Patients Behavioral Intentions toward Using WSN based Smart Home Healthcare Systems: An Empirical Investigation

Transcription

1 th Hawaii International Conference on System Sciences Patients Behavioral Intentions toward Using WSN based Smart Home Healthcare Systems: An Empirical Investigation Ahmad Alaiad Lina Zhou Department of Information Systems, University of Maryland-Baltimore County Baltimore, MD Abstract Advances in Wireless Sensor Network (WSN) have opened up new opportunities for healthcare systems. WSN based smart home healthcare systems (WSN- SHHS) represent innovations in the area of sensor technology that have promised to improve healthcare quality and to stem rising healthcare costs by facilitating effective patient-medical professional collaboration and smart information sharing, and increasing patients health observability and remote monitoring. Most previous research on WSN-SHHS has focused on algorithm development and technical improvement. However, limited research has explored the factors influencing patients adoption of WSN- SHHS, which are of equal importance for successful implementation of WSN-SHHS. To fill the knowledge gap, we contextualized UTAUT to the domain of WSN- SHHS. The model was tested using survey questionnaire. The empirical results confirm that performance expectancy, social influence, life quality expectancy, and cost expectancy have direct effects and effort expectancy has indirect effects on patients behavioral intention to use WSN-SHHS. Several practical and theoretical implications of the research findings are discussed. 1. Introduction Effective health care requires collaboration between patients and physicians and other healthcare professionals [1]. This is particularly the case for the home healthcare setting, where patients family members are also likely to be involved. The effectiveness of the collaboration in turn depends on the communication and information sharing between patients and medical professionals [2]. Poor doctorand nurse-to-patient collaboration efficiency and inability to constantly monitor a patient s health at home are of the main factors that contribute to the high heathcare costs. They also play a role in health care related preventable deaths, which account for approximately 44,000 to 98,000 deaths per year in the U.S., as estimated by the Institute of Medicine [3]. To improve and enhance our lives as well as reduce costs, technology has presented various opportunities. Advances in Wireless Sensor Network (WSN) have opened up new opportunities for healthcare systems. WSN based smart home healthcare systems (WSN- SHHS) represent innovations in the area of sensor technology, which promises to add a truly ambient intelligent component to our daily lives and allows for the integration of tiny, low-power, and smart medical sensor devices into existing electronic and infrastructure already found in patients home [3]. These systems offer a potential solution to the inefficiencies that plague the health care industry and provide an ambient awareness of the home s occupants through an ecosystem of ubiquitous connectivity, disappearing devices, highly-available services, and multi-modal sensing. One of the expected benefits of WSN-SHHS is to help stem rising health care costs by increasing health observability and doctor-to patient efficiency [4]. More importantly, constant monitoring will increase early detection of adverse conditions and diseases for risky patients, potentially saving more lives [5]. Despite the undisputed value of WSN-SHHS for efficient and time-critical medical monitoring, there are some cautionary notes from the human perspective. Despite a plethora of studies on algorithm development and technical improvement for WSN-SHHS [3, 4, 5], there is a lack of exploration of the factors influencing the adoption of WSN-SHHS, which are of equal importance for the technology success. The lack of understanding is one of the main reasons leading to the failure of most previous IT development [6]. For any new system, it is important to identify the factors of adoption as systems perceived negatively by the users are more likely to fail. Since WSN-SHHS are at an early stage of diffusion, it is critical that we understand the factors that influence the adoption of /15 $ IEEE DOI /HICSS

2 WSN-SHHS. There is a stream of technology adoption research in the information systems field [7-10]. However, WSN-SHHS have not been studied from the technology adoption perspective to date. This research aims to fill the knowledge gap by contextualizing UTAUT to the smart home healthcare domain through answering the following research question: What are the main factors influencing patients behavioral intention to use WSN-SHHS? This research makes several contributions to the literature. First, it enriches the technology adoption literature by extending related theories to the domain of WSN-SHHS; second, it extends the technology adoption models by introducing two new constructs such as life quality expectancy and cost expectancy; third, it enhances the theoretical foundation of smart home healthcare research by applying technology adoption models; fourth, it enables home healthcare service providers to better understand patients needs and expectations of WSN-SHHS. The remainder of this paper is organized as follows: the subsequent section reviews the related literature and theories. The third section introduces the research model and hypotheses. Section four describes the research methodology. The fifth section reports the analysis of the results, followed by discussion and conclusion in section six and seven, respectively. 2. Background and theoretical foundation 2.1. WSN-SHHS: roles and tasks Wireless sensor networks (WSN), pervasive computing, and artificial intelligence in combination have built the interdisciplinary concept of ambient intelligence to overcome the challenges we face in everyday life [11]. WSN technologies have the potential to change the way of living in many applications such as entertainment, travel, retail, industry, medicine, and healthcare. Wirelessly networked sensors enable dense spatio temporal sampling of physical, physiological, psychological, cognitive, and behavioral processes in spaces ranging from personal to buildings to even larger scale ones. Advances in WSN have opened up new opportunities for healthcare systems. One of the future trends is the integration of the abundance of existing specialized medical technology with pervasive WSN [3]. They co-exist with the installed infrastructure, augmenting data collection and real-time response. There is incremental integration of wireless sensor networks and patient electronics. WSN based smart home healthcare systems (WSN-SHHS), which is the focus of this research, is one of such systems. They possess the essential elements of future medical applications and could be primarily used to support people in maintaining their independency and mobility at home in everyday life in spite of their restrictions and impairments. WSN-SHHS extend healthcare from the traditional clinic or hospital setting to the patient's home, enabling telecare without the prohibitive costs of retrofitting existing dwellings [12]. Currently, patients visit doctors at regular intervals, self-reporting experienced symptoms, problems, and conditions. Doctors conduct various tests to arrive at a diagnosis and then must monitor patient progress throughout treatment. In WSN-SHHS, the WSN remotely collects data about patient health condition according to a physician's specifications, removing some of the cognitive burden from the patient and providing a continuous record to assist diagnosis. In recent years, WSN-SHHS have started helping in remotely monitoring the patients at home in several ways [4, 5, 11, 12], enabling collaboration in healthcare between patients and healthcare professionals, and between patients home and clinical or hospital settings. Table 1 summarizes the main monitoring tasks of WSN-SHHS at patient s home. Table 1. Main monitoring tasks of WSN-SHHS Monitoring infant health status and notifying parent in emergency such as sleeping on stomach that leads to Sudden Infant Death Syndrome (SIDS) Monitoring elder adult s vital signs at home such as pulse oximetry, and respiration rate Monitoring contextual and environmental information such as how many times patient visits toilet, lighting, air pressure and temperature Monitoring changes in body functions such as range of motion, pain, fatigue, headache, irritability, etc Empowering the hearing impaired with the perception ability of critical audible information in their environment (e.g. doorbell, smoke alarm, crying child) Helping blind people to improve their vision such as finding away Monitoring sleep apnea This research focuses on the monitoring task of the WSN-SHHS, more specifically, in the home settings. Two significant problems are recently emerging from these settings that have bad consequences on patients live: falling and inaccessible activities of daily living (ADL) [16]. Falling can be an unpredictably dangerous event. Falling is the leading cause of injury-induced death and the main cause of admission and extended period of stay in a hospital. ADL involves various routine tasks such as sleeping, bathing, dressing, and transportation. For instance, a continuous monitoring of the patients while sleeping is very important for 825

3 patients live especially for those who had some kind of diseases such as heart attack. Doctors nationwide agree that daily and continuous monitoring of those two problems at home is one of the best ways to detect and prevent more serious health issues [4]. To this end, WSN-SHHS plays a significant role. A variety of WSN-SHHS have been recently developed to perform different monitoring tasks for the patients at home aiming at addressing these two significant problems such as WSN based fall detection system and wearable smart shirt [4]. Most of the current research on the WSN-SHHS has focused on system development and prototyping but neglected the role of user adoption. The latter is a critical step in realizing the potential benefits of WSN- SHHS Technology acceptance model Technology acceptance is defined as an individual s psychological state with regard to his or her voluntary or intended use of a particular technology [15]. Technology acceptance research studies how to promote technology use by exploring the factors that hinder or facilitate the acceptance and use of technologies. In the information systems literature, several theories and models have been proposed to explain user acceptance or use of a new technology. Table 2 summarizes the related theories. Each of these models includes usage intention or actual usage as the dependent variable. However, none of these theories or models has been used to explain WSN-SHHS adoption yet. Table 2. A summary of technology acceptance theories Theory Focus Constructs Theory of Reasoned Action TRA [14]. Technology Acceptance Model TAM [15] Motivational Model MM [16] TRA that is drawn from social psychology. TRA is one of the most fundamental and influential theories of human behavior TAM is more tailored to IS research contexts and is primarily designed to predict information technology acceptance and usage. Within information system domain, David et al (1992) applied motivational theory to understand new technology adaptation and use. attitude toward behavior and subjective norm perceived usefulness, perceived ease of use and subjective norm. extrinsic motivation and intrinsic motivation Theory of Planned Behavior TPB [17] A Combined TAM and TPB (C- TAM-TPB) [18] Model of PC utilization [19] The Innovation Diffusion Theory IDT [20] The Social Cognitive Theory SCT [21] Unified theory of acceptance and use of technology (UTAUT) [22] TPB is extending TRA by adding the construct of perceived behavioral control. C-TAM-TPB is combined the predictors of TPB with perceived usefulness from TAM to provide a hybrid model. It presents a competing perspective to that proposed by TRA and TPB. The nature of the model makes it particularly suited to predict individual acceptance and use of a range of information technologies IDT that is grounded in sociology. IDT has been used since 1960s to study a variety of innovations ranging from agricultural tools to organizational innovation. SCT is one of the most powerful theories of human behavior. Compeau and Higgins (1995) applied and extended SCT to the context of computer utilization. UTAUT integrates elements across the above models attitude toward behavior, subjective norm and perceived behavioral control. attitude toward behavior, subjective norm, perceived behavioral control and perceived usefulness. job-fit, complexity, long-term consequence, affect toward use, social factor, and facilitating conditions. relative advantage, ease of use, image, visibility, compatibility, results demonstrability and voluntariness of use outcome expectationsperformance, outcome expectationspersonal, selfefficacy, affect, and anxiety. Performance expectancy, effort expectancy, social influence and facilitating conditions The most recent model emerging from this long line of study is known as the Unified Theory of Acceptance and Use of Technology (UTAUT) [22]. The UTAUT integrates the above eight user acceptance models, which has been found to explain roughly 70% of the variance in user intention to use information systems in at least six organizations. 3. Research model and hypotheses 826

4 In the healthcare industry, WSN-SHHS are designed as an agent through which information, interactions and activities are exchanged between patients and medical professionals [12]. Accordingly, WSN-SHHS are fundamentally collaborative technology centric application. UTAUT was selected amongst other theories as the theoretical foundation for the current research because it is the most comprehensive theory in technology acceptance. The model suggests four key constructs to explain usage intention and behavior: 1) performance expectancy, 2) effort expectancy, 3) social influence, and 4) facilitating conditions [22], and their impacts are moderated by gender, age, experience, and voluntariness of use. Moreover, the validity, reliability and accuracy of the model have been demonstrated in various contexts [8, 9]. Therefore, this research adapts the UTAUT model to examine patients behavioral intentions towards using WSN-SHHS. The research model is shown in Figure 1. We excluded facilitating conditions from the research model because the construct was used to explain the usage behavior instead of intention in the original UTAUT, which is beyond the scope of the current research. Performance expectancy Performance expectancy can be defined as the extent to which a patient believes that using WSN- SHHS will help him/her improve personal performance. The following five constructs, taken from the first eight models (see Table 2), capture the concept of performance expectancy: perceived usefulness (TAM/TAM2 and C-TAM-TPB), extrinsic motivation (MM), job-fit (MPCU), relative advantage (IDT), and outcome expectations (SCT) [22]. There is extensive evidence showing that performance expectancy is the strongest predictor of intention to use IT [9, 22]. Adapted to the domain of WSN-SHHS, performance expectancy reflects the utility derived from WSN- SHHS on behalf of the patients and indicates that using the systems is expected to help them get better sooner, improve overall productivity and outcomes, perform treatments quickly and flexibly, and access healthcare services effectively. Therefore, we propose the following hypothesis: H1a: Performance expectancy has a positive effect on patients behavioral intention to use WSN-SHHS. Quality of life can be explained in terms of perceived benefits and change in the lifestyles [24]. We predict that bringing all the above aspects of performance expectancy together will increase the benefits to the patients, enhance the overall quality of their life, and help them live independently at home. Thus, we set the following hypothesis: H1b: Performance expectancy has a positive effect on life quality expectancy. Effort expectancy Effort expectancy is defined as the degree of ease a patient feels with respect to the use of WSN-SHHS. The three previous constructs that underlie effort expectancy are perceived ease of use (TAM/TAM2), complexity (MPCU), and ease of use (IDT) [22] (see Table 2). Extensive empirical evidence has shown that effort expectancy has a direct effect on usage intention Figure 1. The research model 827

5 for technology adoption [8, 9]. Accordingly, the adoption of WSN-SHHS should depend on whether the systems are easy to use and free of effort. In particular, the patients will compare how much effort it takes to complete the task with or without the systems. Since WSN-SHHS operate at patients homes by design, it is crucial for patients to learn how to operate the systems by themselves. The more learning effort is required, the more inhibition would there be on the part of the patient to use the systems. If the use of WSN-SHHS involves tedious training, documentation, registration, learning about service terms and conditions, then the interaction with the systems would not be clear and understandable, and accordingly the patient would unlikely to adopt the systems. In addition, patients may be unfamiliar with the systems that represent an emerging service, which may also increase their difficulty in using them. Therefore, we hypothesize: H2a: Effort expectancy has a negative effect on patients behavioral intention to use WSN-HHS. The technology acceptance model [15] proposes the relationship between perceived ease of use and perceived usefulness. In view of the similarity between constructs of effort expectancy and ease of use and between performance expectancy and perceived usefulness [22], effort expectancy is expected to be related to performance expectancy. However, such a relationship is missing from UTAUT. We included this relationship into this study to test if there is any indirect influence of effort expectancy on patients behavioral intention to use WSN-SHHS through performance expectancy. Hence, we propose the following hypothesis: H2b: Effort expectancy has a negative effect on performance expectancy. Social influence Social influence is defined as the extent to which a patient perceives that significant others believe he or she should use WSN-SHHS. Three constructs from Table 2 capture the concept of social influence: subjective norm (TRA, TAM2, TPB and C-TAM- TPB), social factors (MPCU), and image (IDT) [22]. Peer support is a high social need in home healthcare especially for the elderly [23]. Thus, people such as friends, relatives and peers in the society may influence the patient decision to use WSN-SHHS. What those who are important to a person think, agree or disagree, about the person performing a given behavior play a vital role in his/her decision making. People may choose to perform behavior that important others agree, even if they themselves are not favorable toward the behavior or the consequences. Prior studies suggest that social influence is significant in shaping an individual s intention to use new technology [8, 9]. Therefore, we propose the following hypothesis: H3: Social influence has a positive effect on patients behavioral intention to use WSN-SHHS. Life quality expectancy Home healthcare patients would need to spend most of the day alone, so they consider WSN-SHHS as a safety-net system that provides assistance when emergency occurs. WSN-SHHS can ensure that if accidents happen, emergency help can be provided remotely. Further, social well-being is an important aspect of their lives. If the WSN-SHHS isn t conflicting with the patients life demands, they will perceive them as a way to improve the quality of the life and may be willing to adopt them. Remote monitoring technology is soft technology and requires less physical interactions with the patients [11]. The home healthcare patients do not perceive WSN-SHHS to impose any changes on their social lives and stated that their social trends and habits would continue with or without the system [24]. Accordingly, patients would feel more comfortable with them. Therefore, we propose the following hypothesis: H4: Life quality expectancy has a positive effect on patients behavioral intention to use WSN-SHHS. Cost expectancy Perceived cost refers to the degree to which a patient recognizes the possible expenses of using WSN-SHHS such as equipment costs, access cost, and transaction fees [25]. According to behavioral decision theory, the cost-benefit pattern is significant for the use of the technology. In switching between different products and services in various markets, individuals must deal with rising non-negligible costs [26]. Furthermore, frustrating experiences such as slow connections, poor quality, delay, and missing links, have infuriated patients. Unfortunately, they must pay for all these frustrations which increase the overall technology cost. The anticipation is that these early investments will lead to a long-term stream of profits from loyal patients, and that will make up for the expense. Otherwise, WSN-SHHS will not thrive because patients can obtain the same information or results through alternative solutions. Previous studies show that cost is very significant predictors for intention [10, 24]. Hence, the following hypothesis is proposed: H5: Perceived cost has a negative effect on patients behavioral intention to use WSN-SHHS. 828

6 4. Method design 4.1. Data collection procedure Survey was employed in this study to collect data about patients perception of WSN-SHHS to test the research model. The survey was conducted online. To make sure that participants understand the context of WSN-SHHS, we provided a set of scenarios along with some pictures at the beginning of the survey. The scenarios were related to both patients fall problem and ADL problems as described earlier. The proposed model consists of six constructs and each construct was measured using multiple items. All the items for performance expectancy, effort expectancy, social influence and behavioral intention were adapted from [22]. The items for life quality expectancy and cost expectancy were adapted from [24, 25], respectively. The survey items are listed in Table 3. Additional questions were designed to collect demographic information and related knowledge and experience. The descriptions of all the questionnaire items were adapted to the WSN-SHHS context and measured using a 7-point Likert scale, with 1 indicating strongly disagree and 7 strongly agree. The subjects were recruited from a mid-sized university on the east coast of the United States and from online home healthcare communities via mailing list and online postings. The study has been approved by the Institutional Review Board (IRB), in accordance with all applicable regulations. We conducted a power analysis to identify the minimum number of participants required to test the research model. Following a 10 rule of thumb strategy [13], a minimum of 50 participants provides a sufficient power to test the model and generalize the findings. Table 3. Survey instruments Performance Expectancy PE1 I would find WSN-based home healthcare systems useful in my home. PE2 Using WSN-based home healthcare systems would enable me to get treatment more quickly. PE3 Using WSN-based home healthcare systems PE4 would increase my effectiveness in the life. Using WSN-based home healthcare systems would increase my chances of getting better. Effort Expectancy EE1 My interaction with WSN-based home healthcare systems would be clear and understandable. EE2 EE3 EE4 It would be easy for me to become skillful at using WSN-based home healthcare systems. I would find WSN-based home healthcare systems easy to use. Learning how to use WSN-based home SI1 SI2 SI3 QL1 QL2 QL3 QL4 QL5 C1 C2 C3 C4 I1 I2 I Data analysis healthcare systems would be easy for me. Social Influence People who influence my behavior think that I should use WSN-based home healthcare systems for better health. People who are important to me think that I should use WSN-based home healthcare systems for better health. People whose opinions that I value prefer that I should use WSN-based home healthcare systems for better health. Quality of Life Expectancy I think I would receive several benefits when I use WSN-based home healthcare systems. I think that WSN-based home healthcare systems fit my lifestyle. I think that using WSN-based home healthcare systems will not influence my routine social relationships. I think that using WSN-based home healthcare systems will help me in living independently. Overall, I think the overall quality of my life would be improved when I use WSN-based home healthcare systems. Cost Expectancy I think the equipment cost is expensive of using WSN-based home healthcare systems. I think the access cost is expensive of using WSN-based home healthcare systems. I think the transaction cost is expensive of using WSN-based home healthcare systems. Overall, I think the infrastructure cost is expensive of using WSN-based home healthcare systems. Intention to use Given the chance, I intend to use WSN-based home healthcare systems in the near future. Given the chance, I predict I would use WSNbased home healthcare systems in the near future. Given the chance, I plan to use WSN-based home healthcare systems in the near future. Partial least square (PLS) was selected for data analysis in this study using SmartPLS. A number of recent technology acceptance studies have utilized PLS [9, 22]. To evaluate the measurement model, PLS first estimates the internal consistency of each block of indicators. PLS then evaluates the degree to which a variable measures what it was intended to measure. This evaluation is comprised of convergent and discriminate validity. Following Gefen and Straub (2005) [26], convergent validity of the variables is evaluated by examining the t-values of the outer model loadings. Discriminate validity is evaluated by 829

7 examining item loadings to variable correlations and by examining the ratio of the square root of the AVE of each variable to the correlations of this construct to all other variables. For the structural model, path coefficients are interpreted as regression coefficients with the t statistic calculated using bootstrapping, a nonparametric technique for estimating the precision of the PLS estimates. To determine how well the model fits the hypothesized relationship PLS calculates an R 2 for each dependent construct in the model. Similar to regression analysis, R 2 represents the proportion of variance in the endogenous constructs which can be explained by the antecedents. 5. Results 5.1. Sample profile Among a total of 118 responses that were received, 83 were complete and valid, and were thus used in our analysis (70.3%). The sample size was greater than the minimum number of participants required (i.e., 50). Table 4 reports demographic statistics of the subjects who participated in this study. Table 4. Statistics of demographic profile Variable Frequency Percentage Gender Male Female Age Educati on Level Annual Income High school degree or equivalent (e.g., GED) Some college but no degree Associate degree Bachelor degree Graduate degree Less than $30,000 $30,000 - $59,999 $60,000 - $99,999 More than $100, As shown from Table 4, males accounted for a little more than half of the participants (55.4%), almost 80 percent of the participants belonged to the years old age group, about two-fifth of the participant s annual income is less than $30000, and one-fifth of them had graduate degree and one-fifth had some college but no degree. The statistics also shows that all the participants used the Internet and the computer several times a day, more than half of the participants (68.7%) had knowledge about home healthcare, and the majority of them had knowledge about the sensor technology (71%). The diversity of the participant background is beneficial for the purpose of this study Measurement model validation Table 5 summarizes the results for the items comprising the model. All item loadings are significant All AVEs are above 0.5, all CRs are above 0.7 and all alpha values are larger than 0.7, showing excellent reliability. Thus, the results provide evidence for the convergent validity of all the scales [26]. Table 5. Item loadings, AVE, composite reliabilities (CR) and Cronbachs alpha Discriminate validity is confirmed if the square root of AVE is significantly higher than correlations between constructs in the corresponding rows and columns. As shown from Table 6, the instruments demonstrate adequate discriminate validity as the AVEs (bold) are greater than the corresponding correlation values in the adjoining columns and rows. Table 6. AVE scores and correlation of latent variables 5.3. Testing the structural model Figure 2 depicts the structural model showing path coefficients ( ) and R 2. R 2 indicates the percentage of 830

8 the variance in the corresponding construct that is explained by the structural paths leading to it. indicates the strengths of relationships between constructs [13]. The R 2 value for the behavioral intention to use WSN-SHHS indicates that the model explained 69.4% of the variance. R 2 for performance expectancy indicates that effort expectancy explained 20.8% of the variance. R 2 for life quality expectancy indicates that performance expectancy explained 41.9% of the variance. The bootstrap method was used in SmartPLS to assess the statistical significance of the path coefficients. The results of hypothesis testing are reported in Table 7. p<0.05), life quality expectancy ( = 0.377, p<0.001), and cost expectancy ( = 0.132, p<0.05) have significant direct impact on patients behavioral intention to use WSN-SHHS. However, effort expectancy was not found to have a direct impact on the patients behavioral intention to use WSN-SHHS despite that it had an indirect effect through performance expectancy ( = 0.456, p< 0.001). Figure 2. Model testing results Table 7. Hypotheses testing results As shown in Table 7, all the hypotheses have been supported except for H2a. Performance expectancy ( = 0.324, p<0.001), social influence ( = 0.251, 6. Discussion This study primarily sought to identify the factors influencing patients behavioral intention to use WSN- SHHS. The findings suggest that patients behavioral intention to use WSN-SHHS is a function of performance expectancy, social influence, life quality expectancy, and cost expectancy. These factors have substantial predictive power accounting for more than half of the variance in explaining the intention to use WSN-SHHS. Moreover, unlike the original UTAUT which shows that performance expectancy is the strongest predictor of patients behavioral intention, our finding shows that life quality expectancy is the strongest predictor of the intention to use WSN-SHHS. In this study, life quality expectancy was found to have stronger predictive power for the intention than performance expectancy. The finding reveals that patients are much more interested in the improvement on the quality of living from using the WSN-SHHS than task performance of the system itself. Home healthcare service providers and smart home designers should recognize that patients would like to live independently and safely at home and the system is 831

9 expected to send alerts in case of emergency without imposing noticeable changes on the social life of the patients. As expected, performance expectancy has a positive impact on patients behavioral intention to use WSN-SHHS. The finding is consistent with previous UTAUT studies [7, 8]. In addition, it was also found that performance expectancy is the second strongest predictor for the intention. Patients seem to be rational in making their adoption decisions by attending to the usefulness and effectiveness of new home healthcare technologies. In other words, patients are likely to adopt WSN-SHHS when it is considered to be beneficial for improving their health condition. Contrary to our prediction, effort expectancy was not found to have a direct effect, but consistent with our proposed model, effort expectancy was found to have an indirect effect on patients behavioral intention to use WSN-SHHS. The latter confirms the relationship between ease of use and usefulness from the TAM model [15]. It suggests that reducing effort alone does not make home healthcare technologies attractive unless they contribute to improved performance in home healthcare. One explanation for the lack of support of the direct effect is that our sample was drawn from a population with high selfefficacy and education, who are capable of learning how to operate WSN-SHHS with proper training and instructions. Social influence was found to have a positive impact on patients behavioral intention to use WSN- SHHS. Patients are likely to develop dependent evaluations and may consequently place high weight on others opinions. According to social influence theory [27], the process of patients taking recommendations from their important others in using WSN-SHHS is called compliance. Thus, WSN-SHHS providers and manufacturers should provide incentives such as awards and membership upgrades for early adopters to promote the technology to their friends and families. To this end, social media platform such as online communities, discussion forums, and online social networks may play a major role. The proposed negative effect of cost expectancy is confirmed in this study. Patients recognize that transitioning to WSN-SHHS implies some additional expenses including equipment costs, access cost, and transaction fees that make their use more expensive than traditional methods. Home healthcare service providers should consider informing patients of the cost-benefit of WSN-SHHS and highlight the longterm benefits and importance of the system despite of significant initial cost. Smart home designers and manufacturers should develop a concrete plan to reduce all the sources of rising cost of the system; in this context, the costs covers not only the devices but also infrastructure, equipment, access, configuration and maintenance costs. Further, it includes costs resulted from slow connections, delay, and missing links. This research has a number of theoretical and practical implications. Theoretically, the research provides a model for explaining the patients behavioral intention to use WSN-SHHS, which not only enhances the theoretical foundation of WSN research, but also expands the application of technology adoption theories to the domain of home healthcare. To the best of our knowledge, this is of the first quantitative research study in the domain of WSN- SHHS that takes into account the perceptions of patients. We identified and empirically validated new constructs including life quality expectancy and cost expectancy to explain the patients behavioral intention to use WSN-SHHS. In practice, the knowledge acquired from this study can potentially benefit the home healthcare providers, designers, manufacturers and their patients. The strong influence of life quality expectancy on the adoption of WSN-SHHS suggests that smart home designers should focus on mechanisms that increase patients independency at home. Given the positive effect of social influence on the adoption, home healthcare providers should identify individuals with strong personal influence to become advocates for WSN- SHHS use. Smart home designers and manufacturers should develop a concrete plan to reduce all the sources of rising cost of the system. WSN-SHHS benefits all these stakeholders as well. For the providers and manufacturers, an automatic monitoring system is valuable for many reasons. First, it frees human labor from 24/7 physical monitoring, reducing labor cost and increasing efficiency. Secondly, wearable sensor devices can sense even small changes in vital signals that humans might overlook, such as heart rate and blood oxygen levels. Quickly notifying doctors of these changes may save human lives. Thirdly, the data collected from the WSN-SHHS can be stored and integrated into a comprehensive health record of each patient, which helps physicians to make more informed diagnoses. Eventually, the analysis, diagnosis, treatment process may also be semiautomated, so a human physician can be assisted by an electronic physician". Home healthcare patients benefit from improved health as a result of faster diagnosis and treatment of diseases. Other quality-oflife issues, such as dignity and convenience, are supported and enhanced by the ability to provide services in the patient's own home. Accordingly, family members and the smart home healthcare network itself become part of the healthcare team. 832

10 Finally, memory aids and other patient-assistance services can restore some lost independence, while preserving safety. This research has several limitations. A larger sample size would provide higher power. There is a lack of qualitative feedback from the patients. Additional research constructs such as perceived security and moderating effects of gender, age, and culture are worthy of future exploration. 7. Conclusion This research is attempted to explain patients behavioral intention to use WSN-SHHS. The research model extends UTAUT with new constructs. The results show that performance expectancy, social influence, life quality expectancy, and cost expectancy are directly associated with, and effort expectancy is indirectly associated with, patients behavioral intention to use WSN-SHHS. These findings have significant implications for smart home healthcare service providers, patients, manufacturers, and designers. 8. References [1] Y. Lu, P. Zhang, and S. 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