Modeling the Impact of Service Quality Factors on Healthcare Recipients: A Path Analysis Approach

Modeling the Impact of Service Quality Factors on Healthcare Recipients: A Path Analysis Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Modeling the Impact of Service Quality Factors on Healthcare Recipients: A Path Analysis Approach Zafer Yıldız This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6652118/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aims to model and analyze the factors influencing healthcare service quality perceptions among patients using Path Analysis. Data were collected from 402 individuals receiving healthcare services in Sivas, Turkey, through a validated service quality scale developed by Varinli and Çakır (2004), which includes five dimensions: Perceived Quality, Attitude, Value, Satisfaction Level, and Behavioral Intention. Path Analysis results revealed two predictive models. The first model demonstrated that Satisfaction Level is primarily driven by Behavioral Intention, Value, and Attitude. The second model showed that Perceived Quality is significantly influenced by Value and Satisfaction Level. Furthermore, the analysis highlighted the mediating role of Satisfaction Level between Value and Perceived Quality, and the indirect influence of Behavioral Intention on Value through Attitude and Perceived Quality. Findings emphasize the multidimensional and interconnected nature of healthcare service quality perceptions, underscoring the need for holistic evaluation approaches. The results offer valuable insights for healthcare managers aiming to design more patient-centered and quality-focused service delivery strategies. Path Analysis Structural Equation Modeling Healthcare Service Quality Model Development Regression Analysis Figures Figure 1 Introduction The evaluation of service quality in healthcare institutions has become a critical research focus in recent decades, owing to its direct influence on patient satisfaction, health outcomes, organizational reputation, and financial performance (Parasuraman et al., 1988 ; Donabedian, 1988 ; Andaleeb, 2001 ). In an increasingly patient-centered healthcare environment, service quality not only determines immediate patient satisfaction but also affects long-term loyalty, treatment adherence, and overall health system sustainability (Berry & Bendapudi, 2007). Service quality in healthcare is inherently multidimensional, integrating tangible elements such as facilities, medical technology, and environmental hygiene with intangible aspects including provider empathy, interpersonal communication, trustworthiness, and ethical standards (Dagger, Sweeney, & Johnson, 2007 ). The intangibility, heterogeneity, and high involvement characteristic of healthcare services differentiate them markedly from conventional service industries (Zeithaml, Parasuraman, & Berry, 1990). Consequently, assessing healthcare service quality necessitates models that capture both the functional and emotional dimensions of patient experience (Dagger et al., 2007 ; Otani & Kurz, 2004). Traditional conceptualizations of service quality, notably the SERVQUAL framework, have been widely adapted to healthcare to measure dimensions such as reliability, responsiveness, assurance, empathy, and tangibles (Zeithaml, Berry, & Parasuraman, 1996 ). Nevertheless, the unique emotional, psychological, and existential vulnerabilities associated with healthcare consumption demand context-specific evaluation models that transcend conventional service quality metrics (Clemes, Gan, & Kao, 2008 ; Wu, 2011 ). In healthcare settings, the quality of communication and relational trust can significantly outweigh technical aspects in shaping patient satisfaction and perceived value (Andaleeb, 2001 ). Emerging research underscores that patient perceptions of service quality are influenced not merely by isolated experiences but by complex interrelationships among multiple psychological constructs, including perceived quality, perceived value, satisfaction, attitude, and behavioral intention (Lee et al., 2012; Wu, 2011 ; Chaniotakis & Lymperopoulos, 2009). Recognizing and accurately modeling these interdependencies is crucial for building comprehensive frameworks that truly reflect patient-centered service delivery. Path Analysis, a specialized form of Structural Equation Modeling (SEM), offers a robust analytical strategy to simultaneously examine the direct and indirect effects among these multiple variables (Kline, 2015; Byrne, 2013). Unlike traditional regression methods that tend to treat predictors independently, Path Analysis allows researchers to understand the mediating and moderating roles between constructs, revealing latent causal structures that would otherwise remain obscured (Hair et al., 2010 ). Particularly in healthcare quality research, where variables such as satisfaction and perceived value often exert both direct and indirect influences on outcomes like loyalty and behavioral intention, Path Analysis provides superior explanatory power. Despite extensive scholarly attention to healthcare service quality, relatively few studies have systematically modeled the mediating roles of key constructs such as satisfaction and value within integrated analytical frameworks using Path Analysis. Prior research has often relied on bivariate correlations or segmented regression models, which are insufficient to capture the dynamic complexity of patient experiences (Andronikidis et al., 2009; Clemes et al., 2001). Furthermore, many studies have focused predominantly on Western healthcare systems, leaving a research gap in emerging market contexts where cultural, systemic, and infrastructural differences may profoundly shape service quality perceptions (Mosadeghrad, 2014). Addressing these gaps, the present study aims to model and empirically test the structural relationships among perceived quality, perceived value, satisfaction, attitude, and behavioral intention based on data collected from healthcare service recipients. Specifically, the study applies Path Analysis to explore both the direct effects and the mediating mechanisms underlying patient evaluations of healthcare service quality. By developing predictive models, this study seeks to identify the key leverage points for enhancing patient satisfaction and loyalty, providing valuable insights for healthcare managers aiming to design and deliver services that meet both clinical and experiential standards. The findings are expected to contribute to both theoretical advancements in service quality research and practical improvements in healthcare service delivery, particularly in patient-centered care models emphasizing holistic patient experience and engagement. Methodology 2.1 Research Design and Sampling The population of this study consists of individuals receiving healthcare services from medical institutions located in Sivas province, Turkey. A non-probability convenience sampling method was employed to collect data. The use of convenience sampling was justified as no differentiation was made among patients or healthcare institutions based on service types or characteristics. For the scale employed in this study, ethical approval was obtained from the "Sivas Cumhuriyet University Social Sciences Scientific Research Proposal Ethics Committee" (Approval Number: E-99711239-050.01-438071, Date: June 11, 2024). 2.2 Instrumentation The data collection instrument used in this study was a healthcare service quality scale developed by Varinli and Çakır (2004). The scale is specifically designed for healthcare settings and consists of five distinct factors: Perceived Quality, Attitude, Value, Satisfaction Level, and Behavioral Intention. Each of these factors captures different dimensions of patient perceptions regarding the quality of healthcare services received, offering a comprehensive framework for evaluating both the technical and interpersonal aspects of service delivery. 2.3 Data Collection Procedure Data were gathered by administering structured questionnaires to individuals receiving services from healthcare institutions in Sivas. Since convenience sampling was utilized, participants were allowed to fill out the survey forms voluntarily, without any external influence or guidance. A total of 402 valid responses were collected for analysis. 2.4 Data Analysis Path Analysis was employed to examine the structural relationships among the factors identified in the service quality scale. Path Analysis is a statistical technique used to estimate the direct and indirect effects among quantitative variables and to determine the extent to which independent variables influence dependent variables through both direct and mediated pathways (Karagöz, 2019, p. 823). Using Path Analysis, direct and indirect effects among the factors developed by Varinli and Çakır were depicted through a path diagram. The models derived from the analysis were presented with their respective coefficients, providing empirical evidence for the structural relationships among the factors. The models developed allowed for independent measurement of each dependent variable in relation to other factors within the scale. Typically, field studies are conducted for each factor individually; however, Path Analysis enables the simultaneous evaluation of all relationships in a single analysis, thus minimizing potential error rates. The primary advantage of Path Analysis lies in its ability to explore the relational dimensions among multiple factors concurrently, providing a more holistic and accurate understanding of the underlying structures. Results 3.1 Descriptive Statistics Table 1 presents the demographic characteristics of the participants. A total of 402 individuals participated in the study, of whom 186 (46.3%) were female and 216 (53.7%) were male. Regarding marital status, 240 participants (59.7%) were married, while 162 (40.3%) were single. In terms of age distribution, 50 participants (12.4%) were aged 20 years or younger, 127 participants (31.6%) were aged between 21 and 30 years, 84 participants (20.9%) were aged between 31 and 40 years, 53 participants (13.2%) were aged between 41 and 50 years, and 88 participants (21.9%) were aged 51 years and older. Educational background revealed that 65 participants (16.2%) completed primary education, 121 (30.1%) graduated from high school, 46 (11.4%) held an associate degree, 130 (32.3%) earned a bachelor’s degree, and 40 (10.0%) completed postgraduate studies. Occupationally, 56 participants (13.9%) were workers, 84 (20.9%) were public employees, 78 (19.4%) were housewives, 151 (37.6%) were self-employed, and 33 (8.2%) were retired. Table 1 Demographic Characteristics of Participants Variable Category Frequency Percentage (%) Gender Female 186 46.3 Male 216 53.7 Age ≤ 20 years 50 12.4 21–30 years 127 31.6 31–40 years 84 20.9 41–50 years 53 13.2 ≥ 51 years 88 21.9 Marital Status Married 240 59.7 Single 162 40.3 Education Level Primary Education 65 16.2 High School 121 30.1 Associate Degree 46 11.4 Bachelor’s Degree 130 32.3 Postgraduate Degree 40 10.0 Occupation Worker 56 13.9 Public Employee 84 20.9 Housewife 78 19.4 Self-employed 151 37.6 Retired 33 8.2 3.2 Reliability Analysis To ensure the validity of the measurement instrument, internal consistency reliability was assessed using Cronbach’s Alpha coefficients for each of the five factors: Value, Satisfaction Level, Behavioral Intention, Attitude, and Perceived Quality. The results are presented in Table 2 . The reliability analysis revealed that all factors demonstrated satisfactory to excellent levels of internal consistency. According to the guidelines established by Nunnally and Bernstein (1994), a Cronbach’s Alpha value above 0.70 is considered acceptable for exploratory research, while values above 0.80 are indicative of high reliability in applied studies. In this context, the findings confirm the robustness of the measurement scale employed in this study. Specifically, the Value factor exhibited the highest internal consistency with a Cronbach’s Alpha of 0.953 , indicating a very strong correlation among the items designed to measure perceived value. This result suggests that participants interpreted and responded to the items related to value in a highly consistent manner. Similarly, the Satisfaction Level factor demonstrated excellent reliability, with a Cronbach’s Alpha of 0.872 . This indicates that the items measuring satisfaction were highly coherent and that participants’ responses were stable across different service quality dimensions. The Attitude factor also achieved a high reliability score of 0.893 , suggesting that participants consistently evaluated their attitudes towards the healthcare services they received. This reliability is crucial for accurately capturing attitudinal tendencies that influence service perceptions. The Perceived Quality factor reported a Cronbach’s Alpha of 0.789 , reflecting an acceptable level of internal consistency. Although slightly lower than the other factors, this score still falls well within the range deemed reliable for empirical research. Lastly, the Behavioral Intention factor yielded a Cronbach’s Alpha of 0.722 . While it represents the lowest reliability among the five factors, it still exceeds the minimum acceptable threshold, supporting the scale’s ability to reliably measure patients’ behavioral tendencies regarding future healthcare service usage. Overall, the high reliability scores across all factors provide strong evidence of the measurement model’s consistency and precision. This robustness enhances the credibility of the subsequent Path Analysis, ensuring that the relationships among service quality dimensions are examined based on stable and coherent constructs. Table 2 Reliability Coefficients (Cronbach’s Alpha) Factor Cronbach’s Alpha Value 0.953 Satisfaction Level 0.872 Behavioral Intention 0.722 Attitude 0.893 Perceived Quality 0.789 3.3 Path Analysis Results To investigate the structural relationships among the service quality factors, Path Analysis was conducted. The goodness-of-fit indices indicated an excellent model fit, with CMIN/df = 1.778, AGFI = 0.974, GFI = 0.996, NFI = 0.996, CFI = 0.999, TLI = 0.996, and RMSEA = 0.044. These results confirm that the hypothesized model adequately represents the observed data, satisfying the recommended thresholds for structural model evaluation (Hu & Bentler, 1999). The results indicate that Satisfaction Level and Perceived Quality serve as key endogenous variables within the model. Satisfaction Level is predicted by Behavioral Intention, Value, and Attitude, while Perceived Quality is predicted by Value and Satisfaction Level. More specifically, the strongest direct effect observed was between Satisfaction Level and Behavioral Intention (β = 0.938, p < 0.001), highlighting the critical role of patient satisfaction in shaping future behavioral tendencies. Similarly, Perceived Quality had a substantial direct effect on Satisfaction Level (β = 0.831, p < 0.001), suggesting that patients’ perceptions of service quality heavily influence their overall satisfaction. The relatively lower and insignificant direct effect of Behavioral Intention on Value (β = 0.028, p > 0.05) underscores the need to consider mediating variables when analyzing indirect relationships. This finding aligns with prior research emphasizing the complex and mediated pathways in service quality models (Dagger et al., 2007 ; Clemes et al., 2008 ). Overall, the Path Analysis results confirm the multidimensional and interconnected nature of service quality perceptions, supporting the development of more sophisticated predictive models in healthcare service evaluation. 3.4 Regression Weights of the Model The standardized regression coefficients derived from the Path Analysis are summarized in Table 3 . These coefficients provide critical insights into the strength and direction of the hypothesized relationships among the key constructs of the service quality model. Each estimate reflects the direct effect of an independent variable on its corresponding dependent variable, after controlling for the influence of other variables in the model. The significance levels, standard errors, and critical ratios further validate the robustness and statistical relevance of these paths, offering empirical support for the proposed structural framework. Table 3 Regression Weights of the Path Analysis Model Path Estimate S.E. C.R. p-value Value ← Perceived Quality 0.300 0.058 5.131 *** Attitude ← Value 0.361 0.043 8.406 *** Perceived Quality ← Attitude 0.336 0.051 6.590 *** Satisfaction Level ← Perceived Quality 0.831 0.052 15.894 *** Attitude ← Satisfaction Level 0.474 0.046 10.380 *** Value ← Behavioral Intention 0.028 0.026 1.042 0.297 Behavioral Intention ← Satisfaction Level 0.938 0.049 19.236 *** Value ← Satisfaction Level 0.511 0.051 10.037 *** Attitude ← Behavioral Intention 0.100 0.023 4.269 *** (Note: *** indicates p < 0.001.) 3.5 Error Variances of the Dependent Variables In addition to the estimation of structural relationships among variables, the residual variances (error terms) associated with the endogenous constructs were examined to assess the unexplained variability within the model. Table 4 presents the estimated error variances for Perceived Quality and Satisfaction Level. The error variance for Perceived Quality was found to be 0.24 (p < 0.001), indicating that 76% of the variability in Perceived Quality is explained by the predictors included in the model. Similarly, the error variance for Satisfaction Level was estimated at 0.26 (p < 0.001), suggesting that 74% of the variance in Satisfaction Level is accounted for by its respective predictors. The critical ratios (C.R.) for both error variances exceed the threshold of 1.96, and the associated p-values are statistically significant (p < 0.001), confirming that the unexplained portions of variance are meaningful and not due to random sampling error (Byrne, 2013). These relatively low error variances support the robustness of the structural model, demonstrating that the proposed factors—Value, Attitude, and Behavioral Intention—collectively offer a strong explanatory framework for understanding patient satisfaction and perceived service quality. Moreover, the small magnitude of error terms indicates high predictive accuracy, thereby enhancing the overall validity of the model. Thus, the Path Analysis not only identifies the significant predictors of key outcomes but also achieves high explanatory power, validating the appropriateness of the hypothesized model for evaluating healthcare service quality perceptions. Table 4 Estimated Error Variances Dependent Variable Estimate S.E. C.R. p-value Perceived Quality 0.24 0.030 8.036 *** Satisfaction Level 0.26 0.021 12.658 *** 3.6 Model Construction and Interpretation Based on the Path Analysis results, two multiple regression models were derived to elucidate the direct and indirect effects of key service quality factors. First Model: Predicting Satisfaction Level In the first model, Satisfaction Level was identified as the dependent variable, predicted by three independent variables: Behavioral Intention , Value , and Attitude . The resulting multiple regression equation is: Y (Satisfaction Level) = 𝛽0 (Error Term) + 𝛽1𝑋( Behavioral Intention) + 𝛽2𝑋( Value) + 𝛽3𝑋 (Attitude) Y (Satisfaction Level) = 0,26 + 0,938* 𝑋 (Behavioral Intention) + 0,511𝑋 (Value) + 0,474x (Attitude) The regression coefficients suggest the following: Behavioral Intention (β = 0.938) exerts the strongest positive influence on Satisfaction Level, highlighting the critical role of patients' future behavioral tendencies in shaping their satisfaction. Value (β = 0.511) has a substantial but slightly lower effect, indicating the importance of patients' perceived trade-off between cost and benefit. Attitude (β = 0.474) also positively impacts Satisfaction Level, albeit to a lesser extent compared to Behavioral Intention and Value. The relatively small error variance (β₀ = 0.26) indicates a good model fit. Second Model: Predicting Perceived Quality In the second model, Perceived Quality serves as the dependent variable, predicted by Value and Satisfaction Level . The multiple regression equation is: Y (Perceived Quality) = 𝛽0( Error Term) + 𝛽1𝑋( Value) + 𝛽2𝑋 (Satisfaction Level) Y (Perceived Quality) = 0,24 + 0,300𝑋( Value) + 0,831𝑋 (Satisfaction Level) Key findings include: Satisfaction Level (β = 0.831) emerges as the strongest predictor of Perceived Quality, emphasizing the pivotal role of overall satisfaction in shaping patients' perceptions of service quality. Value (β = 0.300) also significantly affects Perceived Quality, albeit to a lesser extent. The small error variance (β₀ = 0.24) further supports the model's robustness. Interrelationship Between the Two Models An integrated view of the models reveals the central role of Satisfaction Level: In the first model, Satisfaction Level is an outcome variable predicted by Behavioral Intention, Value, and Attitude. In the second model, Satisfaction Level acts as a predictor for Perceived Quality. Thus, Satisfaction Level operates both as an outcome and a mediator, bridging patients' behavioral intentions and their perceptions of service quality. Similarly, Value serves as an essential independent variable in both models: In the first model, Value directly influences Satisfaction Level. In the second model, Value directly predicts Perceived Quality. This dual role underscores the importance of patients' value perceptions in influencing both satisfaction and service quality assessments. Factorial Relationships and Mediating Effects The analysis of direct and indirect effects among factors provides further insights: In the first instance, Perceived Quality directly influences Value with a coefficient of β = 0.30. When Satisfaction Level is introduced as a mediator, the total effect increases to β = 1.34, indicating that enhancing patient satisfaction substantially amplifies the perceived value of healthcare services. This finding suggests that healthcare providers aiming to improve perceived value must prioritize strategies that enhance patient satisfaction. In the second instance, Behavioral Intention exhibits an insignificant direct effect on Value (β = 0.03). However, when Attitude and Perceived Quality are introduced as mediators, the total effect becomes significant at β = 0.74. This highlights the critical role of intermediary factors in strengthening the link between patients' future behavioral intentions and their perceived value of healthcare services. Overall, the findings demonstrate the complex interplay between direct and mediated effects in shaping healthcare service perceptions, offering important implications for healthcare managers aiming to optimize patient satisfaction and loyalty through strategic service quality improvements. Discussion 5.1 Interpretation of Findings The findings of this study provide critical insights into the determinants of healthcare service quality perceptions among patients. The Path Analysis revealed that Satisfaction Level acts as a central mediator between patients' Behavioral Intention, Value, and Attitude, while Perceived Quality is primarily influenced by Satisfaction Level and Value. Notably, the strong predictive relationship between Behavioral Intention and Satisfaction Level (β = 0.938, p < 0.001) highlights the importance of patients' future behavioral tendencies in shaping overall satisfaction with healthcare services. Similarly, Satisfaction Level emerged as the most significant predictor of Perceived Quality (β = 0.831, p < 0.001), emphasizing the pivotal role of patient-centered experiences in forming quality judgments. 5.2 Comparison with Previous Literature These results align with earlier research suggesting that patient satisfaction is a dominant factor influencing both behavioral outcomes and quality perceptions in healthcare contexts (Andaleeb, 2001 ; Clemes et al., 2008 ). The mediating role of Satisfaction Level resonates with the findings of Dagger, Sweeney, and Johnson ( 2007 ), who argue that satisfaction bridges cognitive evaluations of service attributes and future behavioral intentions. Furthermore, the relatively weak direct effect of Behavioral Intention on Value reinforces the argument that mediating constructs are essential in healthcare service quality models (Wu, 2011 ; Parasuraman et al., 1988 ). This supports the notion that patients’ evaluations of value and quality are shaped not only by direct experiences but also by intermediate psychological processes. 5.3 Theoretical and Practical Implications Theoretically, this study advances the understanding of healthcare service quality by empirically validating the interconnectedness among Value, Satisfaction Level, Attitude, and Behavioral Intention within a comprehensive Path Model. It underscores the complexity of healthcare service evaluations, where patient satisfaction acts both as an outcome and as a driver of perceived quality. From a practical standpoint, the results suggest that healthcare managers should prioritize strategies aimed at enhancing patient satisfaction to indirectly strengthen quality perceptions and foster positive behavioral intentions. Investments in empathic communication, attitudinal training, and value-driven care delivery are likely to yield substantial returns in patient loyalty and organizational reputation (Zeithaml et al., 1996 ; Donabedian, 1988 ). 5.4 Limitations and Future Research Directions Despite its contributions, this study has several limitations. First, the use of a non-probabilistic convenience sampling method in a single geographical region (Sivas, Turkey) may limit the generalizability of the findings. Future research could extend the model across diverse healthcare settings and cultural contexts to validate its robustness. Second, the cross-sectional design restricts causal inferences. Longitudinal studies are recommended to explore how satisfaction and quality perceptions evolve over time. Finally, while the Path Model captures key relationships among constructs, incorporating additional variables such as trust, empathy, or service recovery efforts could further enrich the understanding of healthcare service quality dynamics (Dagger et al., 2007 ; Wu, 2011 ). Conclusion The provision of high-quality healthcare services to patients during their treatment process is a The provision of high-quality healthcare services during the treatment process is a fundamental pillar of the social welfare state model. In Turkey, both public and private healthcare institutions play a pivotal role in delivering healthcare services, and national health policies are increasingly focused on enhancing service quality to ensure citizen satisfaction and well-being. In this context, a survey-based evaluation was conducted to assess the quality of healthcare services from the perspective of service recipients. The instrument utilized in this study comprised 41 items categorized into five distinct factors: Perceived Quality, Attitude, Value, Satisfaction Level, and Behavioral Intention. Each factor was designed to capture a different dimension of patient experiences. Perceived Quality reflected individuals' assessments of the service quality based on their cumulative healthcare experiences. Attitude measured healthcare personnel's approach towards patients, while Value assessed the extent to which patients felt respected and valued by healthcare providers. Satisfaction Level gauged the overall contentment with the healthcare received, and Behavioral Intention evaluated perceptions of the healthcare staff’s willingness and commitment to providing quality service. The demographic characteristics of the participants—including gender, age, marital status, education, and occupation—revealed a relatively homogeneous distribution, supporting the generalizability of the findings within the study context. In line with the study’s objectives, Path Analysis was employed to examine the interrelationships among the service quality factors. The analysis yielded two distinct multiple regression models. The first model was developed to predict patients’ Satisfaction Level , identifying Behavioral Intention , Value , and Attitude as significant predictors. The second model was constructed to predict Perceived Quality , with Value and Satisfaction Level emerging as key explanatory variables. Importantly, the findings highlight that patients’ evaluations of healthcare services cannot be accurately understood by examining isolated service components. Instead, mediating variables —such as Satisfaction Level—play a crucial role in shaping perceptions. The results show that the Satisfaction Level not only acts as an outcome in the first model but also becomes a significant predictor of Perceived Quality in the second model. Similarly, the Value factor serves as a bridge connecting multiple aspects of patient experiences across the two models. The study demonstrates that assessing healthcare service quality requires a multifactorial perspective, wherein both direct and indirect effects among key variables are considered. The Path Analysis approach enabled the identification of causal pathways between constructs, minimizing estimation errors and providing a robust framework for evaluating healthcare service perceptions. In conclusion, this research shows that by applying Path Analysis to the developed service quality scale, two strong predictive models can be derived. These models offer comprehensive insights into how patients perceive healthcare services, highlighting the interconnected roles of satisfaction, perceived value, attitude, and behavioral intention. The findings underscore the necessity of holistic evaluation approaches in healthcare service research, which can ultimately guide policymakers and healthcare managers in designing more effective, patient-centered service delivery strategies. Policy Implications Building on the findings of this study, several policy recommendations can be proposed to enhance the quality of healthcare services and patient satisfaction: Emphasize Satisfaction-Driven Service Improvements Healthcare organizations should prioritize strategies that directly enhance patient satisfaction, as satisfaction serves both as an outcome and a key driver of perceived service quality. Regular assessments and targeted interventions focusing on patient experiences can foster higher loyalty and positive behavioral intentions (Dagger, Sweeney, & Johnson, 2007 ). Integrate Value-Based Care Principles Institutions should embed value-based care approaches that ensure patients feel respected, valued, and engaged throughout the healthcare process. Strengthening the perception of value can significantly improve both satisfaction levels and quality perceptions (Clemes, Gan, & Kao, 2008 ). Develop Comprehensive Service Quality Monitoring Systems Utilizing Path Analysis models can help healthcare managers continuously monitor and diagnose service quality dimensions. Dynamic modeling tools can reveal causal pathways and highlight critical leverage points for quality improvement initiatives (Kline, 2015; Byrne, 2013). Train Healthcare Staff on Attitudinal and Behavioral Competencies Training programs focusing on empathy, communication, and patient-centered care should be systematically implemented. Healthcare personnel's attitudes and behavioral intentions have profound impacts on patient satisfaction and perceptions of care quality (Andaleeb, 2001 ). Foster Holistic and Multidimensional Evaluations Policymakers should encourage holistic evaluations of healthcare service quality, considering both direct and indirect effects among key factors. This approach can lead to more robust healthcare policies that address complex patient needs beyond traditional service metrics (Donabedian, 1988 ; Wu, 2011 ). These recommendations underscore the necessity for healthcare systems to adopt an integrated and patient-centric approach in service quality management. Implementing such strategies can contribute to building more resilient, equitable, and high-performing healthcare institutions. Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and approved by the Sivas Cumhuriyet University Social Sciences Scientific Research Proposal Ethics Committee (approval number: E-99711239-050.01-438071, Date: June 11, 2024). Consent for publication Informed consent was obtained from all subjects involved in the study. Availability of data and materials The data supporting this study's findings are available on a reasonable request from the corresponding author. Competing interests The authors declare no conflicts of interest. Funding This research received no external funding. Authors' contributions Conceptualization, Z.Y.; methodology, Z.Y; formal analysis, Z.Y; data curation, Z.Y; writing—original draft preparation, Z.Y; writing—review and editing, Z.Y All authors have read and agreed to the published version of the manuscript. Acknowledgement None References Aksu, M. & Kurt, H. (2019). Sağlık Hizmetlerinde Kalite Yönetimi. İstanbul: Beta Yayınları. Andaleeb, S. S. (2001). Service quality perceptions and patient satisfaction: A study of hospitals in a developing country. Social Science & Medicine . Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411 Clemes, M. D., Gan, C., & Kao, T. H. (2008). University student satisfaction: An empirical analysis. International Journal of Educational Management . Dagger, T. S., Sweeney, J. C., & Johnson, L. W. (2007). A hierarchical model of health service quality. Journal of Service Research . Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA . Donabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 260(12), 1743-1748. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312 Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson. Karaca, A. & Dönmez, Y. (2017). Sağlık Hizmetlerinde Kalite Kavramı ve Kalite Geliştirme Süreçleri. Sağlık Akademisi Kastamonu, 2(1), 36-45. Karagöz Yalçın (2019). Spss Amos Meta Uygulamalı Nicel- Nitel- Karma Bilimsel Araştırma Yöntemleri E Yayın Etği, Nobel Yayıncılık Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing . Varinli İnci & Aysel Çakır (2004). Hizmet Kalitesi, Değer, Hasta Tatmini Ve Davranışsal Niyetler Arasındaki İlişki-Kayseri'de Poliklinik Hastalarına Yönelik Bir Araştırma, Sosyal Bilimler Enstitüsü Dergisi Sayı, 17/2, 33-52. World Health Organization (WHO). (2006). Quality of Care: A Process for Making Strategic Choices in Health Systems. Geneva: WHO Press. Wu, C. H. J. (2011). The impact of hospital brand image on service quality, patient satisfaction, and loyalty. African Journal of Business Management . Yılmaz, N., & Öztürk, B. (2016). Sağlık hizmetlerinde kalite ölçümünde kullanılan modellerin karşılaştırılması. Türkiye Klinikleri Halk Sağlığı Hemşireliği - Özel Sayısı, 1, 52-57. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6652118","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":472494360,"identity":"8668ead9-d8f9-4cf2-a027-e6d3f2182006","order_by":0,"name":"Zafer Yıldız","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Zafer","middleName":"","lastName":"Yıldız","suffix":""}],"badges":[],"createdAt":"2025-05-13 06:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6652118/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6652118/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85362238,"identity":"077db3a5-d8c0-4b1a-8271-85210af16803","added_by":"auto","created_at":"2025-06-25 06:17:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56414,"visible":true,"origin":"","legend":"\u003cp\u003eUnnumbered image in the Results section.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6652118/v1/db4d4897cb8d4bb3517ea936.png"},{"id":85365983,"identity":"cf545fb1-5023-4d59-8b00-8d9ee88f0afb","added_by":"auto","created_at":"2025-06-25 06:50:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1185006,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6652118/v1/ff3e56bd-06f0-48bf-8137-7ff5dd97cf43.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling the Impact of Service Quality Factors on Healthcare Recipients: A Path Analysis Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe evaluation of service quality in healthcare institutions has become a critical research focus in recent decades, owing to its direct influence on patient satisfaction, health outcomes, organizational reputation, and financial performance (Parasuraman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Donabedian, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Andaleeb, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In an increasingly patient-centered healthcare environment, service quality not only determines immediate patient satisfaction but also affects long-term loyalty, treatment adherence, and overall health system sustainability (Berry \u0026amp; Bendapudi, 2007).\u003c/p\u003e \u003cp\u003eService quality in healthcare is inherently multidimensional, integrating tangible elements such as facilities, medical technology, and environmental hygiene with intangible aspects including provider empathy, interpersonal communication, trustworthiness, and ethical standards (Dagger, Sweeney, \u0026amp; Johnson, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The intangibility, heterogeneity, and high involvement characteristic of healthcare services differentiate them markedly from conventional service industries (Zeithaml, Parasuraman, \u0026amp; Berry, 1990). Consequently, assessing healthcare service quality necessitates models that capture both the functional and emotional dimensions of patient experience (Dagger et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Otani \u0026amp; Kurz, 2004).\u003c/p\u003e \u003cp\u003eTraditional conceptualizations of service quality, notably the SERVQUAL framework, have been widely adapted to healthcare to measure dimensions such as reliability, responsiveness, assurance, empathy, and tangibles (Zeithaml, Berry, \u0026amp; Parasuraman, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Nevertheless, the unique emotional, psychological, and existential vulnerabilities associated with healthcare consumption demand context-specific evaluation models that transcend conventional service quality metrics (Clemes, Gan, \u0026amp; Kao, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In healthcare settings, the quality of communication and relational trust can significantly outweigh technical aspects in shaping patient satisfaction and perceived value (Andaleeb, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmerging research underscores that patient perceptions of service quality are influenced not merely by isolated experiences but by complex interrelationships among multiple psychological constructs, including perceived quality, perceived value, satisfaction, attitude, and behavioral intention (Lee et al., 2012; Wu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chaniotakis \u0026amp; Lymperopoulos, 2009). Recognizing and accurately modeling these interdependencies is crucial for building comprehensive frameworks that truly reflect patient-centered service delivery.\u003c/p\u003e \u003cp\u003ePath Analysis, a specialized form of Structural Equation Modeling (SEM), offers a robust analytical strategy to simultaneously examine the direct and indirect effects among these multiple variables (Kline, 2015; Byrne, 2013). Unlike traditional regression methods that tend to treat predictors independently, Path Analysis allows researchers to understand the mediating and moderating roles between constructs, revealing latent causal structures that would otherwise remain obscured (Hair et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Particularly in healthcare quality research, where variables such as satisfaction and perceived value often exert both direct and indirect influences on outcomes like loyalty and behavioral intention, Path Analysis provides superior explanatory power.\u003c/p\u003e \u003cp\u003eDespite extensive scholarly attention to healthcare service quality, relatively few studies have systematically modeled the mediating roles of key constructs such as satisfaction and value within integrated analytical frameworks using Path Analysis. Prior research has often relied on bivariate correlations or segmented regression models, which are insufficient to capture the dynamic complexity of patient experiences (Andronikidis et al., 2009; Clemes et al., 2001). Furthermore, many studies have focused predominantly on Western healthcare systems, leaving a research gap in emerging market contexts where cultural, systemic, and infrastructural differences may profoundly shape service quality perceptions (Mosadeghrad, 2014).\u003c/p\u003e \u003cp\u003eAddressing these gaps, the present study aims to model and empirically test the structural relationships among perceived quality, perceived value, satisfaction, attitude, and behavioral intention based on data collected from healthcare service recipients. Specifically, the study applies Path Analysis to explore both the direct effects and the mediating mechanisms underlying patient evaluations of healthcare service quality.\u003c/p\u003e \u003cp\u003eBy developing predictive models, this study seeks to identify the key leverage points for enhancing patient satisfaction and loyalty, providing valuable insights for healthcare managers aiming to design and deliver services that meet both clinical and experiential standards. The findings are expected to contribute to both theoretical advancements in service quality research and practical improvements in healthcare service delivery, particularly in patient-centered care models emphasizing holistic patient experience and engagement.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Design and Sampling\u003c/h2\u003e \u003cp\u003eThe population of this study consists of individuals receiving healthcare services from medical institutions located in Sivas province, Turkey. A non-probability convenience sampling method was employed to collect data. The use of convenience sampling was justified as no differentiation was made among patients or healthcare institutions based on service types or characteristics.\u003c/p\u003e \u003cp\u003e For the scale employed in this study, ethical approval was obtained from the \"Sivas Cumhuriyet University Social Sciences Scientific Research Proposal Ethics Committee\" (Approval Number: E-99711239-050.01-438071, Date: June 11, 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Instrumentation\u003c/h2\u003e \u003cp\u003eThe data collection instrument used in this study was a healthcare service quality scale developed by Varinli and \u0026Ccedil;akır (2004). The scale is specifically designed for healthcare settings and consists of five distinct factors: Perceived Quality, Attitude, Value, Satisfaction Level, and Behavioral Intention. Each of these factors captures different dimensions of patient perceptions regarding the quality of healthcare services received, offering a comprehensive framework for evaluating both the technical and interpersonal aspects of service delivery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection Procedure\u003c/h2\u003e \u003cp\u003eData were gathered by administering structured questionnaires to individuals receiving services from healthcare institutions in Sivas. Since convenience sampling was utilized, participants were allowed to fill out the survey forms voluntarily, without any external influence or guidance. A total of 402 valid responses were collected for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e \u003cp\u003ePath Analysis was employed to examine the structural relationships among the factors identified in the service quality scale. Path Analysis is a statistical technique used to estimate the direct and indirect effects among quantitative variables and to determine the extent to which independent variables influence dependent variables through both direct and mediated pathways (Karag\u0026ouml;z, 2019, p. 823).\u003c/p\u003e \u003cp\u003eUsing Path Analysis, direct and indirect effects among the factors developed by Varinli and \u0026Ccedil;akır were depicted through a path diagram. The models derived from the analysis were presented with their respective coefficients, providing empirical evidence for the structural relationships among the factors.\u003c/p\u003e \u003cp\u003eThe models developed allowed for independent measurement of each dependent variable in relation to other factors within the scale. Typically, field studies are conducted for each factor individually; however, Path Analysis enables the simultaneous evaluation of all relationships in a single analysis, thus minimizing potential error rates. The primary advantage of Path Analysis lies in its ability to explore the relational dimensions among multiple factors concurrently, providing a more holistic and accurate understanding of the underlying structures.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic characteristics of the participants. A total of 402 individuals participated in the study, of whom 186 (46.3%) were female and 216 (53.7%) were male. Regarding marital status, 240 participants (59.7%) were married, while 162 (40.3%) were single. In terms of age distribution, 50 participants (12.4%) were aged 20 years or younger, 127 participants (31.6%) were aged between 21 and 30 years, 84 participants (20.9%) were aged between 31 and 40 years, 53 participants (13.2%) were aged between 41 and 50 years, and 88 participants (21.9%) were aged 51 years and older.\u003c/p\u003e \u003cp\u003eEducational background revealed that 65 participants (16.2%) completed primary education, 121 (30.1%) graduated from high school, 46 (11.4%) held an associate degree, 130 (32.3%) earned a bachelor\u0026rsquo;s degree, and 40 (10.0%) completed postgraduate studies. Occupationally, 56 participants (13.9%) were workers, 84 (20.9%) were public employees, 78 (19.4%) were housewives, 151 (37.6%) were self-employed, and 33 (8.2%) were retired.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026ndash;30 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;51 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssociate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u0026rsquo;s Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate Degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWorker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic Employee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Reliability Analysis\u003c/h2\u003e \u003cp\u003eTo ensure the validity of the measurement instrument, internal consistency reliability was assessed using Cronbach\u0026rsquo;s Alpha coefficients for each of the five factors: Value, Satisfaction Level, Behavioral Intention, Attitude, and Perceived Quality. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe reliability analysis revealed that all factors demonstrated satisfactory to excellent levels of internal consistency. According to the guidelines established by Nunnally and Bernstein (1994), a Cronbach\u0026rsquo;s Alpha value above 0.70 is considered acceptable for exploratory research, while values above 0.80 are indicative of high reliability in applied studies. In this context, the findings confirm the robustness of the measurement scale employed in this study.\u003c/p\u003e \u003cp\u003eSpecifically, the \u003cb\u003eValue\u003c/b\u003e factor exhibited the highest internal consistency with a Cronbach\u0026rsquo;s Alpha of \u003cb\u003e0.953\u003c/b\u003e, indicating a very strong correlation among the items designed to measure perceived value. This result suggests that participants interpreted and responded to the items related to value in a highly consistent manner.\u003c/p\u003e \u003cp\u003eSimilarly, the \u003cb\u003eSatisfaction Level\u003c/b\u003e factor demonstrated excellent reliability, with a Cronbach\u0026rsquo;s Alpha of \u003cb\u003e0.872\u003c/b\u003e. This indicates that the items measuring satisfaction were highly coherent and that participants\u0026rsquo; responses were stable across different service quality dimensions.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003eAttitude\u003c/b\u003e factor also achieved a high reliability score of \u003cb\u003e0.893\u003c/b\u003e, suggesting that participants consistently evaluated their attitudes towards the healthcare services they received. This reliability is crucial for accurately capturing attitudinal tendencies that influence service perceptions.\u003c/p\u003e \u003cp\u003eThe \u003cb\u003ePerceived Quality\u003c/b\u003e factor reported a Cronbach\u0026rsquo;s Alpha of \u003cb\u003e0.789\u003c/b\u003e, reflecting an acceptable level of internal consistency. Although slightly lower than the other factors, this score still falls well within the range deemed reliable for empirical research.\u003c/p\u003e \u003cp\u003eLastly, the \u003cb\u003eBehavioral Intention\u003c/b\u003e factor yielded a Cronbach\u0026rsquo;s Alpha of \u003cb\u003e0.722\u003c/b\u003e. While it represents the lowest reliability among the five factors, it still exceeds the minimum acceptable threshold, supporting the scale\u0026rsquo;s ability to reliably measure patients\u0026rsquo; behavioral tendencies regarding future healthcare service usage.\u003c/p\u003e \u003cp\u003eOverall, the high reliability scores across all factors provide strong evidence of the measurement model\u0026rsquo;s consistency and precision. This robustness enhances the credibility of the subsequent Path Analysis, ensuring that the relationships among service quality dimensions are examined based on stable and coherent constructs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability Coefficients (Cronbach\u0026rsquo;s Alpha)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFactor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach\u0026rsquo;s Alpha\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Path Analysis Results\u003c/h2\u003e \u003cp\u003eTo investigate the structural relationships among the service quality factors, Path Analysis was conducted. The goodness-of-fit indices indicated an excellent model fit, with CMIN/df\u0026thinsp;=\u0026thinsp;1.778, AGFI\u0026thinsp;=\u0026thinsp;0.974, GFI\u0026thinsp;=\u0026thinsp;0.996, NFI\u0026thinsp;=\u0026thinsp;0.996, CFI\u0026thinsp;=\u0026thinsp;0.999, TLI\u0026thinsp;=\u0026thinsp;0.996, and RMSEA\u0026thinsp;=\u0026thinsp;0.044. These results confirm that the hypothesized model adequately represents the observed data, satisfying the recommended thresholds for structural model evaluation (Hu \u0026amp; Bentler, 1999).\u003c/p\u003e \u003cp\u003eThe results indicate that Satisfaction Level and Perceived Quality serve as key endogenous variables within the model. Satisfaction Level is predicted by Behavioral Intention, Value, and Attitude, while Perceived Quality is predicted by Value and Satisfaction Level.\u003c/p\u003e \u003cp\u003eMore specifically, the strongest direct effect observed was between Satisfaction Level and Behavioral Intention (β\u0026thinsp;=\u0026thinsp;0.938, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlighting the critical role of patient satisfaction in shaping future behavioral tendencies. Similarly, Perceived Quality had a substantial direct effect on Satisfaction Level (β\u0026thinsp;=\u0026thinsp;0.831, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that patients\u0026rsquo; perceptions of service quality heavily influence their overall satisfaction.\u003c/p\u003e \u003cp\u003eThe relatively lower and insignificant direct effect of Behavioral Intention on Value (β\u0026thinsp;=\u0026thinsp;0.028, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) underscores the need to consider mediating variables when analyzing indirect relationships. This finding aligns with prior research emphasizing the complex and mediated pathways in service quality models (Dagger et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Clemes et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the Path Analysis results confirm the multidimensional and interconnected nature of service quality perceptions, supporting the development of more sophisticated predictive models in healthcare service evaluation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Regression Weights of the Model\u003c/h2\u003e \u003cp\u003eThe standardized regression coefficients derived from the Path Analysis are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These coefficients provide critical insights into the strength and direction of the hypothesized relationships among the key constructs of the service quality model. Each estimate reflects the direct effect of an independent variable on its corresponding dependent variable, after controlling for the influence of other variables in the model. The significance levels, standard errors, and critical ratios further validate the robustness and statistical relevance of these paths, offering empirical support for the proposed structural framework.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression Weights of the Path Analysis Model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue \u0026larr; Perceived Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude \u0026larr; Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Quality \u0026larr; Attitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction Level \u0026larr; Perceived Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude \u0026larr; Satisfaction Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue \u0026larr; Behavioral Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Intention \u0026larr; Satisfaction Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValue \u0026larr; Satisfaction Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttitude \u0026larr; Behavioral Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e(Note: *** indicates p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Error Variances of the Dependent Variables\u003c/h2\u003e \u003cp\u003eIn addition to the estimation of structural relationships among variables, the residual variances (error terms) associated with the endogenous constructs were examined to assess the unexplained variability within the model. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the estimated error variances for Perceived Quality and Satisfaction Level.\u003c/p\u003e \u003cp\u003eThe error variance for Perceived Quality was found to be 0.24 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that 76% of the variability in Perceived Quality is explained by the predictors included in the model. Similarly, the error variance for Satisfaction Level was estimated at 0.26 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that 74% of the variance in Satisfaction Level is accounted for by its respective predictors.\u003c/p\u003e \u003cp\u003eThe critical ratios (C.R.) for both error variances exceed the threshold of 1.96, and the associated p-values are statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming that the unexplained portions of variance are meaningful and not due to random sampling error (Byrne, 2013).\u003c/p\u003e \u003cp\u003eThese relatively low error variances support the robustness of the structural model, demonstrating that the proposed factors\u0026mdash;Value, Attitude, and Behavioral Intention\u0026mdash;collectively offer a strong explanatory framework for understanding patient satisfaction and perceived service quality. Moreover, the small magnitude of error terms indicates high predictive accuracy, thereby enhancing the overall validity of the model.\u003c/p\u003e \u003cp\u003eThus, the Path Analysis not only identifies the significant predictors of key outcomes but also achieves high explanatory power, validating the appropriateness of the hypothesized model for evaluating healthcare service quality perceptions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimated Error Variances\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatisfaction Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Model Construction and Interpretation\u003c/h2\u003e \u003cp\u003eBased on the Path Analysis results, two multiple regression models were derived to elucidate the direct and indirect effects of key service quality factors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFirst Model: Predicting Satisfaction Level\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the first model, \u003cb\u003eSatisfaction Level\u003c/b\u003e was identified as the dependent variable, predicted by three independent variables: \u003cb\u003eBehavioral Intention\u003c/b\u003e, \u003cb\u003eValue\u003c/b\u003e, and \u003cb\u003eAttitude\u003c/b\u003e. The resulting multiple regression equation is:\u003c/p\u003e \u003cp\u003eY\u003csub\u003e(Satisfaction Level)\u003c/sub\u003e= \u0026#120573;0\u003csub\u003e(Error Term)\u003c/sub\u003e + \u0026#120573;1\u0026#119883;(\u003csub\u003eBehavioral Intention)\u003c/sub\u003e + \u0026#120573;2\u0026#119883;(\u003csub\u003eValue)\u003c/sub\u003e + \u0026#120573;3\u0026#119883;\u003csub\u003e(Attitude)\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eY\u003csub\u003e(Satisfaction Level)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0,26\u0026thinsp;+\u0026thinsp;0,938* \u0026#119883;\u003csub\u003e(Behavioral Intention)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0,511\u0026#119883;\u003csub\u003e(Value)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0,474x\u003csub\u003e(Attitude)\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eThe regression coefficients suggest the following:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eBehavioral Intention\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;0.938) exerts the strongest positive influence on Satisfaction Level, highlighting the critical role of patients' future behavioral tendencies in shaping their satisfaction.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eValue\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;0.511) has a substantial but slightly lower effect, indicating the importance of patients' perceived trade-off between cost and benefit.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAttitude\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;0.474) also positively impacts Satisfaction Level, albeit to a lesser extent compared to Behavioral Intention and Value.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe relatively small error variance (β₀ = 0.26) indicates a good model fit.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSecond Model: Predicting Perceived Quality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the second model, \u003cb\u003ePerceived Quality\u003c/b\u003e serves as the dependent variable, predicted by \u003cb\u003eValue\u003c/b\u003e and \u003cb\u003eSatisfaction Level\u003c/b\u003e. The multiple regression equation is:\u003c/p\u003e \u003cp\u003eY\u003csub\u003e(Perceived Quality)\u003c/sub\u003e= \u0026#120573;0(\u003csub\u003eError Term)\u003c/sub\u003e + \u0026#120573;1\u0026#119883;(\u003csub\u003eValue)\u003c/sub\u003e + \u0026#120573;2\u0026#119883;\u003csub\u003e(Satisfaction Level)\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eY\u003csub\u003e(Perceived Quality)\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0,24\u0026thinsp;+\u0026thinsp;0,300\u0026#119883;(\u003csub\u003eValue)\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0,831\u0026#119883;\u003csub\u003e(Satisfaction Level)\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eKey findings include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSatisfaction Level\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;0.831) emerges as the strongest predictor of Perceived Quality, emphasizing the pivotal role of overall satisfaction in shaping patients' perceptions of service quality.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eValue\u003c/b\u003e (β\u0026thinsp;=\u0026thinsp;0.300) also significantly affects Perceived Quality, albeit to a lesser extent.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe small error variance (β₀ = 0.24) further supports the model's robustness.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInterrelationship Between the Two Models\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAn integrated view of the models reveals the central role of Satisfaction Level:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn the first model, Satisfaction Level is an outcome variable predicted by Behavioral Intention, Value, and Attitude.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn the second model, Satisfaction Level acts as a predictor for Perceived Quality.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThus, Satisfaction Level operates both as an outcome and a mediator, bridging patients' behavioral intentions and their perceptions of service quality.\u003c/p\u003e \u003cp\u003eSimilarly, Value serves as an essential independent variable in both models:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn the first model, Value directly influences Satisfaction Level.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn the second model, Value directly predicts Perceived Quality.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThis dual role underscores the importance of patients' value perceptions in influencing both satisfaction and service quality assessments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFactorial Relationships and Mediating Effects\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe analysis of direct and indirect effects among factors provides further insights:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIn the first instance, Perceived Quality directly influences Value with a coefficient of β\u0026thinsp;=\u0026thinsp;0.30. When Satisfaction Level is introduced as a mediator, the total effect increases to β\u0026thinsp;=\u0026thinsp;1.34, indicating that enhancing patient satisfaction substantially amplifies the perceived value of healthcare services. This finding suggests that healthcare providers aiming to improve perceived value must prioritize strategies that enhance patient satisfaction.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIn the second instance, Behavioral Intention exhibits an insignificant direct effect on Value (β\u0026thinsp;=\u0026thinsp;0.03). However, when Attitude and Perceived Quality are introduced as mediators, the total effect becomes significant at β\u0026thinsp;=\u0026thinsp;0.74. This highlights the critical role of intermediary factors in strengthening the link between patients' future behavioral intentions and their perceived value of healthcare services.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eOverall, the findings demonstrate the complex interplay between direct and mediated effects in shaping healthcare service perceptions, offering important implications for healthcare managers aiming to optimize patient satisfaction and loyalty through strategic service quality improvements.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Interpretation of Findings\u003c/h2\u003e \u003cp\u003eThe findings of this study provide critical insights into the determinants of healthcare service quality perceptions among patients. The Path Analysis revealed that Satisfaction Level acts as a central mediator between patients' Behavioral Intention, Value, and Attitude, while Perceived Quality is primarily influenced by Satisfaction Level and Value. Notably, the strong predictive relationship between Behavioral Intention and Satisfaction Level (β\u0026thinsp;=\u0026thinsp;0.938, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) highlights the importance of patients' future behavioral tendencies in shaping overall satisfaction with healthcare services. Similarly, Satisfaction Level emerged as the most significant predictor of Perceived Quality (β\u0026thinsp;=\u0026thinsp;0.831, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), emphasizing the pivotal role of patient-centered experiences in forming quality judgments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Comparison with Previous Literature\u003c/h2\u003e \u003cp\u003eThese results align with earlier research suggesting that patient satisfaction is a dominant factor influencing both behavioral outcomes and quality perceptions in healthcare contexts (Andaleeb, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Clemes et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The mediating role of Satisfaction Level resonates with the findings of Dagger, Sweeney, and Johnson (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), who argue that satisfaction bridges cognitive evaluations of service attributes and future behavioral intentions. Furthermore, the relatively weak direct effect of Behavioral Intention on Value reinforces the argument that mediating constructs are essential in healthcare service quality models (Wu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Parasuraman et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). This supports the notion that patients\u0026rsquo; evaluations of value and quality are shaped not only by direct experiences but also by intermediate psychological processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Theoretical and Practical Implications\u003c/h2\u003e \u003cp\u003eTheoretically, this study advances the understanding of healthcare service quality by empirically validating the interconnectedness among Value, Satisfaction Level, Attitude, and Behavioral Intention within a comprehensive Path Model. It underscores the complexity of healthcare service evaluations, where patient satisfaction acts both as an outcome and as a driver of perceived quality. From a practical standpoint, the results suggest that healthcare managers should prioritize strategies aimed at enhancing patient satisfaction to indirectly strengthen quality perceptions and foster positive behavioral intentions. Investments in empathic communication, attitudinal training, and value-driven care delivery are likely to yield substantial returns in patient loyalty and organizational reputation (Zeithaml et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Donabedian, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Limitations and Future Research Directions\u003c/h2\u003e \u003cp\u003eDespite its contributions, this study has several limitations. First, the use of a non-probabilistic convenience sampling method in a single geographical region (Sivas, Turkey) may limit the generalizability of the findings. Future research could extend the model across diverse healthcare settings and cultural contexts to validate its robustness. Second, the cross-sectional design restricts causal inferences. Longitudinal studies are recommended to explore how satisfaction and quality perceptions evolve over time. Finally, while the Path Model captures key relationships among constructs, incorporating additional variables such as trust, empathy, or service recovery efforts could further enrich the understanding of healthcare service quality dynamics (Dagger et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe provision of high-quality healthcare services to patients during their treatment process is a The provision of high-quality healthcare services during the treatment process is a fundamental pillar of the social welfare state model. In Turkey, both public and private healthcare institutions play a pivotal role in delivering healthcare services, and national health policies are increasingly focused on enhancing service quality to ensure citizen satisfaction and well-being. In this context, a survey-based evaluation was conducted to assess the quality of healthcare services from the perspective of service recipients.\u003c/p\u003e \u003cp\u003eThe instrument utilized in this study comprised 41 items categorized into five distinct factors: Perceived Quality, Attitude, Value, Satisfaction Level, and Behavioral Intention. Each factor was designed to capture a different dimension of patient experiences. Perceived Quality reflected individuals' assessments of the service quality based on their cumulative healthcare experiences. Attitude measured healthcare personnel's approach towards patients, while Value assessed the extent to which patients felt respected and valued by healthcare providers. Satisfaction Level gauged the overall contentment with the healthcare received, and Behavioral Intention evaluated perceptions of the healthcare staff\u0026rsquo;s willingness and commitment to providing quality service.\u003c/p\u003e \u003cp\u003eThe demographic characteristics of the participants\u0026mdash;including gender, age, marital status, education, and occupation\u0026mdash;revealed a relatively homogeneous distribution, supporting the generalizability of the findings within the study context.\u003c/p\u003e \u003cp\u003eIn line with the study\u0026rsquo;s objectives, \u003cb\u003ePath Analysis\u003c/b\u003e was employed to examine the interrelationships among the service quality factors. The analysis yielded two distinct multiple regression models. The first model was developed to predict patients\u0026rsquo; \u003cb\u003eSatisfaction Level\u003c/b\u003e, identifying \u003cb\u003eBehavioral Intention\u003c/b\u003e, \u003cb\u003eValue\u003c/b\u003e, and \u003cb\u003eAttitude\u003c/b\u003e as significant predictors. The second model was constructed to predict \u003cb\u003ePerceived Quality\u003c/b\u003e, with \u003cb\u003eValue\u003c/b\u003e and \u003cb\u003eSatisfaction Level\u003c/b\u003e emerging as key explanatory variables.\u003c/p\u003e \u003cp\u003eImportantly, the findings highlight that patients\u0026rsquo; evaluations of healthcare services cannot be accurately understood by examining isolated service components. Instead, \u003cb\u003emediating variables\u003c/b\u003e\u0026mdash;such as Satisfaction Level\u0026mdash;play a crucial role in shaping perceptions. The results show that the Satisfaction Level not only acts as an outcome in the first model but also becomes a significant predictor of Perceived Quality in the second model. Similarly, the Value factor serves as a bridge connecting multiple aspects of patient experiences across the two models.\u003c/p\u003e \u003cp\u003eThe study demonstrates that assessing healthcare service quality requires a multifactorial perspective, wherein both direct and indirect effects among key variables are considered. The Path Analysis approach enabled the identification of causal pathways between constructs, minimizing estimation errors and providing a robust framework for evaluating healthcare service perceptions.\u003c/p\u003e \u003cp\u003eIn conclusion, this research shows that by applying Path Analysis to the developed service quality scale, two strong predictive models can be derived. These models offer comprehensive insights into how patients perceive healthcare services, highlighting the interconnected roles of satisfaction, perceived value, attitude, and behavioral intention. The findings underscore the necessity of holistic evaluation approaches in healthcare service research, which can ultimately guide policymakers and healthcare managers in designing more effective, patient-centered service delivery strategies.\u003c/p\u003e"},{"header":"Policy Implications","content":"\u003cp\u003eBuilding on the findings of this study, several policy recommendations can be proposed to enhance the quality of healthcare services and patient satisfaction:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEmphasize Satisfaction-Driven Service Improvements\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHealthcare organizations should prioritize strategies that directly enhance patient satisfaction, as satisfaction serves both as an outcome and a key driver of perceived service quality. Regular assessments and targeted interventions focusing on patient experiences can foster higher loyalty and positive behavioral intentions (Dagger, Sweeney, \u0026amp; Johnson, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntegrate Value-Based Care Principles\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInstitutions should embed value-based care approaches that ensure patients feel respected, valued, and engaged throughout the healthcare process. Strengthening the perception of value can significantly improve both satisfaction levels and quality perceptions (Clemes, Gan, \u0026amp; Kao, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDevelop Comprehensive Service Quality Monitoring Systems\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUtilizing Path Analysis models can help healthcare managers continuously monitor and diagnose service quality dimensions. Dynamic modeling tools can reveal causal pathways and highlight critical leverage points for quality improvement initiatives (Kline, 2015; Byrne, 2013).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTrain Healthcare Staff on Attitudinal and Behavioral Competencies\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTraining programs focusing on empathy, communication, and patient-centered care should be systematically implemented. Healthcare personnel's attitudes and behavioral intentions have profound impacts on patient satisfaction and perceptions of care quality (Andaleeb, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eFoster Holistic and Multidimensional Evaluations\u003c/b\u003e \u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePolicymakers should encourage holistic evaluations of healthcare service quality, considering both direct and indirect effects among key factors. This approach can lead to more robust healthcare policies that address complex patient needs beyond traditional service metrics (Donabedian, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Wu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThese recommendations underscore the necessity for healthcare systems to adopt an integrated and patient-centric approach in service quality management. Implementing such strategies can contribute to building more resilient, equitable, and high-performing healthcare institutions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and approved by the Sivas Cumhuriyet University Social Sciences Scientific Research Proposal Ethics Committee (approval number: E-99711239-050.01-438071, Date: June 11, 2024).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Availability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study\u0026apos;s findings are available on a reasonable request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Z.Y.; methodology, Z.Y; formal analysis, Z.Y; data curation, Z.Y; writing\u0026mdash;original draft preparation, Z.Y; writing\u0026mdash;review and editing, Z.Y All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAksu, M. \u0026amp; Kurt, H. (2019). Sağlık Hizmetlerinde Kalite Y\u0026ouml;netimi. İstanbul: Beta Yayınları.\u003c/li\u003e\n\u003cli\u003eAndaleeb, S. S. (2001). Service quality perceptions and patient satisfaction: A study of hospitals in a developing country. \u003cem\u003eSocial Science \u0026amp; Medicine\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eAnderson, J. C., \u0026amp; Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411\u0026ndash;423. https://doi.org/10.1037/0033-2909.103.3.411\u003c/li\u003e\n\u003cli\u003eClemes, M. D., Gan, C., \u0026amp; Kao, T. H. (2008). University student satisfaction: An empirical analysis. \u003cem\u003eInternational Journal of Educational Management\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDagger, T. S., Sweeney, J. C., \u0026amp; Johnson, L. W. (2007). A hierarchical model of health service quality. \u003cem\u003eJournal of Service Research\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDonabedian, A. (1988). The quality of care: How can it be assessed? \u003cem\u003eJAMA\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eDonabedian, A. (1988). The quality of care: How can it be assessed? JAMA, 260(12), 1743-1748.\u003c/li\u003e\n\u003cli\u003eFornell, C., \u0026amp; Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39\u0026ndash;50. https://doi.org/10.2307/3151312\u003c/li\u003e\n\u003cli\u003eHair, J. F., Black, W. C., Babin, B. J., \u0026amp; Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.\u003c/li\u003e\n\u003cli\u003eKaraca, A. \u0026amp; D\u0026ouml;nmez, Y. (2017). Sağlık Hizmetlerinde Kalite Kavramı ve Kalite Geliştirme S\u0026uuml;re\u0026ccedil;leri. Sağlık Akademisi Kastamonu, 2(1), 36-45.\u003c/li\u003e\n\u003cli\u003eKarag\u0026ouml;z Yal\u0026ccedil;ın (2019). Spss Amos Meta Uygulamalı Nicel- Nitel- Karma Bilimsel Araştırma Y\u0026ouml;ntemleri E Yayın Etği, Nobel Yayıncılık\u003c/li\u003e\n\u003cli\u003eParasuraman, A., Zeithaml, V. A., \u0026amp; Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12\u0026ndash;40.\u003c/li\u003e\n\u003cli\u003eParasuraman, A., Zeithaml, V. A., \u0026amp; Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. \u003cem\u003eJournal of Retailing\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eVarinli İnci \u0026amp; Aysel \u0026Ccedil;akır (2004). Hizmet Kalitesi, Değer, Hasta Tatmini Ve Davranışsal Niyetler Arasındaki İlişki-Kayseri\u0026apos;de Poliklinik Hastalarına Y\u0026ouml;nelik Bir Araştırma, Sosyal Bilimler Enstit\u0026uuml;s\u0026uuml; Dergisi Sayı, 17/2, 33-52. \u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). (2006). Quality of Care: A Process for Making Strategic Choices in Health Systems. Geneva: WHO Press.\u003c/li\u003e\n\u003cli\u003eWu, C. H. J. (2011). The impact of hospital brand image on service quality, patient satisfaction, and loyalty. \u003cem\u003eAfrican Journal of Business Management\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eYılmaz, N., \u0026amp; \u0026Ouml;zt\u0026uuml;rk, B. (2016). Sağlık hizmetlerinde kalite \u0026ouml;l\u0026ccedil;\u0026uuml;m\u0026uuml;nde kullanılan modellerin karşılaştırılması. T\u0026uuml;rkiye Klinikleri Halk Sağlığı Hemşireliği - \u0026Ouml;zel Sayısı, 1, 52-57.\u003c/li\u003e\n\u003cli\u003eZeithaml, V. A., Berry, L. L., \u0026amp; Parasuraman, A. (1996). The behavioral consequences of service quality. \u003cem\u003eJournal of Marketing\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Path Analysis, Structural Equation Modeling, Healthcare Service Quality, Model Development, Regression Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6652118/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6652118/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aims to model and analyze the factors influencing healthcare service quality perceptions among patients using Path Analysis. Data were collected from 402 individuals receiving healthcare services in Sivas, Turkey, through a validated service quality scale developed by Varinli and \u0026Ccedil;akır (2004), which includes five dimensions: Perceived Quality, Attitude, Value, Satisfaction Level, and Behavioral Intention. Path Analysis results revealed two predictive models. The first model demonstrated that Satisfaction Level is primarily driven by Behavioral Intention, Value, and Attitude. The second model showed that Perceived Quality is significantly influenced by Value and Satisfaction Level. Furthermore, the analysis highlighted the mediating role of Satisfaction Level between Value and Perceived Quality, and the indirect influence of Behavioral Intention on Value through Attitude and Perceived Quality. Findings emphasize the multidimensional and interconnected nature of healthcare service quality perceptions, underscoring the need for holistic evaluation approaches. The results offer valuable insights for healthcare managers aiming to design more patient-centered and quality-focused service delivery strategies.\u003c/p\u003e","manuscriptTitle":"Modeling the Impact of Service Quality Factors on Healthcare Recipients: A Path Analysis Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-25 06:17:54","doi":"10.21203/rs.3.rs-6652118/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"89063f43-1485-4d0d-a598-6113c0964f1f","owner":[],"postedDate":"June 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-25T06:17:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-25 06:17:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6652118","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6652118","identity":"rs-6652118","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}