Revitalising tourism through memorable wellness experiences: Paving the path to destination loyalty via engagement and well-being

Abstract

This study investigates the role of Memorable Wellness Tourism Experiences (MWTE) in revitalising Sri Lanka’s tourism industry by examining their influence on destination loyalty. It specifically explores the mediating roles of tourist engagement and well-being within this relationship, addressing existing gaps in wellness tourism literature. Employing a positivist, quantitative research approach, the study collected primary data from 622 international wellness tourists visiting Sri Lanka. Participants were selected through a combination of convenience and stratified multistage cluster sampling across Sri Lanka Tourism Development Authority (SLTDA)-registered spa and wellness centres. A structured questionnaire was administered, and the data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) via SmartPLS software. The results confirm that MWTE significantly influences destination loyalty and positively impacts tourist engagement and well-being. Notably, engagement was found to partially mediate the relationship between MWTE and destination loyalty, highlighting its critical role in cultivating repeat visitation and positive word-of-mouth. Conversely, well-being, while influenced by MWTE, did not directly affect destination loyalty nor mediate the MWTE–loyalty relationship. These findings suggest that wellness tourism providers should prioritise strategies that enhance tourist engagement to reinforce loyalty. Policymakers and destination marketers should focus on creating rich, engaging wellness experiences to stimulate sustainable growth in the sector. This study contributes to the evolving field of wellness tourism by offering empirical validation of the MWTE framework in a wellness-specific context. It addresses theoretical ambiguities and extends understanding of how experiential attributes drive destination loyalty through engagement mechanisms.

Keywords

Wellness tourism memorable wellness tourism experiences destination loyalty tourist engagement tourist well-being

Introduction

Tourism has long functioned as a restorative practice offering escape and rejuvenation (Dillette, 2016), with contemporary wellness tourism emerging as a specialised sector addressing heightened demand for holistic well-being and preventive healthcare (Voigt and Pforr, 2014). The global prioritisation of health-conscious behaviours and increasing stress-related health issues have catalysed demand for wellness tourism experiences that deliver relaxation, rejuvenation, and holistic well-being (Kazakov and Oyner, 2021). According to Kazakov and Oyner (2021), wellness tourism has proven resilient, emerging as a key catalyst for industry recovery, especially after the COVID pandemic. A destination’s tourism success fundamentally hinges on its capacity to craft memorable experiences that substantially enhance tourist satisfaction, foster emotional connections, and cultivate enduring destination loyalty (Kim et al., 2012; Sthapit et al., 2023). Sri Lanka has established itself as a leading global wellness destination (Sri Lanka Tourism, 2021), specialising in Ayurveda and holistic healing (Sri Lanka Export Development Board [EDB], n.d). As demand for wellness tourism grows, examining how MWTE influences engagement, well-being, and destination loyalty becomes crucial for maintaining Sri Lanka’s competitive edge in the wellness sector. Thus, the primary objective of this research is to explore how Sri Lankan tourism might be regenerated through the emerging form of wellness tourism, particularly how MWTE influences destination loyalty.

Memorable tourism experiences (MTE) are inherently multidimensional, encompassing hedonistic pleasure, novelty, meaningfulness, cultural immersion, and psychological rejuvenation (Kim and Fesenmaier, 2017). However, prior studies have not established a universally applicable framework or consistent scale for measuring MTE across different tourism contexts (Hosseini et al., 2021; Wilson, 2019), revealing a theoretical gap. While research has explored MTE in various specialized forms such as cultural (Seyfi et al., 2020), ecotourism (Subramaniam et al., 2018), and marine tourism (Jonas et al., 2020), no studies have yet examined memorable experiences in wellness tourism through the lens of Pine and Gilmore’s (1998) experience economy theory, which is the foremost stimulus for broad interest in, incorporating and practising the experience paradigm (Luo et al., 2018), and Mather and Sutherland’s (2011) arousal-biased competition theory (Rahmani et al., 2014).

Accordingly, findings remain inconsistent, with constructs often being subjective and context-dependent (Chandralal et al., 2015; Seyfi et al., 2020), highlighting the need for further investigation into wellness tourism to develop a distinct theoretical model that advances the conceptual understanding of MTE in wellness sector (Luo et al., 2018; Subramaniam et al., 2018). Wellness tourism destinations critically facilitate memorable experiences by deepening emotional connections and boosting engagement, which enhances repeat visitation intentions and positive word-of-mouth behaviour, as evidenced in recent MWTE dimensional research. Accordingly, Sthapit et al. (2023) established five MWTE antecedents: novelty, co-creation, experiencescape, refreshment, and involvement, while Sthapit et al. (2025) advanced an empirical model examining four formative factors: escapism, co-creation, intensification, satisfaction, and three outcomes: hedonic/eudaimonic well-being, place attachment, proposing future research to expand these dimensions (Sthapit et al., 2023, 2025).

Moreover, destination loyalty, a cornerstone of sustainable tourism, is strongly shaped by experiential quality and engagement intensity (Kim and Brown, 2012). MTE drives satisfaction, repeat visitation, and positive word-of-mouth advocacy, constituting vital strategic assets for destinations (Cossío-Silva et al., 2019). Prior literature on wellness tourism encompasses travel experiences, tourists’ satisfaction, and destination loyalty (Kim and Brown, 2012), yet the impacts of travel experience dimensions on destination loyalty in the context of island destinations have gained lesser emphasis (Sangpikul, 2018). Consequently, studies on tourists’ satisfaction, destination image, engagement, motivation, and loyalty to a destination (Kim et al., 2017; Rasoolimanesh et al., 2019, 2021; So et al., 2016) have not taken the construct of travel experience into account, especially in the case of island destinations where the literature is dearth (Sangpikul, 2018).

Tourist engagement, conversely, is a crucial determinant in shaping wellness tourism experiences, encompasses involvement, enthusiasm, and commitment, and serves as (So et al., 2014). Empirical studies demonstrate that MTE strengthen emotional destination bonds and increases revisit intentions (So et al., 2016; Teng, 2021), while engaged tourists exhibit greater destination affinity, positive word-of-mouth dissemination, and loyalty behaviours (Rasoolimanesh et al., 2019). The limited literature in the context of tourist engagement focusing on different forms of tourism can be found (Ahn and Back, 2018; Teng, 2021); however, with a dearth of literature on wellness tourism, leaving a knowledge gap. When considering wellness tourism, it is critical to examine the concept of well-being as one of the imperative notions. Wellness tourism enhances both hedonic (pleasure-based) and eudaimonic (growth-oriented) well-being through restorative, self-transformative experiences (Filep and Laing, 2019; Sthapit and Coudounaris, 2018). Post-pandemic, the role of wellness tourism in destination recovery has intensified, meeting heightened demand for health and well-being-focused, transformative travel that aligns with sustainable sector growth (Wayne and Russell, 2020). Many of the studies on wellness and well-being tourism focus on conceptualising and their constructs (Longo et al., 2017; Moreno-González et al., 2020), as well as comprehending how well these concepts relate to tourism, while segmenting wellness and well-being tourists into diverse groups were commonly seen in several studies (Konu, 2010; Smith and Puczkó, 2009). However, the association between MWTE and well-being was not empirically disclosed.

Despite growing recognition of MWTE’s importance (Dahanayake et al., 2023a, 2023b; Sthapit et al., 2023, 2025), empirical gaps persist regarding its causal relationships with tourist engagement, well-being, and destination loyalty. Therefore, this study examines these interconnections in wellness tourism, offering theoretical and practical insights for creating experience-driven strategies that foster sustainable sector growth through enhanced visitor retention and advocacy. Accordingly, prior tourism literature reveals critical research gaps, including a theoretical gap, a knowledge gap, and an empirical gap. To address these gaps, this study attempts to explore how MWTE fosters destination loyalty, with tourist engagement and well-being as mediating factors. Focusing on Sri Lanka, it investigates international wellness tourists’ perceptions to determine how MWTE can rejuvenate post-pandemic tourism. The research aims to resolve two key questions: (1) To what extent does MWTE enhance destination loyalty? and (2) How do engagement and well-being mediate this relationship? Accordingly, this study integrates theoretical, empirical, and practical perspectives to advance scholarly discourse on MWTE while providing actionable insights into wellness tourism revitalisation.

Literature review

Theoretical background of memorable tourism experiences in wellness tourism

Memories and their associated memorability play a critical role in shaping tourists’ behavioural intentions, as satisfaction and quality alone are insufficient descriptors of the experience (Kim et al., 2012). However, existing studies on MTE often focus on isolated moments rather than adopting a holistic perspective across pre-, during, and post-experience phases (Rahmani et al., 2014). Memory evolves and is a primary determinant of future travel behaviour, highlighting the need for a comprehensive theoretical model, particularly in the context of wellness tourism. Pine and Gilmore’s (1998) Experience Economy Theory offers a foundational framework for categorising experiences into education, entertainment, escapism, and esthetics, each influencing the quality and memorability of the experience. Although widely applied in tourism research, its capacity to fully explain what makes an experience memorable remains debated (Sthapit, 2013). In wellness tourism, educational, esthetic, and escapist dimensions are particularly relevant (Luo et al., 2018), yet direct empirical links between the experience economy and MWTEs remain underexplored (Dahanayake et al., 2023a, 2023b).

The Arousal-Biased Competition Theory (Mather and Sutherland, 2011), conversely, offers an alternative lens to understand how MTEs are formed. It posits that arousal enhances the perception and long-term memory of high-priority stimuli while diminishing low-priority elements. Tourists’ expectations, goals, and prior knowledge shape their perception (top-down processing), while sensory and environmental stimuli during the experience influence memory retention (bottom-up processing). The interplay between these elements determines the experience’s most memorable aspects (Rahmani et al., 2014). Although this theory provides valuable insights into MTE formation, its application in wellness tourism remains unexplored, warranting further research to bridge this gap.

While the experience economy provides a broad framework for understanding tourism experiences, integrating the arousal-biased competition theory could offer deeper insights into the mechanisms underlying memorable experiences in wellness tourism. Thus, future research should explore the interplay between the experience economy and arousal-biased competition to enhance the design of personalised, impactful, memorable tourism experiences in wellness context that foster sustained engagement, well-being, and destination loyalty. A comprehensive literature review and a bibliometric analysis highlighted a notable paucity of scholarly work and empirical evidence concerning MWTE (Dahanayake et al., 2023a). Notably, limited scholarly attention has been directed toward identifying the specific dimensions that contribute to forming memorable experiences within the context of wellness tourism (Dahanayake et al., 2023a, 2023b; Sthapit et al., 2023).

Sthapit et al. (2023) proposed several antecedents of MWTE, namely, novelty, co-creation, experiencescape, refreshment, and involvement, while calling for further investigation to uncover additional dimensions that may enhance the conceptual clarity of the construct (Dahanayake et al., 2023a). Supporting these insights, a qualitative study exploring the distinct components of MWTEs revealed ten salient dimensions: professionalism, meaningfulness, environmental aesthetics, refreshment, hedonism, hospitality, involvement, novelty, value for money, and authenticity (Dahanayake et al., 2023b). These dimensions were subsequently employed in the present study to serve as the foundation for the construction of a multidimensional MWTE measurement scale.

Influence of memorable wellness tourism experiences on destination loyalty, tourist engagement, and well-being

Extensive research demonstrates that MTE significantly enhance destination loyalty through increased revisit intentions and positive recommendations (Cetin and Dincer, 2014; Moon and Han, 2018), with effect sizes correlating to memory vividness (Kim, 2009; Zare, 2019). This relationship has been empirically validated across diverse contexts, including cultural, culinary, and nature-based tourism (Chen and Rahman, 2018; Huang et al., 2019; Tsai, 2016), establishing MTE as a critical driver of tourist behaviour. However, the specific mechanisms through which MWTEs influence loyalty remain understudied, particularly in high-potential island destinations like Sri Lanka. Given the established link between MTE and destination loyalty in other tourism contexts, it is reasonable to posit that MWTE similarly enhance tourist loyalty. Therefore, the following hypothesis is proposed:

Memorable wellness tourism experience has a significant impact on destination loyalty.

The existing literature demonstrates that MTE significantly influence well-being, with robust evidence for hedonic benefits like pleasure (Chen and Yoon, 2019; Sthapit and Coudounaris, 2018; Trinanda et al., 2022), while eudaimonic outcomes (personal growth) remain less examined (Filep and Laing, 2019; Voigt, 2017). Wellness tourism specifically enhances subjective well-being through positive affect and life satisfaction (Huang et al., 2019; Voigt, 2017), yet the relationship between MWTE and holistic well-being remains under-researched (Mendonça-Pedro et al., 2021). Given MTE established dual impact on hedonic and eudaimonic well-being across various tourism contexts, it is hypothesised that MWTE similarly promote comprehensive well-being, bridging this critical research gap in wellness tourism literature. Accordingly, the following hypothesis is formulated:

Memorable wellness tourism experience has a significant impact on tourists’ well-being.

Tourist engagement, rooted in consumer engagement theory, is significantly influenced by tourism experiences, as demonstrated in various contexts such as film tourism (St-James et al., 2018; Teng, 2021) and storytelling experiences (Zhong et al., 2017). Research suggests that MTE serve as a key driver of tourist engagement, particularly in post-purchase interactions (Kim et al., 2017; Kim and Fesenmaier, 2017). While wellness tourism has been linked to increased tourist engagement (He et al., 2021), studies have yet to explore the role of MTE in this context. Given that engaging and memorable experiences encourage deeper participation and psychological involvement (Ahn and Back, 2018; Baloglu et al., 2019), it is anticipated that MWTE significantly enhance tourist engagement. Thus, the following hypothesis is established:

Memorable wellness tourism experience has a significant impact on tourists’ engagement.

Association amongst tourists’ well-being, engagement, and destination loyalty

Tourists’ well-being has been widely identified as a crucial factor influencing travel decisions, behavioural intentions, and destination loyalty (Jamaludin et al., 2016; Pyke et al., 2016). Positive emotional experiences, such as enjoyment, relaxation, and social connection, significantly enhance tourists’ attachment to destinations, increasing their likelihood of revisiting and recommending them (Han et al., 2017, 2018; Huang et al., 2019). While both hedonic and eudaimonic well-being contribute to destination loyalty, studies suggest that eudaimonic well-being plays a more substantial role (Vada, 2019). In wellness tourism, where experiences are designed to enhance overall well-being, it is anticipated that tourists’ well-being will significantly influence their loyalty to the destination. Therefore, the subsequent hypothesis is proposed:

Well-being of wellness tourists has a significant impact on destination loyalty.

Tourist engagement, conversely, has been extensively recognised as a crucial factor in fostering destination loyalty, with research demonstrating that higher levels of engagement enhance satisfaction, attachment, and the likelihood of revisiting a destination (Rasoolimanesh et al., 2019; So et al., 2016). In various tourism contexts, including heritage sites (Alrawadieh et al., 2019) and festivals (Manthiou et al., 2014), engagement has strengthened tourists’ connection to a destination, ultimately increasing loyalty. Although limited studies have explored this relationship in wellness tourism, existing research suggests that engaged wellness tourists are more likely to return and recommend the destination (Kim et al., 2017; Vasudevan, 2021). Given these findings, tourist engagement in wellness tourism is anticipated to significantly influence destination loyalty. Hence, the following hypothesis is put forward:

Wellness tourists’ engagement has a significant impact on destination loyalty.

Mediating effect of tourists’ well-being and tourists’ engagement

Existing research has established a direct link between MTE and destination loyalty; however, scholars argue that this relationship can also be mediated by well-being (Kim, 2018; Tsai, 2016; Vada et al., 2019). Memorable travel experiences foster place attachment, which, in turn, enhances destination loyalty through well-being-related benefits such as positive emotions and life satisfaction (Dillette et al., 2019; Huang et al., 2019). In the wellness tourism context, studies have demonstrated that emotional well-being derived from memorable experiences significantly impacts loyalty (Baloglu et al., 2019; Monika et al., 2021). Given these findings, it is reasonable to conclude that tourists’ well-being mediates the effect of MWTE on destination loyalty. In light of this, the following hypothesis is suggested:

Tourists’ well-being mediates the influence of memorable wellness tourism experiences on their destination loyalty.

Tourist engagement has also been recognised as a crucial mediator in the relationship between MTE and destination loyalty, influencing tourists’ behavioural intentions and strengthening their connection to a destination (Sudirman and Patwayati, 2021; Teng, 2021). Research suggests that highly engaging experiences enhance tourists’ emotional involvement, leading to increased loyalty and repeat visits (Kim and So, 2022; Seyfi et al., 2020). In the wellness tourism context, memorable experiences foster engagement, which, in turn, strengthens tourists’ commitment and loyalty to the destination (Maharaniputri et al., 2021; Pujiastuti et al., 2022). Given this, it is reasonable to propose that tourist engagement mediates the relationship between MWTE and destination loyalty. Consequently, the following hypothesis is proposed:

Tourists’ engagement mediates the influence of memorable wellness tourism experiences on their destination loyalty.

Drawing upon established theoretical foundations, this study’s conceptual framework (Figure 1) systematically examines the interrelationships between MWTE, tourist well-being, engagement, and destination loyalty, as evidenced in prior literature.

Methodology

Research design

This study adopts a positivist, explanatory research design utilising a deductive, mono-method quantitative approach to examine MWTE’s influence on destination loyalty through the mediating roles of tourist well-being and engagement (Saunders et al., 2019). The methodology employs a cross-sectional survey with structured questionnaires to collect empirical data from large samples, enabling both descriptive and inferential statistical analyses (Creswell and Creswell, 2018; Rezaei, 2017). This approach is particularly suited for testing hypothesised causal relationships while ensuring objectivity and generalizability in tourism research (Rashid et al., 2021; Veal, 2018). The design addresses practical constraints of longitudinal studies while maintaining methodological rigour in examining the proposed theoretical relationships (Saunders et al., 2019).

Data collection techniques and procedures

This study employs a structured data collection and analysis approach, ensuring reliability and validity. The unit of analysis is international wellness tourists in Sri Lanka, as individual experiences best capture the study’s objectives (Kumar, 2018). The target population comprises international tourists who have visited SLTDA-registered spa and wellness centres in Sri Lanka for wellness tourism over the five preceding years, ensuring relevance and generalizability (Veal, 2018). A non-probability convenience sampling technique was employed due to the unavailability of a complete sampling frame for international wellness tourists in Sri Lanka (Etikan, 2016; Han et al., 2018). Convenience sampling is widely used in wellness tourism research to ensure accessibility, affordability, and timeliness (Sthapit, 2013).

Ensuring representativeness, the sample was purposively selected based on predefined criteria; international wellness tourists above the age of 18, English-speaking, with wellness experiences within the last 5 years, and those who have visited spas and wellness centres to engage in wellness tourism activities, to minimizing bias (Gravetter and Forzano, 2011; Saunders et al., 2019). This was done by qualifying the participants before distributing the questionnaire, and also with the assistance of the operators of the spa and wellness centres. Additionally, a stratified multistage cluster sampling technique was applied to proportionally allocate the sample across SLTDA-registered spa and wellness centres (Lohr, 2021). A larger sample size was targeted to improve statistical power and generalizability (Etikan, 2016; Sthapit, 2013).

A sample size of 680 was determined based on guidelines for factor analysis and PLS-SEM requirements (Comrey and Lee, 2013; Hair et al., 2021). While a sample of 300 is considered adequate for exploratory factor analysis, a Monte Carlo estimate suggested a minimum of 619 for robust PLS-SEM analysis, ensuring statistical power and generalizability (Hair et al., 2022; Jhantasana, 2023). To account for missing data and redundancies, a 10% contingency factor was applied, increasing the final sample size to 680 (Sekaran and Bougie, 2016). Personal questionnaire administration was chosen to minimise non-response bias. To capture the perspectives of English-speaking wellness tourists regarding their recent memorable experiences, primary data were collected immediately after wellness treatments/activities under the direct supervision of the principal researcher. Fourteen trained enumerators, strategically allocated across resort regions, conducted surveys, selected for English proficiency and research competence. The researcher ensured data integrity through direct oversight, with incomplete responses discarded during analysis.

The research instrument, a structured questionnaire with 91 questions across six sections (A-F), was developed based on literature and qualitative findings. Section A captured travel behaviour and wellness experiences, while Section B assessed memorable experiences across ten dimensions. Sections C and D measured well-being (positive emotions, relationships, meaning, accomplishment) and engagement (enthusiasm, attention, absorption, interaction, identification), respectively. Section E evaluated destination loyalty (expectations, perceived quality, satisfaction, revisit/recommendation intentions), and Section F collected demographic data. A 7-point Likert scale (1 = strongly disagree, 7 = strongly agree) was used for Sections B-E to enhance response accuracy, while categorical scales were applied for demographics. The English-language questionnaire, requiring 10-15 minutes, targeted English-speaking wellness tourists for reliable feedback.

Data analysis

Quantitative data analysis was conducted using SPSS (Version 25) and SmartPLS (Version 4) to ensure the accuracy and reliability of findings. SPSS facilitated data preparation, missing data handling, descriptive analysis, and exploratory factor analysis (EFA) (Rahman and Muktadir, 2021), while SmartPLS was employed for confirmatory factor analysis (CFA) and structural equation modelling (SEM) to test hypotheses and model relationships (Hair et al., 2010, 2021). The study examined perceptions of four key variables: MWTE, destination loyalty, tourist engagement, and well-being using a 7-point Likert scale.

The analysis encompassed descriptive statistics (Bedeian, 2014; DeVellis and Thorpe, 2022) and verified multivariate assumptions: normality (Mishra et al., 2019), linearity (Garson, 2012; Verma and Abdel-Salam, 2019), multicollinearity (Hair et al., 2017b, 2022; Shmueli et al., 2019), and homoscedasticity (Verma and Abdel-Salam, 2019) using SPSS (Version 25) (Hair et al., 2017b). EFA using maximum likelihood extraction (Carpenter, 2018; Costello and Osborne, 2019) with Promax rotation (Hair et al., 2010) identified MWTE dimensions, supported by KMO (≥0.60) and Bartlett’s test (p ≤ .05) (Carpenter, 2018). Factors were retained based on eigenvalues ≥1, communalities ≥0.40, loadings ≥0.50, no cross-loadings, and ≥3 items per factor (Carpenter, 2018).

Subsequently, CFA in SmartPLS validated MWTE items, confirming internal consistency, composite reliability (CR), and factor loadings (α ≥ 0.70, CR ≥ 0.70, loadings ≥ 0.50 respectively) (Hair et al., 2010, 2017a, Hair et al., 2021, 2022, 2024) while verifying by satisfying the criteria for convergent validity (AVE ≥ 0.50) and discriminant validity (HTMT < 0.90) (Hair et al., 2010, 2017b, 2019; 2017). The study employed PLS-SEM to rigorously evaluate reflective measurement models through a two-stage approach (Becker et al., 2023; Sarstedt et al., 2019).

Phase I assessed lower-order constructs for indicator loadings (≥0.5) (Hair et al., 2017a, 2021, 2022), internal consistency (α ≥ 0.7), composite reliability (CR ≥ 0.7), convergent and discriminant validity (AVE ≥ 0.50, HTMT < 0.90) (Hair et al., 2017, 2019, 2021, 2024). Phase II assessed higher-order constructs using the embedded two-stage method (Becker et al., 2023; Hair et al., 2021; Sarstedt et al., 2019), where first-order construct scores served as indicators (Crocetta et al., 2021), applying identical validation criteria to ensure robustness (Hair et al., 2021; Sarstedt et al., 2019). This comprehensive approach guaranteed construct validity and reliability throughout the analysis (Hair et al., 2022; Shmueli et al., 2019).

The structural model evaluation assessed hypothesised relationships through R² (explanatory power), f² (effect size), and PLSpredict (predictive accuracy) (Becker et al., 2023; Hair et al., 2022), while testing collinearity (VIF ≤ 5) (Hair et al., 2017, 2022; Shmueli et al., 2019) and estimating path coefficients via bias-corrected and accelerated (BCa) bootstrapping (10,000 subsamples) (Streukens and Leroi-Werelds, 2016). Hypothesis testing employed standardised path coefficients (β between −1 and +1), statistical significance thresholds (p < 0.05, t > 1.96), BCa bootstrap confidence intervals, and R² interpretive thresholds (substantial = 0.75, moderate = 0.50, weak = 0.25) to assess explanatory power (Hair et al., 2019). Mediation analysis utilised BCa bootstrapping to distinguish between full, partial (complementary/competitive), and non-mediation effects (Avkiran and Ringle, 2018; Hair et al., 2019), while PLSpredict assessed out-of-sample predictive power through k-fold cross-validation (k = 10), comparing Q², RMSE, and MAE against linear regression benchmarks to assess predictive accuracy (Shmueli et al., 2019).

Results

Demographic characteristics

Demographic analysis of international wellness tourists indicates a predominance of females (64.6%), with the largest age group being 36–45 years (24.6%). Most are married (60.3%), reside in Europe, particularly Germany (21.5%), and are employed full-time (61.3%) or retired (18.6%). They are largely higher-income earners (49.7% earning above $50,001) and well-educated (31.2% with tertiary education), suggesting wellness tourism appeals to financially secure, educated individuals (Table 1). Regarding wellness experiences, 31.2% of respondents undertake annual wellness trips, primarily with a spouse or partner (34.9%) or alone (27.0%). Longer stays (8–14 days) are preferred (40.4%). Key information sources include personal networks (33.1%) and online platforms (26.7%). Popular activities include Ayurveda (66.7%), spa treatments (60.6%), and yoga (43.6%), with Ayurveda being the most preferred (26.4%). The findings underscore a growing inclination toward holistic, traditional healing practices that integrate physical, mental, and spiritual well-being.

Table 1.

Demographic profile of study respondents (n = 622).

Variables	Category	Frequencies	Variables	Category	Frequencies
Gender	Male	220	Employment Status	Full-time employed	381
Gender	Female	402		Part-time employed	32
Age	18–25	34		Unemployed	2
	26–35	126		Self-employed	76
	36–45	153		Student	15
	46–55	124		Retired	116
	56–65	120	Annual Household Income (USD)	Below 10,000	31
	Over 65	65		10,000–20,000	28
Marital Status	Married	375		20,001–30,000	49
	Single (never married)	126		30,001–40,000	84
	Living with partner	67		40,001–50,000	121
	Divorced	27		Above 50,001	309
	Separated	7	Education Level	High school graduate or less	129
	Widowed	20		College/University graduate	194
Country of Residence	Germany	134		Professional qualification	77
	United Kingdom	89		Postgraduate degree	160
	United States of America	73		Doctoral degree	33
	Australia	53		Other	29
	Other	273		Other	29

Descriptive analysis of study variables

Descriptive analysis revealed that international wellness tourists exhibit highly positive perceptions of their memorable experiences, with an overall mean score of 6.03 and relatively low standard deviation (SD) of 0.666 on a 7-point Likert scale, reflecting strong consistency. Key aspects such as Friendly people (M = 6.43) and Welcomeness (M = 6.41) were particularly valued, alongside environmental attributes and emotional fulfilment, while Explain the process (M = 5.66) and affordability-related factors indicated moderate yet improvable performance. Destination loyalty was notably high (M = 6.29, SD = 0.718), with all dimensions including Satisfaction, Recommendation Intent, and Revisit Intention, scoring above 6.0, underscoring strong retention potential. Additionally, Tourist Well-being (M = 5.58, SD = 0.965) was robust, particularly in Positive Emotions (M = 6.04), though Relationships and Accomplishment showed slightly lower scores, while Tourist Engagement (M = 5.45, SD = 0.954) demonstrated strong Enthusiasm and Absorption (M = 5.86 and 5.82, respectively) but relatively weaker Interaction and Identification (M = 5.07 and 4.57), suggesting areas for further enhancement despite overall positive engagement levels.

Factor analysis of memorable wellness tourism experiences

The study adopted sequential analytical approach, employing EFA, identifying latent dimensions of MWTE and their constituent indicators, followed by CFA to validate the derived measurement structure. The Kaiser-Meyer-Olkin (KMO) measure (0.946) and significant Bartlett’s test (χ² = 9894.948; df = 465; p < 0.001) confirmed factor analysis suitability (Carpenter, 2018). Applying established Kaiser’s criteria stated under the methodology section (Carpenter, 2018), six distinct factors were extracted. After iterative refinement, 31 items across six dimensions (Professionalism, Meaningfulness, Environmental Aesthetics, Tranquil Ambience, Novelty, and Value for Money) demonstrated strong reliability (α = 0.87-0.91), with item-total correlations ranging from 0.511 to 0.865, and explained 73.18% total variance.

Following EFA, CFA was conducted to confirm the MWTE measurement model’s robustness. The evaluation adhered to established criteria specified under the methodology section (Hair et al., 2017, Hair et al., 2019, 2024). The refined 29-item scale, following the elimination of two underperforming indicators, underwent rigorous expert assessment to establish both face and content validity. The CFA results (Tables 2 and 3) confirmed robust measurement properties across all six MWTE dimensions refined under EFA, with excellent reliability (α = 0.822–0.948; CR = 0.823–0.949), strong convergent validity (AVE = 0.725–0.878), and high indicator reliability (loadings > 0.740) (Table 2).

Table 2.

CFA results of MWTE measures.

Dimensions and indicators	Factor loading	Cronbach’s alpha (α)	CR	AVE
Professionalism		0.947	0.949	0.741
Empathy	0.840
Well-trained practitioner	0.872
Knowledgeability of practitioners	0.911
Experienced practitioners	0.895
Thoroughness with process	0.912
Preparedness	0.884
Explain the process	0.740
Methodical process	0.819
Meaningfulness		0.929	0.930	0.876
Important	0.927
Intimate	0.950
Self-development	0.932
Environmental Aesthetics		0.822	0.823	0.738
Ambience	0.857
Cleanliness of environment	0.868
Peaceful environment	0.851
Tranquil Ambiance		0.948	0.949	0.725
Relaxing	0.851
Soothing	0.897
Stress less	0.754
Invigorating	0.859
Pleasure	0.925
Impressive	0.893
Enjoyment	0.899
Friendly people	0.780
Welcomeness	0.785
Novelty		0.930	0.931	0.878
Different experience	0.923
Unique experience	0.945
Novel experience	0.941
Value for money		0.900	0.916	0.834
Affordable	0.937
Worth the money	0.933
Competitive rates	0.868

Note. Factor loadings ≥ 0.5; α ≥ 0.70; CR (Composite Reliability (rho_a)) ≥ 0.7; AVE (Average Variance Extracted) ≥ 0.50.

Table 3.

HTMT criterion results of MWTE measures.

Dimension	MWTEEn	MWTEMe	MWTENo	MWTEPr	MWTETrAm
MWTEEn
MWTEMe	0.617
MWTENo	0.558	0.719
MWTEPr	0.625	0.738	0.731
MWTETrAm	0.695	0.818	0.763	0.816
MWTEVa	0.428	0.721	0.729	0.683	0.771

Note. MWTEEn: Environmental Aesthetics; MWTEMe: Meaningfulness; MWTENo: Novelty; MWTEPr: Professionalism; MWTETrAm: Tranquil Ambiance; MWTEVa: Value for money: HTMT < 0.90.

The HTMT analysis (Table 3) confirmed discriminant validity, with values (0.428–0.818) below the recommended threshold, supporting the six-dimensional MWTE construct’s distinctiveness. These findings validate proceeding with the proposed multidimensional conceptualisation.

Measurement model assessment

This study adopts a reflective-reflective higher-order modelling approach, utilising the embedded two-stage method (Becker et al., 2023; Hair et al., 2021; Sarstedt et al., 2019) to assess MWTE (second-order), tourist well-being and engagement (second-order mediators), and destination loyalty (first-order). The measurement model was evaluated sequentially, first validating lower-order constructs before assessing higher-order structures, ensuring adherence to rigorous validity and reliability standards.

Assessment of measurement model – Phase I

The Phase I measurement model results (Tables 4 and 5) confirm all constructs meet rigorous measurement standards, demonstrating a strong reliability (α = 0.822–0.949; CR = 0.823–0.950), excellent convergent validity (AVE = 0.725–0.945), with particularly robust results for Accomplishment, Meaning, and Identification. These findings establish methodologically rigorous foundation for higher-order analysis.

Table 4.

Measurement model assessment (Phase I) - internal consistency and convergent validity.

Variable/Construct	Code	Cronbach’s alpha (α)	CR	AVE
Destination Loyalty	DL	0.949	0.949	0.796
Environmental Aesthetics	MWTEEn	0.822	0.823	0.738
Meaningfulness	MWTEMe	0.929	0.930	0.876
Novelty	MWTENo	0.930	0.931	0.878
Professionalism	MWTEPr	0.947	0.949	0.741
Tranquil Ambiance	MWTETrAm	0.948	0.949	0.725
Value for money	MWTEVa	0.900	0.916	0.834
Absorption	TEAb	0.936	0.939	0.875
Attention	TEAt	0.931	0.934	0.830
Enthusiasm	TEEn	0.937	0.939	0.885
Identification	TEId	0.948	0.949	0.930
Interaction	TEIn	0.936	0.938	0.898
Accomplishment	TWAc	0.949	0.950	0.945
Meaning	TWMe	0.948	0.948	0.933
Positive Emotions	TWPo	0.938	0.940	0.910
Relationships	TWRe	0.934	0.937	0.911

Note. Variable/Construct column consists of lower-order (LO) constructs: α ≥ 0.70; CR (Composite Reliability (rho_a)) ≥ 0.7; AVE (Average Variance Extracted) ≥ 0.50.

Table 5.

Measurement model assessment (Phase I) - discriminant validity: heterotrait-monotrait ratio of correlations (HTMT) criterion.

Variable/Construct	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
DL
MWTEEn	0.579
MWTEMe	0.739	0.617
MWTENo	0.642	0.558	0.719
MWTEPr	0.698	0.625	0.738	0.731
MWTETrAm	0.776	0.695	0.818	0.763	0.816
MWTEVa	0.685	0.428	0.721	0.729	0.683	0.771
TEAb	0.712	0.455	0.692	0.637	0.568	0.699	0.682
TEAt	0.683	0.465	0.652	0.592	0.549	0.636	0.590	0.890
TEEn	0.746	0.524	0.717	0.621	0.622	0.733	0.638	0.836	0.845
TEId	0.415	0.320	0.412	0.400	0.354	0.408	0.341	0.455	0.535	0.516
TEIn	0.497	0.338	0.442	0.433	0.374	0.464	0.447	0.574	0.658	0.554	0.709
TWAc	0.487	0.384	0.533	0.472	0.405	0.468	0.407	0.495	0.618	0.575	0.587	0.545
TWMe	0.536	0.457	0.631	0.578	0.449	0.530	0.499	0.592	0.683	0.664	0.555	0.530	0.899
TWPo	0.770	0.577	0.798	0.717	0.720	0.845	0.733	0.731	0.670	0.783	0.434	0.478	0.561	0.644
TWRe	0.512	0.457	0.549	0.572	0.438	0.512	0.463	0.501	0.607	0.559	0.613	0.598	0.842	0.853	0.609

Note. Variable/Construct column consists of lower-order (LO) constructs: DL = 1, MWTEEn = 2, MWTEMe = 3, MWTENo = 4, MWTEPr = 5, MWTETrAm = 6, MWTEVa = 7, TEAb = 8, TEAt = 9, TEEn = 10, TEId = 11, TEIn = 12, TWAc = 13, TWMe = 14, TWPo = 15, TWRe = 16: HTMT < 0.90.

Discriminant validity was confirmed via HTMT analysis (Table 5), with all values (0.320–0.899) below the 0.90 threshold, demonstrating construct distinctiveness. Figure 2 shows the Phase I measurement model, displaying lower-order (LO) construct loadings and AVE values.

Assessment of measurement model – Phase II

Phase II employed the embedded two-stage approach (Becker et al., 2023; Hair et al., 2021; Sarstedt et al., 2019) to assess reflective-reflective higher-order constructs, using first-order latent variable scores (Ringle et al., 2012). The evaluation applied identical measurement standards as Phase I, with results for higher-order constructs presented in Tables 6 (indicator loadings, internal consistency, convergent validity) and 6 (discriminant validity).

Table 6.

Measurement model assessment (Phase II) – indicator outer loadings, internal consistency, and convergent validity.

HO constructs & indicator codes	Outer loadings of indicators	Cronbach’s alpha (α)	CR	AVE
MWTE		0.914	0.934	0.704
LVS_MWTEEn	0.688
LVS_MWTEMe	0.873
LVS_MWTENo	0.846
LVS_MWTEPr	0.866
LVS_MWTETrAm	0.924
LVS_MWTEVa	0.818
TE		0.893	0.921	0.703
LVS_TEAb	0.898
LVS_TEAt	0.920
LVS_TEEn	0.896
LVS_TEId	0.687
LVS_TEIn	0.766
TW		0.906	0.932	0.775
LVS_TWAc	0.885
LVS_TWMe	0.921
LVS_TWPo	0.823
LVS_TWRe	0.889

Note. First column consists of higher-order (HO) constructs with indicator codes (based on first-order LVS): α ≥ 0.70; CR (Composite Reliability (rho_a)) ≥ 0.7; AVE (Average Variance Extracted) ≥ 0.50.

The results (Table 6) confirm robust measurement properties for all higher-order constructs, with strong outer loadings MWTE (0.688–0.924), TE (0.687–0.920), and TW (0.823–0.921), high reliability (α = 0.893–0.914; CR = 0.921–0.934), and excellent convergent validity (AVE = 0.703–0.775), meeting all established thresholds.

Discriminant validity was confirmed (Table 7) with all HTMT ratios below 0.90 (MWTE-TE = 0.779; MWTE-TW = 0.768; TE-TW = 0.849), demonstrating construct distinctiveness. Figure 3 illustrates the validated Phase II measurement model, displaying higher-order (HO) construct loadings and AVE values, enabling progression to structural analysis.

Table 7.

Measurement model assessment (Phase II) – discriminant validity: heterotrait-monotrait ratio of correlations (HTMT) criterion.

Variable/Construct	MWTE	TE
MWTE
TE	0.779
TW	0.768	0.849

Note. Variable/Construct column consists of higher-order (HO) constructs: HTMT < 0.90.

Structural model assessment and hypothesis testing

Preceding to examining structural relationships, collinearity was assessed using VIF values based on latent variable scores. Collinearity assessment revealed all VIF values below thresholds (MWTE→DL = 2.509; TE→DL = 2.894; TW→DL = 2.976; MWTE→TE/TW = 1.000), confirming predictor independence. Subsequent analysis examined path coefficients’ significance (Figure 4) to evaluate structural relationships. The structural analysis demonstrated significant positive effects of MWTE on DL (β = 0.527, t = 10.692, p < 0.001), TE (β = 0.726, t = 27.379, p < 0.001), and TW (β = 0.735, t = 41.854, p < 0.001), with TE also significantly influencing DL (β = 0.312, t = 5.782, p < 0.001). However, the effect of TW on DL is not statistically significant (β = 0.023, t = 0.491, p = 0.623). The explanatory power of the structural model was assessed through coefficient of determination (R²) and effect size (f²). Results demonstrate strong explanatory power, accounting for 64.3% (DL), 52.7% (TE), and 54.0% (TW) of variance. Effect sizes reveal MWTE’s medium influence on DL (f² = 0.310) and large impacts on TE (f² = 1.115) and TW (f² = 1.175), while TE shows a small DL effect (f² = 0.094) and TW’s DL impact is negligible (f² = 0.001).

The mediation analysis (Table 8) demonstrated that TE significantly mediates the relationship between MWTE and DL (mean = 0.228, 95% CI [0.145, 0.311], t = 5.392, p < 0.001), while TW showed no significant mediating effect (mean = 0.017, 95% CI [−0.052, 0.084], t = 0.491, p = 0.623). The total indirect effect was significant (mean = 0.245, 95% CI [0.163, 0.327], t = 5.835, p < 0.001), indicating partial complementary mediation, with TE enhancing the direct MWTE-DL relationship (mean = 0.525, 95% CI [0.427, 0.620], t = 10.692, p < 0.001). These results confirm TE’s pivotal role in rendering memorable wellness experiences into destination loyalty.

Table 8.

Mediation analysis outcomes.

Effect type	Path	M	95% Confidence interval (with bias correction)	t	p	Significance
Specific indirect effects	MWTE -> TE -> DL	0.228	(0.145; 0.311)	5.392	0.000	Significant
Specific indirect effects	MWTE -> TW -> DL	0.017	(−0.052; 0.084)	0.491	0.623	Insignificant
Total indirect effects	MWTE -> DL	0.245	(0.163; 0.327)	5.835	0.000	Significant
Direct Effect	MWTE -> DL	0.525	(0.427; 0.620)	10.692	0.000	Significant

Note. Sample Mean (M), t values > 1.96, p values < 0.05, Bootstrapped (BCa approach) with 10,000 samples, 95% CI (lower limit = 2.5%, upper limit = 97.5% with non-zero confidence intervals).

The PLSpredict analysis demonstrated moderate predictive performance, with Q²predict values (0.412–0.506) exceeding thresholds, particularly for Perceived Quality (0.506) and Intention to Recommend (0.479). All Destination Loyalty indicators surpassed zero benchmarks, while lower PLS-SEM RMSE values versus linear regression confirmed moderate predictive validity. The empirical results confirm significant positive effects of MWTE on DL (H1: PC = 0.527, t = 10.692, p < 0.001, 95% CI [0.427;0.620]), TW (H2: PC = 0.735, t = 41.854, p < 0.001, 95% CI [0.698;0.767]), and TE (H3: PC = 0.726, t = 27.379, p < 0.001, 95% CI [0.666;0.772]). Furthermore, the analysis validated TE’s significant mediating role in the MWTE-DL relationship (H7: M = 0.228, t = 5.392, p < 0.001, 95% CI [0.145;0.311]), alongside its direct effect on DL (H5: PC = 0.312, t = 5.782, p < 0.001, 95% CI [0.204;0.417]). The analysis rejected H4 (TW→DL: PC = 0.023, t = 0.491, p = 0.623, 95% CI [–0.070;0.114]) and H6 (TW mediation: M = 0.017, t = 0.491, p = 0.623, 95% CI [–0.052;0.084]), confirming a non-significant role of TW in loyalty formation. These findings collectively demonstrate MWTE’s substantial influence on both direct and mediated pathways to DL, while highlighting TE as a key mechanism translating wellness experiences into loyal behaviours.

Discussion and conclusion

Through a systematic and rigorous research process, the study successfully addressed its objectives, providing new insights into the wellness tourism field. Accordingly, study respondents were predominantly mature, well-educated females, with a strong European representation, aligning with prior wellness tourism research (Lee and Kim, 2023; Wangzhou, 2022). Most participants were aged 35–54 (48%), held higher education degrees (68%), and reported a substantial incomes (38%), with Austria and Germany being the most common origins (23% each). Similar demographics were observed in other studies, including a higher proportion of females (56–79.8%), married individuals (76–80.2%), and repeat visitors (Chen, 2023; Sthapit et al., 2023; Thal and Hudson, 2019). Travel patterns varied, with 31.2% visiting annually and 50% exhibiting irregular visit frequencies, while stays typically lasted 8–14 days (40.4%) (Chen, 2023; Sthapit et al., 2023).

Wellness activity preferences highlighted a strong inclination toward holistic experiences, with Ayurveda (66.7%), spa treatments (60.6%), and yoga (43.6%) being the most popular (Deesilatham, 2016; Dillette et al., 2021). Niche activities like sound healing and Reiki further underscored the demand for diverse wellness approaches (Valentine, 2016; Wangzhou, 2022). Preferences varied by demographic, with females favoring forest yoga and beauty spas, while males preferred vigorous activities like marine sports (Dillette et al., 2021). The findings reinforce wellness tourism as a multidimensional concept integrating physical, mental, and spiritual well-being through tailored experiences (Wangzhou, 2022).

This study primarily intends to attain a comprehensive understanding of memorable experiences in wellness tourism and to investigate their effects on destination loyalty, with particular attention to the mediating roles of tourists’ well-being and engagement. Accordingly, the study highlighted six crucial dimensions: Professionalism, Meaningfulness, Environmental Aesthetics, Tranquil Ambience, Novelty, and Value for Money, including 29 indicators, which were refined and validated through EFA and CFA. Professionalism and Meaningfulness align with prior studies emphasising the significance of service quality and personalised experiences (Manhas et al., 2020; Rasoolimanesh et al., 2021), particularly in wellness tourism (Dahanayake et al., 2023b).

Environmental Aesthetics and Tranquil Ambience were identified as key factors influencing MWTE, corroborating findings in related research (Dahanayake et al., 2023b; Kim, 2009). Novelty was also recognised as a central element for creating lasting memories, consistent with the literature on tourism motivation (Dahanayake et al., 2023; Kim, 2017). Value for Money emerged as a critical factor influencing tourists’ satisfaction and perceptions of experience quality (Dahanayake et al., 2023b; Hui et al., 2012). Overall, these measures provide a robust framework for assessing MWTE, offering significant implications for both research and practice in the field (Dahanayake et al., 2023b).

Despite extensive research on the associations between MTEs, well-being, engagement, and destination loyalty, empirical evidence in wellness tourism is scarce. This study addresses this gap by exploring these relationships within wellness tourism and comparing the results with existing literature. Accordingly, the findings confirm that MWTE significantly influences destination loyalty (H1), aligning with existing literature that emphasises the connection between memorable experiences and loyalty (Cetin and Dincer, 2014; Moon and Han, 2018). Additionally, the study supports the work of Kim (2009) and Zare (2019), showing that vivid, well-recalled experiences enhance loyalty.

The research also confirms that MWTE positively affects tourist well-being (H2), consistent with prior studies highlighting the role of wellness tourism in promoting well-being (Sthapit and Coudounaris, 2018; Voigt et al., 2011). Importantly, this study extends existing knowledge by highlighting the dual pathways through which well-being may enrich tourism experience through hedonic well-being, derived from pleasure and enjoyment, and eudaimonic well-being, rooted in meaningfulness and self-realisation (Chen and Yoon, 2019; Sirgy et al., 2011).

The hedonic well-being reinforces immediate satisfaction and emotional bonds with the destination, whereas eudaimonic well-being cultivates deeper, enduring connections by fulfilling tourists’ needs for purpose and personal growth, echoing calls for integrated well-being approaches in tourism (Knobloch et al., 2017; Voigt, 2017). Although the current analysis did not explicitly differentiate these dimensions, their theoretical relevance to loyalty suggests that future research should explore their distinct effects. Furthermore, the study demonstrates that MWTE positively influences tourist engagement (H3), supporting broader tourism literature on the relationship between experiences and engagement (Kim et al., 2017; Lemon and Verhoef, 2016).

Engagement is also found to mediate the relationship between MWTE and destination loyalty (H7), corroborating previous research that links engagement to loyalty (Brodie et al., 2011; Rasoolimanesh et al., 2019). Contrary to prior assumptions, the hypothesis that well-being mediates the relationship between MWTE and destination loyalty (H6) is rejected, reflecting the complex and multidimensional nature of loyalty in wellness tourism (Liu et al., 2023; Robiani et al., 2019). Overall, this study reinforces that MWTE significantly influences tourist engagement and destination loyalty, aligning with previous research and providing new insights into wellness tourism.

Accordingly, this study advances wellness tourism research through significant contributions by developing a validated MWTE scale that enhances experiential assessment. The findings demonstrate engagement’s pivotal mediating role in shaping destination loyalty while challenging conventional assumptions about well-being’s direct influence. These insights advance scholarly understanding and provide actionable strategies for industry practitioners to optimize wellness tourism experiences and foster long-term tourist loyalty.

Theoretical implications

This study makes several significant contributions to the theoretical understanding of memorable tourism experiences, particularly within the wellness tourism context. First, the development and validation of the MWTE measures represents a substantial advancement in scale development literature (Dahanayake et al., 2023b). The comprehensive procedure, resulted in a robust six-dimensional scale encompassing Professionalism, Meaningfulness, Environmental Aesthetics, Tranquil Ambiance, Novelty, and Value for Money with 29 validated indicators. This methodological rigor builds upon established MTE frameworks (Chandralal et al., 2015; Kim, 2009) while addressing the specific nuances of wellness tourism experiences that previous generic scales failed to capture.

The study extends memorable tourism experience theory by demonstrating how general MTE dimensions require context-specific adaptation for wellness tourism. The identification of novel dimensions, particularly Environmental Aesthetics and Tranquil Ambiance, contributes to the theoretical understanding of how physical environments and sensory experiences shape memorable tourism encounters. This aligns with Pine and Gilmore’s (1998) experience economy principles and Kim’s (2009) foundational MTE work, while extending recent niche tourism research by Rasoolimanesh et al. (2021) and Jonas et al. (2020). The emergence of these wellness-specific dimensions suggests that memorable experiences are not universally constructed but are significantly influenced by the tourism context and tourist motivations.

Furthermore, this research advances tourist engagement theory by confirming engagement as a partial mediator between MWTE and destination loyalty. This finding validates broader engagement literature (Brodie et al., 2011) within the wellness tourism context and supports Kim and So’s (2022) comprehensive review highlighting engagement’s mediating role. The study demonstrates that memorable wellness experiences enhance emotional and behavioral attachment to destinations through increased tourist engagement, providing empirical support for the theoretical proposition that engagement serves as a critical bridge between experiential quality and behavioral outcomes (Rasoolimanesh et al., 2019).

Importantly, this study challenges conventional assumptions about the well-being and loyalty relationship in tourism. Accordingly, the tourists’ well-being does not significantly impact destination loyalty directly or mediate the MWTE-loyalty relationship. This finding contradicts expectations based on previous literature (Han et al., 2018; Vada, 2019) and suggests that the relationship between well-being and loyalty is more complex than previously theorized. The results align with Cruz-Milán’s (2023) findings that satisfaction and other factors may overshadow well-being’s direct effects on loyalty, contributing to a more nuanced understanding of tourist behavior in wellness contexts.

Practical implications

The study provides actionable insights for wellness tourism industry stakeholders seeking to enhance tourist experiences and foster destination loyalty. The validated MWTE scale serves as a comprehensive framework for wellness operators to systematically assess and improve their offerings across six critical dimensions. Industry practitioners can utilize the 29 indicators to identify strengths and weaknesses in their service delivery, enabling targeted improvements that directly impact memorable experience creation. The emphasis on professionalism as a critical factor necessitates investment in staff training, emphasizing empathetic service delivery, comprehensive knowledge, and clear communication skills, as supported by Manhas et al. (2020).

Destination marketing organizations and wellness facility managers should prioritize environmental design that combines aesthetic appeal with tranquil ambiance. The study’s findings regarding Environmental Aesthetics and Tranquil Ambiance suggest that investments in naturalness, cleanliness, visual appeal, and peaceful atmospheres significantly contribute to memorable experiences. This aligns with Singh et al. (2022) recommendations for sensory stimulation and Voigt’s (2017) emphasis on aesthetic environment importance. Practitioners should focus on creating holistic environments that engage multiple senses while promoting relaxation and rejuvenation, moving beyond traditional spa settings to encompass broader wellness environments.

The study’s revelation that tourist engagement, rather than well-being enhancement, serves as the primary pathway to destination loyalty has significant strategic implications. Wellness tourism operators should prioritize engagement-focused initiatives over well-being promotion when seeking to build customer loyalty. This involves creating interactive, participatory experiences that foster emotional connections and active involvement, as recommended by Kim et al. (2017). Marketing strategies should emphasize engagement opportunities and memorable experience creation rather than solely focusing on health and wellness benefits, aligning with the empirical evidence that engagement mediates the experience-loyalty relationship (Ahn and Back, 2018).

Value for money emerges as a crucial practical consideration, requiring clear communication of cost-benefit propositions to address pricing concerns identified by Hui et al. (2012). Wellness tourism providers must demonstrate competitive pricing while maintaining high service quality standards, ensuring that tourists perceive their expenditure as worthwhile investments in memorable experiences. This involves transparent pricing strategies, clear service descriptions, and demonstrable value delivery that supports Dillette et al.'s (2021) emphasis on value importance in wellness tourism.

The emphasis on novelty and meaningfulness necessitates personalized experience design that moves beyond standardized wellness offerings. Practitioners should develop unique, individually relevant activities and treatments that provide personal significance and create lasting memories, supporting Sthapit et al.’s (2023) findings on novelty-memory relationships. This requires understanding individual tourist motivations and preferences, enabling customized experience delivery that enhances memorability through personal relevance and uniqueness. Finally, the systematic implementation of the MWTE scale for performance measurement enables continuous improvement through multi-dimensional tracking of experience quality, moving beyond traditional satisfaction metrics to encompass the full spectrum of memorable experience creation.

Study limitations and future research directions

This study’s findings, while valuable for understanding wellness tourism dynamics, must be interpreted within the context of Sri Lanka’s unique socioeconomic and political challenges during the research period. These circumstances may have influenced tourists’ experiences in ways that differ from more stable, post-pandemic environments. The cross-sectional design, though methodologically sound, presents limitations in capturing the temporal evolution of MWTE. Future research should pursue longitudinal studies across diverse geographic and cultural contexts, particularly in developed nations, to enhance the generalizability of these findings. Additionally, incorporating mixed-methods approaches and expanding investigations to include domestic tourist perspectives would provide more comprehensive insights into MWTE’s multidimensional nature. Such methodological refinements would strengthen the theoretical and practical applications of this research in global wellness tourism contexts.

Conclusion

This study comprehensively examined MWTE and their impact on well-being, engagement, and destination loyalty. The demographic analysis revealed a predominance of middle-aged, educated, higher-income females, highlighting wellness tourism’s appeal to financially secure demographics. Key findings identified six MWTE dimensions; Professionalism, Meaningfulness, Environmental Aesthetics, Tranquil Ambiance, Novelty, and Value for Money, validated through a robust 29-item scale. Structural equation modelling demonstrated MWTE’s significant positive influence on destination loyalty and engagement, with engagement partially mediating this relationship, while well-being, though enhanced by MWTE, did not directly affect loyalty. The study challenges prior assumptions by emphasizing engagement’s critical role in fostering loyalty over well-being alone. These insights advance theoretical understanding and offer practical strategies for designing immersive wellness experiences. By bridging methodological and conceptual gaps, this research enriches wellness tourism literature and provides a foundation for future academic and industry applications.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Shakila Dahanayake

Author biographies

Shakila Dahanayake is a senior lecturer, Department of Tourism & Hospitality Management, Faculty of Management Studies, Rajarata University of Sri Lanka, Mihintale, Sri Lanka and a PhD student in Tourism Management - University of Kelaniya, Sri Lanka. Her research interests are Tourism and Hospitality Marketing, Tourism and Hospitality Education, and Wellness Tourism.

Bandara Wanninayake is a Professor in Marketing attached to the Department of Marketing Management/Dean, Faculty of Commerce and Management Studies, University of Kelaniya. PhD in Tomas Bata University, Czech Republic. His research interests and expertise are; Customer Satisfaction, Consumer Behaviour, Retail, Marketing Management, Strategic Marketing, Services Marketing, Branding, and Customer Relationship Management.

Ruwan Ranasinghe is a Professor in Tourism attached to the Department of Tourism Studies/Dean, Faculty of Management, Uva Wellassa University, Sri Lanka. PhD in Tourism Management (School of Tourism Management, Sichuan University, Chengdu, PRC). His research interests and expertise are; Tourism Planning and Management, Tourist Destination Marketing, Cultural and Heritage Tourism, Hospitality Management, Events Marketing and Management, and SDGs and Tourism.

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