Hrev_master Healthcare in Low-resource Settings 2024; volume 12:12276 Hospital brand image and trust leading towards patient satisfaction: medical tourists’ behavioural intention in Malaysia Tham Noi Fook, Low Mei Peng, Yeong Wai Mun Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Selangor, Malaysia Abstract In Malaysia, hospital branding is critical to recruiting medical tourists. Reputation, service quality, and word-of-mouth influence hospital branding. Thus, hospitals and the healthcare tourism sec- tor must understand these elements to gain a competitive edge in the global market. This study investigated the effect of hospital advertising factors on healthcare tourists’ behavioural intentions (BI) in Malaysia, with emphasis on clarifying the nature of hospi- tal brand image and hospital brand trust. Additionally, the study assessed how perceived standards and satisfaction stimulate favourable BI among healthcare tourists. This study used the quantitative research-based deductive approach, where hospitals in Malaysia were the target sector. The results demonstrated that accessibility, cost, and a good web presence influenced hospital marketing for medical tourism. Furthermore, the characteristics of safety and security and effective advertising enhance trust. Moreover, patient satisfaction is critical to reduce the divide between service standards and BI, which emphasises the necessity of prioritising patients in medical facilities. Nevertheless, the find- ings were time-sensitive and not adjusted for healthcare tourism sector alterations or customer habit variations over time. Introduction The term “medical tourism” slightly diverges from normal tourism, as it primarily focuses on the medical services obtained from tourism or moving to distant regions with the desired medi- cal facility. A tourism medical index reported that Malaysia is one of the most prominent medical tourism countries.1 Characteristically, Malaysia provides several services. Reportedly, 850 thousand medical tourists have visited different regions of Malaysia for medical services. The Malaysian Healthcare Travel Council (MHTC) facilitated these visitors by conducting several initiatives by recruiting registered doctors and encouraging public and private industry collaboration.2 Medical tourists have been discussed as a substantial revenue source in Malaysia given that the Malaysian medical tourism sec- tor has a stronger competitive advantage than other Asian counter- parts. The advantages of Malaysia include a favourable exchange rate, highly qualified and trained health workers and doctors, political and effective economic stability, increased population lit- eracy rate, high-demand medical facilities, and economical medi- cal treatments.3 Malaysian medical tourism is a significant and vital revenue source for the economy, which contributed 1.3 bil- lion Malaysian Ringgit in the financial year 2022. Nonetheless, this figure remains far lower than the total 2019 revenue of 1.7 bil- lion Malaysian Ringgit. Malaysian medical tourism is witness to this substantial difference, as the after-effect or supplementary effect of the COVID-19 pandemic.4 Additionally, the MHTC requested that the medical tourism sector take advanced steps and adopt different practices to become an international hub providing optimal tourist medical services.5 To fulfil this dream, hospitals in Malaysia must embrace tourists’ expectations and focus on the factors boosting their service quality and branding. These factors include knowledge of the country, social media, price reasonable- ness (PR), safety and security (SSA), accessibility (ACC), adver- tisement (ADM), and medical tourists’ word-of-mouth. These fac- tors were reported in a recent study on Chinese medical tourists’ behavioural intentions (BI).1 Perceivably, hospitals in Malaysian regions such as Sarawak should improve their service quality and client satisfaction. This improvement would enhance their brand image and visitors’ BI.6- 7 The aforementioned studies used the concepts of brand image, service quality, satisfaction, and BI. Accordingly, this study com- bined an original empirical model and suggestions to design the following research objectives: i) to investigate the influence of factors associated with hospital branding on defining medical tourists’ BI in Malaysia, ii) to elucidate the mediation of hospital brand image (HBI) and trust (HBT) to increase medical tourists’ BI in Malaysia and, iii) to evaluate the catalytic influence of med- ical tourists’ perceived quality and satisfaction ‘ to enhance their favourable BI to visit. To fulfil these research objectives, the researcher targeted hos- pitals in a specific Malaysian region (Sarawak). The target popu- Correspondence: Tham Noi Fook, Faculty of Accountancy and Management, Universiti Tunku Abdul Rahman, Selangor, Malaysia. E-mail: thamnoifook@protonmail.com Key words: patient experience, patient satisfaction, hospital brand image, hospital brand trust, perceived service quality, behavioral intention. Conflict of interest: there is no potential conflict of interest. Funding: this research study is not funding by any institute/agency. Ethics approval: not applicable. Patient consent: not applicable. Data availability: data is available from corresponding author on request. Received: 12 January 2024. Accepted: 1 February 2024. Early access: 19 February 2024. This work is licensed under a Creative Commons Attribution 4.0 License (by-nc 4.0). ©Copyright: the Author(s), 2024 Licensee PAGEPress, Italy Healthcare in Low-resource Settings 2024; 12:12276 doi:10.4081/hls.2024.12276 Publisher's note: all claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organi- zations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher. [Healthcare in Low-resource Settings 2024;12:12276] [page 301] Non -co mmerc ial us e o nly lation was medical tourists from Indonesia. Data were collected using a self-administered questionnaire. A total of 344 valid responses were obtained. This study will be an important information source for formu- lating strategies and means of conduct for Sarawak hospitals to identify, enhance, and increase their services. Consequently, all factors triggering HBI and HBT will increase. Hence, reputations of the hospitals will flourish among medical tourists, who will demonstrate a greater tendency for visiting BI. Furthermore, this study enhances current theoretical knowledge of the factors facili- tating medical tourists’ mindset. Increased convenient service fea- tures would increase HBI and HBT in Malaysia and maximise BI. Literature review Theoretical background This study is based on Berry’s relationship marketing theory.8 The theory emphasises building and maintaining strong, long-term relationships with customers and other stakeholders. Furthermore, the theory promotes a customer-centric approach and centres the customer in all marketing efforts. Moreover, the theory states that understanding the customer’s needs and preferences is a key deter- minant of long and sustainable relationships. This focus on build- ing long and sustainable relationships with consumers represents a long-term approach to nurturing and retaining consumers. The principles of this theory were applied to this study on the factors influencing hospital branding and its relationship with medical tourists’ BI. Regarding healthcare centre branding efforts, the rela- tionship marketing theory encourages prioritising patient, directing all marketing efforts towards building a strong and positive brand image, and positively influencing the patient’s perceived service quality (PSQ) and BI. Determinants of HBI Brand image refers to the consumers’ perception of a specific brand or product.9 The HBI refers to patients’ perceptions of the quality of services offered at a hospital. This study focused on the three determinants of HBI: ACC, PR, and hospital-generated social media (HSM). The ACC The ACC is considered a key HBI component. Talarposhti10 conducted a mixed study to investigate healthcare branding in healthcare centres in Iran. Reportedly, brand accessibility was a key determinant of healthcare branding. Easy access to healthcare services encourages consumers to use healthcare services and maintain their health. Moreover, socio-economic circumstances significantly influence a person’s access to healthcare services. Therefore, healthcare branding should focus on providing fair accessibility to all community members. Similarly, Erlinda and Ratnawati11 analysed the influence of ACC on HBI in hospitals in Indonesia. The authors reported that ACC also significantly influ- enced HBI and considered it the dominant factor among the observed variables in forming the HBI. Thus, the following hypothesis (H) was proposed. H1: The ACC significantly influences HBI The PR Most consumer markets perceive higher prices as a determi- nant of high quality. Nevertheless, the healthcare industry fre- quently challenges this notion of price–quality relation. Thus, interpreting price and quality in the healthcare industry is typically challenging. Therefore, there is a lack of consensus on the costs, prices, and quality of healthcare services offered the healthcare industry in Malaysia. Beauvais et al.12 reported that higher pricing was not associated with high-quality service, thus implying that PR is a desired factor by healthcare facility consumers. Havidz and Mahaputra13 reported the significant influence of perceived price on the brand image of any product, which implied that better price perception leads to a better brand image. Therefore, the following hypothesis was suggested: H2: The PR significantly influences HBI. Social media platforms offer opportunities for consumers to exchange brand-related opinions.14 Cham et al.15 analysed the fac- tors influencing HBI in the Malaysian medical tourism industry. The authors reported the significant influence of HSM on HBI, which suggested the significant influence of social media on consumer per- ception regarding HBI. Nonetheless, the authors also reported the substantial influence of both HSM and user-generated social media (USM) on hospital branding. The findings supported the notion that medical tourists rely on social media platforms to obtain information on healthcare facilities. Similarly, Cham et al.3 reported the signifi- cant influence of HSM on medical tourists’ perception of HBI. Accordingly, the following hypothesis was formulated. H3: The HSM significantly influences HBI. Determinants of HBT Brand trust represents the brand’s promise to meet its con- sumers’ expectations and enhances customer loyalty. Brands build trust with consumers by proving their potential to meet consumers’ expectations. Brand trust also represents brand reliability.16 The healthcare sector is responsible for providing high-quality services to its consumers. Consumers prefer to obtain sufficient information on the services offered at hospitals before visiting them, which demonstrates the importance of the honesty and sincerity of hospi- tals in establishing their consumers’ trust.17 This study focused on the three determinants of HBT: SSA, ADM, and USM. The SSA Elizar et al.18 studied the interplay between customer satisfac- tion, service quality, customer trust, and customer loyalty in Indonesian hospitals. While the authors did not exclusively inves- tigate the influence of SSA on customer trust in hospitals, the find- ings considered prioritising patients’ comfort and safety as a key customer satisfaction determinant. Building trust is a significant factor in sustaining a positive relationship with consumers. Healthcare centres must assure customers of the safety of the pro- cedures offered to gain their trust.19 The World Health Organisation (WHO)20 reported that ~1 in every 10 patients is harmed in health- care facilities. Furthermore, an unsafe environment within hospi- tals leads to over three million deaths annually. The common rea- sons underlying these outcomes are the use of unsafe equipment and procedures, medical errors, and mismanagement. As such, the healthcare sector should focus on medical tourists’ SSA. The fol- lowing hypothesis was suggested as follows. H4: The SSA significantly influences HBT. Article [page 302] [Healthcare in Low-resource Settings 2024;12:12276] Non -co mmerc ial us e o nly The ADM Mohamed21 delineated several means by which the healthcare sector could build trust with patients. A key factor influencing the trust between the hospital and its consumers was the use of emo- tional advertisement campaigns. The author suggested that hospi- tals should use persuasive and emotional advertisements to arouse empathy and hope among patients. This approach could aid the hospitals in building trust with the patients. Cham et al.3 investigat- ed the influence of advertisements on HBT in Chinese medical tourism and reported a positive influence of advertisements on medical tourists’ HBI. The aforementioned influence positively affected medical tourists’ trust in the hospital brand. Heskiano et al.22 also reported the significant influence of social media adver- tisements on brand loyalty and trust in the Indonesian healthcare sector. Thus, the following hypothesis was proposed. H5: The ADM significantly influences HBT. The USM Agnisarman et al.23 investigated the influence of USM on con- sumers’ healthcare facility selection. Resultantly, user-generated anecdotal information on the healthcare facility significantly influ- enced consumers’ choices. The findings implied that USM signifi- cantly influenced HBT. Abuhmeidan24 investigated the influence of digital marketing on the brand equity of hospitals in Jordan. The authors considered two digital marketing dimensions (firm- and user-generated social media content) and reported the significant influence of user-generated content on hospital brand equity. Accordingly, the following hypothesis was formulated. H6: The USM significantly influences HBT. Influence of HBI and HBT on PSQ Healthcare facility PSQ refers to visitors’ views of the quality of services provided.25 Cham et al.15 analysed the influence of HBI on PSQ in the Malaysian healthcare sector and reported the signif- icant influence of HBI on medical tourists’ PSQ. The results sug- gested patients’ heavy reliance on HBI to interpret the quality of service offered at hospitals. Thus, hospitals can utilise their brand image to positively influence medical tourists’ PSQ. Similarly, Sukawati26 analysed the influence of HBI on the PSQ of consumers of healthcare facilities in Bali, Indonesia, and reported the signifi- cant influence of HBI on PSQ. Thus, the authors reaffirmed the importance of maintaining a good HBI to sustain positive PSQ among medical tourists. Taneja27 also reported the positive influ- ence of HBI on healthcare facility PSQ. Gur28 considered customer trust a key PSQ determinant in the healthcare sector and indicated the importance of nurturing trust between the hospital and its con- sumers to promote positive PSQ. There is a significant research gap on the influence of HBT towards PSQ. Thus, the following hypotheses were formulated based on these observations. H7: The HBI significantly influences PSQ. Influence of PSQ on BI Cham et al.15 reported the positive influence of PSQ on medical tourists’ BI regarding healthcare centres in Malaysia. The authors suggested that PSQ significantly influenced consumers’ intention to use specific healthcare facilities. The PSQ might also result in med- ical tourists repeatedly visiting a specific medical centre. Similarly, Liao et al.29 reported the significant influence of PSQ on con- sumers’ BI to purchase a product or service. Shahid Iqbal et al.30 confirmed the positive influence of PSQ on consumers’ BI in the Pakistani service sector. Similarly, Prentice and Kadan31 reported the positive influence of PSQ on consumers’ BI to re-visit and pur- chase a service in the service sector. Fatima etv al.32 suggested the significant influence of PSQ on patients’ behavioural attitudes and loyalty intentions. The authors indicated that the patients’ decisions to re-visit a healthcare facility relied on their perception of the ser- vice quality at the facility. Agyapong et al.33 examined the effect of PSQ on patients’ BI in Ghana and reported a positive and signifi- cant correlation between the two variables. The authors suggested that hospitals should focus on designing consumer-driven strategies to meet their expectations on healthcare service quality. Therefore, the following hypothesis was proposed. H8: The PSQ significantly influences BI. Mediating effect of patient satisfaction (PS) Zehra and Arshad17 studied the mediating effect of customer satisfaction on the relationship between service quality and cus- tomers’ intentions to use healthcare facilities. The authors reported a true mediation, which indicated that the patients’ PSQ signifi- cantly influenced their intentions to use healthcare services when they were satisfied with the service offered. Therefore, PS is the outcome of their reception and perception of a healthcare service. The healthcare sector aims to achieve higher PS levels by provid- ing high-quality healthcare services. Thus, a patient’s perception is crucial in evaluating services and their satisfaction with these ser- vices. Patients frequently experience satisfaction and pleasure due to high-quality service and the hospital staff’s positive demeanour. The higher satisfaction level will influence patients’ loyalty and intentions to re-visit the hospital.34 Similarly, Paradilla et al.35 sug- gested that patients’ satisfaction with service quality significantly influenced their loyalty, which represented their intentions to re- visit the hospital. Ajmal and Risal36 confirmed the significant influ- ence of PS on their loyalty to the hospital. The authors suggested that satisfaction with the service quality encouraged patients to re- visit the hospital. Cham et al.15 highlighted the mediating effect of medical tourists’ satisfaction on the relationship between PSQ and BI. The authors indicated that healthcare facilities should focus on providing high-quality services and achieving higher levels of PS to influence patients’ BI to re-visit the hospital. Therefore, the fol- lowing hypothesis was suggested H9: The PS mediates the correlation between medical tourists’ PSQ and BI. Materials and Methods Figure 1 indicates the research framework of the present study. This study used the quantitative research-based deductive approach, with the target sector being hospitals in Malaysia. Data were obtained using a self-administered questionnaire survey. The data were obtained using non-probability sampling. Medical tourists at various hospitals in Malaysia were invited to participate in the survey. Sampling and data collection This study strictly followed all research ethics and guidelines during data collection. For example, all the individuals were requested to participate voluntarily. The research information and the purpose of data collection were first communicated to the par- ticipants. Subsequently, data were only collected from the volun- Article [Healthcare in Low-resource Settings 2024;12:12276] [page 303] Non -co mmerc ial us e o nly tary participants upon ensuring their trust, confidentiality, and anonymity. Given the involved data sample, the sample size was 340 as suggested previously.37 Typically, surveys record a low response rate. Furthermore, some collected responses have > 25% missing values. Therefore, the desired sample size was achieved by distributing a total of 500 questionnaires in person to the respon- dents. The questionnaire contained demographic questions related to the respondent’s age, gender, education, marital status, number of visits to Malaysia, and how they arranged their visit to Malaysia. Key questions on the study variables are described in the following section. As this study focused on Indonesian medical tourists, the researcher translated the questionnaire into Malay and obtained data via convenience sampling. Measures of the constructs The main questionnaire body was designed following previous empirical studies, which included the complete phrases of variable items. The ACC was measured using four items,15 PR was mea- sured using three items,38 HSM was measured using a three-item scale,39 SSA was measured using a five-item scale,15 USM was measured using three items,39 and ADM was measured using six items.40 The mediators HBI, HBT, PSQ, and PS were measured using three items,15 four items,41 a five-item scale,15 and four items,42 respectively. Lastly, the dependent variable BI was mea- sured using a three-item scale.43 Data were analysed by using the SPSS and CB-SEM. Results Respondents’ demographic profile Table 1 presents the 344 respondents’ demographic profile. Most respondents were male (52.3%), between 25 and 30 years old (52.3%), married (61%), and visited the hospitals for clinical treat- ment options (58.1%). Multicollinearity analysis In a multiple regression model, a higher correlation between multiple independent constructs results in the issue of multi- collinearity.44 In a regression analysis, the variance inflation factor (VIF) is a measure of multicollinearity.45 The VIF threshold is 3 or 5.46 Table 2 presents the VIF results. The resultant values against all construct items were under both threshold ranges, thus indicat- ing the absence of multicollinearity in the dataset. Confirmatory factor analysis The estimated linkages in the reflective measurement model were outer loadings. The outer loadings indicate the direction from the latent constructs to their indicators.47 In structural equational modelling analysis, the outer loadings value ranges from 0 to 1, and the cut-off value is 0.6.48 Table 3 presents the outer loading results, where all values in the table were >0.60. One ACC item, two ADM items, one PS item, two SSA items, and one PSQ item were deleted following low factor loading values. The reliability of variables was measured using Cronbach’s alpha (α). The Cronbach alpha threshold value is >0.70. Table 3 presents the internal consis- tency reliability results. The ACC, ADM, BI, HBI, HBT, HSM, PR, PS, PSQ, SSA, and USM alpha value was 0.88, 0.59, 0.83, 0.90, 0.87, 0.68, 0.84, 0.92, 0.81, and 0.85, respectively. Therefore, the dataset was reliable. Convergent validity is used to examine how closely the measurement tests are associated with the tests used to measure identical variables. Convergent validity is mea- sured with two indicators: average variance extracted (AVE) and composite reliability (CR). The AVE and CR value should be >0.50 and >0.70 to ensure the existence of true reliability in the dataset.49 Table 3 presents the convergent validity results. All resul- tant values met the standard criteria, thus indicating that the data were reliable, normally distributed, and accurate. Discriminant validity Discriminant validity determines whether theoretically unrelated variables are actually unrelated.50 In this study, discriminant validity was measured using Fornell-Larcker’s (1981) criterion. Discriminant validity is evaluated by comparing the AVE square root of each vari- able.51 The results in Table 4 demonstrate that the AVE square root value of each variable was higher than the latent construct correla- tions. Thus, the results established discriminant validity. Article Figure 1. Proposed research model. Table 1. Respondents’ demographic profile. Characteristic Frequency Percentage Gender Male 180 52.3 Female 164 47.7 Total 344 100.0 Age (years) 25–30 110 34.7 31–35 80 24.2 36–40 90 26.1 40–45 48 14.05 > 45 16 0.05 Total 344 100.0 Marital status Married 210 61 Unmarried 134 39 Total 344 100.0 Treatment type Clinical 200 58.1 Surgical 94 27.4 Other 50 14.5 Total 344 100.0 [page 304] [Healthcare in Low-resource Settings 2024;12:12276] Non -co mmerc ial us e o nly R2 The R2 results in Table 5 demonstrate that BI, HBI, HBT, PS, and PSQ contributed 25.8%, 31.4%, 54.8%, 23.3%, and 46.5% to their relevant variables, respectively. Measurement model Figure 2 depicts the measurement model of the study. Model fitness The goodness of model fit was measured using the indicators SRMR and NFI. SRMR is defined as, “the difference between the observed correlation and the model implied correlation matrix.”52 NFI is defined as “NFI is given by the relative location of the cur- rent model between the saturated model with TS=0 and the inde- pendence model TI.”53 According to Dijkstra and Henseler (54), “d_ULS (i.e., the squared Euclidean distance) and d_G (i.e., the geodesic distance) represent two different ways to compute this Article Table 2. Multicollinearity analysis. VIF ACC2 2.363 ACC3 2.714 ACC4 2.386 ADM3 1.369 ADM4 2.678 ADM5 3.436 ADM6 2.506 BI1 1.823 BI2 1.97 BI3 1.942 HBI1 2.548 HBI2 3.642 HBI3 3.358 HBT1 2.217 HBT2 3.319 HBT3 3.417 HBT4 3.015 HSM1 2.245 HSM2 2.981 HSM3 2.304 PR1 1.353 PR2 1.321 PR3 1.326 PS2 1.859 PS3 2.051 PS4 2.212 PSQ2 3.057 PSQ3 3.856 PSQ4 3.608 PSQ5 3.257 SSA1 1.423 SSA2 2.54 SSA3 2.406 USM1 2.058 USM2 2.202 USM3 2.023 Note: ACC, accessibility, PR, price reasonableness, HSM, hospital-created social media, SSA, safety and security, USM, user-generated social media, ADM, advertisement, HBI, hospital brand image, HBT, hospital brand trust, PSQ, perceived service quality, PS, patient satisfaction, BI, behavioural intention. Table 3. Outer loading values. Items Alpha CR (rho_a) CR (rho_c) AVE ACC2 0.902 0.882 0.887 0.927 0.808 ACC3 0.904 ACC4 0.891 ADM3 0.715 0.859 0.86 0.906 0.707 ADM4 0.875 ADM5 0.904 ADM6 0.858 BI1 0.859 0.831 0.831 0.898 0.747 BI2 0.869 BI3 0.864 HBI1 0.904 0.909 0.909 0.943 0.846 HBI2 0.933 HBI3 0.922 HBT1 0.852 0.913 0.913 0.939 0.793 HBT2 0.91 HBT3 0.906 HBT4 0.895 HSM1 0.872 0.876 0.88 0.924 0.801 HSM2 0.924 HSM3 0.889 PR1 0.77 0.686 0.693 0.826 0.613 PR2 0.766 PR3 0.812 PS2 0.857 0.800 0.843 0.905 0.762 PS3 0.874 PS4 0.887 PSQ2 0.891 0.843 0.927 0.948 0.819 PSQ3 0.918 PSQ4 0.907 PSQ5 0.904 SSA1 0.819 0.926 0.818 0.888 0.726 SSA2 0.878 SSA3 0.858 USM1 0.888 0.813 0.859 0.91 0.77 USM2 0.874 USM3 0.871 [Healthcare in Low-resource Settings 2024;12:12276] [page 305] Non -co mmerc ial us e o nly discrepancy”. The SRMR should be < 0.08,55 while the NFI should be ≥ 0.90.56 Table 6 demonstrates that the values of the model were not a good fit overall. The NFI value was < 0.90, as the sample size was small according to item-to-response theory. The small sample size primarily resulted from item deletions based on low factor loading values. Structural equation modelling (SEM) The hypotheses were evaluated using SEM. Table 7 presents the SEM results, where the hypotheses were supported with a p- value < 0.05. There was support for the association between ACC and HBI (p=0.00), ADM and HBT (p=0.007), HBI and PSQ (p=0.00), HBT and PSQ (p=0.00), HSM and HBI (p=0.00), PR and HBI (p=0.048), PS and BI (p=0.00), PSQ and BI (p=0.00), PSQ and PS (p=0.00), and SSA and HBT (p=0.00). Nevertheless, the relationship between USM and HBT was not supported (p=0.436). The PS mediation of PSQ and BI was supported (p=0.00). Discussion This study examined the aspects that influence hospital brand- ing and its relevance to medical tourists’ BI. The examination of multiple hypotheses clarified the complicated dynamics of medical service quality and its influence on patients’ intentions. The first three hypotheses were supported and addressed the influence of ACC, PR, and hospital-created social networking sites on HBI. This result indicated that these variables are important in determin- ing medical tourists’ opinions of the reputation of a hospital. The findings highlighted the necessity of hospitals having quick access, affordable pricing, and a strong internet presence to develop a favourable reputation, which is critical for recruiting medical tourists. The H4–6 focused on the influence of SSA, ADM, and USM on HBT. The H4 and H5 were supported whereas H6 was not. This finding suggested that SSA measures and efficient adver- tising initiatives contribute to the development of trust among Article Table 4. Discriminant validity. ACC ADM BI HBI HBT HSM PR PS PSQ SSA USM ACC 0.899 ADM 0.598 0.841 BI 0.597 0.468 0.864 HBI 0.382 0.611 0.374 0.92 HBT 0.441 0.593 0.386 0.532 0.891 HSM 0.461 0.583 0.421 0.529 0.866 0.895 PR 0.196 0.301 0.211 0.261 0.326 0.298 0.783 PS 0.772 0.653 0.471 0.511 0.558 0.493 0.242 0.873 PSQ 0.445 0.615 0.394 0.533 0.644 0.687 0.45 0.482 0.905 SSA 0.403 0.547 0.35 0.557 0.694 0.721 0.419 0.487 0.81 0.852 USM 0.373 0.419 0.373 0.741 0.355 0.354 0.272 0.379 0.371 0.367 0.878 Figure 2. Measurement model. [page 306] [Healthcare in Low-resource Settings 2024;12:12276] Non -co mmerc ial us e o nly healthcare visitors. Nonetheless, user-generated social networking content had limited influence, which indicated that hospitals should emphasise other aspects to increase trust. The H7 and H8 referred to the influence of HBI and HBT on PSQ, and both were supported. This result suggested that a favourable HBI and HBT positively influence PSQ. Medical visitors tend to correlate these aspects with the standard of medical services, which emphasises the necessity for brand image management and trust development. Additionally, H9 stated that PSQ substantially influences health- care tourists’ BI. The H9 suggested that a favourable impression of service standards leads to a greater desire to return or recommend the hospital facility. The H9 emphasises the importance of service standards in influencing healthcare tourists’ behaviour. Lastly, H10 stated that PS mediated PSQ and BI. This hypothesis suggested that patient happiness is key to narrowing the disparity between PSQ and medical tourists’ aspirations. A satisfied customer is more inclined to display good BI, which highlights the need for hospitals to prioritise patient happiness. The current findings coincided with previous study outcomes on healthcare tourism and medical ser- vice standards. The validation of the influence of ACC, PR, and hospital-created online platforms on HBI paralleled previous research. Cham et al.15 emphasised the importance of these aspects in determining patients’ views and preferences when selecting healthcare facilities. Similarly, accepting the assumptions of secu- rity and protection and the efficacy of commercials corresponded with Yasui57 emphasis on the importance of trust-building and mar- keting methods in healthcare environments. Furthermore, the established ideas on the influence of HBI and HBT towards PSQ correlate with Cham3 study, which demonstrated the interdepen- dence of these factors. Moreover, the findings supported a previ- ously documented association between PSQ and client happiness in assessing medical tourists’ BI. Rahman6 emphasised the signifi- cance of these elements in anticipating healthcare tourists’ inten- tions. The author highlighted that enhanced service experiences and satisfied customers increase the probability of return visits or recommendations. Implications This study provided the following theoretical contributions and practical implications. Theoretical contributions This study on the variables affecting hospital branding and its association with medical tourists’ BI clarified the key variables influencing medical tourists’ decision-making procedures, which eventually affect the healthcare sector and hospital administration. The study underscored the significance of hospital advertising in the healthcare tourism framework. This finding highlighted the importance of hospital credibility, image, and overall quality of offerings when attracting medical visitors. This insight would aid hospitals in recognising the necessity of good branding initiatives to succeed in the international medical tourism industry. The study also identified and examined the characteristics influencing medical visitors’ decision-making. These characteris- tics include service quality, confidence, word-of-mouth, and hospi- tal online presence. Understanding these characteristics would allow hospitals to focus on specific elements that connect their branding efforts with potential healthcare tourists. Additionally, this study examined the relationship between hospital advertising and medical tourists’ BI. The findings demonstrated that the favourable perception of a brand to attract medical travellers and increase their desire to select a specific healthcare facility. Such selection has important consequences for hospitals aiming to engage in the healthcare tourism industry. Article [Healthcare in Low-resource Settings 2024;12:12276] [page 307] Table 7. Discriminant validity. Relationship Original sample Sample mean Standard deviation T statistic ACC -> HBI 0.168 0.168 0.062 2.719 ADM -> HBT 0.292 0.295 0.058 5.03 HBI -> PSQ 0.264 0.265 0.066 3.972 HBT -> PSQ 0.505 0.503 0.061 8.32 HSM -> HBI 0.421 0.421 0.067 6.28 PR -> HBI 0.103 0.107 0.052 1.973 PS -> BI 0.366 0.368 0.055 6.69 PSQ -> BI 0.218 0.218 0.059 3.715 PSQ -> PS 0.482 0.483 0.046 10.425 SSA -> HBT 0.519 0.517 0.052 9.993 USM -> HBT 0.042 0.042 0.054 0.78 PSQ -> PS -> BI 0.177 0.178 0.031 5.751 Table 5. The R2 values. R2 Adjusted R2 BI 0.258 0.254 HBI 0.314 0.308 HBT 0.548 0.544 PS 0.233 0.23 PSQ 0.465 0.462 Table 6. Model fitness. Saturated model Estimated model SRMR 0.061 0.163 d_ULS 2.497 17.683 d_G 2.632 3.667 Chi-square 4168.457 5178.151 NFI 0.664 0.583Non -co mmerc ial us e o nly Practical implications This study presented useful recommendations for hospitals and healthcare organisations aiming to succeed in medical tourism. Hospitals can establish tailored branding and promotional strate- gies by identifying the factors influencing medical visitors’ deci- sion-making. Moreover, hospitals may invest to enhance service quality, establish trust, and increase internet presence, all of which are key to attracting and maintaining medical visitors. This under- standing can increase patient influx and income. Additionally, the findings can aid healthcare tourism destinations and politicians in developing appropriate rules and laws. Politicians as well as healthcare tourism destination countries may use the data to pro- mote and assist hospitals in successful marketing and service qual- ity, which would strengthen the social economy and healthcare system. Conversely, underperforming hospitals may be guided on areas for development and result in broad expansion of the health- care tourism industry. Moreover, the implications for medical tourism are extensive. Medical tourists can make educated deci- sions on their medical options with an awareness of the key factors influencing the reputation and service level of a hospital. Such consciousness can result in better outcomes, increased satisfaction, and a better experience for healthcare tourists, which would benefit the industry image. Limitations and future directions The findings are time-sensitive and not adjusted for healthcare tourism sector changes or customer habit variations over time. Continuous data and tracking were not conducted to precisely record patterns and shifts. Furthermore, cultural differences and changes in healthcare tourism were not adequately considered. Diverse cultural norms, demands, and healthcare systems reflected varying effects on the highlighted parameters. Future research should examine how cultural variations affect medical tourists’ perceptions of hospital advertising and BI. Comparative research across diverse cultural settings could elucidate the complexity of healthcare decisions. Furthermore, the study overlooked external factors, such as political stability, socio-economic situations, and public health emergencies (pandemics), which could substan- tially influence medical visitors’ decision-making. Moreover, future research should investigate the influence of new technolo- gies, which include telemedicine, artificial intelligence, and machine learning, on modifying hospital marketing and healthcare tourists’ selections. Future studies should examine the influence of these advances on the standard and perception of medical services. Lastly, future studies should investigate the influence of public– private collaborations on hospital marketing and healthcare tourist recruitment. Such studies should examine the effect of legislation in fostering such alliances. Conclusions This study investigated the elements influencing hospital branding and its association with healthcare tourists’ BI. The tested hypothesis yielded useful insights into the mechanisms of health- care marketing and its influence on medical visitors’ decision- making processes. The findings indicated that ACC, PR, and HSM contribute to the establishment of a favourable HBI, which affects PSQ. Furthermore, SSA and ADM were key influencers of HBT, which then affected PSQ. Nevertheless, the findings did not sup- port the idea that USM substantially influenced HBT, which emphasised the importance of hospitals prioritising regulated channels of communication. Reputation, trust, quality of service, and patient happiness were highly correlated, all of which were critical to attain healthcare tourists’ goals. Then, the study dis- cussed its implications, limitations and presented research direc- tions to subsequent scholars. References 1. Cham T-H, Lim Y-M, Sia B-C, et al. Medical tourism destina- tion image and its relationship with the intention to revisit: A study of Chinese medical tourists in Malaysia. J China Tourism Res 2021;17:163-91. 2. Statisa. Number of people who travelled to Malaysia for healthcare from 2013 to 2022(in 1,000s)Number of people who travelled to Malaysia for healthcare from 2013 to 2022(in 1,000s). 2023. 3. Cham TH, Lim YM, Sigala M. Marketing and social influ- ences, hospital branding, and medical tourists’ behavioural intention: Before-and after-service consumption perspective. Int J Tourism Res 2022;24:140-57. 4. Statista. Revenue from medical tourism in Malaysia from 2013 to 2022 2023. 5. Nilashi M, Samad S, Manaf AA, et al. Factors influencing medical tourism adoption in Malaysia: A DEMATEL-Fuzzy TOPSIS approach. Computers & Industrial Engineering. 2019;137:106005. 6. Rahman MK. Medical tourism: tourists’ perceived services and satisfaction lessons from Malaysian hospitals. Tourism Rev 2019;74:739-58. 7. Rahman MS, Bag S, Hassan H, et al. Destination brand equity and tourist’s revisit intention towards health tourism: an empir- ical study. Benchmarking: An International Journal 2022;29:1306-31. 8. Berry LL. Relationship marketing of services-growing inter- est, emerging perspectives. J Academy Marketing Sci 1995; 23:236-45. 9. Oktavanny AY, Sulistiadi W. The Determinant Factors of Customer Satisfaction: Promotion, Service quality and Brand image. IJEBD 2022;5:312-22. 10. Talarposhti MA, Mahmoudi G, Jahani M-A. A model for health branding based on a service providers approach. Acta Facultatis Medicae Naissensis 2022;39:347-60. 11. Erlinda MR, Ratnawati A. Increasing Customer Retention through Digital Marketing and Paramedic Competency with Hospital Brand Image as Intervening Variable. e-Academia Journal 2022;11:18270. 12. Beauvais B, Gilson G, Schwab S, et al. Overpriced? Are Hospital Prices Associated with the Quality of Care? Healthcare (Basel) 2020;8:135. 13. Havidz HBH, Mahaputra MR. Brand image and purchasing decision: Analysis of price perception and promotion (litera- ture review of marketing management). Dinasti Int J Econ Finance Accounting 2020;1:727-41. 14. Cheah J-H, Ting H, Cham TH, Memon MA. The effect of self- ie promotion and celebrity endorsed advertisement on deci- sion-making processes: A model comparison. Internet Res 2019;29:552-77. 15. Cham TH, Cheng BL, Low MP, Cheok JBC. Brand image as the competitive edge for hospitals in medical tourism. Eur Business Rev 2020;33(1). 16. Kustini NI. Experiential marketing, emotional branding, and Article [page 308] [Healthcare in Low-resource Settings 2024;12:12276] Non -co mmerc ial us e o nly brand trust and their effect on loyalty on Honda motorcycle product. J Econ Business Accountancy Ventura 2011;14(1). 17. Zehra SJ, Arshad U. Brand trust and image: Effect on cus- tomers’ satisfaction. J Marketing Logistics 2019;2:50-64. 18. Elizar C, Indrawati R, Syah TYR. Service quality, customer satisfaction, customer trust, and customer loyalty in service of Paediatric Polyclinic over Private H Hospital of East Jakarta, Indonesia. J Multidisciplinary Academic 2020;4:105-11. 19. Jameson M. 7 Reasons Branding is Important for Your Healthcare Practice. 2019. 20. WHO. Patient Safety. World Health Organization; 2023. 21. Mohamed NSP. Branded Healthcare Environment: Building Trust among Patients. 2022. 22. Heskiano H, Syah TYR, Hilmy MR. Social Media Marketing Relations, Brand Awareness to Brand Loyalty Through The Brand Image. J Multidisciplinary Academic 2020;4:208-14. 23. Agnisarman S, Ponathil A, Lopes S, Madathil KC. An investi- gation of consumer’s choice of a healthcare facility when user- generated anecdotal information is integrated into healthcare public reports. Int J Industrial Ergon 2018;66:206-20. 24. Abuhmeidan TM. The influence of digital marketing on brand equity for private hospitals in Jordan. PhD thesis. University of Bedfordshire. 2023. 25. Ramez WS. Patients’ perception of health care quality, satis- faction and behavioral intention: an empirical study in Bahrain. Int J Business Social Sci 2012;3(18). 26. Sukawati TGR. Hospital brand image, service quality, and patient satisfaction in pandemic situation. JMMR 2021;10: 120-7. 27. Taneja U. Brand Image Perceived Service Quality Patient Satisfaction And Loyalty An Empirical Study In The Healthcare Sector. Health Serv Manag Res 2021;34:250-7. 28. Gur A. Customer trust and perceived service quality in the healthcare sector: Customer aggressive behaviour as a media- tor. J Trust Res 2020;10:113-33. 29. Liao S-H, Hu D-C, Chou H-L. Consumer Perceived Service Quality and Purchase Intention: Two Moderated Mediation Models Investigation. SAGE Open 2022;12:21582440 221139469. 30. Shahid Iqbal M, Ul Hassan M, Habibah U. Impact of self-ser- vice technology (SST) service quality on customer loyalty and behavioral intention: The mediating role of customer satisfac- tion. Cogent Business & Management 2018;5:1. 31. Prentice C, Kadan M. The role of airport service quality in air- port and destination choice. J Retailing Consumer Serv 2019;47:40-8. 32. Fatima T, Malik SA, Shabbir A. Hospital healthcare service quality, patient satisfaction and loyalty: An investigation in context of private healthcare systems. Int J Quality Reliability Manag 2018;35:1195-214. 33. Agyapong A, Afi JD, Kwateng KO. Examining the effect of perceived service quality of health care delivery in Ghana on behavioural intentions of patients: the mediating role of cus- tomer satisfaction. Int J Healthc Manag 2018;11:276-88. 34. Supangat DW, Noor NB, Thamrin Y. The Effect of Brand Image and Patient Satisfaction on Patient Loyalty in Outstanding Installations Dr. Tadjuddin Chalid Makassar. J Asian Multicultural Rese Med Health Sci Study 2022;3:1-9. 35. Paradilla M, Nurfitriani N, Awawiriam S. The Effect of Brand Image on Loyalty through General Patient Satisfaction as an Intervening Variable in Makassar City Hospital. J Asian Multicultural Rese Med Health Sci Study 2022;3:67-75. 36. Ajmal A, Risal M. Brand Image, Service Quality And Patient Satisfaction On Patient Loyalty. Jurnal Mantik 2022;6:280-5. 37. Hair JF, Ringle CM, Sarstedt M. PLS-SEM: Indeed a silver bullet. J Marketing Theory Practice 2011;19:139-52. 38. Kumar A, Gupta S, Kishore N. Measuring Retailer Store Image: A Scale Development Study. Int J Business Econ 2014;13(1). 39. Cham TH, Lim YM, Aik NC, Tay AGM. Antecedents of hos- pital brand image and the relationships with medical tourists’ behavioral intention. Int J Pharm Healthcare Marketing 2016;10:412-31. 40. Inoni OR. Impact of product attributes and advertisement on consumer buying behaviour of instant noodles. Izvestiya J Varna University Economics 2017;61:393-413. 41. Chaudhuri A, Holbrook MB. The chain of effects from brand trust and brand affect to brand performance: the role of brand loyalty. J Marketing 2001;65:81-93. 42. Panjakajornsak V. A comprehensive model for service loyalty in the context of Thai private hospitals. AU J Management 2008;6:60-73. 43. Choi K-S, Cho W-H, Lee S, et al. The relationships among quality, value, satisfaction and behavioral intention in health care provider choice: A South Korean study. J Business Res 2004;57:913-21. 44. Kim JH. Multicollinearity and misleading statistical results. Korean J Anesthesiol 2019;72:558-69. 45. Oke J, Akinkunmi W, Etebefia S. Use of correlation, tolerance and variance inflation factor for multicollinearity test. GSJ 2019;7(5). 46. Kock N, Lynn G. Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. J Assoc Information Systems 2012;13(7). 47. Rashid A. Structural equation modeling. 2020. 48. Kamis A, Saibon RA, Yunus F, et al. The SmartPLS analyzes approach in validity and reliability of graduate marketability instrument. Social Psychol Educ 2020;57:987-1001. 49. Hanafiah MH. Formative vs. reflective measurement model: Guidelines for structural equation modeling research. Int J Analysis Applications 2020;18:876-89. 50. Tsfati Y. Personality factors differentiating selective approach, selective avoidance, and the belief in the importance of silenc- ing others: Further evidence for discriminant validity. Int J Public Opinion Res 2020;32:488-509. 51. Roemer E, Schuberth F, Henseler J. HTMT2–an improved cri- terion for assessing discriminant validity in structural equation modeling. Industrial Management Data Systems 2021;121: 2637-50. 52. Smart-PLS. Fit Measures in SmartPLS 2023. Available from: https://www.smartpls.com/documentation/algorithms-and- techniques/model-fit/ 53. Bentler PM, Bonett DG. Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bull 1980;88:588. 54. Dijkstra TK, Henseler J. Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis 2015;81:10-23. 55. Higgins GE. Digital piracy: An examination of low self-con- trol and motivation using short-term longitudinal data. CyberPsychology & Behavior 2007;10:523-9. 56. GMBH S. Model Fit 2023 [Available from: https://www.smartpls.com/documentation/algorithms-and- techniques/model-fit/. 57. Yasui KM. Using Community Voice to Build Trust in State Systems. 2023. Article [Healthcare in Low-resource Settings 2024;12:12276] [page 309] Non -co mmerc ial us e o nly