Decoding green servicescape effects on hotel revisit intention: A multi-method analysis of configurational and linear mechanisms

Abstract

This study investigates how a hotel’s green servicescape drives customer revisit intention, addressing the competitive industry challenge of translating sustainability investments into tangible customer loyalty. To this end, we develop and validate a three-dimensional framework of green servicescapes—comprising green physical, human, and brand environments—and construct a multi-item scale to measure customer perceptions. Drawing on a dual-method approach, we integrate partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to uncover both linear relationships and complex configurational mechanisms. The PLS-SEM results reveal that all three dimensions significantly enhance revisit intention among customers, with the green physical environment exerting the strongest effect. Going beyond traditional analysis, fsQCA identifies two effective dimensional-level pathways (“physical + human” and “physical + brand”) and five distinct item-level configurations, highlighting the principle of equifinality and the coexistence of rational (function-oriented) and experiential (emotion-oriented) customer decision logics. This two-tier configuration analysis offers a more nuanced understanding of how sustainability cues interact, providing both theoretical advancement and practical insights for green strategy in hospitality design.

Keywords

green servicescape revisit intention PLS-SEM fsQCA configuration analysis

Introduction

With accelerating global climate concerns, evolving environmental regulations, and growing consumer awareness (Chan and Hon, 2020; Okums et al., 2019), the green transformation of the hotel industry has become an imperative for sustainable development—beyond international agendas such as the UN’s 2030 plan (Chi et al., 2022; Maqsoom et al., 2023; Ruiz-Campillo, 2024). The hotel industry, due to its resource-intensive operations and significant carbon emissions, has drawn increasing attention in industry (Chan et al., 2014; Han et al., 2025; Qiu et al., 2022; Rajak et al., 2023), prompting many hotels to integrate environmental responsibility into core strategies while enhancing customer experience and brand value (Haldorai et al., 2022; Zhang, 2024). In this context, the “green servicescape” has emerged as a key concept, blending sustainability principles with customer experience management (Asghar et al., 2024a; Awan et al., 2020). Studies show that green servicescapes can enhance customer satisfaction, strengthen environmental identification, and significantly boost revisit intention, brand loyalty, and eco-friendly word-of-mouth (Guo et al., 2022; Lee and Chuang, 2022; Nimri et al., 2020; Yarimoglu and Gunay, 2020).

However, hotels are not uniform entities, and customers often interpret green service initiatives differently based on their personal experiences, expectations, and values (Li and Wei, 2021). These perceptual variations can significantly affect how green practices are cognitively evaluated and behaviorally responded to different parties. This phenomenon may result in a persistent challenge for the hotel industry known as the “high investment–low perception” paradox: while hotels invest heavily in eco-friendly infrastructure, employee training, and sustainable branding strategies (Kandampully et al., 2023), customer perceptions of green value and the corresponding behavioral outcomes often fall short of expectations (Chi et al., 2022; Clark et al., 2023; Hashish et al., 2022). The intangible, hard-to-quantify nature of green practices further exacerbates this gap, leading to managerial hesitancy in sustaining green investments (Aksu et al., 2022). This mismatch underscores a fundamental need to better understand the psychological mechanisms linking green servicescapes and consumer behavior.

This gap is rooted in a lack of theoretical clarity and methodological sophistication in current green servicescape research. Despite increasing scholarly interest, the existing literature remains fragmented and underdeveloped. Conceptually, most studies focus on isolated elements—such as green infrastructure, employee conduct, or brand messaging—without offering an integrated framework that reflects the holistic customer perception of green servicescapes (Asghar et al., 2024a; Lee and Chuang, 2022). In terms of measurement, the available scales are often generalized or context-insensitive, lacking the multidimensional specificity required for hotel applications (Lockwood and Pyun, 2020). Methodologically, the dominant use of linear models such as structural equation modeling (SEM) limits the ability to capture causal complexity and configuration-based outcomes—where different combinations of green elements may lead to similar behavioral results (Amer and Rakha, 2022; Asante, 2023; Hashish et al., 2022). These gaps call for a more integrated theoretical research model, refined measurement tools, and complementary analytical methods that can uncover both linear and nonlinear influence mechanisms.

To address the gaps, this study constructs a green servicescape framework, based on customer perception in the hotel context, which integrates three core dimensions: green physical environment, green human environment, and green brand environment. Based on this framework, a standardized measurement scale is developed. Then, relying on survey data, the study applies both partial least squares SEM (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to examine the mechanisms through which green servicescape dimensions influence customers’ revisit intentions. This study contributes to the literature in several key ways. Theoretically, it extends the application of servicescape theory into a sustainability-oriented context by proposing a three-dimensional framework encompassing physical, human, and brand aspects of green servicescapes. Methodologically, it develops a structured and perception-based scale that provides a standardized tool for evaluating green hotel initiatives and managing consumer responses. Analytically, it integrates SEM and fsQCA methods to uncover both linear relationships and complex causal configurations, thereby capturing the multifaceted nature of green decision-making mechanisms.

Literature and Hypothesis development

Concept of green servicescape

The concept of servicescape was first introduced in Bitner (1992), emphasizing that the physical environment, social interactions, and symbolic cues within a service space jointly influence customers’ cognition, emotions, and behavioral responses. Building upon this foundation, the concept of green servicescape has emerged by incorporating principles of sustainability into service environment design. It highlights the creation of green service experiences through green space design, employees’ environmentally responsible behaviors, and brand-driven environmental responsibility communication (Asghar et al., 2024b).

From a conceptual development perspective, the notion of green servicescape has evolved through three key stages: In its early stages, the focus was primarily on tangible physical facilities, such as energy-saving lighting and green architecture, which emphasized the sensory stimulation of customers’ perceptions through visible green elements (Mishra and Gupta, 2019; Tresidder, 2017). As research progressed, attention shifted toward human-centered elements, particularly how employees’ green behaviors and service interactions shape customer experiences (Garmaroudi et al., 2021; Khattak et al., 2021). More recently, scholars have highlighted the importance of green brand communication and corporate social responsibility. For example, Tsou et al. (2022) showed that hotel services, including servicescape and employee service, positively influence customers’ brand experiences and engagement. Similarly, perceptions of a brand’s environmental authenticity, responsibility, and emotional resonance have been found to enhance trust, preference, and pro-environmental behaviors (Bashir et al., 2020; Chua et al., 2024). Specifically, Chua et al. (2024) emphasized that green brand trust fosters positive consumer emotions, translating into green consumption behaviors, while Bashir et al. (2020) demonstrated that perceived functional and emotional benefits strengthen green brand image, driving preferences, trust, loyalty, and corporate image. Collectively, these findings underscore sustainability integration as a key driver of green brand equity (Luyang, 2024).

Customer-perceived green servicescape

Customer perception, as a core psychological mechanism linking external stimuli and behavioral responses, refers to an individual’s subjective interpretation of service objects, environments, and conveyed information based on personal experience and expectations (Decrop, 2014; Agapito et al., 2017). Rooted in the psychological theory of perception, it emphasizes how individuals selectively filter, organize, and interpret external stimuli through their senses to form meaningful internal structures (Schacter et al., 2011; Schiffman and Wisenblit, 2015). In tourism and service management research, the concept has been widely applied to assess service quality, perceived value, emotional responses, and behavioral intentions (Ravald and Grönroos, 1996; Ali et al., 2018). Recent studies have advanced this perspective by linking customer perceptions of green hotel practices to deeper psychological constructs such as green psychological benefits (Kim and Ha, 2022), green consumption value, and brand identification (Dang et al., 2024). These findings suggest that green service perception not only reflects cognitive evaluations but also embodies emotional, symbolic, and identity-based meanings that shape sustainable consumer behavior. To bridge the lack of a structured model for green service perception, this study conceptualizes the green servicescape as comprising three interrelated dimensions: the green physical environ-ment, green human environment, and green brand environment. This structure reflects both functional and symbolic cues, allowing for a holistic examination of how customers cognitively and emotionally engage with sustainability in service contexts.

The green physical environment (GPE) captures customers’ perceptions of eco-friendly facilities and spatial design, that is to say, tangible manifestations of sustainability that shape service evaluations through functional, aesthetic, and emotional effects (Asghar et al., 2024b; Mishra and Gupta, 2019). This study defines this dimension based on four aspects: (1) perceived green space and ecological landscaping, including natural lighting, ventilation, and greenery that foster comfort and environmental affinity; (2) perceived energy efficiency such as smart temperature control, energy-saving devices, and eco-labels enhancing awareness of green technology (Jang, 2021); (3) perceived resource recycling, including solar heating and water-saving fixtures that signal commitment to resource conservation (TM et al., 2021); and (4) perceived environmental signage and infrastructure such as recycling instructions and awareness messages that reinforce pro-environmental behavior (Asghar et al., 2024a).

The green human environment (GHE) reflects customers’ perceptions of employ-ees’ eco-conscious behavior, organizational culture, and service atmosphere. It functions as a conduit through which internal green practices translate into customer experiences (Kandampully et al., 2023; Rosenbaum and Massiah, 2011). For example, GHE involves perceptions of (1) employees’ visible engagement in green actions, (2) their guidance encouraging guests to act sustainably (Jang, 2021; Line and Hanks, 2019), and (3) a coherent green organizational culture that consistently communicates environmental values across all levels (Kandampully et al., 2023).

Building on traditional servicescape theory, this study introduces a third dimension, the green brand environment (GBE), to capture the symbolic and communicative role of branding in sustainability perception. GBE comprises informational cues such as certifications, narratives, and visual symbols that convey a brand’s environmental commitment (Kabadayi et al., 2023; Srivastava and Singh, 2021). It emphasizes a brand’s long-term dedication to ecological responsibility and value alignment. The key com-ponents are perceived green brand impression, reflecting customers’ recognition of sustainability in a hotel’s image and marketing communication (Peco-Torres et al., 2021; Topcuoglu et al., 2022); green brand trust, representing confidence in the hotel’s authenticity, transparency, and consistent CSR efforts (Cho et al., 2023; Junaid et al., 2024; Zameer et al., 2020); and perceived responsibility alignment, capturing the extent to which the hotel’s environmental values align with customers’ personal beliefs (Shen et al., 2021; Srivastava and Singh, 2021).

The relationship between green servicescape and customers’ revisit intention

To evaluate the behavioral outcomes of green servicescapes, this study focuses on customers’ revisit intention (RI) as a critical indicator of loyalty and sustained patronage in the hotel industry (Baker and Crompton, 2000; Han et al., 2018). In a green context, revisit intention not only reflects service satisfaction but also signifies a customer’s behavioral alignment with the hotel’s sustainability values. Understanding the factors that drive revisit intention is therefore essential for designing effective green service strategies. Applying the stimulus–organism–response (SOR) framework, Chang et al. (2024) identified customer satisfaction, shaped by service quality, green practices, and perceived value, as a key driver of hotel customers’ revisit intention, premium payment willingness, and positive word-of-mouth in eco-friendly hotels. This high-lights the behavioral significance of sustainability-oriented service experiences in reinforcing hotel customers’ repeat visitation intention.

Extant research has predominantly adopted a “net effects” paradigm, demonstrating that individual elements, such as green facilities, employee eco-behaviors, or sustainability branding, can independently influence loyalty (Dang-Van et al., 2024; Mohammed et al., 2024). In this regard, servicescape-based studies have provided valuable insights into how environmental cues shape consumer experiences and behaviors. For example, through a case study of the hospitality of The Fat Duck, Tresidder (2017) demonstrated that servicescapes influence consumer behavior by creating multisensory experiences through atmospheric design. Similarly, Alvianna (2024) found that the servicescape directly influences revisit intention in the Nglanggeran Gunung Kidul Tourism Village, operating independently of visitor satisfaction. Together, these studies highlight that both multisensory and direct environmental cues can shape customer behavior beyond mere cognitive evaluation.

However, despite these findings, this variable-centric approach remains ill-suited to address the fundamental complexity of service experiences, where customers perceive and evaluate environmental cues not in isolation but as an interconnected Gestalt. This has created a critical theoretical gap: a lack of understanding of how the multiple dimensions of a green servicescape combine and interact to catalyze hotel consumers’ revisit intention.

To address this gap, we turn to configurational theory (Fiss, 2011; Ragin, 2009). This theory posits that outcomes of interest are rarely the product of single factors operating in isolation but, rather, emerge from the synergistic interplay of multiple conditions. It is characterized by causal complexity, including (1) conjunctural causality; that is, outcomes result from specific combinations of conditions, and (2) equifinality; that is, multiple, distinct pathways can lead to the same outcome. This theoretical lens is uniquely suited to explain the high investment–low perception paradox, as it suggests that the failure to elicit loyalty may not be due to a deficit in any single element but to an ineffective configuration of the physical, human, and brand environments.

Accordingly, this study constructs a configurational model of green servicescape influence. We propose that the green physical environment, green human environment, and green brand environment constitute core conditions that can form multiple, equally effective “causal recipes” for high revisit intention. Rather than asking “which dimension matters most,” our model investigates the core research question: What are the different configurations of physical, human, and brand elements that are sufficient to drive a customer’s decision to revisit? This approach allows us to move beyond linear relationships and uncover the holistic, synergistic mechanisms that underpin customer decision-making in green service settings. On the basis of above reasonings, the following hypotheses are proposed (See Figure 1):

Positive perception of the green physical environment significantly increases customers’ revisit intention.

Positive perception of the green human environment significantly increases customers’ revisit intention.

Positive perception of the green brand environment significantly increases customers’ revisit intention.

Customers’ overall perception of the green servicescape is positively associated with their revisit intention.

Research methodology

Analytical approach

This study adopts a dual-method analytical strategy by integrating partial least squares structural equation modeling (PLS-SEM) with fuzzy-set qualitative comparative analysis (fsQCA) to investigate both linear and configurational relationships in-fluencing customers’ intentions to revisit green hotels. This multi-method design addresses the limitations of previous studies relying on a single analytical lens and aligns with recent advances in sustainability research (Chan and Hon, 2020; Chan et al., 2014; Hair et al., 2019; Rasoolimanesh et al., 2021).

PLS-SEM was chosen for its robustness in modeling complex structures—especially formative second-order constructs—and its suitability for exploratory research with small or non-normally distributed samples (Hair et al., 2019, 2022). In this model, green physical environment (GPE), green human environment (GHE), and green brand environment (GBE) were specified as first-order constructs that jointly form the second-order latent construct of green servicescape (GS). The measurement model was assessed through indicator loadings, composite reliability, average variance extracted (AVE), and discriminant validity. Path significance and predictive relevance in the structural model were evaluated using 5000 non-parametric bootstraps (Rasoolimanesh et al., 2021).

To uncover causal asymmetry and conjunctural complexity, fsQCA was employed as a complementary technique. This method is effective in identifying multiple sufficient combinations of antecedent conditions (i.e., “causal recipes”) that lead to the same outcome—customer revisit intention (Ragin, 2006; Woodside, 2013). Unlike PLS-SEM, which focuses on average net effects, fsQCA emphasizes equifinality and configurational diversity, offering richer insight into how different causal paths may yield the same behavioral outcome (Pappas and Woodside, 2021).

Following the direct calibration method proposed by Ragin (2006), variables were transformed into fuzzy sets with thresholds of 0.95 (full membership), 0.5 (crossover point), and 0.05 (full non-membership). The fsQCA analysis was conducted at two levels: (1) the construct level, using composite scores of GPE, GHE, and GBE; and (2) the item level, using 10 empirically validated indicators. A configuration was considered sufficient if its consistency exceeded 0.80 and its coverage surpassed 0.20 (Pappas and Woodside, 2021).

By integrating PLS-SEM and fsQCA, this study combines symmetric and asymmetric analytical perspectives, enhancing both predictive validity and theoretical gen-eralizability. This approach is particularly suitable for emerging, multidimensional constructs such as green servicescapes, where customer behavior may arise from both structural influences and alternative configurational pathways (Hasan et al., 2022; Rasoolimanesh et al., 2021). In particular, the inclusion of fsQCA enables the examination of synergistic relationships among the green physical, human, and brand environments, capturing how these dimensions jointly shape customer responses through dynamic, configurational mechanisms rather than isolated linear effects.

Scale development and measures

To ensure the content and construct validity of the green servicescape scale, this study employed a systematic, multistage development process integrating theoretical servicescapes framework, item refinement, and empirical methodology validation (see Appendix A.1 for details). Drawing from an extensive green servicescape approach, literature review and field observations in selected hotels, the authors identified key components of green servicescapes (e.g., Asghar et al., 2024b; Mishra and Gupta, 2019) and developed an initial customer perception-based measurement scale comprising 19 items across three dimensions: green physical environment (GPE), green human environment (GHE), and green brand environment (GBE).

To enhance item observability and practical applicability, theoretical constructs were translated into concrete, customer-facing statements. A structured three-round expert consultation process was conducted following the guidelines of Churchill (1979) and Hinkin (1998), with the content validity ratio (CVR) applied to evaluate item quality. In the first round (early November 2024), eight experts—including academic scholars, hotel professionals, branding specialists, and an experienced hotel guest—reviewed the initial 19 items. Items with CVR values below 0.62 were eliminated, resulting in 13 retained items. In the second round (late November 2024), two additional experts with backgrounds in user experience and frequent travel reviewed the item pool for clarity, interpretability, and contextual relevance. Based on their feedback, semantically redundant or overlapping items were merged or deleted, reducing the pool to 12 items. The final round of consultation (end of November 2024) focused on readability, behavioral observability, and measurement feasibility. Following this evaluation, 11 items with CVR values exceeding 0.80 were finalized, demonstrating strong alignment with customer perceptions and practical relevance.

To examine the predictive validity of the newly developed green servicescape scale, this study used customer revisit intention as the dependent variable and tested its as-sociation with the focal constructs, thereby enhancing the overall construct validity of the measurement model. The measurement of revisit intention used the scale developed by Han et al. (2018) with three items: “I intend to stay at this hotel again,” “I would recommend this hotel to my friends and family,” and “I am willing to support similar hotels in the future.”

All items were rated on a 7-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). This measurement approach not only affirms the dimensional completeness of the green servicescape construct but also provides a solid empirical basis for examining customers' revisit behavioral outcomes.

Data collection and scale validation

A two-stage data collection process was implemented to ensure sample independence between the exploratory and confirmatory analyses and to progressively refine and validate the measurement scale.

First, a pilot study was conducted in December 2024 to examine the scale’s factor structure through exploratory factor analysis (EFA) in SPSS 27.0. A total of 206 valid responses were obtained. The EFA results (Table 1) indicated that one item related to green knowledge and communication skills exhibited cross-loadings between the GHE and GBE dimensions and failed to meet the primary loading threshold. Due to its abstract nature and limited customer-side observability, this item was removed. The remaining 10 items all demonstrated factor loadings above 0.60, and the cumulative variance explained reached 71.17%, indicating strong internal consistency and construct validity.

Table 1.

Exploratory factor analysis results (N = 206).

Constructs	Factors	Items	Measurements	Loadings	Variance Explained (%)	Cronbach’s α
GPE	Green space and ecological landscape	GPE1	I Perceive the hotel’s greenery, ventilation, and natural lighting design as comfortable and environmentally friendly	0.818	35.413	0.783
	Energy-saving facilities	GPE2	I Notice that the hotel is equipped with energy-saving lighting and smart temperature control systems that help reduce energy consumption	0.674
	Resource recycling facilities	GPE3	I Notice the use of water-saving shower heads and solar water heating systems in the hotel, which I believe contribute directly to environmental protection	0.778
	Environmental signage and facilities	GPE4	I Notice that the hotel has clear and convenient waste sorting bins and environmental signage in public areas	0.800
GHE	Employee green behavior	GHE1	I Notice employees actively conserve energy and water during service delivery, demonstrating environmental awareness	0.889	18.920	0.884
	Employee green guidance	GHE2	Employees remind me to conserve energy, sort waste, and engage in other green behaviors, showing their environmental values	0.861
	Green organizational culture	GHE3	The green organizational culture encourages guests to participate in environmental practices	0.926
GBE	Green brand recognition	GBE1	I Can clearly identify this hotel’s environmental commitment through its branding, such as its eco-themed stories, slogans, or visual elements	0.610	16.839	0.795
	Green brand trust	GBE2	I Notice the hotel’s eco-certifications and environmental communications, which strengthens my belief in its commitment to environmental values	0.923
	Green brand identification	GBE3	I Feel the hotel’s environmental philosophy and messaging align with my personal values, making me willing to support the brand	0.920

Subsequently, the formal survey was conducted from January to March 2025 using a combination of online and offline channels to minimize the risk of duplicate responses and enhance sample diversity. Online questionnaires were distributed through travel-related communities via WeChat platform, targeting a broad range of respondents with travel experience, and employed IP address verification to prevent multiple submissions from the same device. Offline data collection was conducted in collaboration with partner hotels, where front-line staff were trained to invite guests to complete the survey via QR code scanning during their stay. This hybrid approach ensured the independence of observations for subsequent analysis while enhancing data authenticity and response rate.

Before completing the survey, all respondents were provided with a clear definition of the green servicescape, along with visual and textual examples illustrating typical features of green physical, human, and brand environments. This preparatory information aimed to improve participants’ understanding and response accuracy. After data collection, rigorous data screening was conducted to eliminate invalid responses, incomplete entries, and logically inconsistent answers. A total of 412 valid question-naires were retained for analysis.

Survey results and data validation

Sample characteristics and descriptive statistics

As shown in Table 2, the sample was diverse in terms of key demographic variables. Gender distribution was balanced, with 49.0% male and 51.0% female respond-ents. Most participants were aged between 31 and 40 (48.1%), followed by 41–50 (20.4%) and 26–30 (15.8%), indicating that the majority were middle-aged adults with independent travel decision-making capacity. Monthly disposable income was spread across five brackets, with approximately half of the participants earning over CNY 10,000, suggesting moderate to high purchasing power. Regarding educational background, 85.2% of the respondents had completed undergraduate or postgraduate education, indicating a high level of academic attainment and cognitive capability within the sample. Occupationally, enterprise employees, civil servants, and professionals made up 67.5% of the sample. Additionally, 23.1% of the respondents reported hotel stays one to two times per month, and 4.1% stayed weekly or more—indicating significant exposure to hotel services. Overall, the sample was well qualified for validating the measurement scale and testing the structural model.

Table 2.

Demographic characteristics of respondents (N = 412).

Variable	Description	Frequency	Percentage
Gender	Male	202	49.0
Gender	Female	210	51.0
Age (years)	18–25	51	12.4
	26–30	65	15.8
	31–40	198	48.1
	41–50	84	20.4
	≥51	14	3.4
Education (completed)	Secondary	14	3.4
	Diploma	47	11.4
	Undergraduate	277	67.2
	Postgraduate	74	18.0
Monthly disposable income	≤CNY 5000	104	25.2
	CNY 5001–10,000	110	26.7
	CNY 10,001–15,000	76	18.4
	CNY 15,001–20,000	44	10.7
	≥CNY 20,000	78	18.9
Occupation	Enterprise staff/civil servants	278	67.5
	Other occupations	61	14.8
	Students	59	14.3
	Retirees	14	3.4
Hotel stay frequency	Once a week or more	17	4.1
	1–2 times per month	95	23.1
	1–2 times per half-year	215	52.2
	Once per year	85	20.6

Measurement model evaluation

As one of the core objectives of this study is to validate the green servicescape perception scale, confirmatory factor analysis (CFA) was first conducted prior to structural modeling to assess the reliability and validity of the measurement model (Hair et al., 2019). Using R version 4.5.1, CFA was performed on the four latent constructs—green physical environment (GPE), green human environment (GHE), green brand environment (GBE), and revisit intention (RI)—based on 412 valid responses. The analysis used maximum likelihood (ML) estimation.

Model fit indices indicated a satisfactory model fit: χ² (59) = 145.289, p < 0.001; χ²/df = 2.46; CFI = 0.980; TLI = 0.973; RMSEA = 0.060 (90% CI [0.047, 0.072]); and SRMR = 0.032. All values met the recommended cutoffs proposed by Hu and Bentler (1999).

To evaluate construct reliability and convergent validity, multiple criteria were assessed: Cronbach’s alpha (α), composite reliability (CR), and average variance extracted (AVE). In Table 3, all constructs demonstrated excellent internal consistency, with α values ranging from 0.848 to 0.918, CR values from 0.859 to 0.918, and AVE values from 0.666 to 0.788—all exceeding the conventional thresholds of 0.7 for α and CR and 0.5 for AVE (Chi et al., 2022; Hair et al., 2019; Qiu et al., 2022). All standardized factor loadings were significant (p < 0.001) and ranged from 0.703 to 0.911, indicating good item reliability (Hair et al., 2019).

Table 3.

Factor loadings, internal consistency, and convergent validity.

Constructs	Items	Loadings	Cronbach’s α	CR	AVE
GPE	GPE1	0.822	0.909	0.909	0.715
	GPE2	0.848
	GPE3	0.868
	GPE4	0.843
GHE	GHE1	0.891	0.906	0.907	0.764
	GHE2	0.878
	GHE3	0.853
GBE	GBE1	0.859	0.848	0.859	0.666
	GBE2	0.703
	GBE3	0.875
RI	RI1	0.881	0.918	0.918	0.788
	RI2	0.911
	RI3	0.872

Note. All factor loadings are significant at p < 0.001.

Discriminant validity was evaluated using both the Fornell–Larcker criterion and the heterotraitmonotrait (HTMT) ratio. As shown in Table 4, the square root of AVE (in bold) for each construct was greater than its correlations with other constructs, thereby satisfying the Fornell–Larcker criterion (Hair et al., 2022). In addition, HTMT values presented in Table 5 were all below the conservative threshold of 0.85, con-firming discriminant validity across all constructs (Henseler et al., 2015). These results collectively affirm the psychometric robustness of the measurement model and justify its use in subsequent structural modeling.

Table 4.

Discriminant validity based on Fornell–Larcker criterion.

	GPE	GHE	GBE	RI
GPE	0.846
GHE	0.774	0.874
GBE	0.699	0.631	0.816
RI	0.751	0.664	0.577	0.888

Note. bold values indicate the square root of AVE. Off-diagonal entries are inter-construct correlations.

Table 5.

Heterotraitmonotrait ratio (HTMT) for discriminant validity.

	GPE	GHE	GBE	RI
GPE	-
GHE	0.802	-
GBE	0.752	0.688	-
RI	0.789	0.715	0.642	-

Structural model results using PLS-SEM

To validate the theoretical soundness of the proposed “physical–human–brand” three-dimensional framework of green servicescapes, this study employed PLS-SEM using R version 4.5.1. The analysis examined both the higher-order formative con-struct—green servicescape (GS)—and its three lower-order reflective dimensions—green physical environment (GPE), green human environment (GHE), and green brand environment (GBE)—in relation to revisit intention (RI).

Figure 2 depicts the first-order model and its direct effects. Path analysis confirmed that all three green perception dimensions (GPE, GHE, GBE) had significant positive effects on revisit intention, thus supporting Hypotheses H1–H3. Specifically, GPE exerted the strongest influence (β = 0.457, t = 8.64, p < 0.001), followed by GHE (β = 0.209, t = 4.18, p < 0.001) and GBE (β = 0.132, t = 2.88, p < 0.01). The first-order model explained 51.2% of the variance in revisit intention (R² = 0.512), indicating considerable explanatory power. The asymmetric strengths of the β coefficients suggest that green physical cues play a dominant role in shaping revisit behavior, consistent with prior findings on servicescape salience.

Figure 3 illustrates the second-order formative model and the validation of the overall framework. This model was constructed to empirically test the integrated green servicescape (GS) as a higher-order construct. To evaluate the holistic effect of the in-tegrated green servicescape, a second-order formative model was specified. Results showed a strong and statistically significant effect of the GS on RI (β = 0.716, t = 20.51, p < 0.001), confirming Hypothesis H4. The model demonstrated robust explanatory power (R² = 0.507) and predictive relevance (Q² = 0.502) (Hair et al., 2019). Within the formative structure, all three subdimensions significantly contributed to the higher-order GS construct: GPE (weight = 0.375), GHE (weight = 0.276), and GBE (weight = 0.245), with all weights significant at p < 0.001. The variance inflation factor (VIF) values (2.35, 2.09, and 1.76) were well below the conservative threshold of 3.3, indicating no multicollinearity issues (Rasoolimanesh et al., 2021). Furthermore, the overall goodness-of-fit (GoF) index reached 0.639, surpassing the benchmark of 0.36 suggested by Tenenhaus et al. (2005), thereby supporting the structural stability and theoretical adequacy of the model.

As summarized in Table 6, all four hypotheses (H1–H4) received empirical support. These findings highlight both the integrated value of the green servicescape as a holistic construct and the differentiated contributions of its subdimensions. The results also point to the potential for multiple configuration pathways through which different green cues may activate customer loyalty intentions, underscoring the need for con-text-specific strategies in green service design.

Table 6.

Results of hypotheses testing.

Hypotheses	Path	Path Coefficient	t Statistics	p-values	Supported
H1	GPE → RI	0.457	8.64	<0.001	Yes
H2	GHE → RI	0.209	4.18	<0.001	Yes
H3	GBE → RI	0.132	2.88	<0.01	Yes
H4	GS → RI	0.712	20.8	<0.001	Yes

FsQCA results

To further explore the configurational mechanisms through which green servicescape influences revisit intention, this study employed fsQCA to examine how combinations of GPE, GHE, and GBE interact to produce high revisit intention. Following the direct calibration method proposed by Ragin (2006), the standardized con-struct scores generated from PLS-SEM were transformed into fuzzy-set values ranging from 0 to 1. The calibration thresholds were set at 0.95 for full membership, 0.5 for the crossover point, and 0.05 for full non-membership. Detailed calibration thresholds and descriptive statistics for each variable and dimension are provided in Appendix A.2. The necessity analysis (see Appendix A.3 for details) showed that no individual factor qualifies as a necessary condition for high revisit intention. Therefore, subsequent analysis focused on identifying sufficient configurations. fsQCA produces three types of solutions: complex, intermediate, and parsimonious. Based on methodological recommendations (Rasoolimanesh et al., 2021), this study adopted the intermediate solution as the primary basis for analysis and theoretical interpretation, as it offers a balanced trade-off be-tween empirical complexity and theoretical generalizability.

Table 7 presents the result of dimensional-level configuration analysis. Configurations were first examined across the three aggregate dimensions: GPE, GHE, and GBE. Based on automated calibration, the frequency threshold was set at 11, and the con-sistency threshold was set at 0.908. Finally, two configurations were identified: Configuration 1—high GPE and high GBE (coverage = 0.691, consistency = 0.899), indicating that a combination of physical environmental cues and reliable green brand trust can still effectively stimulate revisit behaviors, and Configuration 2—high GPE and high GHE (coverage = 0.705, consistency = 0.889), suggesting that strong physical infrastructure and high-quality human interaction can effectively trigger revisit intention.

Table 7.

Dimensional-level configurations for high RI.

Configurations	Raw coverage	Unique coverage	Consistency
f (GPE, GHE, GBE)
S1: f (GPE*GBE)	0.691	0.068	0.899
S2: f (GPE*GHE)	0.705	0.083	0.889
Solution coverage	0.773
Solution consistency	0.877

Note. asterisk (*) denotes the logical “AND” operator, indicating the combination of conditions.

Table 8 shows item-level configuration analysis. To capture more granular interactions, item-level fsQCA was conducted using 10 condition indicators. The frequency threshold was set at 2, and the consistency threshold was set at 0.911. As shown in Table 8, five configurations were identified with a solution consistency of 0.896 and a coverage of 0.628. These configurations can be grouped into two neutral permutation sets: S1a–S1c and S2a–S2b, namely, configurations that share the same core conditions but differ in peripheral conditions (Fiss, 2011), indicated by different suffix letters.

Table 8.

Item-level configurations for high RI.

	S1a	S1b	S1c	S2a	S2b
GPE1
GPE2
GPE3
GPE4
GHE1	⊗
GHE2
GHE3
GBE1
GBE2
GBE3
Raw coverage	0.304	0.555	0.560	0.563	0.546
Unique coverage	0.019	0.003	0.012	0.017	0.012
Consistency	0.907	0.919	0.914	0.904	0.917
Solution coverage	0.628
Solution consistency	0.896

Notes. (a) symbol legend: large black circles (“”) denote the presence of a core condition, while small black circles (“”) indicate the presence of a peripheral condition. Crossed-out circles (“⊗”) represent the absence of a peripheral condition. Blank cells indicate a “do not care” scenario, meaning the condition may be either present or absent and is not relevant for the outcome in that specific configuration. (b) Neutral permutations: configurations labeled with the same number but different lowercase letters (e.g., S1a, S1b, S1c; S2a, S2b) are considered neutral permutations. These configurations share the same set of core conditions but differ in peripheral conditions, offering alternative but equally sufficient paths to the outcome. In contrast, configurations with different numbers (e.g., S1 vs S2) indicate structurally distinct combinations of causal conditions driven by different core elements. (c) Coverage and consistency: coverage and consistency are used to assess the explanatory power and empirical relevance of each sufficiency configuration. Raw coverage refers to the proportion of cases that can be explained by a given configuration. Unique coverage indicates the portion of the outcome that is explained only by that specific configuration. Consistency measures the degree to which cases sharing a configuration also exhibit the outcome; it is a robustness indicator of sufficiency. Thresholds of consistency ≥0.80 and coverage ≥0.20 are commonly recommended in the literature (Fiss, 2011; Ragin, 2006). All configurations in Table 6 meet or exceed these thresholds, indicating acceptable robustness and explanatory relevance. (d). For full fsQCA output details, please refer to Appendix A.4.

Neutral permutation S1: S1a–S1c. This set shared six core conditions: high green space and ecological landscape (GPE1), high energy-saving facilities (GPE2), high resource recycling facilities (GPE3), high environmental signage and facilities (GPE4), high green brand trust (GBE2), and high green brand identification (GBE3). This indicated that the combination of physical environment and brand credibility played a fundamental role in shaping customer revisit intention. However, the three configurations were differentiated by their peripheral conditions: S1a did not include high employee green behavior (GHE1) and high green brand recognition (GBE1); S1b emphasized high employee green behavior (GHE1) and high employee green guidance (GHE2); S1c incorporated high green organizational culture (GHE3) and high green brand recognition (GBE1).

Neutral permutation S2: S2a–S2b. This set shared five core conditions: high green space and ecological landscape (GPE1), high resource recycling facilities (GPE3), high environmental signage and facilities (GPE4), high employee green behavior (GHE1), and high green brand trust (GBE2). These configurations reflected a synergistic integration of physical, human, and brand factors. Their differences lay in the peripheral conditions: S2a included high energy-saving facilities (GPE2), high employee green guidance (GHE2), and high green organizational culture (GHE3); S2b comprised high employee green guidance (GHE2), high green organizational culture (GHE3), high green brand recognition (GBE1), and high green brand identification (GBE3), with energy-saving facilities (GPE2) playing a non-core role. The comparison between S2a and S2b revealed a functional substitution mechanism: when energy-saving infrastructure was insufficient, hotels could compensate by enhancing high green brand recognition and high green brand identification.

To ensure the reliability of the fsQCA findings, robustness checks were conducted at both dimensional and item levels. As recommended in prior studies (Pappas and Woodside, 2021; Ragin, 2009), we systematically varied the calibration anchors for dimensions and adjusted the frequency cutoffs for item-level data. At the dimensional level, varying the calibration anchors (across the 95%/50%/5%, 90%/60%/10%, and 85%/40%/15% percentiles) consistently yielded the same core configurations (e.g., GPE*GHE and GPE*GBE), with only minor fluctuations in coverage and consistency (see Appendix A.5). At the item level, raising the frequency cutoff from 2 to 5 revealed that the core solution remained stable around the interplay between items of the GPE and GBE constructs despite some streamlining of peripheral elements (see Appendix A.6). These results collectively support the robustness and interpretability of the configurational model across different methodological specifications.

Discussion

Interpretation of findings

This study employed a dual-method approach by integrating PLS-SEM and fsQCA to investigate the mechanisms through which green servicescape influences customer revisit intention. These two analytical methods—one linear and variable-oriented, the other configurational and case-oriented—demonstrated strong convergence in identifying key drivers while offering complementary insights into the underlying mechanisms (Asante, 2023; Hashish et al., 2022).

First, both approaches consistently highlighted the predominant role of the green physical environment. The PLS-SEM results reveal that GPE exhibits the strongest direct effect on customer revisit intention in the first-order model and the highest formative weight in shaping the second-order green servicescape construct. This finding aligns with the fsQCA results, in which “high green physical environment” consistently emerged as a core condition across all sufficient configurations. These results collectively underscore that GPE serves as a non-compensatory, foundational condition—a necessary building block for constructing effective green servicescapes (Asghar et al., 2024a; Mishra and Gupta, 2019).

However, fsQCA went beyond the linear insights of SEM by revealing complex and nonlinear configurational mechanisms. It identified multiple, equally sufficient configurations that lead to high revisit intention, illustrating heterogeneity in customer behavior and decision pathways (Amer and Rakha, 2022; Chi et al., 2022). For example, configurations S1a–S1c suggest that some customers primarily rely on a core combination of green physical environment and brand environment, showing lower sensitivity to human interaction elements. In such cases, green brand recognition (GBE1) tends to appear when human-related factors are absent or weak (e.g., S1a, S1c), indicating a potential compensatory or amplifying mechanism. This supports earlier findings that green branding can substitute for experiential cues when human interaction is limited (Srivastava and Singh, 2021). In contrast, configurations S2a and S2b highlight another customer segment that values the synergistic interaction of physical, human, and brand dimensions, with a particular emphasis on employee green behavior and organizational culture as indispensable conditions (Garmaroudi et al., 2021; Kandampully et al., 2023).

These divergent pathways reflect underlying differences in customers’ intrinsic motivations and contextual sensitivities. Specifically, these findings resonate with well-established consumer behavior theories. The S1-type configurations align with the concept of utilitarian value and functional benefits, where decision-making is guided by efficiency and brand reliability—typical of consumers with a cognitive processing style. Conversely, S2-type configurations reflect hedonic value and emotional benefits, more closely associated with affective decision-making styles and experiential consumption preferences (Babin et al., 1994; Batra and Ahtola, 1991; Sproles and Kendall, 1986). This dual-path structure mirrors the broader cognitive–affective dichotomy in consumer psychology, enhancing our theoretical understanding of how different green cues resonate with different consumer profiles.

Customers such as business travelers or efficiency-oriented consumers, aligned with S1-type configurations, tend to prioritize functional environmental performance and brand credibility. Their decision-making is driven by cognitive and utilitarian factors, and thus, reinforcing visible green technologies and clear brand commitments is particularly effective for this group.

Customers aligned with S2-type configurations (e.g., families or experience-seeking guests) are more susceptible to atmospheric cues and social norms. They emphasize emotional experience and social identification, showing greater responsiveness to human-centric elements such as employee conduct and cultural ambiance. For this group, enhancing interactive services and immersive environments is essential.

Despite their different preference structures, green physical environment consistently appears as a core condition across all configurations, reaffirming its foundational role. This finding not only corroborates the PLS-SEM results but also, through the lens of fsQCA, demonstrates the presence of multiple concurrent causal mechanisms.

Moreover, customers can achieve similar behavioral outcomes through different combinations of green perception factors, reflecting the plasticity of consumer behavior and the strategic flexibility available to service providers.

Research contributions

This study further highlights the synergistic nature of the green servicescape. The interplay among the physical, human, and brand dimensions demonstrates that customer responses arise from dynamic combinations rather than isolated effects. Specifically, the presence of one green dimension can enhance or compensate for the influence of another, depending on contextual conditions. This finding underscores that the relationships among green dimensions are not merely additive but synergistic, reflecting a dynamic interaction mechanism within sustainable service settings. Integrating insights from both PLS-SEM and fsQCA, this study provides empirical support for a configurational understanding of how multiple green cues jointly shape customer revisit intentions, thereby enriching theoretical perspectives on causal complexity in hospitality research.

Theoretical implications

This study offers several notable theoretical contributions by addressing critical gaps in the green servicescape literature—specifically the lack of a coherent conceptual framework, fragmented measurement tools, and overly linear assumptions about influence mechanisms (Asghar et al., 2024a; Hashish et al., 2022; Lee and Chuang, 2022; Okums et al., 2019;). Through systematic theoretical development and rigorous empirical validation, this research advances the field in three key areas: conceptual framework construction, scale development, and methodological innovation.

Firstly, this study extends the foundational servicescape theory (Bitner, 1992) into the domain of sustainable service environments by proposing an integrated green servicescape framework comprising three interrelated dimensions: the green physical environment, green human environment, and green brand environment. This multidimensional structure, grounded in environmental psychology and green marketing theory (Chua et al., 2024; Garmaroudi et al., 2021), shifts the theoretical lens from a function-oriented view toward a value-oriented perspective, reflecting how modern consumers cognitively and emotionally evaluate green practices within service contexts. By incorporating both tangible and symbolic components, the framework offers a more holistic understanding of how sustainability is experienced, perceived, and acted upon by customers (Asghar et al., 2024b; Chan et al., 2014; Tsou et al., 2022).

Secondly, this study contributes a multidimensional, psychometrically validated scale to capture customer perceptions of green servicescapes. Following best practices in scale development (Churchill, 1979; Hinkin, 1998), this research systematically generated, refined, and validated measurement items, consolidating previously scattered operational definitions into a unified structure encompassing three core dimensions and 10 subdimensions. The resulting instrument demonstrates strong internal consistency, convergent validity, and discriminant validity, offering both theoretical precision and practical adaptability for application across hospitality, retail, and other service contexts (Asghar et al., 2024a; Mishra and Gupta, 2019).

Finally, from a methodological standpoint, this research adopts a hybrid analytical strategy by integrating PLS-SEM and fsQCA, thereby capturing both linear and configurational pathways through which green servicescape elements influence customer revisit intention. While PLS-SEM identifies the independent effects of each construct, fsQCA uncovers equifinal configurations—highlighting the complementarity, substitution, and asymmetry inherent in consumer decision-making. This dual-method approach reflects the broader shift in service and management research toward embracing causal complexity and nonlinear reasoning (Fiss, 2011), offering a richer, multipath explanation of how sustainability cues influence customer loyalty behaviors.

Taken together, these contributions establish a more theoretically grounded, methodologically robust, and practically actionable foundation for future research in green service management and customer experience design.

Managerial implications

This study provides several actionable managerial insights for hotel operators aiming to implement effective green strategies that are both evidence-based and experience-enhancing.

Firstly, the multidimensional green servicescape scale developed in this study offers a robust diagnostic tool to assess customer perceptions across physical, human, and brand-related green attributes. Hotel managers are encouraged to adopt data-driven diagnostics by regularly administering this scale to identify service gaps, evaluate the effectiveness of sustainability investments, and guide resource allocation. This supports a strategic shift from intuition-based to closed-loop environmental management. For example, survey findings may indicate low perception of brand trust or human interaction despite significant investment in facilities, prompting targeted interventions.

Secondly, based on the fsQCA results, managers should adopt differentiated configuration strategies tailored to customer segments and market positioning, as multiple configurations can lead to equally high revisit intention—echoing the equifinality principle in strategic management. For rational, efficiency-driven segments (e.g., business travelers), the optimal approach involves enhancing functional green infrastructure and brand credibility. This can be achieved through features such as smart energy meters, in-room sustainability dashboards, and prominently displayed eco-certifications or carbon footprint data to build trust and transparency. Conversely, for experiential, emotion-driven segments (e.g., families or leisure travelers), the emphasis should shift toward employee green behavior and interactive engagement. Frontline staff can be trained to deliver personalized sustainability messages—such as “By reusing your towel, you‘re helping us protect the coral reef”—while immersive activities like eco-tours or recycling workshops can foster emotional resonance and a sense of shared environmental purpose. These scripts personalize the sustainability narrative, fostering emotional resonance and a sense of shared purpose. This distinction reflects underlying consumer decision-making styles—where some guests are guided by utilitarian logic (Babin et al., 1994), while others are influenced by hedonic or affective responses (Batra and Ahtola, 1991; Sproles & Kendall, 1986).

By aligning green practices with customer motivations, hotels can thereby translate sustainability into both competitive advantage and meaningful guest experiences.

Finally, managers should adopt a systemic mindset that treats the green physical environment as a strategic foundation while promoting synergy across green human and brand dimensions. All valid configurations identified in this study included strong green physical cues, making this dimension a non-negotiable baseline. Managers should avoid standardized approaches and instead align resource deployment based on market segmentation and experience design goals. Guided by resource dependence theory, the flexible integration of environmental technologies, service behaviors, and branding allows hotels to enhance both sustainability performance and customer experience—thereby achieving a sustainable competitive advantage.

Limitations and future research

Despite its theoretical and methodological contributions, this study has several limitations that provide fertile ground for future research.

Firstly, the sample was primarily drawn from hotel customers in mainland China, which may limit the generalizability of findings across different cultural contexts. Cultural values and environmental norms can significantly shape how consumers perceive, evaluate, and respond to green services. In particular, the Chinese cultural context, characterized by collectivism and a high-context communication style, may strengthen the role of collective environmental norms and institutional trust. This suggests that the influence of “green brand trust” identified in this study could be more pronounced than in more individualistic Western markets, where personal autonomy and direct experience may weigh more heavily in consumer decisions. Future studies are encouraged to conduct cross-cultural comparisons using data from diverse regions to validate the model and investigate the potential moderating effects of cultural dimensions.

Secondly, this study adopted a cross-sectional design, which limits the understanding of how customer perceptions of green servicescapes evolve over time. Green experiences often unfold across multiple service phases (e.g., pre-stay, during stay, post-stay), and perceptions may dynamically shift along the customer journey. Moreover, as with much intention-based research, it is important to acknowledge the potential gap between customers’ stated revisit intentions and their actual behavioral choices. Future longitudinal or behavioral tracking studies could help verify whether the observed patterns translate into real revisiting behavior. Future research could employ longitudinal or experience-sampling methods (e.g., diary studies or customer journey mapping) to capture these temporal dynamics and examine how revisit intention translates into long-term green loyalty behaviors.

Finally, the fsQCA results reveal two distinct yet equally effective configurations (S1, S2) that trigger high revisit intention. These findings offer a basis for developing testable consumer typologies in future research. Experimental or quasi-experimental studies could manipulate the physical, human, and brand elements to test whether different combinations evoke distinct psychological mechanisms, such as cognitive trust versus emotional attachment, depending on the customer segment. Such research would deepen our understanding of the boundary conditions of each pathway and enhance the causal robustness of the configurational model.

Footnotes

ORCID iD

Zhen Chen

Consent to participate

Informed consent was obtained from all subjects involved in the study.

Author contributions

S.C., conceptualization, methodology, investigation, analysis, and original draft preparation. Z.C., writing—review and editing. A.H., review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

The authors declare that no specific financial support was received for the research, authorship, and publication of this article. This work was completed as part of the 2025 Huangshan City Social Sciences Innovative Development Research Project Program (Project No. 2025197), which was organized and administered by the Huangshanshi Federation of Social Sciences.

Declaration of conflicting interests

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

Data Availability Statement

Data supporting reported results will be made available by the authors upon request.*

Author biographies

Dr. Suxia Chen is a researcher and lecturer in the School of Business, Shanghai Normal University Tianhua College. Her research interests mainly include hotel and tourism management, user experience, leadership and culture, and sustainable development.

Dr. Zhen Chen is an Associate Professor of Practice in the HNU-ASU International College, Hainan University. His research interests focus on tourism and hospitality management, sustainable tourism, tourism and hospitality entrepreneurship.

Dr. Alice H.Y. Hon is an Associate Professor in the School of Hotel & Tourism Management, The Hong Kong Polytechnic University. Her research focuses on employee motivation and creativity, leadership and culture, and individual and firm performance outcomes.

Appendix

References

Agapito

Pinto

Mendes

(2017) Tourists’ memories, sensory impressions and loyalty: in loco and post-visit study in southwest Portugal. Tourism Management 58: 108–118.

Aksu

Albayrak

Caber

(2022) Hotel customer segmentation according to eco-service quality perception: the case of Russian tourists. Journal of Hospitality and Tourism Insights 5(3): 501–514.

Ali

Kim

, et al. (2018) Make it delightful: customers' experience, satisfaction and loyalty in Malaysian theme parks. Journal of Destination Marketing & Management 7: 1–11.

Alvianna

(2024) The role of servicescape, green tourism and destination image on tourists' return visit interest through satisfaction to nglanggeran unung kidul tourism village. International Journal of Advances in Engineering and Management (IJAEM) 6(1): 434–444.

Amer

Rakha

(2022) How servicescape unleash customer engagement behaviors through place attachment: an investigation in a Non-Western context. Cogent Business & Management 9(1): 2055443.

Asante

(2023) Hotels’ green leadership and employee pro-environmental behaviour, the role of value congruence and moral consciousness: evidence from symmetrical and asymmetrical approaches. Journal of Sustainable Tourism 32(7): 1370–1391.

Asghar

Asif

Akhtar

(2024a) Servicescapes and social servicescapes: impact on perceived usefulness, choice satisfaction and behavior at fast-food restaurants. Journal of Hospitality and Tourism Insights 7(4): 2121–2143.

Asghar

Asif

Akhtar

, et al. (2024b) Do green servicescape and perceived quality determine consumerism and its consequences? Journal of Hospitality and Tourism Insights 7(5): 2662–2683.

Awan

Kraslawski

Huiskonen

(2020) Progress from blue to the green world: multilevel governance for pollution prevention planning and sustainability. In: Handbook of Environmental Materials Management. Springer, 1–22.

10.

Babin

Darden

Griffin

(1994) Work and/or fun: measuring hedonic and utilitarian shopping value. Journal of Consumer Research 20(4): 644–656.

11.

Baker

Crompton

(2000) Quality, satisfaction and behavioral intentions. Annals of Tourism Research 27(3): 785–804.

12.

Bashir

Khwaja

Rashid

, et al. (2020) Green brand benefits and brand outcomes: the mediating role of green brand image. Sage Open 10(3): 2158244020953156.

13.

Batra

Ahtola

(1991) Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing Letters 2(2): 159–170.

14.

Bitner

(1992) Servicescapes: the impact of physical surroundings on customers and employees. Journal of Marketing 56(2): 57–71.

15.

Chan

ESW

Hon

AHY

(2020) Application of extended theory of planned behavior model to ecological behavior intentions in the food and beverage service industry. Journal of Foodservice Business Research 23(2): 169–191.

16.

Chan

ESW

Hon

AHY

Chan

WWH

, et al. (2014) What drives employees’ intention to implement environmental green practices in hotels? The roles of knowledge, awareness, concern, and ecological behavior. International Journal of Hospitality Management 40: 20–28.

17.

Chang

Chanda

Vafaei-Zadeh

, et al. (2024) Assessing green practices on eco-friendly hotel customer loyalty: a partial least squares structural equation modeling and fuzzy-set qualitative comparative analysis hybrid approach. Sustainability 16(9): 3834.

18.

Chi

, et al. (2022) Narrowing the intention-behavior gap: the impact of hotel green certification. International Journal of Hospitality Management 107: 103305.

19.

Cho

Park

Lee

(2023) Do green practices and green image promote consumers’ participation in social network sites (SNS)? Journal of Hospitality Marketing & Management 32(5): 631–648.

20.

Chua

Kim

Baah

, et al. (2024) When hospitality brands go green: the role of authenticity and stereotypes in building customer-green brand relationships. Journal of Sustainable Tourism 32(6): 1118–1141.

21.

Churchill

(1979) A paradigm for developing better measures of marketing constructs. Journal of Marketing Research 16(1): 64–73.

22.

Clark

Kang

Calhoun

(2023) Green meets social media: young travelers’ perceptions of hotel environmental sustainability. Journal of Hospitality and Tourism Insights 6(1): 36–51.

23.

Dang-Van

Vo-Thanh

Wang

, et al. (2024) Green hotel practices and consumer revisit intention: a mediating model of consumer promotion focus, brand identification, and green consumption value. Journal of Vacation Marketing 30(4): 677–693.

24.

Decrop

(2014) Theorizing tourist behaviour. In: The Routledge Handbook of Tourism Marketing. Routledge, 251–267.

25.

Fiss

(2011) Building better causal theories: a fuzzy set approach to typologies in organization research. Academy of Management Journal 54(2): 393–420.

26.

Garmaroudi

King

(2021) Social servicescape’s impact on customer perceptions of the hospitality brand–the role of branded social cues. International Journal of Hospitality Management 93: 102774.

27.

Guo

Yao

Chang

(2022) Research on customer behavioral intention of hot spring resorts based on SOR model: the multiple mediation effects of service climate and employee engagement. Sustainability 14(14): 8869.

28.

Hair

Risher

Sarstedt

, et al. (2019) When to use and how to report the results of PLS-SEM. European Business Review 31(1): 2–24.

29.

Hair

Hult

GTM

Ringle

, et al. (2022) A Primer on Partial Least Squares Structural Equation Modeling. 3rd edition. Sage.

30.

Haldorai

Kim

Garcia

(2022) Top management green commitment and green intellectual capital as enablers of hotel environmental performance: the mediating role of green human resource management. Tourism Management 88: 104431.

31.

Han

Lee

Trang

HLT

, et al. (2018) Water conservation and waste reduction management for increasing guest loyalty and green hotel practices. International Journal of Hospitality Management 75: 58–66.

32.

Han

Kim

Baah

, et al. (2025) Antecedents of customer retention in the green hotel context: exploring the optimum combination of cognitive and affective factors. Journal of Hospitality and Tourism Insights 8(5): 1761–1782.

33.

Hasan

Bao

Chiong

(2022) A multi-method analytical approach to predicting young adults’ intention to invest in mHealth during the COVID-19 pandemic. Telematics and Informatics 68: 101765.

34.

Hashish

MES

Abdou

Mohamed

SAK

, et al. (2022) The nexus between green perceived quality, green satisfaction, green trust, and customers’ green behavioral intentions in eco-friendly hotels: a structural equation modeling approach. International Journal of Environmental Research and Public Health 19(23): 16195.

35.

Henseler

Ringle

Sarstedt

(2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43(1): 115–135.

36.

Hinkin

(1998) A brief tutorial on the development of measures for use in survey questionnaires. Organizational Research Methods 1(1): 104–121.

37.

Bentler

(1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal 6(1): 1–55.

38.

Jang

(2021) The role of customer familiarity in evaluating green servicescape: an investigation in the coffee shop context. International Journal of Contemporary Hospitality Management 33(2): 693–716.

39.

Junaid

Javed

Hussain

, et al. (2024) Responsible brand leadership in services: fostering love, ethics, and empowerment. Service Industries Journal 44(9-10): 686–709.

40.

Kabadayi

Aksoy

Turkay

(2023) How does customer engagement value occur in restaurants? A stimulus-organism-response (SOR) perspective. Service Industries Journal 43(7-8): 497–524.

41.

Kandampully

Bilgihan

Amer

(2023) Linking servicescape and experiencescape: creating a collective focus for the service industry. Journal of Service Management 34(2): 316–340.

42.

Khattak

Nouman

Fayaz

, et al. (2021) Corporate social responsibility and employee green behavior in the hospitality industry: a cross-country study. Sustainability 13(19): 10534.

43.

Kim

(2022) An investigation of customer psychological perceptions of green consciousness in a green hotel context: applying a extended theory of planned behavior. International Journal of Environmental Research and Public Health 19(11): 6795.

44.

Lee

Chuang

(2022) Applying expanded servicescape to the hotel industry. Journal of Hospitality & Tourism Research 46(4): 771–796.

45.

Wei

(2021) Hotel servicescape and customer citizenship behaviors: mediating role of customer engagement and moderating role of gender. International Journal of Contemporary Hospitality Management 33(2): 587–603.

46.

Line

Hanks

(2019) The social servicescape: a multidimensional operationalization. Journal of Hospitality & Tourism Research 43(2): 167–187.

47.

Lockwood

Pyun

(2020) Developing a scale measuring customers’ servicescape perceptions in upscale hotels. International Journal of Contemporary Hospitality Management 32(1): 40–59.

48.

Luyang

(2024) Brand management from the perspective of sustainable development: responsibility marketing and social value creation. Academic. Journal of Business & Management 6(3): 107–112.

49.

Maqsoom

Umer

Alaloul

, et al. (2023) Adopting green behaviors in the construction sector: the role of behavioral intention, motivation, and environmental consciousness. Buildings 13(4): 1036.

50.

Mishra

Gupta

(2019) Green hotel servicescape: attributes and unique experiences. Current Issues in Tourism 22(20): 2566–2578.

51.

Mohammed

Zhou

Abduljabbar Hael

, et al. (2024) The impact of green service quality on consumers’ intentions to revisit green hotels based on theory of planned behavior. Journal of Quality Assurance in Hospitality & Tourism 1–37.

52.

Nimri

Patiar

Kensbock

, et al. (2020) Consumers’ intention to stay in green hotels in Australia: theorization and implications. Journal of Hospitality & Tourism Research 44(1): 149–168.

53.

Okums

Koseoglu

Chan

ESW

, et al. (2019) How do hotel employees’ environmental attitudes and intentions to implement green practices relate to their ecological behavior? Journal of Hospitality and Tourism Management 39: 193–200.

54.

Pappas

Woodside

(2021) Fuzzy-set qualitative comparative analysis (fsQCA): guidelines for research practice in information systems and marketing. International Journal of Information Management 58: 102310.

55.

Peco-Torres

Polo-Peña

Frías-Jamilena

(2021) Brand personality in cultural tourism through social media. Tourism Review 76(1): 164–183.

56.

Qiu

Guo

Sun

(2022) Carbon emission reduction calculation for the green transformation of traditional hotel design. Frontiers in Energy Research 10: 985336.

57.

Ragin

(2006) Set relations in social research: evaluating their consistency and coverage. Political Analysis 14(3): 291–310.

58.

Ragin

(2009) Redesigning Social Inquiry: Fuzzy Sets and Beyond. University of Chicago press.

59.

Rajak

Reddy

Patil

, et al. (2023) Green hotels and their effect on Urban Sustainability. Ecology Environment and Conservation 29: 1871–1876.

60.

Rasoolimanesh

Ringle

Sarstedt

, et al. (2021) The combined use of symmetric and asymmetric approaches: partial least squares-structural equation modeling and fuzzy-set qualitative comparative analysis. International Journal of Contemporary Hospitality Management 33(5): 1571–1592.

61.

Ravald

Grönroos

(1996) The value concept and relationship marketing. European Journal of Marketing 30(2): 19–30.

62.

Rosenbaum

Massiah

(2011) An expanded servicescape perspective. Journal of Service Management 22(4): 471–490.

63.

Ruiz-Campillo

(2024) Post-Paris agreement negotiations: a commitment to multilateralism despite the lack of funding. Environmental Science & Policy 156: 103754.

64.

Schacter

Gilbert

Wegner

(2011) Psychology. 2nd edition. Pearson.

65.

Schiffman

Wisenblit

(2015) Consumer Behavior. 2nd edition. Pearson.

66.

Shen

Huang

Choi

HSC

, et al. (2021) Does brand love matter to casual restaurants? A multi-group path analysis. Journal of Hospitality Marketing & Management 30(5): 630–654.

67.

Sprotles

Kendall

(1986) A methodology for profiling consumers' decision‐making styles. Journal of Consumer Affairs 20(2): 267–279.

68.

Srivastava

Singh

(2021) Do corporate social responsibility (CSR) initiatives boost customer retention in the hotel industry? A moderation-mediation approach. Journal of Hospitality Marketing & Management 30(4): 459–485.

69.

Tenenhaus

Vinzi

Chatelin

, et al. (2005) PLS path modeling. Computational Statistics & Data Analysis 48(1): 159–205.

70.

Kaur

Bresciani

, et al. (2021) What drives the adoption and consumption of green hotel products and services? A systematic literature review of past achievement and future promises. Business Strategy and the Environment 30(5): 2637–2655.

71.

Topcuoglu

Kim

, et al. (2022) Green message strategies and green brand image in a hotel context. Journal of Hospitality Marketing & Management 31(3): 311–325.

72.

Tresidder

(2017) Servicescapes for hospitality and tourism consumers. In: Dixit

(ed) The Routledge Handbook of Consumer Behaviour in Hospitality and Tourism. Routledge, 443–449.

73.

Tsou

Hou

Chen

, et al. (2022) Rethinking sustainability hotel branding: the pathways from hotel services to brand engagement. Sustainability 14(16): 10138.

74.

Woodside

(2013) Moving beyond multiple regression analysis to algorithms: calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research 66(4): 463–472.

75.

Yarimoglu

Gunay

(2020) The extended theory of planned behavior in Turkish customers' intentions to visit green hotels. Business Strategy and the Environment 29(3): 1097–1108.

76.

Zameer

Wang

Yasmeen

(2020) Reinforcing green competitive advantage through green production, creativity and green brand image: implications for cleaner production in China. Journal of Cleaner Production 247: 119119.

77.

Zhang

(2024) Carbon emissions dynamics and environmental sustainability in China’s tourism sector: a 22-year comprehensive regional study. Sustainability 16(16): 7091.