Mobile phones and smartphones have become an important part of daily life and the widespread use of these mobile devices has contributed to the convergence of healthcare services and mobile technologies [1]. Mobile health (mHealth) refers to a type of health service applied to mobile computing, medical sensors, and communication technology [2, 3]. In 2015, more than 165,000 mHealth apps were available for users, and most mHealth apps focused on health management (65%), and disease and treatment management (24%). Participants of mHealth apps include patients and professional physicians, but prior studies little consider physicians’ acceptance and usage behaviors of mHealth apps [4]. Different types of users, including patients and physicians, can easily publish and access health information anytime and anywhere through mHealth apps [5, 6]. Physicians through mHealth apps access medical literature, quickly collect patients’ disease information, and communicate with patients in real-time. For patients, mHealth apps provide rich health services and information for them and they need to find information of high value to promote their health and well-being [7, 8]. Patients who conduct health self-management with physicians’ guidance can easily enable patient-physician partnerships and empower their healthcare by integrating professional knowledge and other patients with similar diseases experiences [9, 10]. The lack of physicians’ participation may limit the valuable use and success of mHealth apps. Therefore, Exploring factors on physicians’ acceptance of mHealth apps is meaningful and greatly improves the effective engagement with mHealth apps [11].
The topic of mHealth apps in recent years has become the focus in the field of healthcare informatics. For example, research on examining the influence of patients’ opinions on physician quality [12], the antecedent variables affecting patients’ acceptances of mHealth services in developing countries [13], and the recommendation of mHealth apps based on behavior change techniques [14]. Though most studies have explored the effect of behavioral intentions and usage behaviors of mHealth services [13, 15], the literature has not yet addressed the issue of mHealth apps adoption from the perspectives of specific types of users. To address this research gap, this study focuses on physician-centric mHealth apps, a form of mobile platform maintained by physicians, in which patients can consult with physicians and other patients who have consulted with the same physicians [9]. We aimed to examine the antecedent factors affecting physicians’ usage behaviors of mHealth apps. In this study, usage behaviors refer to physicians’ ongoing and everyday post-acceptance use of mHealth apps. Based on UTAUT2, we propose a research model to predict physicians’ intentions and behaviors of mHealth app usage. The contribution of this study is that the research model innovatively added altruism, cognitive trust, and online rating in the research model based on the features of mHealth apps. Altruism reflects personal social responsibility and mission [16]. Physicians take altruism as the basic ethics and work in the best interests of patients [17]. Compared with nonmedical service providers, physicians are more willing to sacrifice their benefits to promote patients’ healthcare outcomes [8]. Cognitive trust reflects the ability of mHealth apps to provide information reliably, safely, and accurately [18], which plays a critical role in encouraging physicians to adopt the emerging platforms of health information and services provided. Online ratings are the innovative feature of mobile technology. Online ratings published through mHealth apps reflect the overall evaluation related to online health services of physicians by patients that maybe affect physicians’ intention and behavior of usage of such apps [19]. From the perspective of physicians, online ratings belong to an intrinsic motivation to use mHealth apps, and physicians who obtain high online ratings have a higher sense of self-worth.
Mobile health applications
Mobile technology adoption is an important exploration in the current fields of information systems. Prior studies have explored various factors that affect the acceptance of information technologies [20]. Recently, studies focusing on mHealth have grown rapidly and the value of mHealth based on mobile technologies has gradually been recognized [21, 22]. The use of mobile phones and the development of mHealth apps have aroused the interest of researchers in information systems [23]. Mohammad used the UTAUT2 theoretical framework to study the factors promoting the adoption of mHealth services in the developing country and examined the moderating effects of gender on usage intention and behavior [21]. In addition, Murnane et al. classified mHealth apps at a fine-grained level and examined the perceived efficacy and the factors of potential adoption and abandonment of such apps [24]. Krebs and Duncan investigated the use of mHealth apps in the United States and found that most people did not use mHealth apps and even among those who used mHealth apps at first stopped using such apps, which found that the main reasons may be the heavy burden of information input, loss of interest and potential costs [4]. Based on the above literature, we found that prior studies focused on users’ behaviors in mHealth apps and rarely discussed physicians’ usage behavior in mHealth apps. The guidance of physicians to patients in mHealth apps plays a vital role in patients’ self-management. In contrast to prior studies, this study focuses on the characteristics of physicians using mHealth apps in China and combined with the theoretical framework of UTAUT2, which is widely used in the field of information systems acceptance [24], to explore physicians using behaviors.
Theoretical background
UTAUT is the most comprehensive theory in the field of information systems and is used to understand the acceptance of information technology in various environments [25]. UTAUT assumes that antecedents (performance expectations, effort expectations, social influence) indirectly affect usage behaviors through behavioral intentions, behavioral intentions and facilitating conditions directly affect usage behaviors, and the moderation effects of factors (gender, age, experience, and voluntariness of use) on the relationships between antecedents and usage behaviors [26]. UTAUT2 was proposed which has been widely used in technical user scenarios and expanded the three external constructs of hedonic motivation, price value, and habit into UTAUT [27]. Existing studies have expanded UTAUT2 to different types of users in explaining the process of their technology acceptance. For example, Chuah et al. discussed consumers’ adoption of smartwatches based on UTAUT2 and other dominant technology adoption theories [28]. Wang et al. used UTAUT2 to verify the habitual behavior of Chinese on social media [29]. For special types of users, Stefi et al. explored the influence of reviewing software developers’ reuse of software components in the organization based on UTAUT2 [30]. In addition, Escobar et al. examined the driving factors behind different types of consumer purchases of air tickets, including examining consumer users types and citizen user types [31]. Dwivedi et al. investigated the factors influencing the adoption of mHealth by citizens from different countries [32]. However, previous studies rarely have focused on specific types of user categories, such as teachers, students, tourists, and jobseekers.
To extend previous studies, this study focuses on the physicians using mHealth apps based on UTAUT2. In the context of physicians, hedonic motivation is not the main factor affecting Chinese physicians to use mHealth apps as they have heavy offline work. Thus, this study removed the constructs of hedonic motivation from UTAUT2 and added altruism as physicians’ intrinsic motivation. In addition, considering that physicians have health expertise and high judgment on the professionalism of mHealth apps, we speculate that physicians’ cognitive trust in mHealth apps affects usage behavior because cognitive trust has been proved to be closely related to individuals’ perceived competence [33]. Finally, physicians’ online ratings provided by patients who have consulted health services on mHealth apps may encourage physicians to actively use such apps. Online ratings also reflect physicians’ activity in mHealth apps, which improves physicians’ online social value and attracts other physicians to participate in such apps for communication and health knowledge interaction. Our proposed research model is presented in Fig. 1.
Hypotheses development
Performance expectancy
People expect that using emerging technologies will help them enhance their job performance [19]. Performance expectancy is an important variable of peoples’ behavioral intentions and has been proved to significantly affect individuals’ intention to accept emerging information technologies [26, 34,35,36]. Using smartphones has been proved to be convenient for people to improve their work performance [37]. Performance expectancy represents physicians’ beliefs about mHealth apps before use, and these beliefs may affect physicians’ behavioral intentions [26]. Thus, we propose the following hypothesis:
Effort expectancy
People expect the process of using emerging technologies to be simple and easy. Effort expectancy is a construct about how easy it is to use emerging technologies in prior studies on technology acceptance [26]. Perceived ease of use significantly affects behavioral intentions [38]. With the increase in effort expectancy, emerging technology usage is believed to require minimal effort [39]. Physicians’ behavioral intentions of using mHealth apps may be related to effort expectancy. Thus, we propose the following hypothesis:
Social influence
The opinions of some friends and relatives opinions on the acceptance of emerging technology will influence individuals’ behavioral intentions, which is the definition of social influence [40]. Social influence reflects how individuals’ behaviors of mHealth app usage are influenced by others’ opinions [38]. Most physicians are unfamiliar with mHealth apps that are emerging applications and lack time to participate in actives through such apps [41], so physicians’ intentions of usage mHealth apps may tend to rely on others’ perceptions. Thus, we propose the following hypothesis:
Altruism
Altruism is when people help others without expecting anything in return [42]. Physicians help others voluntarily and selflessly, gaining happiness by showing altruism [43]. Different from patients of mHealth apps, physicians are more willing to help patients at the expense of their interests and enjoy helping patients solve health problems and answer patients’ doubts [44, 45]. Physicians as health professionals instinctively master altruism and work for the best interests of patients [17]. As an intrinsic motivation, altruism plays a significant role in determining emerging technologies’ adoption and usage [46]. In the original model in UTAUT2, the intrinsic motivation of technical users to accept the use of information systems in hedonic motivation [27]. The intrinsic motivation of physicians to participate in mHealth apps is not hedonic motivation. This study supplements intrinsic motivation for altruism to help understand physicians’ intention to use mHealth apps. Thus, we propose the following hypothesis:
Facilitating condition
Facilitating conditions are defined as the extent to which people believe that potential conditions, such as technical infrastructure, human support, and compatibility, exist to support the use of an emerging technology [26]. In the context of technical infrastructure, objective conditions in mHealth apps believed by physicians make the operation easy, including the provision of mobile devices support. In the context of human support, the guidance of mHealth apps is available to physicians in the process of decision-making of user behavior. In the context of compatibility, most physicians are highly educated professionals with the ability to use mHealth apps. The higher the level of hospital support, the more favorable the mHealth app usage is among physicians [47]. Prior studies have found significant relationships between facilitating conditions and usage intentions and behaviors of healthcare information systems [48, 49]. Thus, we propose the following hypothesis:
Habit
Habit refers to the individuals’ tendencies to behaviors based on cumulative learning experience [50]. The results of accumulated learning experiences and habitual behavior might affect individuals’ attitudes and beliefs, which have a significant effect on intentions and usage behaviors [51]. The effects of habits on intentions and actual usage behaviors are reflected in the mobile apps field [52, 53], such as mobile payments and mobile banking services. In the health field, there may be a significant correlation between habit and physicians’ intentions and use behaviors of such apps. Thus, we propose the following hypothesis:
Cognitive trust
Cognitive trust refers to users’ perception confidence in the accurate functions promised by emerging technology providers when using technical processes [54]. Physicians provide health services and information for patients through mHealth apps. If physicians are unable to operate the mHealth apps comfortably, they will become frustrated and consider mHealth apps to be unreliable [55]. Previous studies proposed that people perceive information systems as trustworthy due to their user-friendliness [56]. In addition, cognitive trust is related to physicians’ competence and has been confirmed to affect behavioral intention and use behavior of emerging technologies [57, 58]. When physicians believe that mHealth apps are reliable and help to provide health services, they may be inclined to trust such apps. Physicians’ intentions and behaviors of using mHealth apps may be strengthened due to cognitive trust. Thus, we propose the following hypothesis:
Online rating
Information exchange through mHealth apps allows patients to publish ratings on physicians and share ratings with other patients on such apps [59]. Online rating objectively reflects the average evaluation of patients on the health services provided by physicians. Online ratings, the innovative feature of mobile technology, can motivate physicians to actively use behaviors and more easily help physicians evaluate the experience of using mHealth apps [19, 60]. Prior studies found that a significant correlation between online ratings’ features and individuals’ behaviors [61, 62]. Thus, we propose the following hypothesis:
Behavioral intention and usage behavior
Intentions play an important role in predicting usage behaviors of emerging technologies. Prior studies have confirmed that behavioral intentions are highly related to user behavior and behavioral intention is the main factor in using mobile services [14]. In this study, intentions are considered the extent to which physicians perceive their willingness to use mHealth apps. Usage behaviors are considered the ongoing and everyday post-acceptance use of mHealth apps. We expect that physicians’ behavioral intentions to use mHealth apps may positively affect their usage behaviors. Thus, we propose the following hypothesis: