Resistance of multiple stakeholders to e-health innovations: Integration of fundamental insights and guiding research paths

Consumer/user resistance is considered a key factor responsible for the failure of digital innovations. Yet, existing scholarship has not given it due attention while examining user responses to e-health innovations. The present study addressed this need by consolidating the existing findings to provide a platform to motivate future research. We used a systematic literature review (SLR) approach to identify and analyze the relevant literature. To execute the SLR


Introduction
E-health (or electronic health) technologies refer to various innovations that support the delivery of medical care and other healthcare services via the Internet or mobile apps.The key innovations in the healthcare space are mobile health applications (mHealth apps), webbased telemedicine services, health cloud, smart clothing systems, information technology-based assistive technology services, electronic medical record (EMR) systems, clinical decision support systems (CDSSs), RFID application in managing various forms of healthcare, and computerized physician/provider order entry (CPOE) (Barrett & Stephens, 2017;Bush et al., 2017;de Wit et al., 2019;Dubin et al., 2020).
The use of these innovations has been widely acknowledged to support diagnosis and improve the delivery of medical services.Over the years, digital technology-driven healthcare systems have become more potent, competent, fast, and beneficial in identifying illness and treatment (Kumari et al., 2018;Tanwar et al., 2020).In light of this, one would expect digitally delivered healthcare (e-health hereinafter) to become an integral part of the healthcare interface, beginning from econsultation and ending with recovery through virtual care.However, until the beginning of 2020, this was not the case.Even in most advanced countries, e-health initiatives varying from telemedicine to CDSS have witnessed low diffusion due to resistance from multiple stakeholders, including patients, doctors, clinical staff, and hospital management.For instance, some reports note that in the United States, at the beginning of 2019, only a few healthcare systems had implemented virtual care (e.g., Mehrotra and Prewitt, 2019).However, things changed considerably with the surfacing of the COVID-19 crisis, particularly after the World Health Organization declared it a pandemic in March 2020 (Laato et al., 2020;Miri et al., 2020).With a surge in infections and the consequent social distancing requirements, health systems across the globe made a massive move towards e-health technologies to provide virtual medical treatment (Webster, 2020).However, scholars have recently observed certain trends that do not bode well for plans for the long-term full-fledged shift of healthcare delivery through e-health systems.For instance, Webster (2020) noted that many doctors are still wary of e-health innovations, despite the pandemic.Others suggest that the care has already started shifting back to usual physical interactions (Mehrotra et al., 2020).This raises the question of whether the healthcare systems will relapse to their rather stiff adherence to the physical mode of consultation, treatment, and care once the pandemic recedes; or has the face of healthcare, as far as e-health is concerned, transformed forever?
The query is deceptively simple, but the answer is not easy to ascertain.The issue is not only of habit and adherence to a certain way of doing things.There are more intricacies involved.On the one hand, ehealth systems offer benefits in the form of cost savings, expediency, and inclusiveness (e.g., Totten et al., 2019), but on the other hand, it is believed that these systems may not offer the quality of care that many doctors believe is possible only through physical interaction (e.g., Webster, 2020).Or is this just a matter of perception?Clearly, there is much to understand and evaluate.Indeed, with the healthcare sector as a whole being at crossroads, these aspects warrant deep contemplation and incisive insights such that the concerned stakeholders are able to strike the right balance, thereby benefitting the world through the expansion of e-health systems without compromising the quality of care.
The onus is now on academic research to shed light on varied embodiments, lineation patterns, and perceptions related to the adoption/ non-adoption of e-health innovations such that the underlying dynamics, granularities, and complex nuances are better understood.We argue that to formulate a well-rounded response to the friction between the anticipated trajectory and unforeseen impediments in the way of diffusion of e-health innovations, it is essential to understand and address negative perceptions and barriers of the end-users, including doctors, patients, and hospitals.We further contend that to fully appreciate the potential inhibitors that might obstruct the diffusion of ehealth innovations in times to come, it is essential to look back in the past to diagnose the factors that caused multiple stakeholders to resist these innovations before the onset of the pandemic, during the lockdown phase of the pandemic, and immediately after the lockdown restrictions were eased.In sum, there is a need to understand the consumer/user behavior of different stakeholders in the healthcare ecosystem to offer a clearer perspective on the future diffusion of e-health innovations.
A comprehensive review of the literature evolved through different phases of digitalization of healthcare, from healthcare 1.0 (1970 s), healthcare 2.0 (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005), healthcare 3.0 (2006-2015) to healthcare 4.0 (since 2016) (Tanwar et al., 2020), reveals that although there is a reasonable volume of literature examining end-user perspectives, these studies have largely focused on adoption, considering non-adoption only as a by-product of the absence of adoption drivers.In comparison, studies focusing specifically on factors associated with the non-adoption of e-health innovations are limited.Since the extended literature suggests that non-adoption is not merely an outcome of the absence of factors that motivate adoption, rather it is a manifestation of consumer resistance (e.g., Talwar et al., 2020b), more research exclusively examining consumer resistance towards e-health innovations is required to guide practice better.
We suggest that to encourage future research addressing this specific requirement, there is a need to integrate the current knowledge, which is fragmented across narratives, creating theoretical confusion.In concordance, our study endeavors to synthesize the drivers of nonadoption of e-health innovations by different participants in the healthcare ecosystem.Towards this end, we propose to organize the state-of-the-art literature into meaningful clusters to structure the findings in a more relatable and reproducible way.In addition, we plan to bring forth the dilemmas, contradictions, and limitations in the existing knowledge to map future research needs.Specifically, we propose to address three research questions (RQs): RQ1.Why do multiple internal and external stakeholders resist e-health innovations across various healthcare contexts?RQ2.What are the methodological and conceptual deficiencies in the extant literature that deprive practice of actionable insights?RQ3.What are the potential research paths that can meaningfully drive the future research agenda?We propose to use the systematic literature review (SLR) approach to address these research questions.Our choice of the SLR approach is based on past recommendations that it is an effective way of reviewing and synthesizing the identified literature (Dhir, Talwar, Kaur, & Malibari, 2020;Talwar, Talwar, Kaur, & Dhir, 2020b).
The unique contributions of our study may be summed as follows: (a) It is the first SLR in the area to review studies from the perspective of all key stakeholders-patients, doctors/clinical staff, hospital management, policymakers, and community leaders.By doing so we contribute to the theoretical deepening of the research in the area where discussion on consumer resistance have been rather operational so far; (b) it underscores the need to pay closer attention to the digital transformation dilemma, which poses many challenges despite the rhetorical emphasis on transitioning completely to a digital mode wherever possible; and (c) it scores due to its timeliness by raising the issue of resistance to e-health innovation when the world is settling in the new normal post-easing of the severity of the COVID-19 pandemic.Decisions taken now will determine the future of healthcare delivery and preparedness for meeting future health crises.

Review methodology
To achieve the objective of our study and respond to the proposed research questions, we reviewed the state-of-the-art literature through a broadly accepted approach, i.e., an SLR (e.g., Christofi, Vrontis, & Cadogan, 2021;Christofi et al., 2022).An SLR is a useful review approach since it enables researchers to review and report the existing findings systematically and extensively (Kaur et al., 2021;Seth et al., 2020).The inductive reasoning approach of an SLR offers established criteria for identifying the corpus of literature to be reviewed (Chaudhary et al., 2021;Kraus et al., 2022).
Although scholars have applied different steps to conduct SLRs, there is a common thread that can be observed across most of them.Following recently published SLRs (e.g., Bresciani et al., 2021;Madanaguli et al., 2022, we executed our study through the following steps: (i) setting the conceptual boundary of the review to serve as the basis for identifying the keywords and databases for the literature search, (ii) defining the study selection protocol through clear delineation of the inclusion and exclusion criteria, (iii) shortlisting of congruent studies through multiple rounds of screening, and (iv) reporting the review by undertaking a content analysis of the congruent studies to identify the key themes.

Setting the conceptual boundary of the review
The broad objective of our SLR is to review past studies examining resistance towards and the non-adoption of various e-health innovations envisaged to make the patient − hospital − doctor interface more information technology-driven.Accordingly, we identified the following initial set of keywords: healthcare information technology, resistance/nonadoption, patients, and doctors.Following the common practice, we searched this initial set of keywords on Google Scholar and thoroughly read the first 100 results.Based on the understanding developed from the analysis of these articles, we expanded the keywords list to 13.Thereafter, we sought the opinion of four experts (three professors and one practitioner) from information systems (IS), healthcare, and consumer behavior backgrounds to ensure that our keyword selection is comprehensive.They recommended two more words, resulting in the final list of 15 keywords, presented in Table 1.Finally, we identified two digital databases-Scopus and the Web of Science-to search and shortlist relevant studies for inclusion in the review.The choice of these two databases is guided by the fact that these have been acknowledged to be the most comprehensive indices of academic research by recent studies (e.g., Kaur et al., 2020).

Defining the study selection protocol
Although we followed a very stringent process for the keyword selection and literature search, not all studies found through the search could be expected to be congruent with the topic at hand.Therefore, we also specified certain inclusion and exclusion criteria that could help us filter relevant studies.The criteria are presented in Table 2.

Shortlisting of congruent studies
We searched the identified keywords in the title, abstract, and author keywords using * and two Boolean operators: OR and AND.We used * with each of the keywords; OR was used within the category (keywords related to innovations, keywords related to consumer resistance, and the keywords related to end-users) and AND was used between the three categories.The search string was executed on both databases, setting the relevant time period as all years to date.Details of the document results/ articles found at each stage of filtration are illustrated in Fig. 1.As exhibited in the figure, the filtration process resulted in 154 potentially congruent articles after the reading of the abstracts.
To ensure a robust selection process for shortlisting articles for the SLR, we invited four researchers from the IS, healthcare, and consumer behavior fields to further evaluate our shortlist of 154 articles for their relevance to the topic at hand.At this stage, based on a consensus decision, the four evaluators generated a list of 82 articles to be included in the review.Next, guided by an expert team of three professors and one practitioner who had helped in keyword identification, the author team analyzed the full articles to arrive at a final list of 72 articles that were considered to be congruent with the conceptual boundaries of the present SLR.

Data Analysis: Coding
We analyzed the contents of the selected studies to understand and determine how the existing scholarship has evaluated resistance to ehealth innovations from the point of view of different actors that constitute the complex healthcare sector.Given the scope of our review, we followed recent studies (e.g., Christofi, Pereira, et al., 2021) to analyze the shortlisted studies through a multistep qualitative coding approach.To begin with, we extracted the basic descriptive details of each study, including the author names, year of publication, product examined, country, theoretical framework, data collection approach/ sample size, and method of data analysis.Curation of descriptive details not only revealed the research profile of the short-listed literature but also served as basis for identifying methodological gaps.
Thereafter, we analyzed the content of each study to distil the findings and discussions related to the sources of resistance.Herein, each author coded the data independently.We used MAXQDA to perform the analysis and coding process as it offers a versatile environment for this purpose.To ensure inter-coder reliability, after each round of coding, the authors discussed the codes and resolved the dissensions and issues.Since the stated objective of our review study was to synthesize the literature from the perspective of diverse stakeholders, the key point of discussion was how the perspectives would be presented.The choices were to coagulate the clusters based on theories used, underlying disease discussed, methodology used, the geography of data collection, variables examined, and stakeholders examined.After much debate and advice from our expert panel of three professors and one practitioner who had helped in the keyword identification and study selection, the consensus decision was to present the literature by clustering it simply from each separate stakeholder perspective.The choice was primarily driven by the fact that literature organized from the perspective of the consumer behavior of each type of stakeholder separately would provide a logical and relatable context for the theoretical advancement of research in the area.As a result, the author team decided to cluster the findings from the individual perspective on the one hand and the organizational perspective on the other.
A comprehensive review of the full text of the shortlisted studies also made us realize that individual perspectives should be evaluated from the patients' point of view because the studies related to healthcare professionals had a distinct organizational slant covering stakeholders such as physicians, surgeons, nursing staff, technicians, information technology experts, administrators, and management.As a result, we identified patient resistance as one thematic cluster and organizational resistance as another thematic cluster.In addition, based on the studies that distinctly focused on a multi-stakeholder perspective by examining the resistance of a variety of internal and external stakeholders towards a given innovation, we identified a third thematic cluster, that of multistakeholder resistance to e-health innovations.Accordingly, the following thematic clusters, as presented in Fig. 2, were identified: (a) patients' resistance to e-health innovations, (b) organizational resistance to e-health innovations, and (c) multi-stakeholder resistance to ehealth innovations.The output of the coding process is presented in Appendices I through III.

Patients' resistance to e-health innovations
With the advancement of information and communication technology (ICT), the healthcare sector has also seen a shift in the mode of interaction between patients and healthcare providers, leading to the concept of electronic healthcare or e-healthcare.As such, the use of the   internet and the underlying technology to provide consultations, monitor patient health, maintain patient records, and so on is called ehealth (Bhatnagar et al., 2017).In general, scholars have considered various dimensions of patients' responses to e-health innovations.Specifically, past studies have examined patients in both developed and developing economies to diagnose the guarded and rather unenthusiastic response to e-health innovations, despite their being effective in improving access to healthcare at a reasonable cost.To this end, prior studies have employed both qualitative and quantitative research designs to collect the required data.A total of 14 studies selected for our review have examined patients' perspectives, of which 11 have used a quantitative research design, two have used a qualitative design, and one has used mixed-method design.These studies have examined e-health interfaces such as mobile health apps (Alaiad et al., 2019), mobile health services (mHealth) (Cao et al., 2020;Deng et al., 2014;Hoque & Sorwar, 2017;Mikolasek et al., 2018), and so on, as presented in the table in Appendix I. Scholars have discussed not only the factors that have driven the adoption of these services but also the factors that cause patients to resist them.In doing so, the selected studies have used technology acceptance theories such as the unified theory of acceptance and use of technology (UTAUT; e.g., Alaiad, 2019;Hoque & Sorwar, 2017;Hsieh, 2016) and the technology acceptance theory (TAM; e.g., Cranen, 2011;Kamal et al., 2020;Tsai et al., 2019;Tsai et al., 2020;) amongst others, as presented in the table in Appendix I.
In discussing sources of resistance directly, or making an indirect reference to them, existing scholarship has identified technology anxiety, security and privacy risk, resistance to change, sunk costs, inertia, perceived value, transition costs, and uncertainty as the key factors that can impede the adoption of e-health innovations by patients (Alaiad 2019;Hoque & Sorwar, 2017;Hsieh, 2016).In addition, some studies examined and confirmed the role of socio-demographic attributes in  shaping patients' resistance towards e-health innovations.For example, Deng et al. (2014) found that resistance to change was a key dissuading factor for middle-aged patients, whereas for older users it was technology anxiety.Similarly, Cao et al. (2020) revealed that information overload and system feature overload in mHealth applications contributed to resistance of elderly patients by increasing fatigue and technostress.
In one of the limited efforts to examine barriers to e-health innovation adoption (with reference to telemedicine, specifically), Zobair et al. (2020) revealed lack of organizational effectiveness, motivation of health staff, satisfaction among patients, and trustworthiness as the key barriers.In a similar vein, Kamal et al. (2020) revealed technological anxiety, perceived risk, and resistance to technology as key barriers towards e-health innovations.Tsai et al. (2020) reported similar results in the case of a smart clothing system, revealing technology anxiety to be a key barrier.
We summarize the sources of resistance noted by each of the 14 studies to consolidate them under four broad headings: (i) technologyrelated impediments, (ii) care-related concerns, (iii) status quo bias, and (iv) perceived risks, as presented in Fig. 2. The technology-related impediments comprise factors such as technology anxiety, resistance to technology, lack of technical competence, lack of ability to operate equipment, low computer self-efficacy, and fears of disruption of services.The care-related concerns of patients were manifested through lack of satisfaction with the diagnosis and treatment suggested, lack of trust in organizational effectiveness, and concerns related to the lack of healthcare staff motivation.Next, status quo bias of patients was manifested through resistance to change, inertia, and transition cost.Finally, perceived risks comprised sources of resistance such as uncertainty, security, privacy, lack of trust, threat to identity, information overload, system feature overload, fatigue, and technostress.

Organizational resistance to e-health innovations
Scholars have been mindful of the resistance offered by doctors, clinical staff, and other organizational stakeholders, as evidenced from a reasonable body of extant literature examining their opposition to the adoption and use of e-health systems.For instance, Bhattacherjee and Hikmet (2007) underscored the importance of recognizing and examining user resistance to technology in the specific case of e-health innovations.Lin et al. (2012) also contented that understanding the resistance perspective is important to better diagnose technology rejection.These and other studies examining the resistance of healthcare professionals and other organizational stakeholders to e-health innovations have employed both qualitative and quantitative research designs to collect the required data.A total of 46 studies selected for our review have examined the organizational stakeholders' perspective, of which 20 have used a quantitative and 26 have used a qualitative research design.
These studies have focused on a variety of products, deploying theoretical frameworks used by scholars for examining consumer behavior in different contexts.Some of the predominant aspects of these studies include the following: (a) recognition of the fact that resistance to e-health innovations varies with the type of professionals/organizational stakeholders under discussion, such as physicians versus nurses (e.g., Barrett, 2017); (b) the importance of considering both pre-and post-implementation challenges and engagement, especially since lessengaged groups may develop resistance.Such groups are more likely to use unsanctioned workarounds if they perceive the given system to be inadequate (e.g., Bagot et al., 2020;Cresswell et al., 2017); (c) the importance of workarounds that may lower resistance to change (e.g., Barrett & Stephens, 2017); (d) the role of resistance in lowering adoption-related factors such as perceived usefulness (e.g., Beglaryan et al., 2017); (e) the difference in user versus non-user perceptions of barriers and related solutions from an administrative perspective (e.g., Zandieh et al., 2008); and (f) acceptance of the fact that despite its acknowledged potential to improve the quality of healthcare, health information technology has diffused quite slowly since its introduction in the 1980s (Grabenbauer et al., 2011).
Within these broad boundaries, the e-health products/services examined include electronic health records (e.g., Al-Rayes et al., 2019;Grabenbauer et al., 2011;Heath & Porter, 2019;Hossain et al. 2019;Ngafeeson & Manga, 2021), clinical decision support systems (e.g., Fossum et al., 2011;Litvin et al., 2012;Zakane et al., 2014), etc., presented in the table in Appendix II.The key theories utilized by these studies are the acceptance/adoption theories, such as the technology acceptance model (e.g., Al-Rayes et al., 2019;Bezboruah et al., 2014;Segrelles-Calvo et al., 2017), the unified theory of acceptance and use of technology (e.g., Bush et al., 2017;Hossain et al., 2019), and so on, as presented in the table in Appendix II.In addition to the acceptance perspective, some scholars have provided insights about change management and personnel issues related to the use of e-health innovations by a wide range of professionals, such as physicians, surgeons, technicians, ancillary staff, nurses, physiotherapists, administrators, and other organizational stakeholders (Barrett & Stephens, 2017;Bush et al., 2017;Kelly et al., 2017;Lapointe & Rivard, 2005;Segrelles-Calvo et al., 2017).To this end, they used theories such as the theory of interpersonal influence and leadership (Ilie & Turel, 2020) and so on, as presented in the table in Appendix II.A limited number of studies have utilized resistance theories and perspectives, such as the psychological reactance theory (Ngafeeson & Manga, 2021), status quo bias (Hsieh, 2015;Hsieh & Lin, 2020) and the theory of innovation resistance (de Wit et al., 2019) to specifically focus on the barriers that drive resistance of healthcare professionals and other organizational stakeholders towards the use of ehealth innovations.
Using these theoretical frameworks, the reviewed studies revealed resistance to change brought by the introduction of e-health innovations to be a key driver of resistance towards a given innovation (Al-Rayes et al., 2019;Bhattacherjee & Hikmet, 2007;Barrett & Stephens, 2017;Dubin et al., 2020;McAlearney et al., 2013).Such resistance could be related to changes in workflow and organizational impediments or control concerns (Grabenbauer et al., 2011;Kelly et al.,2017;Litvin et al., 2012;Stronge et al., 2008).Workflow issues may arise if the innovation is not integrated within the existing workflow, requiring the concerned professional to spend additional time to get the work done or increasing the documentation requirement, resulting in productivity losses (McAlearney et al., 2013;Ser et al., 2014;Yu et al., 2013).
Institutional politics may also impede the diffusion and use of these innovations (Ackerman et al., 2012).In addition, institutional pressures, the unsystematic process followed by management for technology adoption, information asymmetry, and issues in communication can also cause resistance (e.g., Bezboruah et al., 2014).
Over and above this, many studies have noted the impact of technical complexities and technical capabilities on resistance towards the use of e-health innovations (Aboelmaged & Hashem, 2018;de Wit et al., 2019;Segrelles-Calvo et al., 2017), which can lead to aggravation due to lack of training (Hossain et al., 2019;Ser et al., 2014).Anxiety among healthcare staff about delivering proper patient care with a new technology, which is driven by self-doubt about their own technical competence, skills, and knowledge, also acts as dissuading factor (Jindal et al., 2018;Taylor et al., 2015).
Overall, the extant findings have reinforced the barriers associated with innovation implementation in general, such as the need for technical support, worries related to technology, perceived threat, perceived risk, cost barriers, user resistance, culture, and the disruption of work routines that have been discussed by the reviewed studies (e.g., Bush et al., 2017;Caffery et al., 2017;Cocosila & Archer, 2016;Hsieh, 2015;Lin et al., 2012;Varsi et al.,2015), as presented in Appendix II.
We summarize the sources of resistance noted by each of these 46 studies to consolidate them under 10 broad headings: (i) personnel impediments, (ii) resource shortfalls, (iii) functional impediments, (iv) technological challenges, (v) external inhibitors, (vi) perceived threats, S. Talwar et al. (vii) work-related concerns, (viii) negative perceptions, (ix) efficacy issues, and (x) status quo bias, as presented in Fig. 2.

Multi-stakeholder perspectives on resistance to e-health innovations
The diffusion of e-health innovations has been challenging because the resistance to their adoption and subsequent use comes from multiple stakeholders at the same time.This implies that every innovation that comes up as an alternative for digitalizing a process or interface encounters barriers from patients, healthcare professionals, administrators, and management concurrently.Thus, resistance is actually a complex outcome of multiple inhibitors perceived by varied stakeholders in the same temporal context.Some scholars, albeit few, have recognized this fact and investigated resistance to a given e-health innovation from the perspective of multiple stakeholders such as patients, physicians, IT staff, nursing staff, consultants, pharmacists, social workers, administration staff, hospital directors, and so on.These studies have employed both qualitative and quantitative research designs to collect the required data.A total of 12 studies selected for our review have examined multi-stakeholder perspectives, of which two used a quantitative research design, nine used a qualitative research design, and one used a mixed-method design.The key findings in this regard are that the level of resistance varies with stakeholder type (e.g., Alajlani & Clarke, 2013;Safi et al.,2018;Wang et al., 2015) as well as with the demographic profile of the respondent (e.g., Alajlani & Clarke, 2013;Poss-Doering et al., 2018;Weitzman et al., 2009).
These broad findings are based on the examination of resistance offered to innovations such as electronic health records (Poss-Doering et al., 2018;Takian et al., 2012), electronic patient-reported outcome mobile application and portal systems (Hans et al., 2018), etc. as presented in Appendix III.The reviewed studies have drawn upon different theoretical frameworks to provide insights about factors that drive the resistance of the internal and external stakeholders to e-health innovations.The key theories utilized are the healthcare information systems evaluation Framework (Wang et al., 2015) and the technology acceptance model (Safi et al., 2018;Wang et al., 2015), amongst others, as presented in as presented in Appendix III.
Examining resistance from a multi-stakeholder perspective enabled the past studies to identify varied dimensions of such resistance such as societal, interpersonal, and individual.Some of the key drivers include integrity, prioritizing health information technology to advance healthcare and policy issues, discomfort and unwillingness of providers to share power and information, lack of technological literacy and low self-efficacy to use the innovation, less commitment to collaborative work, organizational complexity, and lack of conducive organizational culture (Serrano et al., 2020;Weitzman et al., 2009).
As observed in the case of the individual as well as the organizational perspective, the lack of technological skills and knowledge at both patient and organizational levels has continued to manifest during the past decade (Campling et al., 2017;Cijvat et al.,2021;Takian et al., 2012;Weitzman et al., 2009).Furthermore, different stakeholders tend to have different levels of concerns related to privacy protection, with fear of misuse being highest in doctors (Wang et al., 2015).The financial aspect has also been underscored as a reason behind resistance to e-health innovations (Alajlani & Clarke, 2013;Campling et al., 2017;Poss-Doering et al., 2018;Serrano et al., 2020).In addition, data safety and security serve as barriers that drive the resistance of multiple stakeholders to accept/adopt/use e-health innovations (Alrahbi et al., 2022;Poss-Doering et al., 2018;Wang et al., 2015).Cost and usability are also important considerations for organizational stakeholders, along with concern for patient care (Alrahbi et al., 2022;Choi et al., 2019).
Organizational level issues such as power-sharing, management control, disruption in workflow, liability concerns, and impact on the relationship with patients are also seen as key sources of resistance offered by professionals to e-health innovations (Hans et al., 2018;Safi et al., 2018;Takian et al., 2012;Weitzman et al., 2009).In fact, of the key stakeholders, including policy-making officials, healthcare professionals, patients, and industrialists, healthcare professionals emerged as the most resistant group (Choi et al., 2019), with stakeholders responsible for governance and policy-making identifying the highest number of barriers (Serrano et al., 2020).
In sum, as presented in Appendix III, technological concerns, financial aspects, and organizational issues are the key sources of resistance to e-health innovations when seen from a multi-stakeholder perspective.We further organized these barriers under seven broad categories to present an aggregate view of multi-stakeholder resistance, as presented in Fig. 2. The key categories are (i) societal concerns, (ii) resource shortfalls, (iii) functional impediments, (iv) personnel impediments, (v) external inhibitors, (vi) organizational-level constraints, and (vii) individual-level inhibitors.

Discussion
After establishing the exigency of advancing research on the resistance to e-health innovations, we sought to address three research questions (RQs).To respond to RQ1, we analyzed the short-listed studies to develop a comprehensive set of drivers of resistance from the perspective of three user groups: patients, organizational actors, and multiple internal and external stakeholders.The results are discussed in detail in the preceding text and are also presented through Fig. 2 and Appendices I through III.Overall, it is rather surprising that the studies published a decade back as well as those more recently have noted technology-related, training-related, and usability-related issues among the key reasons behind the resistance of healthcare professionals and other organizational stakeholders towards e-health innovations (Hoonakker et al., 2013;Ser et al., 2014;Stronge et al. 2008).The fact that, even after more than a decade, not much progress has been made to address the technology-related issues points to the need for more intensive efforts to overcome resistance.Offering a solution, the existing scholarship in the area argues that the resistance manifested by healthcare professionals and other organizational stakeholders in the form of distrust, inertia, reactance, and so on (Ngafeeson & Manga, 2021) can be countered by organizations by honestly communicating the anticipated obstacles and associated benefits during the initial implementation phase of these innovations (Barrett, 2017) and using workarounds to reduce resistance to change (Barrett & Stephens, 2017).At the same time, scholars caution against using interpersonal influence tactics judiciously since, in some cases, they may actually end up unintentionally increasing resistance (Ilie & Turel, 2020).Succinctly, research in this area needs to expand in volume and mature in coverage to make agenda-setting contributions to practice.
To respond to RQ2, we critically analyzed the short-listed studies to identify methodological and conceptual deficiencies in the extant literature that deprive practice of actionable insights.To begin with, in the case of all three resistant perspectives-patient, organizational stakeholder, and multi-stakeholder-there is a similar kind of methodological shallowness and linearity-spanning limitations in methods of data collection, geographies sampled, and data analysis methods applied.There are three key gaps in this regard.
(i) Methods of data collection: If we consider the total sample of 72 studies, then there is an appreciable balance between the quantitative and qualitative insights available on resistance to e-health innovations.However, if we consider the individual thematic clusters, then in the case of the patients' perspective, of the 14 congruent studies selected for our review, 11 have used a quantitative research design, two have used a qualitative design, and one has used a mixed-method design, indicating not only very limited but also skewed findings.In contrast, in the case of multi-stakeholder perspective, of 12 congruent studies reviewed, only two have used a quantitative approach, with nine offering qualitative insights and one using a mixed-method design, again indicating a narrow and skewed literature base, but on the opposite side in this case.Admittedly, there is a balance in available insights with regard to studies examining the organizational perspective (46 studies of which 20 have used a quantitative and 26 have used a qualitative research design).
Within these broad design-based limitations, the methodology used lacks variety, with most quantitative studies employing a paper and/or web-based, self-reported, single-wave, questionnaire survey for empirical data collection.In comparison, very few studies have employed an experimental design or collected data in multiple waves.In the case of qualitative design, most studies have used focus groups and semistructured interview approaches.In comparison, other qualitative approaches, such as open-ended written essays, observation studies, diary studies, and so on have hardly been employed.Since the data collected and analyzed forms an important part of robust research, there is a need to address these gaps by expanding the repertoire of methodological tools used by researchers.
(ii) Geographies sampled: Most of the studies reviewed from all three perspectives have either focused on an Asian country such as China, Taiwan, Bangladesh, and Pakistan, or on the United States, with some studies examining advanced economies in Europe.As a result, the literature presents skewed findings with limited generalizability.The focus on the US is particularly intriguing, given that, anecdotally, technology resistance has been considered to be a characteristic of emerging and under-developed countries.The reason behind the narrow geographical focus could perhaps be that the US has been at the forefront in introducing e-health innovations, thereby providing suitable field conditions for the studies to take place.Regardless, this serves as an indication that future researchers need to focus on different geographies with an unbiased preconceived notion about technology resistance being rooted only in the level of development.
(iii) Data analysis methods applied: As mentioned above, the reviewed studies have used both qualitative and quantitative data to respond to their identified research questions.The collected quantitative data has been analyzed largely using common data analysis methods such as hierarchical regression analysis, covariance-based structural equation modelling, and variance-based structural equation modelling, amongst others mentioned in Appendices I through III.These are popular yet common methods of data analysis that do not provide advanced insights.This severely limits the width and depth of findings at the disposal of researchers and practitioners endeavoring to develop models and strategies to overcome resistance to innovations.In comparison, the studies based on qualitative data collection have analyzed the data using a variety of methods such as content analysis, narrative analysis, framework analysis, grounded theory approach, and interpretive phenomenological analysis.The preceding discussion indicates that there exists much scope for increasing the methodological width of the literature in the area.
In terms of gaps from a conceptual perspective, we observe that in the case of all three resistant groups-patients, organizational stakeholders, and multi-stakeholders-existing scholarship has adhered to a rather narrow conceptualization by examining a limited set of e-health innovations, employing commonly used acceptance theories and focusing on a comparatively narrow set of respondents in terms of both variety and number.The key gaps in this regard are as follows: (i) The limited set of e-health innovations examined: A limited variety of innovations have been examined, with most studies focusing on a clinical decision support systems (four), computerized physician/provider order entry systems (three), electronic health/medical records (18), telehealth /telemedicine (15), and mHealth interventions/services/apps (seven).Unless the understanding of the drivers of resistance to a wider variety of innovations is clear, it will be difficult for academic research to offer viable strategies for the commercial success of these innovations in a way that the community also benefits.
(ii) The limited set of user groups/activities examined: A close look at the reviewed literature as presented in Appendices I through III and in comparison to the extensive universe of patients, providers, clinicians, IT staff, administrators, and types of healthcare set-ups reveals that what has been examined is really the tip of the iceberg.Thus, the existing literature is considerably constrained and limited in providing the real picture of resistance as it exists on the ground.
(iii) Limited theoretical frameworks utilized: As evident from the preceding discussion and Appendices I through III, the existing scholarship has shown a predominant tendency to use acceptance/adoption theories like TAM, UTAUT, and TPB to examine the resistance offered to e-health innovation.Since prior literature has clearly established that the drivers of adoption are quite distinct from those of resistance, the use of the acceptance lens perhaps weakens the robustness of the resistancerelated insights offered by these studies.
(iv) The limited scope of inquiry: The extant literature has examined a variety of variables; however, resistance has been investigated by most of these studies as an afterthought rather than as a key point of focus.Due to this, the understanding of finer aspects of resistance to e-health innovations in terms of its type (i.e., postponement, opposition, or rejection) or form of its manifestation (active or passive) has been quite deficient so far.
We responded to RQ3 by putting forth tangible recommendations for setting a future research agenda.Herein, we have formulated a conceptual framework and proposed potential research questions grounded in the framework to provide future researchers a comprehensive frame of reference.The framework and the potential research questions are discussed in detail below.

Conceptual framework
Building upon the theoretical insights obtained from our literature review, we propose a conceptual framework.The proposed framework embodies a comprehensive view of resistance of multiple stakeholders towards the adoption and continued usage of e-health innovations.The proposed framework goes beyond mere mapping of drivers of resistance to highlight various nudges and interventions that can be leveraged to overcome resistance, thereby increasing adoption, post-adoption usage, and recommendation intent.In sum, our framework rests on the tripod of (i) barriers and user resistance, (ii) consequents, and (iii) nudges and interventions, as presented in Fig. 3.

Framework constituents
(i) Barriers and user resistance: We have categorized the barriers under three levels-micro, meso, and macro-as per user groups.The mapping of levels to user groups provides a rational context for presenting commensurate and relevant interventions.We have categorized barriers at these three levels by drawing upon Chandler and Vargo's (2011) conceptualization, which was extended by Beirão et al. (2017) to examine the healthcare service ecosystem.Accordingly, the barriers/ sources of resistance at these three levels are discussed below.
Micro-level barriers are drivers of resistance of individual actors represented by patients.We identified barriers under this category on the basis of an understanding evolved from our review of congruent studies, as discussed under thematic analysis and presented in Appendices I and III.For the ease and clarity of presentation, we have classified these barriers under three broad headings: (a) resistance to change, (b) resistance to use, and (c) perceived risk.
Resistance to change captures patients' tendency to avoid e-health innovations due to inertia, concerns about transition costs both in terms of learning effort and monetary expense, comfort with existing practices, inaccurate perceptions about the efficacy of these innovations, and carerelated concerns.Succinctly, resistance to change represents patients' status quo bias.In comparison, resistance to use represents a set of micro-level barriers that arise from patients' technology anxiety, limited computer self-efficacy, and concerns about fatigue and technostress due to information and system feature overloads.In essence, these factors capture the dark side of technology use and self-doubt about using it efficaciously.Finally, perceived risk, representing the third broad set of micro-level barriers, encapsulates resistance arising from patients' perception of threat to identity, privacy concerns, data security worries, and lack of trust.
Meso-level barriers are drivers of resistance of organizational actors such as healthcare professionals, technicians, administrative staff, and management.We shortlisted barriers under this category by consolidating our understanding derived from the reviewed studies, as discussed in the thematic analysis and presented in Appendices II and III.To put forth a structured and reproducible map, we have classified the meso-level barriers under four broad headings: (a) status quo bias, (b) work-related issues, (c) care-quality concerns, and (d) organizational challenges.
Status quo bias represents a set of barriers perceived/manifested by the healthcare providers at an individual level, but in the workplace setting.These barriers include preconceived notions against the said innovation, task-fit issues, low involvement in implementation, perceived helplessness, low perceived value of change, differences in personal innovativeness, and stress on account of change.
Next, work-related issues that may cause resistance from organizational actors encompass a set of factors that includes increased workload, time commitments, changes in workflow, higher documentation burdens, role ambiguity, fear of legal and medical liability, and reduction in autonomy.In the case of care-quality concerns, the meso-level barriers mainly manifest on account of worries related to assessing patient suitability, impact of the system on patients' anxiety, dehumanization of the patient-provider relationship, commercial exploitation of patients, too much standardization of the medical decision-making process, inability to provide the desired patient care, fear of selfmedication or treatment leading to patient isolation, misfit of new technology with logic of care, and lack of clarity about operational and practical aspects of patient care and management.
Coming to organizational challenges, the resistance of healthcare providers may manifest due to lack of training, limited clinical knowledge, low engagement levels of staff, decreased productivity, lack of required routines, communication problems, unplanned and ineffective implementation, limited resources leading to infrastructural shortages, technology usability challenges, design and software barriers, issues in integration with other IT systems, fears associated with system efficacy or performance risk, and doubts about the clinical and cost effectiveness of the system.
Macro-level barriers are drivers of resistance coming from the milieu in which the healthcare sector operates.These barriers can arise on account of (a) policy makers, (b) societal concerns, (c) supporting industrial sectors, (d) globalization, (e) the nature of digital technologies, (f) changing demographics, and (g) evolving healthcare challenges.In other words, the politico-administrative actors, national orientation, socio-demographic profile of the population, regulatory expectations, level of economic development, technological ability and know-how, technology transfer and support systems, and tertiary support of related industrial sectors can all play an important role in driving resistance towards e-health innovations.
Coming to specific details, the barriers attributable to policy makers include regulatory enforcement to implement a system, policy confusion, and the general lack of a supportive environment.Next, societal concerns that hinder the unobstructed diffusion of these innovation span a variety of data, integrity, compensation, information, and societalawareness barriers.Lack of support from tertiary industrial sectors, vendor-related issues, coordination problems, complex supply routes, and other external factors also act to inhibit the adoption and continued usage of these innovations.In addition, the lowering of sovereign boundaries, the fast-paced transition and transformation of digital technologies, aging populations-particularly in lower-and middleincome group countries where healthcare affordability and reach are a challenge-and the rising complexities of healthcare systems also act, covertly but quite detrimentally, as barriers.
(ii) Consequents: We propose adoption, continued usage, and recommendation intentions as the three outcome variables of interest, implying that, ultimately, future research should examine how the user resistance towards e-health innovations can be countered to increase adoption and to ensure continued usage.To elaborate, from the perspective of individual patients, the key consequents could be their willingness to consult, continue treatment, and receive care virtually when required, as well as comply with all information sharing and safety requirements that can support the frictionless use of e-health technologies.In the case of the organizational actors, adoption and continued usage would imply sincere implementation of the e-health technologies for delivering healthcare, committing adequate resources, and avoiding unfaithful workarounds that may reduce the efficacy of the implemented systems.From the perspective of macro actors such as regulators and policy makers, this would imply the formulation of clear policy guidelines and the creation of a conducive environment for the expedient yet voluntary adoption of these technologies by the concerned user groups.
In addition to proposing adoption and continued usage as outcome variables of interest, we have also included recommendation intentions in our framework, since word of mouth and its effect on users' adoption behavior is well-documented in the consumer behavior literature (Talwar, Dhir, et al., 2020;Talwar, Dhir et al., 2021).
(iii) Nudges and interventions: The role of nudges and interventions in inducing individuals to act in a societally or environmentally desirable way is well-established in the literature (Dhir et al., 2020).In addition, prior literature on resistance to digital innovations, in general, has also discussed how interventions in the form of information and guidance ( Talwar et al., 2020b) can lower resistance.Furthermore, the idea that resistance from key stakeholders can be overcome through well-defined strategies is also consistent with the seminal work of Ram and Sheth (1989) in which several marketing strategies were discussed to overcome customer resistance to innovations.
In the present context, we propose seven Is to represent different kinds of interventions and nudges that can be used to counter resistance of different user groups.For instance, information shared through mass media promotion may work well to reduce the patients' resistance.Similarly, insights shared through workshops, seminars, and reading material may help healthcare professionals know more about the available innovations and their usefulness.At the same time, continuous engagement and communication may work well for organizational actors to handle their anxieties and concerns in the pre-implementation stage.Continued interaction with the external stakeholders, such as regulators, suppliers, and societal representatives, can also serve as a nudge and intervention for ensuring that the macro-level barriers are mitigated.Also, involvement in the decision making and seeking feedback from user groups may serve to lower their resistance before it escalates into complete rejection.From the organizational perspective, training diligently, integrating the workflow and routines of healthcare professionals and staff, and motivating them through additional compensation and recognition can also nudge them in the right direction to use the system faithfully and efficaciously.Bringing it all together, we suggest that more such nudges and interventions should be designed and implemented through an approach similar to planned social change driven by reinforcement, inducement, rationalization, and confrontation, as discussed by Sheth and Frazier (1982).

Potential research questions
(i) Barriers and user resistance.PRQ1: Why do patients and organizational actors resist comparatively new e-health innovations such as cloud computing, the epidemic prevention cloud, assistive and welfare technologies, and web-based self-management tools for critical care more than the older, more routine innovations?PRQ2: How is patients' resistance to e-health innovations correlated with the nature of their illness (critical/non-critical), the existence of comorbidities, and the extent of treatment required?PRQ3: How is the resistance of organizational actors different in the case of large hospitals with an established legacy as compared to smaller nursing homes that may be more agile in successfully implementing the innovations but constrained by resource availability?PRQ4: How does the resistance of multiple stakeholders differ in different cultural contexts, such as Confucian-based versus individualistic?
PRQ5: How is the manifestation of resistance of multiple stakeholders different in developed versus developing economies?
(ii) Consequents.PRQ6: How have pandemic-induced anxieties affected the resistance of multiple stakeholders towards the continued use of remote medical consulting and care?PRQ7: Which barriers can slow down or obstruct completely the adoption of emerging (industry 4.0) technologies-driven healthcare innovations such as robotic surgery, smarter pacemakers, smart wearables, and so on?
PRQ8: What are the differences between the resistance of earlyadopters and late-adopters that drive postponement/opposition/rejection of e-health innovations?
PRQ9: How does resistance associated with the size, type, ownership status, and age of healthcare set-ups impact the adoption and continued usage of e-health innovations from both clinical as well as administrative standpoints?
PRQ10: What are the potential interventions that can be effectively used for mitigating pre-and post-implementation resistance towards ehealth innovations?
PRQ11: How can the bottlenecks and obstructions associated with supply-side drivers of resistance be overcome to reduce the external barriers to adoption of e-health innovations?
PRQ12: How can policy measures be used as a positive nudge to mitigate the resistance of different stakeholder groups?
PRQ13: How can social and peer influence be leveraged through recommendations and word of mouth to mitigate the resistance of multiple stakeholders?

Theoretical implications
Our study contributes to the accumulated literature on resistance to e-health innovations in the following three ways.First, our review builds upon and addresses the limitations of the prior reviews on the topic by offering a broader and deeper view of resistance to e-health innovations that incorporates the perspectives of all key stakeholders, covering varied geographies and a variety of e-health innovations.An examination of prior reviews reveals their narrow scope in terms of the coverage of stakeholders, innovations, and geographies.For instance, Almathami et al. (2020) reviewed congruent studies to identify the barriers and facilitators that impede or stimulate the adoption of healthcare home consultation systems, and Niazkhani et al. (2020) identified various barriers that hinder the adoption of electronic personal health records by patients, caregivers, and providers.Similarly, Kumar et al. (2020) examined the pertinent corpus of studies published in information system journals and conferences to present a consolidated view of resistance to healthcare information technology focusing on the interactions of people, practice, and technology.Adding to the growing volume of review literature, Al-Samarraie et al. (2020) analyzed relevant studies to reveal insufficient progress made in the diffusion of telemedicine in Middle Eastern countries, identifying many key challenges such as financial, cultural, and regulatory.Relatively older studies have also shown a similar narrow inclination toward reviewing studies on resistance by remaining confined in terms of publications, user groups, or innovations.For example, Davidson et al. (2018) reviewed the evolution of healthcare research in information system journals since 2004, offering a descriptive narrative of the accumulated findings through three clusters: health information technology adoption and diffusion, physician resistance to health information technology use, and the impact of health information technology on healthcare outcomes, and Kruse et al. (2018) mapped the barriers inhibiting the adoption of telemedicine across the world by reviewing the existing research.In a similar vein, Kruse et al. (2015) conducted an SLR to uncover the drivers of resistance of patients and providers to patient portals, and Lluch (2011) reviewed studies on healthcare professionals' organizational barriers to health information technologies.The review offered a single perspective and categorized the barriers into five categories from the organizational viewpoint.In another noteworthy review, Boonstra and Broekhuis (2010) synthesized studies specifically examining physicians' barriers toward the adoption of electronic medical records.In comparison, the broad coverage of our study provides an integrated view of resistance of different end-users towards e-health innovations, presenting a single point of reference for researchers.
Second, our study lucidly categorizes the resistance literature under three clear perspectives based on user groups: (a) patients, (b) organizations (an umbrella term for physicians, nurses, administrators, technicians, and hospital directors) and (c) multiple other internal and external stakeholders.Such categorization helps synthesize the literature in a more relatable and understandable manner, making it a useful reference for future research and ongoing practice.Our theoretical contribution is further concretized by two key aspects of our study: (i) we have presented a clear narrative of the underlying products, theories, methodologies, and barriers for each user cluster (see Appendices I through III), and (ii) we have specifically summarized the sources of resistance discussed/revealed by each study, offering a comprehensive narrative to balance the literature that has so far majorly focused on adoption, referring to resistance almost as an afterthought.Such summarization is quite valuable, especially in the cases where barriers are not immediately apparent.
Finally, our explicit delineation of methodological and conceptual underpinnings of resistance serves as a coherent basis for suggesting well-defined PRQs for the academic community to work on.By doing so, we bring forth potential areas of research that could not only enrich the insights specific to resistance towards e-health innovations but also contribute to the richness of the methodological literature in general.To make our contribution even more tangible, we have formulated a conceptual framework that brings together potential research paths, serving as a ready reference for researchers keen to advance the academic understanding of the resistance of multiple stakeholders, manifested through rationally experienced or irrationally perceived barriers.Succinctly, our proposed framework comprising barriers classified into micro-, meso-, and macro-categories can help theorists and practitioners evolve more contextual strategies.At the same time, our detailed presentation of methodology-related gaps can provide future researchers with valuable inputs on how to determine a suitable research design and plan better theory-based conceptualizations.For instance, our study suggests that future researchers should consciously draw upon the resistance-related theories such as innovation resistance theory (IRT; Ram & Sheth, 1989) and status quo bias theory (SQB; Samuelson & Zeckhauser, 1988) to formulate models theorizing and testing more resistance-related variables, rather than explaining resistance as the absence of adoption drivers.Recent studies have deployed these theories effectively to explain consumer resistance in different contexts (e.g., Khalil et al., 2023).

Practical implications
Our study offers three key contributions for practice: First, our study reveals that resistance to e-health innovations is not confined to patients or doctors alone, rather multiple internal and external stakeholders who can play a vital role in supporting the adoption of these innovations also have reservations about their efficacy, utility, and implementability (Campling et al., 2017;Choi et al., 2019;Takian et al., 2012).By uncovering such finer dimensions, we bring out the complexity of the issue and provide useful inputs for formulating effective strategies and interventions to overcome such resistance.
Second, we highlight the fact that despite the potential of these innovations to deliver healthcare more efficiently and, ultimately, cost-effectively, there exist several organizational impediments such as power, control, autonomy, workflow, and productivity (Hans et al., 2018;Safi et al., 2018;Takian et al., 2012) that may create frictions in the adoption of e-health innovations.By doing so, we underscore the fact that the adoption of these innovations is not just an operational or a technical decision, rather it is a broader decision, with intangible aspects playing an equally important role.Observing this, we suggest that the organizations adopting these innovations should onboard their human resources department fully to properly plan and execute the implementation as a well-visualized change management process.
Finally, by revealing that the resistance of the implementing organization may come from administrative challenges such as the skill level of staff, training requirements, additional compensation expectations, etc. (Dubin et al., 2020;Plumb et al., 2017;Taylor et al., 2015), as well as from the clinical features that affect the quality of care (Alrahbi et al., 2022;Zwaanswijk et al., 2011), we provide action-oriented inputs to the manufacturers and marketers.To elaborate, we suggest that ease of use should be emphasized continually, and all innovations should be accompanied by proper demo support and self-help videos that can be referred to as and when required.To address the quality-of-care concerns, the manufacturers and marketers of e-health innovations should have a systematic approach to capture reviews, feedback, and testimonials of different user groups to serve two specific purposes: (i) provide inputs for subsequent product improvements, and (ii) reassure potential users about the effectiveness of the concerned innovation.

Limitations and future work
Our study offers a comprehensive, first-of-its-kind review of the state-of-the-art literature on resistance to e-health innovations, yet it has certain limitations that need to be acknowledged.First, although we followed recent studies (e.g., Christofi, Pereira, et al., 2021;Vrontis et al., 2022) to execute a robust and systematic search and review of congruent studies, there might be certain keywords or studies that we have missed due to our inclusion and exclusion criteria.Second, we limited our search to only two digital databases, Scopus and the Web of Science, which could have led us to miss some relevant studies indexed in other databases.However, the two databases are known for their extensive indexing, assuring us that there is unlikely to be any grave omission.Third, apart from the methodological limitations, there is also a possibility of human error in shortlisting and/or synthesizing the reviewed studies, which could have affected the reported analysis.However, the author team tried its best to limit such errors by undertaking independent coding at each stage and comparing the output.
Future researchers can take our review study forward by (a) searching additional databases to include studies that may have been inadvertently excluded by us, (b) conducting a meta-analysis of the studies to provide deeper insights into the underlying literature, and (c) undertaking a bibliometric analysis of the literature in the area to generate a detailed research profile to help future researchers.

Conclusion
Our study rests on the premise that despite being widely acknowledged for supporting efficient and effective delivery of medical services, e-health innovations have not become an integral part of the healthcare system.Even in most advanced countries, a variety of e-health initiatives have received lukewarm response.Such disengagement and lack of acceptance is depriving the healthcare system from delivering costeffective and inclusive medical care.Given the criticality of the issue, the factors contributing to limited adoption of e-health innovations cannot be ignored.Taking cognizance of need, our study examines the reasons behind low diffusion of e-health innovations.We contribute to the literature by curating and critically synthesizing the barriers that comprise resistance of three key stakeholder groupspatients, doctors and clinical staff, and hospital management towards e-health innovations, and suggest potential research questions that need to be addressed.

Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.(continued )
-Response framework Online survey [317 respondents, 60 years old and above, 41.9 % male] Variance-based structural equation modelling (a) Information overload(b) System feature overload(c) Fatigue(d) Technostress Kamal et al. (2020) Telemedicine Pakistan Technology Acceptance Model Face-to-face survey [ 226 respondents, 20-50 years in age, 64.6% male] Variance-based structural equation modelling (a) Technological anxiety (b) Perceived risk(c) Resistance to technology (continued on next page) S. Talwar et al.

Table 1
Selected keywords.

Table 2
Inclusion and exclusion criteria for screening the studies.