Nyamweya is a Kenyan scholar who has done many years of research on a diversity of topics
. Researchable Topic Area
The topic for this research is “A research on success factors of Chinese mHealth Apps for self-care; a case of Ping an Good Doctor”. According to Garcia-Perez et al. (2017), technology has been at the forefront of almost all the activities that are conducted by human beings to increase efficiency and to help in improving accuracy and effectiveness. As such, the health sector has not been left behind especially with the technological evolution of the mobile phone into a smartphone. This evolution has enabled the smartphone to be able to connect to various services through the internet and to transmit real-time data (Zapata et al., 2015). Moreover, the smartphone has become an important gadget in every day’s life of people hence manufacturers have strived to make the most out of its capabilities. In this light, the manufactures have personalised the smart phone to be able to check regularly and give its owner the updates regarding their health and physical fitness through installed applications that are able to collect and synthesise data on a person’s health status in several dimensions (Payne et al., 2015). The relationship between a smartphone owner and their smartphone, which has been named mHealth, has elicited a lot of interest amongst researchers especially in the wake of several lifestyle diseases globally in modern days (Krebs & Duncan, 2015).
Accordingly, the Chinese government has targeted to have everyone able to access affordable basic healthcare by the year 2020 under its ‘Healthy China 2020’ initiative making china one of the most lucrative markets for health companies worldwide (Chen & Zhu, 2015). In this view, especially also because of its vast population and good internet proliferation, mHealth has been gaining popularity due to its ability to help people to self-diagnose themselves as well as its capability to connect patients to doctors remotely. However, the cultural practices in China are deep rooted in the lifestyles of most of the residents especially the ones residing in the rural areas where traditional Chinese medicine practice is upheld (Farquhar, 2018). Consequently, this has made it cumbersome to introduce modern healthcare especially mHealth. Similarly, researchers have neglected this factor as they based their pieces of research in the urban areas in China, which indicate positive uptake of mHealth application. Therefore, this research will explore the factors that are affecting the success of mHealth applications for self-care in the whole of china with specific emphasis on Ping A Good Doctor application.
2. Objectives for the Research
Accordingly, this research will be guided by four objectives, which will enable the researcher to achieve a detailed coverage of the research topic. These objectives are as listed below.
1. To determine the impact of perceived privacy on Chinese consumers’ intention to use Ping An Good Doctor
2. To analyse the impact of social influence on Chinese consumers’ intention to use Ping An Good Doctor
3. To evaluate the impact of performance expectancy on Chinese consumers’ intention to use Ping An Good Doctor
4. To assess the impact of effort expectancy on Chinese consumers’ intention to use Ping An Good Doctor
3. Literature Review:
3.1 Technology acceptance model (TAM)
According to Marangunić & Granić (2015), the technology acceptance model (TAM) is a theory that explains how information system users are able to accept and use a particular system. Fred Davis and Richard Bagozzi developed the model, which is one of the most widely used models to explain user acceptance and usage of technology generally (Wallace & Sheetz, 2014). According to the theory, users consider a number of factors when first introduced to a new technology before they can fully adopt and utilise it.
As such, this study has identified four factors namely, perceived privacy, social influence, performance expectancy and effort expectancy that are highly impactful on consumers when considering the adoption of mHealth applications. Further, the theory states that upon formation of an intention to act in people, such is carried out without limitation (Alharbi & Drew, 2014). Therefore, in this regard, the adoption and use of a system are based on the creation of the intention to use in its users. On the other hand, Gao et al. (2015) noted the increased use of TAM in healthcare systems context has portrayed this theory as a befitting theory to use in establishing the acceptability and adoption of mHealth application in the healthcare. Arguably, despite the model having been developed without the healthcare context in its scope, it is increasingly becoming a useful model to explain the uptake of information technology in healthcare (Strudwick, 2015). Significantly, the theory states that the key to getting users to accept and adopt usage of technology in healthcare lies in making them appreciate technology in the first place (Marangunić & Granić, 2015).
3.2 Impact of perceived privacy on Chinese consumers’ intention of mHealth applications
According to Schnall et al. (2016), for mHealth applications to be able to function effectively, they must be able to capture a huge amount of the user's data and synthesise it so that they can be able to relay accurate information. Additionally, most of the mHealth applications customise themselves to their users by storing their private data, which they are able to update regularly according to the users progress health wise (Olla & Shimskey, 2015). As such, users are not guaranteed of the safety of their private data that is captured through such applications and neither can they be able to control how much data such applications are able to capture (Mense et al., 2016). Therefore, this presents a security challenge for mHealth applications users especially because it is their sensitive information that the applications use to customise their profiles within them. On the other hand, with the rampant data security breaches and hacker threats worldwide, trusting online-based platforms is becoming increasingly difficult especially with private information (Isakovic et al., 2015). As such, there have been claims that American soldiers locations were given away by a fitness application named Strava which is a serious security breach (Hern, 2018). Therefore, in the wake of such incidences, the perceived privacy of such applications is a critical consideration for users despite the usefulness and their willingness to use such applications
3.3 Impact of social influence on Chinese consumers’ intention of mHealth applications
According to Azhar & Dhillon (2016), social influence is the beliefs emanating from users societal background as to whether or not to use a certain system. Phichitchaisopa & Naenna (2013) noted that social influence is significant in determining the acceptance levels of various technologies in the society. For instance, the cultural Chinese society has a high number of barriers that strain the adoption of various technologies as opposed to the western countries such as the USA (Tarhini et al., 2015). In this regard, the Chinese traditional medicine, which is embraced in the Chinese culture especially in the rural areas, has restricted the adoption of mHealth because people are still entangled in the traditional health practice (Sampat, & Prabhakar, 2017). Similarly, empirical evidence indicates that social evidence not only affects the end users of the mHealth applications but also the health practitioners such as hospital personnel who attend to the end users of such applications (Altman & Gries, 2017). This has been evident in mHealth apps that seek to connect patients with the health personnel such as doctors who may be reluctant to attend to patients referred to them by mobile applications (Campbell et al., 2017).
3.4 Impact of performance expectancy on Chinese consumers’ intention of mHealth applications
According to Sampat & Prabhakar (2017), performance expectancy refers to the perceived performance of a certain system and the technology that is associated with it. On a similar note, Azhar & Dhillon (2016) postulated that performance expectancy of an application is similar to the perceived usefulness of an application. Notably, performance expectancy is determined by various factors such as relative advantage, extrinsic motivation and outcome expectation (Kayyali et al., 2017). Additionally, the use of the mHealth application by the targeted users is determined by their belief that using the application will be useful to them in attaining their health goals (Dwivedi et al., 2016). On the other hand, the performance expectancy of such applications by users is also governed by the experiences that users have had with such applications previously. As such, if users have a bad experience with such applications, their expectancy on similar applications will be negatively influenced and vice-versa. According to Adell et al. (2017), performance expectancy constitutes the strongest influence on the intentions of the Chinese customers to use mHealth applications especially die to the high rate of penetration of technology in the country.
3.5 Impact of effort expectancy on Chinese consumers’ intention of mHealth applications
Phichitchaisopa & Naenna (2013) noted that effort expectancy in the context of mHealth applications can be defined as the ease of use of a particular application. Similarly, Sampat & Prabhakar (2017) noted that the perception of how easy to use a certain application is to the end user constitutes the effort expectancy for that user. Accordingly, if users find a system as a complex one to use, they will certainly avoid using it, however, if the system requires little effort from the user in order for them to put it into use, then it becomes preferable to them (Stoddart & Evans, 2017). In this respect, manufacturers of health applications have strived to make them as easy as possible for their targeted customers with some applications being autonomous of any user input in the way they operate. On the other hand, decreasing the effort the user uses in interacting with a mHealth application increases the chances of the application collecting inaccurate information regarding its user hence decreasing the usability and reliability of the application (Schnall et al., 2016). Therefore, for the manufacturers of these applications, it is a delicate balancing act to ensure that the application is as effortless to the use as possible while at the same time, it is capable of capturing reliable and accurate data.
4. Details of your research
Where the data is being collected from
This research will be focused on establishing the success factors that impact on the Chinese mHealth apps for self-care with a specific focus on the Ping An Good Doctor mobile application. According to Ping An Healthcare and Technology Company Limited (2018), the Ping An Good Doctor mobile app was created and managed by the Ping An Helathcare And Technology Company Ltd which is a subsidiary of the Ping An Insurance (Group) Co. of China Ltd, founded in the year 1988 by Ming Zhe Ma. Notably, the company is headquartered in Shanghai, China and deals with the provision of internet based healthcare solutions in the republic of China. Moreover, the company has the largest internet based healthcare platform comprising of 192 million registered users as of the end of the year 2017 (Sito, 2017). Despite launching the internet service in the year 2015, the company recorded tremendous success in all its online medical and wellness services consisting of family doctor services, consumer healthcare, health mall/ health management and wellness interactions (Ping An Healthcare and Technology Company Limited, 2018). Accordingly, the data for this research will be collected from this company because of it’s vast knowledge and experience in the matters pertaining the internet healthcare provision. Additionally, the company is based in China hence accessing its offices will be easy increasing the ability to conduct and in-depth study of the research objectives. As such, the researcher will seek the help of the management team to identify the suitable personnel to provide the data required by this research.
Data collection methods
This research will combine the interview method together with the questionnaire method to capture data from the staff of the Ping An Healthcare and Technology Ltd and from the customers of the company respectively. To begin with, the questionnaire method was chosen to collect data from the customers of the company due to its ability to be sent over internet platforms without distorting the format or the content of the questionnaires (Phellas et al., 2011). This is achieved by ensuring that each respondent receives a similar set of questions as the other regardless of the distance or the number of people involved. Similarly, Sekaran & Bougie (2016) notes that questionnaires can be used to collect data over a large sample, widely spread across a large geographical location within a short timeframe and with minimal costs. This would be an added advantage in this research as a researcher would be targeting a few of the over 192 million customers of Ping An Healthcare and Technology Ltd.’s Ping An Doctor app (Sito, 2017). On the other hand, data gathered thorough the questionnaire method can easily be downloaded through various analytical tools for analysis purposes hence saving on the time of recording the data again (Zohrabi, 2013). However, the return rate of questionnaires can be low because of the relativity of the targeted respondents hence affecting the reliability of the data that is captured through them (Bryman, 2015). However, the researcher aimed at alleviating this aspect by issuing a large quantity of the questionnaires and lowering the expected return rate to 70% because the questionnaires will be internet based. Additionally, to further increase the reliability of the data captured in the study, the researcher will supplement the questionnaire method with interviews, which will be targeted on the customer support staff of the Ping An Healthcare and Technology Ltd Company. According to Chenail (2011), interview method is desirable in data collection because of the opportunity it gives the researcher to read the body language of the respondents hence enabling them to capture more and accurate data. Additionally, the researcher is able to focus on points of interest to acquire more information from the respondents as they may deem necessary. However, Phellas et al. (2011) noted that the method is prone to researcher bias, a factor that will be alleviated by the supplementary questionnaire data as questionnaires are arguably researcher bias free.
How the data will be collected
The data will be collected in two phases simultaneously; firstly from 5 customer support staff members of the Ping An Healthcare and Technology Ltd Company through the interview method and secondly from the customers of the Ping An Healthcare and Technology Ltd Company through a questionnaire that will be posted through the application the user's smartphones. Accordingly, the interview data will be aimed at qualifying the questionnaire data from the side of the application vendor regarding the success factors affecting the Ping An Good Doctor app. Therefore, this explains the few staff number to be involved in the interview process, which will further enhance the time and cost saving measures of the research. Moreover, the targeting of customer support staff of the company was as a result of their constant engagement with the customers hence being capable to understand the issues they undergo while in the process of utilising the application. As such, to gain access to the customer support staff, the researcher will seek an appointment with the senior management of the company through a friend that works at the company who will organise an appointment for the researcher and the senior management. Upon getting the appointment, the researcher will explain the intention of the study to the senior management and request that they facilitate the researcher to utilise the organisation for the purpose of the study. Further, the researcher will request to work with the human resource department, which will allocate 5 staff from the customer support department to participate in the interview with the researcher. Additionally, the interviews, which will be semi structured will each take 30 approximately minutes and will be scheduled within timelines that the interviewees will deem convenient for them but all have to be completed within one week.
On the other hand, the questionnaire, which will be sent to the customers through the Ping An Good Doctor mobile app feedback section, will be aimed at collecting a total of 90 fully completed questionnaires. As such, to achieve the set return rate of 70%, the researcher will post 129 questionnaires over the mobile application. The questionnaires will require the customers to choose multiple answers from a provided list for each question. Once the customers fill out the questionnaires, they will then submit them and the questionnaires will be reverted to the company. The researcher will allow a period of two weeks after which the filled questionnaires will be requested from the company for the purposes of compiling and analysis.
How analysis will be done
The analysis of the collected data from the interview method will undertake the content analysis method while the data collected through the questionnaires will be analysed statistically through descriptive, frequency and trend analysis method. As such, the interview data will be coded according to the similarity and differences rate in a bid to identify trends and patterns in the data signifying the success factors of the Chinese mHealth apps for self-care. The content analysis method used to analyse the interview data was considered as sufficient due to its ability to effective analyse textual data emanating from social interactions (Zohrabi, 2013). On the other hand, the use of statistical methods to analyse the questionnaire data was preferable as a result of its ease of analysis because it was already in software form.
5. Research ethics:
This study will observe various research ethics that are applicable to its scope of research. Firstly, the researcher will seek permission from the senior management of the Ping An Healthcare and Technology Ltd Company to use their organisation and staff to conduct the research. Secondly, the research will ensure that all the people that will respond to the research questions in both methods do it out of their own will as no one will be coerced to participate. Further, the researcher will maintain the confidentiality of sensitive company data and personal information of the participants at all times. Similarly, the researcher will avoid capturing personal details of the respondents to both the interviews and the questionnaires in a bid to uphold their confidentiality. On the other hand, the researcher will ensure that the interview avoids any questions that may make the respondents uncomfortable.
Accordingly, this research is aimed at achieving the findings that will enable the researcher to establish the success factors influencing the Chinese mHealth apps for self-care adoption. Factually, perceived privacy, social influence, performance expectancy and effort expectancy are expected to emerge strongly as significant factors that influence the adoption of such applications. Evidently, from the literature review section, these factors play a major role in the adoption of mHealth applications elsewhere in the world and the same relationship is expected to replicate in the Chinese environment. On the other hand, the findings of this study will be significant in enabling the healthcare firms to understand the Chinese market clearly before investing in the markets healthcare sector. Similarly, the research will provide a single source of empirical evidence explaining the implementation and adoption of mHealth application in the Chinese market.
7. Timetable for your research:
Dissertation Activity (parts)
Refining research objectives
Verifying data collection methods to be used
Organising data collection procedure in the company/ Collecting data
Analysing data and writing the data analysis chapter
Writing the discussions and conclusions chapter
Proof reading the whole dissertation and submission
Adell, E., Várhelyi, A., & Nilsson, L. (2017). Modelling acceptance of driver assistance systems: Application of the unified theory of acceptance and use of technology. In Driver Acceptance of New Technology (pp. 23-34). CRC Press.
Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155.
Altmann, V., & Gries, M. (2017). Factors influencing the usage intention of mHealth apps: An Empirical Study on the example of Sweden.
Azhar, F. A. B., & Dhillon, J. S. (2016). A systematic review of factors influencing the effective use of mHealth apps for self-care. In Computer and Information Sciences (ICCOINS), 2016 3rd International Conference on (pp. 191-196). IEEE.
Campbell, J. I., Aturinda, I., Mwesigwa, E., Burns, B., Haberer, J. E., Bangsberg, D. R., ... & Siedner, M. J. (2017). The technology acceptance model for resource-limited settings (TAM-RLS): a novel framework for mobile health interventions targeted to low-literacy end-users in resource-limited settings. AIDS and Behavior, 21(11), 3129-3140.
Chen, & Zhu. (2015). Healthy China 2020: Strategic Research Report. Elsevier.
Chenail, R. J. (2011). Interviewing the investigator: Strategies for addressing instrumentation and researcher bias concerns in qualitative research. The Qualitative Report, 16(1), 255.
Dwivedi, Y. K., Shareef, M. A., Simintiras, A. C., Lal, B., & Weerakkody, V. (2016). A generalised adoption model for services: A cross-country comparison of mobile health (m-health). Government Information Quarterly, 33(1), 174-187.
Farquhar, J. (2018). Knowing practice: The clinical encounter of Chinese medicine. Routledge.
Gao, Y., Li, H., & Luo, Y. (2015). An empirical study of wearable technology acceptance in healthcare. Industrial Management & Data Systems, 115(9), 1704-1723.
Garcia-Perez, C., Diaz-Zayas, A., Rios, A., Merino, P., Katsalis, K., Chang, C. Y., ... & Morris, D. (2017). Improving the efficiency and reliability of wearable based mobile eHealth applications. Pervasive and Mobile Computing, 40, 674-691.
Hern, A. (2018) Fitness tracking app Strava gives away location of secret US army bases Available at: https://www.theguardian.com/world/2018/jan/28/fitness-tracking-app-gives-away-location-of-secret-us-army-bases (accessed 27th August 2018)
Isakovic, M., Cijan, J., Sedlar, U., Volk, M., & Bester, J. (2015, April). The Role of mHealth applications in societal and social challenges of the future. In Information Technology-New Generations (ITNG), 2015 12th International Conference on(pp. 561-566). IEEE.
Kayyali, R., Peletidi, A., Ismail, M., Hashim, Z., Bandeira, P., & Bonnah, J. (2017). Awareness and use of mHealth apps: a study from England. Pharmacy, 5(2), 33.
Krebs, P., & Duncan, D. T. (2015). Health app use among US mobile phone owners: a national survey. JMIR mHealth and uHealth, 3(4).
Marangunić, N., & Granić, A. (2015). Technology acceptance model: a literature review from 1986 to 2013. Universal Access in the Information Society, 14(1), 81-95.
Mense, A., Steger, S., Sulek, M., Jukic-Sunaric, D., & Mészáros, A. (2016). Analyzing privacy risks of mHealth applications. Stud Health Technol Inform, 221, 41-45.
Olla, P., & Shimskey, C. (2015). mHealth taxonomy: a literature survey of mobile health applications. Health and Technology, 4(4), 299-308.
Payne, H. E., Lister, C., West, J. H., & Bernhardt, J. M. (2015). Behavioral functionality of mobile apps in health interventions: a systematic review of the literature. JMIR mHealth and uHealth, 3(1).
Phellas, C. N., Bloch, A., & Seale, C. (2011). Structured methods: interviews, questionnaires and observation. Researching society and culture, 3.
Phichitchaisopa, N., & Naenna, T. (2013). Factors affecting the adoption of healthcare information technology. EXCLI journal, 12, 413.
Ping An Healthcare and Technology Company Limited (2018) About us available at: http://www.pahtg.com/en/about-us/company-overview/ (accessed 27th august 2018)
Sampat, B. H., & Prabhakar, B. (2017). Privacy Risks and Security Threats in mHealth Apps. Journal of International Technology and Information Management, 26(4), 126-153.
Schnall, R., Rojas, M., Bakken, S., Brown III, W., Carballo-Dieguez, A., Carry, M., ... & Travers, J. (2016). A user-centered model for designing consumer mobile health (mHealth) applications (apps). Journal of biomedical informatics, 60, 243-251.
Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. USA: John Wiley & Sons.
Sito,P. (2017) Ping An’s Good Doctor app is using AI to get smarter as its user base tops 180 million Available at: https://www.scmp.com/business/companies/article/2121339/ping-ans-medical-app-good-doctor-ramp-reliance-ai-user-base-tops (accessed 27th august 2018)
Stoddart, G. L., & Evans, R. G. (2017). Producing health, consuming health care. In Why are some people healthy and others not? (pp. 27-64). Routledge.
Strudwick, G. (2015). Predicting nurses’ use of healthcare technology using the technology acceptance model: an integrative review. CIN: Computers, Informatics, Nursing, 33(5), 189-198.
Tarhini, A., Hone, K., & Liu, X. (2015). A cross‐cultural examination of the impact of social, organisational and individual factors on educational technology acceptance between B ritish and L ebanese university students. British Journal of Educational Technology, 46(4), 739-755.
Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management, 51(2), 249-259.
Zapata, B. C., Fernández-Alemán, J. L., Idri, A., & Toval, A. (2015). Empirical studies on usability of mHealth apps: a systematic literature review. Journal of medical systems, 39(2), 1.
Zohrabi, M. (2013). Mixed method research: Instruments, validity, reliability and reporting findings. Theory and Practice in Language Studies, 3(2), 254