Exploring the factors affecting cruise passengers’ perceptions of value for money expressed in online reviews

The study addresses how cruise ship attributes and onboard/onshore experience dimensions shape passengers’ perceptions of value for money (VfM) and whether these determinants differ by market positioning (standard, premium, luxury). Against the backdrop of a maturing North American and European cruise market and growing Asia-Pacific demand, the paper argues that high VfM is critical for customer satisfaction, loyalty, and market expansion. Existing work highlights VfM as a driver of revisit intention but lacks a systematic analysis of which experiential and physical attributes drive VfM in cruises and how effects vary across brand segments. The authors set two objectives: (1) identify key factors influencing perceived VfM using content analysis, regression, and multigroup comparison on a large corpus of online reviews; and (2) compare determinants across standard, premium, and luxury segments to inform targeted managerial strategies.

Value for money is commonly conceptualized as performance/quality relative to price and, more broadly, as a trade-off between benefits acquired and sacrifices (price and other costs). While widely used in engineering, construction, trade, and ICT (often operationalized as quality or performance divided by price), VfM has seen limited focused study in tourism. In service sectors (airlines, hotels), perceived VfM correlates with satisfaction, loyalty, and WOM, with differences by customer segment and service class. In tourism, VfM is linked to revisit/repurchase intentions, yet few studies explicitly identify which attributes drive VfM perceptions. Methodologically, earlier VfM studies used expert judgment, AHP, clustering, and surveys with SEM; with UGC proliferation, content analysis of online reviews has become prevalent. This study leverages UGC to examine cruise-specific experiential attributes (embarkation, cabin, dining, entertainment, service, fitness, activities, public rooms, shore excursions) and physical ship attributes (size, launch year) as potential VfM drivers and compares their roles across market segments.

Data source: Cruise Critic, a leading third-party review platform. The authors scraped cruise physical attributes and user-generated reviews using Python. Initial dataset >160,000 reviews; after removing records missing attribute scores, N=97,559 reviews remained, covering >360 ocean cruise ships from >120 cruise lines. Market segments followed SeaTrade Cruise News (2017): standard (N=64,049; 65.7%), premium (N=32,118; 32.9%), luxury (N=1,392; 1.4%). Variables: Dependent variable: perceived value for money (1–5). Independent variables: nine experience attributes scored by reviewers—embarkation, cabin, dining, entertainment, service, fitness, activities, public room, shore excursion—and two physical attributes—ship size (maximum passenger capacity, log-transformed) and launch year (ship newness, log-transformed). Attributes were also informed by NVivo word-frequency content analysis of review texts, merging related terms (e.g., room/cabin/balcony as ‘cabin’, food/buffet/dinner as ‘dining’). Analytical procedure: (1) Content analysis with NVivo to identify salient attributes (top word frequencies). (2) Stepwise multiple regression in SPSS to estimate effects of attributes on VfM for the overall sample and separately by segment; diagnostics indicated VIF<10 and Durbin–Watson ≈2. (3) Multigroup comparison with Amos to assess cross-segment differences in path strengths; model fit assessed via χ²/df, RMSEA, NFI, RFI, IFI, TLI, CFI, with restrictions tested by Δχ² between constrained vs. unconstrained models.

- Descriptives (N=97,559): Mean VfM=3.69 (SD=1.31) on 1–5 scale. Highest average experience scores: service (4.11), embarkation (4.11), cabin (4.10), public rooms (4.08). Lower relative scores: activities (3.48), shore excursions (3.63), entertainment (3.73). - Overall regression (R²=0.698; F=53.540; DW=1.905; all p<0.001; VIF<10): All nine experience attributes positively predict VfM. Standardized effects (Beta, approximate ordering): dining (0.200), service (0.182), shore excursion (0.165), public room (0.133), cabin (0.131), activity (0.101), entertainment (0.091), embarkation (0.062), fitness (0.048). Physical attributes: ship size positive (B=0.106, Beta=0.015), launch year negative (B=-52.26, Beta=-0.057). - By segment: • Standard (R²=0.704; F=45.817): All nine experience attributes significant and positive. Physicals: ship size negative (B=-0.458), launch year negative (B=-18.443). • Premium (R²=0.694; F=56.792): All nine experience attributes significant and positive. Physicals: ship size positive (B=0.174), launch year negative (B=-37.108). • Luxury (R²=0.734; F=5.685): All experience attributes except shore excursion? (table shows nine positives listed; shore excursion is positive and significant too) are significant and positive; physicals (ship size, launch year) not significant. - Multigroup model fit: Good across segments (e.g., RMSEA≤0.040; NFI/IFI/CFI ≈ 0.999–1.000). - Cross-segment differences (Δχ² tests): • Standard vs Premium: Significant differences for activity, dining, public room, service, fitness, cabin, embarkation (generally stronger effects in standard, except service stronger in premium); no significant difference for entertainment, shore excursion, ship size, launch year. • Standard vs Luxury: Significant differences for activity, dining, public room, entertainment, service, fitness, launch year, ship size, cabin, embarkation; shore excursion not significantly different. Standard tends to show stronger effects for activity, public rooms, service, fitness, embarkation; luxury stronger for dining and entertainment. • Premium vs Luxury: Significant differences for activity, public room, entertainment, service, fitness, launch year, ship size, embarkation; no significant differences for dining, shore excursion, cabin. Premium generally shows stronger effects for activity, public rooms, entertainment, service, fitness, ship size; luxury shows weaker sensitivity to launch year. - Overall insight: Experiential attributes consistently and positively drive VfM, whereas the impact of physical attributes depends on segment: ‘big and new’ lowers VfM in standard, mixed in premium (newer lowers, bigger increases), and is largely irrelevant in luxury.

Findings demonstrate that passengers’ perceived value for money is primarily shaped by experiential touchpoints across the journey—embarkation, accommodation, dining, entertainment, service, fitness, onboard activities, public spaces, and shore excursions. These attributes directly address the research question by identifying which elements most influence VfM and how their effects differ by market segment. The consistent positive relationships underscore the centrality of service and experience design in enhancing VfM. Segmental differences clarify that physical ship investments (size, newness) do not uniformly translate into better VfM: in standard cruises, larger/newer ships may raise prices without proportionate perceived benefits, lowering VfM; premium guests value larger ships but penalize newer ships’ price premia; luxury guests’ VfM perceptions are largely decoupled from physical attributes, emphasizing premium experiential quality instead. This nuance informs differentiation strategies and resource allocation, highlighting where improvements will most effectively elevate perceived value and loyalty.

The paper contributes by (1) systematically integrating UGC-based content analysis with regression and multigroup comparisons to reveal the determinants of cruise VfM across a large, diverse sample; (2) establishing that experiential attributes robustly and positively drive VfM across segments, with dining, service, and shore excursions particularly salient; and (3) evidencing that physical ship attributes have segment-contingent effects—negative in standard for size and newness, mixed in premium, and negligible in luxury. Managerially, cruise lines should prioritize enhancing key experiential dimensions aligned with segment expectations and clarify positioning to optimize perceived value. Future research should incorporate richer traveler characteristics, triangulate with surveys/interviews for theory-building, and employ additional qualitative approaches to unpack mechanisms behind attribute-value linkages.

- The UGC dataset aggregates diverse reviewers with heterogeneous demographics (age, residence, economic status) not explicitly modeled; subgroup heterogeneity may influence results. - Variable selection was constrained by available platform ratings and scraped attributes; other relevant constructs (e.g., price paid, itinerary length, service touchpoint specifics) were not included. - The study is data-driven; future work should augment with literature-based constructs, interviews, and surveys to strengthen construct validity and causal inference, and apply robust qualitative analyses to unpack specific elements and formation mechanisms of VfM perceptions.