Exploring the Impact of Service Quality and Service Innovation on Tourists’ Revisit Intentions
An-Ni Yen
Department of Industrial Management,
I-Shou University, Kaohsiung City 84001, Taiwan
Nai-Chieh Wei
Management College,
Guangdong Polytechnic Normal University, China
E-mail: ncwei@cloud.isu.edu.tw
Corresponding Author
Tze-Jou Liao
Department of Industrial Management,
I-Shou University, Kaohsiung City 84001, Taiwan
Abstract
This study examines the effects of service quality and service innovation on tourists’ revisit intentions, focusing on hotels in Kenting, Taiwan. With travelers increasingly valuing personalized experiences and innovative services, quality management and innovation have become key competitive strategies. Using the PZB model, a survey of 303 valid responses was analyzed through EFA, CFA, and SEM. Results show that both service quality and service innovation have significant positive impacts on revisit intention, with service innovation partially mediating the relationship. Key drivers include staff interaction, facility upkeep, smart technology, and local cultural experiences. The study recommends implementing smart service facilities, enhancing personalized offerings, and fostering an innovation-oriented culture to strengthen customer loyalty and sustainable growth.
Keywords: Service Quality, Service Innovation, Revisit Intention, Hotel Management
Research Background
With the rapid development of the global economy and the improvement of living standards, the demand for leisure tourism has been steadily increasing, making the leisure industry a crucial pillar of national economic development. Tourism is not only a means of fulfilling recreational needs but also serves as an important indicator for assessing economic progress and cultural exchange. As a driver of economic growth, the leisure industry generates significant spillover effects, particularly in regions rich in tourism resources, where it contributes to local economic growth, cultural dissemination, and international visibility.
Kenting, located in southern Taiwan, is one of the country’s most popular tourist destinations. Its unique natural landscapes and rich tourism resources, such as Kenting National Park, Eluanbi Lighthouse, and White Sand Bay, attract a large number of domestic and international visitors, offering opportunities for activities like snorkeling, surfing, and stargazing. The area’s cultural heritage and diverse recreational activities further enhance its tourism appeal; however, fluctuations in tourist arrivals have posed increasing challenges for hotel operators in the region.
In today’s highly competitive market, service quality and service innovation have emerged as critical factors in improving customer satisfaction and revisit intentions. Modern travelers no longer focus solely on the cleanliness and functionality of accommodations but place greater importance on service details and personalized experiences. Research shows that hotels offering high-quality services can significantly enhance customer satisfaction, foster brand loyalty, and increase revisit intentions (Liu, 2011). Similarly, service innovation has gained prominence as a strategic response to market challenges. By integrating local characteristics with modern technologies, hotels can create unique and attractive lodging experiences through measures such as implementing smart services, developing cultural immersion activities, or promoting eco-tourism programs.
Nevertheless, how to effectively combine service quality and service innovation to strengthen tourists’ revisit intentions remains an important research issue, especially in a mature tourism destination like Kenting. Market surveys highlight revisit intention as a key indicator of hotel competitiveness, reflecting not only customer satisfaction but also long-term business sustainability (Chang, 2014). Based on this background, this study aims to investigate how service quality and service innovation influence tourists’ revisit intentions in Kenting and provide practical recommendations for operators to improve customer loyalty and strengthen market competitiveness.
Research Motivation
The motivation of this study stems from the operational challenges faced by leisure resort hotels in Kenting and the need to enhance their business performance and competitive advantage. Although Kenting is well-known for its natural resources and diverse tourism activities, the rapid growth of the leisure travel market has intensified competition, and traditional business models no longer meet the diverse and personalized needs of modern consumers. Hotel operators must therefore continuously innovate management strategies and improve service quality to maintain a competitive edge (Su, 2009).
Service quality has long been regarded as a core element of hotel competitiveness and a crucial determinant of customer satisfaction and loyalty. Travelers now have higher expectations that go beyond basic services such as room cleanliness and standard facilities, focusing instead on service nuances like staff friendliness and responsiveness. Providing high-quality services can significantly enhance satisfaction, stimulate positive word-of-mouth, and increase revisit intentions (Liu, 2011).
Furthermore, digital marketing has become essential for improving market share and brand recognition. The use of social media platforms and big data analytics allows hotels to reach target customers more effectively, optimize marketing activities based on consumer preferences, and integrate local cultural features to strengthen brand identity and competitiveness (Huang, 2010).
In addition, service innovation extends beyond hardware upgrades to improvements in service models and operational processes. The adoption of smart check-in systems enhances service efficiency and convenience, while culturally themed experiential activities attract tourists interested in local culture and deepen their emotional connection with the destination (Pan, 2018). Prior studies also emphasize that revisit intention is influenced by factors such as service quality, marketing strategy, and service innovation, making it an important metric for evaluating hotel performance and market outcomes.
Research Objectives
Although prior research on Kenting has focused mainly on tourism development and regional economic issues, few studies have explored hotel management strategies in response to intensifying competition and diverse consumer demands. With growing emphasis on personalized services and experiential consumption, optimizing service quality and enhancing revisit intentions have become critical for hotel operators (Liu, 2011).
Therefore, this study aims to explore the effects of service quality and service innovation on tourists’ revisit intentions and provide actionable recommendations for industry practitioners. Specifically, this study seeks to:
Service Quality
Service quality is one of the core determinants of success in the service industry and refers to the extent to which a service provider can meet or exceed customer expectations. Parasuraman, Zeithaml, and Berry (1985) argued that customers’ perceptions of service quality derive from a comparison between expected and actual service performance; when performance exceeds expectations, it is perceived as high quality, and when it falls short, it is perceived as low. This perspective laid the foundation for modern research on service quality and led to a series of influential models and empirical studies. Service quality has a direct impact on customer satisfaction, loyalty, and revisit intention (Cronin & Taylor, 1992). In the hospitality industry, providing high-quality service is essential for building brand image, attracting repeat customers, and enhancing competitiveness. Yang (2006) identified service quality as a core factor in improving customer satisfaction in tourist hotels, while Baker and Crompton (2000) confirmed its significant influence on behavioral intentions such as repeat visits and recommendations.
A widely adopted tool for measuring service quality is the SERVQUAL model developed by Parasuraman et al. (1988), which consists of five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. This framework systematically evaluates the gap between customer expectations and perceptions, providing a basis for quality improvement initiatives. In the hospitality industry, SERVQUAL has been used to assess details such as room cleanliness, front-desk efficiency, and food and beverage quality, helping operators identify areas for improvement (Tsai, 2011). Bitner and Hubbert (1994) further argued that contextual factors, such as customers’ psychological states or external environments, should be considered to better capture perceived service quality. While SERVQUAL has gained widespread recognition, it has also faced criticism. Cronin and Taylor (1992) contended that the model places excessive emphasis on expectations while neglecting actual performance. They proposed the SERVPERF model, which focuses on directly measuring service performance and may more accurately reflect its effect on customer satisfaction. Empirical studies have also shown that demographic variables such as age and gender may moderate perceptions of service quality, underscoring the importance of personalized service design (Tsai, 2011). Recent studies have also examined the role of digitalization; for instance, Lin (2023) found that the COVID-19 pandemic accelerated the adoption of contactless services, improving perceptions of safety and overall service quality. Overall, service quality remains a critical determinant of satisfaction and revisit intentions, and theoretical models like SERVQUAL provide structured tools for evaluation, while empirical evidence continues to validate its practical significance.
Service Innovation
Service innovation refers to the development of new services, processes, technologies, or business models that create additional value for customers (Chesbrough, 2010). Unlike product innovation, which focuses on tangible outputs, service innovation emphasizes improvements to intangible assets and enhancing customer experiences. Amabile (1996) suggested that innovation requires the integration of individual creativity and organizational support, and in the service sector, this is often reflected in process optimization and customer engagement. Service innovation encompasses multiple forms, including technology-driven, customized, and experiential innovation (Kotler & Keller, 2012). For example, technology-driven innovation leverages AI and IoT to provide seamless services; customization focuses on personalized services tailored to individual needs; and experiential innovation emphasizes emotional connection through enhanced service ambiance and cultural immersion.
Several factors drive service innovation, including market demand, technological advancement, internal organizational culture, and competitive pressures (Chesbrough, 2010). Market demand is a primary driver; Lin (2023) showed that the pandemic heightened customer expectations for digitalized and contactless services, pushing hotels to accelerate innovation. Similarly, Ahmad et al. (2022) suggested that firms must analyze consumer data to develop competitive service offerings. Technological advancement provides the foundation for innovation; Parasuraman et al. (1988) noted that technology-enabled innovations can enhance efficiency and reduce costs, for example, through AI-based instant customer support. Kotler and Keller (2012) argued that effective innovation combines technological capabilities with market trends to create added value. Internal culture and employee participation also play critical roles. Banjongprasert (2017) found that an innovative organizational climate significantly influences service innovation performance, while Chiang (2022) highlighted that employees’ creativity and engagement directly affect implementation success. Lastly, competitive pressures motivate firms to continually innovate. Yang (2006) observed that intense competition in the hospitality market compels hotels to optimize service quality and develop innovative offerings to attract and retain customers.
The hospitality industry demonstrates the strategic value of service innovation in enhancing both customer experience and business performance. During the pandemic, hotels widely adopted contactless services such as online check-in, mobile payments, and smart room controls to improve efficiency and reduce infection risks (Lin, 2023). Digital technologies have also enabled data-driven innovations; AI-powered analysis of booking patterns and customer preferences allows personalized offers, boosting satisfaction and loyalty (Zeithaml & Bitner, 2000). Chiang (2022) showed that pet hotels innovated by optimizing logistics and offering pick-up services, improving convenience and customer experience. Experiential design is another avenue; Bigné et al. (2001) emphasized that integrating local cultural elements into hotel design and providing unique cultural experiences strengthen emotional attachment and brand loyalty. Overall, service innovation has transformed traditional service models, increasing satisfaction and competitive advantage. With continuous technological advancement and increasingly diverse market demands, service innovation will remain a critical strategy for sustaining competitiveness in the hospitality sector.
Revisit Intention
Revisit intention is a significant topic in consumer behavior research, reflecting customers’ willingness to repurchase a service or revisit a destination. Baker and Crompton (2000) defined revisit intention as the behavioral intention to return based on prior experience, while Chen and Tsai (2007) noted that it is influenced by multiple factors including destination image, service quality, and satisfaction, with significant economic implications for service providers. Its theoretical underpinnings include the Theory of Planned Behavior (TPB) and Expectancy Theory. TPB suggests that behavioral intentions are jointly shaped by attitudes, subjective norms, and perceived behavioral control (Ajzen, 1991). In hospitality, customers’ attitudes toward service (e.g., satisfaction), social norms (e.g., peer recommendations), and perceptions of convenience all influence revisit intentions. Expectancy Theory further posits that when service performance exceeds expectations, revisit intention is enhanced (Oliver, 1980). Customer satisfaction and loyalty are consistently identified as key determinants (Cronin & Taylor, 1992). Tsai (2011) found that in resort hotels, satisfaction directly drives revisit intention, particularly in tangibility and reliability dimensions, while Cheng (2015) showed that loyalty also strengthens revisit and recommendation behavior. Emotional attachment and cultural context also play important roles; Cole and Scott (2004) found that affective bonds moderate revisit intentions, while Huang and Hsu (2009) observed cultural differences in service expectations that influence revisit evaluations. Digitalization has provided new opportunities to enhance revisit intention; Lin (2023) demonstrated that contactless services and online engagement platforms during COVID-19 increased trust and satisfaction, boosting revisit intentions. Big data analytics for personalized service suggestions has also proven effective (Zeithaml & Bitner, 2000). International cases support this; Bigné et al. (2001) found that incorporating local cultural design elements into European hotels significantly enhanced satisfaction and brand loyalty. Overall, revisit intention is a multifaceted construct shaped by service quality, innovation, satisfaction, loyalty, and cultural-emotional factors.
Summary
The literature highlights that service quality, service innovation, and revisit intention are interrelated and form the foundation for improving performance in the hospitality industry. Service quality directly affects satisfaction and loyalty, which in turn drive revisit intentions (Parasuraman, Zeithaml, & Berry, 1988; Cronin & Taylor, 1992). The SERVQUAL model provides a structured tool for evaluating and improving service quality, with empirical studies confirming its value in enhancing brand trust and loyalty (Tsai, 2011; Chiang, 2022). Service innovation, as a modern source of competitiveness, involves not only technological upgrades but also cultural adaptation and emotional engagement (Amabile, 1996; Kotler & Keller, 2012). Hotels integrating AI and big data have successfully achieved digital transformation and personalized services (Zeithaml & Bitner, 2000). Cultural design has also been shown to strengthen emotional attachment and revisit intentions (Bigné, Andreu, & Gnoth, 2001). Revisit intention is thus the outcome of multiple factors, including satisfaction, loyalty, emotional connection, and cultural fit. To remain competitive, hospitality operators must continuously optimize service quality, promote innovation aligned with market and technology trends, and design personalized, culturally adapted experiences. These strategies are essential for enhancing revisit intentions and achieving long-term business sustainability.
Research Framework
This study adopts a quantitative research design to examine the relationships among service quality, service innovation, and revisit intention. It explores consumers’ perceptions of service quality and service innovation and investigates how these factors influence their intention to revisit. A questionnaire survey is used to collect data from respondents with prior hotel experience, aiming to provide empirical evidence to assist hotel operators in understanding key factors affecting customer loyalty and repeat patronage. The findings are intended to help practitioners adjust their business strategies and service designs to enhance customer satisfaction and strengthen competitiveness. The conceptual framework is illustrated in Figure 3.1.
Figure 3.1 Research Framework
Research Hypotheses
Based on the research objectives and framework, the following hypotheses are proposed:
Research Instruments
A structured questionnaire was developed as the primary data collection instrument. It consists of three main constructs: service quality, service innovation, and revisit intention as well as demographic variables such as gender, age, marital status, number of children, education, occupation, income, travel frequency, and travel preferences. These variables help examine how respondents with different backgrounds evaluate hotel services and their revisit intentions.
Research Subjects
The target respondents are consumers with prior hotel experience. Using a convenience sampling approach, questionnaires were distributed and collected via Google Forms by graduate researchers targeting individuals and their acquaintances who have stayed at hotels. This method ensures a sufficient sample size for subsequent statistical analysis and provides diverse perspectives from different traveler backgrounds.
Data Analysis
After data collection, responses will be analyzed using JASP statistical software. The following analyses will be conducted:
Sample Structure Analysis
A total of 303 valid questionnaires were collected. Respondents’ demographics included gender, age, marital status, education level, region of residence, monthly income, and occupation (see Table 4.1). The sample was predominantly female (61.4%) and mainly middle-aged; the largest age group was 50–59 years (36.3%), followed by 40–49 years (21.5%) and 30–39 years (20.5%). Most respondents were married (60.1%) and held a university degree (48.2%). Over half resided in southern Taiwan (55.8%), indicating strong participation from residents near Kenting. The majority reported stable income, with the largest group earning NT$30,001–40,000 (27.1%). Most respondents worked in the service industry (37.3%), followed by industry/commerce and self-employment. This distribution suggests respondents have sufficient economic capacity and relevant travel experience for evaluating service quality and innovation.
Table 4.1 Sample Demographic Profile
|
Variable |
Category |
Frequency |
Cumulative Percentage |
|
Gender |
Female |
186 |
61.39% |
|
Male |
117 |
100.00% |
|
|
Age |
20–29 years |
31 |
10.23% |
|
30–39 years |
62 |
30.69% |
|
|
40–49 years |
65 |
52.15% |
|
|
50–59 years |
110 |
88.45% |
|
|
60 years and above |
35 |
100.00% |
|
|
Marital Status |
Married |
182 |
60.07% |
|
Single |
121 |
100.00% |
|
|
Education Level |
Junior High School or below |
6 |
1.98% |
|
University (Bachelor’s) |
146 |
50.17% |
|
|
College (Associate degree) |
45 |
65.02% |
|
|
Graduate School (Master’s) |
35 |
76.57% |
|
|
Senior High/Vocational School |
71 |
100.00% |
|
|
Region of Residence |
Central Taiwan |
73 |
24.09% |
|
Northern Taiwan |
61 |
44.22% |
|
|
Southern Taiwan |
169 |
100.00% |
|
|
Monthly Income |
NT$30,000 or below |
49 |
16.17% |
|
NT$30,001–40,000 |
82 |
43.23% |
|
|
NT$40,001–50,000 |
54 |
61.06% |
|
|
NT$50,001–60,000 |
44 |
75.58% |
|
|
NT$60,001–70,000 |
25 |
83.83% |
|
|
NT$70,001 and above |
49 |
100.00% |
|
|
Occupation |
Others |
40 |
13.20% |
|
Student |
8 |
15.84% |
|
|
Homemaker |
11 |
19.47% |
|
|
Industry/Commerce |
57 |
38.28% |
|
|
Service Industry |
113 |
75.58% |
|
|
Freelance/Self-employed |
33 |
86.47% |
|
|
Military/Public Sector |
28 |
95.71% |
|
|
Retired |
13 |
100.00% |
Reliability Analysis
Reliability refers to the stability of a measurement tool and the internal consistency of its items. This study employed both Cronbach’s α and McDonald’s ω to assess the reliability of the scales. As shown in Table 4.2, all constructs demonstrated excellent internal consistency, with both coefficients exceeding 0.90.
Table 4.2 Reliability Analysis of Scales
|
Scale |
Dimension/Overall |
Coefficient ω |
Coefficient α |
|
Service Quality Scale |
Employee Interaction & Responsiveness |
0.974 |
0.974 |
|
Facility & Environmental Quality |
0.947 |
0.942 |
|
|
Overall Service Quality |
0.977 |
0.979 |
|
|
Service Innovation Scale |
Experiential Context |
0.955 |
0.955 |
|
Technological Interaction |
0.91 |
0.906 |
|
|
Overall Service Innovation |
0.967 |
0.96 |
|
|
Revisit Intention Scale |
Perceived Value |
0.942 |
0.94 |
|
Experience Evaluation |
0.946 |
0.939 |
|
|
Overall Revisit Intention |
0.952 |
0.955 |
Validity Analysis (Concise)
Confirmatory Factor Analysis (CFA) was conducted to evaluate the construct validity and measurement quality for all three key scales: service quality, service innovation, and revisit intention. Following Fornell and Larcker’s (1981) guidelines, standardized factor loadings, Average Variance Extracted (AVE), Composite Reliability (CR), and discriminant validity were examined to ensure the adequacy of the measurement model.
For the Service Quality Scale, two latent constructs were identified: Employee Interaction & Responsiveness (14 items) and Facility & Environmental Quality (6 items). All standardized factor loadings ranged from 0.780 to 0.909 and were significant at p < .001, as shown in Table 4.3. The covariance between these two constructs was 0.893 (see Table 4.4). AVE and CR values were 0.728 and 0.979 for Employee Interaction & Responsiveness, and 0.739 and 0.967 for Facility & Environmental Quality, exceeding recommended thresholds (see Table 4.5).
For the Service Innovation Scale, two latent constructs were also validated: Experiential Context (10 items) and Technological Interaction (2 items). As shown in Table 4.6, all factor loadings ranged between 0.761 and 0.935 with p < .001. The correlation between the two constructs was 0.841 (see Table 4.7). AVE and CR values for Experiential Context were 0.690 and 0.977, while for Technological Interaction they were 0.836 and 0.941 (see Table 4.8).
For the Revisit Intention Scale, CFA results confirmed two latent constructs: Perceived Value (5 items) and Experience Evaluation (3 items). All loadings were significant and ranged from 0.842 to 0.961, as presented in Table 4.9. The correlation between these two constructs was 0.829 (see Table 4.10). AVE and CR values were 0.772 and 0.956 for Perceived Value, and 0.857 and 0.940 for Experience Evaluation (see Table 4.11).
Overall, the CFA results across all three scales demonstrated strong convergent validity (AVE > 0.50 and CR > 0.70) and discriminant validity (inter-factor correlations < ±1), confirming that the measurement instruments are reliable and suitable for subsequent SEM analysis.
Table 4.3. Service Quality Scale – CFA Factor Loadings (Standardized Estimates)
|
Factor |
Indicator |
Std. Estimate |
Std. Error |
z-value |
p |
|
Employee Interaction & Responsiveness |
Q1-7 |
0.831 |
0.019 |
44.756 |
< .001 |
|
Q1-8 |
0.852 |
0.016 |
51.777 |
< .001 |
|
|
Q1-9 |
0.827 |
0.019 |
43.88 |
< .001 |
|
|
… (Q1-10 to Q1-22) … |
0.780–0.909 |
— |
— |
< .001 |
|
|
Facility & Environmental Quality |
Q1-1 to Q1-6 |
0.780–0.909 |
— |
— |
< .001 |
Table 4.4. Service Quality Scale – Factor Covariance
|
Relationship |
Std. Estimate |
Std. Error |
z-value |
p |
95% CI Lower |
95% CI Upper |
|
Employee Interaction ↔ Facility & Environmental |
0.893 |
0.014 |
63.133 |
< .001 |
0.865 |
0.921 |
Table 4.5. Service Quality Scale – AVE and CR
|
Factor |
AVE |
CR |
|
Employee Interaction & Responsiveness |
0.728 |
0.979 |
|
Facility & Environmental Quality |
0.739 |
0.967 |
Table 4.6. Service Innovation Scale – CFA Factor Loadings
|
Factor |
Indicator |
Std. Estimate |
Std. Error |
z-value |
p |
|
Experiential Context |
Q2-1 to Q2-12 (10 items) |
0.761–0.888 |
— |
— |
< .001 |
|
Technological Interaction |
Q2-5, Q2-6 |
0.889–0.935 |
— |
— |
< .001 |
Table 4.7. Service Innovation Scale – Factor Covariance
|
Relationship |
Std. Estimate |
Std. Error |
z-value |
p |
95% CI Lower |
95% CI Upper |
|
Experiential Context ↔ Technological Interaction |
0.841 |
0.021 |
39.255 |
< .001 |
0.799 |
0.883 |
Table 4.8. Service Innovation Scale – AVE and CR
|
Factor |
AVE |
CR |
|
Experiential Context |
0.69 |
0.977 |
|
Technological Interaction |
0.836 |
0.941 |
Table 4.9. Revisit Intention Scale – CFA Factor Loadings
|
Factor |
Indicator |
Std. Estimate |
Std. Error |
z-value |
p |
|
Perceived Value |
Q3-4 to Q3-8 |
0.842–0.913 |
— |
— |
< .001 |
|
Experience Evaluation |
Q3-1 to Q3-3 |
0.843–0.961 |
— |
— |
< .001 |
Table 4.10. Revisit Intention Scale – Factor Covariance
|
Relationship |
Std. Estimate |
Std. Error |
z-value |
p |
95% CI Lower |
95% CI Upper |
|
Perceived Value ↔ Experience Evaluation |
0.829 |
0.021 |
39.551 |
< .001 |
0.788 |
0.87 |
Table 4.11. Revisit Intention Scale – AVE and CR
|
Factor |
AVE |
CR |
|
Perceived Value |
0.772 |
0.956 |
|
Experience Evaluation |
0.857 |
0.94 |
Structural Equation Modeling (SEM)
Structural Equation Modeling (SEM) was employed to examine the structural relationships among service quality, service innovation, and revisit intention, with multiple model fit indices evaluated to confirm a well-fitting model. Using JASP software, the overall fit indices indicated acceptable model adequacy. The chi-square value was 2,193.421 with 731 degrees of freedom, and the chi-square/df ratio was 3.00, which falls within the recommended range. The RMSEA value was 0.081 with a 90% confidence interval of [0.077, 0.085], meeting the suggested threshold. Incremental fit indices, including CFI (0.899), TLI (0.893), and IFI (0.900), were close to or slightly below the 0.90 cut-off, while SRMR (0.047) and GFI (0.724) also supported an acceptable model fit. A detailed summary of the model fit indices is presented in Table 4.12.
The measurement model results further confirmed that each latent construct was significantly reflected by its corresponding dimensions, demonstrating strong construct validity. As shown in Table 4.13, service innovation was measured by experiential context (β = 1.021) and technological interaction (β = 0.823); service quality was indicated by employee interaction & responsiveness (β = 0.950) and facility & environmental quality (β = 0.939); revisit intention was measured by perceived value (β = 0.896) and experience evaluation (β = 0.928). All factor loadings were statistically significant (p < .001), with z-values well above the acceptable range.
Regarding the structural model, path analysis results (see Table 4.14) revealed significant positive effects among the latent variables. Specifically, service quality positively influenced service innovation (β = 0.836) and revisit intention (β = 0.977), while service innovation also positively affected revisit intention (β = 0.891). All paths were significant at p < .001, with z-values exceeding 30, confirming stable parameter estimates and a well-fitting overall model. These findings further suggest that service innovation serves as a partial mediator, indicating that high service quality directly enhances revisit intention and also indirectly strengthens it through customers’ perceptions of innovative services.
Table 4.12. Model Fit Indices
|
Index |
Value |
|
Comparative Fit Index (CFI) |
0.899 |
|
Tucker–Lewis Index (TLI) |
0.893 |
|
Bentler–Bonett Non-Normed Fit Index (NNFI) |
0.893 |
|
Bentler–Bonett Normed Fit Index (NFI) |
0.857 |
|
Parsimony Normed Fit Index (PNFI) |
0.803 |
|
Bollen’s Relative Fit Index (RFI) |
0.847 |
|
Bollen’s Incremental Fit Index (IFI) |
0.9 |
|
Relative Noncentrality Index (RNI) |
0.899 |
|
RMSEA |
0.081 |
|
RMSEA 90% CI (Lower–Upper) |
0.077–0.085 |
|
RMSEA p-value |
0 |
|
Standardized RMR (SRMR) |
0.047 |
|
Hoelter’s Critical N (α = .05) |
110.823 |
|
Hoelter’s Critical N (α = .01) |
114.673 |
|
Goodness of Fit Index (GFI) |
0.724 |
|
McDonald Fit Index (MFI) |
0.09 |
|
Expected Cross-Validation Index (ECVI) |
7.826 |
|
Log-likelihood |
−7607.551 |
|
Number of Free Parameters |
89 |
|
Akaike Information Criterion (AIC) |
15393.1 |
|
Bayesian Information Criterion (BIC) |
15723.63 |
|
Sample-size Adjusted BIC (SSABIC) |
15441.36 |
Table 4.13. SEM Path Analysis Results
|
Latent Variable |
Indicator Dimension |
Std. Estimate |
Std. Error |
z-value |
p |
|
Service Innovation |
Experiential Context |
1.021 |
0.014 |
71.135 |
< .001 |
|
Technological Interaction |
0.823 |
0.025 |
33.426 |
< .001 |
|
|
Service Quality |
Employee Interaction & Responsiveness |
0.95 |
0.01 |
94.977 |
< .001 |
|
Facility & Environmental Quality |
0.939 |
0.011 |
81.782 |
< .001 |
|
|
Revisit Intention |
Perceived Value |
0.896 |
0.015 |
57.907 |
< .001 |
|
Experience Evaluation |
0.928 |
0.013 |
73.347 |
< .001 |
**Measurement Model (Latent → Dimension)
Table 4.14: Structural Model Path Analysis Results
|
Independent Latent |
Dependent Latent |
Std. Estimate |
Std. Error |
z-value |
p |
|
Service Quality |
Service Innovation |
0.836 |
0.023 |
36.337 |
< .001 |
|
Service Quality |
Revisit Intention |
0.977 |
0.012 |
80.347 |
< .001 |
|
Service Innovation |
Revisit Intention |
0.891 |
0.021 |
43.312 |
< .001 |
**Structural Model (Latent → Latent)
Research Conclusions
This study investigated the impact of service quality and service innovation on tourists’ revisit intention in Kenting’s hotel industry using 303 valid survey responses analyzed through EFA, CFA, reliability/validity tests, and SEM. Results confirmed that:
The SEM model demonstrated acceptable fit (e.g., CFI ≈ 0.90, RMSEA = 0.081). Service quality was measured by employee interaction & responsiveness and facility/environment quality, while service innovation consisted of experiential and technological dimensions. All constructs showed high reliability and validity, and all path coefficients were significant (e.g., service quality → revisit intention β = 0.977; service innovation → revisit intention β = 0.891, p < .001). Findings highlight that high-quality service combined with innovative experiences—especially smart technologies and cultural engagement—can strengthen customer loyalty and promote repeat visits.
Recommendations
Based on these findings, Kenting hotel operators should prioritize smart technologies (e.g., self-check-in systems, app-based guest services) to enhance convenience and efficiency, while also integrating local culture into the guest experience (e.g., themed rooms, cultural tours). Personalized services based on guest preferences and increased customer participation in service design can deepen emotional connections and satisfaction.
Internally, hotels should foster an innovation-friendly culture by encouraging staff creativity and offering training or reward programs for service improvement ideas. Data analytics and digital marketing can help tailor services and strengthen brand competitiveness. Overall, continuous innovation combining technology, culture, and personalized experiences is essential to attract new guests, retain loyal customers, and sustain competitive advantages.
References:
