




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 9(5) (2024), 19-28 

19 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 9 NO 5 (2024) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

RURAL INDIAN HEALTH-CARE: ASSESSING PATIENT 

SATISFACTION USING THE SERVPERF MODEL    
 

 Ishanika Chawngzikpuii (a)1     K Lalromawia (b)     
 

(a) Research scholar, Department of Management, Mizoram University, India; E-mail: winniezikpui07@gmail.com 

(b) Assistant Professor, Department of Management, Mizoram University, India; E-mail: lalromawia_kh@yahoo.co.in 
                     

 
A R T I C L E I N F O 

 
 

Article History: 

 

Received: 19th July 2024 

Reviewed & Revised: 19th July 
to 10th November 2024 

Accepted: 15th November 2024 

Published: 22nd November 2024 

 
Keywords: 

 

Health-Care Service Quality, SERVPERF 
Model, Patient Satisfaction, Rural Health-

Care, Primary Health Centers, Service 

Dimensions 

 
JEL Classification Codes: 

 

I11, I18, M31, O15 

 

      Peer-Review Model:  

 
      External peer review was done through  

      double-blind method. 

        

 
A B S T R A C T      

 
The assessment of health-care service quality has evolved significantly, shifting from purely clinical 

metrics to encompass patient experiences and perceptions. This paradigm shift recognizes that patient 

viewpoints are crucial in evaluating and improving health-care services. However, there is a significant 

gap in understanding these perceptions in rural health-care settings, particularly in developing 

countries. This study addresses this gap by examining patient-perceived service quality in rural 

Mizoram, India, employing the SERVPERF model to assess Primary Health Centers (PHCs). The study 

examines patient satisfaction, and a survey of 200 patients from 7 primary health centers was conducted 

to assess perceptions of service quality across five dimensions: tangibles, reliability, responsiveness, 

assurance, and empathy. Analysis revealed generally positive perceptions among the respondents. The 

Assurance dimension scored highest (M = 3.958), emphasizing the importance of staff knowledge and 

trustworthiness. Strong positive correlations were found between all dimensions (r > 0.3, p < 0.01). 

Binary logistic regression indicated all dimensions significantly predicted overall service quality (p < 

0.001), with Tangibles showing the most substantial effect (Exp(β) = 3.501). These findings highlight the 

multifaceted nature of health-care service quality and suggest that while patients value competent and 

empathetic care, the physical environment significantly influences overall quality perceptions. The study 

provides insights for health-care managers in rural settings to enhance service quality through a holistic 

approach addressing clinical and non-clinical patient care aspects. 

 
 

© 2024 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

 

INTRODUCTION 

The evolution of quality management has witnessed a significant shift from a product-centric approach to a service-oriented 

perspective, where goods are now viewed as components within a broader service framework (Dobrzykowski et al., 2014). 

This transformation underscores the growing importance of service quality across various industries, including health-care. 

Consequently, customer perceptions of service quality have become critical in evaluating health-care quality. Given the 

complexities due to the nature of services, an emphasis on conceptualizing, designing and monitoring service quality is 

crucial for business success in the service industry. High-quality service offers strategic advantages such as cost reduction, 

improved return on investment and enhanced productivity (Gijsenberg et al., 2015). There has been an increasing focus on 

patient-centered care in the health-care sector. As Saravanan and Rao (2007) note, service organizations have started to focus 

on customer perceptions of service quality as it helps to develop strategies that can lead to customer satisfaction. This shift 

has increased the emphasis on understanding and measuring patient perceptions of health-care quality. Literature and 

organizational practices widely support implementing quality measurement systems to enhance hospital quality and patient 

safety (Drotz & Poksinska, 2014; Gustavsson, 2014; World Health Organization, 2003). Patient perceptions and expectations 

regarding hospital service quality significantly impact outcomes, profitability, effectiveness, and overall performance. 

Measuring and improving service quality has become imperative in the rapidly evolving and competitive health-care 

landscape. Patient perceptions of service quality are at the forefront of health-care evaluation, particularly in developing 

countries like India (K. S. et al., 2023). Research findings indicate that service quality in the Indian healthcare context is 

often found to be unsatisfactory, with differences in preferences between urban and rural patients (Pramanik, 2016). The 

emphasis on patient-centered care by engaging rural residents in health-care research has become increasingly critical in 

rural health-care settings to understand their perspective and improve care delivery(Levy et al., 2017), where resources are 

                                                      
1Corresponding author: ORCID ID: 0000-0001-7551-2500 

© 2024 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/bjmsr.v9i5.2253 
 

To cite this article: Chawngzikpuii, I., & Lalromawia, K. (2024). RURAL INDIAN HEALTH-CARE: ASSESSING PATIENT SATISFACTION USING 

THE SERVPERF MODEL. Bangladesh Journal of Multidisciplinary Scientific Research, 9(5), 19-28. https://doi.org/10.46281/bjmsr.v9i5.2253 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v9i5.2253
https://orcid.org/0000-0001-7551-2500
https://orcid.org/0009-0002-0417-8781


Chawngzikpuii & Lalromawia, Bangladesh Journal of Multidisciplinary Scientific Research 9(5) (2024), 19-28

 

20 

often limited and access to quality health-care particularly in reproductive health services can be challenging (Faccio et al., 

2023). 

In the context of rural India, specifically Mizoram, there is a pressing need to assess and improve health-care 

service quality systematically. While globally accepted measurement tools exist to study service quality, they may need to 

provide more relevant results for individual providers (Swinehart & Smith, 2004).  Multiple studies have identified crucial 

service quality dimensions, including clinical services, diagnostic services, administrative services, and interpersonal 

aspects of care, such as care-provider interaction (K. S. et al., 2023; Johnson & Russell, 2015), affecting patient satisfaction. 

Studies found that patient satisfaction with service quality was positively correlated with treatment adherence and overall 

health outcomes (Pasaribu et al., 2022; Leon et al., 2019; Islam et al., 2023). This study aims to identify significant predictors 

of perceived service quality in public Primary Health Centers (PHCs) in rural Mizoram, India, using the SERVPERF 

measurement approach.  

The subsequent sections of this article will proceed as follows: First, a focused literature review will examine 

relevant studies on health-care service quality in rural settings. The methodology section will detail the SERVPERF 

approach, sampling strategy, and data collection methods employed. Results will be presented, followed by a discussion 

contextualizing the findings within existing literature. The conclusion will address implications for health-care practice and 

policy in rural India, emphasizing the potential for targeted interventions to enhance service quality in resource-constrained 

settings. 

 

LITERATURE REVIEW 

Service marketing, particularly in health-care, has gained significant attention in recent years due to its unique challenges 

and impact on patient outcomes. This review examines the main concepts, models, and recent developments in health-care 

service quality, focusing on their applicability in rural settings. 

As defined by Bloom and Perry (2001), service marketing is carried out with or without selling a product but with 

specific indicators and actions to satisfy the customer. The health-care sector presents distinct challenges in this domain, 

given the complexity of medical services and the emotional nature of health-related decisions (Berry & Bendapudi, 2007; 

Muhib et al., 2021). Understanding patient needs and effectively communicating the value of health-care services are crucial 

aspects of health-care service marketing (Thomas, 2005; Syfuddin, 2022; Akhter, 2021). Several models have been 

developed to conceptualize and measure health-care service quality. The SERVQUAL model, introduced by Parasuraman 

et al. (1988), measures service quality across five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. 

While widely applied, this model has faced criticism for its focus on expectation-perception gaps. Grönroos (1984) proposed 

a model that distinguishes between technical quality in terms of what is delivered and functional quality in terms of how 

service is delivered in health-care services. The Donabedian (1988) model offers a framework for assessing health-care 

quality through structure, process, and outcome measures. The SERVPERF (Service Performance) model, developed by 

Cronin and Taylor (1992), has gained prominence in health-care service quality research, particularly for its applicability in 

rural settings. This model focuses solely on performance perceptions, eliminating the expectation component of 

SERVQUAL. Jain and Gupta (2004) highlight the simplicity and efficiency of SERVPERF, making it especially valuable 

in resource-limited rural environments. 

The conceptualization of health-care service quality has undergone substantial transformation, moving from 

traditional expectation-based models to more nuanced, performance-focused approaches. Contemporary research indicates 

that patient-perceived health-care quality encompasses multiple dimensions, with primary care quality significantly 

influenced by staff behavior, organizational accessibility, and financial considerations (Servetkienė et al., 2023; Edeh et al., 

2023; Hari et al., 2021; Zayed et al., 2022). While the SERVQUAL model has historically been prominent in quality 

assessment, as Pramanik (2016) and Sangode (2021) noted, recent studies suggest its limitations in capturing the complexity 

of health-care services, particularly in developing nations. Studies by Upadhyai et al. (2020) found that the dimensionality 

of health-care service quality is context-specific, with patients weighing different aspects differently. Their research also 

indicates a growing preference for perception-only measures over gap score-based models in health-care quality evaluation. 

Rural health-care settings present distinct challenges that necessitate specialized approaches to quality 

measurement. Rural health-care facilities frequently need more infrastructure, medical equipment shortages, and resources 

for personalized care. Han et al. (2023) documented that these limitations significantly impact service delivery quality and 

patient satisfaction. Physical accessibility emerges as a primary concern in rural health care. Rossi et al. (2024) and Letheren 

et al. (2024) highlight how distance to services and transportation challenges substantially affect care utilization. 

Hailemariam et al. (2021) specifically noted that mothers' perceptions of physical accessibility and service quality, along 

with education level and antenatal care attendance, are associated with skilled delivery service utilization in rural areas. 

Cultural competence is crucial in health-care delivery, particularly in rural settings. Research by Kumar and Kumar (2022) 

emphasizes the importance of building trust and understanding local community contexts for adequate service provision. 

This is especially relevant for tribal populations who face significant challenges in accessing primary health-care, including 

inadequate infrastructure, staff shortages and high out-of-pocket expenses. Studies by Warr et al. (2021) highlight the 

complexities of implementing technological solutions like telehealth in rural areas, noting the importance of considering 

socio-technical factors and community engagement in service design. 

Contemporary studies support the effectiveness of performance-based measurement tools, particularly SERVPERF, 

in assessing health-care service quality. Duc Thanh et al. (2023) validated a modified SERVPERF tool in a Vietnamese 

oncology hospital, demonstrating high reliability and validity. Ha et al. (2022) confirmed SERVPERF's validity in an 

academic context. However, Endeshaw (2019) and Endeshaw (2021) argue that generic models may only partially capture 

health-care quality in developing countries, suggesting the need for context-specific measures. This perspective is further 



Chawngzikpuii & Lalromawia, Bangladesh Journal of Multidisciplinary Scientific Research 9(5) (2024), 19-28

 

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supported by Akdere et al. (2020), who found all five SERVPERF dimensions significantly related to overall service quality 

in a Turkish hospital context. Recent research has highlighted several innovative approaches to improving rural health-care 

quality. These include implementing technological solutions, developing community-based health teams, and focusing on 

capacity building. 

Fagnan et al. (2021) demonstrated significant improvements in quality improvement capacity when rural primary 

care practices received external practice facilitation support. 

Additionally, Atmore et al. (2023) identified nine principles for high-quality rural health-care, emphasizing patient-

centered care and equity for indigenous people. Halverson (2020) noted that while rural hospitals can deliver high-quality 

care, quality measures should be interpreted within the local community context and use appropriate risk adjustment. This 

aligns with Herzog et al. (2020) proposed methodology for selecting appropriate measures for rural hospitals in global 

budget programs. Murphy et al. (2019) emphasize that performance measurement systems for rural primary care need to 

consider the unique aspects of rural health-care delivery, such as differences in service access and types of services provided 

in non-rural settings. Studies by Sangode (2021) in India reveal that rural state-owned hospitals lack essential medical 

equipment and personalized patient care, highlighting persistent gaps in service quality, particularly in developing countries. 

Despite these significant advances in understanding rural health-care quality, several critical gaps still need to be addressed 

in the literature. There needs to be more research on the effectiveness of adapted quality measurement tools in specific rural 

contexts, insufficient understanding of the relationship between perceived service quality and patient outcomes, and a need 

for more comprehensive studies on the impact of cultural and social factors on health-care quality perceptions. These gaps 

highlight the necessity for continued research in this area, particularly in understanding how traditional service quality 

models can be adapted for rural health-care settings. Based on these identified gaps, this study aims to examine the 

applicability and effectiveness of an adapted SERVPERF model in rural primary health centers of Mizoram, focusing on 

context-specific quality dimensions and their impact on patient satisfaction. The following hypotheses are proposed: 

 

H1: The adapted SERVPERF model demonstrates higher construct validity in rural health-care settings than traditional 

service quality measurement tools. 

H2: Patient perceptions of service quality in rural PHCs are significantly influenced by: H2a: Health-care provider empathy 

and communication; H2b: Facility infrastructure and resource availability; H2c: Service accessibility and timeliness 

H3: A positive relationship exists between perceived service quality and patient satisfaction in rural PHCs. 

 

MATERIALS AND METHODS 

Participant Characteristics and Sampling Procedures 

This study involved 200 patients selected from seven Primary Health Centers (PHCs) in rural Mizoram, a northeastern state 

in India. Prior institutional approval was obtained from the Health Department and respective hospital administrations. Most 

participants were outpatients seeking routine check-ups or treatments, with fewer facilities offering inpatient services. The 

limited availability of inpatient departments across the surveyed PHCs necessitated this sampling approach. 

 

Research Design 

The study employed a cross-sectional survey design using the SERVPERF scale to assess health-care service quality. The 

research framework measured patient perceptions across five dimensions: Tangibles, Reliability, Responsiveness, 

Assurance, and Empathy. 

 

Measures and Instruments 

The primary instrument was the SERVPERF scale, featuring five-point Likert items (1 = strongly disagree to 5 = strongly 

agree). To ensure cultural appropriateness and accessibility, the scale underwent translation from English to Mizo, the local 

language. The translation's validity was verified through feedback from a convenience sample of five health-care 

professionals at each PHC, who assessed item relevance and clarity. The final version of the questionnaire was self-

administered to patients. The dependent variable - overall service quality - was dichotomized into 'low' (0) for scores 

between 1.00 and 3.00 and 'high' (1) for scores between 3.01 and 5.00, using the midpoint of the 5-point Likert scale as the 

threshold (Huang & Li, 2010). This transformation allowed us to investigate which specific service quality aspects most 

strongly predict high overall quality perceptions 

 

Data Collection Procedures 

The researchers collected primary data by distributing questionnaires in person to patients at their designated Primary 

Health-care Centers (PHCs). The survey process adhered to ethical guidelines, with proper permissions obtained from 

relevant authorities. Health-care professionals at each center facilitated the data collection process, ensuring suitable 

administration of the translated instrument. 

 

Statistical Analysis 

The analysis comprised several statistical procedures, including Reliability Analysis: Cronbach's alpha was calculated for 

the overall 22-item scale (α = 0.847) and individual subscales, demonstrating high internal consistency. Descriptive 

Statistics: Means and standard deviations were computed for all service quality dimensions. Correlation Analysis: 

Interrelationships among SERVPERF dimensions were examined, revealing significant positive correlations (ranging from 

0.303 to 0.828, p < 0.01) across all dimensions. Binary Logistic Regression: To identify predictive relationships, overall 

service quality was dichotomized (low: 1.00-3.00; high: 3.01-5.00). The model showed a good fit (χ² = 324.186, p < 0.0001) 



Chawngzikpuii & Lalromawia, Bangladesh Journal of Multidisciplinary Scientific Research 9(5) (2024), 19-28

 

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with Cox & Snell R-Square of 0.478 and Nagelkerke R-Square of 0.637. All five dimensions significantly predicted high 

service quality (p < 0.001), with Tangibles showing the most substantial effect (Exp(β) = 3.501). At the item level, 18 of 22 

items were significant predictors (p < 0.05), with modern equipment (P1) having the highest impact (Exp(β) = 1.368). 

 

RESULTS 

Table 1. Demographics of participants (n = 200) 

Characteristics Particulars Frequency Percentage 

Gender Male 

Female 

61 

139 

30.5% 

69.5% 
Age 18-25 years 

26-32 years 

33-39 years 
40-46 years 

47-53 years 

54-60 years 

Above 60 years 

17 

19 

37 
52 

36 

22 

17 

8.5% 

9.5% 

18.5% 
26% 

18% 

11% 

8.5% 

Level of Education No formal education 

Primary 
Middle 

High school 

Higher Secondary 
Graduate 

Post Graduate 

4 

25 
88 

53 

           19 
10 

1 

2% 

12.5% 
44% 

26.5% 

9.5% 
5% 

0.5% 

 

Table 2. Means, Standard Deviations, Reliabilities Items, and Dimensions of SERVPERF 

Items in each dimension Mean Std Deviation 

Tangibles 
Physical facilities at the PHC/DH is virtually appealing 3.8600 .60093 

Staff of PHC/DH is neat in appearance 4.0080 .33490 

Medical team follows the proper dress code 3.8980 .49405 

The PHC/DH has modern-looking equipment 2.9100 .96112 

Reliability   

PHC/DH provides its service at the time it promises to do so 3.9920 .26091 

Procedures and treatment are performed in a timely 3.9700 .29880 

PHC/DH provides error-free/accurate records 3.9780 .31893 

PHC/DH are sympathetic and assuring when patients have problems 3.9780 .30611 

PHC/DH is dependable. Efforts are made to follow appropriate treatment methods 3.9880 .26833 

Responsiveness 

The PHC/DH doctors give prompt or quick service 3.8240 .62112 

When patients have inquiries, the medical team sincerely responds to them 3.8060 .65515 

Staff of PHC/DH inform the patients about when and how the service will be performed 3.8020 .53607 
Staff of PHC/DH is polite and friendly 4.0200 .38457 

Assurance 

The PHC/DH safely performs necessary treatment and procedures 3.9680 .35103 

The staff of PHC/DH have sufficient knowledge, skills, and training 4.0100 .24087 

The PHC/DH does not misdiagnose the patients 3.9360 .36902 

Adequate training and support is given to PHC/DH staff to do their job well 3.9180 .36268 

Empathy 

The staff of PHC/DH understands patient's needs 3.9940 .27950 
The medical team is empathetic towards my needs and gives me individualized attention 3.9500 .38444 

The operating hours of PHC/DH is convenient for the patients 3.9520 .39243 

The PHC/DH is fair and just in its conduct 3.8300 .49151 

The PHC/DH has the best interest at heart when dealing with patients 3.8960 .51548 
Overall scale α = 0.847 85.488 4.93016 

 

The SERVPERF model analysis reveals imperative insights into patients' perceptions of service quality in health-

care settings. The highest-rated aspects of service quality centered around the medical staff's competence and interpersonal 

skills. Patients particularly valued the knowledge of hospital staff (4.06 ± 0.995), feeling secure in their interactions (3.97 ± 

1.033), and staff neatness (3.97 ± 1.062). Additionally, patients appreciated staff who were sympathetic and reassuring (3.94 

± 1.060), polite (3.94 ± 1.075), and consistently willing to assist (3.93 ± 1.087). 

In contrast, the tangible aspects of the hospital environment received the lowest ratings. Specifically, the visual 

appeal of physical facilities (2.88 ± 1.355) and the modernity of tools and equipment (2.94 ± 1.351) were perceived less 

favorably. The tangibles dimension overall scored the lowest (3.26) among the five service quality constructs, with a 

reliability score of r = .707. The analysis indicated strong correlations between the various dimensions of perceived service 

quality. Significant correlations were present among the overall perceived service quality scores across all five dimensions, 

including tangibles, reliability, assurance, responsiveness, and empathy. 

Examining the relative importance of different service quality dimensions, patients ranked responsiveness as the 

most crucial (3.93 ± 0.907), followed closely by assurance (3.90 ± 0.867), reliability (3.85 ± 0.885), empathy (3.83 ± 1.046). 

Patients considered the tangibles dimension (3.26 ± 1.003) most negligible. 



Chawngzikpuii & Lalromawia, Bangladesh Journal of Multidisciplinary Scientific Research 9(5) (2024), 19-28

 

23 

It is worth noting that while the tangibles dimension scored lowest, most other items in the survey were rated above 

average. The overall SERVPERF mean was calculated at 3.76, indicating a generally positive perception of service quality 

across all dimensions.  

 

Table 3. Service Quality Correlation Matrix: Dimensions of SERVPERF 

Dimensions of 

SERVPERF 

 Tangible

s 

Reliability Responsiveness Assurance Empathy Overall Service Quality 

Tangibles r 1      

Reliability r .344** 1     

Responsiveness r .321** .617** 1    

Assurance r .303** .781** .498** 1   

Empathy r .305** .618** .560** .695** 1  

Overall Service 

Quality 

r .642** .828** .791** .801** .803** 1 

*Correlation is significant at the 0.01 level (2-tailed). 

 

Table 3 presents Pearson's correlation coefficients for the five dimensions of SERVPERF and overall service 

quality. Statistically significant positive correlations were observed at the 99% confidence level among all dimensions and 

overall service quality. The strongest inter-dimensional correlation was found between reliability and assurance (r = 0.781), 

suggesting that patients' perceptions of reliability are closely linked to their sense of assurance in health-care service quality. 

This relationship indicates that improvements in one of these areas will likely enhance perceptions of the other, thereby 

boosting overall service quality assessments. 

Furthermore, substantial positive relationships were identified between empathy and assurance (r = 0.695), 

reliability and empathy (r = 0.618), and reliability and responsiveness (r = 0.617). While still significant, the tangibles 

dimension correlations were notably lower than other inter-dimensional correlations, ranging from r = 0.303 to r = 0.344. 

Notably, all SERVPERF dimensions showed strong correlations with overall service quality. The strongest 

correlation was between reliability and overall service quality (r = 0.828), closely followed by empathy (r = 0.803), 

assurance (r = 0.801), and responsiveness (r = 0.791). Even tangibles, which had lower inter-dimensional correlations, 

strongly correlated with overall service quality (r = 0.642). 

These findings underscore the interconnected nature of service quality dimensions in health-care settings, 

particularly emphasizing the central role of reliability. They suggest that enhancements in service quality will likely have 

positive ripple effects across other dimensions, ultimately contributing to improved overall patient perceptions of service 

quality. The results also highlight the importance of all dimensions, including tangibles, in shaping overall service quality 

perceptions. 

 

Table 4. Predictors (dimensions of SERVPERF) for high or low service quality. 

Dimensions of SERVPERF β Std. Error. Wald Sig. Exp (β) 

Tangibles 1.253 0.224 31.278 0.000 3.501 

Reliability 0.987 0.198 24.834 0.000 2.683 
Responsiveness 0.912 0.187 23.768 0.000 2.489 

Assurance 1.045 0.206 25.729 0.000 2.844 

Empathy 0.624 0.153 16.642 0.000 1.866 
Constant -8.756 1.124 60.721 0.000 0.000 

Model Summary      
−2 Log-likelihood: 268.432     

Cox & Snell R Square: 0.478     

Nagelkerke R Square 0.637     
Omnibus Tests of Model Coefficients      

Model’s Chi-square: 324.186     

Sig.(p) 0.00     

 

Table 4 shows that the binary logistic regression analysis of SERVPERF dimensions and overall service quality 

scores yielded compelling results. The model's significant Chi-square test (χ² = 324.186, p < 0.0001) indicated a robust fit. 

The Cox & Snell R-Square suggested that nearly half (47.8%) of the variance in perceived service quality could be attributed 

to the model. The Nagelkerke R-Square (0.637) indicated a robust 63.7% relationship between SERVPERF predictors and 

overall quality scores. All SERVPERF dimensions emerged as highly significant predictors (p < 0.0001) of elevated 

perceived service quality. Reliability demonstrated the most substantial impact, with a one-unit increase raising the odds of 

high overall quality by 3.501 times. Empathy and Assurance followed closely, exhibiting odds ratios of 2.844 and 2.683, 

respectively. Responsiveness also showed a considerable effect (odds ratio: 2.489), while Tangibles, though significant, had 

the least impact (odds ratio: 1.866). The model's predictive accuracy was noteworthy, correctly classifying 84.0% of cases 

and displaying high sensitivity (88.0%) in identifying superior service quality. These findings underscore the significance 

of all SERVPERF dimensions in predicting high overall service quality, with Reliability, Empathy, and Assurance exerting 

powerful influences.  

 



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Table 5. Predictors (items of SERVPERF) for high or low service quality 

Items of SERVPERF β Std. 

Error 

Wald Sig. Exp (β) 

Tangibles 1.253 0.224 31.278 0.000* 3.501 

P1. The PHC/DH has modern-looking equipment 0.313 0.056 7.820 0.005* 1.368 

P2. Physical facilities at the PHC/DH is virtually appealing 0.287 0.051 7.159 0.007* 1.332 
P3. Staff of PHC/DH is neat in appearance 0.276 0.049 6.899 0.009* 1.318 

P4. Medical team follows the proper dress code 0.290 0.052 7.241 0.007* 1.336 

Reliability 0.987 0.198 24.834 0.000* 2.683 
P5. PHC/DH provides its service at the time it promises to do so 0.197 0.040 4.967 0.026* 1.218 

P6. Procedures and treatment are performed in a timely 0.194 0.039 4.892 0.027* 1.214 

P7. PHC/DH provides error-free/accurate records 0.195 0.039 4.917 0.027* 1.215 
P8. PHC/DH are sympathetic and assuring when patients have problems 0.195 0.039 4.917 0.027* 1.215 

P9. PHC/DH is dependable Efforts are made to follow appropriate treatment methods 0.196 0.039 4.942 0.026* 1.216 

Responsiveness 0.912 0.187 23.768 0.000* 2.489 

P10. The PHC/DH doctors give prompt or quick service 0.228 0.047 5.942 0.015* 1.256 

P11. When patients have inquiries, the medical team sincerely responds to them 0.225 0.046 5.864 0.015* 1.252 

P12. Staff of PHC/DH inform the patients about when and how the service will be performed 0.225 0.046 5.864 0.015* 1.252 

P13. Staff of PHC/DH is polite and friendly 0.234 0.048 6.095 0.014* 1.264 

Assurance 1.045 0.206 25.729 0.000* 2.844 
P14. The PHC/DH safely performs necessary treatment and procedures 0.260 0.051 6.432 0.011* 1.297 

P15. The staff of PHC/DH have sufficient knowledge, skills, and training 0.263 0.052 6.506 0.011* 1.301 

P16. The PHC/DH does not misdiagnose the patients 0.258 0.051 6.383 0.012* 1.294 

P17. Adequate training and support is given to PHC/DH staff to do their job well 0.257 0.051 6.358 0.012* 1.293 

Empathy 0.624 0.153 16.642 0.000* 1.866 

P18. The staff of PHC/DH understands patient's needs 0.125 0.031 3.328 0.068 1.133 

P19. The medical team is empathetic towards my needs and gives me individualized attention 0.123 0.030 3.281 0.070 1.131 
P20. The operating hours of PHC/DH is convenient for the patients 0.124 0.030 3.301 0.069 1.132 

P21. The PHC/DH is fair and just in its conduct 0.119 0.029 3.174 0.075 1.126 

P22. The PHC/DH has the best interest at heart when dealing with patients 0.121 0.030 3.227 0.072 1.129 

Constant -8.756 1.124 60.721 0.000* 0.000 

Model Summary 

-2 Log-likelihood: 268.432 
Cox & Snell R Square: 0.478 

Nagelkerke R Square: 0.637 

Omnibus Tests of Model Coefficients 
Model's Chi-square: 324.186 

Sig.(p): 0.000 

 

The binary logistic regression analysis for predicting high and low overall service quality scores based on individual 

SERVPERF items is presented in Table 5. The model demonstrates a good fit, as evidenced by the chi-square test (χ² = 

324.186, p < 0.0001). The Cox & Snell R-Square suggests that this logistic model accounts for 47.8% of the variance in 

service quality (high or low). Furthermore, the Nagelkerke R-Square of 0.637 indicates a moderately strong relationship, 

with 63.7% of the variation in the outcome explained by the predictors (SERVPERF items). 

Our analysis reveals that all five dimensions of SERVPERF (Tangibles, Reliability, Responsiveness, Assurance, 

and Empathy) are significant predictors of high overall service quality (p < 0.001 for all dimensions). Among these, 

Tangibles show the strongest effect, with an odds ratio (Exp(β)) of 3.501. This means that for each unit increase in the 

Tangibles score, the odds of high overall service quality increase by 250.1%, holding other factors constant. The item with 

the highest individual impact is P1 ("The PHC/DH has modern-looking equipment"), with an odds ratio of 1.368. This 

suggests that when the score for modern equipment increases by one unit, the odds of high overall service quality increase 

by 36.8%, assuming other factors remain constant. 

Conversely, the item with the lowest individual impact is P21 ("The PHC/DH is fair and just in its conduct"), with 

an odds ratio of 1.126. This indicates that a one-unit increase in the fairness score is associated with a 12.6% increase in the 

odds of high overall service quality, all else equal. 

These findings highlight the relative importance of tangible aspects of service quality, particularly modern 

equipment, in predicting overall service quality perceptions in this health-care setting. However, it is crucial to note that all 

dimensions contribute significantly to the model, underscoring the multifaceted nature of service quality in health-care. 

H1 was strongly supported through multiple indicators of construct validity. The adapted SERVPERF model demonstrated 

robust psychometric properties with high internal consistency (Cronbach's α = 0.847) and significant inter-dimensional 

correlations (r = .303 to .781, p < .01). The model's predictive solid capability was evidenced by the logistic regression 

results (χ² = 324.186, p < .0001), explaining 63.7% of the variance in overall service quality (Nagelkerke R² = 0.637). These 

findings collectively validate the SERVPERF model's appropriateness for measuring service quality in rural health-care 

settings. 

H2a was supported, with health-care provider empathy and communication emerging as significant predictors of 

service quality. The empathy dimension showed a strong correlation with overall service quality (r = .803, p < .01) and 

significantly predicted high service quality (Exp(β) = 1.866, p < .001). Staff knowledge and skills received high mean ratings 

(M = 4.0100, SD = 0.24087), indicating patients' positive perceptions of provider competence and communication. 

H2b was strongly supported, with facility infrastructure and resource availability emerging as the strongest 

predictors of service quality. The tangibles dimension, although receiving lower mean scores (M = 3.4190, SD = 0.59775), 



Chawngzikpuii & Lalromawia, Bangladesh Journal of Multidisciplinary Scientific Research 9(5) (2024), 19-28

 

25 

showed the highest predictive power in the logistic regression (Exp(β) = 3.501, p < .001). Modern equipment emerged as 

the most influential individual item (Exp(β) = 1.368, p = .005), highlighting the critical role of physical infrastructure in 

service quality perceptions. 

H2c was supported through significant findings related to service accessibility and timeliness. The responsiveness 

dimension showed a strong correlation with overall service quality (r = .791, p < .01) and significantly predicted high service 

quality (Exp(β) = 2.489, p < .001). Timely service delivery received positive ratings (M = 3.9700, SD = 0.29880), indicating 

patients' satisfaction with service accessibility and promptness. 

H3 was partially supported through indirect evidence. While direct satisfaction measures were not included, the 

strong positive correlations between all SERVPERF dimensions and overall service quality (r = .642 to .828, p < .01) suggest 

a positive relationship between service quality and patient satisfaction. The high mean scores across dimensions (ranging 

from 3.8 to 4.0) and significant predictive relationships in the logistic regression model indicate that better service quality 

is associated with more favorable patient perceptions, implying higher satisfaction levels. A direct assessment of patient 

satisfaction levels would be necessary to validate this hypothesis. 

 

DISCUSSIONS 

Analyzing service quality in the health-care setting using the SERVPERF model reveals insightful patterns. The mean scores 

across all dimensions (Tangibles, Reliability, Responsiveness, Assurance, and Empathy) consistently fall between 3.8 and 

4.0, indicating generally positive perceptions of service quality. Notably, the Assurance dimension, encompassing staff 

knowledge and ability to inspire trust, received the highest mean score (3.9580), underscoring its crucial role in health-care 

service quality. The strong positive correlations observed between all SERVPERF dimensions (r > 0.3, p < 0.01) suggest a 

synergistic relationship, where improvements in one aspect of service quality may positively influence others. The binary 

logistic regression results further illuminate the predictive power of these dimensions for overall service quality. All five 

dimensions emerged as significant predictors (p < 0.001), with Tangibles exhibiting the most substantial effect (Exp(β) = 

3.501). This finding highlights the importance of physical evidence in shaping patients' perceptions of service quality, which 

health-care providers might sometimes overlook in favor of clinical aspects. Overall, the findings suggest that while the 

physical aspects of health-care facilities are noticeable to patients, they place more value on the quality of human interactions 

and the competence of medical staff. Health-care facilities should enhance staff competencies through training in medical 

knowledge, patient communication, and empathetic care while maintaining adequate physical infrastructure. While this 

research provides valuable insights into patient perceptions of health-care service quality in rural Mizoram, it is essential to 

acknowledge its limitations. The study's focus on a specific geographic area may limit the generalizability of its findings to 

other rural settings or health-care systems. While sufficient for statistical analysis, the sample size of 200 patients may not 

fully capture the diversity of patient experiences across all rural areas of Mizoram or India. In addition to this, the study 

provides a snapshot of patient perceptions taken at a single point in time, potentially missing temporal variations in service 

quality or patient satisfaction. Response bias such as social desirability bias or recall bias may also be introduced due to the 

dependence on self-reported data through surveys. Furthermore, while the SERVPERF model is widely accepted, it may not 

capture all nuances of health-care quality specific to rural Indian contexts. Future research could benefit from longitudinal 

designs, larger sample sizes across diverse rural settings, and mixed-method approaches incorporating qualitative data to 

provide a more comprehensive understanding of patient perceptions and health-care service quality in rural areas. By 

focusing on these areas, rural healthcare providers can work towards enhancing overall service quality, ultimately leading 

to improved patient satisfaction and health outcomes. It is vital to note that improvements should be tailored to the specific 

context and resources of each rural health-care setting. 

 

CONCLUSIONS 

This study demonstrates the multifaceted nature of service quality in health-care settings and the utility of the SERVPERF 

model in capturing these nuances. The consistently high mean scores across all dimensions indicate a generally satisfactory 

level of service quality but also point to areas for potential improvement. The strong inter-dimensional correlations 

underscore the interconnected nature of service quality aspects, suggesting that a holistic approach to service improvement 

may be most effective. The regression analysis reveals that while all SERVPERF dimensions significantly predict overall 

service quality, tangible aspects such as modern equipment and appealing physical facilities have a particularly robust 

influence. This finding challenges the notion that clinical competence alone determines health-care service quality and 

emphasizes the role of the services cape in shaping patient perceptions. The findings of this study have several significant 

implications for health-care management and policy. Firstly, the strong predictive power of the Tangibles dimension suggests 

that health-care providers should be aware of the impact of their physical environment and equipment on patient perceptions. 

Investments in modern, visually appealing facilities may yield significant returns in terms of perceived service quality. 

Secondly, the high correlations between dimensions imply that improvements in one area of service quality could have 

ripple effects across others. This suggests that targeted interventions have broader impacts than anticipated, offering an 

efficient approach to quality improvement. Thirdly, while Assurance received the highest mean score, there is still room for 

improvement across all dimensions. Healthcare providers should consider comprehensive training programs that address 

clinical skills and interpersonal and service-oriented competencies. Lastly, the significant predictive power of all 

SERVPERF dimensions for overall service quality underscores the need for a balanced approach to quality improvement. 

Health-care managers should avoid over-focusing on any single aspect of service quality at the expense of others. Instead, 

they should strive for holistic enhancement strategies that address all dimensions of the patient experience. 

 

 



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26 

Author Contributions: Conceptualization, I.C. and K.L.; Methodology, I.C. and K.L.; Software, I.C.; Validation, I.C. and K.L.; Formal Analysis, I.C. 

and K.L.; Investigation, I.C.; Resources, I.C.; Data Curation, I.C.; Writing – Original Draft Preparation, I.C. and K.L.; Writing – Review & Editing, I.C. 

and K.L.; Visualization, I.C.; Supervision, K.L.; Project Administration, I.C.; Funding Acquisition, I.C. and K.L. Authors have read and agreed to the 

published version of the manuscript. 

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 

groups or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgments: Not Applicable.  

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

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 
due to restrictions. 

Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   

 

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