Impact of Patient-Physician Co-creation on Joint Decision-Making, Patient Satisfaction,

and Patient Loyalty: The moderating role of Personal Innovativeness, Digital Literacy
and Resistance to Change

A Doctoral Dissertation Proposal

Submitted by

Vaidik Bhatt
Research Scholar
18FMRCHH010012

DAC Members:
Dr. Nikhat Afshan(Convener)

ICFAI Business School (IBS) Hyderabad

IFHE
(Deemed-to-be University under Section 3 of the UGC act, 1956)
Impact of Patient-Physician Co-creation on Joint Decision-Making, Patient Satisfaction,
and Patient Loyalty: The moderating role of Personal Innovativeness, Digital Literacy
and Resistance to Change
Abstract
Technology has transformed the way business operates in different sectors, and healthcare is
no exception. This is evident by the growing number of online healthcare platforms in the world,
including India. Healthcare platforms have enabled healthcare service providers to offer several
healthcare services online. The adoption of healthcare technology showed exponential growth during
the pandemic. However, there is not much research done in this area to explore how patient-
physician co-creation impacts joint decision-making, patent satisfaction and patent loyalty. The study
tries to investigate how digital healthcare platform helps to co-create value through the lens of
service-dominant logic and DART (Dialogue; Access; Risk-Assessment; and Transparency)
framework. The study proposes a research model depicting the linkages between patient-physician
co-creation, joint decision-making, patient satisfaction, and loyalty. The study further also explores
the moderating effects of personal innovativeness, digital literacy, and resistance to change on the
relationship between value co-creation and digital literacy, patient satisfaction, and patient loyalty.
The study aims to collect data from 500 respondents who have used digital healthcare platforms to
avail of healthcare services. The target population would consist of students, faculty members, non-
teaching staff, and research scholars of a leading private university. Partial Least Square Structural
Equation Modelling (PLS-SEM) using SmartPLS 4.0 version will be conducted to test the proposed
model. The study's findings will have implications for physicians, hospital administrators, healthcare
platform developers, founders and entrepreneurs in the healthcare sectors. The findings will also
have important implications for policy-makers to make a suitable policy regarding the better use of
digital healthcare platforms.

Key Words: Digital Healthcare; Digital Healthcare Platforms, Co-creation; Joint decision-making;
Patient Satisfaction; Patient Loyalty; Personal Innovativeness; Digital Literacy; Resistance to
Change

1. Introduction
Technology is essential in providing high-quality services without temporal or spatial
constraints (Fan et al., 2022) . Despite several advantages of using digital technologies, its usage by
patients was limited in the pre-covid era. This can be attributed to a lack of trust in technology
(Accenture report)1, physicians' preferences of physical interaction with patients (ITU report) 2,3.
However, the pandemic changed the scenario and forced people to use digital healthcare
technologies to fulfil their healthcare needs and avoid any covid related infections
(Baudier et al., 2021, 2022)
. Digital healthcare, also known as eHealth or digital health, refers to the use of digital
technologies, such as computers, mobile devices, the internet, and various software applications, to
improve healthcare services, enhance access to medical information, support clinical decision-
making, and facilitate communication between healthcare providers and patients (Pant et al., 2022) .
The rising number of patients during the pandemic forced healthcare organizations to adopt digital
transformation in a short span of time. Digital technologies enabled healthcare organizations to
segregate people who needed physical diagnostics from people who could be provided online
consultations for their healthcare needs. This helped them serve more patients with limited physical
1
Digital Technology Adoption in Healthcare | Accenture
2
How COVID-19 accelerated digital healthcare - ITU Hub
3
Insurers (payers) and the governments were underpaying for the online healthcare consultancies, physicians lost the motivation to provide
healthcare services in online form.

2
resources (Wang et al., 2020) . Digital technology helps people seek doctors’ advice over mobile
devices from their homes.
This is supported by the fact that virtual consultancy increased to 32% in the middle of 2021
from just 7% in early 2020. Almost half of these patients (48%) were reported as new patients
(Accenture report1). As per the McKinsey & Company report, more than 70% of patients have
cancelled in-person visits and registered themselves on digital health platforms during the pandemic.
In 2019, 11% of patients reporting themselves on e-health platforms increased to 76% in 2020 4;
Physicians also reported a significant surge in patient registrations on digital portals by 50-175 times
as per the same report. The report suggests that fifty-seven per cent of physicians considered digital
healthcare technologies a favourable medium for providing healthcare services.
To ensure transparent and ethical usage of digital healthcare platforms in India, the
Government of India also introduced several guidelines for digital healthcare. A program like e-
sanjeevani5 was introduced as a part of the digital India mission for inclusive growth of the digital
healthcare ecosystem. Some of these initiatives include the emergency response and health systems
preparedness package, which financially supports states in strengthening public healthcare systems
(Ministry of Health and Family Welfare- Press Release) 6. The government has also approved a US$
1 billion loan towards India’s Pradhan Mantri-Ayushman Bharat Health Infrastructure Mission in
July 2022 (IBEF-report)7. The Ministry of Health and Family Welfare has also launched several e-
health and telemedicine initiatives, such as the National Health Portal, which provides information
on health, government programs, and services in the health sector. The Online Registration System
(ORS) allows citizens to make online registration and appointments, pay fees, view diagnostic
reports, and inquire about blood availability in various public hospitals (Ministry of Health and
Family Welfare - report)8. The government is leveraging existing digital infrastructure through the
National Digital Health Mission (NDHM) to create a cohesive digital system (Financial Express
Report)9.
Digital technology uses electronic tools like mobile devices and computers to fulfil patients'
healthcare-related needs. Healthcare has become more accessible for patients through digital
technologies. The digital platform provides several advantages to patients, such as booking online
appointments (Fan et al., 2022) , booking video consultations (Birkmeyer et al., 2021) , and availing
other healthcare services. Patients can book a video consultation with the doctor, and the healthcare
encounter process has been shifted to virtual platforms where physical presence is not required
(Ayabakan et al., 2023). Patients can be segregated based on the disease conditions and the criticality
of the procedure. Thus, patients with less critical conditions do not need to travel to healthcare
facilities and wait in a queue for a longer time.
With the help of the utilisation of digital technologies in healthcare, physicians have a choice
to provide high-quality healthcare at a reasonable cost
(Bao et al., 2020; Dal Mas et al., 2023; Rezaei et al., 2021; S
. Patients can utilise digital healthcare platforms
for basic healthcare-related needs like mental health, skin, metabolic diseases
(Ayabakan et al., 2023)
, and various chronic and acute diseases
(Baudier et al., 2021; He et al., 2021; Liu et al., 2020; Thompson et al., 2020)
. Digital technologies also empower patients to consult the doctor post-
discharge for follow-ups (Li et al., 2020). Patients using digital technologies as a preferred medium
to interact with physicians before physical interactions allow early disease detection (Thompson et
al., 2020), reduce congestion in emergency rooms (Sun et al., 2020), and thus reduce the overall
burden on the healthcare system. Digital technology enables seamless information exchange
(Joiner & Lusch, 2016). It enables
patients to participate in healthcare through improved engagement and
4
https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality
5
https://esanjeevani.in
6
Initiatives to Promote Indian Healthcare Industry (pib.gov.in)
7
Healthcare System in India, Healthcare India - IBEF
8
e-Health & Telemedicine | Ministry of Health and Family Welfare | GOI (mohfw.gov.in)
9
India’s journey towards a digital healthcare ecosystem: taking the leap of faith | The Financial Express

3
shared decision-making, increasing their commitment to compliance and adherence to treatment
protocols (Osei-Frimpong et al., 2018).
Though digital technology offers several advantages, human and psychological factors such
as personal innovativeness (Martínez-Caro et al., 2018) , digital literacy (Baek et al., 2021), and
resistance to change (Hoque & Sorwar, 2017) bring many challenges. On one side, where a user's
personal innovativeness boosts the user's morale to use and adopt the technologies, resistance to
change negatively impacts the users’ mindset (Hoque & Sorwar, 2017) . Apart from this, digital
literacy also influences the users’ behaviour. A user with a high digital literacy tends to adopt and
use technology more frequently (Zahoor et al., 2023) . Patients are more satisfied with healthcare
services when actively involved in decision-making (De Rosis & Barsanti, 2016) , which can
ultimately lead to loyalty (Martínez-Caro et al., 2018) . It is important to have interaction and
involvement from both parties in the healthcare process to enable a smooth flow of information and
services (Akter et al., 2022).
Thus, the study takes co-creation activities between physicians and patients as a starting point
and evaluates the impact of patient-physician co-creation on joint decision-making, patient
satisfaction, and loyalty. This study also focuses on the moderating effects of resistance to change,
digital literacy, and personal innovativeness on the relationship between patient-physician co-
creation and joint decision-making, patient satisfaction, and loyalty. This study explores two research
questions.
1. How do patients perceived value co-creation, that is, the level of interaction between
physician and patients on joint decision-making, patient satisfaction, and patient loyalty in
digital healthcare platforms?
2. How do personal innovativeness, digital literacy, and resistance to change moderate the
impact of co-creation enabled by digital healthcare platforms on joint decision-making,
patient satisfaction, and patient loyalty?
Section 2 discusses the motivation behind the study, followed by a literature review of
relevant articles and constructs description in section 3. The research gaps and objectives are in
sections 4 and 5, followed by a theoretical framework and hypothesis development in sections 6 and
7. Finally, section 8 suggests research methods and sampling methods. The study’s anticipated
implications and contributions are enlisted in section 9.

2. Motivation
In recent years, there has been a significant increase in healthcare service requirements due to
rising cases of acute and chronic diseases. A sedentary lifestyle also escalates the lifestyle disorders
like diabetes and cardiac arrhythmia among youth. The healthcare ecosystem is more vulnerable and
burdensome because of more patients and fewer resources. As per the IBEF report (September
2021)10, the country’s current doctor-to-patient ratio is 1:1456. This ratio is much less than the WHO
standard of 1:1000. This clearly means India needs more doctors and healthcare facilities. Although
adding a medical staff can be one of the approaches to solving access issues, the quality of healthcare
services is still a question. To cater to this need, government and private players are coming together
to introduce advanced technologies to build resilient healthcare systems 11. IT has huge potential to
provide various solutions irrespective of the temporal dimension
(Ayabakan et al., 2023; Bharadwaj, 2000)
. Technology has made it possible to provide quality healthcare at much affordable rates along
with increased reach
(Ayabakan et al., 2023; Bao et al., 2020; A. N. Mishra et al., 2022; Sun et al., 2020; Tong e
. McKinsey & Company’s report on Payers’

10
https://www.ibef.org/research/newstrends/adoption-of-digital-technology-to-accelerated-funding-in-indian-healthcare-sector
11
Resilient healthcare system is an ability of a healthcare ecosystem to be prepared for shocks as well as minimize the negative impact of
disruptions and recover at a faster rate to serve the basic needs of a people.

4
perspective12 suggests that digital technology will take the lead in the healthcare ecosystem with its
state-of-the-art capabilities. The improvement in technology is likely to enhance access, experience
and trust in the healthcare ecosystem (Accenture13).
To make Indian healthcare digitally advanced, the government of India has taken several
initiatives to meet the nation’s healthcare-related requirements. One such initiative is National
Health Policy (NHP), which guides towards inclusive care and universal health coverage (KPMG,
2021)14. NHP is likely to increase the reach of quality healthcare and decrease the burden on the
government and healthcare systems. Apart from government lead initiatives, private players also
contributed in the digitalisation of healthcare (Economic Times Report, 2022) 15. For example, Tata
Sons paid an undisclosed amount for a majority stake in health-tech start-up 1mg. The adoption of
technology in the Indian healthcare system has attracted funding from health tech start-ups by 124
time in the last 8 years (Annexure 4a), and revenue and market share is projected to increase by 35
times by 2030 (Annexure 4b). Technology usage in the healthcare system allows patients to avail
several services in digital mode, and physical visits in hospitals and clinics are likely to decrease by
8% and 9%, respectively (Annexure 4c).
The digitisation of healthcare has attracted the attention of researchers and practitioners on
this important area of research. However, most of the researchers have focused on the adoption of
digital technologies
(Alam et al., 2020; Hoque & Sorwar, 2017; Jansen-Kosterink et al., 2019; Khatun et al., 20
with the little focus on patient-
physician co-creation activities and leading consequences. To the best of our knowledge, there have
been very few studies that have focused on value co-creation. Osei-Frimpong et al. (2018) suggest
that patient-physician co-creation activities enhance service engagement and patients’ compliance
toward treatment. In addition, findings from Akter et al., (2022) suggest that co-creation activities
enhance service innovation, perceived value, and patient welfare. However, no study has investigated
the impact of patient-physician co-creation activities on joint decision-making, patient satisfaction,
and patient loyalty. Further, the moderating role of personal innovativeness, resistance to change,
and digital literacy on the relationship between patient-physician co-creation and joint decision-
making, patient satisfaction, and patient loyalty is also an important avenue for research.
3. Literature Review
This section provides a brief understanding of the constructs included in the study.
3.1 Overview of Constructs
The focal constructs used in the study are patient-physician co-creation, joint decision-
making, patient satisfaction, and patient loyalty to validate the direct effects of patient-physician co-
creation impact on joint decision-making, patient satisfaction, patient loyalty, personal
innovativeness, resistance to change, and digital literacy.
3.1.1 Patient-Physician Co-creation
Value co-creation is an interactive process in which different actors in the network have an
effective role in creating value. It results from direct and indirect interaction and exchange and
combining resources among different network actors (T. Zhang et al., 2020) . Value co-creation has
been a topic for researchers in the past
(Al-Omoush et al., 2023; Anshu et al., 2022; Leone et al., 2021; Schiav
. The co-creation
capabilities has been studied in different context like manufacturing, engineering, shipping, and

12
https://www.mckinsey.com/industries/healthcare/our-insights/digital-health-ecosystems-a-payer-perspective
13
https://www.accenture.com/_acnmedia/PDF-161/Accenture-Digital-Adoption-In-Healthcare.pdf
14
https://kpmg.com/in/en/home/insights/2021/02/india-healthcare-sector-transformation-in-the-post-covid-19-era.html
15
The Changing Landscape of Digital Healthcare in India, Health News, ET HealthWorld (indiatimes.com)

5
services (Struwe & Slepniov, 2023) . In healthcare, patients are co-creators of services who are
actively involved with healthcare service providers (Mende, 2019).
In this study, value co-creation has been adopted from Akter et al., (2022) where it has been
operationalised as a second-order formative construct consisting of dimensions of the DART
framework as first-order constructs.
The first dimension of DART, 'dialogue,' which is the foundation for all communications.
Dialogue is a series of formal and informal communications between a user and the service provider
through various channels. It is also applicable to B-2-B and B-2-C scenarios. Furthermore, it enables
building a platform on the capacity and willingness of all parties involved, creating an environment
that will benefit both parties (Akter et al., 2022; Taghizadeh et al., 2016).
The second dimension of DART, 'access,' relates to the accessibility and influence of
information and knowledge resources already available in the network, emphasising ownership and
openness. The parameter highlights sharing valuable and pertinent information timely
(Prahalad & Ramaswamy, 2004b)
.
The third dimension of DART, ‘Risk-assessment’, describes the prediction ability of network
actors and analyses the implications of their interactions using the available information
(Prahalad & Ramaswamy, 2004b)
. This element suggests informed decision-making and awareness regarding the
stakes involved in a relationship. This parameter reveals the network participants' motives, agendas,
plans, and goals.
The fourth dimension of DART, ‘Transparency’. It is the fourth dimension of the DART
framework and facilitates efficient information exchange, improves visibility and enhances
performance (Konstantinos et al., 2017; Prahalad & Ramaswamy, 2004a; Taghizadeh et al., 2016) .
Efforts have been made in the literature to define transparency and explore its significance in the
buyer-supplier relationship within the supply chain context. Transparency facilitates value creation,
efficient information exchange, reduction of speculation, and improved visibility, ultimately leading
to enhanced performance (Fan et al., 2022).
3.1.2 Joint Decision-Making
Joint decision-making is a coordination mechanism that can address the inherent complexity
of business-to-business (B2B) processes (Nurhayati et al., 2023) . Joint decision-making refers to a
collaborative process in which multiple individuals, such as patients and healthcare providers, come
together to make decisions regarding healthcare treatment or management (Vogel et al., 2021) .
Given the significance of patient well-being, involving patients and enabling decision-making
processes is imperative. Nowadays, patients actively seek to participate in these processes, as they
are the ones who expect and value the outcomes of their healthcare experiences. Joint decision-
making helps the patients to adhere to the treatment protocols (Osei-Frimpong et al., 2018). In recent
years, patients have had access to various data sources and can search for relevant information
regarding the disease during, before, and after online physician interaction
(Osei-Frimpong et al., 2018)
. Patients' behaviour of seeking information and asking questions influences their self-
awareness and well-being (Dahl et al., 2021). Informed patients are involved in the decision-making
related to treatment options (Kraus et al., 2021).
3.1.3 Patient Satisfaction
Patient satisfaction measures patients’ opinions and attitudes toward healthcare services
(Dhakate & Joshi, 2023) . Some of the studies define patient satisfaction as an outcome of quality
services desired and received by patients from healthcare services (Otani et al., 2011) . Patient
satisfaction is subjective, unique, and personal (Stefanini et al., 2021). The digital healthcare
platform's high functional and technical quality enhances patient satisfaction (Lu et al., 2021) .
Conversely, factors like trust (Sun et al., 2022), ease of convenience, and technology usefulness
enhance patient satisfaction, enabling continuous usage of digital healthcare platforms (A. Mishra et al., 2023; B

6
 . Patient satisfaction is important to measure and improve
healthcare service quality (Lu et al., 2021).
3.1.4 Patient Loyalty
Patient loyalty refers to the level of commitment, trust, and preference that a patient has
towards a healthcare provider, facility, or healthcare system
(Nguyen & Nagase, 2021; Y. Zhang et al., 2018)
. In the marketing context, customer loyalty is set to exist when recurring purchase activity
reflects a favourable opinion toward the brand (Keller, 1993). In healthcare, service quality, word of
mouth, and relationship with physicians impact patients' loyalty to outpatient services
(Dayan et al., 2022)
.. Hospital image, perceived medical-service quality, and satisfaction also improve patients’
loyalty (Lan et al., 2016). This study takes patient loyalty as a favourable attitude of patients towards
digital healthcare platforms, resulting in continuous usage.
3.1.5 Personal Innovativeness
Personal innovativeness refers to the personal traits affecting a user's adoption of a novel
technology (Eom et al., 2012). In other words, personal innovativeness is an individual’s response to
adopting new technology. It is one of the most extensively studied constructs in the IT literature.
Many authors have studied personal innovativeness as an extension of the unified theory of
acceptance and use of technology (UTAUT). People with high personal innovativeness show
friendly behaviour and adopt technology faster than the laggards with low personal innovativeness
(Jeong et al., 2009; Patil et al., 2020; Wu & Yu, 2022) . Personal innovativeness improves
behavioural intention to adopt and continue using new technology (Chen, 2022). Many studies have
been conducted in different contexts of digital learning (Chen, 2022; Wu & Yu, 2022) , mobile
payment (Patil et al., 2020), and mobile RFID (radio frequency identification) services
(Jeong et al., 2009)
.
In healthcare, Personal innovativeness impacts the perceived usefulness of wearable device
users (Martínez-Caro et al., 2018) . Conversely, physicians with high personal innovativeness have
been found to show higher compliance while adopting EHR (electronic health records)
(Hossain et al., 2019)
.
3.1.6 Resistance to Change
Resistance to change is explained as an emotion of fear and anxiety (Huang et al., 2021) .
Resistance to change has been identified as an inhibitor of technology adoption behaviour in the
information technology literature (Hoque & Sorwar, 2017; Hossain et al., 2019; Anshu et al., 2022).
Resistance to change is studied in several contexts, such as mobile wallets (Leong et al., 2020) , in-
vehicle infotainment (Information and Entertainment) systems (Kim & Lee, 2016) , digital contact
tracing apps (Prakash & Das, 2022) , mobile app updates (Fu et al., 2023) , innovation resistance
(Anshu et al., 2022) . Ali et al., (2016) summarised the causes of the resistance in IT adoption.
Similarly, in the context of digital health, many scholars have studied the impact of resistance to
change on behavioural intention to use the technology. Resistance to change negatively influences
physicians’ behavioural intention to adopt EHR (electronic health record) (Hossain et al., 2019) and
patients’ behavioural intention to adopt mHealth (Hoque & Sorwar, 2017) . This study takes the
resistance to change as a physiological barrier that inhibits the usage of healthcare technology, as
taken by Hoque & Sorwar, (2017); Hossain et al., (2019).
3.1.7 Digital Literacy
Digital literacy refers to “the ability to understand and use information in multiple formats
from a wide variety of sources when it is presented via computers”, particularly through the use of
the internet (Pool, 1997) . Digital literacy is all about how the user uses digital technology and
communication tools to receive or create the information to function. Digital literacy provides a
facilitating condition for older adults, which is one of the important factors in technology adoption
studies (Tirado-Morueta et al., 2018) . Digital literacy has been found to moderate the negative

7
impact of age and socioeconomic factors on internet usage. Managers’ digital literacy helps
organisations in digital transformation by enhancing the usage of digital platforms
(Zahoor et al., 2023)
. Digital literacy of managers helps a firm to remain agile in the rapidly changing technological
environment (Rialti et al., 2019).
In the healthcare context, several research has been conducted on digital literacy
(Chang et al., 2021; Guitton, 2021; Le et al., 2023;
. Le et al., (2023) performed a study on
eHealth literacy among medical students in Vietnam, whereas studies performed by
Chang et al., (2021); Quinn et al., (2017)
explored the behaviour of a consumer while seeking health-related
information through the internet.
3.2 Review of Seminal Papers
This section summarises the significant papers in the research area.
3.2.1 Studies on value co-creation and DART
Akter, S., Babu, M. M., Hossain, M. A., & Hani, U. (2022). Value co-creation on a shared healthcare
platform: Impact on service innovation, perceived value and patient welfare. Journal of Business
Research, 140, 95–106 [ABDC - A].

The objective of this study is to understand how patients co-create value through a shared
healthcare platform and its impact on perceived value, perceived service innovation, and patients’
welfare. The research also examined the indirect effect of value co-creation (VCC) on perceived
value and patients’ perceived welfare through perceived service innovation. The study
operationalises VCC as a second-order construct consisting of dialogue, access, risk, and
transparency constructs. The study collected 251 usable responses (response rate = 51%) from
Shashto Bataon [Mobile App] users on a 5-point Likert scale. The proposed research model was
tested using Smart PLS 3.0. The research suggest that dialogue, access, risk assessment, and
transparency are important elements of the higher-order value co-creation construct. The finding
suggested that value co-creation is a major predictor of perceived service innovation, perceived
value, and customer welfare. Despite these factors' relevance, the findings show that transparency is
the best predictor of value co-creation, followed by risk, access, and dialogue in the shared
healthcare platform.
Critique
The study captures insights into service innovation, perceived value, and patients’ welfare.
However, it pays little attention to patient satisfaction and patient loyalty. The study collects data
from the neighbouring country Bangladesh. The study was performed only in Bangladesh, so the
results cannot be generalised. On the other hand, the study utilised the services from a professional
market research firm but did not explain the sampling method used for selecting the respondents.

Albinsson, P. A., Perera, B. Y., & Sautter, P. T. (2016). DART scale development: Diagnosing a
firms readiness for strategic value co-creation. Journal of Marketing Theory and Practice, 24(1),
42–58. [ABDC - B]

This article develops the DART scale that measures dimensions of Dialogue, Access, Risk
assessment, and Transparency with Churchill's (1979) guidelines in customer interactions within the
service experience environment. Further, the article investigates the impact of shared responsibility
on DART and the impact of DART on customer loyalty. The article suggests that companies must
shift towards more collaborative environments that create shared value instead of a firm-centric
closed environment. The study builds on the building blocks of interaction for value co-creation
DART (Prahalad & Ramaswamy, 2004b, 2004a) . From the initial pool of 50 items representing the
DART framework, after content and face validity, 37 items were finalised. The study collected data

8
on a seven-point Likert scale from the 327 students of two universities in the US. Confirmatory
factor analysis was performed on the remaining 36 items. The results indicated that shared
responsibility positively affects dialogue, access, risk assessment and transparency, whereas only
dialogue and access have significant positive access on service loyalty. The study does not provide
any statistical support for risk assessment and transparency on loyalty.
Critique
The study performed the scale development process on DART, where respondents are
university students. While answering the questionnaire, students were instructed to think about a
specific existing product or service irrespective of whether they have used it or not. This would have
led to a stimulated or imaginative response rather than an actual one. Although the study developed
measurement items for the DART model to assess the customers’ perspective, the study failed to
identify the real essence of value co-creation.
3.2.2 Studies on Patient Satisfaction
Stefanini, A., Aloini, D., Gloor, P., & Pochiero, F. (2021). Patient satisfaction in emergency
department: Unveiling complex interactions by wearable sensors. Journal of Business Research, 129,
600–611. [ABDC – A]

The objective of the study is to examine healthcare service providers’ behaviour in verbal and
non-verbal communication and team dynamics affects the patient's satisfaction in an emergency
department of a hospital. This study analysed the interaction between patients and providers in the
emergency department of a large university hospital. The emergency department's interactions
between patients and healthcare providers are complex in nature. Sociometric badges - wearable
devices developed by MIT media lab were used to obtain quantitative data reliably. Sociometric
Badges can automatically and directly measure individual and collective behaviours using different
sensors: an accelerometer, a microphone, and Bluetooth. It is possible to collect behavioural
measures that are impossible to gather with surveys, interviews, or direct observations, without
compromising on privacy. Moreover, the patients were asked to fill out a questionnaire at the time of
discharge from the hospital related to patients’ perception of treatment. Data from the sociometric
was extracted and pre-processed using the software provided by the vendor of the sociometric
badges. Patients’ perception was aggregated for overall satisfaction, care effectiveness, and team
responsiveness. Multiple regression analysis was used to explore potential determinants of patient
satisfaction. The study suggested that the doctors’ posture activity and mirroring audio font network
deviation have a negative impact on overall satisfaction and care effectiveness. Whereas, Patients’
audio back and nurses’ body movement activity have a positive impact on care effectiveness. Team
responsiveness was found to be negatively influenced by doctors’ posture activity and doctor’s
speech overlap.
Critique
The study has identified the critical factors responsible for the patient’s satisfaction in the
emergency room of the hospital, where the interactions are critical. The study used the data from IoT
(Internet of Things) enabled badges and patients’ perceptions. However, the study does not explain
the sampling process clearly.

Lu, W., Hou, H., Ma, R., Chen, H., Zhang, R., Cui, F., Zhang, Q., Gao, Y., Wang, X., Bu, C., Zhao,
J., & Zhai, Y. (2021). Influencing factors of patient satisfaction in teleconsultation: A cross-sectional
study. Technological Forecasting and Social Change, 168. [ABDC – A]

The objective of this study is to empirically investigate the antecedents of patient satisfaction
in the context of teleconsultation. The study examined the direct effect of medical expenses, patient
cognition, technical quality and functional quality on patient satisfaction. Functional quality is
operationalised as a second-order reflective construct consisting of tangibility, reliability,

9
responsiveness, assurance, and empathy as primary constructs. The study also analysed the
moderating effects of medical expenses and patient cognition on the relationships between technical
quality and patient satisfaction as well as functional quality and patient satisfaction. The study uses
the theoretical lenses of cognitive evaluation theory. The study collected data from the National
Telemedicine Center for China (NTCC) database and conducted an anonymous online survey on
patients’ satisfaction. Along with the effective response rate of 43.1%, the study received 459 usable
responses. The statistical analysis was performed using AMOS 24.0 by testing the measurement
model followed by the structural model. The goodness of model fit was tested by chi-square by the
degree of freedom, the goodness of fit index (GFI), comparative fit index (CFI), and root mean
square error of approximation (RMSEA), which was followed by path analysis to estimate the path
coefficients. The direct effects of technical quality, functional quality, medical expenses, and patient
cognition were significant on patient satisfaction. The study also shows that patient cognition
significantly moderates the effect of technical quality on patient satisfaction. At the same time,
medical expenses moderate the effect of functional quality on patient satisfaction.
Critique
The study does not clearly emphasise the sampling procedure. In China government has strict
policies and control over essential services. The study collected responses from the users (patients)
of telemedicine, which is one of the parts of digital healthcare services. The patient satisfaction for
overall digital healthcare platform users is explained in a limited manner.
3.2.3 Studies on Joint Decision-Making
Osei-Frimpong, K., Wilson, A., & Lemke, F. (2018). Patient co-creation activities in healthcare
service delivery at the micro level: The influence of online access to healthcare information.
Technological Forecasting and Social Change, 126, 14–27. [ABDC – A].

The objective of the study is to understand patients’ motivation in searching online health
information and its impact on patients’ commitment to comply with medical protocols. Clinical
encounters in healthcare have undergone a transformation phase due to technological advancement.
Following the transformation, the healthcare sector changed from being paternalistic (directed
entirely by physicians) to being patient-centric to satisfy the patient as a consumer. The study
adopted a mixed-method qualitative and quantitative analysis. To understand patients’ use of online
resources in seeking health-related information, interviews were conducted with twenty patients and
seven doctors. The data from the interviews were used to refine the measurement scales, which were
pretested with the 20 outpatients. The theoretical model was developed and validated by taking
responses from 360 patients from 20 different hospitals. In the first step of analysing the data,
exploratory factor analysis (EFA) was performed using SPSS 21, followed by confirmatory factor
analysis (CFA) using Amos 21. Cronbach alpha, composite reliability, and average variance
extracted were analysed, and discriminant validity. The results of the study provided significant
linkages between the pre-encounter information search by patients and significantly influenced
interactions, patient-provider orientation, and shared decision-making, which further has a positive
impact on service engagement and commitment to compliance.
Critique
The study context was Ghana, an emerging but lower-middle group country with its own
ethnicity and culture. The study focused majorly on the pre-encounter information search. This can
be compared with any other information search on the internet. In this manner, the study provides
limited attention to digital healthcare platforms where the resources are integrated, which needs
further exploration.
3.2.4 Studies on Loyalty

10
Martínez-Caro, E., Cegarra-Navarro, J. G., García-Pérez, A., & Fait, M. (2018). Healthcare service
evolution towards the Internet of Things: An end-user perspective. Technological Forecasting and
Social Change, 136, 268–276. [ABDC – A].

The objective of the study is to explore the antecedents of e-loyalty in the context of Internet-
based healthcare information services. This study examines the relationship between patients’
capabilities for effectively using information technology-enabled devices and the success of IoT-
based healthcare services. The study considers the constructs like personal innovativeness, self-
efficacy, perceived usefulness, satisfaction, and loyalty. The study collected patients’ data from five
different municipalities of the Murcia region in Spain that used IoT devices for healthcare
consultation. The study analysed the data using LISREL 8.50. The goodness of fit and other
parameters like validity and reliability were conducted before the path analysis. The study finds the
relationship between personal innovativeness and self-efficacy on perceived usefulness, satisfaction
and loyalty. The findings of the study suggest that self-efficacy and personal innovativeness have a
positive impact on perceived usefulness, perceived usefulness has a positive effect on satisfaction,
and satisfaction has a positive effect on loyalty.
Critique
The study's results may not be generalisable and need further validation in the other context.
The study takes the responses from the users of IoT devices in healthcare. In a way, the study
concentrates only on one aspect of digitalisation and digital transformation of healthcare. However,
the generalizability regarding the overall digital technology adoption by patients is not provided.
3.2.5 Studies on Digital Literacy

Quinn, S., Bond, R., & Nugent, C. (2017). Quantifying health literacy and eHealth literacy using
existing instruments and browser-based software for tracking online health information-seeking
behavior. Computers in Human Behavior, 69, 256–267. [ABDC – A].

This study aimed to explore the relationships between obtaining online consultancy services
usage by individuals and their level of health literacy and eHealth literacy. This study also
investigated the associations between health literacy, e-health literacy, and individuals’ online
information-seeking behaviour. The study recruited 54 participants within the age group of 18 – 59
years (mean age of 26.76 years) and instructed them to search for information on six different
questions pertaining to health literacy via the Internet. The HCI browser was used as search engine
which recorded all the important data with the time stamp, such as webpage visits, website visits, and
other(s). At the end of the tasks, participants were asked to rate the difficulty in retrieving
information related to the six questions on a five-point Likert scale (1= Very easy, 5= Very difficult).
Multiple regression analysis was performed to determine whether the demographic variables
were significant predictors of the newest vital syndrome (NVS) and eHealth Scores. NVS score was
used to measure patients’ level of health literacy, and the results indicated that 77.4% of participants
had adequate health literacy skills, 14.8% had intermediate health literacy skills, and others had low
health literacy skills. However, supplementary items of eHealth literacy suggested that, in general,
participants had a positive perception of internet-based health resources. Many of the participants
appeared to be confident in their ability to use the internet resources. 85.2% of the participants
agreed that they knew how to use the Internet to answer health-related questions, and 66.7% agreed
that they had the skills required to evaluate resources found on the Internet. The age of the
participants does not found to be a significant predictor. The study suggested that, despite of having
adequate health and eHealth literacy, participants relied on the information on the Internet to answer
the questions. 96.3% of participants utilized unaccredited health information to answer some
questions. This clearly means that it is not always the case that eHealth literate individuals will
always use effective online search strategies.

11
Critique
The context of the study is focusing credible sources of information over the internet.
However, the context of the country is not explained in the study. On the other hand, the study
selected only 54 respondents, so this sample size might not be adequate. Moreover, this study
provides limited attention to how the respondents were selected. Moreover, 77.8% of the participants
have adequate health literacy skills, and 85.2% of participants agreed that they already knew how to
use internet-based resources to answer health-related questions. The respondents' group is not diverse
enough to conclude the study. Moreover, this study only concentrates on health-related information-
seeking behavior.

Chang, Y. S., Zhang, Y., & Gwizdka, J. (2021). The effects of information source and eHealth
literacy on consumer health information credibility evaluation behavior. Computers in Human
Behavior, 115. [ABDC – A].

There are three main objectives. (a) To evaluate the usage frequencies of different types of
indicators and criteria (Content related criteria, design criteria, individual criteria, sources indicators,
design indicators etc.) to evaluate different sources by consumers with different eHealth literacy
levels. (b) To examine how consumers with different eHealth literacy levels use different types of
website indicators to evaluate online health information from different sources. (c) To examine how
consumers with different eHealth literacy levels use different types of credibility criteria to evaluate
online health information from different sources. Patients are using online information. However, the
quality of information available on the internet is an area of concern. Health outcomes vary because
of plenty of non-credible information consumed by the patient regards to health.
The study recruited 25 participants from the university's mailing list for students, staff,
faculty, and alumni. The respondents' average age was 23.3 years, with a standard deviation of 5.50
years. Out of all the participants, only 8% of the respondents were in high school. Other participants
were in their graduation or post-graduation courses or already graduated. Five simulated search tasks
were given to the participants, and for each task, three different websites (government, commercial,
and an online forum) were presented to the participants. Participants were asked to evaluate each
webpage (a total of 15 web pages for 5 tasks). Tobii TX-300 eye-tracker was used to capture eye
movement. Interactions with computer mouse movement were recorded by iMouse software. After
completing the tasks, participants were asked to record a video using Camtasia software by
Techsmith.com about the experience of the tasks. Participants were measured on a scale developed
with eight items on 5 point Likert scale for eHealth literacy (=0.79). The data was analysed using
the Generalised Liner Mixed Models using Template Model Builder (GLMMTMB) package of R.
The finding suggests that most of the participants used content-related criteria and indicators
to identify credible sources. Participants used source indicators more than design indicators to
evaluate the government websites. On the other hand, participants used design criteria more than
individual criteria while evaluating the commercial websites. eHealth literacy did not found to be
significant in credibility evaluation criteria. However, it showed a marginally significant impact on
the information sources on the number of indicators used.
Critique
This study examines the relationship between health-related information searches on the
internet, information credibility, and eHealth literacy. The context of the study focuses on credible
sources of information over the internet. The study recruited 25 respondents. This sample size might
not be adequate. Moreover, this study provides limited attention to how the respondents were
selected. Only 8% of the respondents were in high school. All the other participants have either
completed the graduation or post-graduation or are completing the same. The mean age of the
participants was 23.3 years, with a standard deviation of 5.50 years. Moreover, the recruitment
criteria suggested that the participants' age should be at least 18 years. This indicates that a majority

12
of the participants were young participants. Other studies have already found that literate participants
and youngsters behave favourably regarding technology adoption and usage. Thus, the sample
selection does not provide adequate information on the results.
2.3.6 Studies on personal innovativeness and Resistance to Change

Hossain, A., Quaresma, R., & Rahman, H. (2019). Investigating factors influencing the physicians’
adoption of electronic health record (EHR) in the healthcare system of Bangladesh: An empirical
study. International Journal of Information Management, 44(September 2018), 76–87. [ABDC –
A*].

The study explored antecedents of physicians' adoption of electronic health records. The
study used the UTAUT framework, personal innovativeness, and resistance to change as antecedents
to EHR adoption. The data for the study was collected from 300 physicians of 30 private and public
hospitals in Dhaka using 5 points Likert scale. This resulted in 249 usable responses with a response
rate of 83%. Partial Least Square Structural Equation Modelling (PLS-SEM) was used to analyse the
data. In the first step, the measurement model was examined to identify the reliability and validity of
the measurement instruments, followed by the path analysis to test the hypotheses.
The empirical results show that performance expectancy, effort expectancy, and resistance to
change do not significantly impact physicians’ adoption of EHR. On the other side, facilitating
conditions, social influence, and personal innovativeness significantly influence physicians' adoption
of EHR. The results suggest that physicians who are high on personal innovativeness generally do
not experience resistance to change. In other words, users with high personal innovativeness are
generally more adaptable to change.
Critique
The study selected 30 hospitals in Dhaka, but no rational was provided behind the selection
of the 30 hospitals. Moreover, the study does not discuss the sampling methodology implemented.
The study only represents the direct effects of the constructs on behavioural intention to adopt the
new technologies by users. however, the study remains silent on the consequences of technology
adoption and hence needs further investigation.

Hoque, R., & Sorwar, G. (2017). Understanding factors influencing the adoption of mHealth by the
elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–
84. [ABDC – A].

The objective of the study is to analyse the factors influencing the adoption of mHealth by
elderly patients. This study used the UTAUT framework, technology anxiety, and resistance to
change as antecedents to mHealth adoption. The data for the study was collected from 300 elderly
patients (above 60 years of age) on a five-point Likert scale. The study analysed the 274 usable
responses using PLS-SEM to analyse the data. The empirical results show that facilitating conditions
do not significantly affect behavioural intention or usage behaviour. On the other hand, performance
expectancy, effort expectancy, and social influence positively affect behavioural intentions, while
technology anxiety and resistance to change negatively impact behavioural intentions to adopt the
technology.
Critique
The study does not clearly report the rationale behind the selected sampling method.
Moreover, the study only concentrates on the elderly population and needs further exploration of the
antecedents from the young participants, who are more oriented towards technology.
4. Research Gaps
Based on the literature review, this study identified the following gaps.

13
 4.1 Previous studies have examined the value of co-creation and its consequences in different
industries like telecom (Taghizadeh et al., 2016) , hospitality (Konstantinos et al., 2017) ,
social media (Schiavone et al., 2014) , retail (Albinsson et al., 2016) . However, limited
studies explored the co-creation phenomenon in the healthcare sector (Akter et al., 2022) .
Although the studies have extensively explored the value co-creation process between
customers and firms (B2C) and between the firms (B2B), the value co-creation between
physicians and patients may take place very differently given its nature. However, limited
studies have been conducted on patient-physician co-creation through digital healthcare
platforms.
4.2 Previous studies on value co-creation and the DART framework have studied the influence
of value co-creation on innovation strategy (Taghizadeh et al., 2016) , customer loyalty
(Albinsson et al., 2016) , overall positive experience (Konstantinos et al., 2017) , service
innovation, perceived value, and patient welfare (Akter et al., 2022) . However, the
influence of the patient-physician co-creation process on joint decision-making, patient
satisfaction, and patient loyalty has not been explored.
4.3 Personal Innovativeness has mostly been studied as an extension of UTAUT theory in
different contexts such as e-commerce (Jackson et al., 2013) , mobile payment
(Patil et al., 2020)
, digital learning (Chen, 2022; Wu & Yu, 2022) , RFID services (Jeong et al., 2009).
However, there is very little research on personal innovativeness in healthcare literature.
4.4 The studies on digital literacy have confirmed a positive relationship between an
individual’s level of digital literacy and their ability to find credible information
(Chang et al., 2021; Quinn et al., 2017)
. However, most of these studies have conducted field
experiments where respondents were asked to complete certain information searches, and
the computer system was used to track their keyboard, mouse, and eyeball movement to
validate whether participants could find credible information. However, limited studies
have been performed on the impact of digital literacy on the adoption and consequences of
digital healthcare technology and platforms.
4.5 Resistance to change is studied in a limited manner as an extension of the UTAUT
framework (Hoque & Sorwar, 2017; Hossain et al., 2019) . Most of the studies have tested
the negative impact of resistance to change on dependent variables such as adoption.
However, the moderating role of resistance has not been explored, particularly in the
healthcare context.
5. Research Objectives
5.1 How the value co-creation between physicians and patients is facilitated while interacting
on a digital healthcare platform.
5.2 To investigate the impact of value co-creation on joint decision-making, patient
satisfaction, and patient loyalty.
5.3 To examine the moderating role of personal innovativeness on the relationship between
value co-creation and joint decision-making; value co-creation and patient satisfaction;
value co-creation and patient loyalty.
5.4 To examine the moderating role of digital literacy on the relationship between value co-
creation and joint decision-making; value co-creation and patient satisfaction; value co-
creation and patient loyalty.
5.5 To examine the moderating role of resistance to change on the relationship between value
co-creation and joint decision-making, value co-creation and patient satisfaction; value co-
creation and patient loyalty.
6. Theoretical Framework
Service-Dominant Logic (S-D Logic / SDL) and value co-creation framework

14
 Service-Dominant logic acknowledges the value created by a customer and an enterprise,
known as the value co-creation process. Unlike traditional goods-dominant logic of ‘value in
exchange, the service-dominant logic emphasises ‘value in use’ (Srivastava & Shainesh, 2015). This
logic views the co-creation activities between the network actors, where the customer participates
actively along with the service providers (Vargo & Lusch, 2008). This changed the traditional notion
of co-producing the services following the dyadic relationship (Joiner & Lusch, 2016) . The logic
fundamentally supports the continuing process in a relationship, where a customer is continuously
active in the value-creation process along with the enterprise. This reasoning also indicates that the
two parties should have equal engagement, involvement, and exchange. S-D logic propagates that the
value is defined by customers rather than service providers (Vargo & Akaka, 2009).
As per the S-D logic, all the important actors and Social, economic, and technical factors act
as resource integrators. For example, in healthcare settings, all the actors and physical resources,
such as healthcare professionals, healthcare facilities, and the infrastructure involved, act as resource
integrators. Despite being resource integrators, these resources and actors do not carry any intrinsic
value (Joiner & Lusch, 2016) . However, these actors and resources play a vital role in a value co-
creation process. The S-D logic postulates that value is co-created by the collective actions of
network actors.
In today's world, healthcare consumers (patients) co-create relevant content during their
exchanges with physicians and other healthcare network actors. They want to be heard and trusted by
being engaged in fruitful dialogues with service providers to create better outcomes. Most often, the
patients are impacted by bad experiences because of the knowledge asymmetry while participating in
the co-creation process. This results in hesitation to participate and doubt their knowledge related to
the process (Anshu et al., 2022) . Hence, it is important for a network to satisfy four basic
requirements of dialogue, access, risk assessment, and transparency, called DART, for the co-
creation process.
Value co-creation and DART framework
Technology adoption in healthcare enables a robust platform to co-create value between
physicians and patients. Patients should actively participate and coordinate with physicians to create
value and well-being.
Dialogue represents effective communication from both sides and refers to ‘the continuous
interaction between patients and physicians in a content-rich way’ (Zaborek & Mazur, 2019) .In
healthcare service settings, two-way communication is considered a basic requirement to exchange
knowledge and thoughts. Patients must communicate clearly regarding their illness and health issues;
physicians should also communicate treatment plans with patients in an easy language. In digital
healthcare platforms, efficient communication through the secure platform enables patients to better
engage in the care management process. Patients can engage with physicians on the management of
the disease, discuss treatment protocols, discuss lab results, and seek medication advice.
In addition to the dialogues, access also plays a major role in the co-creation process. Patients
need to have access to the physician as well as treatment. Digital technologies provide that access to
patients (A. N. Mishra et al., 2022; Tong et al., 2022) . Digital healthcare platforms enable the
synchronisation of healthcare data throughout the system of network actors. Thus, physicians and
patients have access to the required data related to each other.
In healthcare settings, patients have the right to know all the risks associated with different
healthcare services (Prahalad & Ramaswamy, 2004a; Taghizadeh et al., 2016) . Hence, it is an
obligation of physicians to let their patients know about the possible risks of each treatment
alternative. The physician’s rating on the digital healthcare platform helps patients to know more
about the physician, their success rates for the particular type of illness, and other patients’ views
about them (Fan et al., 2022; Wang et al., 2020) . Thus, risk assessment plays a crucial role in
patients’ co-creation activities.

15
 Transparency is the other vital element that enables co-creation activities. Transparency is all
about equality while discussing co-creation activities (Ranjan & Read, 2014) . Physicians’
willingness to share transparent information empowers patients to participate in co-creating value
actively. Digital healthcare platforms facilitate such transparency.
Hence, it is evident from the above discussion that all four components of the DART
framework fulfil essential conditions to co-create healthcare value between service providers and
patients.
Figure 1 - Proposed Model

7. Hypothesis Development
7.1 The linkage Between Value Co-creation and Joint Decision-Making
In value co-creation, the patient is always involved with actors and other resources that act as
resource integrator (Joiner & Lusch, 2016) . This active participation is also extended to patient
involvement in the informed decision-making process (Vargo & Lusch, 2017) , where the physician
provides information on health outcomes and alternative procedures. Patients have the flexibility to
evaluate and select from the treatment alternatives. With the increase in internet usage, patients have
much more information available and want active participation in decision-making (Anshu et al.,
2022). The co-creation process helps physicians involve patients in the value chain and takes their
input. The study has shown that components of the DART framework significantly influence
patients’ well-being (Akter et al., 2022). Hence, the study hypothesises that,

H10: Value co-creation between patients and healthcare service providers does not impact joint
decision-making
H1a: Value co-creation between patients and healthcare service providers positively impacts joint
decision-making.

7.2 The Linkage Between Value Co-creation and Patient Satisfaction

Patient participation in healthcare services has received much attention in healthcare research.
Patients involved in a co-creation process show higher compliance and adherence to the medical
protocols (Osei-Frimpong et al., 2018) . Adherence to medical protocols helps patients to recover
faster and enhances their well-being. Co-creation process also improves clarity for the patients on the

16
roles they need to play to maintain their health (Osei-Frimpong et al., 2020) . Co-creation process
enables patients to have better access and communication with the physicians. It also enables
transparent services through pre-encounter information search and helps in provider-patient
orientation (Osei-Frimpong et al., 2018, 2020) . With effective communication, a higher degree of
patient satisfaction can be achieved (Dayan et al., 2022). Co-creation of service between patients and
physicians improves patients’ well-being and improves patient satisfaction (Kuipers et al., 2019) .
Hence, the study hypothesises that,

H20: Value co-creation between patients and healthcare service providers does not impact patient
satisfaction.
H2a: Value co-creation between patients and healthcare service providers positively impacts patient
satisfaction.

7.3 The Linkage Between Value Co-creation and Patient Loyalty

Value co-creation facilitates the essence of joint responsibilities between a customer and a
service provider (Y. Zhang et al., 2022) . This feeling of joint responsibility can be converted into
loyalty as the customer shows repeat purchase behaviour (Albinsson et al., 2016) . In the context of
healthcare, the empowerment of patients helps in achieving positive health output. The patient
involved in decision-making tends to repeat the consultation in future with the same physician
(Laidsaar-Powell et al., 2013) , showing loyalty. Value co-creation through digital platforms
enhances patients’ welfare and the perceived value of healthcare services (Akter et al., 2022) . Co-
creation enables access, communication, and transparency between patients and physicians and thus
improves their relationship (Osei-Frimpong et al., 2018) . The improved patient-physician
relationship enhances patient loyalty (Fatima et al., 2018). Hence, the study hypothesises that,

H30: Value co-creation between patients and healthcare service providers does not impact patient
loyalty.
H3a: Value co-creation between patients and healthcare service providers positively impacts patient
loyalty.

7.4 Moderating Role of Personal Innovativeness

Personal innovativeness refers to an individual’s willingness to try new ideas and
technologies (Hossain et al., 2019) . Users with higher personal innovativeness tend to experiment
more with the technology, and thus it enhances their compatibility and usefulness
(Jackson et al., 2013)
. Technology is easy to use when a user has a propensity to learn it quickly. A higher level of
personal innovativeness is also attributed to a higher attitude (Patil et al., 2020) and behavioural
intentions (Jackson et al., 2013), and continuous usage intention (Chen, 2022) of new technologies.
7.4.1 Personal Innovativeness and Joint decision-making
In the healthcare context, patients with high personal innovativeness have been found to
exhibit technology innovativeness (Thakur et al., 2016) . These patients may use digital healthcare
more frequently as it positively moderates the performance expectancy (Shaw & Sergueeva, 2019) .
With adequate health information, they may be willing to participate in the co-creation process.
Patient-physician co-creation involves the active participation of both parties in the healthcare
process (Shirazi et al., 2021). This can include sharing information, discussing treatment options, and
making decisions together (Osei-Frimpong et al., 2018) . Joint decision-making is a key aspect of
value co-creation. Patients with high personal innovativeness may be more open to new ideas and
approaches and more willing to participate in co-creation activities with their physicians. Personal
innovativeness can act as a moderator in the relationship between patient-physician co-creation and
joint decision-making. Hence, the study hypothesises that,

17
H4A0: Personal Innovativeness does not moderate the relationship between Value Co-creation and
Joint Decision-Making.
H4Aa: Personal Innovativeness moderates the relationship between Value Co-creation and Joint
Decision-Making.

7.4.2 Personal Innovativeness and Patient Satisfaction

User with innovative mindset tends to utilise technology more favourably
(Patil et al., 2020; Shaw & Sergueeva, 2019)
. Digital healthcare platforms empower patients to actively engage in the
healthcare process rather than passively receiving information from physicians. Users with higher
levels of personal innovativeness may find the technology easy to use and start using the same with
less effort (Twum et al., 2022) . Personal innovativeness moderates the relationships between trust
and perceived fluency; trust and integration quality; and trust and assurance quality
(Tran Xuan et al., 2023)
. Patients can better participate in the healthcare process and tend to co-create services
because of the information that patients receives because of personal innovativeness. Personal
innovativeness is likely to enhance patient satisfaction. Thus, the study hypothesises that,

H4B0: Personal Innovativeness does not moderate the relationship between Value Co-creation and
Patient Satisfaction.
H4Ba: Personal Innovativeness moderates the relationship between Value Co-creation and Patient
Satisfaction.

7.4.3 Personal Innovativeness and Patient Loyalty

Personal innovativeness plays an important role in the technology's ease of use and
usefulness, leading to user loyalty (Purani et al., 2019). Personal innovativeness is also considered a
critical factor in improving customer engagement (Tran Xuan et al., 2023) . In a healthcare context,
personal innovativeness is found to indirectly influence patient loyalty through usefulness and
satisfaction (Martínez-Caro et al., 2018). Thus, the study hypothesises that,

H4Ca: Personal Innovativeness does not moderate the relationship between Value Co-creation and
Patient Loyalty.
H4Ca: Personal Innovativeness moderates the relationship between Value Co-creation and Patient
Loyalty.

7.5 Moderating Role of Digital Literacy

7.5.1 Digital Literacy and Joint decision-making
With the advancement in technologies, patients have access to the medical services.
However, patients still need to be able to access technology and have the skills to utilise it
(Guitton, 2021)
. Patient-physician co-creation process involves an exchange of information through
communication channels. However, with more information availability, credibility and accuracy of
information is also crucial for patients’ active participation. Information that patients receive due to
their digital literacy must be from credible sources. Patients with high digital literacy skills can
generally find credible sources of information compared to patients with low levels of digital literacy
(Chang et al., 2021; Quinn et al., 2017) . Patients with higher levels of digital literacy tend to use
technological innovations more frequently and can participate more efficiently in the joint decision-
making process. Thus, the study hypothesises that,

H5A0: Digital Literacy does not moderate the relationship between Value Co-creation and Joint
Decision-Making.

18
H5Aa: Digital Literacy moderates the relationship between Value Co-creation and Joint Decision-
Making.

7.5.2 Digital Literacy and Patient Satisfaction

Patients can search for treatment, technology, and organisation information. With high levels
of digital literacy, credible information can be found. This credible information generates trust, and
patients show trust-related behaviour (Velsen et al., 2017). Patients can also identify the information
related to the physicians on healthcare platforms before consulting with the physician
(Osei-Frimpong et al., 2018, 2020)
. Patients with a higher level of digital literacy can identify more
credible information on physicians’ qualifications, experience, and other patients’ views of the
physicians by physicians' ratings, physician reviews, and other related components. This enables
informed decision-making in the selection of physicians over the digital healthcare platform
(Fan et al., 2022)
. Thus, the study hypothesises that,

H5B0: Digital Literacy does not moderate the relationship between Value Co-creation and Patient
Satisfaction.
H5Ba: Digital Literacy moderates the relationship between Value Co-creation and Patient
Satisfaction.

7.5.3 Digital Literacy and Patient Loyalty

Patients can have better communication, access, and participation in online communities with
higher levels of digital literacy skills. Along with the prescribed medical advice, patients get more
information on the disease and health status through different information sources. Digital literacy is
one of the motivating factors for online information searches for patients (Xiao et al., 2014) .
Searching and understanding related information and medical advice helps patients recover faster
and achieve well-being. Digital literacy can enhance customer loyalty by improving customers’
perceptions of relationship marketing efforts (Lee-Kelley et al., 2003). Thus, the study hypothesises
that,

H5Ca: Digital Literacy does not moderate the relationship between Value Co-creation and Patient
Loyalty.
H5Ca: Digital Literacy moderates the relationship between Value Co-creation and Patient Loyalty.

7.6 Moderating Role of Resistance to Change

Resistance to change is an adverse reaction of users to the proposed change
(Hoque & Sorwar, 2017; Ho
. It is generally
associated with the users’ psychology of not adopting the technology. With a higher degree of
resistance to change, users do not adopt new technologies.

7.6.1 Resistance to Change and Joint decision-making

With a higher degree of resistance to change, users abstain themselves from adopting digital
platforms (Hoque & Sorwar, 2017), and they cannot take advantage of technology-enabled value co-
creation. Also, they abstain from getting information through which others participate in joint
decision-making. Thus, the study hypothesises that,

H6A0: Resistance to change does not moderate the relationship between Value Co-creation and
Joint Decision-Making.

19
H6Aa: Resistance to change negatively moderates the relationship between Value Co-creation and
Joint Decision-Making.

7.6.2 Resistance to Change and Patient Satisfaction

Resistance to change reduces an ability of an individual to embrace the change. Patients’
resistance to change is observed in different contexts, reducing the behavioural intention to adopt
technology (Hoque & Sorwar, 2017; Talukder et al., 2020) . In healthcare settings, apart from the
non-adoption of technology, higher resistance also reduces the patient’s ability to adhere to the
medical protocols. This may a prolonged disease period (Li et al., 2020) . Ultimately, the patient
cannot reach a state of well-being, and satisfaction cannot be achieved. Thus, the study hypothesises
that,

H6B0: Resistance to change does not moderate the relationship between Value Co-creation and
Patient Satisfaction.
H6Ba: Resistance to change negatively moderates the relationship between Value Co-creation and
Patient Satisfaction.

7.6.3 Resistance to Change and Patient Loyalty

With a higher degree of resistance to change, the user shows less affection for the technology
(Li et al., 2020; Xiaofei et al., 2021) and cannot find information regarding health and well-being.
Also, users find themselves helpless to find trustworthy information regarding the platform and
physician. Due to the absence of credible and trustworthy information, health outcomes may not be
achieved (Jansen-Kosterink et al., 2019; Velsen et al., 2017) , and the patient does not repeat the
healthcare encounter process with the same service provider in future. Thus, the study hypothesises
that,

H6C0: Resistance to change does not moderate the relationship between Value Co-creation and
Patient Loyalty.
H6Ca: Resistance to change negatively moderates the relationship between Value Co-creation and
Patient Loyalty.
8 Proposed Research Methods
8.1 Measurement Items and Scale
The study adopted measurement scales from the previous literature. The details of the
adopted measurement items are reported in Table 1.
Table 1 - Measurement Items & Scale

Dimensions Number of Source Context

[11] Items [37]
Dialogue 3 Items (Akter et al., 2022) Developing Nation
Access 3 Items (Akter et al., 2022) Developing Nation
Risk-Assessment 3 Items (Akter et al., 2022) Developing Nation
Transparency 3 Items (Akter et al., 2022) Developing Nation
Value Co-creation Second-order formative construct. This construct represents all the
items of Dialogue, Access, Risk-Assessment, and Transparency as
directed by (Akter et al., 2022).
Joint Decision-Making 5 Items (Osei-Frimpong et al., 2018) Developing Nation

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 Patient Satisfaction 3 Items (Birkmeyer et al., 2021) Developed Nation
Patient Loyalty 3 Items (Martínez-Caro et al., 2018) Developed Nation
Personal Innovativeness 3 Items (Hossain et al., 2019) Developing Nation
Resistance to Change 3 Items (Hoque & Sorwar, 2017) Developing Nation
Digital Literacy 8 Items (Baek et al., 2021) Developing Nation
Scale for value co-creation as a second order formative construct is adopted from
(Akter et al., 2022),
consisting of items from dialogue, access, risk-assessment and transparency. The five-item
scale for joint decision-making is adopted from (Osei-Frimpong et al., 2018). Three Items for patient
satisfaction were adopted from (Birkmeyer et al., 2021), and three items scale for patient loyalty was
adopted from (Martínez-Caro et al., 2018). The 3-item scale was adopted from (Hossain et al., 2019)
and (Hoque & Sorwar, 2017) for personal innovativeness and resistance to change. The eight-item
scale for digital literacy is adopted from (Baek et al., 2021). The scales have been modified to suite
the context of the current study—the modified scale, along with the items listed in appendix 2.
8.2 Equations for hypothesis testing
Table 2 - Model Specifications

Hypothesis Equation
H1 JDM = 0 + 1 (CO) + 2 (AG) + 3 (G) + 4(FI) + 
H2 PS = 0 + 1 (CO) + 2 (AG) + 3 (G) + 4(FI) + 
H3 PL = 0 + 1 (CO) + 2 (AG) + 3 (G) + 4(FI) + 
Moderating role of Personal Innovativeness (PI)
H4A JDM = 0 + 1 (CO) + 2 (PI) + 3 (CO*PI) + 4(AG) + 5 (G) + 6 (FI) + 
H4B PS = 0 + 1 (CO) + 2 (PI) + 3 (CO*PI) + 4(AG) + 5 (G) + 6 (FI) + 
H4C PL = 0 + 1 (CO) + 2 (PI) + 3 (CO*PI) + 4(AG) + 5 (G) + 6 (FI) + 
Moderating role of Digital Literacy (DL)
H5A JDM = 0 + 1 (CO) + 2 (DL) + 3 (CO*DL) + 4(AG) + 5 (G) + 6 (FI) + 
H5B PS = 0 + 1 (CO) + 2 (DL) + 3 (CO*DL) + 4(AG) + 5 (G) + 6 (FI) + 
H5C PL = 0 + 1 (CO) + 2 (DL) + 3 (CO*DL) + 4(AG) + 5 (G) + 6 (FI) + 
Moderating role of Resistance to Change (RC)
H6A JDM = 0 + 1 (CO) + 2 (RC) + 3 (CO*RC) + 4(AG) + 5 (G) + 6 (FI) + 
H6B PS = 0 + 1 (CO) + 2 (RC) + 3 (CO*RC) + 4(AG) + 5 (G) + 6 (FI) + 
H6C PL = 0 + 1 (CO) + 2 (RC) + 3 (CO*RC) + 4(AG) + 5 (G) + 6 (FI) + 
Note: all 0 = Intercept
all i = slope (i=1,2,3…n)
all  = error term
NOTE: CO = Co-creation; JDM = Joint Decision-making; PS = Patient Satisfaction; PL = Patient
Loyalty; PI = Personal Innovativeness; DL = Digital Literacy; RC = Resistance to Change
Control Variables: AG = Age; G = Gender; FI = Annual Family Income

21
8.3 Sampling and Data Collection
8.3.1 Target Population
The target population for this study are patients or users who have used digital healthcare
platforms like 1mg, Netmeds, Apollo 247, Mfine, Medibuddy, Prato etc. through mobile apps,
laptops, computers, and other digital devices recently to fulfil their healthcare-related needs.
This study aims to collect the data by taking the students, faculty members, and
administrative staff members of one of the top private universities in India. The university has
campuses for the master's program in management in cities of Hyderabad, Bangalore, Mumbai,
Pune, Ahmedabad, Kolkata, and Gurugram. The aim of collecting data from the various campuses
spread across India is to include users' diversity in culture and thought processes.
8.3.2 Sampling Procedure
In the first step, the study aims to collect information from the students, faculty members,
administrative staff, and Ph.D. research scholars from the administrative office. A circular systematic
sampling procedure will be followed to select the respondents for the study. The study aims to take a
proportionate sample. Permission from the respective campus and school directors will be obtained
to collect the data. Students will be approached with the permission of the respective faculty
members taking the session at the time of the study. Conversely, faculty members, staff members,
and research scholars will be approached personally in their free time for data collection.
8.3.3 Determination of Sample Size
The study aims to collect data from 370 respondents (370 items * 10), as suggested by
Hair et al., (2010)
. However, in a survey based research generally low response rate is observed. Other
studies performed in similar contexts have received a response rate between 75.33% (Li et al., 2020)
to 91.33% (Hoque & Sorwar, 2017). The study will target 500 complete responses to obtain desired
sample size.
8.3.4 Data Collection
A questionnaire will be designed to collect the responses on a five-point Likert scale. The
questionnaire will be divided into four different sections. The first section will consist of the
screening question. This question is to validate whether the respondent used digital healthcare
technology or platform in the past to consult a physician for healthcare-related needs. The second
part collects the demographic information and the third part collects the responses on a five-point
Likert scale. The last part consists of a qualitative question on why respondents did not use the
digital platforms. Survey Monkey will be used to collect the data from the respondents. The study
will share the link with the respondents. A Quick-Response (QR) code will be played on the
classroom projector. Students can scan the QR from mobile devices and fill out the questionnaire.
The link will be shared with faculty members, administrative staff, and PhD students to collect their
responses.
8.4 Data Analysis
The study will follow the following analytical methods to analyse the data systematically.
 Exploratory Factor Analysis (EFA) on each construct will be performed to analyse the
underlying structure of the construct. This helps in measuring the uni-dimensionality of latent
variables.
 Confirmatory Factor Analysis (CFA) will be performed to measure the validity and
reliability of each construct. Average Variance Extracted (AVE), Cronbach’s alpha,
Composite reliability, and discriminant validity.
 Path Analysis will be performed to test the causal relationships.
CFA and Path analysis are part of Structural equation Modelling. They will be performed based on
Partial Least Square method using SmartPLS 4.0 as the study contains a second-order formative
construct.

22
 9. Expected Contribution
The proposed study aims to add value to the existing literature on digital healthcare in the
context of an emerging economy. It provides a more comprehensive understanding of the
relationship between patients and physicians in the presence of digital healthcare platforms to
interact. The study provides information on the relationship between value co-creation and joint
decision-making, patient satisfaction, and patient loyalty. The study also tests the moderating role of
three variables: personal innovativeness, digital literacy, and resistance to change on the relationship
between value co-creation and joint decision-making; value co-creation and patient satisfaction; and
value co-creation and patient loyalty. The study provides an understanding of digital healthcare
platforms and how patients co-create value through the platform.
 Academic Implications
From an academic perspective, the study is novel regarding digital healthcare platforms.
Previous studies focused on identifying the antecedents to adopting digital healthcare technologies
from different perspectives. This study fulfils the gaps in the previous literature. It also provides
information on the importance of co-creation activities on patient satisfaction and loyalty. The study
will be useful and serve as a reference for researchers who are motivated to study this area.
 Managerial Implications
The study will have several managerial implications for physicians, hospital administrators,
digital platform developers, and health-tech start-up founders. The study's contribution lies in its
attempt to include the patient’s perspective in the value co-creation process, which healthcare service
providers often overlook. The study's results shall provide clear guidelines to health-tech start-up
founders and entrepreneurs.
 Societal Implications
The outcomes of this study will be used to determine the delivery strategy most likely to raise
the standard of a patient's medical treatment through digital healthcare platforms. The findings of this
study will enable patients to use available digital healthcare platforms better for their well-being. The
outcome of the study will provide the role of patients plays in value co-creation related to patient
satisfaction and further patient loyalty. The study provides implications for the government and
policy-makers to create accessible healthcare platforms.
10. Future Scope
Although the study will provide a contribution to academics, practice, and society, the study
has some limitations which can be addressed in the future depending on technological advancement.
First, this study aims to collect data from a top-tier private university with campuses across India.
The study will only collect data from the urban area, as the institute has its centres in metro cities.
Learning about digital healthcare technology diffusion in 2nd-tier, 3rd-tier cities, towns, and rural
India can be the scope for future study. Second, the study does not concentrate on the type of
ailments and illnesses patients use or do not use the digital healthcare platforms and co-creates value.
The future scope includes the study based on the types of illnesses and ailments, cost, feasibility, and
availability of healthcare platforms. Third, the study has the scope to compare different healthcare
apps and find the features for which higher co-creation and higher patient satisfaction and loyalty
may be achieved. Fourth, the study only concentrates on the patient's perspective. However, co-
creation must also include the physician's and other healthcare providers' perspectives, as they act as
resource integrators. In the future, the study can be replicated with the outcome variables related to
the physicians’ and healthcare network actors’ perspectives regarding the co-creation and resource
integration activities from their side.

23
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 Appendix – 1 Commonly used healthcare apps in India16 and Feature Comparison
Features Apollo Netmeds MFine Medibuddy Practo 1 mg Flipkart Health &
247 / DocsApp Medplus
Video Consultation
Audio/chat
Consultation
Physician Ranking
Physician Experience
Information
Consultation Fees
Information
Offline Appointment
Booking
Medicine Dispatch
Path lab
Radio Lab Booking
Prescription
Medical Records
upload
Link to ABHA ID
Subscription for
consultation
Health Articles
Other Services
Table 3 Comparison of different features and services of Digital Healthcare Platforms usually
used in India by patients

16
https://www.fusioninformatics.com/blog/best-healthcare-apps-in-india/

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 Appendix – 2 Construct Definitions and Measurement Scales
Dialogue - Dialogue refers to the continuous interaction between patients and healthcare providers
(Akter et al., 2022).
1. The digital healthcare platforms communicate and listen to me.
2. The digital platform uses a 24/7 channel in order to share and exchange ideas with me about
services.
3. The digital platform facilitates the communication of ideas and suggestions about its services
with me.
Access - Access refers to the healthcare service providers and patients gathering, sharing and
gaining accessibility of the resources and conversation with each other (Akter et al., 2022; Anshu
et al., 2022).
1. The digital platform allows me to personalize services.
2. I have numerous service options to select for my healthcare needs.
3. It's easy to receive information through digital platforms.
Risk-Assessment - Risk-Assessment refers to the risk that healthcare service providers and
patients are likely to face during value co-creation (Akter et al., 2022; Anshu et al., 2022).
1. The digital healthcare platform offers comprehensible information that allows the advantages
and disadvantages of services to be assessed.
2. The digital healthcare platform offers many possibilities to present complaints regarding
problems that may arise.
3. The digital healthcare platform repeatedly urges me to familiarize myself with the possible
risks involved in using the services.
Transparency - Transparency refers to equity while discussing value co-creation (Akter et al.,
2022).
1. The digital healthcare platform provides transparent information in order to assess and
improve the services it offers.
2. I have access to all information that may be of use.
3. The digital healthcare platform offers public and transparent information regarding prices
associated with various services.
Joint decision-making - Joint decision-making refers to the collaborative process in which
patients and healthcare providers come together to make decisions regarding healthcare treatment
and health management (Osei-Frimpong et al., 2018).
1. My digital healthcare platform asks for suggestions from me regarding treatment options.
2. My doctor on digital healthcare platform encourages suggestions about appropriate treatment
of my illness.
3. The doctor discussed the prescription with me over the digital healthcare platform.
4. Together, the doctor and I set goals and discuss treatment options over the digital healthcare
platform.
5. Doctor takes my help in planning the treatment over the digital healthcare platform.
Patient Satisfaction – Patient satisfaction refers to the assessment of patients' subjective
experiences, perceptions, and evaluations of the digital healthcare services they receive through
online platforms or mobile applications.

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1. I am satisfied with the range of services offered by digital healthcare platform.
2. Overall, I am satisfied with the digital healthcare platform.
3. I would recommend the digital healthcare platform to my friends or acquaintances.
Patient Loyalty – patient loyalty refers to the degree of commitment, trust, and continued
engagement exhibited by patients towards a specific digital healthcare platform or service provider
(Martínez-Caro et al., 2018).
1. Assuming that I have access to the digital healthcare platform, I intend to reuse it.
2. I will frequently use digital healthcare platform for future healthcare needs.
3. I will strongly recommend others to use digital healthcare planform for their healthcare-
related needs.
Personal Innovativeness - Personal innovativeness refers to the personal traits affecting users’
adoption of novel technology (Vogel et al., 2021; Hossain et al., 2019).
1. If I heard about a new information technology, I would look for ways to experiment with it.
2. Among my peers, I am usually the first to try out new information technologies.
3. In general, I am not hesitant to try out new information technologies.
Resistance to Change – Resistance to change refers to the reluctance, opposition, or hesitancy
demonstrated by individuals when adopting or accepting new technologies, processes, or practices
within a digital healthcare platform (Hoque & Sorwar, 2017).
1. I don’t want digital healthcare platforms to change the way I deal with health-related
problems.
2. I don’t want digital healthcare platforms to change the way I keep myself healthy.
3. I don’t want digital healthcare platform to change the way I interact with physicians.
Digital Literacy - Digital literacy refers to the individuals’ ability to understand and use
information in multiple formats from wide variety of sources when it is presented via computer
(Zahoor et al., 2023; Beak et al., 2021).
1. I know what kind of the health resources are available on the internet.
2. I know where to find helpful health resources on internet.
3. I know how to find useful health resources on internet.
4. I know how to use the internet to answer my questions about health.
5. I know how to use the health information I find on the Internet to help me.
6. I have the skills I need to evaluate the health resources I find on the Internet.
7. I can tell high-quality health resources from low-quality health resources on the Internet.
8. I feel confident in using information from the Internet to make health decisions.

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 Appendix – 3 Questionnaire
Hello!

I am Vaidik Bhatt. I am currently Pursuing PhD from IBS Hyderabad. My major work and research area are based on the new
technology adoption and its consequences/outcome in healthcare. The study's objective is around patient satisfaction through
healthcare technology adoption. I need help from the patients like you - who have used technologies like digital healthcare
platforms to connect with your doctor for your healthcare needs.

Please fill-up the questionnaire and help me accelerate my research. I know about your privacy issues, and I take it seriously. As a
result, I have decided to keep your responses safe, and in any circumstances, individual responses will not be shared with anyone.
The results of the study will be published as an aggregate response only.

Did you use digital healthcare platforms to connect with the physician in the last 6 months? Yes | No
Which of the following app you most recently used to connect with your physician?
Apollo 247 Netmeds MFine Medibuddy / Practo TATA - 1 Flipkart Health & Other
DocsApp mg Medplus

Demographic Details
State / UT of your domicile
Your Age 18-30 Years 30-42 Years 42-60 Years Above 60 Years
Annual Family Income Under 5 Lac INR Between 5 – 10 lac Between 10 – 20 Above 20 Lac INR
INR lac INR
Education Diploma / Any Graduate PG and Above Professional Qualification (CA,
Schooling CFA, CS etc.)
Gender Male Female

You are requested to share your genuine perception/opinion based on the service and technology you are currently using / recently used. There
are no right or wrong answers. Please be assured that there are no questions from which anyone can identify you and the results will be
prepared and on the aggregate level only and not on an individual level.
Questions Strong Disagre Neutra Agre Strong
Disagre e l e Agree
e
The digital healthcare platforms communicate and listen to me.
The digital platform uses a 24/7 channel in order to share and exchange ideas with me
about services.
The digital platform facilitates the communication of ideas and suggestions about its
services with me.

The digital platform allows me to personalize services.

I have numerous service options to select for my healthcare needs.
It's easy to receive information through digital platforms.

The digital healthcare platform offers comprehensible information that allows the
advantages and disadvantages of services to be assessed.
The digital healthcare platform offers many possibilities to present complaints regarding
problems that may arise.
The digital healthcare platform repeatedly urges me to familiarize myself with the
possible risks involved in using the services.

The digital healthcare platform provides transparent information in order to assess and
improve the services it offers.
I have access to all information that may be of use.
The digital healthcare platform offers public and transparent information regarding
prices associated with various services.

35
My digital healthcare platform asks for suggestions from me regarding treatment
options.
My doctor on digital healthcare platform encourages suggestions about appropriate
treatment of my illness.
The doctor discussed the prescription with me over the digital healthcare platform.
Together, the doctor and I set goals and discuss treatment options over the digital
healthcare platform.
Doctor takes my help in planning the treatment over the digital healthcare platform.

I am satisfied with the range of services offered by digital healthcare platform.

Overall, I am satisfied with the digital healthcare platform.
I would recommend the digital healthcare platform to my friends or acquaintances.

Assuming that I have access to the digital healthcare platform, I intend to reuse it.
I will frequently use digital healthcare platform for future healthcare needs.
I will strongly recommend others to use digital healthcare planform for their healthcare-
related needs.

If I heard about a new information technology, I would look for ways to experiment with
it.
Among my peers, I am usually the first to try out new information technologies. `
In general, I am not hesitant to try out new information technologies.

I don’t want digital healthcare platforms to change the way I deal with health-related
problems.
I don’t want digital healthcare platforms to change the way I keep myself healthy.
I don’t want digital healthcare platform to change the way I interact with physicians.

I know what kind of the health resources are available on the internet.
I know where to find helpful health resources on internet.
I know how to find useful health resources on internet.
I know how to use the internet to answer my questions about health.
I know how to use the health information I find on the Internet to help me.
I have the skills I need to evaluate the health resources I find on the Internet.
I can tell high-quality health resources from low-quality health resources on the
Internet.
I feel confident in using information from the Internet to make health decisions.

If you have not used digital healthcare platforms kindly describe the reasons why you have not used.

Note: This question will be visible only to those users who have not used any digital healthcare platforms for interacting with their physicians.

36
 Appendix – 4 Statista Data17

4a. Private Equity and Venture Capital Investment in the Indian Health-tech Sector

4b. Revenues of Indian Health-Tech Start-ups

4c. Market share by Patients’ visits in Indian Healthcare systems

17
These data were downloaded from statista.com during the 15 days trial period provided to the IFHE university.

37
38