Nursing Staff’s Behavior Intention to Use Mobile Technology: An Exploratory Study Employing the UTAUT 2 Model

Abstract

Mobile technology has become increasingly common in recent years with its booming development across the globe, leading to the gradual adoption of mobile health, which can reduce the risk of caregivers being infected. The widespread use of mobile health has brought many benefits to the healthcare industry, including increased efficiency for frontline caregivers and a better patient–physician relationship. However, it also presents some challenges. This study aimed to examine caregivers’ behavior intention to use mobile technology in medical practice with a unified theory of acceptance and the use of technology two model. Satisfaction and self-efficacy were included as external variables and mobile technology identity as a moderator variable. A total of 281 valid questionnaires were collected from the nursing staff, and a structural equation model was used to test the hypotheses of this study. The results of the study showed that (1) self-efficacy had a significant effect on satisfaction and effort expectancy, (2) effort expectancy significantly affected performance expectancy, (3) performance expectancy and satisfaction had significant effects on behavior intention, and (4) mobile technology identity moderated the relationship between satisfaction and behavior intention. These results could inform medical institutions and facilities in launching mobile technology policies and education or training programs.

Keywords

UTAUT 2 mobile technology identity self-efficacy

Introduction

Mobile technology has spread rapidly worldwide in recent years, leading to the increased popularity of mobile health (mHealth) applications, with mHealth penetration being greater in high-income countries than in low-income countries (Alam et al., 2020; Byambasuren et al., 2019; Guo et al., 2016). The World Health Organization defined mHealth as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” (Li et al., 2019; World Health Organization [WHO], 2011). The use of mHealth will provide new opportunities for healthcare professionals to track their patients’ health status to obtain feedback in real time (Berman et al., 2018; Fatehi et al., 2017; Miyamoto et al., 2016), while providing frontline caregivers with a number of benefits, such as acquired knowledge, streamlined work, saved time, and improved care quality (P. Johansson et al., 2014). Giles-Smith et al. (2017) notes that clinical caregivers who are interested in and familiar with the use of mobile technology have a great advantage in accessing and sharing information. The use of mobile technology, in turn, can also facilitate the flow of medical and medication information to improve self-efficacy (Goldsworthy et al., 2006; P. E. Johansson et al., 2013). The COVID-19 outbreak in 2019 presented a huge challenge to healthcare systems. mHealth can be used for remote monitoring, thus avoiding exposing caregivers to the risk of infection, and it is an indispensable tool to ensure the health of frontline caregivers and reduce the risk of infection. Ferrua et al. (2021) proposed the use of mHealth to help caregivers monitor the status of cancer patients and those in home isolation, allowing for both care monitoring and caregiver protection; therefore, the use of mHealth may reduce the risk of infection for caregivers. Taken together, the widespread use of mHealth has brought many benefits to the healthcare industry, including increased efficiency for frontline caregivers and an improved patient–physician relationship.

Considering the numerous benefits, many scholars have applied academic theories to explore mHealth-related issues, particularly the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2; Giles-Smith et al., 2017; Gücin & Berk, 2015; Gurupur & Wan, 2017). Among them, UTAUT 2 is more comprehensive and has a higher explanatory power for the applicability to the user environment (S. Pan & Jordan-Marsh, 2010; Verdegem & De Marez, 2011; Yu et al., 2021). Some scholars have used UTAUT 2 to explore the problems encountered in using mHealth and found that performance expectancy and habit were important factors influencing caregivers’ use of mobile technology. They also demonstrated the good explanatory power of the model (Owusu Kwateng et al., 2021; Venkatesh et al., 2012).

Meanwhile, higher levels of self-efficacy are associated with more positive attitudes toward using mHealth and have significant effects on effort expectancy and satisfaction (Aggelidis & Chatzoglou, 2009; Judge & Bono, 2001; Melas et al., 2011; Schaper & Pervan, 2007; Shiferaw & Mehari, 2019; Wu et al., 2007). Experiences related to information technology (IT) use and mobile technology identity show a significant relationship with behavior intention for mHealth (Al-Azzam et al., 2019). From the preceding discussion, it is clear that mobile technology identity has a direct effect on behavior intention; however, only a few studies use mobile technology identity as a moderator. Therefore, this study explored caregivers’ behavior intention to use mobile technology by including variables such as self-efficacy and satisfaction in the UTAUT 2 model. In summary, this study aimed to extend the UTAUT 2 model to explore the factors that influence caregivers’ behavior intention to use mobile technology by incorporating variables such as satisfaction, mobile technology identity, and self-efficacy. Furthermore, this study considered mobile technology identity as a moderating variable and explored how it moderates the effects of satisfaction, performance expectancy, and effort expectancy on behavior intention. Thus, the specific purposes of this study were (1) to examine the decision-making process of caregivers’ behavior intention to use mobile technology by extending the UTAUT 2 model; (2) to understand the effects of self-efficacy on performance expectancy, effort expectancy, and satisfaction; and (3) to explore how mobile technology identity moderates the effects of satisfaction, performance expectancy, and effort expectancy on behavior intention. The results of the study could inform mobile technology policies and education or training programs to be launched by medical institutions and facilities.

Literature Review and Hypotheses

Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2)

The use and acceptance of IT originated from the Theory of Reasoned Action (TRA; Fishbein & Ajzen, 1975, 1977) and the Theory of Planned Behavior (TPB; Ajzen, 1991). The two theories are also the basis for the Technology Acceptance Model (TAM; Davis, 1989). Venkatesh et al. (2003) extended the theoretical explanation by combining perceived usefulness and behavior intention to explain the social influence process, resulting in evidence of perceived ease-of-use and perceived usefulness and improved understanding of user behavior. Then, Venkatesh et al. (2003) integrated eight technology-related theoretical models—Theory of Reasoned Action, Theory of Planned Behavior, Technology Acceptance Model, Motivation Model, Social Cognitive Theory, Model of PC Utilization, Combined TAM and TPB, and Innovation Diffusion Theory—to propose UTAUT. The model has four main dimensions. Performance expectancy is the degree of performance improvement after users use IT. Effort expectancy is the degree of users’ efforts to use IT. The information system must be designed to be user-friendly, easy to operate, and easily acceptable to users. Social influence is the extent to which people who are important to the user influence their use of IT. Facilitating conditions is the extent to which users feel that the organization or design can support the use of IT. Facilitating conditions are determinants of behavior intention. Perceived ease-of-use in Technology Acceptance Model is categorized into effort expectancy, or the degree of ease in using IT, while perceived usefulness is categorized as performance expectancy, or the extent to which using IT enhances performance (Davis, Bagozzi, & Warshaw, 1989; Hsieh et al., 2016). Hoque and Sorwar (2017) explored the behavior intention to use mHealth by taking Bangladeshi elders as a study population. Their results suggested that performance expectancy, effort expectancy, social influence, and facilitating conditions had a positive and significant effect on behavior intention. Therefore, this study proposed hypotheses 1–2:

H1: Performance expectancy positively impacts behavior intention to use mobile technology.

H2: Effort expectancy positively impacts behavior intention to use mobile technology.

In addition to its considerable explanatory power in exploring users’ usage behaviors, with the popularity of mHealth, UTAUT has also been used in studies exploring users’ use of mHealth (Bertrand & Bouchard, 2008; Coyle et al., 2007; Park, 2009). Ben Arfi et al. (2021) found that users’ behavior intention to use mHealth was influenced by performance expectancy and effort expectancy. Factors such as performance expectancy, social influence, and facilitating conditions are also determinants of caregivers’ use of mobile technology, and the model has good explanatory power (M. Pan & Gao, 2021). Alam et al. (2020) found high levels of acceptance of mHealth among young people and the significant effects of performance expectancy, social influence, and facilitating conditions on behavior intention. Therefore, this study proposed hypotheses 3–4:

H3: Social influence positively impacts behavior intention to use mobile technology.

H4: Facilitating condition positively impacts behavior intention to use mobile technology.

Venkatesh et al. (2012) extended UTAUT by adding the three constructs of hedonic motivation, price value, and habit to propose UTAUT 2. The latter can explain users’ reasons for adopting IT (Yu et al., 2021). In this model, hedonic motivation, which indicates users’ enjoyment in using IT, occupies an important place in the studies of information acceptance and is also the most appropriate predictor of integrating UTAUT (Brown & Venkatesh, 2005; Thong et al., 2006; Van der Heijden, 2004). Price value is a measure of the perceived benefits of using IT versus the actual costs incurred. In general, users are more concerned with cost, which can influence decision making. Habit can be considered the accumulation of prior experiences. The degree to which users can perform a particular behavior on their own can be an important determinant of their use of mHealth, meaning that users have positive attitudes toward the mHealth system. It has been shown that habit has less influence on intention as users become more accustomed to IT use (S. S. Kim et al., 2005; Limayem et al., 2007; Owusu Kwateng et al., 2021). Duarte and Pinho (2019) found that performance expectancy and habit were effective predictors of caregivers’ use of mHealth technology. Yu et al. (2021) use UTAUT 2 to explore patients in hospitals who use mobile medical education websites and the results are effort expectancy positively impacts performance expectancy. Therefore, this study used the habit variable of UTAUT 2 and proposed hypotheses 5-6:

H5: Habit positively impacts behavior intention to use mobile technology.

H6: Effort expectancy positively impacts performance expectancy.

In recent years, numerous researchers have used UTAUT 2 to explore the behavioral intention to use mHealth (Table 1). The application of UTAUT 2 in mHealth has been quite popular and has precipitated several advantages, but most of the research is discussed from the perspective of patients, older adults, and different groups, and rarely explores behavioral intentions from the perspective of nurses. The intention of front-line personnel to use mHealth is an extremely important topic; thus, this study utilized UTAUT 2 to explore intentions to use mHealth from the perspective of nursing staff.

Table 1.

Research of UTUAT 2.

Authors/Years	Subject of article	Findings
Hoque and Sorwar (2017)	Exploring the behavior intention to use mHealth amongst Bangladeshi older adults	It is effective to predict the Bangladeshi older adults’ intention of using mHealth.
Duarte and Pinho (2019)	Combining Partial Least Squares (PLS-SEM) and fuzzy-set qualitative comparative analysis to explore the user’s intention of mHealth	Performance expectancy and habit were effective predictors of caregivers’ use of mHealth technology.
Alam et al. (2020)	Understanding the determinants of mHealth apps’ adoption in Bangladesh	High levels of acceptance of mHealth among young people
Salgado et al. (2020)	Understanding the factors of accepting mHealth in patients with chronic diseases and non-chronic diseases	Patients with chronic diseases are more motivated to adopt mHealth than those without chronic diseases.
Owusu Kwateng et al. (2021)	Understanding the factors of adopting health information systems by healthcare workers in developing countries	UTAUT 2 should be effective to explain the uptake of health information systems by healthcare workers.
Yu et al. (2021)	Exploring the patients’ intention to use mobile health education website.	UTAUT 2 should be effective to predict the patients’ intention of mobile health education website.
Taneja and Bharti (2022)	Summary of growth trends in UTAUT 2 research from 2012 to 2019	The research on UTAUT 2 is increasing.
Tian and Wu (2022)	Integrating ECM-ISC and UTAUT to explore the factors of continuous use of mHealth in older adults with chronic diseases.	It can effectively predict intentions of older adults with chronic diseases to continue to use mHealth, and provide a reference for developers.
Zhu et al. (2021)	Exploring the user’s intention of the Mobile Chronic Disease Management Service (MCDMS)	The influencing factors for young people and older adults are different, and marketing suggestions for different age groups can be provided.

Satisfaction

Satisfaction refers to the user’s level of satisfaction or disappointment in terms of the desired performance or outcome of product use (Kotler et al., 2016). Satisfaction has been widely used to explore its relationship with users’ behavior intention to use and is also an important factor predicting behavior intention to use (Sharma & Sharma, 2019). In addition, satisfaction is also a major factor that influences users to continue using mobile technology and can be used to judge the satisfaction level of using a product or service. If the satisfaction level is higher than expected, the intention to use it again is higher (Bhattacherjee, 2001; Chow & Shi, 2014; M. C. Lee, 2010; Leung & Chen, 2019; Wang & Liao, 2007). Satisfaction can also involve exploring the relationship between users’ behavior intention to use mHealth and satisfaction. Barutçu et al. (2018) found that satisfaction with mHealth has a positive effect on intention to use it. W. I. Lee et al. (2021) explored users’ use of mHealth based on UTAUT and found that satisfaction has a significant effect on behavior intention to use; therefore, this study incorporated the satisfaction variable and proposed hypothesis 7:

H7: Satisfaction positively impacts behavior intention to use mobile technology

Self-efficacy

Bandura (1986) defines self-efficacy as an individual’s ability to plan and execute goals and to achieve a specified level of performance. Self-efficacy in the field of mobile technology is defined as the extent to which users believe they can use mobile technology to accomplish a task or job, and it is also an important factor influencing their use of mobile technology (Bandura, 1986; Compeau & Higgins, 1995). Previous studies have examined the impact of self-efficacy on behavior intention to use mobile technology. Self-efficacy also directly influences users’ perceived ease-of-use (Asimakopoulos et al., 2017; Balapour et al., 2019; Jaradat et al., 2018; Zhang et al., 2017); the higher the level of self-efficacy of healthcare workers in using mobile technology, the more positive their attitude toward using it. In addition, younger age groups are more enthusiastic about operating diverse mobile technologies and are more familiar with using and understanding mobile technologies (K. H. Kim et al., 2019; Strudwick et al., 2016). Davis, Bagozzi, and Warshaw (1989) categorized perceived ease-of-use of TAM as effort expectancy and perceived usefulness as performance expectancy. Hsieh et al. (2016) used the UTAUT model to study personal health system use behavior and found that self-efficacy has a positive relationship with perceived ease-of-use and perceived usefulness. Moreover, many prior health care-related studies found that self-efficacy significantly affects effort expectancy (Aggelidis & Chatzoglou, 2009; Melas et al., 2011; Schaper & Pervan, 2007; Shiferaw & Mehari, 2019; Wu et al., 2007) and has a significant effect on satisfaction (Judge & Bono, 2001). Therefore, this study used self-efficacy as an external variable and proposed hypotheses 8 to 10:

H8: Self-efficacy positively impacts satisfaction

H9: Self-efficacy positively impacts performance expectancy

H10: Self-efficacy positively impacts effort expectancy

Mobile Technology Identity

Identity is an important indicator of an individual’s continuity of self-perception and enhanced self-assessment (Burke & Stets, 2009). It creates a stable and continuous environment for the individual and validates the importance of personal information within the organization as well as the understanding and response to the surrounding environment. Thus, identity theory can explain the influence of the way in which the self is viewed and the social structure of the individual as well as how the individual operates within the social framework (Reychav et al., 2019). M. Carter and Grover (2015) proposed identification with mobile technology based on existing identity theories to argue the importance and expectancy that individuals internalize, thus explaining individual behavior (Reychav et al., 2019; Stets & Biga, 2003). Therefore, mobile technology identity is the extent to which individuals view mobile technology as part of their self-cognition and also the extent to which individuals feel about themselves, their sense of dependence, and the behaviors that drive adoption and resistance (M. S. Carter, 2012; M. Carter & Grover, 2015; Clayton & Opotow, 2003; Reychav et al., 2019; Stets & Biga, 2003). Users with high levels of mobile technology identity are fond of using mobile technology and rely on the benefits of using it, thus increasing their intention to use mHealth and identity (Balapour et al., 2019; M. Carter & Grover, 2015). From the above, it is clear that the influence of mobile technology identity on behavior intention has been explored in the past, and significant effects have been identified; however, mobile technology identity has rarely been used as a moderating variable. Therefore, this study used mobile technology identity as a moderating variable and proposed the following hypotheses.

M1: Mobile technology identity moderates satisfaction and intention behavior

M2: Mobile technology identity moderates performance expectancy and intention behavior

M3: Mobile technology identity moderates effort expectancy and intention behavior

Methodology

This study was based on the UTAUT 2 of Venkatesh et al. (2012), with external variables such as satisfaction and self-efficacy and the moderating variable mobile technology identity being incorporated to explore caregivers’ behavior intention to use mobile technology. Structural Equation Modeling (SEM) was used for hypothesis testing. SEM can be used to analyze the causal relationships among variables. It has been widely used to explore people’s satisfaction and behavioral intentions in various fields. Zeng and Li (2021) used SEM to analyze and explore the relationship between satisfaction, revisit intention, and the experience value of passengers. Cempena et al. (2021) used SEM to analyze the causal relationship between service quality, brand quality, tourism products and satisfaction, and customer value as an Intervening Variable. Chen et al. (2021) integrated the knowledge-attitude-practice theory and theory of planned behavior to explore the intention of responsible tourism behavior of travelers at the risk of COVID-19.

In recent years, mobile technology has become popular, and some scholars have used SEM to analyze people’s behavioral intentions to use mobile technology. Tran (2021) used SEM to analyze consumers’ behavioral intentions to use Mobile Food Delivery Applications during COVID-19 pandemic and the impact on ongoing behavior. Vo et al. (2022) used SEM to analyze people’s behavioral intentions to shop using mobile technology. Salgado et al. (2020) explored the behavioral intention of using mHealth from the perspective of patients and used SEM for analysis and verification. Alam et al. (2020) used SEM to analyze people’s acceptance of mHealth and found high levels of acceptance among young people. From the above points, it can be concluded that SEM is an effective method for modeling and verification in the study of people’s behavioral intentions. Figure 1 presents the framework of this study.

Figure 1.

Research model.

Data Collection and Measures

The population of this study included nursing staff in central Taiwan, and the questionnaire method was used to collect data. The behavior intention, performance expectancy, effort expectancy, social influence, facilitating conditions, and habit of the UTAUT 2 model were mainly measured with scales from Venkatesh and Zhang (2010), Sok Foon and Chan Yin Fah (2011), Sripalawat et al. (2011), and Al-Azzam et al. (2019), with minor revisions. They were used to measure the perceptions of the caregivers. Mobile technology was measured with scales from M. Carter and Grover (2015), Al-Azzam et al. (2019), and Reychav et al. (2019), with some revisions; these were used to measure caregivers’ reliance on mobile technology and self-cognition. The self-efficacy scale was based on Liao et al. (2009) and Balapour et al. (2019), with some changes to measure the extent to which caregivers themselves used mobile technology to accomplish their work or tasks. Satisfaction was measured based on the scales of Natarajan et al. (2017) and W. I. Lee et al. (2021) with some revisions, mainly involving caregivers’ satisfaction with the use of mobile technology and its effects on behavior intention. This study adopted 5-point Likert scales, with 1 = strongly disagree and 5 = strongly agree. Corrections were made before distributing the questionnaire by incorporating expert inputs about the correction and constructed scales. A pre-test questionnaire was also distributed after the correction and analyzed. The results showed that Cronbach’s α ranged from 0.926 to 0.976—above 0.7, as suggested by Hair et al. (2010), which indicates a good internal consistency of the questionnaire in this study.

Result

Respondents’ Background Information

In this study, a total of 300 questionnaires were sent to nursing staff in a regional hospital in central Taiwan, and after deducting those with incorrect or incomplete answers, 281 questionnaires were considered valid, with an effective rate of 94%. Across the ranks, clinical nursing staff constituted the majority of the sample, accounting for 89.7%. Most (92.2%) of the respondents had college degrees and had served for 15 to 20 years (29.5%). Among the nursing staff, most were nurses (45.5%). The respondents were predominantly aged 40 to 49 years, accounting for 38.4% of the total. Regular staff accounted for more than 90% of the respondents. The basic information of the respondents is shown in Table 2.

Table 2.

Basic Information of the Respondents (N = 281).

Rank	Frequency	Percentage	Education	Frequency	Percentage
Nursing director	18	6.4	Nursing high schools	13	4.6
Clinical nursing personnel	252	89.7	Graduate	259	92.2
Others	11	3.9	Postgraduate above	9	3.2
Job tenure	Frequency	Percentage	Nursing level	Frequency	Percentage
Less than 1 year	11	3.9	Nurse head	11	3.9
1 to less than 3 years	29	10.3	N0	9	3.2
3 to less than 5 years	38	13.5	N1	71	25.3
5 to less than 10 years	38	13.5	N2	128	45.6
10 to less than 15 years	68	24.2	N3	55	19.6
15 to less than 20 years	83	29.5	N4	7	2.5
20 years above	14	5.0	Age	Frequency	Percentage
Positions	Frequency	Percentage	20–29	42	14.9
Full-time staff	269	95.7	30–39	97	34.5
Contractor	12	4.3	40–49	108	38.4
			50+	37	12.1

Analysis of Measurement Models

In this study, individual item reliability, composite reliability (CR), and average variance extracted (AVE) were used to analyze reliability and validity (Bagozzi & Yi, 2012). The results in Table 3 show that the factor loadings of each construct are >0.5 (0.744–0.952), meeting the criteria of factor loadings suggested by Hair et al. (2010), and significant (p < .05). The Cronbach’s α of each construct is above 0.7, which is consistent with the suggestion (alpha value >0.7) of Hair et al. (2010). Thus, the questionnaire of this study has good internal consistency and high reliability. The CR values, which measure the consistency among variables, are all greater than 0.6—is in line with Fornell and Larcker (1981), which suggested that CR values must be >0.6. The AVE values of the constructs measuring the convergent validity of the latent variables are all greater than 0.5, which is also in line with Fornell and Larcker’s (1981) recommendation that AVE values should be >0.5, indicating that the questionnaire has good convergent validity. In addition, the square root of AVE is greater than the correlation coefficient of each construct, and the correlation coefficient is less than 1, consistent with Fornell and Larcker’s (1981) recommendation, indicating that this questionnaire has good discriminant validity.

Table 3.

Construct Reliability Results.

Construct	No. of items	Item loading	Cronbach’s α	CR	AVE	DV
Self-efficacy	3	0.926–0.936	0.923	0.951	0.866	1.336
Satisfaction	5	0.913–0.935	0.955	0.966	0.848	1.202
Performance expectancy	5	0.842–0.908	0.933	0.949	0.789	2.428
Effort expectancy	5	0.878–0.915	0.937	0.952	0.798	1.493
Social influence	5	0.789–0.886	0.896	0.923	0.706	1.321
Facilitating condition	5	0.815–0.921	0.917	0.938	0.753	1.417
Habit	4	0.728–0.929	0.890	0.924	0.755	1.502
Mobile technology identity	7	0.788–0.921	0.948	0.957	0.763	1.087
Behavior intention	3	0.931–0.955	0.936	0.959	0.887	1.257

Note. AVE = average variance extracted; CR = composite reliability; DV = discriminant validity

Henseler et al. (2015) proposed a new method for assessing validity, namely HTMT, which should be at least less than 0.9. Table 4 shows that the HTMT values of this study are less than 0.9, and the HTMT values obtained by the method of 5,000 own samples show that the discriminant validity of this study is acceptable.

Table 4.

Correlations Among Major Constructs.

Construct	1	2	3	4	5	6	7	8	9
Self-efficacy	0.931	0.857	0.385	0.671	0.667	0.718	0.635	0.774	0.735
Satisfaction	0.805	0.921	0.473	0.644	0.718	0.644	0.734	0.881	0.888
Performance expectancy	0.358	0.448	0.888	0.539	0.628	0.426	0.551	0.461	0.521
Effort expectancy	0.626	0.609	0.503	0.893	0.798	0.785	0.714	0.591	0.544
Social influence	0.608	0.663	0.570	0.731	0.840	0.777	0.742	0.676	0.636
Facilitating condition	0.663	0.602	0.393	0.729	0.703	0.867	0.707	0.544	0.547
Habit	0.580	0.674	0.498	0.653	0.662	0.640	0.869	0.771	0.667
Mobile technology identity	0.724	0.838	0.434	0.557	0.624	0.507	0.709	0.874	0.780
Behavior intention	0.683	0.840	0.487	0.510	0.578	0.506	0.603	0.734	0.942

Note. The bold numbers in the diagonal are the square root of AVE for each construct. Above the diagonal are the HTMT values. Below the diagonal are correlation between construct.

Analysis of Structural Models

In this study, structural equation modeling (SEM) was used to test whether the hypotheses of this study are valid. At the significance level of p < .05, performance expectancy showed a positive and significant effect on behavior intention (β = .161, t = 2.799); thus, hypothesis 1 was supported. Effort expectancy showed a positive and significant effect on performance expectancy (β = .463, t = 6.542); therefore, hypothesis 6 was supported. Satisfaction showed a positive and significant effect on behavior intention (β = .654, t = 7.658); thus, hypothesis 7 was supported. Self-efficacy showed a positive and significant effect on performance expectancy (β = .626, t = 10.756); therefore, hypothesis 9 was supported. Self-efficacy showed a positive and significant effect on habit (β = .806, t = 29.625); thus, hypothesis 10 was supported. As effort expectancy, social influence, facilitating conditions, and habit did not reach the significance level for their effects on behavior intention, hypotheses 2, 3, 4, and 5 were not valid. Self-efficacy had no significant effect on performance expectancy, and therefore, hypothesis 8 was rejected. Regarding hypotheses about the moderating effects, mobile technology identity moderated the relationship between satisfaction and behavior intention; thus, the moderating hypothesis 1 is valid. However, mobile technology identity did not moderate the relationships between performance expectancy and behavior intention and between effort expectancy and behavior intention, meaning that the moderating hypotheses 2 and 3 were not valid. A summary of the path analysis results is shown in Table 5. Figure 2 presents the path of the models.

Table 5.

Estimation Results.

Hypotheses	Path from/to	Model 1		Model 2		Model 3
Hypotheses	Path from/to	β	t-Value	β	t-Value	β	t-Value
H 1	Performance expectancy →Behavior intention	.143*	2.535	.143*	2.545	.161*	2.799	Accept
H 2	Effort expectancy →Behavior intention	–0.077	1.259	−0.078	1.218	−0.069	1.118	Reject
H 3	Social influence →Behavior intention	.007	0.094	.001	0.013	.052	0.792	Reject
H 4	Facilitating condition →Behavior intention	–0.009	0.170	.000	0.005	.025	0.459	Reject
H 5	Habit →Behavior intention	.065	1.048	.047	0.672	.085	1.375	Reject
H 6	Effort expectancy →Performance expectancy	.463*	6.512	.463*	6.496	.463*	6.542	Accept
H 7	Satisfaction →Behavior intention	.780*	8.825	.732*	8.518	.654*	7.658	Accept
H 8	Self-efficacy →Performance expectancy	.071	1.001	.071	0.994	.071	1.002	Reject
H 9	Self-efficacy →Effort expectancy	.626*	1.0846	.626*	10.618	.626*	10.756	Accept
H10	Self-efficacy →Satisfaction	.806*	29.594	.806*	29.248	.806*	29.625	Accept
Interaction effect
M 1	Satisfaction * Mobile technology identity →Behavior intention					−.077*	2.010	Accept
M 2	Performance expectancy * Mobile technology identity →Behavior intention					.044	0.961	Reject
M 3	Effort expectancy * Mobile technology identity →Behavior intention					.015	0.346	Reject
R ²
Satisfaction		.649			.649		.649
Performance expectancy		.260			.260		.260
Effort expectancy		.392			.392		.392
Behavior intention		.726			.728		.739

p < .1.

Figure 2.

Path coefficients for the research model.

Conclusion and Discussion

Mobile technology has led to significant changes in the healthcare industry and radically changed people’s medical behavior. This study was based on the UTAUT 2, with the external variables of self-efficacy and satisfaction and the moderating variable of mobile technology identity to explore caregivers’ behavior intention to use mobile technology. The analysis of the study results showed that performance expectancy had a positive and significant effect on behavior intention. If the use of mobile technology could improve their work efficiency, caregivers would be more willing to use it (Ben Arfi et al., 2021; M. Pan & Gao, 2021). The positive effect of effort expectancy on performance expectancy was similar to that of Yu et al. (2021). The reason for this is that when caregivers perceive mobile technology as easy to use and not requiring much time to familiarize themselves with it, they expect the use of mobile technology to contribute significantly to productivity improvement.

As the UTAUT 2 model indicates that effort expectancy has a significant effect on behavior intention, the results of this study showed no significant effect. The finding is similar to that of Jewer (2018), and it can be explained by the fact that nearly 50% of the nursing staff in this study were young and relatively familiar with mobile technology and thus did not need much time to familiarize themselves with it. Therefore, the effect of effort expectancy on behavior intention was not significant. Social influence did not have a significant effect on behavior intention, similar to that suggested by de Veer et al. (2015). That is, when caregivers’ significant others did not feel strongly about using mobile technology, it would lead to the caregivers’ perception that using mobile technology was not particularly important. The reason for the lack of a significant effect of facilitating conditions on behavior intention is that the respondents had been working for more than 10 years and had a set of established patterns for their work; thus, they were less concerned about the convenience of using mobile technology for their work. The finding is similar to that of Hoque and Sorwar (2017). Habit did not have significant effects on behavior intention—a result similar to those of S. S. Kim et al. (2005) and Limayem et al. (2007). The reason is that when young caregivers are already accustomed to the use of mobile technology, even to the extent of automatic execution, then the effect on behavior intention will be reduced. For users in different environments, the finding must be further verified (Macedo, 2017). In addition, 50% of the respondents in this study have more than 10 years of work experience, and it is inferred that they are already accustomed to the work logic, which could make them less willing to change established work modes. Therefore, hospital administrators can plan relevant educational trainings and explain the advantages of using mobile technology so that more senior caregivers will be willing to use mobile technology and thus make the use of mobile technology part of their career planning.

Satisfaction had a positive and significant effect on behavior intention. The result is consistent with that of W. I. Lee et al. (2021), suggesting that caregivers’ behavior intention to use mobile technology will be enhanced when they perceive that using mobile technology brings satisfaction or a sense of accomplishment at work. Self-efficacy had a positive and significant effect on effort expectancy, which echoes the results of Shiferaw and Mehari (2019), Strudwick et al. (2016), and K. H. Kim et al. (2019); this is because nearly 50% of the young caregivers in this study may be more enthusiastic about the operation and use of mobile technology, more familiar with its procedures, and thus more familiar with its use. Self-efficacy also had a positive effect on satisfaction. When caregivers perceive using mobile technology as easy and straightforward, they are satisfied with the convenience and efficiency of mobile technology (W. I. Lee et al., 2021); however, contrary to what Davis, Bagozzi, and Warshaw (1989) has suggested, the effect of self-efficacy on performance expectancy in this study was not significant. The higher the level of self-efficacy, the more positive the caregivers’ attitude toward the use of mobile technology, and the easier it is for them to accomplish any task in life and at work; therefore, the increase in productivity would seem to be less related to the use of mobile technology.

In terms of moderating effect, the analysis results showed that mobile technology identity moderated the relationship between satisfaction and behavior intention. As users with a higher mobile technology identity love using mobile technology (Balapour et al., 2019; M. Carter & Grover, 2015), an indirect effect is that users are more willing to use mobile technology when satisfaction is increased. However, mobile technology identity did not significantly moderate the relationship between performance expectancy and behavior intention. The reason is that performance expectancy had a positive and significant effect on behavior intention. That is, as long as caregivers believe that the use of mobile technology can improve work efficiency or patient–physician relationships, their behavior intention to use mobile technology will only be marginally affected by their low dependence on mobile technology or whether they are fond of the technology. In addition, mobile technology identity did not moderate the relationship between effort expectancy and behavior intention because the effect of effort expectancy on behavior intention was not significant, meaning that younger caregivers had a better grasp of mobile technology. Therefore, the effect of effort expectancy on behavior intention was not significant, regardless of whether they were dependent on mobile technology or expected to use it.

In summary, with the UTAUT 2 model, only performance expectancy had a significant effect on behavior intention, and effort expectancy significantly affected performance expectancy. Effort expectancy, social influence, facilitating conditions, and habit did not substantially affect behavior intention. Meanwhile, self-efficacy had a significant effect on satisfaction and effort expectancy but no significant effect on performance expectancy. The first key point is that nursing staff would be more willing to use mobile technology given that using it can improve work efficiency, although effort expectancy, social influence, facilitating conditions, and habit produce no significant effect. Moreover, caregivers who perceive the convenience of using mobile technology will be less affected by colleagues and an environment discouraging its use. The respondents in this study have worked for more than 10 years, are familiar with the working patterns, and are unwilling to change them. Therefore, to encourage nursing staff to use mobile technology in the future, the management can strengthen relevant education and training and make staff aware of the benefits of using mobile technology.

Research Limitation

The limitation of this study is that the study population only comprises nursing staff in a central Taiwan hospital, and the scope of data collection is narrow. In addition, considering the differences in policy planning for mobile technology across regions, the findings cannot be widely applied to nursing staff in other regions. Data collection from several regions may reveal region-specific factors affecting caregivers’ use of mobile technology. In addition, this study used a self-reported questionnaire to conduct the survey, which is more subjective in terms of data presentation. Objective data such as actual usage could be incorporated in the future. Furthermore, as it is difficult to determine the effect of time with cross-sectional data, it is recommended to extend the observation period to explore the effect of time on the data. Finally, this study has incorporated self-efficacy and satisfaction, and mobile technology identity was included as a moderating variable. Future studies can explore factors affecting caregivers’ use of mobile technology with an alternative set of variables.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the St. Joseph’s Hospital, Taiwan

An Ethics Statement

This study was reviewed and approved by the Institutional Review Board of Changhua Christian Hospital, IRB number 200611.

ORCID iD

Mei-Chen Chuang

References

Aggelidis

V. P.

Chatzoglou

P. D.

(2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78(2), 115–126.

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Alam

M. Z.

Kaium

M. A.

Hoque

M. R.

Alam

M. M. D.

(2020). Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach. Technology in Society, 61, 1–18.

Al-Azzam

M. K.

Bader

Khalid

(2019). MHealth for decision making support: A case study of EHealth in the public sector. International Journal of Advanced Computer Science and Applications, 10(5), 381–387.

Asimakopoulos

Spillers

(2017). Motivation and user engagement in fitness tracking: Heuristics for mobile healthcare wearables. Informatics, 4(1), 5.

Bagozzi

R. P.

(2012). Specification, evaluation, and interpretation of structural equation models. Journal of the Academy of Marketing Science, 40(1), 8–34.

Balapour

Reychav

Sabherwal

Azuri

(2019). Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps. International Journal of Information Management, 49, 58–68.

Bandura

(1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.

Barutçu

Ünal Adıgüzel

(2018). A technology acceptance analysis for mhealth apps: The case of Turkey. Balkan and Near Eastern Journal of Social Sciences, 4(4), 104–113.

10.

Ben Arfi

Ben Nasr

Khvatova

Ben Zaied

(2021). Understanding acceptance of eHealthcare by IoT natives and IoT immigrants: An integrated model of UTAUT, perceived risk, and financial cost. Technological Forecasting and Social Change, 163, 1–17.

11.

Berman

M. A.

Guthrie

N. L.

Edwards

K. L.

Appelbaum

K. J.

Njike

V. Y.

Eisenberg

D. M.

Katz

D. L.

(2018). Change in glycemic control with use of a digital therapeutic in adults with type 2 diabetes: Cohort study. JMIR Diabetes, 3(1), 1–9.

12.

Bertrand

Bouchard

(2008). Applying the technology acceptance model to VR with people who are favorable to its use. Journal of Cyber Therapy and Rehabilitation, 1(2), 200–210.

13.

Bhattacherjee

(2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25, 351–370.

14.

Brown

S. A.

Venkatesh

(2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. Management Information Systems Quarterly, 29(3), 399.

15.

Burke

P. J.

Stets

J. E.

(2009). Identity theory. Oxford University Press.

16.

Byambasuren

Beller

Glasziou

(2019). Current knowledge and adoption of mobile health apps among Australian general practitioners: Survey study. JMIR mhealth and uhealth, 7(6), 1–8.

17.

Carter

Grover

(2015). Me, my Self, and I (T): Conceptualizing Information Technology Identity and its implications. MIS Quarterly, 39(4), 931–957.

18.

Carter

M. S.

(2012). Information technology (IT) identity: A conceptualization, proposed measures, and research agenda [Doctoral dissertation. Clemson University].

19.

Cempena

I. B.

Brahmayanti

I. A. S.

Astawinetu

E. D.

Panjaitan

F. A. B.

Kartini

I. A. N.

Panjaitan

(2021). The role of customer values in increasing tourist satisfaction in Gianyar Regency, Bali, Indonesia. Journal of Asian Finance Economics and Business, 8(8), 553–563.

20.

Chen

Dai

Liu

Jia

(2021). Can the COVID-19 risk perception affect tourists’ responsible behavior intention: An application of the structural equation model. Journal of Sustainable Tourism, 31, 1–20.

21.

Chow

W. S.

Shi

(2014). Investigating students’ satisfaction and continuance intention toward e-learning: An extension of the expectation – Confirmation Model. Procedia - Social and Behavioral Sciences, 141, 1145–1149.

22.

Clayton, S., & Opotow, S. (Eds.) (2003). Identity and the natural environment: The psychological significance of nature. MIT Press.

23.

Compeau

D. R.

Higgins

C. A.

(1995). Application of social cognitive theory to training for computer skills. Information Systems Research, 6(2), 118–143.

24.

Coyle

M. K.

Duffy

J. R.

Martin

E. M.

(2007). Teaching/learning health promoting behaviors through telehealth. Nursing Education Perspectives, 28(1), 18–23.

25.

Davis

F. D.

(1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.

26.

Davis

F. D.

Bagozzi

R. P.

Warshaw

P. R.

(1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003.

27.

de Veer

A. J.

Peeters

J. M.

Brabers

A. E.

Schellevis

F. G.

Rademakers

J. J.

Francke

A. L.

(2015). Determinants of the intention to use e-Health by community dwelling older people. BMC Health Services Research, 15(1), 103–109.

28.

Duarte

Pinho

J. C.

(2019). A mixed methods UTAUT2-based approach to assess mobile health adoption. Journal of Business Research, 102, 140–150.

29.

Fatehi

Gray

L. C.

Russell

A. W.

(2017). Mobile health (mHealth) for diabetes care: Opportunities and challenges. Diabetes Technology & Therapeutics, 19(1), 1–3.

30.

Ferrua

Mathivon

Duflot-Boukobza

Abbas

Charles

Barrais

Legendre

Mendes

Pons

Merad

Minvielle

Mir

Scotté

(2021). Nurse navigators’ telemonitoring for cancer patients with COVID-19: A French case study. Supportive Care in Cancer, 29(8), 4485–4492.

31.

Fishbein

Ajzen

(1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.

32.

Fishbein

Ajzen

(1977). Belief, attitude, intention, and behavior: An introduction to theory and research. Contemporary Socioiogy.

33.

Fornell

Larcker

D. F.

(1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

34.

Giles-Smith

Spencer

Shaw

Porter

Lobchuk

(2017). A study of the impact of an educational intervention on nurse attitudes and behaviours toward mobile device use in hospital settings. Journal of the Canadian Health Libraries Association, 38(1), 12–29.

35.

Goldsworthy

Lawrence

Goodman

(2006). The use of personal digital assistants at the point of care in an undergraduate nursing program. Computers, Informatics, Nursing, 24(3), 138–143.

36.

Gücin

N. Ö.

Berk

Ö. S.

(2015). Technology acceptance in health care: An integrative review of predictive factors and intervention programs. Procedia - Social and Behavioral Sciences, 195, 1698–1704.

37.

Guo

Zhang

Sun

(2016). The privacy–personalization paradox in mHealth services acceptance of different age groups. Electronic Commerce Research and Applications, 16, 55–65.

38.

Gurupur

V. P.

Wan

T. T.

(2017). Challenges in implementing mHealth interventions: A technical perspective. mHealth, 3, 32.

39.

Hair

Jr Black

W. C.

Babin

B. J.

Anderson

R. E.

(2010). Multivariate data analysis: A global perspective (7th ed.). MacMillan.

40.

Henseler

Ringle

C. M.

Sarstedt

(2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

41.

Hoque

Sorwar

(2017). Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model. International Journal of Medical Informatics, 101, 75–84.

42.

Hsieh

H. L.

Kuo

Y. M.

Wang

S. R.

Chuang

B. K.

Tsai

C. H.

(2016). A study of personal health record user’s behavioral model based on the PMT and UTAUT integrative perspective. International Journal of Environmental Research and Public Health, 14(1), 8.

43.

Jaradat

M. I. R.

Imlawi

Mashaqba

(2018). Investigating the moderating effects of self-efficacy, age and gender in the context of nursing mobile decision support systems adoption: A developing country perspective. International Journal of Interactive Mobile Technologies, 12(2), 113–129.

44.

Jewer

(2018). Patients’ intention to use online postings of ED wait times: A modified UTAUT model. International Journal of Medical Informatics, 112, 34–39.

45.

Johansson

Petersson

Saveman

B. I.

Nilsson

(2014). Using advanced mobile devices in nursing practice–the views of nurses and nursing students. Health Informatics Journal, 20(3), 220–231.

46.

Johansson

P. E.

Petersson

G. I.

Nilsson

G. C.

(2013). Nursing students' experience of using a personal digital assistant (PDA) in clinical practice - an intervention study. Nurse Education Today, 33(10), 1246–1251.

47.

Judge

T. A.

Bono

J. E.

(2001). Relationship of core self-evaluations traits–self-esteem, generalized self-efficacy, locus of control, and emotional stability–with job satisfaction and job performance: A meta-analysis. E-Journal of Applied Psychology, 86(1), 80–92.

48.

Kim

K. H.

Kim

K. J.

Lee

D. H.

Kim

M. G.

(2019). Identification of critical quality dimensions for continuance intention in mHealth services: Case study of onecare service. International Journal of Information Management, 46, 187–197.

49.

Kim

S. S.

Malhotra

N. K.

Narasimhan

(2005). Research note—two competing perspectives on automatic use: A theoretical and empirical comparison. Information Systems Research, 16(4), 418–432.

50.

Kotler

Keller

K. L.

MarkKotler

Keller

K. L.

(2016). Marketing Management. Global Edition (Vol. 15e). Global Edition.

51.

Lee

M. C.

(2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506–516.

52.

Lee

W. I.

H. P.

Mendoza

Liu

T. Y.

(2021). Determinants impacting user behavior towards emergency use intentions of m-health services in Taiwan. Healthcare, 9(5), 535.

53.

Leung

Chen

(2019). E-health/m-health adoption and lifestyle improvements: Exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommunications Policy, 43(6), 563–575.

54.

Liao

Palvia

Chen

J. L.

(2009). Information technology adoption behavior life cycle: Toward a technology continuance theory (TCT). International Journal of Information Management, 29(4), 309–320.

55.

Limayem

Hirt

S. G.

Cheung

C. M.

(2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31, 705–737.

56.

Ding

Lin

(2019). Mobile health technology (WeChat) for the hierarchical management of community hypertension: Protocol for a cluster randomized controlled trial. Patient Preference and Adherence, 13, 1339–1352.

57.

Macedo

I. M.

(2017). Predicting the acceptance and use of information and communication technology by older adults: An empirical examination of the revised UTAUT2. Computers in Human Behavior, 75, 935–948.

58.

Melas

C. D.

Zampetakis

L. A.

Dimopoulou

Moustakis

(2011). Modeling the acceptance of clinical information systems among hospital medical staff: An extended TAM model. Journal of Biomedical Informatics, 44(4), 553–564.

59.

Miyamoto

S. W.

Henderson

Young

H. M.

Pande

Han

J. J.

(2016). Tracking health data is not enough: a qualitative exploration of the role of healthcare partnerships and mHealth technology to promote physical activity and to sustain behavior change. JMIR mHealth and uHealth, 4(1), e4814.

60.

Natarajan

Balasubramanian

S. A.

Kasilingam

D. L.

(2017). Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services, 37, 8–22.

61.

Owusu Kwateng

Appiah

Atiemo

K. A. O.

(2021). Adoption of health information systems: Health professionals perspective. International Journal of Healthcare Management, 14(2), 517–533.

62.

Pan

Gao

(2021). Determinants of the behavioral intention to use a mobile nursing application by nurses in China. BMC Health Services Research, 21(1), 1–11.

63.

Pan

Jordan-Marsh

(2010). Internet use intention and adoption among Chinese older adults: From the expanded technology acceptance model perspective. Computers in Human Behavior, 26(5), 1111–1119.

64.

Park

S. Y.

(2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Journal of Educational Technology and Society, 12(3), 150–162.

65.

Reychav

Beeri

Balapour

Raban

D. R.

Sabherwal

Azuri

(2019). How reliable are self-assessments using mobile technology in healthcare? The effects of technology identity and self-efficacy. Computers in Human Behavior, 91, 52–61.

66.

Salgado

Tavares

Oliveira

(2020). Drivers of mobile health acceptance and use from the patient perspective: Survey study and quantitative model development. JMIR mhealth and uhealth, 8(7), 1–22.

67.

Schaper

L. K.

Pervan

G. P.

(2007). ICT and OTs: A model of information and communication technology acceptance and utilisation by occupational therapists. International Journal of Medical Informatics, 76, S212–S221.

68.

Sharma

S. K.

Sharma

(2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44, 65–75.

69.

Shiferaw

K. B.

Mehari

E. A.

(2019). Modeling predictors of acceptance and use of electronic medical record system in a resource limited setting: Using modified UTAUT model. Informatics in Medicine Unlocked, 17, 100182.

70.

Sok Foon

Chan Yin Fah

(2011). Internet banking adoption in Kuala Lumpur: An application of UTAUT model. International Journal of Business and Management, 6(4), 161.

71.

Sripalawat

Thongmak

Ngramyarn

(2011). M-banking in metropolitan Bangkok and a comparison with other countries. Journal of Computer Information Systems, 51(3), 67–76.

72.

Stets

J. E.

Biga

C. F.

(2003). Bringing identity theory into environmental sociology. Sociological Theory, 21(4), 398–423.

73.

Strudwick

Booth

Mistry

(2016). Can social cognitive theories help us understand nurses’ use of Electronic Health Records? Computers, Informatics, Nursing, 34(4), 169–174.

74.

Taneja

Bharti

(2022). Mapping unified theory of acceptance and use of technology (UTAUT) 2: A taxonomical study using bibliometric visualisation. Foresight, 24(2), 210–247.

75.

Thong

J. Y. L.

Hong

S. J.

Tam

K. Y.

(2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799–810.

76.

Tian

X. F.

R. Z.

(2022). Determinants of the mobile health continuance intention of elders with chronic diseases: An integrated framework of ECM-ISC and UTAUT. International Journal of Environmental Research and Public Health, 19(16), 9980.

77.

Tran

V. D.

(2021). Using mobile food delivery applications during the COVID-19 pandemic: Applying the theory of planned behavior to examine continuance behavior. Sustainability, 13(21), 1–20.

78.

Van der Heijden

(2004). User acceptance of hedonic information systems. MIS Quarterly, 28, 695–704.

79.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425–478.

80.

Venkatesh

Thong

J. Y.

(2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36, 157–178.

81.

Venkatesh

Zhang

(2010). Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management, 13(1), 5–27.

82.

Verdegem

De Marez

(2011). Rethinking determinants of ICT acceptance: Towards an integrated and comprehensive overview. Technovation, 31(8), 411–423.

83.

T. H. G.

Luong

D. B.

K. H.

(2022). Understanding Consumer Purchase intention via mobile shopping applications: An empirical study from Vietnam. Journal of Asian Finance Economics and Business, 9(6), 287–295.

84.

Wang

Y. S.

Liao

Y. W.

(2007). The conceptualization and measurement of m-commerce user satisfaction. Computers in Human Behavior, 23(1), 381–398.

85.

World Health Organization. (2011). Global observatory for eHealth series Volume 3: mHealth: new horizons for health through mobile technologies. Author.

86.

J. H.

Wang

S. C.

Lin

L. M.

(2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International Journal of Medical Informatics, 76(1), 66–77.

87.

C. W.

Chao

C. M.

Chang

C. F.

Chen

R. J.

Chen

P. C.

Liu

Y. X.

(2021). Exploring behavioral intention to use a mobile health education website: An extension of the UTAUT 2 model. Sage Open, 11(4), 1–12.

88.

Zeng, L., & Li, R Y M, (2021). Tourist satisfaction, willingness to revisit and recommend, and mountain Kangyang tourism spots sustainability: A structural equation modelling approach. Sustainability, 13(19), 10620.

89.

Zhang

Han

Dang

Meng

Guo

Lin

(2017). User acceptance of mobile health services from users’ perspectives: The role of self-efficacy and response-efficacy in technology acceptance. Informatics for Health and Social Care, 42(2), 194–206.

90.

Zhu

Liu

Cao

Dong

(2021). Factors affecting customer intention to adopt a mobile chronic disease management service: Differentiating age effect from experiential distance perspective. Journal of Organizational and End User Computing, 34(4), 1–23.