M (2024) An fsQCA analysis of service quality for hotel customer satisfaction. Int J Hospitality Manage 122:11\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQIN Y, LUO C, NGAI EW (2025) Deconstructing customer satisfaction recipes: A dynamic configurational framework leveraging the power of online reviews in tourism contexts. Tour Manag 110:105181\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRAGIN CC (2009) Redesigning social inquiry: Fuzzy sets and beyond. University of Chicago Press\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRASOOLIMANESH SM (2026) Applying fsQCA, NCA and NConfA in tourism research. Annals Tourism Res 116:104069\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRASOOLIMANESH SM, GANNON M, TAHERI B, MIKULIć J (2025) Assessing a model of customer rage: does customer entitlement moderate relationships between rage emotions, expressions and behaviors? J Travel Res 64:462\u0026ndash;484\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eREIMERS N, GUREVYCH I (2019) Sentence-bert: Sentence embeddings using siamese bert-networks. Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), 3982\u0026ndash;3992\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR\u0026ouml;DER M, BOTH A, HINNEBURG A (2015) Exploring the space of topic coherence measures. Proceedings of the eighth ACM international conference on Web search and data mining, 399\u0026ndash;408\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSKAANING S-E (2011) Assessing the robustness of crisp-set and fuzzy-set QCA results. Sociol Methods Res 40:391\u0026ndash;408\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSTEIGER R, SCOTT D (2020) Ski tourism in a warmer world: Increased adaptation and regional economic impacts in Austria. Tour Manag 77:104032\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVIS B, DUL J (2018) Analyzing relationships of necessity not just in kind but also in degree: Complementing fsQCA with NCA. Sociol methods Res 47:872\u0026ndash;899\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG R, WU C, WANG X, XU F, YUAN Q (2024) E-Tourism information literacy and its role in driving tourist satisfaction with online travel information: A qualitative comparative analysis. J Travel Res 63:904\u0026ndash;922\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWEIERMAIR K, FUCHS M (1999) Measuring tourist judgment on service quality. Annals tourism Res 26:1004\u0026ndash;1021\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWOODSIDE AG (2014) Embrace\u0026bull; perform\u0026bull; model: Complexity theory, contrarian case analysis, and multiple realities. J Bus Res 67:2495\u0026ndash;2503\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWU J, ZHAO N, YANG T (2024) Wisdom of crowds: SWOT analysis based on hybrid text mining methods using online reviews. J Bus Res 171:114378\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXIANG Z, MA DUQ, Y., FAN W (2017) A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tour Manag 58:51\u0026ndash;65\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXU J, SHI, P.-H., CHEN X (2025) Curators or creators: Role configurations of digital innovation strategy in museum tourism destination and the principles underlying their attractiveness. Tour Manag 106:105024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYANG C, KIM Y (2025) Enhancing topic coherence and diversity in document embeddings using LLMs: A focus on BERTopic. Expert Syst Appl 281:127517\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHANG C, XU Z, GOU X, CHEN S (2021) An online reviews-driven method for the prioritization of improvements in hotel services. Tour Manag, 87\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Complexity theory, Natural Language Processing (NLP), Fuzzy-set Qualitative Comparative Analysis (fsQCA), Necessary Condition Analysis (NCA), User-generated content (UGC), Customer experience","lastPublishedDoi":"10.21203/rs.3.rs-9182910/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9182910/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding user experiences in complex service ecosystems has long relied on traditional symmetric, net-effect methodologies. Drawing on complexity theory, this study examines the non-linear and asymmetric formation of experience quality (EQ) in ski tourism destinations by integrating BERTopic, Necessary Condition Analysis (NCA), and fuzzy-set Qualitative Comparative Analysis (fsQCA). Based on large-scale multi-platform online reviews, BERTopic was first used to identify seven core dimensions of ski tourism experience from the bottom up. The NCA results show that ticketing and on-site processes constitute the strongest bottleneck condition, while slope and snow quality and rental and equipment also impose substantial threshold constraints. The fsQCA results reveal three equifinal configurations associated with high EQ, indicating that core skiing resources, rental support, and price fairness/value jointly form a shared baseline for superior tourist evaluations. In contrast, low EQ is associated with four asymmetric configurational paths, revealing a non-mirror-image logic of deterioration. Some low-EQ paths are characterised by overlapping absences of key conditions, whereas others indicate that isolated strengths in selected dimensions cannot compensate for critical weaknesses elsewhere. These findings extend the application of complexity theory in tourism research and provide a scalable analytical framework for transforming unstructured online reviews into configurational evidence. Practically, the study suggests that ski tourism destinations should first remove critical bottlenecks and then adopt differentiated improvement strategies aligned with their specific resource endowments.\u003c/p\u003e","manuscriptTitle":"Deconstructing the formation of ski tourism destination experience quality: a configurational framework based on online reviews","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 17:10:53","doi":"10.21203/rs.3.rs-9182910/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-19T10:08:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-18T06:53:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T16:29:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T13:18:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122140124733892302075248216889388433826","date":"2026-04-23T09:56:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90040889958137250850425103851527492088","date":"2026-04-22T16:49:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172924619077574085558235963449131301810","date":"2026-04-21T18:23:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93412258431834489287182720728489825565","date":"2026-04-19T13:29:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176509574460858592047890322790630267177","date":"2026-04-19T13:03:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"290833339660652485071414589268334991094","date":"2026-04-17T11:57:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-17T10:45:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-13T10:42:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-08T15:51:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-04-08T15:18:46+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":"224d92f1-d100-465a-8ba2-78fb90163461","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-19T10:08:52+00:00","index":42,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-18T06:53:30+00:00","index":41,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T16:29:11+00:00","index":40,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T13:18:43+00:00","index":39,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66872820,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":66872821,"name":"Scientific community and society/Geography"},{"id":66872823,"name":"Social science/Geography"},{"id":66872825,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-04-26T17:10:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 17:10:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9182910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9182910","identity":"rs-9182910","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}