2. Performance Expectancy (PE)
Performance Expectancy is “the extent to which an individual believes that utilizing the technology will assist him or her to realize benefits in job performance” [18]. The value of an eHealth service is in the degree to which the facility affords a meaningful contribution to a patient’s health. After all, perceived usefulness is a major part of trust and in this case health related usefulness. Thus, it is expected that an effective eHealth service will provide a healthier balance between outcome and effort required [4]. Consumers’ positive attitude toward eHealth’s ability to attain intended healthcare objectives can stimulate adoption. Therefore, we posit that:
Hypothesis 1: Performance expectancy will positively affect behavioral intention to use e-health services
3.
Effort Expectancy (EE)
Effort Expectancy is “the extent of ease related to the system use” [18]. High ease of use is fundamental to effort expectancy and relates to how easily users can navigate a technological platform. For instance, how easily one can navigate an eHealth website. Difficulties in finding information due to vaguely structured menus and web pages means a waste of time and is one of the major risk factors for eHealth. This suggests that a user-friendly and navigable structures should supersede improved visual appeal. Then again, if the visual appearance decreases significantly, this may also decrease trust in the service [4]. Thus, the assumption here is that the more user-friendly, expedient, and easy-to-use the technology is, the greater the likelihood that consumers will intend to use the service. Therefore, we posit the following:
Hypothesis 2: Effort expectancy will have a positively affect behavioral intentions to use eHealth services
4. Social Influence
Social influence is “the extent to which an individual perceives that important others believe he or she should use the new system”. It is dependent on an extensive range of provisional stimuli with three mechanisms affecting individual behavior namely; compliance, internalization, and identification. Changing an individual’s intention in reaction to societal forces is a function of compliance mechanism while the latter two mechanisms concern altering and amending an individual’s beliefs structure and/or initiating the individual’s reaction to possible situational benefits. Thus, adoption or resistance to change me be as a result of stimulus from associates. [18]. This study posits that:
Hypothesis 3: Social Influence will positively affect behavioral intentions to use e-health services
5. Facilitating Conditions
Facilitating Conditions is “the extent of belief an individual has in the existence of an organizational and technical infrastructure to facilitate system use”. It combines three key variables derived from preceding theories: firstly, perceived behavioral control which is individual insight into the existence or lack of necessary means and prospects; facilitating conditions defined as impartial environmental features that participants approve enable easy accomplishment of an act; and compatibility defined as the extent to which a novelty is observed to be well-suited to prospective users’ prevailing standards, desires, and previous involvements” [18]. The study therefore examines how facilitating conditions affect intention to use. The following is therefore hypothesized:
Hypothesis 4: Facilitating conditions will positively affect behavioral intentions to use eHealth services
6. Trust in internet (ToI)
With reference to trust in internet, the expectation is that there should be minimal or no technical errors [4]. Reliability of technical artifacts is often deemed to be a preferable feature [19]. Thus in this case, technical reliability relates to whether the service will experience a systems fault or other failures. Researchers have identified technical failures as a significant risk for Dutch eHealth adoption, as they may lead to higher time consumption and higher costs [20]. Thus, in this study, we propose that the degree to which an individual has confidence in the benefits which using the technical artifact will provide positively affects their belief in the reliability of the technical artifact. Therefore, we posit the following:
Hypothesis 5: Performance expectancy positively affects Trust of internet
With reference to e-Government, scholars theorize that trusting beliefs in the internet will positively affect behavioral intentions [21]. This paper explores this relationship with reference to eHealth. Thus, we posit the following:
Hypothesis 6: Trust of internet positively affects behavioral intentions to use eHealth services
7. Trust in online healthcare providers (ToH)
Benevolence is defined as the degree to which the object warranting an attitude of trust, which is the trustee, is thought to have good intentions towards the trustor. It concerns the trustors assessment of the trustee’s good intentions [22]. Illustrations of this will frequently relate to privacy issues such as providers’ intentions when handling patient data for purposes the patient is inadequately aware of. In this case, the trustee is not the eHealth system itself because technology cannot have its own intentions. Therefore, this domain applies to the providers of the eHealth services such as technology developers and the healthcare institution [4]. In this study it is theorized that consumers’ positive attitude toward eHealth’s ability to attain intended healthcare objectives will positively affect trust in online healthcare service providers. The following is therefore posited:
Hypothesis 7: Performance expectancy will positively affect Trust of online e-health service providers
It is hypothesized that trust in government will positively affect behavioral intentions. However, results of the analysis of this relationship found that this relationship was not significant and therefore not supported. This paper explores this relationship in the context of eHealth and hypothesizes the following:
Hypothesis 8: Trust of online e-health service providers will positively affect intentions to use e-health services
Figure 1: Proposed research model
Table 1: Hypotheses
Number
|
Hypothesis
|
H1
|
Performance expectancy will positively affect behavioral intention to use e-health services
|
H2
|
Effort expectancy will positively affect behavioral intentions to use e-health services
|
H3
|
Social Influence will positively affect behavioral intentions to use e-health services
|
H4
|
Facilitating conditions will positively affect behavioral intentions to use e-health services
|
H5
|
performance expectancy will positively affect Trust of internet
|
H6
|
Trust of internet will positively affect behavioral intentions to use e-health services
|
H7
|
Performance expectancy will positively affect Trust of online e-health service providers
|
H8
|
Trust of online e-health service providers will positively affect behavioral intentions to use e-health services
|