Factors influencing behavior intentions to telehealth by Chinese elderly: An extended TAM model

International Journal of Medical Informatics 126 (2019) 118–127

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International Journal of Medical Informatics journal homepage: www.elsevier.com/locate/ijmedinf

Factors influencing behavior intentions to telehealth by Chinese elderly: An extended TAM model

T

Min Zhoua,b,c, Lindu Zhaob, , Nan Kongc, Kathryn S. Campyd, Shujuan Que, , Song Wanga ⁎

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a

College of Business Administration, Hunan University of Commerce, Changsha, China School of Economics and Management, Southeast University, Nanjing, China c Weldon School of Biomedical Engineering, Purdue University, West Lafayette, USA d Center for Public Health Initiatives, University of Pennsylvania, Philadelphia, USA e The Third Xiangya Hospital, Central South University, Changsha, China b

ARTICLE INFO

ABSTRACT

Keywords: Medical Services Satisfaction Acceptance Telehealth Elderly TAM China

Background and purpose: Telehealth bring significant benefits including improved quality of healthcare, efficiency and cost containment, especially for chronic patients and the elderly. China is the second largest country of investment in telemedicine systems, but the acceptance and behavioral intentions of the technology are still low in the elderly. The objective of this study is to explain the micro-mechanism that determines the behavioral intentions to use telemedicine systems from the perspective of elderly patients based on an extended Technology Acceptance Model. Methods: A sample consisting of 436 elderlies selected through multistage cluster sampling from four cities in mainland China. The empirical study was conducted to examine the proposed model by two aspects: measurement model and structural model. Results: The study determined that medical service satisfaction (t = 6.770, β = 0.332), ease of use (t = 5.200, β = 0.179), information quality (t = 12.540, β = 0.639) had a significant impact on the elderly patients’ acceptance to telehealth, and the acceptance had a significant impact (t = 14.356, β = 0.697) on the elderly patients’ behavior intentions of telehealth. The results also show that the variable of acceptance has significant mediating effects among the theoretical model. Conclusions: This study confirms the applicability of the extended Technology Acceptance Model in the behavioral intentions among elderly people in China using telehealth systems. The results indicate that relationship between telehealth systems and physical medical services are mutually reinforcing rather than alternative. The study will help technology developers better understand the behavioral characteristics of the elderly and encourage healthcare providers to better understand the true need of telehealth systems. These findings provide valuable information to telehealth system developers, governments, investors, and hospitals to promote the use of this technology by elderly patients.

1. Introduction Rapidly growing elderly population has caused healthcare resources to become scarce and difficult to access. Telehealth provides medical services remotely through various telecommunication tools, including landline phone, mobile phone, smartphone, and other mobile wireless devices [1]. It is increasingly used as an innovative strategy to alleviate the shortage of medical services because of its significant advantages of low cost and high efficiency. In recently years, significant increase of smartphone users provides a market environment for telemedicine.

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Meanwhile, further improved wireless telecommunication technologies provide necessary technical support. However, what remains unclear is factors that influence consumer intention on accepting the suggested use of telehealth technology, despite of increased smartphone applications in the area of in-house care management. It is evident that telehealth can improve care of chronic conditions and offer timely treatment of acute episodes [1,2]. It mainly targets elderly and disabled people residing at home and in the community [3]. For example, when an elderly person feels uncomfortable at home and in community, she or he can use a smart sensing device to conduct self-examination, and

Corresponding author at: No. 2 Sipailou Road, School of Economics and Management, Southeast University, Nanjing, 210096, China. Corresponding author at: No.138 Tongzipo Road, The Third Xiangya Hospital, Central South University, Changsha, 410013, China. E-mail addresses: [email protected] (L. Zhao), [email protected] (S. Qu).

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https://doi.org/10.1016/j.ijmedinf.2019.04.001 Received 1 June 2018; Received in revised form 10 October 2018; Accepted 2 April 2019 1386-5056/ © 2019 Elsevier B.V. All rights reserved.

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the relevant biomedical data will be transmitted to her or his doctor in a timely manner. After receiving the data, the doctor will provide diagnosis on the condition and offer self-care recommendations via smart health devices. If the condition deserves further attention, the elderly may be recommended to make an outpatient consultation appointment in a timely manner through the telehealth system connecting to the appointment system. In general, with aid of a telehealth system, home and communitybased residents do not have to wait in hospital lengthily for even simple medical services, which can greatly improve patient satisfaction and often lead to improved care quality. Together with artificial intelligence technology, telehealth can improve rationality and patient-centeredness of medical practice for providers. Meanwhile, from a population care perspective, telehealth may be able to help curb ever-increasing medical expenditure with effective control of care access and clinical pathway. The movement of adopting telehealth technology is supported by the governments of many countries. For example, the United States government has taken the telehealth as one of important measure for health care reform with the belief that telehealth will help provide Americans a low-cost, high-quality health care system [4,5]. The British and Japanese governments have considered recommendation of measuring vital signs parameters (VSPs) and conducting remote audio/ video consultations (“virtual visits”) as part of their health care reforms. Providing sufficient medical services in rural and remote communities is a continuing challenge faced by the Chinese government. A unified nationwide telehealth technology standard and communications protocol were built by the Chinese government in recent years [6,7]. It is worth mentioning investors in China have relatively high interest on telehealth R&D and implementation projects. In 2016, the total amount of investment on telehealth was $ 4.9 billion in China, of which the largest project, “PING AN health”, received investment of $400 million [6]. Despite of recent success in telehealth technology and systems solutions, two significant challenges remain: (1) data transmission efficiency and accuracy for telehealth technology and consumer acceptance of adopting the technology and use of telehealth-based systems. In this paper, we focus on the latter challenge. Our specific aim is to identify the factors (e.g., Medical Service Satisfaction, Ease of Use, Information Quality and Acceptance to Telehealth Systems) that influence elderly Chinese consumers’ acceptance and behavioral intentions of adopting telehealth technology and using relevant system solutions in their care management. The existing literature has provided some interesting findings on acceptance and behavioral intentions of using telehealth system solutions in clinical practice. The main factors affecting the behavioral intentions of telemedicine systems among doctors[n1] are attributed to “ease of use” and “perceived usefulness” [8]. The elderly are the major group of having chronic diseases and the potential customers of telehealth applications;but elderly are conservative users who pose a great challenge to adopt telehealth services. Computer anxiety will have a strong negative effect on the behavioral intentions of the elderly to accept telehealth [9,10]. Compared with the elderly, young people are more likely to express positive attitudes toward the telehealth system. For example, young people in Taiwan are more optimistic at both socioeconomic factors and technological factors (“ease of use” and “perceived usefulness”) [11]. The mainstream theoretical research frameworks used in the previous literature are TAM (Technology Acceptance Model) and UTAUT (Unified Acceptance and Use of Technology) [8,12,13]. The above theoretical frameworks are conducted from the perspective of technology acceptance and behavioral intentions. Although the existing literature provides important evidence about acceptance and behavioral intentions to the telehealth system, there is limited discussion on the mutual influence mechanism of various social and economic factors. A telehealth system is not only based on emerging technology,thus there is the issue of technology usability and adoption.Moreover, a telehealth system is affective to improving

healthy living and social functioning in general; thus it is important to understand how socio-economic factors influence the acceptance. It is different from the general application of new technologies and should focus on socio-economic factors. Social trust, institutional trust, and social participation, these socio-economic factors have a significant impact on acceptance, which has greater impacts than technical factors [13]. Recent studies have emphasized that patients respond differently to the telehealth system and are closely related to social and economic factors such as age, gender, social status, and user’s experience. For example, the perception of medical services will significantly affect the patient's expectations to telehealth and the degree of the medical burden will also change the patient's acceptance of telehealth [1,4,6]. Based on the hypotheses of technology acceptance and ignored the social attributes of patients, these are the main limitations of the previous research framework. The theoretical analysis framework of TAM and UTAUT is based on an important assumption: the patients’ behavioral intentions to accept and use telehealth mainly depends on their understanding and acceptance of this new technology. Although these studies have significant value in exploring the influencing factors of patients’ acceptance of the health information technology, this approach ignores the socioeconomic impact of telehealth systems that differ from the general information technology hardware or software. Future studies need to regard the patient as a “Social Being” rather than a “Natural Being”, so her or his behavioral intentions will be obviously influenced by past experiences and feelings. Few studies have analyzed patients' differences in acceptance of telehealth applications and other new technologies [14,15]. The "healthcare or medical" features of the telehealth system have not received enough attention. Can medical service satisfaction affect patient acceptance of the telehealth system? Does the patient's acceptance of the telehealth system affect their behavioral decisions, including the intention to use the telehealth system and the behavioral intentions of using physical healthcare services? What is the relationship between the telehealth system and the physical medical service system, mutual reinforcing or alternative? Motivated by previous studies, the current research aims to reexamine these gaps from a socio-economic perspective. Therefore, the main objectives of this study are two-fold: First, we explore the relationship between patient satisfaction, ease of use, usefulness, and acceptance of telehealth, which based on the extended -TAM model theory; secondly, we tested the mediating effect of telehealth acceptance on the influence of telehealth behavioral intentions and physical medical service behavior intentions. We developed a theoretical structural model to analyze the relationship between patient's medical service satisfaction, ease of use, usefulness and telehealth acceptance, as an intermediary variable, telehealth acceptance will further influence telehealth behavioral intention and physical medical service behavioral intention. A questionnaire survey was conducted and 436 valid data were collated. This study launched in mainland China where has the second-highest investment in telehealth system and the largest elderly population in the world. Research in this area will be valuable to other developing countries. 2. Material and methods 2.1. Research framework In the telehealth environment, ease of use and information quality are the focus of attention of patients. In this study, patient acceptance is influenced by these two factors, thus we designing constructs and measurements such as the technology acceptance model (TAM) [16,17]. Because telehealth is a supplement and replacement for on-site medical care, patients' satisfaction with existing medical services will affect their intentions to adopt telehealth. The proposed theoretical model (Fig. 1) extends TAM in two ways. Firstly, the research framework expands the effect of medical service quality perception (MSS) on the acceptance (ACC) of telehealth systems. When consumer shifts from 119

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Fig. 1. Theoretical model.

environment of the hospital will have a positive impact on the patient's feelings, especially the clean sheets and wards [20]. With regard to the service methods of doctors and nurses, such as notification forms of malignant tumors, the gender of doctors, and the health education of nurses, these will have a positive impact on the psychological perception of patients [22,23]. There are also studies that demonstrate the influence of hospital decoration style on the patient's psychological perception [24]. Professionalism (PRO) of doctors and nurses mainly refers core knowledge and basic skills, as well as interpersonal skills, lifelong learning, and the ability to integrate core knowledge into clinical practice. Other aspects such as clinical reasoning, expert judgment, ambiguity management, time management, learning strategies and teamwork also contribute to multidimensional assessment while maintaining sufficient reliability and effectiveness [25,26]. In addition, medical services provided in an efficient and prescriptive way and advanced medical equipment are also important indicators that can represent professionalism [26]. Safety (SAF) is the basic requirement of the patient for medical treatment. The Patient Safety Culture Survey (HSOPS) is used to assess the hospital's safety culture and can also serve as an important index of patient perception of medical safety. There are many factors that can affect the patient's sense of security, including complete medical facilities, experienced medical teams and nursing teams, and hospital security measures [5,27,28]. For patients, the most direct factor affecting their perception of medical services is waiting time (WAT). Some hospitals reduce the average waiting time by implementing measures such as management reforms, appointment system, staffing schedule optimization, and physical space reorganization. After the implementation of management strategies, the patient's waiting time and total stay time will be significantly reduced, and patient's satisfaction will also be improved. Types of waiting times that mainly affect satisfaction include appointment, diagnosis, nursing, and payment [29,30]. The difference between the actual waiting time and the possible waiting time for being notified by the hospital is also an important factor that affects the patient's satisfaction [31]. Medical Service Satisfaction (MSS) is the patient's perception of the services of the existing healthcare system and it affects the patient's medical experience. For the acceptance of the telehealth system, it will produce an expectation.

one medical service to another, the experience of current service will affect the perception of the new one. Therefore, this study regards the service quality perception as an important factor that affecting the acceptance of telehealth systems, with two other factors: ease of use (EOU) and information quality (IQ). Secondly, the acceptance to telehealth systems will affect the patient's behavioral intentions, including two aspects: telehealth behavioral intention (TBI) and current medical behavioral intentions (PBI). ACC will have a positive impact on patients' use of telehealth (TBI), which is the basic consensus of the existing literature [17–19]. Based on the above theoretical framework, current research hopes to answer three questions: Is patient satisfaction with medical services a determinant of their acceptance of telehealth? Does the patient's acceptance of telehealth determine his or her behavioral intentions? Will the patients’ behavioral intentions of physical medical systems (PBI) be changed because of the acceptance to telehealth systems? The basic logic of the research framework are as follows: patients' satisfaction with medical services (MSS) is measured by five factors, including affordability (AFF), comfortable (COM), professionalism (PRO), safety (SAF), and waiting time (WAT). Then, MSS, ease of use (EOU) and information quality (IQ) jointly determine the acceptance of telehealth systems (ACC). Last, ACC acts as the predictive factor and determines the patient's behavioral intentions of telehealth systems (TBI) and physical offline-medical services (PBI). 2.2. Hypotheses 2.2.1. Medical Service Satisfaction (MSS) In the past 30 years, the standardizations of medical care services have been rapidly implemented. The requirements of healthcare recipients for their own rights make patient satisfaction became an important evaluation factor for the performance of medical services [20,21]. Patients' satisfaction with medical services mainly includes five aspects: affordability (AFF), Comfortable (COM), Professionalism (PRO), safety (SAF), and the waiting time (WAT). Affordability (AFF) of medical services has always been a problem faced by governments and the public. The hot issues including medical expenditure and medical insurance. Doctors' fees, health checkups, and the costs of medicines are the main sources of medical expenses [20]. Without the help of medical insurance, people will face severe medical financial matters. Improving medical insurance coverage has become a key factor to relieve the financial pressure on the total cost of medical. Among the people covered by medical insurance, the proportion of medical insurance reimbursement is also very important, which will affect the patient's own medical expenses [18]. Comfortable (COM) of medical services is patients' psychological feeling and will have long-term effects on their satisfaction. The overall

H1. MSS has a positive impact on the elderly’s acceptance of telehealth systems (ACC). 2.2.2. Ease of Use (EOU) Ease of Use (EOU) is defined as "the difficulty of using the new 120

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system." Past researches show that EOU has a positive impact on user’ s acceptance to new software systems or hardware devices [9,10]. The decision of doctors to accept telehealth also shows that the ease of use is one of the key factors. An easy-to-use telehealth system will be more likely to be accepted by patients. Therefore, the following assumptions are made:

telehealth services are the first choice. Physical offline-medical services behavioral intentions (PBI) mainly examine the behavioral intentions of elderly patients for current medical services. According to the application of Theory of Planned Behavior in marketing, the revised items of PBI include three aspects: Individuals are willing to recommend this hospital to friends; individuals are willing to receive medical services again in this medical institution; individuals are willing to make this hospital the first choice.

H2. EOU has a positive impact on the elderly’s acceptance of telehealth systems (ACC).

2.3. Measurement instruments 2.2.3. Information quality (IQ) Information quality (IQ) is the critical factor in determining the competitiveness of telehealth systems. Accessibility of medical records and patients directly affects the perceived usefulness of telehealth, and the advice from physicians have a significant positive impact on the patients' acceptance of it [8]. After some elderly patients have used the telehealth system for some time, they have shown a more positive attitude towards practicality [9,16].

Variables measuring included: affordability (AFF), Comfortable (COM), Professionalism (PRO), safety (SAF), waiting time for service (WAT), and their second-order construct is Medical Service Satisfaction (MSS); Ease of Use (EOU), Information Quality (IQ), Acceptance to Telehealth Systems (ACC), Telehealth behavioral intentions (TBI), Physical offline-medical services behavioral intentions (PBI). To ensure the validity of all measures, the measurement items of the latent constructs in the model were developed from previous studies. The detailed items of each construct are listed in Table 1. The literature [20] systematically summarizes the patient's satisfaction with medical services (MSS) mainly including five aspects: affordability (AFF), comfortable (COM), professionalism (PRO), safety (SAF) and waiting time for services (WAT). Therefore, the questionnaires of patient's satisfaction were designed a two-layer composite structure, and the MSS includes the above five facets. Affordability medical expenses are one of the core concerns of most patients, including: total cost of medical care, doctors' fees, drug and other equipment charges, cost of medical examination. Because medical insurance can cover a part of the cost, the reimbursement of medical insurance is also the focus of patients' concerns. According to the literature [18,20], the questionnaire on AFF was revised and designed. The patient's perception of medical services is a complex psychological process involving many influencing factors. The literature [22,23] provides a good reference to measure it, including: clean and sanitary, medical services from doctors and nurses, hospital decoration style. In the subsequent actual investigation, the hospital decoration style was removed because it was not a significant influence variable. Regarding the professionalism of doctors and nurses, the literature [25,26] gives a good explanation, mainly including core knowledge and basic skills, as well as interpersonal skills, lifelong learning and the ability to integrate core knowledge into clinical practice. However, it is difficult for patient to perceive the professionalism of medical services. Therefore, medical services and advanced medical equipment provided in an effective and standardized manner are also important indicators that can represent professionalism. The Patient Safety Culture Survey (HSOPS) gives useful tips to investigate patient safety perceptions, including both hardware and software [27,28]. Hardware refers to the hospital's medical equipment and medicines, and the software is medical services from doctors and nurses. For Chinese patients who rarely prevent disease, emergency assistance is an important source of safety perception. The literature [29,30] gives three main factors for patient waiting time: appointment, nurse care and diagnosis. The above three variables are used in the questionnaire and the results showed good reliability. Ease of Use (EOU) is a common construct in the information technology acceptance model and is widely used in the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT) and Theory of Reasoned Action (TRA). The current research uses and revise the questionnaires from literature [9,10]. At first there were four items, one was removed in the reliability test, and finally three items were retained. Like perceived usefulness, information quality (IQ) reflects the benefits of using telehealth. Many publicity announcements and advertisements showcase the advantages of telehealth, such as lower costs, less waiting time and more accessible medical resources. However, for patients, these benefits are not available in the first place

H3. IQ has a positive impact on the elderly’s acceptance of telehealth systems (ACC). 2.2.4. Acceptance to Telehealth Systems (ACC) Five predictors were verified in the previous study on the acceptance of telehealth among elderly users: monitoring health, feeling safe, convenience, privacy protection, and remote assistance [9]. There is a negative correlation between telehealth acceptance and technical anxiety among elderly users. Through self-efficacy sensory and informal support, older users will increase their interest in the telehealth system. In addition, the doctor's advice will also have positive effects. The acceptance to telehealth of elderly users is the leading factor and predictive indicator of their behavioral intentions to telehealth (TBI). In the short term, the elderly patients who accept the telehealth system will always produce actual adopt behaviors. Monitoring health usefulness and feeling safety and convenience are the main factors affecting patient acceptance. The long-term used telehealth systems are considered to be private and secure, and it is always useful in self-care reminding, convenience, connectivity with healthcare providers [9,12]. The association between acceptance (ACC) to telehealth of elderly users and physical offline-medical services behavioral intentions (PBI) were discussed in this study. Few studies have discussed the promotive or competitive influence of patients' acceptance of telehealth system on existing medical service system. There are some researches to discuss the impact of online stores on physical retail stores. Online shopping as a substitute for in-store shopping can make most of the traditional retail industry less attractive. There is a close correlation between the high vacancy rate of retail properties and the rapid growth of E-commerce. However, due to the respective advantages and disadvantages associated with online and offline shopping, physical stores will not completely be replaced by virtual retail shopping [32,33]. So, will the development of telehealth system be like that of E-commerce? H4. ACC has a positive impact on the elderly’s telehealth behavioral intentions (TBI). H5. ACC has a positive impact on the elderly’s physical offline-medical services behavioral intentions (PBI). Telehealth behavioral intentions (TBI) is the tendency of an individual to take a telehealth service. In the decision-making process of behavior choice, individuals express the intensity of this behavior. Therefore, behavioral intention is the necessary process of any behavior, and it is the decisive factor before the behavior appears. Many social and health psychology theories believe that intentions translate into behavior. There are many ways to measure the behavioral intentions of telehealth systems, including the willingness to use telehealth services, the degree of reliance on telehealth services, and whether 121

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Table 1 Measurement items of constructs. Construct Medical Service Satisfaction (MSS)

Affordability (AFF)

Comfortable (COM) Professionalism (PRO) Safety (SAF)

Waiting (WAT) Ease of Use (EOU) Information Quality (IQ) Acceptance (ACC)

Telehealth Behavior Intentions (TBI) Physical Medical Behavior Intentions (PBI)

Variables

Measurement Items

Source

aff1 aff2 aff3 aff4 aff5 com1 com2 com3 pro1 pro2 pro3 saf1 saf2 saf3 saf4 wat1 wat2 wat3 eou1 eou3 eou4 iq1 iq2 iq3 acc1 acc3 acc4 acc5 tbi1 tbi2 tbi3 pbi1 pbi2 pbi3

The total cost of medical care is affordable. Doctors' fees for medical services are reasonable. Drug and other equipment charges are reasonable. The cost of medical examination is reasonable and clear. The proportion of medical insurance reimbursement is reasonable. The hospital is clean and sanitary. The medical service of the doctor is kind and caring. The medical service of the nurses is kind and caring. Doctors have good theoretical knowledge and professional skills. Nurses have good theoretical knowledge and professional skills. Medical services provided by this hospital are very efficient and prescriptive. The hospital facilities and drugs are very homogeneous. The doctor's treatment is scientific and safe. The nurse's service is scientific and safe. The hospital can handle complex emergency cases. The waiting time for appointments is tolerable. The waiting time for nurse care is tolerable. The waiting time for diagnosis is tolerable. It is easy to record my health condition by telehealth. It is easy to use the telehealth service. It is easy to learn how to use a new APP for telehealth. Telehealth can provide useful information about hospital and doctor information. Telehealth can provide useful information about common disease prevention. Telehealth can provide useful information about first aid measures. I think using telehealth will help monitor my health. I think using telehealth can improve the convenience of medical services. I think telehealth is protected for my privacy. I think telehealth is a good idea to provide medical assistance. I will use telehealth services in the future. I will get medical advice by telehealth services. I will consider telehealth as the first choice. I will introduce this hospital's good service to friends. I will perform health checks and medical services in this hospital. I will continue to use this hospital as my first choice in the future.

[17,19]

[21,22] [24,25] [26,27]

[28,29] [9,10] [8] [9]

[11] [11]

Each item is measured on a 7-point Likert scale with 1 = totally disagree to 7 = totally agree.

when using telehealth. The revelation comes from the literature [8], the current study summarizes three items to measure the information quality: useful information about hospitals and doctors, knowledge of disease prevention and first aid measures. The literature [9] detailed five predictive factors regarding the acceptance of telehealth by the elderly, including: monitoring health, safety perception, convenience, privacy protection and medical assistance. The current study adopted the above findings and performed reliability analysis on the survey data. The safety perception variable is removed because it is not significant. Regarding the measurement of behavioral intentions, different research topics have similar items, such as information technology, blog, and organic food [16,17,34]. The current study revises the behavioral intent measurement items from the literature [12] on telehealth systems. The relationship between telehealth and on-site medical services is like e-commerce and offline stores, with both competition and mutual support. The current study uses similar items to measure Telehealth Behavior Intentions (TBI) and Physical Medical Behavior Intentions (PBI).

Health_APP (2 options). The structured questionnaire part includes the problems of the different structures presented in the theoretical model. There are 36 questions and each item are measured on a 7-point Likert scale. The survey was conducted in four cities, Location1-4 represents Nanjing City, Changsha City, Wuhan City, and Hengyang City. The research population consists of elderly urban residents in mainland China. Patient sample selection includes three principles: age 60 years or older, chronic patients, experience with smartphones. Because the current study is for the elderly and the retirement age in China is 60 years old, those who have retired are selected as potential respondents. Patients with critical illnesses are unlikely to be investigated, so the survey respondents were chronically ill patients. These chronic diseases include hypertension, coronary heart disease, diabetes, gout, tremor paralysis, senile degenerative osteoarthrosis, senile chronic bronchitis, hyperlipidemia and other diseases that are not life-threatening. In China's demographics, the proportion of elderly people in cities with chronic diseases is 78.93% [36]. Because of the shortage of medical resources, when elderly urban residents go to hospitals to check their bodies or see a doctor, they often must wait a long time to get services. During this waiting period, they accepted the investigation of this study. All respondents have experience in using smartphones, and most of them have smartphones (China’s smartphone capacity reaches 1.1 billion, which is the world’s number one). Most smartphones have telehealth functions such as pedometers and health monitoring, and most phones have installed health management APPs such as Pingan Health, Pocket pharmacies or Spring Rain Doctor [7,36]. Regarding the optimal sample size, there are different opinions on the results observed in the literature. Statistical analysis of the structural equation model (SEM): the sampling of 200 as fair and 300 as good; exceeding 200 as the basic requirement; the sample size should exceed 10 times the number of items [37–40]. The number of items in

2.4. Data collection First, a structured questionnaire in English was developed by a native English speaker. Then we translated it into a Chinese questionnaire and compared it repeatedly to ensure that the content expressed in both versions was consistent. In this process of translation, some important inspirations for research have been obtained [7,35]. The questionnaire was divided into two parts, one is the demographic information and other is the structured questionnaire. Demographic information includes: Gender (2 options), Age (4 options), Education (4 options), Family Year (4 options), Own_smart_phone (2 options), 122

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the study was 36 and was conducted in four cities. To improve reliability and validity, 500 samples were selected for investigation. The potential respondents were randomly selected from elderly people who came to the hospital for treatment, from February 2016 to March 2017. Random sampling ensures the randomness of the survey sample without any bias. The data was collected using a Chinese questionnaire. All investigators were trained on the same standards and cooperated with local hospitals. The investigators personally distributed the questionnaire. Each questionnaire received the explicit consent of the respondents and signed the name. During the questionnaire, the respondents have the right to withdraw at any time. This study has ethical approval from the Ethics Committee of Central South University, China.

Table 3 Measurements and confirmatory factor analysis. Constructs

Items

Regression Weights

Loading

SMC

C.R.

AVE

Cronbach’s alpha

AFF

aff1 aff2 aff3 aff4 aff5 com1 com2 com3 pro1 pro2 pro3 saf1 saf2 saf3 saf4 wat1 wat2 wat3 eou1 eou3 eou4 iq1 iq2 iq3 acc1 acc3 acc4 acc5 tbi1 tbi2 tbi3 pbi1 pbi2 pbi3

1 0.99 0.98 0.905 0.971 1 1.337 1.091 1 0.977 0.866 1 0.831 0.895 1.103 1 0.737 0.743 1 1.054 1.015 1 1.004 1.100 1 1.037 1.06 1.129 1 1.035 0.986 1 0.912 0.918

0.835 0.816 0.788 0.775 0.765 0.679 0.914 0.662 0.862 0.867 0.761 0.915 0.730 0.767 0.819 0.898 0.581 0.599 0.736 0.817 0.712 0.836 0.828 0.913 0.805 0.860 0.848 0.966 0.865 0.896 0.879 0.791 0.748 0.766

0.697 0.666 0.621 0.601 0.585 0.461 0.835 0.438 0.743 0.752 0.579 0.837 0.533 0.588 0.671 0.806 0.338 0.359 0.542 0.667 0.507 0.699 0.686 0.834 0.648 0.740 0.719 0.933 0.748 0.803 0.773 0.626 0.560 0.587

0.896 　 　 　 　 0.801 　 　 0.870 　 　 0.884 　 　 　 0.743 　 　 0.800 　 　 0.895 　 　 0.927 　 　 　 0.912 　 　 0.812 　 　

0.634 　 　 　 　 0.578 　 　 0.691 　 　 0.657 　 　 　 0.501 　 　 0.572 　 　 0.740 　 　 0.760 　 　 　 0.775 　 　 0.591 　 　

0.896

COM PRO SAF

2.5. Data analysis First, Cronbach’s alpha was used to check the reliability and the verification standard is alpha > 0.7. Then, confirmatory factor analysis (CFA) was used to discuss the structural validity and internal consistency of each construct; the average variance extracted values(AVE) was used to measure the correlation between different structures [38]. The structural equation modeling(SEM) was used to analyze the fitness of the whole model and the factor loadings of different paths [38,39]. Mediating effects and regulatory effects are also used to analyze the relationship between latent variables.

WAT

3. Results

TBI

3.1. Demographic characteristics

PBI

EOU IQ ACC

The researchers distributed 500 questionnaires, of which 436 were valid for further analysis, Table 2 shows demographic results. In this study, 282 respondents were male (64.7%); about half of the respondents are between the ages of 60–69, and only 17.2% of the respondents are older than 76, the average expected age of the Chinese. Most respondents were less educated, and only 20.6% of respondents had received college or higher education. Most respondent’s annual household income less than 150,000 yuan (81.7%), and 44.3% less than 50,000 yuan, that is, their family's daily income is less than 20 dollars. Most of the respondents had smartphones (79.8%) and 67.4% of respondents had installed healthy Apps in their smartphones. It should be noted that during the investigation, some elderly people did not feel that the investigation was meaningless because they did not have a smartphone, and they stopped the survey.

0.788 0.869 0.879

0.720 0.797 0.894 0.924

0.912 0.812

3.2. Measurement model The measurement model was tested by Cronbach's alpha and confirmatory factor analysis. The internal reliability is verified by the Cronbach's alpha and > 0.7 is an acceptable internal consistency indicator. Convergence validity was tested by CFA, the major indicator is the average variance extracted values (AVE), > 0.5 is an acceptable convergence validity indicator. The loadings, SMC, Composition Reliability, AVE, and Cronbach's alpha are listed in Table 3, the calculated Cronbach's α values range from 0.720 to 0.924 and the composite reliability values range from 0.743 to 0.927, which supports

Table 2 Demographics of respondents (N = 436).

Gender Age

Education Income

Smart Phone Health APP

male female 60-64 years old 65-69 years old 70-75 years old over 76 years old High school education and lower Bachelor degree Master's degree or others below $10,000/year $10,000-$20,000/year $20,000-$30,000/year More than $30,000/year no yes no yes

Frequency

Percent

Cumulative Percent

Mean

SD

282 154 25 190 146 75 382 43 11 193 163 57 23 88 348 142 294

64.7 35.3 5.7 43.6 33.5 17.2 87.6 9.9 2.5 44.3 37.4 13.1 5.3 20.2 79.8 32.6 67.4

64.7 100 5.7 49.3 82.8 100 87.6 97.5 100 44.3 81.7 94.7 100 20.2 100 32.6 100

1.35

0.479

2.62

0.834

1.15

0.422

1.79

0.862

0.8

0.402

0.67

0.469

123

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Table 4 Correlation matrix and square root of the AVE.

AFF COM PRO SAF WAT EOU IQ ACC TBI PBI

AFF

COM

PRO

SAF

WAT

EOU

IQ

ACC

TBI

PBI

0.947 0.543 0.512 0.442 0.322 0.233 0.295 0.487 0.310 0.120

0.895 0.631 0.516 0.311 0.265 0.358 0.490 0.257 0.226

0.933 0.427 0.258 0.275 0.344 0.490 0.204 0.134

0.940 0.281 0.179 0.282 0.412 0.237 0.163

0.862 0.104 0.195 0.287 0.112 0.120

0.894 0.114 0.372 0.198 0.008

0.946 0.825 0.651 0.102

0.963 0.689 0.077

0.955 0.005

0.901

effects is not sufficient [43,46]. In all cases, the best balance between Type I error and statistical power is the test of the joint significance of the two effects, including the effect of intervention variables. This paper uses the Bootstrap method to test the mediation effects and distinguish between complete mediation and partial mediation [41,47]. Table 6 shows that ACC has positive partial mediation on the path of TBI < — MSS, but the direct effect is negative; ACC has complete mediation on the path of TBI < — EOU and partial mediation on the path of TBI < — IQ. There are three paths include PBI < — MSS, PBI < — EOU and PBI < — IQ, their total effect is not significant, which is consistent with the hypothesis test results.

internal reliability. It can also be seen from Table 3 that the estimated loading range is from 0.581 to 0.966 and the AVE range from 0.501 to 0.775, which are greater than the recommended level. Therefore, the data in this study satisfies the conditions of convergence validity. The cross-validation of latent variables needs to be compared with AVE square root and correlation coefficient. The square root of the AVE for any constructs must be greater than its correlation coefficient with other latent variables [38,41,42]. It can be seen from Table 4 that the Correlation of each construct is all below the square root of the AVE, so the discriminant validity of the data in this study is confirmed. To test the total fit of the theoretical model, the following indicators were used: CHI / DF, NFI, IFI, RFI, P, TLI, CFI, GFI, AGFI, RMSEA, and the maximum likelihood method adopted in the structural equation model to test hypotheses [43–45]. The results show as following: CHI/ DF = 1.343, which is lower than the recommended value 3; NFI = 0.927, IFI = 0.980, RFI = 0.920, TLI = 0.978, CFI = 0.980, GFI = 0.919, AGFI = 0.906, and they are higher than the recommended value 0.9 and RMSEA = 0.028 less than 0.08.

4. Discussion 4.1. Theoretical implications This study was set out intending to determine the factors affecting elderly patients' acceptance of telehealth systems and their behavioral intentions, and an extended-TAM model (Technology Acceptance Model) was applied. The empirical findings provide new insights for building extended-TAM models, such as the impact of Medical Service Satisfaction (MSS), Ease of Use (EOU), and Information Quality (IQ)on the acceptance (ACC) of telehealth systems, and ACC has a positive impact on the behavioral intentions of telehealth systems. Most of our findings are consistent with previous findings regarding the application and patients' behavior of telehealth systems. Expected performance, expected effects, promotion conditions, and perceived security, these factors have a significant influence on the behavioral intentions of adopting Telehealth Services [9]. Ease of use, pleasure, as well as knowledge sharing, is positively related to users' attitudes, furthermore, the social factors and attitudes significantly influence the users' intention of blogs [48]. It is the critical factors to influence user's acceptance that the availability of telehealth solutions and real-world applicability. [49]. Performance expectations, effort expectancy, social impact, technical anxiety, and resistance to change have a significant impact on users' behavioral intentions to adopt mobile health services, while there is no significant relationship between promotional conditions and users’ intentions to use mobile health services [50]. Effort expectations have a significant effect on the behavioral intentions of older people to participate in telehealth, and gender has no regulatory effects on these variables [51]. The results provide some meaningful theoretical

3.3. Hypothesis testing To determine the relationship between constructs, Bootstrap confidence based on the bias-corrected percentile method was used for load analysis in the study model. Hypothetical test results are listed in Table 5. The results show that the paths include ACC < —MSS (t = 6.770, β = 0.332), ACC < —EOU (t = 5.200, β = 0.179), ACC < —IQ (t = 12.540, β = 0.639) and TBI < —ACC (t = 14.356, β = 0.697) are significant, so H1, H2, H3, H4, H7b, H7d were supported. Although COM < — TBI (t=-2.363, β=-0.161), PRO < — TBI (t=-2.944, β=-0.200) has a significant correlation, but it is negatively correlated, so H6b and H6c are not supported in this study. However, the path of PBI < —ACC (t = 1.418, β = 0.077) is not significant, so H5 was not supported in this study. 3.4. Mediation effects The mediators act as a bridge to pass the effects of latent variables to dependent variables. In this study, the impact of MSS, EOU, IQ on TBI and PBI has an intermediary impact by ACC as mediator. Many studies have conducted mediation tests using the recommendations of Baron & Kenny (1986), but the validity of “Sobel-tests” to investigate mediating Table 5 Hypothesized relationships. Hypotheses

Paths

H1 H2 H3 H4 H5

ACC ACC ACC TBI PBI

<— <— <— <— <—

MSS EOU IQ ACC ACC

t

β

Lower

Upper

P

Comments

6.353 5.680 12.540 14.251 1.590

0.277 0.198 0.685 0.693 0.087

0.180 0.122 0.617 0.631 −0.009

0.362 0.279 0.750 0.750 0.194

0.001 0.001 0.001 0.001 0.091

Supported Supported Supported Supported Not Supported

Note: 2,000 bootstrap samples. 124

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Table 6 Mediation effects. Path

TBI < — MSS PBI < — MSS TBI < — EOU PBI < — EOU TBI < — IQ PBI < — IQ

Effects

Total Effects Direct Effects Indirect Effects Total Effects Direct Effects Indirect Effects Total Effects Direct Effects Indirect Effects Total Effects Direct Effects Indirect Effects Total Effects Direct Effects Indirect Effects Total Effects Direct Effects Indirect Effects

Estimate

0.602 −0.316 0.919 0.250 0.339 −0.088 0.234 −0.073 0.306 0.006 −0.017 0.023 0.861 0.350 0.511 0.084 0.099 −0.015

Product of Coefficients

Bootstrap

Comments

Bias-Corrected 95% CI

Percentile 95% CI

S.E.

Z

lower

upper

lower

upper

0.099 0.121 0.114 0.075 0.103 0.057 0.065 0.055 0.046 0.042 0.046 0.017 0.061 0.110 0.092 0.045 0.105 0.087

6.081 −2.612 8.061 3.333 3.291 −1.544 3.600 −1.327 6.652 0.143 −0.370 1.353 14.115 3.182 5.554 1.867 0.943 −0.172

0.408 −0.588 0.717 0.111 0.142 −0.197 0.124 −0.164 0.238 −0.066 −0.093 −0.001 0.752 0.127 0.344 −0.007 −0.114 −0.183

0.800 −0.087 1.158 0.417 0.552 0.024 0.341 0.016 0.393 0.073 0.060 0.053 0.996 0.560 0.699 0.172 0.291 0.158

0.418 −0.564 0.723 0.109 0.153 −0.205 0.104 −0.184 0.221 −0.079 −0.110 −0.007 0.746 0.126 0.347 −0.004 −0.101 −0.192

0.811 −0.084 1.163 0.415 0.557 0.016 0.355 0.012 0.401 0.091 0.076 0.057 0.987 0.559 0.705 0.174 0.307 0.147

Partial Mediation Not Significant Complete Mediation Not Significant Partial Mediation Not Significant

Note: 2000 bootstrap samples.

study supports the second view through analysis of mediation effects. That is, users' satisfaction with current medical services will increase their acceptance of telehealth and thus have a positive impact on behavioral intentions.

contributions to the literature. This study extends the TAM model and combines the UTAUT model to develop a psychosocial model to determine the behavioral intentions of elderly people using telehealth services in developing countries. In addition to the above technical acceptance factors, socio-economic factors and medical service perception factors are integrated into the current research framework, which makes this theoretical model more comprehensive and balanced. Although acceptance (ACC) has a positive impact on the patients’ behavioral intentions of telehealth systems, there is no significant correlation between ACC and patients’ behavioral intentions to physical offline-medical services (PBI). This shows that the acceptance of telehealth systems by elderly people will not affect their behavioral intentions for existing medical services, in other words, telehealth systems will not be a substitute for physical medical systems. The analysis results of ACC mediation also prove that medical service satisfaction (MSS), ease of use (EOU), and information quality (IQ) have no direct or indirect influence on PBI. As the results of ACC mediating effects between MSS, EOU, IQ, and patients’ behavioral intentions to telehealth (TBI): the direct effect of MSS on TBI is negative, the indirect effect is positive, while the total effect is positive. It indicates that ACC plays an important bridge role in this process. MSS will have a negative impact on TBI if there is no influence from ACC, on the contrary, MSS will have a positive impact on TBI by the mediating effect of ACC. The direct effect of EOU on TBI is not significant, and the overall effect is positive, indicating that ACC has complete mediation, and EOU can only affect the behavioral intention of elderly patients by changing their acceptance of telehealth system. The direct effect, indirect effect and the total effect of ACC on the path between IQ and TBI all are positive, indicating that the ACC acts as a partial mediation to enhance the positive influence. These conclusions confirm that the intervention measures to enhance user acceptance has positive short-term behavioral results, further demonstrating that ease of use (EOU) must be effective for patients’ behavioral intentions to telehealth (TBI) by improving users’ acceptance, and information quality (IQ) impacts on TBI enhanced by acceptance [9,10]. There are two different conjectures about the relationship between medical service satisfaction and telehealth acceptance: patients will increase their intention to use telehealth because they are not satisfied with current medical services, the other opinion is patients are willing to try telehealth because they are satisfied with and trust the current medical service [5,13,15,19,52]. This

4.2. Managerial implications Most of the previous studies were mainly conducted in developed countries or in countries with relatively small populations, while the present research focuses on mainland China - a developing country with the largest population. The elderly population in this area reached 230 million in 2017 and is expected to reach 248 million by 2020. China is the largest market of elderly people in healthcare and telehealth application in the world. In 2017, China’s investment in the telehealth system was the second largest in the world. However, the number of elderly people using telehealth was not very optimistic. The results of this study have practical implications for improving the use of telehealth systems for elderly people in developing countries. A better understanding of the elderly’s behavioral intentions for the telehealth system will help to promote the willingness of the elderly to accept and adopt it [11,50]. For technology developers, they need to inspect the elderly's intentions and demands to telehealth systems from social and psychological views, not just from the perspective of technology acceptance such as useful features and ease of use. For hospital administrators and medical service providers, they do not need to fear the challenges from telehealth systems. On the contrary, they should adapt to this change and actively participate in the telehealth system, because such action would further improve the patient's satisfaction with the existing medical services. On the other hand, hospitals should improve medical services quality to promote the behavioral intention of elderly patients to use the telehealth system. The government and investors should take measures to improve the elderly's acceptance of telehealth systems, such as user-friendly software design, practical application functions, and easy to use it. 4.3. Limitations and further research This study was conducted to measure the behavioral intentions of elderly people to telehealth systems in mainland China. Although socioeconomic and technical acceptance factors were discussed, the actual use behaviors were not investigated. We analyzed the relationship 125

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among behavioral intentions of the telehealth system of elderly patients and medical services satisfaction, and further research should discuss the impact mechanism between these factors and use behaviors. On the other hand, the data used in this study were collected mainly in big cities (Nanjing, Wuhan, Changsha, and Hengyang) of central China. From the perspective of data integrity, it should also include elderly people in small towns and rural areas, then the cross-regional comparative analysis can be conducted. It is foreseeable that researchers are likely to observe more valuable conclusions based on survey data from diverse regions. Although previous studies have shown that gender has no significant effect on the behavioral intention of the telehealth system, this study did not analyze the interference effects of other control scalars, such as age and income [51]. Therefore, further research should analyze the possible regulatory effects of these control variables.

Funding This work was supported by NSFC (National Natural Science Foundation of China) [grant number 71601043, 71671039, 71671040] and 2017 Youth Innovation Driven Project in Hunan University of Commerce [grant number 17QD06]. Summary Points What was already known on the topic:

• TAM is a widely used model for evaluating consumer acceptance of new technologies; • The acceptance of telehealth is affected by ease of use and •

5. Conclusion Based on an extended TAM model, this study attempts to find the key factors that influence the behavioral intentions of elderly for telehealth systems. The results indicate that medical service satisfaction (including affordability, comfortable, professionalism, safety, and waiting time), ease of use, and information quality are decisive variables that influencing elderly’s acceptance, and acceptance will have a positive impact on the behavioral intentions while no significant impact on physical medical services behavioral intentions. The results indicate that relationship between telehealth systems and physical medical services are mutually reinforcing rather than alternative. The study will help technology developers better understand the behavioral characteristics of the elderly, and encourage hospitals and healthcare providers better understand the significance of telehealth systems. These findings provide valuable information to telehealth system designers, governments, investors and hospital administrators to promote the use of this technology by the elderly.

usefulness, but it is not clear whether it will be affected by the quality of existing medical services. The Telehealth system has positive implications for improving the health status of the elderly, and the elderly are the main potential consumers of the system. What this study added to our knowledge:

• The study confirms that the extended TAM model can effec• • •

tively analyze the intentions of telehealth services for the elderly in China. The relationship between the telehealth system and existing medical services is mutually reinforcing rather than alternative. The medical service quality has a positive impact on the acceptance of telehealth by the elderly. Acceptance has a positive impact on telehealth intentions while no significant effect on behavioral intentions of existing health services by the elderly.

Conflict of interest

References

We wish to draw the attention of the Editor to the following facts which may be considered as potential conflicts of interest and to significant financial contributions to this work. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from zhouminlaoshi@ 163.com and [email protected].

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