Exploring patient loyalty in telemedicine through bibliometric analysis and future horizons Abstract: Purpose: In an era of rapid telemedicine expansion, patient loyalty is paramount for effective healthcare delivery. This study aims to understand loyalty behaviours in telemedicine to refine services. The primary objectives are to elucidate the current state of scholarly inquiry concerning loyalty within the telemedicine sphere and to address existing research deficiencies within this domain. This exploration seeks to provide valuable insights and contribute to the advancement of knowledge in this critical area of inquiry. Design/methodology: This study employs a bibliometric analysis to investigate patient loyalty in telemedicine. By reviewing existing literature and analysing bibliometric data, the research identifies key deficiencies and addresses pertinent research questions within the telemedicine loyalty domain. This methodological approach aims to offer a comprehensive understanding of the current state of research and highlight areas requiring further investigation. Findings: The study reveals significant gaps in existing research on telemedicine loyalty, identifying a need for more focused studies on patient loyalty behaviours. Through a bibliometric analysis, the findings highlight critical areas for improvement and potential strategies for enhancing patient loyalty in telemedicine. These insights are crucial for refining telemedicine services and ensuring effective healthcare delivery. Research limitations: The findings may not capture all dimensions of patient loyalty in telemedicine, requiring further empirical studies. Future research should expand on these limitations by incorporating diverse methodologies and broader datasets to validate and extend the study’s insights. Practical implications: The insights from this study can help healthcare providers refine their telemedicine services to enhance patient loyalty. By understanding loyalty behaviours, providers can develop targeted strategies to improve patient satisfaction and retention. These practical implications are essential for the continuous improvement of telemedicine services, ensuring they meet patient needs and expectations effectively. Social Implications: Enhancing patient loyalty in telemedicine leads to significant societal benefits, particularly by improving healthcare access for underserved populations in rural or economically disadvantaged areas. Continuous and trusted care helps reduce healthcare disparities and fosters health equity, positively impacting quality of life through timely medical consultations. In the context of medical tourism, telemedicine facilitates reliable remote consultations, boosting confidence in healthcare systems abroad and benefiting local economies. Additionally, tourists can access healthcare services while traveling, enhancing their sense of safety and well-being. Overall, these advancements highlight telemedicine's potential to create a more equitable and accessible healthcare landscape. Originality/value: This study fills a critical gap in telemedicine research by focusing on patient loyalty, an area often overlooked in existing literature. The bibliometric analysis offers a novel approach to understanding and addressing loyalty behaviours. The findings contribute valuable knowledge, advancing the discourse on telemedicine loyalty and providing a foundation for future research and service improvements. Keywords: Telemedicine; loyalty; telehealth; telecare; ehealth; mhealth; bibliometric analysis 1. Introduction In an era where telemedicine is transforming healthcare, understanding the complexities of patient loyalty become a critical area with profound implications for society. Loyalty in telemedicine goes beyond mere patient-provider relationships; it serves as the foundation of trust, engagement, and accessibility in remote healthcare (Kautish et al., 2023). As virtual consultations rise in popularity, fostering loyalty between patients and telemedicine providers is essential, not only for effective healthcare delivery but also for addressing healthcare disparities and optimizing resource utilization (Chakraborty et al., 2023; Cobelli et al., 2023). In this dynamic industry, innovations are underway to enhance patient engagement and loyalty, including the integration of virtual reality (VR) platforms for immersive patient experiences and the deployment of artificial intelligence (AI) algorithms for personalized treatment recommendations. In this technological context, “telemedicine” refers to medical services delivered at a distance, while “telehealth” encompasses administration and education functions. Additionally, “eHealth” pertains to computer network applications (apps) in healthcare (Adetunji et al., 2021; Fatehi & Wootton, 2012). Complementing these concepts, “telecare” provides support to patients, often alongside existing care models (Stowe & Harding, 2010), while “mHealth” focuses on mobile information and communication technologies, empowering users to engage in their own healthcare programs (Rivera-Romero et al., 2023). This research is crucial in exploring loyalty behaviours in telemedicine for several reasons. Firstly, as remote healthcare solutions become more prevalent, understanding the factors influencing patient loyalty can significantly enhance the design and implementation of effective telemedicine services. Secondly, given the sensitive nature of healthcare, where trust is essential, examining loyalty behaviours helps identify areas where telemedicine platforms may need improvement to foster patient satisfaction and retention. Additionally, with the increasing competition in the telemedicine market, gaining insights into loyalty behaviours can give service providers a competitive advantage by enabling them to personalise their marketing strategies and service offerings more effectively. Lastly, considering ongoing global health challenges, such as the COVID-19 pandemic, where telemedicine is vital in ensuring continued access to healthcare while minimizing virus transmission, understanding and supporting patient loyalty in telemedicine services is essential for maintaining public health resilience and healthcare accessibility. Given this context, our research aims to comprehensively explore loyalty studies in the telemedicine domain through a bibliometric analysis. Our objectives are twofold: to clarify the current state of research on loyalty within telemedicine and to identify existing gaps within this research stream. These deficiencies include: (1) recognizing the importance of analysing loyalty in the telemedicine landscape; (2) identifying the level of interest and attention given to loyalty in telemedicine; (3) examining specific themes and focal areas within loyalty in telemedicine; and (4) analysing publication patterns and distribution trends in loyalty studies in telemedicine. Through this multifaceted approach, we aim to provide valuable insights and contribute to knowledge advancement in this critical area. Accordingly, this research seeks to address the following research questions (RQs) to offer academic and managerial contributions: (RQ1) Does research support the concept of loyalty behaviours in telemedicine? (RQ2) Is there increasing interest in studying loyalty in telemedicine? (RQ3) What are the specific factors influencing patient loyalty in telemedicine? (RQ4) How have publication trends in loyalty studies within telemedicine evolved, and what emerging themes or interests are evident? In this context, examining loyalty in telemedicine through a bibliometric analysis offers invaluable insights that enable scholars to explore its varied dimensions and its potential to revolutionize healthcare access, outcomes, and cost-effectiveness. This article offers a comprehensive overview of the existing literature on loyalty in telemedicine, highlighting key trends, gaps, and implications, thus paving the way for future research endeavours and practical interventions. 2. Methodological approach To address the research gaps and questions, a bibliometric analysis was conducted on loyalty studies in telemedicine. Bibliometrics studies are essential methods of literature review in scientific research (Cano-Marin et al., 2023). This research design was chosen based on the following objectives (Chaudhuri et al., 2023): (1) ensure quality in literature review and analysis findings; (2) review a large number of articles; (3) minimize biases and errors; (4) ensure validity and transparency by making the analysis replicable; (5) synthesize existing literature and structure search data within research domains; and (6) provide theoretical and managerial contributions. This design is also necessary when the study’s subject matter is underexplored, facilitating an understanding of the current state and ensuring the study’s relevance within the context (Deepa, 2024). In this review, the research design cycle was created through five successive advances, adapted from the PRISMA procedure which contains four primary stages: identification, screening, eligibility, and inclusion (Moreno-Lobato et al., 2023). 2.1 Step 1: Question formulation Figure 1 illustrates the five-stage process, beginning with the initial step of question formulation, inspired by Beloskar et al.’s study (2024). [Figure 1] 2.2 Step 2: Identification According to Deepa et al. (2024) and Moreno-Lobato et al. (2023), the second step, identification, involves selecting specific databases and defining Boolean operators to locate relevant articles. The Web of Science (WoS) and Scopus databases were used for the bibliometric analyses. The decision criteria employed in both bibliometric analyses were based on previous research (Beloskar et al., 2024; Liu & Avello, 2021; Kaur, 2024; Novitzky et al., 2023). Table 1 outlines the Boolean criteria approach used for each bibliometric analysis, focusing on loyalty within telemedicine domains. These searches were conducted on April 26, 2024, using the main keywords “loyalty” combined with “telemedicine,” “telehealth,” “telecare,” “eHealth” (or “e-health”), and “mHealth” (or “m-health”). The inclusion criteria were based on document type, limited to articles, and language, restricted to English. Table 1 shows the inclusion criteria regarding WoS categories and Scopus subject areas for the bibliometric analysis (Moreno-Lobato et al., 2023). Table 2 presents the number of records obtained from searches on the five topics. This process in the creation of ten databases, each corresponding to one of the five search criteria across the two databases (i.e., WoS and Scopus). [Tables 1 and 2] 2.3 Step 3: Screening The third step involved removing duplicate articles by merging WoS and Scopus databases for each of the five topics. Biblioshiny was used to eliminate duplicate DOI and SR values, entries without author keywords, and those lacking DOI or SR values (Cano-Marin et al., 2023; Kaur, 2024). Table 3 shows the record outcomes. [Table 3] 2.4 Step 4: Eligibility In this phase, each database was cleaned, by removing irrelevant records after reviewing abstracts, titles, and keywords (Cano-Marin et al., 2023). 2.5 Step 5: Included A second review of duplicate DOI and SR values was conducted in the merged database. After this process, a final sample of 52 records for loyalty studies in the telemedicine domain was obtained, as detailed in Table 5. The sample size was considered appropriate (Chaudhuri et al., 2023; Moreno-Lobato et al., 2023). The final step involved conducting bibliometric analyses within various software tools, including Biblioshiny, a graphical interface of the Bibliometrix R package, commonly used in modern bibliometric studies (Beloskar et al., 2024; Makaya et al., 2023; Oludapo et al., 2024). Tableau and Microsoft Excel were also employed for analysis and visualization (Alhashmi et al., 2024; Del Gesso et al., 2024). Table 4 provides an overview of key bibliometric metrics. The articles included in the study sample were published from 2004 to 2024. The average citation per document of 11.14. [Table 4] 3 Comprehensive literature review 3.1 Publication frequency by year and geographical origin Following the initial publication on loyalty in telemedicine in 2004, research activity remained low until 2017 (Table 5). However, 82.7% of the articles were published after 2017. Until April 2024, the findings indicate a Price’s Index of 67.3%, representing the percentage of references that are less than five years old (Gong, 2023; Price, 1970). Given the relatively high values of the Price Index (Liu & Avello, 2021), loyalty in telemedicine studies is considered a relevant and prominent research area. It is commonly observed that the advancement of a scientific discipline follows an exponential growth pattern, doubling in size every 10 to 15 years (Price, 1963). This evolution typically involves four stages: the precursor phase, the exponential growth period, the consolidation of knowledge, and the decline in production. Figure 2 shows that research in the field is still in its precursor phase (Liu & Avello, 2021). However, given the significant growth observed since 2021, with 26 articles published, it remains to be seen how loyalty in telemedicine will evolve. [Table 5] [Figure 2] Loyalty studies in telemedicine are primarily concentrated in the United States of America, China, Canada, the Netherlands, and Spain, as shown in Figures 3 and 4. Figure 3 also highlights evidence from emerging economies, indicating the need for further studies in developing countries, as previously suggested (Albahari et al., 2022). [Figure 3] [Figure 4] 3.2 Key thematic focuses and research directions 3.2.1 Keywords Analysing the frequency of keyword appearances provides insights into the main topics covered (Chaudhuri et al., 2023; Kaur, 2024; Liu & Avello, 2021). As shown in Table 6, terms such as “telemedicine”, “female”, “male”, “adult”, “human”, and “motivation” are the top six most frequently mentioned concepts. As observed in the word clouds in Figure 5, most of all these terms relate to gender, age, human and motivation (refer to Table 6). The tree map in Figure 6 visually represents the data from Table 6, highlighting additional topics explored, such as “telehealth”, “trust”, “controlled study” or “acceptance”. [Table 6]. [Figure 5]. [Figure 6]. 3.2.2 Co-word analysis Biblioshiny generated the correlation network map shown in Figure 7. This figure illustrates that loyalty studies in telemedicine are divided into two distinct clusters. In interpreting the correlation network map, nodes of the same colour form a cluster, with closer proximity between nodes indicating a stronger relationship (Haba et al., 2023; Kaur, 2024; Liu & Avello, 2021). Cluster 1, represented by the blue network and titled “Telemedicine”, focuses on the relationship between gender, adults, and motivation, encompassing aspects such as telehealth, physicians, mobile apps, attitude to health, customer satisfaction, questionnaires, among others. Cluster 2, shown in the red network and labelled “Model”, includes themes related to user acceptance and adoption, including information technology, satisfaction, customer engagement, and other related concepts. [Figure 7]. 3.2.3 Thematic maps Biblioshiny generates thematic maps for each bibliometric analysis, using authors’ keywords as units of analysis to uncover critical themes (Oludapo et al., 2024). These thematic maps are interpreted based on centrality, indicating their significance, and density, representing their growth, of various research themes (Kaur, 2024). In Figure 8, thematic map of loyalty studies in telemedicine highlights themes such as “loyalty”, “model”, and “satisfaction”, along with “physical activity” as fundamental themes relevant to general research. Conversely, the theme “patient satisfaction” shows a declining trend, while “benefits and financial incentives”, “logic, management, and strategies”, and “antecedents and care” are emerging themes with limited representation but rapid growth. Furthermore, themes such as “adolescents, engagement, and exposure”, “information-technology, services, and impact”, “adoption, customer engagement, and trust”, “behaviour, customer satisfaction, and questionnaire”, and “telemedicine, human, and female” are identified as motor themes driving research in the field. Scholars are encouraged to further develop these motor themes, given their significance and potential for future research on loyalty in telemedicine. [Figure 8]. 3.3 Research domain 3.3.1 Leading scholarly publications The significance of journals was assessed based on the number of articles they published and their citation metrics (Bengoa et al., 2023; Liu & Avello, 2021). Table 7 provides details on the most influential journals in loyalty studies within telemedicine, specifically those that published more than two articles in the sample of 52. The journals with the highest number of articles include the Journal of Medical Internet Research (10 articles), JMIR mHealth and uHealth (4 articles), Healthcare (2 articles), International Journal of Healthcare Management (2 articles), JMIR Research Protocols (2 articles), and Telemedicine and e-Health (2 articles) (refer to Table 7). [Table 7]. 3.3.2 Highly cited articles Analysing the most cited articles within a discipline offers valuable insights into the literature deemed most significant by the research community. The number of citations is a key indicator of both influence and attention within the scientific community. It is important to note that the articles listed in Table 8 were retrieved on April 26, 2024. While the articles remain consistent upon repeated searches, the number of citations may vary as new citations accumulate over time (Bengoa et al., 2023; Liu & Avello, 2021). Table 8 presents the 52 articles analysed in this research, along with their citation counts and the percentage of citations per year. A total of 19 articles received at least 20 citations, which is relatively modest number of citations compared to more established fields. The most cited article, with 75 citations, focuses on mobile health apps (Taki et al., 2017). Following is the research by Cheung et al. (2018), which has 40 citations and is a longitudinal study on user engagement in apps for depression and anxiety treatment. [Table 8]. 3.3.3 Predominant research techniques Research methods are essential for the systematic collection and analysis of real-world data, contributing to the advancement of scientific knowledge (Liu & Avello, 2021). Table 9 shows that most studies are empirical, accounting for 94%. Among these, quantitative methods are predominantly used (86%), with surveys as the most common method for data collection (53%), followed by experiments (20%). For data analysis techniques, covariance, and variance-based Structural Equation Modelling (SEM) methods are popular choices (47%). [Table 9] 4 Conclusions and future research agenda The bibliometric analysis conducted in this study provides a clear understanding of loyalty behaviours in telemedicine, offering valuable insights with significant implications for academia, management, society and industry. The aim is to illuminate telemedicine and its improvement, particularly from a marketing perspective. This section will address the research questions in detail. The findings reveal a significant rise in research on loyalty in telemedicine, particularly since 2017, aligning with Price’s model of scientific discipline advancement. This surge, reflected in a Price’s Index of 67.3%, underscores both the timeliness and resilience of loyalty studies in telemedicine, indicating a field poised for transformative innovations (Gong, 2023; Price, 1970). This exponential growth shows increasing scholarly interest, positioning it as a strong and expanding area of research for further exploration (RQ1 and RQ2). This analysis transcends geographical boundaries, revealing interesting patterns in research focus and themes. While loyalty studies are concentrated in regions like the United States of America, China, and others, it highlights the need for broader global representation, particularly in developing countries. A global perspective enriches scholarly discussions, fosters cross-cultural collaboration and enhances understanding of loyalty behaviours in diverse healthcare contexts worldwide (Albahari et al., 2022). Regarding co-occurrence of words, this study identifies a diverse range, with gender, age generation, motivation, and user acceptance emerging as the most frequent. These keywords highlight the multidimensional nature of loyalty behaviours and indicate that research has focused on examining these aspects. The correlation network map offers precise insights into the thematic structure of loyalty studies in telemedicine. Cluster 1, “Telemedicine”, emphasizes the role of gender, adult demographics, and motivational factors in shaping patient attitudes with themes such as telehealth, physician-patient interactions, and mobile apps highlighting the complexity of patient engagement. Cluster 2, “Model”, underscores the importance of user acceptance and adoption in driving patient loyalty, with insights into information technology, satisfaction metrics, and customer engagement, guiding providers in optimizing services and enhancing patient retention (RQ3). The thematic maps generated by Biblioshiny offer valuable insights into the landscape of loyalty studies in telemedicine, highlighting key themes driving research. Central themes like “loyalty”, “model”, and “satisfaction” underscore the importance of patient engagement and outcomes. “Physical activity” emerges as a critical theme with broader healthcare implications. While “patient satisfaction” shows a declining trend, emerging themes such as “benefits and financial incentives”, “logic, management, and strategies”, and “antecedents and care” show rapid growth. Motor themes like “adolescents, engagement, and exposure”, “information-technology, services, and impact”, and “adoption, customer engagement, and trust” propel research forward, suggesting areas for further exploration (Kaur, 2024; Oludapo et al., 2024) (RQ4). Within the scholarly landscape, this analysis identifies leading journals and highly cited articles shaping loyalty studies in telemedicine. Journals like the Journal of Medical Internet Research and JMIR mHealth and uHealth stand out for disseminating research on digital health. Highly cited articles, such as those on mobile health apps and user engagement, reflect evolving research interests (Cheung et al., 2018; Taki et al., 2017). Despite the growing attention, the relatively modest citation counts opportunities for further engagement and collaboration (Bengoa et al., 2023; Liu & Avello, 2021). In terms of research methods, this study highlights the dominance of empirical studies and quantitative methods in loyalty studies in telemedicine. Surveys are the most frequently used data collection method, emphasizing the importance of patient feedback in improving services. Additionally, covariance and variance-based SEM methods are common for data analysis, enabling researchers to examine complex relationships and causal pathways within the telemedicine framework. Future research in loyalty and telemedicine offers exciting potential, particularly in revealing how diverse factors impact patient retention. While current studies have explored loyalty implications in telemedicine, future avenues should explore further into understanding the neurological substructures of patient loyalty. For example, researchers could investigate how specific elements of telemedicine platforms, such as user interface design or communication methods, influence patients’ subconscious reactions and satisfaction by using neuromarketing techniques like electroencephalography (EEG) or eye-tracking. Moreover, studying the correlation between loyalty and patient health outcomes could offer valuable insights into how telemedicine interventions contribute to overall well-being and healthcare effectiveness. Moreover, there are several interesting avenues for further research in this field. Researchers could examine the impact of personalized telemedicine experiences on patient loyalty, by assessing how personalized treatment plans or communication strategies affect patient engagement and retention. Additionally, studying the role of telemedicine in promoting health equity and accessibility among underserved populations could provide insights into its potential to reduce healthcare disparities. Another area for exploration involves the integration of emerging technologies, such as AI or VR, into telemedicine platforms, which may offer novel ways to enhance patient experiences and foster loyalty. VR-enabled telemedicine solutions create immersive experiences, allowing patients to virtually engage with healthcare providers, enhancing the sense of presence and trust that is essential in building patient loyalty. Meanwhile, AI-powered chatbots deliver round-the-clock support, guiding patients through their care journey, potentially elevating patient satisfaction by offering immediate responses and tailored recommendations. Additionally, wearable devices and remote monitoring technologies track patients’ health metrics in real-time, empowering providers to make proactive interventions and develop personalized care plans. These innovations not only elevate the quality of care but also provide patients with convenient and cutting-edge healthcare experiences. Such technology-driven approaches are key to differentiating telemedicine services in a competitive market while fostering loyalty and engagement by meeting patients’ evolving needs for accessible and reliable care. Furthermore, the societal impact of loyalty in telemedicine goes beyond individual healthcare outcomes. Strengthening patient loyalty enhances access for underserved populations, promoting health equity and reducing disparities. Telemedicine also facilitates medical tourism by providing continuous remote care to international patients, building trust and boosting the local economy. This broader societal and economic impact is crucial for advancing equitable healthcare across various sectors. In conclusion, the field of loyalty and telemedicine has the potential to revolutionize healthcare delivery by improving patient satisfaction and fostering lasting relationships between patients and healthcare providers. The societal benefits are substantial, as increased loyalty can enhance health equity, bolster public trust, and support the medical tourism industry. Future research can uncover the underlying mechanisms driving patient loyalty, ultimately contributing to patient-centred care. 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Table 2     Number of articles within databases scope (without filtering): Distribution per source.     Database in telemedicine and loyalty telemedicine telehealth telecare ehealth mhealth Web of Science Core Collection 10 5 2 19 21 Scopus Elsevier 22 8 1 16 18 Total 32 13 3 35 39  Source: Authors own work.           Table 3     Articles within databases scope: After deleting missing DOI values, duplicates DOI values, duplicates or no SR values and no author keywords. Merged database: Web of Science Core Collection and Scopus Elsevier telemedicine telehealth telecare ehealth mhealth             Database in telemedicine and loyalty 20 1 1 20 17 Source: Authors own work. Table 4     Study sample technical characteristics.   Description Results   Timespan 2004:2024   Documents 52   Annual Growth Rate % 0,00   Document Average Age 4,85   Average citations per doc 11,14   References 2195   Authors 261   Authors of single-authored docs 1   Co-Authors per Doc 4,10   International co-authorships % 19,230         Source: Authors own work. Table 5       Frequency of publication of articles related to telemedicine and loyalty per year. Year Frequency Percentage Cumulative relative frequency 2004 1 1,9% 1,92% 2005 0 0,0% 1,92% 2006 0 0,0% 1,92% 2007 0 0,0% 1,92% 2008 0 0,0% 1,92% 2009 1 1,9% 3,85% 2010 0 0,0% 3,85% 2011 1 1,9% 5,77% 2012 1 1,9% 7,69% 2013 2 3,8% 11,54% 2014 2 3,8% 15,38% 2015 1 1,9% 17,31% 2016 0 0,0% 17,31% 2017 4 7,7% 25,00% 2018 4 7,7% 32,69% 2019 3 5,8% 38,46% 2020 6 11,5% 50,00% 2021 7 13,5% 63,46% 2022 11 21,2% 84,62% 2023 7 13,5% 98,08% 2024 1 1,9% 100,00% Source: Authors own work. Figure 2. Growth of scientific production. Source: Authors own work. Figure 2. Figure 3. Countries’ scientific production concerning telemedicine and loyalty articles. Source: Authors own work. Figure 4. Countries’ scientific production over time. Source: Authors own work. Figure 5. Articles concerning telemedicine and loyalty keywords co-occurrence network in word cloud format. Source: Authors own work. Figure 6. Articles concerning telemedicine and loyalty keywords co-occurrence network in tree map format. Source: Authors own work. Table 6. Frequency of occurrence of articles concerning telemedicine and loyalty keywords (>10 times). Keywords Frequency Percentage telemedicine 21 24,14% Female 16 18,39% Male 15 17,24% Adult 13 14,94% human 11 12,64% motivation 11 12,64% Source: Authors own work. Figure 7. Telemedicine and loyalty correlation map between keywords. Source: Authors own work. . Figure 8. Thematic strategic map concerning telemedicine and loyalty. Source: Authors own work. Table 7.. Top journals that have published more than 2 articles concerning telemedicine and loyalty.   Sources NA April. 2024 TC 2022 TCA 2022 JCR 2022 JCR edition JCR category JIF quartile 2022 JCI Rank 2022 Journal of Medical Internet Research 10 43,923 3 7.4 Science Citation Index Expanded (SCIE) MEDICAL INFORMATICS - SCIE; HEALTH CARE SCIENCES & SERVICES - SCIE Q1; Q1 5/31; 3/106 JMIR mHealth and uHealth 4 12,330 3 5 Science Citation Index Expanded (SCIE) MEDICAL INFORMATICS - SCIE; HEALTH CARE SCIENCES & SERVICES - SCIE Q2: Q1 10/31; 14/106 Healthcare 2 10,328 2 2.8 Social Sciences Citation Index (SSCI); Science Citation Index Expanded (SCIE) HEALTH POLICY & SERVICES - SSCI; HEALTH CARE SCIENCES & SERVICES - SCIE Q2; Q3 43/87; 57/106 International Journal of Healthcare Management 2 972 1 2.1 Emerging Sources Citation Index (ESCI) HEALTH POLICY & SERVICES - ESCI Q2 57/117 JMIR Research Protocols 2 4,104 1 1.7 Emerging Sources Citation Index (ESCI) PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH - ESCI; HEALTH CARE SCIENCES & SERVICES - ESCI Q3; Q3 248/400; 113/168 Telemedicine and e-Health 2 7,591 2 4.7 Emerging Sources Citation Index (ESCI) HEALTH CARE SCIENCES & SERVICES - SCIE Q1 17/106 Note: NA number of articles, TC total cites, TCA total cites per article, JCR Journal Citation Reports Impact, JIF Journal Impact Factor.   Source: Authors own work. Table 8. Articles published concerning telemedicine in marketing. Authors Year Title Journal TC TC per year Normalized TC DOI Taki, S., Lymer, S., Russell, C. G., Campbell, K., Laws, R., Ong, K. L., Elliott, R., & Denney-Wilson, E. 2017 Assessing user engagement of an mhealth intervention: Development and implementation of the growing healthy app engagement index. JMIR MHealth and Uhealth 75 9,38 2,88 10.2196/mhealth.7236 Cheung, K., Ling, W., Karr, C. J., Weingardt, K., Schueller, S. M., & Mohr, D. C. 2018 Evaluation of a recommender app for apps for the treatment of depression and anxiety: an analysis of longitudinal user engagement. Journal of the American Medical Informatics Association 40 5,71 1,64 10.1093/jamia/ocy023 Crutzen, R., Cyr, D., & de Vries, N. K. 2011 Bringing loyalty to e-health: Theory validation using three internet-delivered interventions. Journal of Medical Internet Research 37 2,64 1,00 10.2196/jmir.1837 Mitchell, M., White, L., Lau, E., Leahey, T., Adams, M. A., & Faulkner, G. 2018 Evaluating the carrot rewards app, a population-level incentive-based intervention promoting step counts across two Canadian provinces: Quasi-experimental study. JMIR MHealth and Uhealth 29 4,14 1,19 10.2196/mhealth.9912 Mitchell, M., White, L., Oh, P., Alter, D., Leahey, T., Kwan, M., & Faulkner, G. 2017 Uptake of an incentive-based mhealth app: Process evaluation of the carrot rewards app. JMIR MHealth and Uhealth 26 3,25 1,00 10.2196/mhealth.7323 Smith, A. D., & Manna, D. R. 2004 Exploring the trust factor in e‐medicine. Online Information Review 22 1,05 1,00 10.1108/14684520410564271 Lee, H., Uhm, K. E., Cheong, I. Y., Yoo, J. S., Chung, S. H., Park, Y. H., Lee, J. Y., & Hwang, J. H. 2018 Patient satisfaction with mobile health (mhealth). Application for exercise intervention in breast cancer survivors. Journal of Medical Systems 22 3,14 0,90 10.1007/s10916-018-1096-1 Lo Presti, L., Testa, M., Marino, V., & Singer, P. 2019 Engagement in healthcare systems: Adopting digital tools for a sustainable approach. Sustainability 21 3,50 1,36 10.3390/su11010220 Dale, L. P., White, L., Mitchell, M., & Faulkner, G. 2019 Smartphone app uses loyalty point incentives and push notifications to encourage influenza vaccine uptake. Vaccine 20 3,33 1,30 10.1016/j.vaccine.2018.04.018 antos-Vijande, M. L., Gómez-Rico, M., Molina-Collado, A., & Davison, R. M. 2022 Building user engagement to mhealth apps from a learning perspective: Relationships among functional, emotional and social drivers of user value. Journal of Retailing and Consumer Services 19 6,33 4,98 10.1016/j.jretconser.2022.102956 Cobelli, N., & Chiarini, A. 2020 Improving customer satisfaction and loyalty through mHealth service digitalization. The TQM Journal 19 3,80 1,61 10.1108/TQM-10-2019-0252 Stanczyk, N. E., Crutzen, R., Bolman, C., Muris, J., & de Vries, H. 2013 Influence of delivery strategy on message-processing mechanisms and future adherence to a Dutch computer-tailored smoking cessation intervention. Journal of Medical Internet Research 18 1,50 1,44 10.2196/jmir.2153 Handayani, P. W., Gelshirani, N. B., Azzahro, F., Pinem, A. A., & Hidayanto, A. N. 2020 The influence of argument quality, source credibility, and health consciousness on satisfaction, use intention, and loyalty on mobile health application use. Informatics in Medicine Unlocked 17 3,40 1,44 10.1016/j.imu.2020.100429 Crutzen, R., Ruiter, R. A., & de Vries, N. K. 2014 Can interest and enjoyment help to increase use of Internet-delivered interventions? Psychology & Health 16 1,45 1,33 10.1080/08870446.2014.921300 Schuster, L., Proudfoot, J., & Drennan, J. 2015 Understanding consumer loyalty to technology-based self-services with credence qualities. Journal of Services Marketing 16 1,60 1,00 10.1108/JSM-01-2015-0021 Lim, W. M., & Ting, D. H. 2012 Healthcare marketing: Contemporary salient issues and future research directions. International Journal of Healthcare Management 15 1,15 1,00 10.1179/204797012X13293146890048 Brower, J., LaBarge, M. C., White, L., & Mitchell, M. S. 2020 Examining responsiveness to an incentive-based mobile health app: Longitudinal observational study. Journal of Medical Internet Research 12 2,40 1,01 10.2196/16797 Anil Kumar, K., & Natarajan, S. 2020 An extension of the Expectation Confirmation Model (ECM) to study continuance behavior in using e-Health services. Innovative Marketing 12 2,40 1,01 10.21511/im.16(2).2020.02 Zečević, M., Mijatović, D., Kos Koklič, M., Žabkar, V., & Gidaković, P. 2021 User perspectives of diet-tracking apps: Reviews content analysis and topic modeling. Journal of Medical Internet Research 12 3,00   10.2196/25160 Wu, T., Deng, Z., Chen, Z., Zhang, D., Wu, X., & Wang, R. 2019 Predictors of patients’ loyalty toward doctors on web-based health communities: Cross-sectional study. Journal of Medical Internet Research 9 1,50 0,58 10.2196/14484 van Velsen, L., Flierman, I., & Tabak, M. 2021 The formation of patient trust and its transference to online health services: the case of a Dutch online patient portal for rehabilitation care. BMC Medical Informatics and Decision Making 9 2,25   10.1186/s12911-021-01552-4 Clarke, H., Clark, S., Birkin, M., Iles-Smith, H., Glaser, A., & Morris, M. A. 2021 Understanding barriers to novel data linkages: Topic modeling of the results of the lifeinfo survey. Journal of Medical Internet Research 9 2,25   10.2196/24236 Gu, D., Humbatova, G., Xie, Y., Yang, X., Zolotarev, O., & Zhang, G. 2021 Different roles of telehealth and telemedicine on medical tourism: An empirical study from Azerbaijan. Healthcare 8 2,00   10.3390/healthcare9081073 Hwang, J. Y., Kim, K. Y., & Lee, K. H. 2014 Factors that influence the acceptance of telemetry by emergency medical technicians in ambulances: An application of the extended technology acceptance model. Telemedicine and E-Health 8 0,73 0,67 10.1089/tmj.2013.0345 Rodrigues Lucena, T. F., Queiroz Negri, L., Marcon, D., & Yamaguchi, M. U. 2020 Is whatsapp effective at increasing the return rate of blood donors? Telemedicine and E-Health 8 1,60 0,68 10.1089/tmj.2019.0024 Soni, M., Jain, K., & Jajodia, I. 2021 Mobile health (mHealth) application loyalty in young consumers. Young Consumers 8 2,00   10.1108/YC-10-2020-1236 George, B. P., & Henthorne, T. L. 2009 The incorporation of telemedicine with medical tourism: A study of consequences. Journal of Hospitality Marketing & Management 7 0,44 1,00 10.1080/19368620902950097 Uei, S. L., Tsai, C. H., & Yang, M. S. 2013 Telecare service use among Taiwanese aged 60 and over: Satisfaction, trust, and continued use intention. Social Behavior and Personality: An International Journal 7 0,58 0,56 10.2224/sbp.2013.41.8.1309 Li, J., Yu, K., Bao, X., Liu, X., & Yao, J. 2021 Patterns of ehealth website user engagement based on cross-site clickstream data: correlational study. Journal of Medical Internet Research 7 1,75   10.2196/29299 Das, D., & Sengar, A. 2022 A fuzzy analytic hierarchy process-based analysis for prioritization of barriers to the adoption of eHealth in India. International Journal of Medical Informatics 6 2,00 1,57 10.1016/j.ijmedinf.2022.104830 Rosa, W. E., Lynch, K. A., Hadler, R. A., Mahoney, C., & Parker, P. A 2022 “It took away and stripped a part of myself”: Clinician distress and recommendations for future telepalliative care delivery in the cancer context. American Journal of Hospice and Palliative Medicine 5 2,50 6,43 10.1177/10499091221101883 Mönninghoff, A., Fuchs, K., Wu, J., Albert, J., & Mayer, S. 2022 The effect of a future-self avatar mobile health intervention (futureme) on physical activity and food purchases: Randomized controlled trial. Journal of Medical Internet Research 4 1,33 1,05 10.2196/32487 Shi, Y., Li, P., Yu, X., Wang, H., & Niu, L. 2018 Evaluating doctor performance: Ordinal regression-based approach. Journal of Medical Internet Research 4 0,57 0,16 10.2196/jmir.9300 Verma, P., Kumar, S., & Sharma, S. K. 2021 Evaluating the total quality and its role in measuring consumer satisfaction with e-healthcare services using the 5Qs model: a structure equation modeling approach. Benchmarking: An International Journal 4 1,33 1,05 10.1108/BIJ-09-2020-0467 Stoffel, S. T., Law, J. H., Kerrison, R., Brewer, H. R., Flanagan, J. M., & Hirst, Y. 2022 Testing the effectiveness of an animated decision aid to improve recruitment of control participants in a case-control study: Web-based experiment. Journal of Medical Internet Research 4 1,33 1,05 10.2196/40015 Lacasta Tintorer, D., Manresa Domínguez, J. M., Jiménez-Zarco, A., Rodríguez-Blanco, T., Flayeh Beneyto, S., Torán-Monserrat, P., Mundet Tuduri, X., & Saigí-Rubió, F. 2020 Efficiency as a determinant of loyalty among users of a Community of Clinical Practice: a comparative study between the implementation and consolidation phases. BMC Family Practice 3 0,60 0,25 10.1186/s12875-020-1081-x Fraze, T. K., Beidler, L. B., De Marchis, E. H., Gottlieb, L. M., & Potter, M. B. 2022 “Beyond just a supplement”: Administrators’ visions for the future of virtual primary care services. The Journal of the American Board of Family Medicine 2 0,67 0,52 10.3122/jabfm.2022.03.210479 Nunn, A., Crutzen, R., Haag, D., Chabot, C., Carson, A., Ogilvie, G., Shoveller, J., & Gilbert, M. 2017 Examining e-loyalty in a sexual health website: Cross-sectional study. JMIR Public Health and Surveillance 2 0,25 0,08 10.2196/publichealth.5393 Priescu, I., & Oncioiu, I. 2022 Measuring the impact of virtual communities on the intention to use telemedicine services. Healthcare 1 0,33 0,26 10.3390/healthcare10091685 Alzahrani, A., Qureshi, M. S., & Thayananthan, V. 2017 RFID of next generation network for enhancing customer relationship management in healthcare industries. Technology and Health Care 1 0,13 0,04 10.3233/THC-170934 Azad, M. A. K., Rumman, N. S., Connolly, R., Wanke, P., & Mumu, J. R. 2022 Towards an improved understanding of the antecedents of digital health service loyalty during a pandemic: An fsQCA approach. Socio-Economic Planning Sciences 1 0,33 0,26 10.1016/j.seps.2022.101423 Sumaedi, S., Sumardjo, S., Saleh, A., & Syukri, A. F. 2022 Factors influencing males’ loyalty toward functional foods during the COVID-19 pandemic. International Journal of Public Health Science (IJPHS) 1 0,33 0,26 10.11591/ijphs. v11i1.20886 Li, P. 2023 Ability first or opportunity first in the m-health era? A hybrid SEM-ANN approach. Aslib Journal of Information Management. 1 0,50 1,29 10.1108/AJIM-11-2022-0497 Gómez‐Rico, M., Santos‐Vijande, M. L., Molina‐Collado, A., & Bilgihan, A. 2023 Unlocking the flow experience in apps: Fostering long‐term adoption for sustainable healthcare systems. Psychology & Marketing 1 0,50 1,29 10.1002/mar.21824 Alabdali, M. A., & Husain, K. S. 2023 Understanding the relationship between patient satisfaction and loyalty through telemedicine platform quality: An empirical study. 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T. 2022 The effects of service characteristics on intention to use mobile health services: The moderating role of innovativeness and privacy concern. American Journal of Health Behavior 0 0,00 0,00 10.5993/AJHB.46.1.7 Horgan, O. Z., Crane, N. T., Forman, E. M., Milliron, B. J., Simone, N. L., Zhang, F., & Butryn, M. L. 2022 Optimizing an mhealth intervention to change food purchasing behaviours for cancer prevention: Protocol for a pilot randomized controlled trial. JMIR Research Protocols 0 0,00 0,00 10.2196/39669 Liu, P., Wang, L., & Wang, F. 2024 Evaluation of Chinese HIV mobile apps by researchers and patients with HIV: Quality evaluation study. JMIR MHealth and Uhealth 0 0,00   10.2196/52573 Faber, J. S., Al-Dhahir, I., Reijnders, T., Chavannes, N. H., Evers, A. W. M., Kraal, J. J., van den Berg-Emons, H. J. G., & Visch, V. T. 2021 Attitudes toward health, healthcare, and ehealth of people with a low socioeconomic status: A community-based participatory approach. Frontiers in Digital Health       10.3389/fdgth.2021.690182 Source: Authors own work. Table 9. Main research methods used.       Frequency Percentage Type of study (n=52)     Conceptual / review 3 6% Empirical 49 94%       Research methodology (n=49)     Qualitative 7 14% Quantitative 42 86% Mixed methods 0 0%       Data collection method (n=49)     Interviews/focus groups 6 12% Experiment 10 20% Survey 26 53% Mixed data collection 0 0% Others 7 14%       Data analysis technique (n=49)     Co-variance and variance-based SEM 23 47% Qualitative content analysis 4 8% Bivariate 0 0% Multivariate 4 8% Other statistics 18 37%       Source: Authors own work.