Investigating Factors Impelling the Adoption of e-Health: A Perspective of African Expats in China

Abstract

The purpose of the study was to analyze the factors affecting the acceptance and adoption of e-Health applications among African expats in China from the patient’s perspective. This research used extended technology acceptance model (TAM) with the addition of privacy and trust. The survey method with the structured questionnaire was used to collect data from more than 300 African expats in three Tier 1 cities of China. Hypotheses were tested using partial least squares (PLS) method, a structural equation modeling (SEM) based statistical technique. The results show that perceived usefulness, perceived ease of use (PEU), and privacy have significant effects on intention to use (INT) e-Health technologies. Moreover, PEU endorses positive impact on perceived usefulness. The results also revealed that gender was partially associated with the acceptance and use of e-Health technologies. However, surprisingly, trust had an insignificant effect on INT of e-Health. The results of the current research provide the foundation for the development of policies and strategies for the enhancement of e-Health services to African expats in China. Moreover, the acceptance model of current study can be modified easily because of its generic approach for the investigation of adoption of e-Health services among other developing countries’ expats in China.

Keywords

adoption African expats e-Health technology acceptance model health information technologies

Introduction

With the progress of information communication technology (ICT) and the rapid development of Internet communication, the e-Health project has been introduced and accepted as an important and basic element of the medical industry. e-Health has been defined by the World Health Organization (WHO; 2003) as

the leveraging of information and communication technology (ICT) to connect providers, patients, and governments; to educate and inform health care professionals, managers and consumers; to stimulate innovation in care delivery and health system management; and to improve our health care system.

The term can encompass a range of services or systems that are at the edge of medicine/health care and information technology (IT), including Electronic health record; Computerized physician order entry; ePrescribing; Clinical decision support system; Telemedicine; Consumer health informatics; Health knowledge management; Virtual health care teams; mHealth or m-Health; Medical research using grids; Health informatics/health care information systems (Neto & Flynn, 2018). The current study encompasses the adoption of e-Health in terms of recording and managing patient’s data, which can be used by doctors for effective prescription of medicine based on the patient medical history. Therefore, these e-Health services, including Electronic health record; Computerized physician order entry; ePrescribing; and Health informatics/health care information systems, are related to the current study.

Moreover, developed nations have invested their substantial resources and will continue to invest in e-Health systems for the reduction of cost and enhancement of health care (Cruickshank, Packman, & Paxman, 2012; Farahani et al., 2018; Lohman, 2010; Yaylacicegi & Mitchell, 2012). The adoption and implementation of an e-Health system have aroused wide attention in academic and nonacademic circles (Mair et al., 2012). The details of the role of Internet adoption, including ICT infrastructure, IT professional education and training, and e-Health implementation state have been examined in prior literature and found a significant impact on the adoption of e-Health system (Chhanabhai & Holt, 2007; Demiris et al., 2013; Hossain, Quaresma, & Rahman, 2019; Lafky & Horan, 2011; Tavares & Oliveira, 2018; Wilkowska & Ziefle, 2012).

Chetley, Davies, Trude, McConnell, and Ramirez (2006) observed the importance of ICT in the implementation of health care systems in developing countries similar to the finding observed in developed countries. Recent literature shows that e-Health is the part of ICT which contributes toward the improvement of health care systems in developing nations (Baryashaba, Musimenta, Mugisha, & Binamungu, 2019; Hossain et al., 2019). Developing countries are also adopting ICT due to well-documented patient benefits for the solution of access, cost, and quality problems of health care. Moreover, the speed of knowledge diffusion, specifically health care information, would also be enhanced through the implementation of ICT (Kujala, Rajalahti, Heponiemi, & Hilama, 2018). The adoption of e-Health remains a challenge even with its benefits, specifically in developing countries. Therefore, this study is going to investigate the key factors of e-Health adoption, particularly in context to African expats living in mainland China.

China has the world largest population of approximately 1.42 billion as of June 2019 (Worldometers, 2019). According to the Ministry of Health care of China, in 2014, people in China visited doctors around 6.87 billion times in 23,000 hospitals, and the per-person health care expenditures in public hospitals were around 180.2 renminbi (RMB) (about US$30). In China, dating back to the 1990s, some health care institutions had built their electronic invoicing system and registration for the improvement of work efficiency. A hierarchical health information network was built by the end of 2002, covering 2,861 counties, 333 cities, and 31 provinces, which played a significant role in the battle against the severe acute respiratory syndrome outbreak in 2003 (Li, 2011). As per the National Development Programming Compendium for Health Information Construction (2003-2010) by Chinese Ministry of Health, government enhanced the resource investment for the improvement of national health information system and began the construction of health care service information systems, preventive health care information systems, community health care service platforms, health surveillance information systems, and so on. (Wang, 2012). These information systems enable the foundation for the development of e-Health system in mainland China.

Meanwhile, a large number of African expat community reside in mainland China and are facing health problems due to dissimilar environmental conditions of China such as air pollution, weather change, unavailability of home country food, and so on (Bodomo, 2012). Most of the African expats are students, studying in first tier cities of China (Ministry of Education, 2015). Moreover, the Chinese government is investing in the development of its own health sector along with active participation in global health governance, specifically in Africa, which enhanced China–Africa health cooperation and will not only benefit the population of Chinese and Africans but also help enhance the health and well-being of all mankind (Jianlan, 2017). The recent Chinese health reform has overlooked the coverage of expats health. Provision of medical and health services to the African expats is consistent with China’s health care reforms, but the system has failed to close the treatment gap. In addition, doctors in China are not trained in culturally adapted care or in dealing with the expats in China, particularly, Africans (Hall, Chen, Latkin, Ling, & Tucker, 2014; Lin et al., 2015). Moreover, Chinese doctors are unable to provide adequate information and time to foreign patients; therefore, patients were left confused about the exact diagnosis and treatment after their medical consultation (Lin et al., 2015). Therefore, the adoption of e-Health from expat patients is necessary for mitigation of such problems. Access to e-Health for these expats may differ from that of Chinese for several, interrelated aspects, such as the allocation of needs for care, demographic, socioeconomic, territorial and cultural characteristics, and their interaction with related institutional factors. However, only a split of the observed inequalities in access is associated with inequities and calls for policy actions. These inequities exist if there are systematic differences in access that are unrelated to health needs and if these differences are beyond the individual’s control (Allin, Grignon, & Le, 2010). The matter is theoretically delicate in nature and empirically challenging. Therefore, this study aims to fill the existing gap in the research to address this particular issue by identifying the key adoption factors of e-Health.

Based on the above discussion, service provider perspective (e.g., nurses and physician) was the main concern of prior researchers in finding the attitude toward e-Health adoption (Hans, Gray, Gill, & Tiessen, 2018; Hossain et al., 2019). Moreover, most of the studies have been conducted in developed countries’ perspective, whereas few types of research are available on the adoption of e-Health in developing nations from a user perspective (Hoque, Bao, & Sorwar, 2016; Van Velthoven & Cordon, 2019). Meanwhile, the less focus of the prior researchers on this particular issue motivate authors to comprehensively investigate health issues faced by African expats in mainland China, which has not been investigated before (as per our best knowledge). Therefore, current research contributes to filling the gap by evaluating the problems associated with the adoption of e-Health in the context of African expats in mainland China from the patient’s perspective; utilizing the extended version of technology acceptance model (TAM) to develop a comprehensive understanding regarding these issues.

Theoretical Framework and Hypothesis

Numerous theories have been developed, for example, diffusion of innovation theory (Rogers, 2010), theory of reasoned action (Ajzen & Fishbein, 1975), diffusion model (Bass, 1969), TAM (Davis, 1989), hype cycle (Fenn & Raskino, 2008), technology life cycle (Harris, Shaw Jr, & Sommers, 1983), social cognitive theory (Bandura, 1991), theory of planned behavior (Ajzen, 1991), and matching person and technology model (Scherer, 2002), to evaluate the intention and use of new technologies. TAM is the most effective model among these for the adoption of information systems. Literature shows that TAM is the most important tool in the field of Health information research (Orruño, Gagnon, Asua, & Abdeljelil, 2011). In the field of health care, an extension of TAM model has been applied known as UTAUT (unified theory of acceptance and use of technology); the TAM constructs validity remains supported by evidence from the literature (Or et al., 2011). Research proves that TAM predicts a significant part of the adoption of health IT (Aggelidis & Chatzoglou, 2009). Numerous researches prove that TAM is a most suitable theory in health care context for theoretical and empirical testing (Holden & Karsh, 2010). Next section provides the brief introduction of the basic TAM model and its application in e-Health systems.

TAM

The base of the TAM is the theory of reasoned action that elaborates the usage behavior and acceptance of users about IT (Fishbein & Ajzen, 1977). As per TAM, intention to use (INT) leads to the actual use of technology (ACT). According to TAM, INT is influenced by perceived ease of use (PEU) and perceived usefulness (PU) (Davis & Venkatesh, 2004). Perceived usefulness is “the degree to which a person believes that using a particular technology will enhance his or her performance,” and PEU is “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989).

Numerous researchers have elaborated the importance of TAM in the adoption of technology, including e-Health (Harst, Lantzsch, & Scheibe, 2019; King & He, 2006). A study conducted on the older people’s adoption of e-Health concludes that PEU and PU are the primary factor of INT e-Health (Jung & Loria, 2010). Literature proves that TAM provides a means to understand the important influencing variables on patient’s acceptance and adoption of e-Health (Wilson & Lankton, 2004). A study identified that Internet efficacy, perceived ability, and perceived usefulness influence the use and attitude of online health information (Kim, Han, Yoo, & Yun, 2012). Another recent research conducted on the acceptance of web-based interactive self-management technology used TAM for the identification of important factors, for example, PU, PEU, and health care knowledge (Or et al., 2011). PU and PEU influence the physician’s INT telemedicine and INT leads to the actual use of telemedicine (Hu, Chau, Sheng, & Tam, 1999). Based on the above discussion, the following hypotheses were proposed for this research:

Hypothesis 1: Perceived usefulness positively related to the INT e-Health.

Hypothesis 2: Perceived ease of use positively related to the INT e-Health.

Hypothesis 3: Perceived ease of use positively related to perceived usefulness of e-Health.

Hypothesis 4: Intention to use positively related to the actual use of e-Health.

Literature suggests that additional factor beyond PU and PEU may be essential for the specific technological context (Askool, Pan, Jacobs, & Tan, 2019; Kibelloh & Bao, 2014a, 2014b; Wong, 2018) . Research also suggests that TAM does not focus on cultural, group, privacy, and social factors that have an essential impact on technology adoption (Pan, Jacobs, Tan, & Askool, 2018). Tung, Chang, and Chou (2008) conducted research in health information system and applied extended TAM for e-Logistics information system adoption. These studies confirmed the impact of additional factor, including compatibility and trust, and impact the adoption of health information system. The trust factor was also utilized in Electronic Healthcare Records (EHCR) systems in addition to PEU and PU for the measurement of its usage attitude (Egea & González, 2011).

Based on the above discussion, it can be concluded that the factor affecting the usage and acceptance of new technology depends on the targeted users. Therefore, current research utilized the extended TAM (Figure 1) by including two important factors: trust and privacy. Gender played a moderating role in the proposed model, as will be discussed in the following section.

Figure 1.

Theoretical framework.

Privacy and Trust

Trust and privacy factors have been identified along with numerous human and social factors whose effect on the successful use and adoption of e-Health. The importance of privacy (PRI) is increasing in ICT, specifically in the health care sector. It is essential for patients to share their personal information with physicians for the treatment and diagnosis (Appari & Johnson, 2010). Research proves that the patients hesitate to share information such as genetic information, sexual preference, HIV, and psychiatric behavior because they think it may lead to discrimination and societal disgrace (Appelbaum, 2003). Usage of health technology is directly associated with the patient’s concern about privacy (Hossain et al., 2019). Research proves that patient trust more on physicians than their employers and even their family members in sharing of their personal information (Diamond, Busch, Jilch, & Tscheligi, 2018; Pan et al., 2018). And patients have a serious concern about the privacy of this information (Goldman, Westin, & Pearson, 2006). Therefore, the current research proposed the following hypothesis:

Hypothesis 5: Privacy concern directly associated with the INT e-Health.

Trust factor (TRU) also has an association with acceptance of technology like privacy. Trust can be defined as “willingness to rely on an exchange partner in whom one has confidence” (Moorman, Zaltman, & Deshpandé, 1992, 1993). It plays a significant role in an individual’s willingness to share their medical information and status in health care systems (Ozawa & Sripad, 2013). Literature shows that there is a positive association available between patient’s failure to disclose health information and diagnostic error that increases the risk and harm for the patient (Zwaan, Thijs, Wagner, & Timmermans, 2013). Research also proves that trust is one of the most important elements impacting a patient’s decision to adopt e-Health systems (Misra, Bisui, & Singh, 2019). Moreover, Briggs, Burford, De Angeli, and Lynch (2002) prove that a reduction in perceived risk could enhance people’s willingness to trust a site. Furthermore, the success of ICT, specifically e-medicine systems depends on loyalty and trust (Smith & Manna, 2004). Researches in the field of health care system also suggest that trust is an important element for adoption (Abbas, Carroll, & Richardson, 2018; Zhao, Fang, & Jin, 2018). Therefore, the following hypothesis was proposed:

Hypothesis 6: Trust factor positively associated with the INT e-Health.

The Moderating Role of Gender

Literature shows that females and males behave differently and play different roles in society (Saad & Gill, 2000). Males are more prone to adventure, use of technology, and exploring new things than females. Recent literature utilized gender as moderator like Venkatesh, Morris, Davis, and Davis (2003) used into the application of unified theory of acceptance and use of technology model (UTAUT), Zhang, Guo, Lai, Guo, and Li (2014) used it in m-Health adoption and Bao, Xiong, Hu, and Kibelloh (2013) utilized gender as a moderator in mobile learning adoption. Moreover, Orser and Riding (2018) evaluate the influence of gender on the adoption of technology among SMEs. Literature shows that gender plays a significant role in the adoption of e-Health, specifically in developing countries (Duarte & Pinho, 2019; Hoque et al., 2016). Research shows that PU strongly influences male in the adoption of technology, whereas PEU influence more on female (Venkatesh & Morris, 2000). Numerous researchers had utilized gender as moderator in the application of TAM and proved that females have lower level of computer self-efficacy than males (Busch, 1995; Dutta, Peng, & Sun, 2018), differently process information (Venkatesh et al., 2003), comparatively less active in utilizing technology (Slyke, Comunale, & Belanger, 2002), and less inclined to use Internet than male (Ono & Zavodny, 2003). However, no research has evaluated the impact of gender on e-Health adoption in the context of African expats in China.

Above discussion suggest that gender has a significant impact on the INT and ACT. Therefore, the current study addressed the moderating effect of gender on the adoption of e-Health (Figure 1) and formulated the following hypotheses:

Hypothesis 7: Gender moderates the relationship between PU and INT e-Health.

Hypothesis 8: Gender moderates the relationship between PEU and INT e-Health.

Hypothesis 9: Gender moderates the relationship between privacy and the INT e-Health.

Hypothesis 10: Gender moderates the relationship between trust and INT e-Health.

Research Design and Methodology

Exploratory research design in most appropriate as it is the first research in the context of the adoption of e-Health specifically in the context of African expats in China (Zikmund, Carr, Griffin, Babin, & Carr, 2013). For the investigation of intention and adoption of e-Health in the current study, a positivist approach is most appropriate (Lee, 1991). A survey of African expats living in first, second, and third tier cities (Beijing, Wuhan, and Harbin, respectively) of China was conducted for the testing of empirical model and hypothesis. Targeted sampling area of current research is appropriate as most of the Chinese hospitals are equipped with the latest technologies, including the Internet, which is the prerequisite for e-Health (Azam, 2013). Data collection design was based on two objectives: to increase the number of participants and to keeping the monetary cost and time at the optimal level. Therefore, convenience sampling (nonprobability sampling) was used as the survey instrument of current research, which is a widely used sampling technique in information systems research (Eze, Manyeki, & Har, 2011). The target sample was African expats residing in China. It was necessary that respondents must have used e-Health services at least once in China. Hence, only those African expats were sampled who meet the minimal e-Health usage criteria. The study was conducted with a personally administered questionnaire at different universities of the aforementioned cities of China. The objective of the research was elaborated properly to the prospective subjects, and the informed consensus was sought from the participants before the beginning of the survey.

Measures

All the latent constructs measures of the theoretical model were developed from the previous literature and amended in the context of e-Health adoption. The items of the latent constructs including perceived usefulness, PEU, INT, and actual use were adapted from prior literature (e.g., Chau & Hu, 2002; Chellappa, 2003; Chellappa & Pavlou, 2002; Davis, 1989; Davis & Venkatesh, 2004; Featherman & Pavlou, 2003; Korgaonkar & Wolin, 1999; Taylor & Todd, 1995). Measures of trust were obtained from Gefen, Karahanna, and Straub (2003), Tung et al. (2008), and Yoon (2002).

Questionnaire and Sampling Design

Relevant data of construct measurement in research hypothesis were extracted. This study adopts a structured questionnaire survey. The first half of the questionnaire contains demographic information for the establishment of descriptive characteristics (e.g., gender, age, education, and IT using experience) of the sample. The second half of the questionnaire was about the different constructs in the theoretical model. The 5-point Likert-type scale ranging from 1= strongly disagree to 5= strongly agree was used to measure the latent constructs. A pilot study was conducted, and the effectiveness of the final questionnaire was designed and tested by utilizing its results. A personally administered, face-to-face, active recruitment strategy was utilized to recruit participants to current research. The study distributed 320 questionnaires, and 291 were returned with a response rate of 91%. Three incomplete questionnaires were eliminated from the sample for analysis. Thus, 287 complete questionnaires were subjected to further analysis. The partial least squares (PLS) technique, a statistical analysis method based on structural equation modeling (SEM), was utilized for testing and validation of the proposed extended TAM and hypothesized constructs.

Data Analysis and Results

Descriptive Statistics

The male and female response rate was not significantly different, as shown in Table 1. The sample consists of 44% female and 56% males. Most of the respondents were in the age group of 18-28 years because data were collected from the universities, and most of the respondents were students. Moreover, 46% of the respondents were enrolled in doctorate, and 59% of the respondents had more than 7 years of IT using experience. Only those participants were selected who have used e-Health technologies and e-Health services at least once in China. In all, 83% of the respondents used e-Health services once in China, whereas remaining respondents used e-Health services more than one time.

Table 1.

Descriptive Statistics.

	Frequency	%
Gender
Female	126	44
Male	161	56
Education
Undergraduates	37	13
MBBS	19	7
Master’s degree	91	32
PhD	131	46
Others	9	3
Age
18-28 years	150	52
29-34 years	97	34
35-40 years	28	10
More than 40 years	12	4
IT usage experience
Less than 1 year	2	1
1-3 years	25	9
4-6 years	92	32
7-9 years	126	44
More than 10 years	42	15
Experience of using e-Health services
Once	239	83
2-5 times	23	8
6-10 times	18	6
More than 10 times	7	3

Note. MBBS = Bachelor of Medicine and Bachelor of Surgery; IT= information technology.

Analytical Approach and Measurement Model

Internal reliability, discriminant validity, and convergent validity were examined for the assessment of the measurement model. Composite reliability and Cronbach’s alpha were used to evaluate the internal reliability, and a threshold level of .70 was considered as an acceptable internal consistency indicator (Hair & Anderson, 2010).

Convergent validity was measured through average variance extracted (AVE) with the minimum value of 0.50 and item loadings more than 0.50 (Hair & Anderson, 2010). Item loadings, Cronbach’s alpha, composite reliability, and AVE are shown in Table 2. The computed values of Cronbach’s alpha are greater than the recommended level .70 (ranged from .76 to .88), support strong internal reliability. Moreover, estimated loadings (ranged from 0.71 to 0.92) and AVE (ranged from 0.66 to 0.81) are also greater than the threshold levels, satisfy the conditions of convergent validity.

Table 2.

Measurement Model.

Variable	Items	Loadings	CR	Cronbach’s α	(AVE)
Actual use (ACT)	e-Health service is a pleasant experience	0.86	0.85	.76	0.66
	I use e-Health service currently	0.75
	I spend a lot of time on e-Health service	0.83
Intention to use (INT)	I have high INT the e-Health service	0.87	0.90	.83	0.75
	I intend to learn about using e-Health services	0.88
	I plan to use e-Health services to manage my health	0.84
Perceived ease of use (PEU)	Learning to operate the e-Health services will be easy for me	0.84	0.90	.86	0.70
	I can easily become skillful at using the e-Health services	0.84
	I can get the e-Health services to do what I want it to do	0.84
	Overall, the e-Health services are easy to use	0.82
Privacy (PRI)	I believe privacy of e-Health participants is protected	0.91	0.93	.88	0.81
	I believe personal information stored in e-Health system is safe	0.92
	I believe e-Health systems to keep participants information secure	0.86
Perceived usefulness (PU)	Using the e-Health services will improve my life quality	0.87	0.92	.88	0.74
	Using the e-Health services will make my life more convenient	0.88
	Using the e-Health services will make me more effective in my life	0.86
	Overall, I find the e-Health services to be useful in my life	0.82
Trust (TRU)	Based on my experience with the e-Health in the past, I know it is trustworthy	0.86	0.86	.77	0.68
	Based on my experience with the e-Health in the past, I know that it is not opportunistic	0.71
	Based on my experience with e-Health in the past, I know that it keeps its promises to its patient	0.89

Note. AVE = average variance extracted; CR = composite reliability.

The square root of AVE and cross-loading matrix was used to measure the discriminant validity. The square root of the AVE of a construct must be greater than its correlation with other constructs for the approval of discriminant validity (Henseler, Ringle, & Sinkovics, 2009). Table 3 showed that the square root of AVE of each construct is greater than its correlation with other constructs, approving the discriminant validity of the data.

Table 3.

Correlation Matrix and Square Root of AVE.

	ACT	INT	PEU	PRI	PU	TRU
ACT	.81
INT	.70	.86
PEU	.53	.62	.83
PRI	.51	.49	.49	.90
PU	.65	.73	.67	.49	.86
TRU	.75	.59	.56	.63	.68	.82

Note. AVE = average variance extracted; ACT = actual use of technology; INT = intention to use; PEU = perceived ease of use; PRI = privacy; PU = perceived usefulness; TRU = trust.

Diagonal boldface values show the Square Root of the AVE.

Structural Model

To identify the relationships among the constructs, we develop a structural model. The current research utilizes the bootstrapping technique (p < .05) to test the hypotheses. The study tested the relationship between explanatory and explained variables by path coefficient (β) and t statistics at the first stage. In the second stage, the study explored the moderating effect of gender on these relationships. Table 4 shows the calculated path coefficients with significance and corresponding t statistics. The calculated model explains 58.5% of the variance in INT e-Health and 49.6% of the variance in the actual use of e-Heath. The results show that perceived usefulness (β = .49, p < .01), PEU (β = .19, p < .01), and privacy (β = .12, p < .05) have significant effects on INT. Moreover, PEU endorses positive impact (β = .67, p < .01) on perceived usefulness. Thus, the results are supporting Hypotheses 1, 2, 3, and 5. However, surprisingly, trust (β = .09, p > .05) had an insignificant effect on the INT e-Health, contradicting Hypothesis 6. Contradicting with discussed literature, PU is a more important factor than PEU for the African expats residing in China. Furthermore, the results of the single linear regression proved that INT has a positive effect on the actual use of e-Health (β = .71, p < .01), in support of Hypothesis 4.

Table 4.

Structural Model.

	Coefficients	t statistics	p values
INT → ACT	0.70	21.58	.00
PEU → INT	0.19	3.38	.00
PEU → PU	0.67	16.06	.00
PRI → INT	0.10	1.83	.07
PU → INT	0.49	7.47	.00
TRU → INT	0.09	1.46	.14

Note: R² of INT is 58.5% and R² of ACT is 49.6%. INT = intention to use; ACT = actual use of technology; PEU = perceived ease of use; PU = perceived usefulness; PRI = privacy; TRU = trust.

Moderating Effect of Gender

Table 5 shows that females had a significantly higher level of INT e-Health than males regarding PU (0.71 vs. 0.32, p < .01), in support of Hypothesis 7. All the remaining constructs show insignificant results. Thus, the results prove that perceived usefulness is a more significant factor for African females than African males living in China for the adoption of e-Health. Although PEU and privacy impact on the intention of e-Health use irrespective of gender differences. Thus, results partially support the role of gender as a moderating variable, and gender have not a significant impact on the decision to use e-Health for the African expats living in China.

Table 5.

Moderating Effect of Gender.

	Female			Male			Comparison
	β	t statistics	p value	β	t statistics	p value	t statistics	p value
INT → ACT	0.78	26.98	.00	0.62	11.95	.00	2.59	.01
PEU → INT	0.14	2.26	.02	0.30	3.15	.002	1.40	.16
PEU → PU	0.62	7.25	.00	0.70	17.52	.00	0.89	.37
PRI → INT	0.12	1.68	.09	0.08	0.94	.35	0.35	.72
PU → INT	0.70	9.90	.00	0.31	3.07	.002	2.98	.003
TRU → INT	−0.02	0.24	.81	0.10	1.07	.28	0.96	.34

Note. INT = intention to use; ACT = actual use of technology; PEU = perceived ease of use; PU = perceived usefulness; PRI = privacy; TRU = trust.

Discussion and Conclusion

The aim of the current research is to investigate factor influencing the African expat’s INT e-Health in China. The focus on the relative influence of different determinants demonstrates how male and female differ in their decision-making process regarding e-Health adoption and use.

The results of the current research are in line with the previous researches on the utilization of TAM in the adoption of e-Health (Askool et al., 2019; Hoque et al., 2016; Pan et al., 2018). African expats in China are prone to use e-Health if the technology appears to be useful as proven from results PU is a significant predictor of the INT e-Health. The findings are in agreement with the previous research of Lim et al. (2011) who proved that PU is a significant factor of the INT e-Health through a mobile phone for the Singaporean women. The findings of the current research prove that PEU is a significant factor in the INT e-Health. PEU is also the predictor of PU and was found to be a strong predictor of it. Findings suggest that PU is essential than PEU for acceptance of technology, including the adoption of e-Health. These findings are contradicting with previous researches (e.g., Hoque et al., 2016; Wu, Li, & Fu, 2011), they indicate that PEU in more important than PU for the adoption of e-Health. The possible reason for this finding is that most of our participants are African students studying in China, and 60% of the respondents have more than 7 years of IT using experience. University students are prone to adopt new technologies (Margaryan, Littlejohn, & Vojt, 2011), so they do not feel e-Health application difficult to adopt. Thus, perceived usefulness of e-Health is more important for them than PEU.

Current research identifies and investigates trust and privacy in addition to the basic TAM model. The findings of the current study found the relationship between privacy and INT e-Health. Our results are in line with previous research (Pan et al., 2018). Privacy has a direct relationship with technology adoption that proved, by numerous researchers (Diamond et al., 2018; Hossain et al., 2019), the impact of privacy on acceptance and usage of medical assistive technologies. The possible explanation of the current finding is that African expats are more concerned about the confidentiality of their health record and treatment. But Chinese doctors did not provide satisfactory privacy during consultations, which raise medical privacy concerns about confidentiality (Lin et al., 2015). Results show no significant relationship between trust and African expat’s adoption of e-Health in China. This result is apparently surprising, given that numerous researchers confirm a direct relationship between trust and adoption of technology. For example, Abbas et al. (2018) and Misra et al. (2019) proved that trust is an important element for the adoption of e-services, such as e-commerce, e-Health, and e-governance. The Same type of relationship had been proved by Zhao et al. (2018). The findings of the current research may be the reflection of the fact that African expats in China are not more concerned with trust and reveal all information to doctors, which is because of certain reasons, such as previous research proved that patients are willing to share information with doctors but not with others, such as employer and even with family members (Sankaranarayanan & Sallach, 2014).

Current research also investigated the moderating effect of gender on the INT and adoption of e-Health by the African expats in China. The results show that gender partially influences the INT e-Health. The findings prove that PU is a more important factor for African females than males in the adoption of e-Health. These findings reflect that African females are more concerned with the perceived usefulness of e-Health. This might be explained by the fact that females have to visit doctors frequently, in case of pregnancy or pregnancy-related problems and e-Health are more helpful to track the records of their pregnancy or pregnancy-related problem digitally, whereas male patients have little concern with the previous visit of a doctor. The results show that gender does not influence the impact of PEU, privacy, and trust on the INT e-Heath.

Implications

This study has meaningful theoretical and practical implications in the stream of e-Health acceptance and adoption among the expats in developing countries, specifically African expats in China. The findings of the current research highlight the need to undertake and intervene in specified measures to enhance internal and international expat’s access to health care in China. Moreover, expat’s specific health care desires and demands need to be met through targeted health care services. TAM is used widely in the researches of technology acceptance of individuals. However, few studies in the literature have tested the validity of TAM constructs in e-Health, and the current study has been limited to the developing countries context. The current study extended and justified the existing TAM model by including trust and privacy as additional variables in the context of African expats in China. Moreover, this study analyzed how gender moderates the relationship between PU and INT e-Health. The results underline the significance of enhancing patient responsiveness and orientation to expat’s needs in health care provision of China.

The empirical results of the current research may provide guidelines in the establishment of an effective plan for the successful implication of e-Health services to expats in China. The finding of the current research proved that PU and PEU are the most influencing factors for African expat’s INT e-Health. The results suggest that user-friendly interfaces are particularly sensitive to gender (Duarte & Pinho, 2019; Orser & Riding, 2018; Venkatesh, Thong, & Xu, 2012) and that the efficacy and usefulness of e-Health technologies are censoriously important to avail the adoption of these technologies at a large scale (Cotten & Gupta, 2004).

Although the findings of the current research show an insignificant relationship between trust and e-Health adoption. As discussed above, this finding may be surprising but may truly reflect the socioeconomic and cultural context of African expats. But, privacy has a significant relationship with acceptance of e-Health. These findings provide guidelines to policymakers and suggest that although patients are willing to share information with doctors; inadequate legislation about privacy may generate issues. Appealing to the pragmatism inherent in an e-Health approach might help to increase attention to the resources for addressing expat health. Strengthening diplomatic ties can enhance action more effectively than appeals solely to humanitarian goodwill (Valentino, 2011). Adoption of e-Health by African expats in China is a potentially effective strategy/notion, and one that is aligned with both China’s economic interests and China’s trend toward assuming a greater role as a donor country and global health leader.

Limitations and Future Work

This study contributes to advancing knowledge about the adoption and acceptance of e-Health systems, specifically in the context of African expats, which may provide a fruitful avenue for future research. The current study was conducted on a sample population selected from three Tier 1 cities of China. Hence, the results may not provide an actual reflection of the attitudes toward the acceptance and adoption of e-Health of all the African expats living in China. Thus, future research with extended sample size, more diverse sampling locations, and multiple sampling techniques is needed.

Our research is exclusively focused on African Expats living in China, and due to funding issues, the study sample mostly are students, who are most of the times medically insured and the scholarship council pays for it. Still, a valuable starting point in future reviews could be the inclusion of more expat population groups like other nationalities or comparison with multiple social groups of multiple countries living in China or other developing countries. Furthermore, the model of the current research can be applied to the acceptance and adoption of other types of e-Health services, such as telemedicine and mHealth (mobile Health), in the context of expats in China or other developing countries.

Footnotes

Acknowledgements

The authors gratefully acknowledge the insightful proofreading from Mr. Ken Margel and valuable suggestions and comments from colleagues (Dr Adeel Luqman, Dr Muhammad Abdullah, Dr Han Dongping, Dr Sohail, Akhtar Wahab, Umar Ajmal, and Tanawar Khan).

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Imran Khan

Fakhar Shahzad

Author Biographies

Imran Khan is lecturer at department of Management Sciences, The Islamia University of Bahawalpur, Bahawalnagar Campus, Pakistan. He attained his PhD in marketing from the School of Management, Harbin Institute of Technology, China. His research interests include the social media marketing, e-Health, information system and Big Data analytics.

Guo Xitong is professor at department of Management Science and Engineering, Harbin Institute of Technology, China. His research interests include e-Health, Big Data and Business analytics.

Zeeshan Ahmad is an assistant professor of marketing at the department of Business Administration, Air University, Multan Campus, Pakistan. He attained his PhD from Northeast Agriculture University, Harbin, China. His research interests include brand management, customer purchase intention and consumer behavior.

Fakhar Shahzad has completed his PhD from Harbin University of Science and Technology, China. His research interests include diffusion of information system, human-computer interaction and social media research.

References

Abbas

R. M.

Carroll

Richardson

(2018, June 4-7). In technology we trust: Extending TAM from a healthcare technology perspective (ICHI). Paper presented at the 2018 IEEE International Conference on Healthcare Informatics, New York, NY.

Aggelidis

V. P.

Chatzoglou

P. D.

(2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78, 115-126.

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

Ajzen

Fishbein

(1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

Allin

Grignon

G. J.

(2010). Subjective unmet need and utilization of health care services in Canada: What are the equity implications? Social Science & Medicine, 70, 465-472.

Appari

Johnson

M. E.

(2010). Information security and privacy in healthcare: Current state of research. International Journal of Internet and Enterprise Management, 6, 279-314.

Appelbaum

P. S.

(2003). Privacy in psychiatric treatment: Threats and responses. American Journal of Psychiatry, 159, 1809-1818.

Askool

Pan

Y.-C.

Jacobs

Tan

(2019). Understanding proximity mobile payment adoption through technology acceptance model and organizational semiotics: An exploratory study. Retrieved from https://repository.uwl.ac.uk/id/eprint/5825/

Azam

M. S.

(2013). Examining the influence of national culture on adoption and use of information and communication technology: A study from Bangladesh’s SME perspective. International Technology Management Review, 3, 116-126.

10.

Bandura

(1991). Social cognitive theory of self-regulation. Organizational Behavior and Human Decision Processes, 50, 248-287.

11.

Bao

Xiong

Kibelloh

(2013). Exploring gender differences on general and specific computer self-efficacy in mobile learning adoption. Journal of Educational Computing Research, 49, 111-132.

12.

Baryashaba

Musimenta

Mugisha

Binamungu

L. P.

(2019. May 1-3). Investigating the adoption of an integrated hospital information system in rural Uganda: A case of Kisiizi Hospital. Paper presented at the International Conference on Social Implications of Computers in Developing Countries, Dar es Salaam, Tanzania

13.

Bass

(1969). A new product growth model for consumer durables. Management Sciences, 15, 215-227.

14.

Bodomo

A. B.

(2012). Africans in China: A sociocultural study and its implications on Africa-China relations. Amherst, NY: Cambria Press.

15.

Briggs

Burford

De Angeli

Lynch

(2002). Trust in online advice. Social Science Computer Review, 20, 321-332.

16.

Busch

(1995). Gender differences in self-efficacy and attitudes toward computers. Journal of Educational Computing Research, 12, 147-158.

17.

Chau

P. Y. K.

J. H.

(2002). Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories. Information & Management, 39, 297-311.

18.

Chellappa

R. K.

(2003). Consumers’ trust in electronic commerce transactions: The role of perceived privacy and perceived security. Retrieved from https://pdfs.semanticscholar.org/7e2f/bad4fa4877ea3fd8d197950e335d59ebeedf.pdf

19.

Chellappa

R. K.

Pavlou

P. A.

(2002). Perceived information security, financial liability and consumer trust in electronic commerce transactions. Logistics Information Management, 15, 358-368.

20.

Chetley

Davies

Trude

McConnell

Ramirez

(2006). Improving health connecting people: The role of ICTs in the health sector of developing countries. Retrieved from http://www.infodev.org/infodev-files/resource/InfodevDocuments_84.pdf

21.

Chhanabhai

Holt

(2007). Consumers are ready to accept the transition to online and electronic records if they can be assured of the security measures. MedGenMed Medscape General Medicine, 9(1), 8.

22.

Cotten

S. R.

Gupta

S. S.

(2004). Characteristics of online and offline health information seekers and factors that discriminate between them. Social Science & Medicine, 59, 1795-1806.

23.

Cruickshank

Packman

Paxman

(2012). Personal health records: Putting patients in control? Retrieved from http://www.2020health.org/2020health/Publications/publications-2012/Public-Health-Records.html

24.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340.

25.

Davis

F. D.

Venkatesh

(2004). Toward preprototype user acceptance testing of new information systems: Implications for software project management. IEEE Transactions on Engineering Management, 51, 31-46.

26.

Demiris

Thompson

Boquet

Chaudhuri

Chung

(2013). Older adults’ acceptance of a community-based telehealth wellness system. Informatics for Health & Social Care, 38, 27-36.

27.

Diamond

Busch

Jilch

Tscheligi

(2018, September 3-6). Using technology acceptance models for product development: Case study of a smart payment card. Paper presented at the Proceedings of the 20th International Conference on Human-Computer Interaction With Mobile Devices and Services Adjunct, Barcelona, Spain.

28.

Duarte

Pinho

J. C.

(2019). A mixed methods UTAUT2-based approach to assess mobile health adoption. Journal of Business Research, 102, 140-150.

29.

Dutta

Peng

M.-H.

Sun

S.-L.

(2018). Modeling the adoption of personal health record (PHR) among individual: The effect of health-care technology self-efficacy and gender concern. Libyan Journal of Medicine, 13(1), 1500349.

30.

Egea

J. M. O.

González

M. V. R.

(2011). Explaining physicians’ acceptance of EHCR systems: An extension of TAM with trust and risk factors. Computers in Human Behavior, 27, 319-332.

31.

Eze

U. C.

Manyeki

J. K.

Har

L. C.

(2011). Factors affecting internet banking adoption among young adults: Evidence from Malaysia. In International Proceedings of Economics Development & Research. Retrieved from https://pdfs.semanticscholar.org/b262/e31eb0646fa87a68cb16d545414659c9cd30.pdf

32.

Farahani

Firouzi

Chang

Badaroglu

Constant

Mankodiya

(2018). Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare. Future Generation Computer Systems, 78, 659-676.

33.

Featherman

M. S.

Pavlou

P. A.

(2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59, 451-474.

34.

Fenn

Raskino

(2008). Mastering the hype cycle: How to choose the right innovation at the right time. Cambridge, MA: Harvard Business Press.

35.

Fishbein

Ajzen

(1977). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

36.

Gefen

Karahanna

Straub

D. W.

(2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27, 51-90.

37.

Goldman

Westin

Pearson

(2006, December 7). Connecting Americans to their health care: Improving access and protecting privacy. Advancing America’s Future. Retrieved from https://www.markle.org/publications/938-connecting-americans-their-health-care-improving-access-and-protecting-privacy

38.

Hair

J. F.

Anderson

R. E.

(2010). Multivariate data analysis with readings. Upper Saddle River, NJ: Prentice Hall.

39.

Hall

B. J.

Chen

Latkin

Ling

Tucker

J. D.

(2014). Africans in south China face social and health barriers. The Lancet, 383, 1291-1292.

40.

Hans

P. K.

Gray

C. S.

Gill

Tiessen

(2018). The provider perspective: Investigating the effect of the Electronic Patient-Reported Outcome (ePRO) mobile application and portal on primary care provider workflow. Primary Health Care Research & Development, 19, 151-164.

41.

Harris

J. M.

Shaw

R. W.

Jr Sommers

W. P.

(1983). The strategic management of technology. Planning Review, 11, 28-35.

42.

Harst

Lantzsch

Scheibe

(2019). Theories predicting end-user acceptance of telemedicine use: Systematic review. Journal of Medical Internet Research, 21(5), e13117.

43.

Henseler

Ringle

C. M.

Sinkovics

R. R.

(2009). The use of partial least squares path modeling in international marketing. Social Science Electronic Publishing, 20(4), 277-319.

44.

Holden

R. J.

Karsh

B.-T.

(2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43, 159-172.

45.

Hoque

M. R.

Bao

Sorwar

(2016). Investigating factors influencing the adoption of e-Health in developing countries: A patient’s perspective. Informatics for Health and Social Care, 4, 1-17.

46.

Hossain

Quaresma

Rahman

(2019). Investigating factors influencing the physicians’ adoption of electronic health record (EHR) in healthcare system of Bangladesh: An empirical study. International Journal of Information Management, 44, 76-87.

47.

P. J.

Chau

P. Y.

Sheng

O. R. L.

Tam

K. Y.

(1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16, 91-112.

48.

Jianlan

(2017). China, Africa enhance cooperation in health. FOCAC. Retrieved from https://www.focac.org/eng/zfgx_4/zzjw/t1457549.htm

49.

Jung

M.-L.

Loria

(2010). Acceptance of Swedish e-health services. Journal of Multidisciplinary Healthcare, 3, 55-63.

50.

Kibelloh

Bao

(2014a). Can online MBA programmes allow professional working mothers to balance work, family, and career progression? A case study in China. The Asia-Pacific Education Researcher, 23, 249-259.

51.

Kibelloh

Bao

(2014b). Perceptions of international female students toward e-learning in resolving high education and family role strain. Journal of Educational Computing Research, 50, 467-487.

52.

Kim

N. E.

Han

S. S.

Yoo

K. H.

Yun

E. K.

(2012). The impact of user’s perceived ability on online health information acceptance. Telemedicine and E-health, 18, 703-708.

53.

King

W. R.

(2006). A meta-analysis of the technology acceptance model. Information & Management, 43, 740-755.

54.

Korgaonkar

P. K.

Wolin

L. D.

(1999). A multivariate analysis of web usage. Journal of Advertising Research, 39, 53-68.

55.

Kujala

Rajalahti

Heponiemi

Hilama

(2018). Health professionals’ expanding eHealth competences for supporting patients’ self-management. Studies in Health Technology and Informatics, 247, 181-185.

56.

Lafky

D. B.

Horan

T. A.

(2011). Personal health records consumer attitudes toward privacy and security of their personal health information. Health Informatics Journal, 17, 63-71.

57.

Lee

A. S.

(1991). Integrating positivist and interpretive approaches to organizational research. Organization Science, 2, 342-365.

58.

(2011). Intelligent healthcare development in China: Situation, problems and countermeasures (Master’s thesis). University of Science and Technology of China, Hefei, China.

59.

Lim

Xue

Yen

C. C.

Chang

Chan

H. C.

Tai

B. C.

. . . Choolani

(2011). A study on Singaporean women’s acceptance of using mobile phones to seek health information. International Journal of Medical Informatics, 80, e189-e202.

60.

Lin

Brown

K. B.

Yang

Wang

Schrock

J. M.

. . . Nehl

E. J.

(2015). Health care experiences and perceived barriers to health care access: A qualitative study among African migrants in Guangzhou, Guangdong province, China. Journal of Immigrant and Minority Health, 17, 1509-1517.

61.

Lohman

(2010, April 15). Australian e-health spending to top $2 billion in 2010. Computerworld. Retrieved from https://www.computerworld.com.au/article/343220/australian_e-health_spending_top_2_billion_2010/

62.

Mair

F. S.

May

O’Donnell

Finch

Sullivan

Murray

(2012). Factors that promote or inhibit the implementation of e-health systems: An explanatory systematic review. Bulletin of the World Health Organization, 90, 357-364.

63.

Margaryan

Littlejohn

Vojt

(2011). Are digital natives a myth or reality? University students’ use of digital technologies. Computers & Education, 56, 429-440.

64.

Misra

S. C.

Bisui

Singh

(2019). A study on the role of trust factor in adopting personalised medicine. Behaviour & Information Technology. Advance online publication. doi:10.1080/0144929X.2019.1614225

65.

Ministry of Education. (2015, September 3). Number of international students in China. Retrieved from http://en.moe.gov.cn/Resources/Statistics/

66.

Moorman

Zaltman

Deshpandé

(1992). Relationships between providers and users of market research: The dynamics of trust within and between organizations. Journal of Marketing Research, 29, l4-29.

67.

Moorman

Zaltman

Deshpandé

(1993). Factors affecting trust in market relationships. Journal of Marketing, 57, 81-101.

68.

Neto

A. P.

Flynn

M. B.

(2018). The internet and health in Brazil: Challenges and trends. New York, NY: Springer.

69.

Ono

Zavodny

(2003). Gender and the internet*. Social Science Quarterly, 84, 111-121.

70.

C. K.

Karsh

B.-T.

Severtson

D. J.

Burke

L. J.

Brown

R. L.

Brennan

P. F.

(2011). Factors affecting home care patients’ acceptance of a web-based interactive self-management technology. Journal of the American Medical Informatics Association, 18, 51-59.

71.

Orruño

Gagnon

M. P.

Asua

Abdeljelil

A. B.

(2011). Evaluation of teledermatology adoption by health-care professionals using a modified Technology Acceptance Model. Journal of Telemedicine and Telecare, 17, 303-307.

72.

Orser

B. J.

Riding

(2018). The influence of gender on the adoption of technology among SMEs. International Journal of Entrepreneurship and Small Business, 33, 514-531.

73.

Ozawa

Sripad

(2013). How do you measure trust in the health system? A systematic review of the literature. Social Science & Medicine, 91, 10-14.

74.

Pan

Y.-C.

Jacobs

Tan

Askool

(2018, July 16-18). Extending technology acceptance model for proximity mobile payment via organisational semiotics. Paper presented at the International Conference on Informatics and Semiotics in Organisations, Reading, UK.

75.

Rogers

E. M.

(2010). Diffusion of innovations. New York, NY: Simon & Schuster.

76.

Saad

Gill

(2000). Application of evolutionary psychology in marketing. Psychology & Marketing, 17, 1005-1034.

77.

Sankaranarayanan

Sallach

R. E.

(2014). Rural patients’ access to mobile phones and willingness to receive mobile phone-based pharmacy and other health technology services: A pilot study. Telemedicine and E-health, 20, 182-185.

78.

Scherer

M. J.

(2002). Assistive technology: Matching device and consumer for successful rehabilitation. Washington, DC: American Psychological Association.

79.

Slyke

C. V.

Comunale

C. L.

Belanger

(2002). Gender differences in perceptions of web-based shopping. Communications of the ACM, 45, 82-86.

80.

Smith

A. D.

Manna

D. R.

(2004). Exploring the trust factor in e-medicine. Online Information Review, 28, 346-355.

81.

Tavares

Oliveira

(2018). New integrated model approach to understand the factors that drive electronic health record portal adoption: Cross-sectional national survey. Journal of Medical Internet Research, 20(11), e11032.

82.

Taylor

Todd

(1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19, 561-570.

83.

Tung

F.-C.

Chang

S.-C.

Chou

C.-M.

(2008). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International Journal of Medical Informatics, 77, 324-335.

84.

Valentino

B. A.

(2011). The true costs of humanitarian intervention: The hard truth about a noble notion. Foreign Affairs, 90, 60-73.

85.

Van Velthoven

M. H.

Cordon

. (2019). Sustainable adoption of digital health innovations: Perspectives from a stakeholder workshop. Journal of Medical Internet Research, 21(3), e11922.

86.

Venkatesh

Morris

M. G.

(2000). Why don’t men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly, 24, 115-139.

87.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478.

88.

Venkatesh

Thong

J. Y.

(2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157-178.

89.

Wang

X. Y.

(2012). The digitalization of Chinese healthcare industry speeds up, half population got health records. China News. Retrieved from http://finance.chinanews.com/jk/2012/02-27/3701936.shtml

90.

World Health Organization. (2003). Eastern Mediterranean region health observatory. Retrieved from http://www.emro.who.int/ehealth/

91.

Wilkowska

Ziefle

(2012). Privacy and data security in E-health: Requirements from the user’s perspective. Health Informatics Journal, 18, 191-201.

92.

Wilson

E. V.

Lankton

N. K.

(2004). Modeling patients’ acceptance of provider-delivered e-health. Journal of the American Medical Informatics Association, 11, 241-248.

93.

Wong

A. T. T.

(2018). A study of consumer acceptance of mobile payment services in Hong Kong. Journal of Economics, Management and Trade, 21, 1-14.

94.

Worldometers. (2019). China population. Retrieved from https://www.worldometers.info/world-population/china-population/

95.

I. L.

J. Y.

C. Y.

(2011). The adoption of mobile healthcare by hospital’s professionals: An integrative perspective. Decision Support Systems, 51, 587-596.

96.

Yaylacicegi

Mitchell

(2012). Differential impacts of four types of EHR implementation on small, medium, and large hospitals. Paper presented at the AMCIS. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.895.8880&rep=rep1&type=pdf

97.

Yoon

S. J.

(2002). The antecedents and consequences of trust in online-purchase decisions. Journal of Interactive Marketing, 16, 47-63.

98.

Zhang

Guo

Lai

K. H.

Guo

(2014). Understanding gender differences in m-health adoption: A modified theory of reasoned action model. Telemedicine and E-health, 20, 39-46.

99.

Zhao

Fang

Jin

(2018). Modeling and quantifying user acceptance of personalized business modes based on TAM, trust and attitude. Sustainability, 10, 356.

100.

Zikmund

W. G.

Babin

B. J.

Carr

J. C.

Griffin

(2013). Business research methods. Nashville, TN: Southwestern Publishing.

101.

Zwaan

Thijs

Wagner

Timmermans

D. R.

(2013). Does inappropriate selectivity in information use relate to diagnostic errors and patient harm? The diagnosis of patients with dyspnea. Social Science & Medicine, 91, 32-38.

	ACT	INT	PEU	PRI	PU	TRU
ACT	.81
INT	.70	.86
PEU	.53	.62	.83
PRI	.51	.49	.49	.90
PU	.65	.73	.67	.49	.86
TRU	.75	.59	.56	.63	.68	.82

	ACT	INT	PEU	PRI	PU	TRU
ACT	.81
INT	.70	.86
PEU	.53	.62	.83
PRI	.51	.49	.49	.90
PU	.65	.73	.67	.49	.86
TRU	.75	.59	.56	.63	.68	.82

	ACT	INT	PEU	PRI	PU	TRU
ACT	.81
INT	.70	.86
PEU	.53	.62	.83
PRI	.51	.49	.49	.90
PU	.65	.73	.67	.49	.86
TRU	.75	.59	.56	.63	.68	.82