




































AMERICAN INTERNATIONAL JOURNAL OF SOCIAL SCIENCE RESEARCH 15(1) (2024), 12-20 

 

12 

 

SOCIAL SCIENCE RESEARCH 

AIJSSR VOL 15 NO 1 (2024) P-ISSN 2576-103X   E-ISSN 2576-1048 
 

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

Published by American Social Science Society, USA 

ANTECEDENTS OF REVISIT INTENTIONS ON HOSPITAL CHOICE 

IN THE DEVELOPING COUNTRY: A SEM ANALYSIS          
 

  A. M. Shahabuddin (a)1     Mohammad Toufiqur Rahman    Syed Md Hasib Ahsan    Md Shahidul Islam   Kulsuma Akter   

 
(a) Associate Professor, Department of Business Administration, International Islamic University Chittagong, Chittagong, Bangladesh; E-mail: 

ams_iiuc@yahoo.com 
(b)Associate Professor, Department of Business Administration, International Islamic University Chittagong, Chittagong, Bangladesh; E-mail: 

mtr.iiuc@gmail.com 
(c) Associate Professor, Department of Business Administration, International Islamic University Chittagong, Chittagong, Bangladesh; E-mail: 

hasib27.ahsan@gmail.com 
(d)Service Engineering Division, Bangladesh Forest Research Institute, Chittagong, Bangladesh; E-mail: engr.shahidul.islam@gmail.com 
(e) Assistant Professor, Department of Business Administration, International Islamic University Chittagong, Chittagong, Bangladesh; E-mail: 

nahidkulsuma@iiuc.ac.bd 
 

 
A R T I C L E I N F O 

      
 

Article History: 
 

Received: 12th February 2024 

Reviewed & Revised: 13th February 2024 
to 20th April 2024 

Accepted: 24th April 2024 

Published: 30th April 2024 

 
Keywords: 

AMOS 24, Bangladesh, Factor 

Analysis, Hospitals, Revisit Intention, 

SEM Analysis 

 
      JEL Classification Codes:  

 

      M1, M3, H51  

  

      Peer-Review Model:  
 

      External peer review was done through  

      Double-blind method. 

       

 
A B S T R A C T 

 

In today's rapidly evolving healthcare landscape, understanding the factors that drive patient 
decision-making regarding hospital revisit intention is critical for hospitals to remain competitive 

and thrive. Despite the availability of world-class private hospitals, many patients from 

developing countries such as Bangladesh routinely travel beyond for medical treatment. Hence, 
developing countries are taking the initiative to strengthen their healthcare industries to increase 

their GDP. This study aims to shed light on this issue by exploring the antecedents that affect 
hospitals in a developing country, Bangladesh. Data were collected by interviews at private 

hospitals in Chattrogram from November 2023 to December 2023 from 417 individuals using a 

randomized block design, and AMOS 24 was used for SEM analysis. The process in both EFA 
and CFA confirmed construct, convergent, and discriminant validity. The findings of this study 

demonstrate that the brand image of private hospitals affects patient revisit intent both directly 

and indirectly through service quality and patient satisfaction. A brand image does not influence 
the probability of returning to the same hospital. Moreover, commodity costs have a negative 

impact on revisit intention—service quality is associated with patient satisfaction, which is 

moderated by the brand image of selected hospitals in Bangladesh. Hospital management can 
better position itself to meet patients' expectations, the government may contribute to improving 

health outcomes, and academicians may develop extensions of theory for developing countries.  
 

 

© 2024 by the authors. Licensee American Social Science Society, USA. This article is an open 

access article  distributed under the terms and conditions of the Creative Commons Attribution (CC 

BY) license (http://creativecommons.org/licenses/by/4.0/).

 

 
INTRODUCTION 

Although poor healthcare infrastructure puts Bangladesh in the lowest place among South Asian nations (Mohiuddin, 

2020), with few resources, Bangladesh is advancing rapidly in meeting international healthcare standards. Developing 

high-income nations are advancing rapidly, but progress in low- and middle-income nations (such as Bangladesh) could 

be faster. A hospital must assess patients' needs and expectations to provide care that meets them. However, patients and 

relatives often have to sell assets or borrow money to cover the costs when loved ones need expensive medical care for 

conditions like heart disease, kidney disease, or oncology, putting their health and well-being at risk in the process. Even 

patients in Bangladesh who are dissatisfied with their prospects have begun to go to neighbouring nations like India, 

Thailand, and Singapore in search of better medical care (Ali & Medhekar, 2018). To retain these people in receiving 

healthcare services from Bangladesh, we fill a gap in the literature by investigating the connection between service quality 

(SQ), patient satisfaction (PS), revisit intentions (RI), brand image (BI), and healthcare expenditures in developing nations 

like Bangladesh. 

                                                      
1Corresponding author: ORCID ID: 0000-0001-6469-1873 
© 2024 by the authors. Hosting by American Social Science Society. Peer review under responsibility of American Social Science Society, USA.  

https://doi.org/10.46281/aijssr.v15i1.2206 

  
To cite this article: Shahabuddin, A. M., Rahman, M. T., Ahsan, S. M. H., Islam, M. S., & Akter, K. (2024). ANTECEDENTS OF REVISIT 

INTENTIONS ON HOSPITAL CHOICE IN THE DEVELOPING COUNTRY: A SEM ANALYSIS. American International Journal of Social Science 

Research, 15(1), 12-20. https://doi.org/10.46281/aijssr.v15i1.2206 

http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/aijssr.v15i1.2206
https://orcid.org/0000-0001-6469-1873
https://orcid.org/0000-0003-3494-0861
https://orcid.org/0000-0002-4286-5032
https://orcid.org/0000-0001-5755-2365
https://orcid.org/0000-0001-9232-9740


Shahabuddin et al., American International Journal of Social Science Research 15(1) (2024), 12-20

  

13 
 

Researchers in Bangladesh and other poor countries devote closer attention to healthcare issues than their 

governments do (Javed & Ilyas, 2018). Cost savings are one-way hospitals with loyal patients can attract more patients. 

However, more studies are needed to analyze how brand image correlates with service quality and client retention (Cham 

et al., 2016). When deciding on a doctor, many patients consider the clinic's or hospital's name alone (AlSaleh, 2019; 

Hyder et al., 2019). In recent years, interest has risen in the correlation between consumer perceptions of a brand and their 

likelihood to return. 

Many studies have examined how SQ affects PS or how satisfaction affects the likelihood that a customer will 

return (Lin & Bowman, 2022; Ali et al., 2018; Shafiq et al., 2017). However, more research must be conducted to analyse 

how well-satisfied customers influence a company's reputation and how often they return. The literature needs to include 

this sort of discussion. As a result, this study tries to respond to the following healthcare-related research questions: What 

role does hospital BI play in determining whether or not a patient will return to a given facility? Specifically, how do SQ 

and PS relate to the likelihood of a customer making a return visit? The literature was reviewed to provide answers to 

these questions. In the subsequent sentences, we will discuss the review's objectives and analyze how the literature has 

changed. We next moved on to SEM after reviewing the factor analysis. 

Although Chattroram is the commercial hub of Bangladesh and the country's second-largest city, this research aims to 

determine whether or not patients are satisfied with the care they receive at the private hospitals there. The following aims 

can be attained with the help of the survey data: 

 To Determine The Relationship Between BI On RI; 

 To Identify The Relationship Between SQ And PS On RI And 

 To Evaluate The Relationship Between Commodity Cost And RI; 

 

LITERATURE REVIEW 

Research on the connections between the significant variables that affect patients' choices to return to hospitals in 

Chittagong, Bangladesh, remains sparse. In particular, few studies use an integrated approach to comprehend how 

variables such as "Revisit Intent (RI)," "Brand Image (BI)," "Service Quality (SQ)," "Patient Satisfaction (PS)," and 

"Commodity Cost (CC)" relate to one another. Many Bangladeshis travel to other South Asian nations each year for 

medical care because those countries provide better medical facilities at lower costs. Besides the unavailability of 

treatments and lengthy wait times for diagnostics and specialist treatment support services, Anvekar (2012) revealed 

that high treatment costs are the primary reason people travel abroad for care.  

 

CC and RI 

Hospital spending refers to a commodity expense (Shetty & Ananthakrishnan, 2016) or patient out-of-pocket costs 

(like doctor's visits, hospital stays, medicine, and transportation to and from medical appointments, which are those not 

covered by insurance) paid directly to hospitals. For instance, if a patient cannot afford the out-of-pocket costs, they may 

decide against receiving hospital service or delay receiving it. The authors argue that treating healthcare costs like any 

other commodity expense will allow hospitals to develop more robust brand image initiatives that win the trust of  

their patients. Thus, the hypothesis is developed as follows: 

 

H1: There is no significant influence of commodity cost to revisit the intention of the same hospital. 

 

BI and RI 

Researchers contend that a hospital's brand image encompasses elements like its reputation, marketing efforts, and 

patients' satisfaction, which affect patients' propensity to return and their loyalty to the hospital (Chaudhuri & Holbrook, 

2001; Kim & Kim, 2017). Thus, a favourable hospital Brand Image may increase patient continuance intention in a 

hospital setting. So, a more reputable hospital will have a more significant number of loyal patients. 

 

H2: There is no significant influence of brand image on revisiting the intention of the same hospital. 

 

BI and SQ 

Patients' impressions of the hospital and their probability of returning or recommending the facility remain significantly 

affected by the quality of their care (Choi et al., 2004). However, Physicians' recommendations of specific hospitals to 

their patients may influence their decisions and the public's perception of those facilities in Bangladesh. Thus, this 

research examined how hospital marketing and social media affected patients' perceptions of the hospital's brand. This 

leads to the following hypothesis: 

 

H3: BI in hospitals is not strongly influenced by the quality of the services  

 

BI and PS 

Research (Kim et al., 2008) shows that patients' satisfaction levels with a hospital are directly related to the hospital's 

reputation. While branding and marketing initiatives may help, they should not be used as a substitute for providing 

excellent medical treatment and satisfying patient experiences. This leads to the following hypothesis: 

 

H4: Patient Satisfaction in hospitals is not significantly influenced by the hospital's BI  

 



Shahabuddin et al., American International Journal of Social Science Research 15(1) (2024), 12-20

  

14 
 

SQ and PS 

Patients' satisfaction with hospital care in Iran significantly correlated with the quality of care they received (Tavakol et 

al., 2018). Numerous studies have looked at how satisfied customers are related to service quality. Aspects of healthcare 

service quality include practitioner competence, treatment efficacy, and consistency (Omoregie et al., 2019). This leads to 

the following hypothesis: 

 

H5: Service quality has no significant influence on hospital patient satisfaction. 

 

PS and RI 

Patient satisfaction has been demonstrated to correlate positively with revisit intention in the healthcare setting (Vuori & 

Vänskä, 2018). Thus, it is reasonable to assume that patients who are happy with their care will be more loyal to their 

hospital. This leads to the following hypothesis: 

 

H6: There is no significant influence of patient satisfaction to revisit the intention of the same hospital. 

 

MATERIALS & METHODS 

Sample and Procedure 

We examined patients to identify the revisit intention from the top five sample private hospitals providing heart centres, 

kidney dialysis, and critical care in Chattrogram, Bangladesh, with more than 100 beds. We took a convenience sample 

from 500 respondents when the patients were present at these hospitals. These patients are the leading representatives of 

the target market, and they are conveniently reachable in person at a particular location (Andrade, 2021) from November 

2023 to December 2023. Participants' responses were voluntary and anonymous, and privacy was maintained. We asked 

questions of those who had visited the sample hospitals within six months. Among the valid respondents, 312 are male 

and 105 are female, of which 45% are below 35 and 65% are over 40 years old. Moreover, demographics indicate that 

around 73.5% of respondents were above SSC educational qualification. 

 

Measurement 

The questionnaire was pre-tested with the selected 30 patients from different groups and two academicians to ensure their 

instrument suitability and content validity, and then necessary corrections and modifications were made according to their 

suggestions. We used a modified version of Kim et al.’s (2008) “brand image” items and Parasuman et al. (1988) “service 

quality” items. The study developed a 23-item (Table 1) validated scale by adopting items from prior literature with minor 

changes. The corrected and finalized survey questionnaire was distributed by purposefully sampling in a randomized 

block design method to the selected 500 patients from different hospitals in Chittagong City.  

Method of Analysis  

The complete opinions of 417 respondents (83%) are selected and coded (as some respondents answered all the questions 

in the same rank and did not answer many questions) in IBM SPSS AMOS 24, which is sufficient for further analysis 

(Saunders et al., 2007) with 5% margin of error, and a 50% population proportion.  

Afterwards, we check the data from our survey response variables for the normality test using the Kolmogorov-

Smirnov and Shapiro-Wilk tests. Based on the normality test's result, median values are considered to classify the 

response data into a factor analysis. From the factor analysis's result, the following conceptual model is developed (Figure 

1) to test the hypothesis. 

 
Figure 1. Conceptual model 

 

 



Shahabuddin et al., American International Journal of Social Science Research 15(1) (2024), 12-20

  

15 
 

RESULTS 

Normality Test 

Table 1 depicts the descriptive statistics and normality test for the respondents' disclosed values on their intention to 

return. 

 

Table 1. Descriptive statistics and normality test  

 
Sl. 

No. 

Constru

cts 

Questionnaire Min Max Kolmogorov– 

Smirnov Test 

(Sig) 

Shapiro–Wilk 

Test (Sig) 

Median 

1. Q1 If I need hospital care, I can get admitted 

without any trouble 

1 4 0.267 (0.000) 0.862 (0.000) 2.0 

2. Q2 When I need medical attention, I will consider 

this hospital. 

1 4 0.240 (0.000) 0.872 (0.000) 2.0 

3. Q3 The hospital has my highest possible 

recommendation 

1 4 0.223 (0.000) 0.878 (0.000) 2.0 

4. Q4 I want to promote this institution as a desirable 

destination shortly. 

1 4 0.211 (0.000) 0.880 (0.000) 2.5 

5. Q5 I will participate in awareness programs 

organized by this hospital in the future 

2 4 0.254 (0.000) 0.868 (0.000) 2.0 

6. Q6 This hospital is conveniently located 2 5 0.213 (0.000) 0.878 (0.000) 3.0 

7. Q7 The follow-ups after treatment are good 2 5 0.211 (0.000) 0.869 (0.000) 3.0 

8. Q8 Medical records are error-free. 1 5 0.196 (0.000) 0.877 (0.000) 3.0 

9. Q9 This hospital's personnel provided prompt 
service. 

2 5 0.213 (0.000) 0.874 (0.000) 3.0 

10. Q10 The staff is educated enough to respond to my 

questions. 

2 5 0.220 (0.000) 0.867 (0.000) 4.0 

11. Q11 This hospital's personnel gives me individual 
care. 

2 5 0.222 (0.000) 0.860 (0.000) 4.0 

12. Q12 This hospital's physical amenities are modern 

and visually appealing. 

2 5 0.224 (0.000) 0.850 (0.000) 4.0 

13. Q13 I am sure that I will be able to obtain the 
medical treatment I require without incurring 

financial hardship. 

2 5 0.226 (0.000) 0.867 (0.000) 4.0 

14. Q14 People did not have to wait long for care in this 

hospital. 

2 5 0.218 (0.000) 0.862 (0.000) 4.0 

  Brand Image      

15. Q15 The brand's social media exposure is good for 

this hospital. 

1 5 0.219 (0.000) 0.900 (0.000) 3.0 

16. Q16 I felt safe taking treatment in this hospital 1 5 0.196 (0.000) 0.906 (0.000) 3.0 

17. Q17 The doctors gave me ample opportunity to ask 
questions, and their answers addressed all my 

worries 

1 5 0.209 (0.000) 0.902 (0.000) 3.0 

18. Q18 The hospital seems to be equipped with the 
latest equipment 

1 5 0.167 (0.000) 0.910 (0.000) 3.0 

19. Q19 I have a favourable impression of this 

hospital's name because of recommendations 

from my loved ones. 

1 5 0.176 (0.000) 0.912 (0.000) 3.0 

20. Q20 The cost of doctor fees is affordable 1 5 0.182 (0.000) 0.909 (0.000) 3.0 

21. Q21 The cost of renting a cabin or bed is 

inexpensive 

1 5 0.167 (0.000) 0.914 (0.000) 3.0 

22. Q22 The price of medicine is reasonable 1 5 0.174 (0.000) 0.908 (0.000) 3.0 

23. Q23 The price of an ambulance service is high 1 5 0.163 (0.000) 0.914 (0.000) 3.0 

 

The minimum and maximum values for each survey questionnaire response are 1 to 2 and 4 to 5, respectively. At 

the 0.000 significance level, each response ranges from 0.163 to 0.267 on the Kolmogorov-Smirnov test statistic and from 

0.850 to 0.914 on the Shapiro-Wilk test statistic. 

 

Factor Analysis 

Since the survey response values are not normally distributed (p < 0.05), the factor analysis calculates median values for 

mean rank comparison. The median values of each survey questionnaire response are 2 to 4. As the median values vary 

widely, a factor analysis is conducted to classify the survey questionnaire into different factors with similar responses. As 

the KMO is 0.793, we can apply the factor analysis method to divide the questionnaire response values into different 

factors (Table 2).  

 

Table 2. Factor analysis and Convergent Validity 

 
Rotated Component Matrixa Convergent Validity 

Questionnaire Component Construct Cronbach’s 

Alpha 

AVE Square 

Root of 

AVE 1 2 3 4 5    

Q10 0.963     Service 

Quality 

0.931 0.734 0.857 

Q13 0.945        



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Q11 0.855        

Q14 0.837        

Q12 0.800        

Q2  0.925    Revisit 

Intension 

0.921 0.718 0.847 

Q5  0.869       

Q3  0.852       

Q1  0.851       

Q4  0.804       

Q18   0.918   Brand 
Image 

0.916 0.660 0.813 

Q15   0.915      

Q17   0.908      

Q19   0.792      

Q16   0.766      

Q23    0.943  Commodity 
Cost 

0.959 0.833 0.913 

Q21    0.930     

Q20     0.924    

Q22    0.859     

Q9     0.878 Patient 

Satisfaction 

0.901 0.698 0.835 

Q7     0.850    

Q6     0.832    

Q8     0.809    

a. Rotation converged in 5 iterations. 

 

From the factor analysis table, the factor loading values are classified into five-factor variables: Service quality 

with Question10 to Question14 (factor leading 0.800 to 0.963), Revisit intention with Question1 to Question5 (factor 

leading 0.804 to 0.925), Brand Image with Question15 to Question19 (factor leading 0.766 to 0.918), Commodity cost 

with Question20 Question23 (factor leading 0.859 to 0.943) and Patient satisfaction Question6 to Question9 (factor 

leading 0.809 to 0.878) respectively. All the constructs had factor loadings above 0.70, Cronbach alphas above 7.0, and 

AVE over 0.50, showing they were highly trustworthy, valid, and consistent (Hair et al., 2006). 

 

Structure Equation Model 

Based on the above factor analysis result, a structural equation model of revisit intention based on commodity cost, brand 

image, service quality, and patient satisfaction (Figure 2)has been developed to test the hypothesis. 

 
Figure 2. Structure equation model of revisit intension 

 

According to the above structural equation model, the standardized regression weights for commodity cost are 

0.79 to 1.00, brand image is 0.53 to 1.00, service quality is 0.70 to 1.00, patient satisfaction is 0.79 to 0.90 and revisit 

intention is 0.74 to 1.01 respectively (which are nearly between –1 to 1). In these cases, each factor loading of indicator 

variables is exceptionally high and statistically significant (p < 0.05). The covariance values between errors in the model 

range from – 0.14 to 0.46 and are also important (p < 0.05). 

The final model fits the data quite well, as shown by the computed global fit measures (χ2 /df = 2.614, GFI= 

0.90, CFI= 0.966, IFI= 0.966, TLI= 0.960, NFI= 0.946, RFI= 0.938, and RMSEA = 0.064, which is 0.08). The model 

index values meet all of the survey's requirements for a well-fitted model; consequently, the chosen model is appropriate 

(Awang et al., 2015).  

 

 



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Convergent and Discriminant Validity 

Table 3 shows the results of a convergent validity test using the average variance expected (AVE). In contrast, Table 3 

shows the results of a discriminant validity test using the maximum shared variance (MSV). 

 

Table 3. Path coefficient and discriminant validity 

 
Regression Weights: (Path coefficient) Discriminant validity 

Exogenous  Endogenous Variable  Estimate P Maximum shared variance (MSV) 

Service quality <--- Brand image 0.033 0.401 0.042 

Patient satisfaction <--- Brand image – 0.012 0.739 – 0.009 

Patient satisfaction <--- Service quality 0.155 *** 0.177 

Revisit intension <--- Commodity cost – 0.079 0.021 – 0.184 

Revisit intension <--- Brand image 0.108 0.001 0.145 

Revisit intension <--- Patient satisfaction 0.222 *** 0.251 

Source: Authors Calculation 

 

The average variance expected (AVE) to test convergent validity (from Table 2) for service quality is 0.734, 

revisit intension is 0.718, brand image is 0.660, commodity cost is 0.833, and patient satisfaction is 0.698, respectively. 

Here, the AVE values are more significant than 0.5, which indicates that the model has achieved convergent validity 

(Arbuckle, 2006). 

Maximum shared variance (MSV) was examined to test the discriminant validity. As depicted in Table 3, the 

MSV between BI and SQ was 0.042, which is less than the square root of the Average Variance Extracted(SRAVE) for BI 

(0.813) and service quality (0.857). Similarly, the MSV between BI and Patient Satisfaction(PS) was 0.009, which is 

smaller than both the SRAVE for BI (0.813) and PS (0.835). Furthermore, the MSV between SQ and PS was 0.177, less 

than the SRAVE for SQ (0.857) and PS (0.835). Additionally, the MSV between commodity price and intention to return 

was found to be 0.184, smaller than the SRAVE for commodity price (0.913) and return intent (0.918). (0.847). Moreover, 

the SRAVE for brand image (0.813) and the AVE for revisit intention (0.813) were both smaller than the MSV of brand 

image and revisit intention (0.145). (0.847). Accordingly, these findings support discriminant validity (Henseler et al., 

2015). 

 

Hypothesis Testing 

The path coefficient of brand image to patient revisit intention is 0.108 (p = 0.001). This indicates that null hypothesis 2 is 

rejected and that the brand image significantly influences revisiting intention to the same hospital (p < 0.05). It suggests 

that as brand image improves (social media communication, safe treatment, enough time spent by a doctor with a patient, 

equipped with the latest equipment, patient positive evaluation, etc.), the patient revisit intention into the same hospital 

will increase significantly.  

Hospital service quality is correlated with the brand image at a path coefficient of 0.033 (p = 0.401). Hence, the 

evidence does not support discarding null hypothesis 3. An organization's internal and external communication channels 

indicate its reputation and efficacy in the community from the standpoint of the signalling theory (Soliha et al., 2021; Kim 

et al., 2010). A company's name, product line, and perceived quality all play a role in determining whether or not a 

customer would return to make a purchase. 

The structure equation model's path coefficient of commodity cost to revisit intention is – 0.079 (p = 0.021). 

Therefore, commodity cost significantly negatively influences the intention to return to the same hospital, thus rejecting 

the null hypothesis 1. As a result, with the increased commodity costs (doctors' fees, cabin or bed rent, medicine prices, 

etc.), the patient revisits intention significantly and vice versa. In line with previous research, the higher commodity 

expenses were associated with a lower level of satisfaction and a reduced likelihood of return for hospital care and vice 

versa (Yang et al.,2016) 

Similarly, the correlation between brand image and hospital patient satisfaction was found to be -0.012 (p = 

0.739), refuting null hypothesis 4. While previous studies explored the relationship between BI and PS (Liu et al.,2018), a 

negative correlation was observed in Chattrogram, Bangladesh, due to physician associations of Chattrogram not 

favouring hospital management branding; other factors may also contribute to this outcome, which was not accounted for 

in previous studies. 

The path coefficient of hospital service quality to patient satisfaction is 0.155 (p < 0.000). Therefore, service 

quality has influenced patient satisfaction, thus rejecting the null hypothesis 5(p < 0.05). As a result, with the increase in 

service quality (error-free medical reports, prompt service, enough knowledge of staff, individual attention to the patient, 

physical facilities, etc.), the hospital's patient satisfaction increases significantly and vice versa. This increased patient 

satisfaction also substantially increases the intention of the patient to revisit the same hospital (Hypothesis 6). The 

connection between patient satisfaction and future hospital readmission is 0.2222 (p=0.000). We reject null 

hypothesis 6 (p < 0.05) because there is strong evidence that patients' level of satisfaction promotes their desire to 

return to the same hospital. Similar correlations between service quality and patient satisfaction have been identified 

in prior research (Fook & Dastane, 2021; Sanyal & Hisam, 2016). As a result, with increased patient satisfaction in 

hospitals, the intention to revisit patients increases significantly, and vice versa.  

 

 

 



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DISCUSSIONS 

Despite a large body of prior theoretical and empirical work, our results suggest that investigation into the connection 

between BI and follow-up visits is still in its infancy in the healthcare industry. The model's soundness was tested by 

Structural Equation Modeling (SEM). Hospital reputation, service quality, patient loyalty, and satisfaction were all shown 

to be significantly correlated. Our investigation extends further than the already available SQ and PS frameworks, both 

capable of delving into BI. To enhance patients' intentions to return to the same hospital, it is necessary to considerably 

cut the cost of commodities, such as doctors' fees, cabin or bed rent, pharmaceutical prices, ambulance service costs, etc. 

Brand image has a significant positive influence on patients' revisiting intentions to the same hospital, so the 

brand image (social media communication, safe treatment, enough time spent by a doctor with a patient, equipped with the 

latest equipment, patient positive evaluation, etc.) must be increased significantly to increase the patient's revisit intention 

to the same hospital.  

It has also been observed that brand image does not significantly influence patient satisfaction in hospitals in 

developing countries. However, in our study, brand image has no significant influence on the service quality of hospitals 

in the business capital city of Chattrogram in the developing country. Physicians’ contribution has a profound impact on 

increasing patient satisfaction rather than a brand image in Chattrogram. Leading two foreign affiliated and cobranded 

private hospitals could not attract and retain patients in their hospital by creating a brand image. The lack of authorization 

of foreign doctors to practice in the selected foreign cobranded hospitals in Bangladesh had a problem for boosting revisit 

intention.  

Moreover, as a result, show, the service quality of a hospital has a positive significant influence on patient 

satisfaction in a hospital, so the service quality (error-free medical report, prompt service, enough knowledge of staff, 

individual attention to patients, physical facilities, etc.) of the hospital must be increased significantly to increase the 

patient satisfaction in hospital in the developing country like Bangladesh. This increased patient satisfaction also 

substantially increases the intention of the patient to revisit the same hospital (Hypothesis 6). 

We found that patient satisfaction in hospitals significantly influences patient revisit intention in the same 

hospital; thus, patient satisfaction in hospitals must be increased significantly to increase the patient revisit intention in the 

same hospital in a developing country like Bangladesh. This increase in patient satisfaction in hospitals significantly 

increases patient revisit intention. This increased patient revisit intention also increases the revenue earning of the hospital 

significantly and ultimately makes a higher profit. 

From a managerial perspective, this study illuminates patient-centred care in a developing economy for 

healthcare organizations. Coordinated care pathways foster a patient-centred hospital culture (Lin & Bowman, 2022; 

Srivastava & Singh, 2020). Management can improve morale among workers and patients by adjusting the hospital's 

physical space. Building a solid reputation in this sector might increase the number of times patients visit a healthcare 

facility. In today's globally competitive market, patients have numerous options regarding where to acquire healthcare. 

This should serve as an incentive for hospital administrators to work on improving their institutions' public profiles. 

 

CONCLUSIONS 

The study found a favourable association between a hospital's brand image and the likelihood of a patient returning for 

health care. At the same time, a negative association between commodity cost and return intent was also found. However, 

in hospitals in developing countries, brand image has no appreciable effect on service quality or patient satisfaction. 

Furthermore, patient satisfaction in hospitals is significantly impacted by the quality of hospital services. However, there 

needs to be an overarching conceptual framework in the literature. The study focused on patients in private hospitals. This 

might limit the generalizability of the results. Future research will likely broaden the degree of generalizability to different 

cities or healthcare settings. The small sample size falls short of the targeted sample size. Studies often use cross-sectional 

study methodologies, but longitudinal studies should be conducted afterwards to glean extra knowledge. This gap in the 

literature can be filled by future conceptual and empirical research. It would be beneficial to extrapolate the results to 

countries like India, Pakistan, Malaysia, Singapore, etc. if the study were replicated in diverse settings. Health 

communication data gleaned from research on service quality gaps could contribute to the economic and social 

development of Bangladesh and other comparable developing countries. 

Finally, this research reveals that mere brand image does not increase patient satisfaction; a brand image with 

service quality can satisfy patients. Brand hospitals with high prices may not satisfy patients. Quality time doctors provide 

in brand hospitals, patient care by the staff, and structural ties could motivate patients to revisit hospitals. Customer 

surveys, suggestion boxes, and other feedback methods can be used to improve patients' levels of satisfaction. They can 

set themselves apart by raising their brand's awareness, fostering greater patient trust, generating significant returns, and 

expanding their organization. Hospital administrators can integrate numerous marketing tactics such as patient 

communication, service training for their workers, advertising, public relations, and online marketing to develop BI and 

gain a competitive advantage. The hospital administration should emphasize any ideas made by the patients for purposes 

of improvement and identical brand image creation. 

 

 
Author Contributions: Conceptualization, A.M.S.; Methodology, A.M.S. S.M.H.A. and  M.T.R.; Software, M.S.I.; Validation, S.M.H.A.; Formal 
Analysis, M.S.I.; Investigation, K.A.N., S.M.H.A. and A.M.S.; Resources, A.M.S., M.T.R. and K.A.N.; Data Curation, S.M.H.A.; Writing – Original 

Draft Preparation, A.M.S., M.T.R. and S.M.H.A.; Writing – Review & Editing, A.M.S. and M.T.R.; Visualization, M.S.I.; Supervision, A.M.S.; Project 

Administration, A.M.S.; Funding Acquisition,  A.M.S., M.T.R., S.M.H.A., M.S.I. and K.A. 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 the research does not deal with vulnerable 



Shahabuddin et al., American International Journal of Social Science Research 15(1) (2024), 12-20

  

19 
 

groups or sensitive issues. 

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

Acknowledgement: 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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