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Mobile health (mHealth) as an innovative form of information and communications technology can efficiently deliver high-quality health care by enhancing communication and health management, reducing costs, and increasing access to health services. An individual’s internal health locus of control (HLOC) is found to be associated with the behavioral intent to adopt mHealth. However, little is known about the underlying mechanism of this association.
The primary objective of this study was to test the mediation influence of the Unified Theory of Acceptance and Use of Technology (UTAUT) on the relationship between internal HLOC and the behavioral intention to use mHealth.
A total of 374 responses were collected from Malaysian adult users of mHealth, using convenience and snowball sampling methods. Partial least squares structural equation modeling was used to analyze the data. Data were collected for variables, including demographics, internal HLOC, and modified UTAUT constructs (ie, performance expectancy, effort expectancy, and social influence).
The results showed that there was no direct relationship between internal HLOC and the behavioral intention to use mHealth (β=−0.039,
This study developed an integrative model, where a health-related disposition (internal HLOC), mHealth-related beliefs (performance expectancy and effort expectancy), and normative pressure (social influence) were combined to explain the underlying mechanism of the behavioral intent to adopt mHealth. The results showed that the intention to adopt mHealth is mediated by the influence of UTAUT factors, while HLOC has no direct effect on adoption intention. The findings provide insights into augmenting mHealth adoption among the public by enhancing the perceived benefits of mHealth, helping design more effective and user-friendly mHealth tools, and capitalizing on social normative influence to adopt mHealth. This study utilized the constructs of the UTAUT model to determine the intention to use mHealth. Future research should focus on other health- and technology-related theories to ascertain other possible factors influencing the behavioral intent of mHealth adoption.
Over the past decades, health care systems in most countries around the world have experienced tremendous changes because of the rapid advancement in information and communications technology (ICT). Mobile health (mHealth), as an innovative form of ICT, is one of the most prominent services with remarkable effects on the development of the health care system [
The popularity of mHealth programs has grown worldwide as evidenced by a Statista report in 2017 regarding the estimated number of mHealth app downloads, which has exponentially increased from 1.7 billion in 2013 to 3.7 billion in 2017 [
The importance and implications of mHealth have inspired researchers to investigate the factors in the adoption of mHealth. A cluster of studies viewed mHealth as a perceived technology–driven behavior and attempted to find the correlates of such behavior using technology adoption theories [
Among health-related factors, the belief that health events are caused by one’s own actions is one of the major predictors of health behaviors such as greater engagement in health/disease management, healthier lifestyle, and better physical and mental quality of life [
Although the direct relationship between internal HLOC and behavioral intention to use mHealth provides a noteworthy tenet to knowledge, little is known regarding the underlying mechanism of this relationship. Focusing on mediating factors, which facilitate the relationship between internal HLOC and the intent to use mHealth, can provide a better insight to Malaysian health policy makers and health care professionals for embedding mHealth use in daily life and promoting mHealth functions (such as health/disease information seeking, communicating for health-related purposes, and downloading and using any health-related apps) for health management and more importantly active participation in disease prevention, thus reducing the need for health care services and consequently the toll on the Malaysian health care system.
Therefore, this study aims to contribute to the literature by introducing 3 constructs of the Unified Theory of Acceptance and Use of Technology (UTAUT) (ie, performance expectancy, effort expectancy, and social influence) as mediators between internal HLOC and the intention to use mHealth, which, to the best of our knowledge, has not been examined thus far. The UTAUT is one of the most widely accepted technology adoption theories with a wide applicability and a high explanatory power to predict the intent to adopt technology [
Locus of control (LOC) is a psychological construct that is derived from the social learning theory of personality [
In general, higher levels of internal HLOC are more likely to drive healthy behaviors and more successful changes in health behaviors and preventive health behaviors, whereas higher levels of external HLOC are not. Stronger internal HLOC orientations were found to be related to greater engagement in health-enhancing behaviors (such as exercise and diet) [
Research into technology adoption has relied on LOC as a construct that explains adoption behavior. Empirical studies provided support for the association between LOC and higher propensity of adopting technology in an array of technologies where differences in internal and external LOC tend to differentiate the behaviors between these 2 groups [
Limited research on the relationship between internal HLOC and behavioral intent to adopt mHealth calls for further investigation into the possibility of other variables that could underlie this relationship. Therefore, this study intends to suggest the mediating effect of UTAUT constructs to test the indirect relationship between internal HLOC and the intention to use mHealth. Venkatesh et al developed a unified model that has an overall comprehensive explanatory power to conceptualize and predict acceptance behavior, known as the UTAUT [
Since its emergence, the UTAUT has been empirically tested across domains [
Individuals who score high in internal LOC, also known as internals, cherish innovative ideas [
In light of the above literature, we would expect internal HLOC to predict UTAUT constructs, which may, in turn, predict behavioral intent to adopt mHealth. In other words, instead of a direct relationship between HLOC and mHealth, we would expect an indirect relationship that could provide an underlying mechanism to explain how those high on internal HLOC are disposed toward mHealth use. Individuals who tend to assign the cause of health outcomes to their internal characteristics rather than to outside forces are more likely to perceive that mHealth is useful and easy to use. However, they may not use mHealth because of social influences as they do not believe that external forces, such as others, can motivate them to use mHealth. Because of their belief in mHealth usefulness and ease of use, they may have an intention to adopt mHealth. However, their resistant to social influence may hinder them from adopting mHealth. Hypotheses and justifications for the hypotheses are presented in
It was found that there is a relationship between locus of control (LOC) and technology adoption in developing agriculture [
Individuals with oropharyngeal head and neck cancer with a high propensity for an internal HLOC orientation showed their support toward telepractice models of care telerehabilitation [
A cross-sectional study revealed that the amount of control college students believed they had over their health predicted willingness to use health apps and online health trackers [
In a study to examine the intention of elderly people aged 57 to 77 years to use eHealth apps, expected performance and effort were highly related to the intention to use eHealth while social influence was not [
A study revealed that Unified Theory of Acceptance and Use of Technology factors, namely effort expectancy, expectancy performance, and social influence, were significant determinants of the intention for mHealth adoption behavior in citizens like diabetic patients who were taking traditional health care services repeatedly from any medical hospital for diabetes, blood pressure, and cholesterol monitoring in the United States, Canada, and Bangladesh [
A study on the intention to use a mobile electronic health record (MEHR) system in a sample of health care professionals (doctors and nurses) showed that the intention to use the MEHR system was indirectly influenced by effort expectancy and performance expectancy through attitudes toward the MEHR system, while social influence was found to be directly associated with the intention to utilize the MEHR system [
Venugopal et al [
Individuals with high internal LOC are more likely to seek new information when the information is personally relevant, and obtain valuable knowledge and skills to enhance their performance [
Internals commonly display a favorable attitude toward technology [
Individuals with internal LOC attributed perceived difficulty toward technology to their own abilities and attempted to use technology more effectively [
Internals have more experience in using technologies and find technology, such as e-learning, easy to use [
Individuals with higher internal LOC are resistant to social influence as they feel they have more self-control and self-reinforcement over their life and things that happened to them [
They are not easily persuaded and do not conform to others’ influence [
Performance expectancy and effort expectancy were found to be positive and significant mediators among website design, customer service, and customer’s intention to adopt internet banking [
Performance expectancy and effort expectancy were found to be linked to user adoption in context awareness and Alipay, a third-party mobile and online payment platform [
Fong et al [
Among 374 participants in this study, there were 145 males and 229 females. The participant age ranged from 18 to 68 years (mean 28.01 years, SD 11.10). Almost 45% (166/374, 44.4%) of the participants were Chinese, and 40.7% (152/374) were Malays. In terms of health status, 47.4% (177/374) of the participants perceived their health status as good, 27.5% (103/374) perceived it as fair, and 18.2% (68/374) perceived it as very good. Participants were also asked whether they had an ongoing or a serious health problem that included heart disease, arthritis, or a mental health condition requiring frequent medical care, such as regular visits to doctors or daily medications. The majority (315/374, 84.3%) of the participants indicated that they did not have any ongoing or serious health problem, while 12.0% (45/374) reported that they did not know of any serious health problems. A small percentage (14/374, 3.7%) of participants had an ongoing disease or serious health problem. Lastly, regarding mobile phone usage experience, 40.4% (151/374) of the participants had 8 to 10 years of experience, 39.0% (146/374) had 4 to 7 years of experience, 15.0% (56/374) had more than 10 years of experience, and 5.6% (21/374) had 1 to 3 years of experience.
Demographic profile of the respondents.
Background variable | Value (N=374), n (%) | ||
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Male | 145 (38.8) | |
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Female | 229 (61.2) | |
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Malay | 152 (40.7) | |
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Chinese | 166 (44.4) | |
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Indian | 47 (12.5) | |
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Others | 9 (2.4) | |
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Do not know | 3 (0.8) | |
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Poor | 9 (2.4) | |
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Fair | 103 (27.5) | |
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Good | 177 (47.4) | |
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Very good | 68 (18.2) | |
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Excellent | 14 (3.7) | |
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Yes | 14 (3.7) | |
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No | 315 (84.3) | |
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Do not know | 45 (12.0) | |
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1-3 years | 21 (5.6) | |
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4-7 years | 146 (39.0) | |
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8-10 years | 151 (40.4) | |
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>10 years | 56 (15.0) |
This study used a questionnaire-based cross-sectional design to collect the required data. A total of 400 questionnaires were distributed to Malaysian adults residing in Kuala Lumpur, Malaysia. The subjects for this study were drawn from mHealth users. A screening self-report question was included in the survey to identify mHealth users. Participants were asked if they have ever used their smartphones for any health-related purposes, such as seeking health- and disease-related information online, texting messages for health-related purposes (such as reminders/alerts for appointments, taking medications, and consultations), and downloading and using any health-related apps (such as fitness apps, and apps for health tracking and medication tracking). Participants who reported having used their smartphones at least for one of these purposes were included in the analysis.
The questionnaire comprised an informed consent form, demographic profiles, and questions related to internal HLOC and the modified UTAUT constructs for mHealth use. Data were collected using convenience and snowball sampling methods. A research assistant was recruited for data collection. Participation was voluntary where confidentiality was ensured, and respondent consent was obtained before commencing the survey. Participants were given an absolute right of withdrawal at any time and without giving any reason. The protocol of the study (including the research procedure, the rights and safety of the participants, and the method of data collection) was approved by the review board of Xiamen University Malaysia to ensure compliance with research ethics. The approval number is REC-1911.01.
The following 2 rules of thumb are used for choosing the sample size when partial least square is to be used for model analysis: (1) “10 times the scale with the largest number of formative (ie, causal) indicators (note that scales for constructs designated with reflective indicators can be ignored),” and (2) “10 times the largest number of structural paths directed at a particular construct in the structural model.” [
In measuring internal HLOC, 6 items were adopted from the Multidimensional Health Locus of Control scale developed by Wallston et al [
Items to measure performance expectancy, effort expectancy, social influence, and intention to use mHealth were directly extracted from the original UTAUT model [
Questions related to internal HLOC and the modified UTAUT constructs for mHealth use are included in
In this study, partial least squares structural equation modeling (PLS-SEM) was used to examine the proposed conceptual framework using SmartPLS software. By using PLS-SEM, the direct and indirect effects of multiple independent and dependent variables can be tested simultaneously, which provides greater statistical power. PLS-SEM is also able to accommodate a study with a small sample size despite the complexity of the models [
The first step in the analysis concerning the measurement model was to examine the factor loading. In this study, the factor loadings of the items varied from 0.632 to 0.945 (
Assessment results of the measurement model.
Constructs and items | Loading | CRa | AVEb | |||
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0.873 | 0.535 | |||
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IHLC1 | 0.714 |
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IHLC2 | 0.662 |
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IHLC3 | 0.799 |
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IHLC4 | 0.747 |
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IHLC5 | 0.817 |
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IHLC6 | 0.632 |
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0.915 | 0.783 | |||
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PE1 | 0.853 |
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PE2 | 0.920 |
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PE3 | 0.880 |
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0.937 | 0.789 | |||
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EE1 | 0.875 |
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EE2 | 0.890 |
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EE3 | 0.926 |
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EE4 | 0.861 |
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0.954 | 0.874 | |||
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SI1 | 0.919 |
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SI2 | 0.945 |
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SI3 | 0.940 |
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0.891 | 0.732 | |||
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BI1 | 0.785 |
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BI2 | 0.898 |
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BI3 | 0.880 |
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aCR: composite reliability.
bAVE: average variance extracted.
Lastly, discriminant validity was determined using the heterotrait-monotrait (HTMT) ratio of correlation as recommended by Henseler et al [
Discriminant validity using the heterotrait-monotrait (HTMT) ratio.
Construct | Internal health locus of control | Performance expectancy | Effort expectancy | Social influence | Behavioral intention |
Internal health locus of control | N/Aa | 0.390 | 0.373 | 0.212 | 0.213 |
Performance expectancy | 0.390 | N/A | 0.886 | 0.687 | 0.745 |
Effort expectancy | 0.373 | 0.886 | N/A | 0.636 | 0.678 |
Social influence | 0.212 | 0.687 | 0.636 | N/A | 0.682 |
Behavioral intention | 0.213 | 0.745 | 0.678 | 0.682 | N/A |
aN/A: not applicable.
Multicollinearity is assessed using the variance inflation factor (VIF). In this study, all VIF values were below 5, which indicated no violation of the multicollinearity assumption. The structural model was assessed using
Direct, total indirect, and specific indirect effects.
Path |
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Beta | ||||||||
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0.472 |
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Internal health locus of control → behavioral intention |
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−0.039 | 0.999 | .32 | ||||
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Performance expectancy → behavioral intention |
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0.316 | 4.859 | <.001 | ||||
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Effort expectancy → behavioral intention |
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0.169 | 2.672 | .008 | ||||
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Social influence → behavioral intention |
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0.307 | 5.715 | <.001 | ||||
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0.109 |
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Internal health locus of control → performance expectancy |
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0.330 | 6.522 | <.001 | ||||
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0.109 |
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Internal health locus of control → effort expectancy |
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0.329 | 7.020 | <.001 | ||||
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0.034 |
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Internal health locus of control → social influence |
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0.186 | 3.621 | <.001 | ||||
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0.472 |
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Internal health locus of control → behavior through performance expectancy, effort expectancy, and social influence |
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0.217 | 5.554 | <.001 | ||||
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Internal health locus of control → performance expectancy → behavioral intention |
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0.104 | 3.813 | <.001 | |||||
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Internal health locus of control → effort expectancy → behavioral intention |
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0.056 | 2.389 | .02 | |||||
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Internal health locus of control → social influence → behavioral intention |
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0.057 | 3.123 | .002 |
Based on the structural model, hypothesis 1 was not supported because internal HLOC (β=−0.039,
Overall, the results supported the model of this study where the total indirect effect was significant (β=0.217,
Path coefficients of the structural research model. mHealth: mobile health; ns: not significant. *
For hypothesis 9, effort expectancy as the mediator for the relationship between internal HLOC and the intention to adopt mHealth was tested. The direct effect from internal HLOC to effort expectancy and the direct effect from effort expectancy to the intention to use mHealth were significant, but the direct effect from internal HLOC to the intention of adopting mHealth was not significant. This indicated that effort expectancy fully mediated the relationship between internal HLOC and the intention to use mHealth (β=0.056,
In
Previous studies have demonstrated the role of LOC in the tendency to use technology [
In this study, internal HLOC was not found to be significantly related to the intention to use mHealth (hypothesis 1). This result is inconsistent with previous research providing evidence that individuals with internal LOC beliefs tend to utilize health apps [
The UTAUT constructs, namely performance expectancy, effort expectancy, and social influence, were found to be significant predictors, with a positive relationship for the intention to use mHealth (hypotheses 2, 3, and 4), lending support to past studies that consistently showed the association between UTAUT determinants and eHealth and mHealth adoption [
To further explore whether internal HLOC can be suitably applied in the UTAUT model, the relationships between internal HLOC and 3 constructs of the UTAUT (ie, performance expectancy, effort expectancy, and social influence) in the mHealth context were postulated in this study (hypotheses 5, 6, and 7). The results showed that internal HLOC had a significant positive effect on performance expectancy, effort expectancy, and social influence in mHealth use, which suggests that the more internal the users are, the higher the perceived usefulness and ease of use they will have and the more likely they will conform to important others. These results are in line with the findings of previous studies that showed the significant influence of LOC on perceived usefulness and ease of use for mobile learning adoption [
In testing the mediating role of performance expectancy, effort expectancy, and social influence, this study found that these 3 constructs fully mediated the relationship between internal HLOC and the intention to use mHealth, supporting hypotheses 8, 9, and 10. Internal HLOC was positively related to the intention to adopt mHealth through performance expectancy. Effort expectancy had a mediating effect on the relationship between internal HLOC and the intention to use mHealth (hypothesis 9), similar to the results found in previous research [
The findings of this study have several implications. Theoretically, this study has contributed to mHealth literature by investigating the direct and indirect relationships between internal HLOC and the intention to use mHealth. The indirect relationship provided a more multifaceted understanding of mHealth adoption behavior, where both health and technology come into play in the adoption decision process. Moreover, the results attested the robustness of the UTAUT in mHealth adoption. To the best of our knowledge, this study is the first attempt to examine the indirect effect of internal HLOC on the behavioral intent of mHealth. The results of the effect of UTAUT dimensions on the intention to adopt mHealth have significant implications for health providers seeking methods to enhance mHealth engagement behavior. They can leverage cognitive and normative factors related to technology (ie, performance expectancy, effort expectancy, and social influence) to increase individuals’ preferences to use mHealth for health purposes.
The limitations of this study can be attributed to the urban sample concentrated in the most developed part of Malaysia. A more representative sample should be considered in future studies. Nonprobability sampling methods (ie, convenience and snowball) employed for the sample selection and the unbalanced gender makeup of the sample can jeopardize the generalizability of the results. The cross-sectional design used in this research does not provide definite information about cause and effect relationships. Moreover, social desirability bias can be a problem with self-report measurements used in this study. The framework proposed in this study predicted 42.7% of the variance in mHealth, while a more extended model encompassing more cognitive factors can augment the prediction power of the model. Given the absence of a significant association between internal HLOC and the intent to use mHealth in this research, replication studies are suggested, which can include a broader framework of multiple health-related factors (such as perceived health susceptibility, perceived health severity, perceived health status, and health consciousness) and personality factors in order to advance the frontier of knowledge regarding HLOC and technology adoption, since technology will become even more important in the future with “Industrial Revolution 4.0.” Moreover, incorporating perceived health risk factors along with HLOC will enable researchers to determine whether mHealth is a proactive/preventive health behavior, a reactive behavior, or both.
Questions related to internal health locus of control and the modified Unified Theory of Acceptance and Use of Technology constructs.
average variance extracted
health locus of control
heterotrait-monotrait
information and communications technology
locus of control
mobile health
partial least squares structural equation modeling
Unified Theory of Acceptance and Use of Technology
variance inflation factor
This study was funded by Xiamen University Malaysia Research Grant (XMUMRF/2019/C3/IART/0004).
None declared.