The past several decades have witnessed a kaleidoscope of technological innovations, many unprecedented and life-changing. In particular, innovations in the digital space have impacted individuals’ personal and professional lives in many ways, ultimately transforming how we communicate, interact, and transact. While the disruptive innovations were evolving at their own pace, the pandemic of 2020 occurred, changing all permutations and combinations. The pandemic served as a tipping point in more than one way, accelerating digital transformation in some sectors and stalling it entirely in others.
One sector where transformation has been spectacular is the healthcare sector, with growing footprints of digital innovations becoming visible in the healthcare space with each subsequent phase of the pandemic. However, as academic researchers have noted, the acceptance/adoption of digital innovations in most spheres, including healthcare, has not been as expected, with end-users displaying a noticeable resistance to changing the status quo. i ii
In this blog, we explore the adoption-resistance quandary that innovating firms face on account of lukewarm end-user response to digital innovation in the healthcare space. We draw upon some recently published articles to explicate the resistance of medical practitioners, clinical staff, hospital administrators, and patients toward the innovations, which range from remote delivery of consultation and care to automation of records.
Digital innovations have revolutionized the day-to-day lives of individuals and digital products, and most industries have been leveraging the benefits. Parallel to the unfolding of digital innovations, a growing resistance to their adoption is also becoming quite apparent. Consumer resistance is described as the unwillingness among consumers to try newer innovations in the market, and it can often fail any innovation by delaying or impeding the adoption of the same. Existing scholarship has attempted to diagnose the reasons behind the unwillingness to adopt innovations that are better alternatives to how things are being done. Their revelatory findings list various psychological, social, practical, and random factors. Some studies have also traced the source of resistance to demographic factors such as income, gender, age, and education. These factors were found to be quite evident in the case of mobile banking, with age and gender being crucial components.
The resistance to digital innovations can be non-adoption, rejection, and postponement, representing adherence to the status quo. The drivers of resistance and its impact vary with the types of innovations. Existing scholarship has examined resistance to a lesser extent, offering limited theoretical insights. However, the seminal work of Ram and Sheth (1989), Innovation Resistance Theory (IRT) iii , provides a detailed and practically useful elucidation of consumer resistance through different barriers that hinder the adoption of an innovation. According to IRT, the resistance could arise from functional barriers, including usage, value, and risk, and psychological barriers, including tradition and image. Diving deeper into the functional barriers, the usage barrier describes the usability of the innovation and the changes the consumers have to undergo to use it; the value barrier refers to the performance-to-price value of the service to its competitors. Lastly, the risk barrier is the consumer’s perceptions of the risks associated with the innovations. In comparison, the tradition and image barriers in the psychological barriers category refer to the habit of doing services in specific ways and the ease of usage, respectively.
The healthcare sector provides a vast scope and platform for implementing successful digital innovation. The pandemic and the subsequent lockdowns have accelerated the impact of innovations in the sector, which, before the onset of the pandemic, had been progressing at a rather creeping pace. Through the pandemic, these innovations appeared at all levels, from administrative to patient care, and helped address the unique healthcare challenges during the lockdown. The innovation-enabled response served as a boon to the geriatric community and people with comorbidities. Some examples include online consultations with patients (the eSanjeevani platform by the MoHFW), online care delivery, remote doctor consultation, e-health records, etc. iv Past studies have acknowledged that these initiatives have reduced the cost of healthcare, promoted and supported healthcare infrastructure, and reduced the disparities in access to healthcare facilities. v
Despite the rise in use during the pandemic, once the pandemic receded and presented an opportunity to revert to the traditional way of discharging healthcare, an unanticipated and robust resistance to the continued use of many digital innovations surfaced. The resistance was not confined to individuals, with all end-user groups, including the patients, healthcare providers, and caretakers, resisting either the adoption or continued usage of digital innovations in the healthcare sector. Academic research sought answers to this rising phenomenon and found that the resistance was a result of barriers related to the different aspects of the healthcare sector, ranging from data management, ease of use, policy insufficiency, infrastructure constraints, and aversion to technology, which causes a sense of anxiety and indifference towards technology usage. Mapping the resistance to IRT, one can observe several barriers as sources of resistance, including security and privacy concerns, which represent risk barriers along with the tradition and image barriers driven by presumptions about the quality and efficacy of care. For instance, the view that in-person consultations are more effective than online consultations is a commonly acknowledged barrier source in this context.
It is logically arguable that barriers, however irrational, can lead to innovation failure and impede transitions that could have potentially been exceedingly useful, saving both time and money. It is, therefore, indisputable that there needs to be a well-thought-out approach to overcome the resistance towards digital innovations. Speaking broadly, comprehensive measures are required to overcome each barrier by addressing the concerns of different stakeholder groups. Scholars have discussed the value of introducing interventions in this regard.
To begin with, the most rudimentary requirement is to acknowledge that barriers exist, and differences exist in the type and the extent of resistance due to socio-demographic profile variations. Accordingly, some essential steps that can be taken towards reducing the barriers include appropriate management of information to address privacy and data-related concerns, safety labels and usage instructions where required to address usage barriers, creation of end-user awareness through educational and training programs, deployment of commensurate communication strategies to enhance consumers’ perception of power and control, provision of adequate support service to improve perceived usefulness, personalization of services, and involvement of relevant end-users at an early stage of product conception.
Admittedly, this is easier said than done. Overcoming the resistance to digital innovations is indeed a complex mechanism, especially in the healthcare sector. Still, the interventions /mechanisms mentioned above can serve as a good beginning point for supporting the diffusion of disruptive digital innovations in the sector.
i Talwar, S., Talwar, M., Kaur, P., & Dhir, A. (2020, November). Consumers’ Resistance to Digital Innovations: A Systematic Review and Framework Development. Australasian Marketing Journal, 28(4), 286–299.
ii Talwar, S., Dhir, A., Islam, N., Kaur, P., & Almusharraf, A. (2023, November). Resistance of multiple stakeholders to e-health innovations: Integration of fundamental insights and guiding research paths. Journal of Business Research, 166, 114135.
iii Ram, S., Sheth, J.N., 1989. Consumer resistance to innovations: the marketing problem and its solutions. J. Consum. Mark.
iv Alves, R., Caneiras, C., Santos, A. I., Barbosa, P., Cardoso, J., Caseiro, P., Vitorino, M. J., Pereira, J., & Escoval, A. (2020, November 25). Medical Electronic Prescription for Home Respiratory Care Services (PEM-CRD) at a Portuguese University Tertiary Care Centre (2014–2018): A Case Study. Sustainability, 12(23), 9859.
v Jung, C., & Padman, R. (2015). Disruptive digital innovation in healthcare delivery: the case for patient portals and online clinical consultations. In Springer eBooks (pp. 297–318).