CHAPTER 3-RESEARCH MODEL AND HYPOTHESIS
3.1 Research Model
Based on our literature review, we propose the following research framework.
Figure 3-1 Research framework
Based on expectation-confirmation theory (ECT) (Anderson and Sullivan, 1993;
Dabholkar et al., 2000; Oliver 1980, 1993; Patterson et al. 1997; Tse and Wilton 1988), we divided the e-health usage process into the following three stages: intention to adopt, adoption, and continued use. Patients’ intention to use the e-health service is highly
E-health usage process
Service Features z By whom:
1. By social relations 2. By self-evaluation 3. By professional
suggestions z What
1. Physiological data recording
2. Symptom Analysis 3. Health education
information
4. Medical Information 5. Alarm service
Frequency of service supply
z How
--Platform
1. Medical 2. Operational --Service
1. Medical 2. Operational
Continued use Adoption
Intention
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related to their perception of the value of e-health and their expectations of this service.
The intention leads to the initial decision to adopt this service. The initial experience then influences the decision to repurchase the service and continue using it. In this research, we are interested in discovering the important service features that affect the intention to adopt, adoption, and continued use of e-health. Based on a review of the literature, we use the service concept to categorize our interest in the following 5 dimensions of e-health service features: by whom, what, for whom, when, and how.
3.2.1 Service features - By whom
The “by whom” dimension refers to the channels and devices by which customers choose a given service. The channels can be doctors or friends.
According to the Technology Acceptance Model (TAM), social influence and subjective norms can explain an individual’s intentional or voluntary adoption of a technology (Hsu and Lu, 2004, Karahanna and Straub, 1999, Liao et al., 1999, Liker and Sindi, 1997, Taylor and Todd, 1995 and Venkatesh and Davis, 2000). Past studies have also showed that a consumer’s choice is the result of a complex interplay of cultural, social, personal, and psychological factors (Anilkumar and Joseph, 2012).
Furthermore, Ajzen and Fishbein (1980) postulated that a consumer’s intent to purchase and his or her purchase patterns are influenced by personal and social factors.
Thus, we can assume that people would choose a service or product because of relatives or friends’ recommendations or experiences with that product.
A previous study noted that self-evaluation is an important aspect of the consumer decision-making processes (Grewal, Cline & Davies, 2003). People make a purchase decision based on their own evaluation of a product’s price and functions.
Moreover, professional suggestions from doctors or nurses might affect patient attitudes toward e-health. Previous have studies found that doctors’ suggestions usually determine patients’ decisions about their own medical care (Hart et al., 2006).
Therefore, we assume that professional suggestions from doctors or medical professionals affect patients’ intention to adopt and continued use of an e-health service.
The following hypotheses are proposed:
H1a: Social relations have an impact on a patient’s intention to adopt e-health.
H1b: Self-evaluation has an impact on a patient’s intention to adopt e-health.
H1c: Professional suggestions have positive impact on a patient’s intention to adopt e-health.
H2a: Social relations have an impact on a patient’s continued use of e-health.
H2b: Self-evaluation has an impact on a patient’s continued use of e-health channels or devices.
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H2c: Professional suggestions have an impact on a patient’s continued use of e-health channels or devices.
3.2.2 Service features – What
The following key service features of e-health were determined from a study of the literature: physiological data recording, symptom analysis, health education information, medical information, alarm service, and caring service. These key service features are assumed to influence patients’ perceived usefulness of e-health.. TAM shows that when people perceive usefulness and ease of use of information technology, they will adopt that technology. Furthermore, according to the Post Acceptance Model of IS Continuance, when a user perceives the usefulness of a specific information technology, he/she will continue to use that technology. We propose the following hypotheses:
H3a: Having a physiological data recording would have an impact on a patient’s intention to adopt E-health.
H3b: Having symptom analysis would have an impact on a patient’s intention to adopt E-health.
H3c: Having health education information would have an impact on a patient’s intention to adopt E-health.
H3d: Having medical information would have an impact on a patient’s intention to adopt E-health.
H3e: Having an alarm service would have an impact on a patient’s intention to adopt E-health.
H3f: Having a caring service would have an impact on a patient’s intention to adopt E-health.
H4a: Having physiological data recording would have an impact on a patient’s continued use of E-health.
H4b: Having symptom analysis would have an impact on a patient’s continued use of E-health.
H4c: Having health education information would have an impact on a patient’s continued use of E-health.
H4d: Having medical information would have an impact on a patient’s continued use of E-health.
H4e: Having an alarm service would have an impact on a patient’s continued use of E-health.
H4f: Having a caring service would have an impact on a patient’s continued use of E-health.
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3.2.3 Service features - For whom
Previous research has shown that demographic and other external effects affect the development and adoption of Internet-related technologies in various sectors. It also suggested that e-health service design should take into account differences in sex, age, location, and size of the medical practice (Egea et al., 2010). In the case of e-health, age is often considered in the design of a service. Issues such as how to reduce IT complexity to encourage elderly people to adopt e-health services have often been discussed in the literature. In addition to age, disease type is an important factor that service designers should consider. For example, different types of disease may require different frequencies of service supply. We propose the following hypotheses:
H5a: Age would have an impact on a patient’s intention to adopt E-health.
H5b: Disease type would have an impact on a patient’s intention to adopt E-health.
H6a: Age would have an impact on a patient’s continued use of E-health.
H6b: Disease type would have an impact on a patient’s continued use of E-health.
3.2.4 Service features – When
This section attempts to determine when the service will be needed to fit user demands. Because storing or maintaining an inventory of a service is impracticable (Parasuraman et al., 1988), understanding the connection between supply and user demand is an important issue (Lovelock, 1983). In the context of e-health, service frequency is an important design issue. For example, Juretic and Meghan (2012) have discussed what response frequency would be suitable for a home telehealth service.
We expect that patients might demand a high frequency of services because intensive service usually results in service satisfaction. Thus, we propose the following hypotheses:
H7: The frequency of service supply would have an impact on a patient’s intention to adopt E-health.
H8: The frequency of service supply would have an impact on a patient’s continued use of E-health.
3.2.5 Service features – How
In e-health, service functions can be categorized into medical functions and operational functions. The whole service process can be divided into service and platform. Both functions and processes require an assessment of quality. According to Parasuraman (1988), service quality has become the most powerful means for a service organization to establish a competitive advantage and achieve success. Czepiel and Akerele (1974) also believed that customer satisfaction can be considered an
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integrated response to the evaluation of service quality. ECT also holds that consumers’ intention to repurchase a product or continue service use is determined primarily by their satisfaction with prior use of that product or service (Anderson and Sullivan 1993; Oliver 1980, 1993). We can expect that when patients feel satisfied about functions and processes of a service, they will adopt and continue to use that service. The following hypotheses are proposed:
H9aa: Medical satisfaction with the platform would have an impact on a patient’s intention to adopt E-health.
H9ab: Operational satisfaction with the platform would have an impact on a patient’s intention to adopt E-health.
H9ba: Medical satisfaction with the service would have an impact on a patient’s intention to adopt E-health.
H9bb: Operational satisfaction with the service would have an impact on a patient’s intention to adopt E-health.
H10aa: Medical satisfaction with the platform would have an impact on a patient’s continued use of E-health.
H10ab: Operational satisfaction with the platform would have an impact on a patient’s continued use of E-health.
H10ba: Medical satisfaction with the service would have an impact on a patient’s continued use of E-health.
H10bb: Operational satisfaction with the service would have an impact on a patient’s continued use of E-health.
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CHAPTER 4-RESEARCH PLAN