Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 8057-8080 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate © 2024 by the authors; licensee Learning Gate * Correspondence: sutrisno@thamrin.ac.id Exploration of patient loyalty through value integration and customer satisfaction in class B university hospitals on Java Island Sutrisno1*, Tri Andjarwati2, Endah Budiarti3 1Management Study Program, Mohammad Husni Thamrin University, Jl. Salemba Tengah, No. 5 Paseban, Kec. Senen, Kota Jakarta Pusat, Daerah Khusus Ibukota Jakarta, 10440, Indonesia; sutrisno@thamrin.ac.id (S.). 2,3Faculty of Economics and Business, University of 17 August 1945 Surabaya, East Java, Indonesia; triandjarwati@untag- sby.ac.id (T.A.) endahbudiarti@untag-sby.ac.id (E.B.). Abstract: This study aims to analyze the relationship between accessibility, brand image, and service quality on customer value, customer satisfaction, and revisit intention at the University Hospital (RSPT). The main focus of the study is the mediating role of customer value and customer satisfaction in influencing patient loyalty. With a holistic approach, this study highlights the unique dynamics of RSPT that integrates education and health care functions. The findings indicate that accessibility and service quality have a significant influence on customer value, while brand image has a more limited contribution in this context. In addition, customer satisfaction plays an important role in bridging the relationship between customer value and revisit intention, making it a strategic dimension in increasing patient loyalty. A statistical approach using the SmartPLS method was carried out to evaluate the validity and reliability of the research model. The test results showed that the Average Variance Extracted (AVE) value of all constructs was greater than 0.50, reflecting that convergent validity was met. The Composite Reliability and Cronbach's Alpha values were each greater than 0.70, indicating good internal consistency. Discriminant Validity is assessed through the Heterotrait-Monotrait Ratio (HTMT) with a value of less than 0.85, ensuring clear differences between constructs. The results of the path analysis show that accessibility (X2) significantly affects customer value (Z) with a coefficient of 0.826 (T = 6.993, p < 0.000). Likewise, service quality (X3) has a positive effect on customer value with a coefficient of 0.291 (T = 2.521, p < 0.012). In contrast, brand image (X1) does not have a significant effect on customer value (coefficient -0.192, T = 1.783, p = 0.075). Customer value has a significant effect on customer satisfaction (coefficient 0.922, T = 55.068, p < 0.000), which in turn affects patient revisit intention (coefficient 0.922, T = 53.495, p < 0.000). This study provides theoretical contributions by expanding the healthcare management literature, especially in the unique context of RSPT. Practically, these findings recommend improving accessibility and service quality to enhance patient loyalty. This study also opens up opportunities for further research with a longitudinal approach to understand changes in patient perceptions over time. Keywords: Accessibility, Brand image, Customer satisfaction, Patient loyalty, Service quality. 1. Introduction Since the implementation of the National Health Insurance (JKN) in 2014, Indonesia has experienced a significant transformation in its health care system. This policy has changed the landscape of the hospital industry, with a sharp increase in the number of hospitals (RS).(Nugraheni & Hartono, 2017; Sri Wahyuni Rochmawati et al., 2021; Yuniar & Handayani, 2016). Data shows that the number of hospitals has almost doubled, from 1,632 in 2010 to 3,072 in 2022, with private hospitals growing faster than government hospitals. Government hospitals recorded growth from 751 to 1,043 units in the 2010- 2020 decade, focusing on improving facilities in remote areas and providing complex health services as referrals. Meanwhile, private hospitals recorded a remarkable growth of 92%, from 990 to 1,900 units. Private hospitals play a vital role in meeting the needs of the upper middle class, especially in urban 8058 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate areas, with technology and specialist services that are often more advanced. Of course, this is a parameter for improving the quality of service in the hospital industry.(Meesala & Paul, 2018; Pradana et al., 2024). Figure 1. Number of Indonesian hospitals, 2012-2022. The Bed Occupancy Rate (BOR) in 2020 reached 64.1% nationally, but several provinces such as DKI Jakarta and West Java reported figures of up to 85%, reflecting service capacity challenges. The bed-to-population ratio also increased from 1.16 in 2017 to 1.4 per 1,000 population in 2022. The emergence of the State University Hospital (RSPTN) is another interesting aspect. With 13 units, RSPTN not only serves as a health service center but also as an education and research facility, supporting medical innovation integrated with community service (Figure 1). State University Hospitals (RSPTN) in Indonesia present a revolutionary concept that combines health services, education, and research. With the presence of 13 RSPTN throughout Indonesia, such as the University of Indonesia Hospital and Gadjah Mada University Hospital, these facilities not only serve general patients but also become centers of medical innovation and clinical training for medical students. As part of the National Health System (SKN), RSPTN plays a vital role in ensuring quality health services that are aligned with educational and community service efforts. RSPTN faces challenges in winning the increasingly tight competition in the world of health services. Factors such as increasing life expectancy, demographic changes, and the threat of disease outbreaks, such as COVID- 19, force hospitals to innovate in their services (Gunawan et al., 2022; Ilbeigipour et al., 2022; Soto- Castellón et al., 2023). This is where it is important to understand revisit intention, which is the patient's intention to return. This phenomenon is influenced by a combination of positive experiences, brand image, and the quality of services provided by the hospital. A positive brand image plays a key role in building patient trust. Patients who are satisfied with their experience are more likely to return to the same hospital rather than trying another institution. Therefore, RSPTN needs to utilize a customer-based approach to strengthen relationships with patients through improved service quality, fair pricing, and innovative marketing programs. With this step, RSPTN can ensure the sustainability of their role as a strategic institution in the national health system while building sustainable patient loyalty.State University Hospitals (RSPTN) in Indonesia present a revolutionary concept that combines health services, education, and research. With the presence of 13 RSPTN throughout Indonesia, such as the University of Indonesia Hospital and Gadjah Mada University Hospital, these facilities not only serve general patients but also become centers of medical innovation and clinical training for medical students. As part of the National Health System (SKN), RSPTN plays a vital role in ensuring quality health services that are aligned with educational and community service efforts. RSPTN faces challenges in winning the increasingly tight competition in the world of health services. Factors such as increasing life expectancy, demographic changes, and the threat of disease outbreaks, such as COVID- 8059 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate 19, force hospitals to innovate in services. This is where it is important to understand revisit intention, namely the patient's intention to return. This phenomenon is influenced by a combination of positive experiences, brand image, and the quality of services provided by the hospital. A positive brand image plays a key role in building patient trust. Patients who are satisfied with their experience are more likely to return to the same hospital rather than trying another institution. Therefore, RSPTN needs to utilize a customer-centric approach to strengthen relationships with patients through improved service quality, fair pricing, and innovative marketing programs. With this step, RSPTN can ensure the sustainability of their role as a strategic institution in the national health system while building sustainable patient loyalty. Studies(Sunarta et al., 2020), The results of the study show that a good hospital reputation has a very significant influence on the hospital's brand image and the patient's intention to revisit in the near future. In other words, a strong reputation can strengthen the relationship between hospital brand equity and patient revisit intention.(Puspitasari et al., 2019), The results of the study show that perceived quality does not directly affect tourist satisfaction or revisit intention. However, perceived quality significantly affects revisit intention through perceived value.(Alam Wiguna et al., 2023), The results of the study indicate that perceived medical quality, perceived service quality, and price fairness have a significant influence on consumer satisfaction. In addition, satisfaction and trust are proven to be significant mediators in influencing consumer intentions to revisit health clinics.(Putri et al., 2022), The results of the study showed that doctor services and nurse services had a significant positive influence on patient satisfaction.(Jung & Sung, 2018), The results of the study showed significant differences between patients in general care units and comprehensive nursing care units in satisfaction with nursing services (t=14.73, p<.001), commitment to the hospital (t=7.52, p<.001), and intention to revisit (t=6.01, p<.001).(Ongkaruna & Kristaung, 2023), The results of the study indicate that the quality of hospital services has a positive effect on patient engagement. Furthermore, patient engagement has a positive effect on patient loyalty. Patient loyalty has been shown to have a positive effect on patient revisit intentions and word-of-mouth.(Angelica & Bernardo, 2023), The results of the study showed that people factors (personnel), physical evidence, and price fairness had a significant positive effect on patient satisfaction. However, the process factor did not have a positive effect on patient satisfaction. Furthermore, patient satisfaction was found to have a significant positive effect on revisit intention.(Octaviani et al., 2023), The results of the study indicate that electronic word of mouth (EWOM) is the most dominant antecedent factor that influences cognitive experience (CE) and affective experience (AE), with a greater influence on CE than AE. Meanwhile, servicescape has no significant influence on CE. AE was found to have a slightly stronger influence on revisit intention (RI) compared to CE.(Lee & Kim, 2017), The results showed that the average score of medical service quality was 5.72 out of 7, patient satisfaction was 88.88 out of 100, and revisit intention was 4.59 out of 5. There was a positive relationship between medical service quality and satisfaction, medical service quality and revisit intention, and satisfaction and revisit intention. Factors such as medical services by doctors, visit paths, and responsiveness of medical services explained about 23.8% of the variability in patients' revisit intention.(Rahman et al., 2023), The results of the study showed that service quality has a direct influence on word of mouth. In addition, the image of the hospital also directly affects word of mouth. Word of mouth was found to have a direct influence on revisit intentions.(Hai et al., 2021), This study found a significant relationship between hospital service quality and hospital brand image with patient satisfaction and loyalty. Service quality has a direct influence on patient satisfaction and revisit intention, which are indicators of patient loyalty. Meanwhile, hospital brand image has a direct influence on patient loyalty, although it does not affect patient satisfaction.(Rusmahafi & Wulandari, 2020), The results of the study indicate that the brand image variable does not have a significant effect on customer satisfaction of Bank Muamalat Indonesia. However, the service quality and customer value variables have a significant positive effect on customer satisfaction. A good brand image, which is identical to a trusted and Islamic sharia bank, needs to be continuously strengthened and socialized to increase customer satisfaction.(Guspianto et al., 2022), The results of the study indicate that the variable indicators used are valid and reliable, and found five significant influence frameworks. The influences are: service quality on patient value (β = 0.203), service quality on patient satisfaction (β = 0.429), 8060 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate service quality on revisit intention (β = 0.254), patient value on patient satisfaction (β = 0.156), and patient satisfaction on revisit intention (β = 0.539). However, patient value does not have a significant effect on revisit intention (β = 0.057).(Woo & Choi, 2021) , The results of the study showed that in direct effects, outpatients have a sequence of factors that influence patient satisfaction, namely doctor's practice services, hospital environment, and patient satisfaction.(Mohamed Diaa, 2022), The results showed that virtual layout and design as well as virtual social presence have a significant positive effect on revisit intention to online stores. In contrast, virtual atmosphere and virtual theater do not have a significant effect on revisit intention.(Nguyen et al., 2021), This study identified four dimensions of service quality, namely emotion, function, social influence, and trust. These dimensions mostly have a significant influence on customer perceived value and customer satisfaction, except for emotion which is not significant on perceived value, and function which is not significant on customer satisfaction.(Fengmin et al., 2022), This study found that nutritional knowledge, perceived quality of medical services, and trust in physiologists significantly influenced the revisit intention of medical tourists in China. All of these variables had a strong positive relationship with tourists' intention to return for treatment in China.(Akthar et al., 2023), The results of the study indicate that perceived service quality has a significant effect on patient trust through partial mediation by patient satisfaction. In addition, patient satisfaction significantly affects patient behavioral intentions through partial mediation by trust.(Yuniarti & Hidayat, 2021), The results of the study indicate that the quality of medical services and the quality of non-medical services have a positive and significant influence on patient satisfaction. Furthermore, patient satisfaction positively and significantly affects patient trust and revisit intention.(Granados et al., 2021), The results of the study show that the Service Quality- Customer Value-Customer Satisfaction-Trust/Commitment-Customer Loyalty chain applies to corporate customers. Service quality has a significant influence on customer value, which in turn increases customer satisfaction.(Kurnianingrum & Hidayat, 2020). The research findings show that service quality and price perception have a positive and significant influence on consumer trust. Furthermore, consumer trust has a significant influence on revisit intention.(Pighin et al., 2022), The research findings show that the direct route between health service quality and satisfaction is not statistically significant. However, the direct relationships between other variables, such as health program quality to trust, satisfaction to intention to return, and trust to intention to return, are statistically significant (Siripipatthanakul, 2021), indicating that patient satisfaction is a mediator between service quality and its outcomes in the form of WOM and intention to return. Of the elements of service quality, empathy has the greatest influence on patient satisfaction (Beta = 0.411, p < 0.001), followed by reliability (Beta = 0.183, p < 0.05), tangible (Beta = 0.119, p < 0.05), while assurance (Beta = 0.077, p> 0.05) and responsiveness (Beta = 0.053, p> 0.05) have no significant influence. Patient satisfaction can predict intention to return by 53.4% (Beta = 0.731, p < 0.001, R² = 0.534), and WOM by 42.9% (Beta = 0.655, p < 0.001, R² = 0.429).(Rajput & Gahfoor, 2020), that food quality, restaurant service quality, physical environment quality, and customer satisfaction have a positive relationship with the intention to return to fast food restaurant customers. However, WOM does not positively moderate the relationship between customer satisfaction and intention to return.(Rifa & Bernardo, 2023), the physical environment of the hospital, registration services, waiting time, doctor services, and nurse services have a positive influence on patient satisfaction. In addition, patient satisfaction also has a positive influence on revisit intentions. Studies on university hospitals (PTHs) still face a number of prominent limitations in the literature. Many previous health studies used limited samples, eitherfrom the aspect of number or region, such as focusing on one type of hospital, one clinic, or a specific geographic area. This limits the generalizability of the results, especially when considering hospitals with unique backgrounds such as RSPT, which combine educational, health care, and research functions. In addition, the use of convenience sampling or purposive sampling methods in many studies often creates selection bias, so that the results of the study are less representative of the wider population. The majority of studies also apply a cross-sectional approach, which cannot capture the dynamics of relationships between variables or changes in patient experience over time. In the context of RSPT, these limitations highlight the need for a more inclusive, longitudinal, and representative approach to understand the operational complexity and its contribution 8061 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate to the health and education sectors more comprehensively. Although many studies have made significant contributions to the understanding of health service quality and its relationship to patient satisfaction, loyalty, and revisit intentions, there is still a gap in the context of tertiary hospitals (RSPT). Most studies focus on public hospitals, private hospitals, or health clinics with a standard service approach. However, RSPT has unique characteristics because it plays a dual role as an educational institution and a health service provider. The available literature does not sufficiently discuss how the integration of education, research, and health service functions in RSPT affects the quality of services holistically, both from the perspective of patients, medical personnel, and students. In addition, there are not many studies that explore the contribution of RSPT in supporting medical tourism through academic and technology-based service innovations. Therefore, in-depth research is needed to develop a service strategy that reflects the operational complexity of RSPT and its contribution to the health and education sectors. Research on University Hospitals (RSPT) still faces a significant gap in the literature. Most previous studies have focused on public or private hospitals with a standard service approach, thus ignoring the unique characteristics of RSPT that integrate education, health care, and research functions. This has led to a lack of understanding of how the dual role of RSPT affects service quality from the perspective of patients, medical personnel, and students. In addition, existing studies often use limited samples in terms of both number and geographical area, and adopt a cross-sectional design that is less able to capture long-term dynamics. This study offers a new approach by examining the contribution of RSPT in supporting the health and education sectors through the analysis of relationships between variables using a mediation model. In this study, the Smart-PLS method is used to utilize the advantages of path analysis with a mediation model, which is able to capture complex relationships between variables such as accessibility, service quality, customer value, satisfaction, and revisit intention. The advantage of this approach is its ability to test the mediating role of variables such as Customer Value (Z) and Customer Satisfaction (M) in the relationship between exogenous variables (Accessibility, Service Quality) and endogenous variables (Revisit Intention). This mediation model provides more comprehensive insights than simple analysis, because it is able to show how intervening variables influence causal relationships in depth. In addition, this study addresses the literature gap by highlighting the integration of education and health service functions in RSPT. This approach allows for a more holistic analysis of the impact of academic-based services and technological innovation on patient satisfaction and loyalty. The study also explores the potential of RSPT in supporting medical tourism, an area that has received little attention in the literature. With a longitudinal and representative research design, this study not only contributes to the development of theories in health service management but also provides relevant strategic recommendations for the operational management of RSPT. The results are expected to be a foundation for increasing the competitiveness of RSPT in Indonesia, while strengthening their contribution to the health, education, and national economic sectors. This study aims to explore the relationship between accessibility (Accessibility, X2), brand image (Brand Image, X1), and service quality (Service Quality, X3) on customer value (Customer Value, Z), customer satisfaction (Customer Satisfaction, M), and revisit intention (Revisit Intention, Y) at a University Hospital (RSPT) in Indonesia. Specifically, this study analyzes the mediating role of customer value and customer satisfaction in influencing patient intention to reuse RSPT services. Using the Smart-PLS method, this study aims to identify significant relationship pathways between these variables, as well as reveal factors that have a dominant impact on RSPT customer behavior. This study will contribute theoretically by extending the literature on healthcare management, particularly in the unique context of RSPT. The findings suggest that accessibility and service quality play a significant role in creating customer value, while brand image has a more limited impact in RSPT. This will strengthen the idea that direct customer experience is more important than brand perception in the complex context of healthcare. Furthermore, customer satisfaction will prove to be an important link between customer value and revisit intention, making the customer satisfaction dimension a strategic focus in enhancing patient loyalty. Practically, this study provides recommendations to RSPT management to prioritize accessibility development and service quality 8062 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate improvement. This includes efforts to ensure easy access for patients, such as improving transportation infrastructure, as well as providing responsive and reliable services. By creating a satisfying experience, RSPT can increase patient loyalty while strengthening its position as a leading healthcare provider. 2. Research Methodology 2.1. Design This study uses a quantitative design with a mediation model, which is analyzed using Smart Partial Least Square (SmartPLS). This approach is very suitable for understanding the complex relationships between the variables that are the focus of the study, namely Brand Image (X1), Accessibility (X2), Service Quality (X3), Customer Value (Z), Customer Satisfaction (M), and Revisit Intention (Y). As the object of research, State University Hospitals (RS PTN) in Indonesia provide a relevant empirical context, given their role as providers of health services integrated with academic and research functions.(Pradana, Luh, et al., 2023). The advantage of using SmartPLS is its ability to handle structural models with latent variables, including complex mediation models. This model allows the analysis of direct and indirect relationships between exogenous variables (X1, X2, X3) with endogenous variables (Y) through mediating variables, namely Customer Satisfaction (M) and Customer Value (Z)(Pradana et al., 2024). For example, positive Brand Image and good Accessibility can increase the perception of Service Quality, which in turn strengthens Customer Value. The mediating variable Customer Satisfaction plays an important role in explaining how patient satisfaction affects the intention to return (Revisit Intention). This approach also excels in handling data with small to medium sample sizes, such as in this study, which involved outpatients at a class B state-owned hospital in Java. With the ability to handle non-normal data and measure both reflective and formative indicators, SmartPLS provides high analytical flexibility. In addition, this approach allows exploration of the complex interactions between patient perceptions of hospital image, ease of access, and the quality of service they receive, which ultimately impact their decision to return to the hospital. This design provides strategic insights that state-owned hospital management can use to improve services based on empirical data, create patient-focused experiences, and strengthen the competitiveness of institutions in the increasingly dynamic health sector. 2.2. Population and Sampling This study focuses on a limited population, namely outpatients at State University Hospitals (RS PTN) in Indonesia. Based on the classification, there are 13 PTN hospitals, of which 9 hospitals are categorized as class C and 4 are class B hospitals. Of the class B hospitals, three are in Java (Airlangga University Hospital, Gadjah Mada University Hospital, University of Indonesia Hospital), while one is in Sulawesi (Hasanuddin University Hospital). The target population is productive age patients (15-64 years) according to the classification of the Indonesian Ministry of Health (2017). With this approach, the study targets a homogeneous population to ensure the relevance of the results to service needs. The sample used was outpatients at the internal medicine polyclinic at a class B PTN hospital in Java. The sampling technique used convenience sampling which allows easy access to respondents, considering the limitations of time and cost. According to Hair et al. (2020), the number of samples is determined with a minimum scale of five times the number of research parameters. This study used 125 respondents, because it is sufficient for large-scale social research, ensuring a margin of error of 5%(Almaqtari, 2024; Qomariyah & Dwiridotjahjono, 2024). This non-probability sampling technique is considered ideal for achieving population representation in relevant social and geographical contexts. 2.3. Procedures and Data Collection Data collection in this study was carried out through a series of methods aimed at obtaining valid, relevant, and accurate information according to research needs. The researcher applied three main approaches, namely literature study, preliminary study, and field study. Literature study was conducted by collecting various references in the form of books, journals, and relevant literature to compile a theoretical basis in the literature review. This stage is very important to provide a solid conceptual framework as a basis for research. Furthermore, a preliminary study was used to test the research 8063 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate instrument by distributing a manipulation questionnaire to 30 participants. This step aims to ensure that the instrument has been understood by the respondents and is able to capture information efficiently. After the instrument was tested, a field study was carried out as the main study by distributing questionnaires to the main respondents, namely outpatients of productive age (15–64 years) at class B State Hospitals in Java. The questionnaire was distributed online via the WhatsApp application using the Google Form platform to reach respondents practically and quickly. The purposive sampling technique was applied to ensure that respondents met the research criteria. The instrument used in this study was a questionnaire designed by the researcher himself using a Likert scale. This scale is used to measure the attitudes, opinions, and perceptions of respondents towards social phenomena that are the focus of the study. Respondents gave assessments in five categories: strongly agree (score 5), agree (4), neutral (3), disagree (2), and strongly disagree (1). The statements in the questionnaire are entirely favorable statements, which means supporting the research object. This aims to facilitate data analysis and interpretation of research results. This instrument is expected to produce measurable, consistent, and valid data in explaining research variables. Data collection that is in accordance with the method allows the data analysis process to run optimally. With a Likert scale-based instrument, the collected data is analyzed quantitatively using statistical tools to measure the relationship between variables. The reliability of the instrument is guaranteed through an initial validation stage, such as manipulation tests and initial response analysis from preliminary studies. Questionnaires designed based on relevant theories support a more standardized analysis process. The online method used also reduces the potential for data input errors, ensures that respondents meet the criteria, and makes it easier for researchers to recap and process data. This supports conclusions that are more accurate, relevant, and applicable to the research context. 2.4. Operational Variables This study uses six main variables with construct items and questions designed to measure specific aspects of each variable. First, Brand Image (X1) consists of two construct items: favorability and strength of brand association. The favorability of brand association reflects customers' positive perceptions of the brand, while the strength of association assesses how strongly customers identify with the brand's value. This variable has four questions that focus on brand image as a driver of positive impressions. Second, Accessibility (X2) has three construct items: availability of public transportation, more effective travel distance and time, and travel costs. This variable has six questions designed to evaluate the ease of customer access to a particular location. Its focus on practical elements such as the effectiveness of travel time and cost provides insight into accessibility factors that affect customer experience. Third, Service Quality (X3) includes five construct items based on the Servqual dimensions: tangible, responsiveness, assurance, empathy, and reliability. With a total of 10 questions, this variable aims to evaluate service quality from the customer's perspective, covering the physical aspects of the service (tangible) to customer trust in the service provider (reliability) (Table 1). 8064 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate Table 1. Operational variables. No Variables Construct items Question items Reference 1 Brand image (X1) 1. The advantages of brand association 2. The power of brand association 4 questions (Djunaedi et al., 2022; Lidiawan, 2024; Pangastuti et al., 2023; Prayitno & Yap, 2024) 2 Accessibility (X2) 1. Availability of public transportation 2. More effective travel distance and time 3. Travel expense 6 questions (Amador-Jimenez & Serrano, 2017; Antony et al., 2024; Zulkarnain et al., 2024) 3 Service quality (X3) 1. Tangible 2. Responsiveness 3. Assurance 4. Empathy 5. Reliability 10 questions (Cahyaningrum et al., 2024; Pradana, Hariastuti, et al., 2023; Pradana, Luh, et al., 2023) 4 Customer value (Z) 1. Emotional value 2. Social Value 4 questions (Fauzi et al., 2024; Teangsompong et al., 2024) 5 Customer satisfaction (M) 1. Overall satisfaction 2. Expectation satisfaction 3 questions (Komari, 2017, 2023; Komari et al., 2019) 6 Revisit intention (Y) 1. Intention to recommend 2. Intention to revisit 3. Resistance to change 5 questions (Lin, 2024) Fourth, Customer Value (Z) is measured through two construct items, namely emotional value and social value. Emotional value assesses the emotional value felt by customers, while social value measures the social benefits obtained. The four questions in this variable focus on how emotional and social values drive customer perceptions of service benefits. Fifth, Customer Satisfaction (M) includes two construct items: overall satisfaction and expectation satisfaction. With three questions, this variable evaluates the extent to which the customer experience meets or exceeds their expectations, as well as the overall level of satisfaction after receiving the service. Finally, Revisit Intention (Y) consists of three construct items: intention to recommend, intention to revisit, and resistance to change. Five questions are used to measure customers' intention to recommend, revisit, or remain loyal to a service despite changes in the market. 2.5. Research Procedures This study by testing the statistical model with SmartPLS in detail, the testing steps involve several main statistical parameters that must be met to ensure the validity, reliability, and significance of the model. The first stage is Convergent Validity, which is evaluated through the Average Variance Extracted (AVE) value. The AVE value must be greater than 0.50, which indicates that more than 50% of the indicator variance is successfully explained by the measured construct. Furthermore, Composite Reliability and Cronbach's Alpha are used to measure the internal consistency and reliability of the construct(Fuaddi & Pradana, 2024; Laely & Lidiawan, 2024). The required value for both indicators is more than 0.70, which reflects that the construct has good consistency in measuring the variables. Then, R-Square is used to evaluate the ability of the independent and mediating variables to explain the dependent variable. The R-Square value is interpreted based on the following criteria: ≥ 0.67 is considered strong, 0.33-0.67 is considered moderate, and <0.33 is considered weak. In addition, Path Coefficients provide information on the direction and strength of the relationship between variables in the model. This value is accompanied by statistical testing through T-Statistics, where the T value must 8065 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate be more than 1.96 at a significance level of 5% (p <0.05) to ensure that the influence between variables is statistically significant. The next stage is to ensure Discriminant Validity, which is measured through the Heterotrait-Monotrait Ratio (HTMT)(Hasan et al., 2024; Nugraha et al., 2022; Roziq & Ilma Ahmad, 2024; Zamnur & Harjatno, 2023). The HTMT value must be less than 0.85 to ensure that each construct is clearly different and does not overlap with other constructs. In addition, the Indirect Effect test is needed to evaluate the mediation (intervening) effect.(Catania et al., 2024; Ridley et al., 2002). This effect was assessed using bootstrapping to ensure that the confidence interval values did not include zero, which would indicate that the mediation effect was significant. 3. Results and Discussion 3.1. Results 3.1.1. Descriptive Study The value of descriptive studies, fromBrand Image(X1) at State University Hospitals in Indonesia, it can be seen that the indicators used have varying values. Indicator X1.1 shows an average value (mean) of 3.984 with a standard deviation of 0.737, a median of 4.000, a minimum value of 2.000, and a maximum value of 5.000. This indicator has a negative skewness of -0.702, indicating that the data distribution tends to be skewed to the left. Indicator X1.2 has a higher average value, namely 4.056 with a standard deviation of 0.842, a median of 4.000, a minimum value of 2.000, and a maximum value of 5.000. Skewness of -0.677 also indicates that the data is skewed to the left. Indicator X1.3 shows an average of 3.952, standard deviation of 0.788, median of 4.000, minimum value of 2.000, maximum value of 5.000, and skewness of -0.509. Meanwhile, X1.4 has an average of 3.824 with the highest standard deviation of 0.801, median of 4.000, minimum value of 1.000, maximum value of 5.000, and skewness of 1.160 which shows a right-skewed distribution. Overall, these data reflect that the brand image of the State University Hospital is considered good by respondents with a tendency for data distribution to vary in each indicator. This indicates that public perception of the hospital's brand image is quite positive, but more attention is needed to aspect X1.4 which shows a higher variation in assessment and a tendency for asymmetric data. Accessibility (X2) at State University Hospitals (RSPTN) in Indonesia, the average score (mean) ranges from 3,808 to 4,016, indicating that hospital accessibility is generally considered good by respondents. The median value for all indicators is 4,000, indicating that most respondents gave a positive assessment of accessibility. The range of minimum values 1,000 to maximum 5,000 indicates significant differences in perception between respondents, possibly influenced by variations in conditions or experiences of service users at RSPTN. The highest standard deviation is found in indicator X2.3 at 0.883, indicating a greater level of data spread than other indicators, while indicator X2.5 has the lowest standard deviation (0.685), indicating a more homogeneous perception among respondents. The skewness value ranges from -0.382 to -0.896, with a negative tendency indicating that the majority of respondents gave an above average assessment. In addition, the kurtosis value varies between 0.355 to 1.690, indicating that the data distribution is mostly close to a normal distribution, although in some indicators such as X2.2, the distribution is more prominent. Overall, these data reflect that accessibility at RSPTN is considered quite adequate, but there is room for improvement, especially in reducing the perception gap between user groups, in order to ensure more equitable and inclusive services. Service Quality (X3) at State University Hospitals (RSPTN) in Indonesia showed a mean score that varied between 3.712 and 4.120 on a scale of 1-5, with a consistent median score of 4.00. This indicates that the majority of respondents have a positive perception of the quality of hospital services. The minimum score range of 2.00 to a maximum of 5.00 indicates a variation in perception among respondents. The standard deviation value ranged from 0.749 to 0.875, indicating a relatively low level of data dispersion, meaning that perceptions of service quality tend to be homogeneous. The skewness values that were mostly negative, for example in indicators X3.3 (-0.680) and X3.7 (-0.356), indicate a data distribution that is skewed towards high values, while several indicators such as X3.9 (0.396) and X3.8 (0.369) have positive skewness indicating that a small portion of respondents gave lower values. The kurtosis values for most indicators are below zero, reflecting a wider or near-normal distribution of data. Overall, these results indicate that RSPTN in Indonesia has succeeded in meeting most patients' expectations in terms of service quality, although there is still room 8066 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate for improvement to ensure a more consistent positive perception across all service dimensions. Customer Value (Z) includes Z1, Z2, Z3, and Z4, with an average score ranging from 3.856 to 4.016. This indicates that customer perceptions of service value at State University Hospitals (RSPTN) in Indonesia are generally high, approaching a maximum scale of 5. The minimum values for several indicators such as Z1 and Z3 reach 1.000, indicating a variation in negative experiences for a small number of respondents. Conversely, the maximum value that is consistent at 5.000 for all indicators indicates that some customers have very positive experiences. The standard deviation ranges from 0.681 to 0.936 indicating a moderate level of data spread, with Z2 having the smallest variation (0.681), indicating higher consistency compared to other indicators. Negative skewness, such as in Z1 (-1.368) and Z3 (-0.994), indicates a data distribution that is skewed towards high scores. This strengthens the indication that the majority of customers rate RSPTN services as high value. This assessment is important to support the development of customer value-based service improvement strategies, such as improving aspects that still have low minimum ratings to ensure a more equitable positive experience for all patients. This combination of data provides in-depth insights for RSPTN managers to continue to increase customer trust and loyalty. Customer satisfaction at State University Hospitals (RSPTN) in Indonesia shows significant variation. The mean values for M1, M2, and M3 are 4.016, 3.816, and 4.048, respectively.which shows a tendency for customer satisfaction to be in the "satisfied" category with a value approaching 4 (satisfied). The median for the three indicators is 4,000, indicating that most respondents give consistent assessments at that number. The minimum and maximum score ranges, namely 2 to 5 for M1 and M3 and 1 to 5 for M2, indicate the diversity of customer perceptions of hospital services. The standard deviation (SD) for the three indicators ranges from 0.725 to 0.880, indicating a fairly low level of data variation, indicating that most respondents have similar perceptions of service satisfaction (Table 2). 8067 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate Table 2. Descriptive value. Variables Question items Missing Mean Median Min. Max. Standard deviation Excess Kurtosis Skewness Brand image (X1) X1.1 0.000 3.984 4.000 2.000 5.000 0.737 0.805 -0.702 X1.2 0.000 4.056 4.000 2.000 5.000 0.842 -0.049 -0.677 X1.3 0.000 3.952 4.000 2.000 5.000 0.788 0.008 -0.509 X1.4 0.000 3.824 4.000 1.000 5.000 0.801 1.160 -0.899 Accessibility (X2) X2.1 0.000 3.888 4.000 2.000 5.000 0.740 0.395 -0.536 X2.2 0.000 3.880 4.000 1.000 5.000 0.845 1.690 -0.896 X2.3 0.000 3.808 4.000 1.000 5.000 0.883 1.087 -0.813 X2.4 0.000 3.912 4.000 1.000 5.000 0.849 0.524 -0.704 X2.5 0.000 3.944 4.000 2.000 5.000 0.685 0.355 -0.382 X2.6 0.000 4.016 4.000 1.000 5.000 0.810 0.898 -0.761 Service quality (X3) X3.1 0.000 3.936 4.000 2.000 5.000 0.756 -0.182 -0.342 X3.2 0.000 3.880 4.000 2.000 5.000 0.806 0.002 -0.519 X3.3 0.000 3.768 4.000 2.000 5.000 0.868 -0.680 -0.196 X3.4 0.000 3.920 4.000 2.000 5.000 0.765 0.183 -0.513 X3.5 0.000 3.816 4.000 2.000 5.000 0.784 -0.468 -0.166 X3.6 0.000 3.912 4.000 2.000 5.000 0.839 -0.081 -0.571 X3.7 0.000 3.712 4.000 2.000 5.000 0.875 -0.356 -0.489 X3.8 0.000 3.912 4.000 2.000 5.000 0.749 0.369 -0.549 X3.9 0.000 4.120 4.000 2.000 5.000 0.776 0.396 -0.734 X3.10 0.000 3.904 4.000 2.000 5.000 0.774 0.258 -0.565 Customer value (Z) Z1 0.000 3.856 4.000 1.000 5.000 0.936 2.609 -1.368 Z2 0.000 3.992 4.000 2.000 5.000 0.710 0.929 -0.667 Z3 0.000 3.856 4.000 1.000 5.000 0.864 1.978 -0.994 Z4 0.000 4.016 4.000 2.000 5.000 0.681 1.090 -0.635 Customer satisfaction(M) M1 0.000 4.016 4.000 2.000 5.000 0.737 0.586 -0.631 M2 0.000 3.816 4.000 1.000 5.000 0.880 0.727 -0.770 M3 0.000 4.048 4.000 2.000 5.000 0.725 0.890 -0.711 Revisit intention(Y) Y1 0.000 3.856 4.000 1.000 5.000 0.936 2.609 -1.368 Y2 0.000 3.992 4.000 2.000 5.000 0.710 0.929 -0.667 Y3 0.000 3.856 4.000 1.000 5.000 0.864 1.978 -0.994 Y4 0.000 4.016 4.000 2.000 5.000 0.681 1.090 -0.635 Y5 0.000 3.568 4.000 2.000 5.000 0.897 -0.750 -0.039 8068 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate The negative skewness value on all indicators (M1: -0.631, M2: -0.770, M3: -0.711) indicates a data distribution that is skewed towards high scores, meaning that many customers give positive assessments. This data indicates that RSPTN has succeeded in providing satisfactory services, although there is still room for improvement, especially for indicator M2 which has a lower minimum score. This evaluation is important to ensure consistency and improvement in service quality in order to optimally meet patient expectations. Revisit Intention (Y) shows an average score that varies between indicators. Indicators Y1 and Y3 have a mean of 3.856, with a median score of 4.000, a range of values between 1.000 and 5.000, and standard deviations of 0.936 and 0.864, respectively, indicating a fairly homogeneous distribution of data but still indicating some variability in respondent responses. Indicator Y2 has the highest average of 3.992 with a median of 4.000, a range of values of 2.000-5.000, and the smallest standard deviation of 0.710, reflecting a more consistent respondent perception. Meanwhile, Y4 shows an average of 4.016 as the highest value among all indicators, with a standard deviation of 0.681 indicating the most stable data. In contrast, Y5 has the lowest average of 3.568 and the largest standard deviation of 0.897, reflecting greater variability in responses. Overall, the negative skewness scores on most indicators indicate that respondent perceptions tend to be higher than the measured average values. In the context of research at State University Hospitals in Indonesia, these data indicate a strong tendency for patients to consider repeat visits, especially on indicators reflecting service satisfaction. This variability can also be the basis for improving hospital management strategies to improve 3.2. Outer Model Customer Satisfaction (M)shows that the indicators used, namely M1, M2, and M3, have high loading factor values, respectively 0.940; 0.873; and 0.949. All of these values are above the minimum threshold of 0.75, indicating that these indicators are valid and appropriate for measuring customer satisfaction at State University Hospitals (PTN) in Indonesia. In addition, the Variance Inflation Factor (VIF) values for each indicator are also within reasonable tolerance limits, namely 4.671 for M1, 2.222 for M2, and 4.975 for M3. This indicates that there is no multicollinearity problem, so that the analysis model can be relied on. These values reflect a high level of patient satisfaction with the services received, indicating that the hospital has been able to meet or even exceed patient expectations, both in terms of service quality, accessibility, and brand image. With this significant indicator performance, State Hospitals in Indonesia can use this data to design strategies to improve service quality, ensure high customer value, and strengthen patient intentions to return to their services in the future. This finding is relevant for policy making in improving the competitiveness of academic-based hospitals in Indonesia. Brand Image (X1) which is measured through four indicators, namely X1.1 to X1.4, meets the eligibility criteria based on a factor loading value greater than 0.75. Indicator X1.1 has a value of 0.915 with a variance extracted (VE) value of 3.330, indicating that this indicator is very relevant in representing the Brand Image variable. Furthermore, indicator X1.2 records a factor loading value of 0.832 with a VE of 2.011, while X1.3 and X1.4 each have values of 0.868 and 0.882 with VEs of 2.582 and 2.608. All indicators are declared fit, because they meet the minimum factor loading standard (> 0.75), which indicates a strong relationship between the indicators and the variable construct. In the context of research at State University Hospitals (RSPTN) in Indonesia, this finding confirms that Brand Image is an important dimension that reflects public perception of the quality of service and reputation of the hospital. With high statistical values, it can be concluded that strengthening brand image is a strategic element for RSPTN to increase patient competitiveness and trust, especially in meeting expectations for professional and quality higher education-based health services. Accessibility (X2) in this study, which measures the accessibility of services at State University Hospitals in Indonesia, obtained results that all indicators have loading factor values above 0.75, indicating the feasibility of the construction in the model. Indicator X2.1 has a loading factor value of 0.859 with a t-value of 2.928, which indicates a significant contribution in describing accessibility. Furthermore, X2.2 has the highest value of 0.927 with a t-value of 7.137, indicating that this aspect has the greatest influence on the accessibility variable. Indicator X2.3 recorded a loading factor value of 0.884 with a t-value of 5.593, indicating strong relevance in the model. Meanwhile, X2.4 to X2.6 each have loading factor values of 0.853, 0.809, and 0.807 with t-values of 2.780, 2.620, and 2.168, respectively, all of which are within the feasible range. 8069 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate These findings indicate that State University Hospitals in Indonesia have met the criteria for good accessibility, including ease of access, affordable services, and responsiveness to patient needs. This is important in increasing patient satisfaction and supporting the role of hospitals as health facilities that support the tri dharma of higher education, especially in education and community services. Figure 2. Structural equation outer model intervening. Service Quality(X3) has a loading factor value greater than 0.75, indicating that each indicator in this variable has a strong contribution to the Service Quality construct (Figure 2). The loading factor value for each indicator ranges from 0.763 to 0.878, indicating that these items are highly relevant in describing service quality. In addition, the t-statistic value for all items is greater than 1.96, with the highest value reaching 5.307 on indicator X3.6, meaning that each indicator is statistically significant. In other words, these results indicate that the Service Quality variable has a significant influence in influencing the quality of service perceived by patients. Relating it to the research object, namely State University Hospitals in Indonesia, high service quality is very important to ensure patient satisfaction and improve the hospital's reputation. Hospitals as health service institutions that focus on education and research must ensure that all aspects of service, from patient admission to treatment, meet high quality standards. Therefore, each indicator in Service Quality must be considered seriously to improve the patient experience in the hospital (Table 3). 8070 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate Table 3. Outer loading model value. Variables Indicator items Accessibility (X2) Brand image (X1) Customer satisfaction(M) Customer value (Z) Revisit intention(Y) Service quality (X3) Decision VIF Value decision Customer satisfaction(M) M1 0.940 > 0.75 4,671 Worthy M2 0.873 > 0.75 2.222 Worthy M3 0.949 > 0.75 4.975 Worthy Brand image (X1) X1.1 0.915 > 0.75 3.330 Worthy X1.2 0.832 > 0.75 2.011 Worthy X1.3 0.868 > 0.75 2,582 Worthy X1.4 0.882 > 0.75 2,608 Worthy Accessibility (X2) X2.1 0.859 > 0.75 2,928 Worthy X2.2 0.927 > 0.75 7.137 Worthy X2.3 0.884 > 0.75 5,593 Worthy X2.4 0.853 > 0.75 2.780 Worthy X2.5 0.809 > 0.75 2.620 Worthy X2.6 0.807 > 0.75 2.168 Worthy Service quality (X3) X3.1 0.821 > 0.75 3.079 Worthy X3.10 0.861 > 0.75 5.000 Worthy X3.2 0.797 > 0.75 3.559 Worthy X3.3 0.852 > 0.75 4.379 Worthy X3.4 0.847 > 0.75 3.032 Worthy X3.5 0.878 > 0.75 4.936 Worthy X3.6 0.815 > 0.75 5.307 Worthy X3.7 0.857 > 0.75 3.342 Worthy X3.8 0.806 > 0.75 2.652 Worthy X3.9 0.763 > 0.75 2.396 Worthy Revisit intention(Y) Y1 0.881 > 0.75 3.520 Worthy Y2 0.795 > 0.75 1.987 Worthy Y3 0.946 > 0.75 6.086 Worthy Y4 0.849 > 0.75 2,610 Worthy Y5 0.799 > 0.75 2.149 Worthy Customer value (Z) Z1 0.895 > 0.75 3.516 Worthy Z2 0.799 > 0.75 1.887 Worthy Z3 0.946 > 0.75 5.247 Worthy Z4 0.870 > 0.75 2.609 Worthy 8071 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate Loading factor value that shows the quality of indicator measurement in measuring the variable Intention to Revisit (desire to revisit). Each item has a loading factor value greater than 0.75, namely Y1 (0.881), Y2 (0.795), Y3 (0.946), Y4 (0.849), and Y5 (0.799), which indicates that each indicator is very good at reflecting the intended construct. The t-statistic values listed in the next column, namely 3,520, 1,987, 6,086, 2,610, and 2,149, are all greater than the critical value of 1.96, indicating that the results of this measurement are statistically significant. Thus, the five indicators are worthy of use in the research model. In the context of the research object, namely State University Hospitals in Indonesia, these results reflect that the level of patient or visitor desire to return to the hospital is greatly influenced by factors measured through these indicators, such as satisfaction with services, facilities, and quality of care provided. This shows the importance of improving the quality of health services in state university hospitals to maintain or increase patient loyalty. Customer Value (Z) shows very good results. The Z1 value is 0.895, Z2 is 0.799, Z3 is 0.946, and Z4 is 0.870, all exceeding the threshold of 0.75, indicating that each indicator has a quality that is worthy of being used as a reference. Other statistical values such as t-statistics also show significant numbers, with Z1 reaching 3,516, Z2 reaching 1,887, Z3 reaching 5,247, and Z4 reaching 2,609, all of which are greater than 1.96, indicating that these values do not occur by chance and have a significant influence. In the context of the research object, namely State University Hospitals in Indonesia, these results illustrate that the dimensions measured related to Customer Value have good and decent quality. State university hospitals have high scores in terms of patient service and satisfaction, which can indicate that they have met or even exceeded customer expectations. Thus, these hospitals have a strong foundation in providing quality health services, oriented to patient needs and satisfaction, which are important indicators in improving the image and sustainability of hospital services (Table 3). Table 4. Outer loading average variance extracted model value. Variables Cronbach's Alpha rho_A Composite reliability Average variance extracted (AVE) Decision Accessibility (X2) 0.928 0.937 0.943 0.735 Fulfilled Brand image (X1) 0.897 0.901 0.929 0.765 Fulfilled Customer satisfaction(M) 0.911 0.916 0.944 0.849 Fulfilled Customer value (Z) 0.901 0.911 0.931 0.773 Fulfilled Revisit intention(Y) 0.908 0.918 0.932 0.733 Fulfilled Service quality (X3) 0.950 0.952 0.957 0.690 Fulfilled Average Variance Extracted (AVE) shows that each construct in this study meets the requirements for convergent validity, with an AVE value greater than 0.5. Organizational Citizenship Behavior (X2) has an AVE value of 0.655, indicating that this construct has good validity. Likewise, Performance of Legislative Functions (Y) obtained an AVE value of 0.710, indicating a very good level of validity. Meanwhile, Professional Commitment (X1) has an AVE value of 0.579, which also meets the expected convergent validity standards. Finally, Understanding of Legislative Functions (Z) has an AVE value of 0.604, which is also more than 0.5, so it can be considered statistically valid. In the context of research at State University Hospitals in Indonesia, these results indicate that the variables studied, such as professional commitment, organizational citizenship behavior, and understanding of legislative functions, play an important role in influencing hospital legislative performance. Good legislative performance can improve the effectiveness of hospital management, thereby making a major contribution to more optimal health services in the higher health education environment in Indonesia (Table 4). 8072 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate Table 5. Outer loading discriminant variable. Variables Accessibility (X2) Brand image (X1) Customer satisfaction(M) Customer value (Z) Revisit intention(Y) Service quality (X3) Decision Accessibility (X2) 0.857 Fulfilled Brand image (X1) 0.905 0.875 Fulfilled Customer satisfaction(M) 0.856 0.757 0.922 Fulfilled Customer value (Z) 0.910 0.805 0.922 0.879 Fulfilled Revisit intention(Y) 0.917 0.805 0.922 0.989 0.856 Fulfilled Service quality (X3) 0.885 0.854 0.845 0.859 0.867 0.830 Fulfilled Outer loading discriminant between variables, which is the basis of convergent validity in the SEM- PLS model. The outer loading value of each indicator for its respective variables ranges from 0.830 to 0.989, all of which meet the criteria for discriminant validity (value > 0.70). The highest value is achieved by the Revisit Intention (Y) indicator against the Customer Value (Z) variable with a value of 0.989, indicating a significant contribution of the variable to the prediction of revisit intention. Conversely, the lowest value for the Service Quality (X3) variable is 0.830, but is still within the standard limit. Each indicator has a different value between variables which proves the diversity of its role. The overall validity decision is "Fulfilled" (Table 5). Discriminant validity using the Heterotrait-Monotrait (HTMT) ratio. All HTMT values are below the critical limit of 0.90, indicating no multicollinearity problems between latent variables. For example, the relationship between Professional Commitment (X1) and Performance of Legislative Functions (Y) is only 0.500, indicating a moderate relationship. In contrast, the highest HTMT value is 0.538 between Understanding of Legislative Functions (Z) and Organizational Citizenship Behavior (X2), but it is still within safe limits. Based on this analysis, the discriminant validity for HTMT has been met. Table 6. Value eligibility Heterotrait-Monotrait ratio. Organizational citizenship behavior (X2) Performance of legislative functions (Y) Professional commitment (X1) Average variance extracted (AVE) Standard Decision Organizational citizenship behavior (X2) 0.655 >0.5 Fulfilled Performance of legislative functions (Y) 0.372 0.710 >0.5 Fulfilled Professional commitment (X1) 0.167 0.500 0.579 >0.5 Fulfilled Understanding of legislative functions (Z) 0.538 0.115 0.195 0.604 >0.5 Fulfilled The Average Variance Extracted (AVE) value is another indicator in the evaluation of convergent validity. All variables show AVE above the 0.50 limit. The Performance of Legislative Functions (Y) variable has the highest AVE value, which is 0.710, indicating that this variable is able to explain more than 71% of the variance of its indicator. The variable with the lowest AVE value is Professional 8073 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate Commitment (X1) at 0.579, which still meets the minimum requirement of 0.50. The overall AVE eligibility decision is "Fulfilled" (Table 6). Figure 3. Structural equation inner model. 3.3. Inner Model The hypothetical path shown in the table shows a significant relationship between the various variables in the study focusing on "State University Hospitals in Indonesia (Figure 5)." There is a very strong positive relationship between accessibility (X2) and customer value (Z) with a value of 0.826, which has a T-Statistics of 6.993 and a P-Value of 0.000, which means the hypothesis is accepted. This shows that hospital accessibility greatly influences the perception of value felt by patients. Meanwhile, brand image (X1) does not show a significant effect on customer value (Z), with a T-Statistics of 1.783 and a P-Value of 0.075, which means this hypothesis is rejected. In addition, customer satisfaction (M) shows a very strong influence on revisit intention (Y), with a coefficient value of 0.922 and a very high T-Statistics, 53.495, indicating that the more satisfied patients are, the more likely they are to return. The customer value variable (Z) also showed a significant positive relationship with customer satisfaction (M) and revisit intention (Y), with a very small P-Value, confirming that patient experience of service value plays an important role in their satisfaction and decision to return to the hospital. This study confirms the importance of improving accessibility, service quality, and hospital image to improve patient satisfaction and loyalty. 8074 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate Table 7. Value of mediation and partial path hypothesis. Hypothesis path Original sample (O) Sample mean (M) Standard deviation (STDEV) T statistics (|O/STDEV|) P values Decision Accessibility (X2) -> Customer value (Z) 0.826 0.823 0.118 6,993 0.000 Hypothesis accepted Brand image (X1) -> Customer value (Z) -0.192 -0.189 0.108 1.783 0.075 Hypothesis rejected Customer satisfaction(M) -> Revisit intention(Y) 0.922 0.922 0.017 53.495 0.000 Hypothesis accepted Customer value (Z) -> Customer satisfaction(M) 0.922 0.921 0.017 55.068 0.000 Hypothesis accepted Service quality (X3) -> Customer value (Z) 0.291 0.292 0.116 2.521 0.012 Hypothesis accepted Accessibility (X2) -> Customer value (Z) -> Customer satisfaction (M) 0.762 0.758 0.110 6.901 0.000 Hypothesis accepted Brand image (X1) -> Customer value (Z) -> Customer satisfaction (M) -0.177 -0.174 0.099 1.781 0.075 Hypothesis rejected Service Quality (X3) -> Customer Value (Z) -> Customer satisfaction (M) 0.269 0.269 0.106 2.523 0.012 Hypothesis accepted Accessibility (X2) -> Customer value (Z) -> Customer satisfaction (M) -> Revisit intention (Y) 0.702 0.700 0.105 6.665 0.000 Hypothesis accepted Brand image (X1) -> Customer value (Z) -> Customer satisfaction (M) -> Revisit intention (Y) -0.163 -0.160 0.092 1.776 0.076 Hypothesis rejected Customer value (Z) -> Customer satisfaction (M) -> Revisit intention (Y) 0.850 0.850 0.031 27.619 0.000 Hypothesis accepted Service quality (X3) -> Customer value (Z) -> Customer satisfaction (M) -> Revisit intention (Y) 0.248 0.248 0.098 2.526 0.012 Hypothesis accepted The Accessibility variable (X2) shows an F Square value of 0.600, which means its contribution to the model is high. Meanwhile, Brand Image (X1) has an F Square of 0.041, which indicates a small contribution to the model. Customer Satisfaction (M) shows a very high F Square value, which is 5.666, with an R Square of 0.850, which indicates that customer satisfaction contributes greatly to the success of this model. The same thing also happens to Customer Value (Z), which has an F Square of 5.657 and an R Square of 0.848, which indicates a high contribution to the model. On the other hand, Service Quality (X3) shows an F Square of 0.112, which indicates a small contribution to the model. As for Revisit Intention (Y), which has an R Square value of 0.850, indicating that the intention to revisit the hospital contributes greatly to this research model. Connecting these results to the research object, namely State University Hospitals in Indonesia, it can be concluded that factors such as customer 8075 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate satisfaction, perceived value by patients, and accessibility have a very large influence on patient intention to return to the hospital. Meanwhile, service quality and brand image factors, although important, have a smaller contribution in shaping the overall patient experience (Table 8). Table 8. Path value decision f² and R². Variables F square R square Decision Accessibility (X2) 0.600 High contribution Brand image (X1) 0.041 Contribute small Customer satisfaction(M) 5.666 0.850 High contribution Customer value (Z) 5.657 0.848 High contribution Service quality (X3) 0.112 Contribute small Revisit intention(Y) 0.850 High contribution There are two statistics used to measure model fit, namely SRMR (Standardized Root Mean Square Residual) and d_ULS (distance-based unweighted least squares). For SRMR, the value in the estimated model is 0.080, while the value in the saturated model is 0.064. The lower SRMR indicates that the estimated model has a good fit with the observed data, although slightly larger than the value in the saturated model, but still within acceptable limits. For d_ULS, the value in the estimated model is 3.368, while in the saturated model it is 2.192. This difference in value indicates a higher deviation in the estimated model, but it is still within acceptable limits because the d_ULS value remains close to the value in the saturated model. Overall, these two statistics indicate that the estimated model is acceptable. If we relate these results to the research object of "State University Hospitals in Indonesia," these results reflect that the model applied in the context of state university hospitals can be used as a valid basis in analyzing various factors that influence performance and services in these hospitals (Table 9). Table 9. Fit model eligibility. Saturated model Estimated model Contribution SRMR 0.064 0.080 Model accepted d_ULS 2.192 3.368 Model accepted 3.4. Discussion This findinghighlighted the importance of various dimensions of service quality, patient perceptions, and other supporting elements in influencing satisfaction, trust, and revisit intention in various contexts, from hospitals to the medical tourism sector. The importance of hospital reputation in strengthening brand image and revisit intention and quality perception alone is not enough without the mediation of value perception. Price fairness and perceived quality, these elements are significant to patient satisfaction and revisit intention, although there are differences in the role of service processes, so that comprehensive care on patient loyalty. EWOM and virtual social presence, show a shift to the digital era, where virtual factors become important antecedents in improving patient experience. Service quality elements, such as empathy and tangible, have a very large influence on satisfaction and revisit intention, although not all dimensions are significant. This study with the complexity of the relationship between service quality, perceived value, and institutional image in various geographic contexts and sectors. Although there is consistency in the importance of satisfaction as a mediator, the dynamics of antecedents vary depending on service and patient characteristics. (Sunarta et al., 2020), a good hospital reputation has a very significant influence on the hospital's brand image and the patient's intention to revisit in the near future. In other words, a strong reputation can strengthen the relationship between hospital brand equity and patient revisit intention. (Puspitasari et al., 2019), perceived quality does not directly affect tourists' satisfaction or revisit intention. However, perceived quality significantly affects revisit intention through perceived value.(Alam Wiguna et al., 2023), perceived medical quality, 8076 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate perceived service quality, and price fairness have a significant influence on consumer satisfaction. In addition, satisfaction and trust are proven to be significant mediators in influencing consumer intention to revisit a health clinic.(Putri et al., 2022), doctor services and nurse services have a significant positive influence on patient satisfaction.(Jung & Sung, 2018), significant differences between patients in general care units and comprehensive nursing care units in satisfaction with nursing services (t=14.73, p<.001), commitment to the hospital (t=7.52, p<.001), and revisit intention (t=6.01, p<.001)(Ongkaruna & Kristaung, 2023). Hypothesis path analysis showing significant relationships between variables, the right strategy for University Hospitals in Indonesia can be built by focusing on several important aspects that influence patient experience and revisit intentions. Accessibility (X2) has a very strong influence on customer value (Z), which means that increasing hospital accessibility will strengthen patient perceptions of the value of the services provided. Therefore, University Hospitals in Indonesia need to improve accessibility by expanding operating hours, increasing the number of transportation facilities, and improving the registration and information systems that make it easier for patients to access hospital services. This emphasis on ease of access can create a more positive experience for patients, increase perceived value perceptions, and encourage long-term patient loyalty. Furthermore, although brand image (X1) does not show a significant influence on customer value, the role of brand image is still important in creating a good first impression. University Hospitals need to focus on efforts to strengthen their reputation and brand image through clear information campaigns about superior services, quality of doctors, and medical facilities. This will improve the image of the hospital in the eyes of the public and strengthen positive perceptions, although its contribution to customer value is smaller compared to accessibility. In addition, good brand image management will also contribute to customer satisfaction and patient repeat visit intentions. Customer satisfaction (M) has been shown to have a significant effect on revisit intention (Y), with a very high coefficient value. Therefore, an effective strategy should include improving the overall quality of hospital services. Focusing on patient satisfaction can be done by improving communication between patients and medical staff, providing faster and more responsive services, and providing appropriate solutions to patient complaints. In addition, training for medical and non-medical staff to provide friendly and empathetic services can improve patient experience, which has an impact on their intention to return to the hospital. Customer value (Z) has a significant relationship with customer satisfaction (M) and revisit intention (Y). Therefore, improving patient experience related to service value is very important. University Hospital can implement a loyalty or reward program for patients who use services repeatedly, which can improve patient perceptions of the hospital's value. In addition, customer value enhancement strategies can be carried out by offering additional services that focus on patient needs, such as free follow-up consultations or special health service packages for patients with certain conditions. To complement this strategy, hospitals must continue to pay attention to overall service quality, especially in terms of medical and non-medical services, as well as the use of technology to facilitate patient interaction and access. With this strategy, University Hospitals in Indonesia can significantly increase patient satisfaction, loyalty, and revisit intentions, which in turn will improve the performance and success of hospitals in providing quality and affordable health services to the community. 4. Conclusion and Suggestions This study shows that accessibility (Accessibility, X2), service quality (Service Quality, X3), and customer value (CV, Z) have a significant relationship to customer satisfaction (Customer Satisfaction, M) and revisit intention (Revisit Intention, Y) in a University Hospital in Indonesia. The path Accessibility -> Customer Value (O=0.826, p=0.000) has the strongest influence, indicating that ease of access greatly determines the value perceived by customers. Likewise, service quality contributes significantly to customer value (O=0.291, p=0.012). However, the effect of brand image (Brand Image, X1) on customer value is not significant (O=-0.192, p=0.075), indicating that in the context of a teaching hospital, brand reputation is not a major factor compared to accessibility and service quality. The mediation relationship was also confirmed to be significant, such as the path Customer Value -> 8077 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8057-8080, 2024 DOI: 10.55214/25768484.v8i6.3746 © 2024 by the authors; licensee Learning Gate Customer Satisfaction -> Revisit Intention (O=0.850, p=0.000), which confirms the role of satisfaction as a link between value and revisit intention. This study shows that the management of university hospitals in Indonesia. Management needs to focus on improving accessibility and service quality to create optimal customer value. For example, improving the transportation system to the hospital or providing technology-based services can strengthen the perception of accessibility. In terms of service quality, improving the dimensions of empathy and reliability can encourage patient trust and loyalty. The finding of the low influence of brand image suggests that university hospitals need to prioritize direct patient experience over extensive brand image campaigns. By instilling a sense of satisfaction through direct experience, hospitals can increase patients' intention to return and recommend their services. Future studies on digital healthcare innovation or patient trust in educational institutions that manage hospitals. In addition, comparative studies between university hospitals in urban and rural areas can provide insights into differences in patient preferences based on geographic location. External factors, such as government policies related to referral systems and health insurance, can also be integrated to understand how these elements affect the relationship between variables. 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