Responsible Artificial Intelligence (AI) for Value Formation and Market Performance in Healthcare: the Mediating Role of Patient’s Cognitive Engagement

In healthcare, AI has a crucial role in improving the clinical outcomes and overall efficiency of managerial activities. Prior studies have outlined how AI can improve healthcare delivery by augmenting human abilities, supporting mental health, and precise diagnosis (WHO, 2020b; Wu et al., 2021). The AI technology allows us to gain information, process it, and generate a well-defined output for medical professionals (Reddy, 2018; Wang et al., 2018). AI technologies possess data-mining and pattern recognition capabilities that enable the prediction, diagnosis and treatment (Kok et al., 2013). Early attempts to apply AI technologies in medicine were intended to replicate the functions of the human brain and set up a rule-based system to assist medical reasoning (He et al., 2019; Warwick, 2013). Further developments in AI were focused on replicating the intellectual function of the physician. However, the modern products of AI technologies are overcoming the various limitations of clinicians and complexities in the care process (Reddy, 2018; Wang et al., 2020). The application of AI converts analytical insights into cognitive engagement solutions that enhance diagnosis, improve predictive interventions, and optimize clinical productivity (Fox & James, 2020; Porra et al., 2020; Wang et al., 2018).

AI technology is capable of accelerating the shift from traditional hospital settings to customer-focused care sites like ambulatory surgical centers, retail clinics, and home care (Barello et al., 2016; Manyika et al., 2013). Further, home care and wearable devices may lower the overall health costs by 20 to 32 % (Saha & Ray, 2019; Sultan, 2015). AI-enabled technologies have opened up a new vista of home infusion and observation care models, which are expected to grow by more than 18 % over the next five years (OECD, 2019). Specifically, it is becoming easier to understand patient’s health patterns, with improvements in predictive analytics, enabling clinicians to remotely monitor patients under home care or through connected devices. Thus, healthcare professionals have benefitted from improved prevention, diagnosis, and care processes.

On the other hand, the inherent power of AI creates substantial threats to organizations, stakeholders, and the industry supply chain (Obermeyer et al., 2019; Sivarajah et al., 2017). Past studies have reported that the more advanced the AI, the greater are the challenges and threats it poses humanity. Risks associated with AI include (but are not limited to) the safety of critical AI applications, security and privacy of user data, and social risks (Joubert et al., 2021; Porra et al., 2020; Sharma & Sharma, 2019; Vellido, 2019). For instance, the collection of patient data by AI algorithms raises serious issues of privacy invasion, transparency, and information leakage (Fox & James, 2020). Wang et al. (2020) catalogue several risks for healthcare deliveries like compromising transparency standards, neglecting fair clinical deployment, and ignoring the algorithmic biases. Thus, the utilization of such advanced technologies has exposed individuals to many risks at various levels of data collection and processing. Consequently, there has been an increasing concern about the ethical issues and legitimacy associated with AI technologies (Deven & Joshua, 2017; Lui & Lamba, 2018). In recent years, tremendous concern about AI has been a expressed in various forums, corporations, and government departments (NAH, 2020; NeHA, 2016; Thomas, 2020) in India. The Medical Council of India has implemented data protection regimes to regulate ‘private data’ (MCI, 2016). Consequently, a shift in focus has been observed to the development and implementation of AI technologies that are socially responsible.

Wang et al. (2020) define responsible AI as “the integration of ethical and responsible use of AI into the strategic implementation and planning process.“ Responsible AI primarily aims to design ethical, transparent, and accountable solutions (Fox & James, 2020; Shaikhina & Khovanova, 2017; Winter & Davidson, 2019). To Abosaq (2019), responsible AI is a tool helpful for organizations to improve trust and minimize privacy invasion. Past studies highlight the importance of investigating ethical considerations, technical skills concerning data and algorithms, and risk mitigation strategies, in leveraging AI developments (Shaikhina & Khovanova, 2017; Zink & Rose, 2020; Zuboff, 2015). Several studies have furthered this discussion of responsible AI and suggest that different technical and analytical skills are required to build and use AI responsibly (Chatterjee, 2020; Gupta & George, 2016; Hung et al., 2007). Technical skills should focus on the alignment of stakeholders’ expectations for the use of data. The magnitude of risk often dictates the importance of risk mitigation strategies. The term responsible AI encompasses all these risk-mitigating activities. Such strategies will need wider collaborations (Bengatson & Kock, 2000; Campbell, 2007). Burkhardt et al. (2019) argue that it is generally the external regulations and internal motivation that force a typical AI firm to strive for safer AI products. Such efforts, however, do not always lead to increased profit or reduced litigation cost. Hence, it is crucial to identify factors that can facilitate co-operative development of responsible AI, rather than a competitive battle in the market. Trust among stakeholders and high shared gains from cooperation are a couple of such factors (Hung et al., 2007). Several researchers opined that AI-based systems are intrinsically autonomous, interactive, and adaptable (Chace, 2015; Hsu et al., 2021; Shaikhina & Khovanova, 2017). While exploring AI, Wang et al. (2020) integrate it with the ART principles: accountability, responsibility and transparency. Accountability would ensure that all the decisions and outcomes are justified to all the stakeholders. These decisions should be derivable from the original set of data or information used. Responsibility would ensure that all the developers and researchers are aware of their commitment, more so in cases where the impact of AI on society is significant and direct. Finally, transparency would ensure that the mechanism through which AI makes a particular decision is described to the stakeholders, who should be able to further inspect and reproduce the process of decision making. This is a significant departure from the existing, more popular black-box approach of AI (Barrat, 2013; Chopra, 2019; Kok et al., 2013).

Though these concepts of accountability, responsibility, and transparency may be considered essentials of a responsible AI, their applicability dictates acceptance boundaries. For instance, Wang et al. (2018) hold explainability to be core to responsible AI. However, the expectations of a typical end-user, especially in the healthcare domain, might vary. The authors explain this with an example of a healthcare application that predicts skin cancer with the help of images. In this context, most patients would care less about the model’s nuances and more about its accuracy of prediction and recommendations. Thus, the same principles need to be applied differently in diverse application areas. Many studies further point to the importance of appropriate data acquisition, data-set suitability, fairness of AI outputs, and regulatory compliance and engagement, in the context of leading an organization to use responsible AI (Burkhardt et al., 2019; Duan et al., 2019; Dwivedi et al., 2021; Grover et al., 2020; Fuentes, 2015; Wearn et al., 2019). Al-quaness et al. (2020) utilized COVID-related responses to highlight the gaps and potential pitfalls in the existing AI approach. These include algorithm bias and discrimination, adverse data impact, lack of process transparency, and model interpretability. According to the authors, the challenges to autonomy, privacy, and public trust are also evident in various COVID-related contact tracing applications. In this context, it was also observed that some of these solutions tend to shift focus from supporting effective medical response to the concerns of mass surveillance and politics of public distrust. A responsible AI approach in such a context would emphasize reproducibility, replicability, and transparency. Numerous studies further propose key steps for responsible AI, including advice to open science, share data responsibly, adopt ethical principles, generate public trust, foster equitable innovation and protect the interests of the vulnerable (Dwivedi et al., 2021; GDPR, 2019; Modjarrad et al., 2016).

It is evident from the above discussion that responsible AI is a multi-dimensional construct focusing on technical challenges and skills, ethical concerns, and risk mitigation. The critical appraisal of the existing literature thus reveals the constituent elements of responsible AI. This study conceptualizes responsible AI as a third-order factor (see Fig. 1).

In healthcare, value creation is recognized as an essential element of the service provider’s competitive strategy (Da Silva et al., 2015; Sergio, 2015). Management researchers have identified that actively managing value and devising ways to develop value propositions are crucial for sparking growth (Chen et al., 2017).The means-end theory (Rokeach, 1973) states that personal values include instrumental values and terminal values, holding that personal values influence individual behavior. According to Parry (2001), the means-end theory suggests that personal values are the end that people seek, while the means help achieving such end. In the current study context, the means are patients’ experience of positive consequences of responsible AI. Based on past studies (Amit & Zott, 2001; De Sarbo et al., 2001), we posit that patients perceive different benefits from these technologies that affect instrumental and terminal values. Responsible AI makes patient interactions more convenient and trustworthy. These platforms deliver the financial benefit of cost reduction and psychological benefits of variety and creating instrumental value of cheerfulness. Besides, these platforms provide functional benefits through reduced hospitalization and a hassle-free care process. Thus, the patients perceive the instrumental values of cheerfulness, self-control, and responsibility. Chen et al. (2017) found that experiential benefits create customer terminal value. Easy access and risk reduction by engaging with responsible AI infuses confidence in patients in sharing personal information. Responsible AI solutions deliver psychological benefits of risk reduction, making patients feel happy and comfortable. Risk reduction by utilizing AI technologies creates the terminal value of nostalgia (Almquist et al., 2016). Magids et al. (2015) explored emotional-connection-driven opportunities and found that these technologies make sense of freedom and belongingness, thus driving customer behavior and creating terminal value. Thus, responsible AI provides benefits that are perceived as offering more terminal value. Therefore, it is hypothesized that:

Market performance is defined as the capability to enter new markets and introduce new services faster than the competitors (Ravichandran & Lertwongsatien, 2005). Wang et al. (2012) posit that market performance indicates a more significant market share and success rate than the competitors. Several researchers and practitioners argue that customer value is an essential element of a firm’s market performance. According to Chen et al. (2017), customer value management is a strategic tool to enhance market performance. Many studies indicate a significant relationship between perceived value and market-based outcomes (Javalgi et al., 2005; Johnson et al., 2003; Ravichandran & Lertwongsatien, 2005). Lusch and Nambisan (2015.p.159) stress that “the organization’s performance will fall or rise based on the perceived value.“ Many studies have shown that the customer’s perceived value is a critical measure of the firm’s market performance (Brozovic et al., 2016; Gronroos & Ravald, 2010). According to Chen et al. (2017), a customer’s perceived value is a subjective indicator of a firm’s market performance. Ravichandran and Lertwongsatien (2005) showed that customer value improves market share and enables firms to remain competitive in the market. Researchers believe that the several value characteristics (e.g., courage, cheerfulness, responsibility, and self-control) perceived by the customers indicate the firm’s success and thus affect market performance. Similarly, many researchers posit that customer’s perceived terminal values (e.g., pleasure, comfort and self-respect) help in the introduction of new services and affect market performance. Existing studies aver that perceived value remains a vital issue for healthcare services (Da Silva et al., 2015; Nair et al., 2013). The overall market performance of a healthcare firm is a function of perceived values. Patients’ evaluation of a specific service attribute is likely to affect their relationships with the service provider, which affects the firm’s market performance. Past studies (Bate & Robert, 2007) indicate that the beliefs and impressions customers hold toward a service provider form a brand image, which influences the latter’s market performance. Likewise, the perceived instrumental and terminal values of socially responsible AI technologies being utilized in various care procedures generate the best performance outcomes.

Thus, we postulate that:

Cognitive engagement is connected to what the patient knows, understands, and how he/she makes sense of the disease, its treatments, its possible development, and its monitoring (Serino et al., 2014). As a significant part of the overall learning and experience process, cognitive engagement enables patients to immerse in in-depth learning processes situated in realistic healthcare problems (Gen, 2015). AI technologies in healthcare aim to increase patients’ cognitive engagement with those applications and tools providing personalized care (Reddy, 2018). A recent study (Singhal & Carlton, 2019) states that AI technologies are designed to help patients’ to improve their health conditions, make informed decisions, and engage effectively and efficiently with the healthcare system. Graffigana et al. (2015) described the dynamic nature of cognitive engagements as a psychosocial phenomenon resulting from the conjoint beliefs, goals, and epidemiology enactment of individuals towards their health condition and management. Extant literature also indicates that the particular benefits and value patients perceive concerning any given technology for their care process, influence their cognitive engagement (Agarwal et al., 2013; Bashshur et al., 2011; Coulter & Ellins, 2007; Hibbard et al., 2007).

The healthcare sector describes cognitive engagement mainly as a critical factor in obtaining adequate and customized disease management plans for patients (Linn et al., 2011). The importance of involving patients in learning and utilizing new technologies lies in fostering their self-management skills (Faber et al., 2017). Therefore, cognitive engagement in the context of responsible AI in healthcare is explained as a pivotal element in legitimizing the patient’s expression of physical and emotional needs, thus better orienting professional interventions (Marano & Nicolantonio, 2015). Responsible AI technologies offer valuable devices and platforms to facilitate patient activation and engagement in self-care and preventive behaviors (Gambhir et al., 2016). The perceptions of reasonable AI encourage patients to engage with them actively. For instance, promises to prevent the harmful effects, fairness in dealing with patients and their data, and transparency in the processes will increase patients’ cognitive engagement with such technologies. Thus, cognitive engagement with responsible AI is oriented by a broader vision of healthcare that goes beyond the organizational boundaries of healthcare settings. Responsible AI technologies seek to learn more about health concerns and collaborate with physicians in making treatment decisions, and communicate and share information with them (Kim, 2015; Wimmer et al., 2016).

Cognitive engagement with responsible AI technology has several positive consequences, one of which is perceived value (Chen et al., 2017). Numerous researchers have established significant relationships between cognitive engagement and perceived value. For example, Brodie et al. (2011) argued that cognitive engagement with particular circumstances and service environment leads to higher perceived values. Some studies have found that cognitive engagement is linked to the assurance of instrumental and experiential values and pleasure and satisfaction (Amit & Zott, 2001). Researchers have also commented on the propensity of cognitive engagement to strengthen perceived value (Eggers & Kaplan, 2013). In addition, the customer’s evaluation of the consumption experience is fundamental in creating instrumental and terminal values (Zeithmal, 1988). In healthcare, patients perceive several benefits by the consumption of various service products that are adjacent to their social and self-perception, which in turn affects their ‘value-in-use’ (Gronroos & Gummerus, 2014). It has also been established that firms essentially need to focus on customer engagement, to add an array of values to the service products. Recent studies offer cognitive engagement with service products as a unit of analysis for value creation (Chen et al., 2017). Patient benefits derived from cognitive engagement with responsible AI technologies and artifacts are essential for perceived values. Cognitive engagement with responsible AI technologies forms the preferences for the process of care and channelizes the instrumental and terminal values by the rendered services. In this context, it appears that cognitive engagement by patients with responsible AI technologies corresponds to their perceived instrumental and terminal value (see Fig. 2).

Hence, we postulate that:

In the first stage (Study 1), an exploratory qualitative study was conducted with in-depth interviews. A purposive sampling strategy was utilized to recruit the participants. The sampling criteria ensured the likelihood of healthcare professionals (senior medical officers, senior nursing staff, and technical officers) expressing their understanding of the constituents of responsible AI in healthcare. The criteria for the interviewees were experience in and knowledge of responsible AI. The exploratory interviews emphasized the participants’ perception of responsible AI and their views as to the constituents and consequences of responsible AI. The interview guide was prepared from the concepts derived from the extant literature. We utilized a few open-ended questions (Appendix Table 9) to explore the concepts under study and identify the additional items to establish responsible AI constructs. A total of 26 interviews were conducted in multiple rounds (Appendix Table 10). Typically, interviews were conducted in the respective offices of the respondents and lasted between 40 and 60 min.

We analyzed the transcriptions utilizing framework analysis (Harrison & Reilly, 2011). The exploratory interviews confirmed the relevance of the concepts and constructs drawn from the literature. Respondents expressed their perceptions of various facets of responsible AI (Table 1). Two of them described the technical issues involved with responsible AI, explaining how the data and algorithm are sensitive issues for responsible AI. Research has shown that technical skills related to data set acquisitions and processing are crucial in the design of AI systems (Leslie, 2019). Evidence suggests that choosing the right data and the right algorithms delivers a fair outcome (Amoore & Raley, 2017; Deven & Joshua, 2017).

Further, the algorithms should be robust and better informed. Respondents substantiate the data- related factors (Gupta & George, 2016) and emphasize the use of relevant and quality data. Past studies have emphasized the importance of aligning with the stakeholder’s expectations for the use of their data (Hota et al., 2015). Two respondents expressed the patients’ concern of their data being manipulated and of privacy invasion issues as service ethics, remarking that they were trying to minimize privacy invasion to build a level of trust. In the exploratory interviews, respondents revealed potential threats of ethical issues and their impact on a responsible AI system. For instance, they expressed concern for the safety of human lives in robotic surgery. Shaikhina and Khovanova (2017) posited that AI could obtain personal information that may give rise to ethical issues. The interview analysis confirms various risk management strategies as a vital factor of responsible AI. Three respondents agreed that adaptive capacity and transparency are essential to design responsible AI systems and artifacts and that responsible AI developments can be visualized as a collective action problem with risk mitigation, requiring successful coordination with different activities.

The interviews revealed four items of the various constructs utilized in the next stage measurement scale, the quantitative study. Consequently, we added four measurement items- (DD2, Alg1, SRE1, Cop2) in our model to conduct the survey (Appendix Table 11).

A measurement scale was constructed for the quantitative assessment of the proposed model. The measurement parameters were adopted from the extant literature, and four items were the outcome of exploratory interviews (Appendix Table 11). The scale items were adapted from previous studies and modified to suit the context of the study and ensure face validity. A pre-test of the survey instrument was administered to seven academicians and six healthcare professionals, to establish the questionnaire’s content validity. We also followed the recommendations of Malhotra et al. (2006) while designing the questionnaire, to reduce the effects of common method bias (CMB). We provided brief descriptions of the scale items and also ensured the anonymity of the respondents. The final scale (five-point Likert-scale with 1 = strongly disagree to 5 = strongly agree) for the survey (Study I) consisted of seven constructs and 26 items.

We conducted a survey of healthcare professionals in India, consisting of all the first-order constructs of responsible AI (seven constructs). The sampling frame was the top five performing hospitals in India. The inclusion criteria were bed strength, the volume of patients, and healthcare firms’ market performance. We approached the potential respondents (Table 2) through email and requested them to participate in the survey. A total of 290 valid responses were collected (55.17 % male, with more than 77 % between 28 and 48 years). The survey participants first responded to items about their experience with AI-enabled tools and platforms. A further explanation on scales was provided to ensure clarity and accuracy.

After establishing the Resp AI construct as a third-order formative factor, the next stage of the study intended to assess the strength of the proposed relationships. Data for study III were collected from those in charge of various medical units and patients. A separate survey was created by adding questions of market performance construct and the refined set of items for Resp AI. We checked whether any respondent from the initial survey was also involved in this data collection stage. Further, these respondents were requested to provide the name and contact details of at least two or three patients. These participants (patients) had been using AI-enabled tools or other platforms for the last one to two years (e.g., - ‘FitBit’, wearable ECG, chat boats). Thus, we further found the respondents’ list, which consisted of the other end of the healthcare provider-patient dyad (Gooty & Francis, 2011). The patients’ questionnaire consisted of three constructs with four measurement items: cognitive engagement, instrumental value, and terminal value. Snowball sampling was utilized to reach the other end of the healthcare provider-patient dyad (Gummenson, 2005). Thus, a sample of 174 dyads was formed.

We further utilized PLS-SEM to assess the strength of the relationships depicted in the hypothesized model (Fig. 2). A similar method was followed to evaluate the outer loadings of the first order constructs, which were found to be significant (> 0.7), and construct reliability was also established (Table 6). For reflective constructs, the AVE values were greater than 0.5. The discriminant validity results are given in Table 7.

The structural model was estimated through bootstrapping. The direct impact of Resp AI on INV (β = 0.531, t = 14.665, p = 0.000) and TNV (β = 0.465, t = 12.257, p = 0.000) was significant. Thus, the hypothesis H1 and H2 are supported (Table 8). Mediation analysis was performed as recommended by Baron and Kenny (1986). The strength of this relationship was suppressed (Table 3) when the mediating variable (Cog Eng) was introduced. However, the impact of Resp AI on Cog Eng (β = 0.561, t = 15.427, p = 0.000) and the relationships of Cog Eng-INV (β = 0.245, t = 3.708, p = 0.001) and Cog Eng-TNV (β = 0.220, t = 3.413, p = 0.001) were significant. Moreover, the indirect relationships Resp AI-INV (β = 0.205, t = 3.075, p = 0.002) and Resp AI-TNV (β = 0.205, t = 3.075, p = 0.002) were also significant. Thus, the mediation mechanism (H3) of Cog Eng was established. Finally, the impact of INV on MKP (β = 0.0.270, t = 2.071, p = 0.038) was significant, while that of TNV on MKP (β = 0.151, t = 1.198, p = 0.231) was insignificant (Fig. 6). Thus, hypothesis H4 was supported, while H5 was not supported.

Based on our research findings, there are several implications for theory. First, our study responds to the recent calls by existing studies (Barello et al., 2016; Reddy, 2018; Shukla & Sushil, 2020; Wang et al., 2020) to identify the constituents of responsible AI. The current study precisely explains how technical skills, ethical concerns, and risk mitigation factors affect responsible AI in the healthcare context. Though much effort has gone into exploring the technological understanding of AI implementation, studies seeking to leverage the ecosystem of responsible AI are scarce. Despite the emerging importance of micro-foundations of psychological underpinnings (Huang & Chang, 2012; Swar et al., 2017), very little is known about the patient’s cognitive engagement with AI and its impact on value formation and market performance. This study fills this knowledge gap.

Second, several versions of the constructs of AI and its antecedents have been utilized in the literature (Duan et al., 2019; Grover et al., 2020; Wang et al., 2018). Many studies across various contexts have outlined different independent variables that could influence responsible AI (Shareef et al., 2021; Wearn et al., 2019). To the contrary, only a few papers are found on what and how responsible AI might influence. Although these noble pursuits invite attention, we examined a wealth of literature relating to responsible AI. This study is the first step to establishing responsible AI as a multi-dimensional third-order construct and investigating a mixed-method approach.

Third, recent researchers have explored the managerial cognitive perspectives of customer benefit and value creation, by considering previous foundations of means-end theory and service-dominant logic (Chen et al., 2017; Skålén et al., 2015). Our empirical studies extend these frameworks by integrating customers’ psychological perspectives and contribute to marketing literature by explaining the dynamics of customer interactions with AI-enabled technologies and service products.

Fourth, the current study’s findings conjoin the IS and marketing literature by investigating the under-represented fields of data privacy, ethical concerns, and co-operative developments and their impacts on market performance. This study also goes beyond the customer-dominant logics of value formation (Brodie et al., 2011; Heinonen et al., 2013), by revealing how responsible AI contributes to their perceived value. Further, our study pushes back the existing frontiers of knowledge concerning technology adoption (Rahman et al., 2016) by the customers and argues that responsible AI systems and artifacts facilitate their cognitive engagement, which is essential from marketing perspectives.

Finally, the findings of our study are also consistent with some previous studies that accept the role of cognitive engagement with different facets of service deliveries and its positive outcomes (Barello et al., 2014; 2016; Graffigana et al., 2015). Our study further explains that cognitive engagement with AI-enabled technologies in healthcare by patients has several benefits, affecting value creation. The study also supports prior studies on data security, privacy invasion, and ethical concerns in healthcare. Of particular note is the study’s finding regarding privacy invasion, quick recovery from malfunctions, and cooperation with technology vendors. The implicit discussions regarding these elements exist in the literature. Only our mixed-method study included these parameters into a comprehensive model and clarified how they are important in implementing responsible AI in healthcare.

The study’s findings are also aligned with the need for innovations in data science and artificial intelligence, to support global efforts to combat outbreaks of a pandemic like COVID-19 (Al-quaness et al., 2020; Wang et al., 2018). Implementing AI solutions that are socially responsible would enable medical scientists and technologists to address a wide range of biomedical and epidemiological challenges. This study depicts specific steps to tackle COVID-19 challenges by utilizing responsible AI, such as generating public trust through transparency, respecting individual dignity, and facilitating evidence-based clinical decisions (Khalifa et al., 2019; WHO, 2020a). We further argue that understanding cognitive engagement perspectives will provide insights to automate, target, and personalize healthcare marketing activities. For example, the perceived value of wearable devices and other AI-based health monitoring tools will lead to market growth. The fair and safe use of AI tools will increase the patient’s engagement in influencing healthcare firms’ market performance. Our study’s findings shed light on the unexplored ramification of psychological mechanisms of cognitive engagement and provide direction to managers for developing value propositions (e.g., increasing awareness programs, providing assurance of safety, and communicating the convenient use of such AI-based tools).

Further, the study complements and extends the service product perspective by linking the responsible production mechanisms of AI with the patients’ perceived terminal and instrumental value. The rigorous model depicted in this study allows firms to develop appropriate combinations of patient benefits for their AI-enabled tools and platforms. In this way, healthcare firms become patient-centric (Barello et al., 2016), by focusing on marketing capabilities driven by such socially responsible AI technologies.

The study findings suggest that healthcare firms should design service products in general and AI-enabled tools, particularly by taking into account both medical modernization and ethical concerns. The current findings also provide guidelines for healthcare practitioners and policymakers to implement responsible AI for better clinical outcomes, as well as patient benefits such as standard operating procedures for AI tools, regulation for data privacy protection, training to improve technical skills, reducing harmfulness, quick actions for recovery, and conducting audits of the transparency. Our study results suggest that healthcare policymakers should adopt a broader perspective of strategic choices and abandon the rigid delivery models. Moreover, the findings suggest how healthcare managers can focus on developing dynamic cognitive capabilities (Almquist et al., 2016) to identify responsible AI’s perceived benefit and its linkage with value formation. Further, the model proposed in this study may also facilitate scrutiny of healthcare practices and offer value propositions through a wide array of AI-enabled technologies that are socially responsible. From the patients’ perspective, cognitive engagement with AI-enabled technologies will facilitate positive perceptions and create a level of trust while interacting with them.

The digital divide concerns and its possible negative social implication on society’s vulnerable sections have always remained a critical apprehension for researchers. Efforts to solve this challenge in the context of an emerging technology must be made since the initial days of its conceptualization. Responsible AI is, in fact, an attempt to solve this social challenge of equitable distribution of benefits. By identifying dimensions of responsible AI, this study would help the citizens group in two key ways. First, by identifying the list of possible vulnerabilities like discrimination, autonomy, and privacy invasion, this study would help them understand the possibility of unequal distribution of benefits from AI application in the domain of healthcare. Second, by identifying risk mitigating strategies like adaptability, cooperation, and transparency, this study would further help the citizens group in directing their resources towards established and scientifically proven managerial solutions. Active citizen groups working towards the cause of equitable access to healthcare facilities would further benefit by learning various aspects of applying responsible AI in healthcare.

Similarly, the managers and owners of healthcare firms would benefit from a better understanding of responsible AI. They would be able to serve the diverse population with effective and efficient healthcare offerings. This study would also equip them with knowledge of balancing growth and sustainability, thus providing long-term benefits to society. The study would further guide the younger generation in identifying the requirement of skills in these emerging domains – the scope for which is expected to follow a sharp upward trajectory. It will thus help them in preparing themselves for the future job market. The findings suggest how the responsible AI solutions can lower the barriers between hospitals and patients and improve access to care, particularly in tier II and tier III cities of India. The findings also provide ways to develop a vibrant start-up ecosystem that goes beyond the clinical services to a new business model of wellness, prevention, monitoring services, even in non-metro cities of India.

The present study, though it has certain limitations, generates some avenues for future research. First, the study was conducted in an Indian context. Future research should test the framework’s generalizability in contrasting contexts (emerging country healthcare) to provide a robust understanding. A fruitful avenue of future research is the extension of the framework by considering the cultural characteristics and collecting the multi-national samples to assess the strength of the relationships. Second, the study contributes to the constituents of ‘Responsible AI’ in healthcare by considering several psychological and social aspects. However, there may be other contextual variables affecting the formation of Responsible AI, which remain unexplored. Future studies should also be conducted to better understand the antecedents of responsible AI. Third, we believe that the nature of tasks and the degree of involvement with AI-enabled technology may also moderate this relationship. For example, different users may use different tools and platforms as per their requirements. Task-related and rational motives may affect the perceived benefits and value differently. Future researchers should take into account the moderating roles of such variables and explore these issues of personal habits and task characteristics, which the current study has not considered. Finally, theories of psychology suggest that the factors responsible for cognitive engagement are affected by the brands of service products, and may also change over time. Therefore, a longitudinal study considering the brand values of the AI-enabled technologies will provide more useful insights to strengthen the theorizing in this context.