A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies

Journal Pre-proof A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies

Da Tao, Tieyan Wang, Tieshan Wang, Tingru Zhang, Xiaoyan Zhang, Xingda Qu PII:

S0747-5632(19)30351-6

DOI:

https://doi.org/10.1016/j.chb.2019.09.023

Reference:

CHB 6147

To appear in:

Computers in Human Behavior

Received Date:

24 January 2019

Accepted Date:

23 September 2019

Please cite this article as: Da Tao, Tieyan Wang, Tieshan Wang, Tingru Zhang, Xiaoyan Zhang, Xingda Qu, A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies, Computers in Human Behavior (2019), https://doi.org/10.1016/j.chb. 2019.09.023

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Journal Pre-proof Title page Title: A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies Authors: Da Tao1, Tieyan Wang2, Tieshan Wang3, Tingru Zhang1, Xiaoyan Zhang1,4, Xingda Qu1 1Institute

of Human Factors and Ergonomics, College of Mechatronics and Control

Engineering, Shenzhen University, Shenzhen, China 2Marketing 3School 4Key

Management Committee, Xiamen Meiya Pico Information Co.,Ltd

of Management, Xi'an Polytechnic University, Xi'an, China

laboratory of Optoelectronic Devices and Systems of Ministry of Education and

Guangdong Province, Shenzhen University, Shenzhen, 518060, China Corresponding Address: Xingda Qu, PhD Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, 3688 Nanhai Avenue, Shenzhen City, Guangdong Province, China Phone: +86-755-86965716, E-mail: [email protected], Fax: +86-755-26557471

Journal Pre-proof Title: A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies

Abstract (199 words) This study was conducted to synthesize existing studies on user acceptance of consumer-oriented health information technologies (CHITs) through a systematic review and meta-analysis. We searched four electronic databases in August 2018 for studies that empirically examined user acceptance of CHITs based on theoretical frameworks of Technology Acceptance Model (TAM). Meta-analysis was used to estimate effect sizes of pairwise relationships among TAM constructs, while subgroup analysis was performed to investigate potential factors that may moderate TAM relationships. Sixty-seven studies were identified and included for analysis. The results show that TAM was a robust model in examining user acceptance of CHITs. The results also identified a number of significant relationships between several antecedents (self-efficacy, subjective norm, trust, perceived behavioral control and facilitating conditions) and the core TAM constructs. In addition, many of the relationships could be moderated by study characteristics such as country of origin, type of user and type of technology. The findings demonstrated that TAM represents a good ground theory for examining factors that influence consumer acceptance of CHITs. Further efforts can be dedicated to contextualize the use of TAM theories in CHIT domain and to further examine factors that are able to moderate the model relationships. Keywords: Health information technologies, technology acceptance, consumer health informatics, meta-analysis

Journal Pre-proof 1. Introduction Health information technologies (HITs) have long been promoted as a key to improve efficiency and quality of care, support health care delivery, and achieve cost savings.1-4 While a considerable portion of work has been focusing on how health care providers and organizations could use HITs for delivery of health care services,4,5 there is a growing recognition that consumers also want to be actively engaged in their own health care activities.6,7 Consumer-oriented HITs (CHITs) are therefore emerging as promising tools to meet such requirements.8 CHITs refer to “consumer-centered electronic tools, technologies, applications, or systems that are interacted with directly by health consumers (i.e., individuals who seek or receive health care services) to provide them with data, information, recommendations, or services for promotion of health and health care”.9 They are believed to be increasingly prevalent because that consumers require convenient and efficient tools to engage in their health care activities, and that current healthcare system is encountered with high pressure to transform patient care from hospitals to other less costly environments (such as homes) where CHITs can play an essential role.10 CHITs in theory can benefit consumers in such ways as providing patients with prompt personalized health records, enhancing the accessibility of health care resources, promoting communication and relationships between clinicians and patients, and overcoming geographical barriers and logistical inconvenience when seeking health care services.11-13 Previous reviews have showed positive effects of CHITs on patients’ health outcomes among various conditions, for example, better glycaemic control for diabetic patients,9,14-16 reduced rehospitalization and mortality rates for heart failure patients,17-19 and improved medication adherence for chronically 1

Journal Pre-proof ill patients.20,21 In spite of their benefits, the introduction of CHITs to consumers has proven difficult and rates of technology use have been limited.12,22 In fact, the history of HIT development is littered with a number of projects that were rejected or under-used by intended users, because designers fail to attend to key factors underlying user acceptance.12,23-25 This is a significant concern because not only does non-acceptance or non-use of HITs mean a loss of return on investment, but also users will not realize the full benefit from the technologies. In this regard, an increasing number of studies were conducted to examine user acceptance of varied HITs by replicating or extending well-known IT acceptance theories, among which, the Technology Acceptance Model (TAM) has been the most widely used (Figure 1).26-28 Adapted from theories in social-psychological/behavioral literature (mainly the Theory of Planned Behavior,29 Figure 1a), TAM states that the most proximal antecedent to actual technology use is behavioral intention, which is now commonly regarded as the agent of acceptance.30 Behavioral intention is influenced by individuals’ attitude, which, in turn, is determined by two key constructs: perceived ease of use and perceived usefulness.30 Additionally, perceived usefulness is specified to have a direct impact on behavioral intention, and perceived ease of use has a direct impact on perceived usefulness.30 Later efforts have led to several important updates of the original TAM model (Figure 1b), such as TAM2 (Figure 1c)31 and Universal Theory of Acceptance and Use of Technology (UTAUT) (Figure 1d).32 Specifically, TAM2 removes the attitude construct from the original TAM, and adds subjective norm to capture the social pressure and influence from important others’ beliefs about the use of technology. TAM2 proposes that subjective norm has a direct impact on both perceived usefulness and behavioral intention.31 2

Journal Pre-proof Similarly, UTAUT adds a new variable, facilitating conditions, to capture an individual’s perception of availability of internal and external resources necessary for using the technology. It proposes that facilitating conditions is a determinant of behavioral intention.32 UTAUT also uses a different set of terms (i.e., performance expectancy, effort expectancy and social influence) to represent perceived usefulness, perceived ease of use and subjective norm, respectively. In addition, a number of studies have incorporated other psychological/behavioral theories and variables into TAM to increase its explanation power. For example, the role of self-efficacy,33 trust34 and perceived behavioral control29 are frequently examined and they have been demonstrated to be important predictors of key constructs of the original TAM.35,36 So far, the TAM model and its extensions have been consistently shown to be able to explain technology acceptance in varied contexts, such as social network applications,37 automated vehicles,38 health informatics,39 and mobile banking.40 A summary of definitions for the abovementioned variables is presented in Table 1. ---------------------------------------------------Insert Table 1 about here ---------------------------------------------------In spite of the existence of many empirical TAM studies in health informatics, little work has been done to synthesize existing evidence (especially quantitatively) to provide a comprehensive picture of relationships between antecedent variables and CHIT acceptance.28,41 Or and Karsh summarized 94 variables that could predict CHIT acceptance in their narrative review.42 However, their findings came from a number of heterogeneous studies and many variables were not empirically tested and validated in theoretical frameworks. The reviews by Holden and Karsh26 and Yarbrough and Smith27 showed that TAM was indeed capable of predicting substantial portion of 3

Journal Pre-proof clinician acceptance of HITs. However, the reviews examined technology acceptance by clinicians, instead of health consumers, who were highly likely to respond differently to the technologies due to their voluntary usage and lack of professional medical knowledge.43 In addition, none of previous reviews provided quantitative synthesis of evidence.26-28,42 Therefore, the mechanisms for causal relationships among TAM constructs in CHITs cannot be clearly understood. Moreover, the magnitude of reported relationships varied much across studies, perhaps due to heterogeneity of study characteristics. Unfortunately, sources for the heterogeneity were not examined in previous reviews, which leaves practitioners without clear guidance in technology design and implementation in specific contexts. In fact, there is evidence that TAM relationships could be moderated by factors such as culture (or country of origin),44 user type44-48 and technology type.41,48 The purpose of this study was to systematically review existing studies on CHIT acceptance within TAM frameworks, to meta-analytically estimate the magnitude of relationships among antecedent variables and TAM constructs, and to reveal potential factors that can moderate the relationships.

2. Methods 2.1 Literature search and study selection This study was conducted in accordance with the Cochrane Collaboration Guidelines for Systematic Reviews.49 A systematic literature search was conducted with databases of MEDLINE and Academic Search Complete via EBSCOhost Research Databases, PsycINFO and PsycARTICLES via ProQuest for studies published from 1989 (the year when TAM was developed) to August 2018. The search strategy included combinations of two sets of terms related to acceptance 4

Journal Pre-proof (technology acceptance OR TAM OR UTAUT OR (Universal Theory of Acceptance AND Use of Technology)) and CHIT (health* OR medical* OR healthcare OR patient* OR eHealth OR mHealth OR mobile phone OR smart phone OR telemedicin* OR telemonitor* OR telehealth) (See Appendix 1 for detailed search strategy for the four databases). We intentionally used a broad search terms, including both keywords and associated controlled vocabularies, to reduce the chance of missing relevant studies. The titles and abstracts of the citations identified in initial search were first screened to determine their relevance. The full texts of potentially relevant studies were further reviewed for final inclusion. Reference lists of the included studies and several relevant review studies 26-28,41,42 were also manually searched to catch any possibly missed articles.

2.2 Inclusion and exclusion criteria Studies were included if they: (1) quantitatively tested relationships between antecedent factors and CHIT acceptance; (2) examined technology acceptance under theoretical frameworks of TAM or its updates and extensions; (3) examined patients and other consumers who directly used CHITs for the purpose of health care or disease management; and (4) were written in English. For multiple studies using the same sample information (e.g., studies by Lin,50 and Lin and Yang51), we only included the one with a more comprehensive model (e.g., study by Lin50). We excluded studies that did not use TAM as theoretical frameworks, because that our pilot, non-systematic review revealed that most relevant studies used TAM models as theoretical frameworks to examine CHIT acceptance, and that such exclusion criterion reduced heterogeneity among studies and allowed for more reliable meta-analysis across the studies. We excluded studies that examined point of 5

Journal Pre-proof care devices, simple short messaging services,52 and HITs for provision of healthcare services by medical professionals.26

2.3 Data extraction A coding scheme, which described what and how data should be extracted, was pre-constructed based on previous reviews41,44-47 to guide data extraction. The data extracted included study characteristics (e.g., sample size, user type, technology type, country where the study was conducted, and ground acceptance theories), and the statistics of relationships among antecedent variables and TAM constructs (e.g., correlation coefficient (r), construct reliability, standardized path coefficient (β) and any other statistics (e.g., regression weights, t value and F value) that could be converted to r or β by using procedures described in previous studies46,53). For studies used different terminologies for the same construct, we combined the terminologies into a single categorization. For example, performance expectancy, effort expectancy and social influence were consolidated with perceived usefulness, perceived ease of use and subjective norm, respectively. For studies that tested models with separate samples, we extracted the data separately and considered each sample as separate trial.49 Two authors (DT and TYW) independently assessed the studies in all stages of the study selection and data extraction. The other author (TSW) then cross-checked the extracted data. Any discrepancies were resolved through discussion among the three authors until consensus was reached.

2.4 Data analysis

6

Journal Pre-proof Meta-analysis was used to analyze the evidence for the pairwise relationships among antecedent variables and TAM constructs. As Pearson correlation coefficient (r) and standardized path coefficient (β) are commonly used to represent relationships between variables and are widely used effect sizes in previous meta-analysis studies (especially in technology acceptance domain),44,46,54,55 both were adopted as indicators of effect size in this study. While correlation values are simpler to interpret in terms of practical importance, standardized path coefficients are more useful in understanding structural relationships among TAM constructs.56 In fact, the β value indicates the change in the dependent variable for each one-unit change in the independent variable, controlling for other independent variables.55 For example, a β of 0.41 for perceived usefulness→behavioral intention indicates that one-unit change of perceived usefulness would lead to 0.41 unit change of behavioral intention. As measurement error is likely to systematically lower correlations between variables, original correlations were corrected before meta-analysis by using the attenuation correction procedures (i.e., dividing original correlations by the square root of the product of the reliabilities of the two variables).57 We applied Fisher Z transformation for each of the correlations in individual studies, and calculated the weighted mean Z by considering sample size. Then, the weighted mean correlation was computed using inverse Z–r transformation.49 Random-effects models were used to pool the effect sizes as the studies were assumed a sample of trials that could be possibly conducted in this domain, unless a low level of heterogeneity was detected, where fixed-effects models were applied.58 Heterogeneity was assessed by the I2 statistic and Cochran’s Q test. The I2 statistic measures the extent of inconsistency among the results of studies, with I2 values of 25%, 50%, and 75% indicating low, moderate and high levels of heterogeneity, 7

Journal Pre-proof respectively, while a significant Q statistic indicates that the null hypothesis of homogeneity between studies is rejected.59 The possibility of publication bias was examined using Egger’s regression test, with a p value < 0.05 considered as the existence of publication bias.60 Meta-analysis was only performed for relationships with at least three coefficients available, the typical minimum standard.56 In response to substantial variability across studies (based on the I2 test for heterogeneity), subgroup analysis was performed to explore potential factors that would moderate the relationships among TAM constructs. Since the meta-analysis of correlation and path coefficients resulted in similar results and there were more studies reporting path coefficients, we performed subgroup analysis based on path coefficients. The following factors were examined in subgroup analysis: country of origin (Western, Asian or others), user type (general consumers or specific patients), and technology type (mHealth technology or non-mHealth technology). The mHealth technology refers to CHITs that use mobile devices for the delivery of health information and services.61 The between-group effect, represented by between heterogeneity coefficient (QB), was examined by the test of homogeneity between groups.58 The meta-analysis and subgroup analysis were conducted using Comprehensive Meta-Analysis Version 2.

3. Results The process of literature search and study selection is illustrated in Figure 2. We identified 67 eligible studies from a screening of 5371 initial citations and manual search.35,36,39,50,62-124 We extracted two trials from study by Deng et al., 2014,76 as this study examined middle-aged and older user samples separately. Therefore, a total of 68 individual trials were included for analysis. 8

Journal Pre-proof ---------------------------------------------------Insert Figure 2 about here ----------------------------------------------------

3.1 Study characteristics The trials were mostly conducted in past eight years (79%), and widely distributed across varied regions, such as Asia (62%), North America (19%), and Europe (15%). The sample size in individual trials ranged from 32 to 958, with a median of 277. CHITs examined included mHealth technologies (38%) and nonmHealth technologies (62%). Fifty-five trials reported data on variance of acceptance explained by their proposed models, ranged from 27% to 88%, with a median of 54%. Most trials examined extensions or modified version of TAM (84%), while other trials based their theoretical frameworks on UTAUT (16%) (Please see Appendix 2 for detailed information of the 68 trials).

3.2 Meta-analysis Seventeen correlations and 15 path coefficients were examined at least by three trials and therefore included in our analysis. The examined variables included five core constructs in TAM (i.e., perceived ease of use, perceived usefulness, attitude, behavioral intention and U) and five antecedents: subjective norm, self-efficacy, trust, perceived behavioral control and facilitating conditions. Table 2 presents the results of meta-analysis, and tests of heterogeneity and publication bias. High levels of heterogeneity across trials were detected for all path coefficients, except for path of trust→behavioral intention. The Egger’s regression tests indicated the presence of

9

Journal Pre-proof publication bias for path of facilitating conditions→behavioral intention (t=3.436, p=0.041). ---------------------------------------------------Insert Table 2 about here ---------------------------------------------------The meta-analysis results showed that the pooled path coefficients were all positive and significant, except for path of facilitating conditions→behavioral intention. The paths among the five core constructs of TAM were all supported. The 95% confidence intervals for pooled paths of perceived ease of use→perceived usefulness, perceived usefulness→behavioral intention and perceived ease of use→ behavioral intention were narrower than that for other paths, indicating that these paths were robust and consistent across the trials. In addition, self-efficacy, subjective norm, trust and perceived behavioral control played important roles in developing positive perceived usefulness, perceived ease of use and behavioral intention. Specifically, attitude was the strongest predictors of behavioral intention (β = 0.64), followed by perceived usefulness (β = 0.41), self-efficacy (β = 0.24), perceived ease of use (β = 0.21), subjective norm (β = 0.19), trust (β = 0.17), and perceived behavioral control (β = 0.14). Perceived usefulness could be predicted by subjective norm (β = 0.23) and trust (β = 0.43), while perceived ease of use could be predicted by self-efficacy (β = 0.51). Behavioral intention was also a significant predictor of U (β = 0.47). Similarly, the meta-analysis results showed that all pooled relationships were positive and significant, except for correlation of trust-perceived usefulness. The correlations among the five core constructs of TAM were all supported, and lied in large effect size ranges (r >0.5). Specifically, attitude (r = 0.67) and perceived 10

Journal Pre-proof usefulness (r = 0.66) yielded the strongest correlations with behavioral intention, followed by perceived ease of use (r = 0.58) and subjective norm (r = 0.57). Both perceived usefulness (r = 0.76) and perceived ease of use (r = 0.69) exerted strong correlations with attitude. There were also large correlations of subjective normperceived usefulness (r = 0.52), subjective norm-perceived ease of use (r = 0.50), selfefficacy-perceived ease of use (r = 0.56) and perceived behavioral control-behavioral intention (r = 0.57). Overall, self-efficacy, subjective norm, trust, facilitating conditions and perceived behavioral control were all significantly correlated with perceived usefulness, perceived ease of use or behavioral intention.

3.3 Subgroup analysis Seven paths among the five core TAM constructs were examined in subgroup analysis. The results are summarized in Table 3 and depicted in Figure 3. In general, all the examined factors yielded significant moderating effects on at least four paths. Paths regarding the country of origin varied. Asian people showed larger path coefficients for perceived ease of use→perceived usefulness (QB = 21.10, p < 0.001), perceived ease of use→attitude (QB = 14.13, p < 0.001), attitude→behavioral intention (QB = 584.63, p < 0.001) and behavioral intention→U (QB = 173.57, p < 0.001) than Western people. ---------------------------------------------------Insert Table 3 and Figure 3 about here ---------------------------------------------------User type distinguished specific patients from general consumers. While specific patients exerted a larger path coefficient for perceived usefulness→attitude (QB = 20.71, p < 0.001) than general consumers, they yielded smaller path coefficients for 11

Journal Pre-proof perceived ease of use→perceived usefulness (QB = 9.57, p < 0.01) and attitude→ behavioral intention (QB = 189.33, p < 0.001). Technology type moderated all the relationships except paths of perceived usefulness→attitude and perceived ease of use→behavioral intention. MHealth technologies produced a larger path coefficient for behavioral intention→U (QB = 31.83, p < 0.001), while non-mHealth technologies showed larger path coefficients for perceived usefulness→behavioral intention (QB = 147.96, p < 0.001), perceived ease of use→perceived usefulness (QB = 4.77, p < 0.05), perceived ease of use→ attitude (QB = 11.91, p < 0.01), and attitude→behavioral intention (QB = 664.80, p < 0.001).

4. Discussion While an increasing number of CHITs are available for consumer utilization, their wider application is largely inhibited by users’ low acceptance or underuse of the technologies. As such, the purpose of the present study was to systematically review and assess the current state of evidence concerning CHIT acceptance based on TAM frameworks. Our review encompassed 67 studies that quantitatively investigated CHIT acceptance with more than 21,000 participants. It shows that the original TAM relationships were supported in CHIT domain. The results also identified a number of significant relationships between several antecedents and the core TAM constructs. In addition, the relationships among TAM constructs could be moderated by study characteristics such as country of origin, user type and technology type.

4.1 Primary findings While most of previous meta-analysis studies40,41,44,45,47,48,53 synthesized 12

Journal Pre-proof relationships among TAM constructs with either correlations or path coefficients, our study synthesized TAM relationships with both coefficients. We found that the two coefficients provided relatively similar results on the direction and statistical significance of relationships among TAM constructs. Specifically, all the synthesized relationships from the two types of coefficients were positive and statistically significant, except that the synthesized correlation of facilitating conditionsbehavioral intention is significant, while its synthesized path coefficient is nonsignificant. There also existed difference in the magnitudes between the two types of synthesized coefficients due to the natural difference in their statistical techniques.125 Such difference may not be an issue in understanding the TAM relationships, as the two types of synthesized coefficients are highly correlated with each other (i.e., a pairwise relationship that yielded a larger synthesized correlation coefficient had a larger synthesized path coefficient as well). In addition, correlations and path coefficients explain TAM relationships from different perspectives. While the correlations show associations of TAM constructs and are simpler to interpret in terms of practical importance, path coefficients demonstrate predicting effects of one construct on another and are more useful in understanding casual relationships among TAM constructs based on theoretical assumptions.125 Overall, our meta-analysis results show that existing CHIT acceptance studies supported causal links in TAM models, which are highly reliable and can be used in a variety of CHIT contexts. The magnitudes of these relationships found in our study are also comparable to those found in previous meta-analysis reviews of varied technologies (Table 4).40,41,44-47 The most influential predictors in driving CHIT acceptance are attitude, usefulness, ease of use, self-efficacy, subjective norm, and perceived behavioral control. These results provide strong justification for integrating 13

Journal Pre-proof additional variables into the TAM to increase its predicting power. ---------------------------------------------------Insert Table 4 about here ---------------------------------------------------The relative impacts varied for the significant predictors, indicating that not all predictors play equally important role in driving CHIT acceptance. In particular, while our study showed the significance of both perceived usefulness and perceived ease of use with respect to attitude and behavioral intention, the results demonstrated a stronger relationship with perceived usefulness than with perceived ease of use. This indicates a more important role of utility than that of ease of use in determining individuals’ acceptance of CHITs. In contrast, previous qualitative reviews suggested that perceived ease of use is less likely to affect physician acceptance of HITs.26,27 This is not beyond our expectation, as physicians usually own higher intelligence levels and more professional experience with HITs, and therefore are easier to learn to use the technologies compared with consumers.126 Venkatesh et al. also found that perceived ease of use only exerted effects in pre-implementation stage, rather than in post-implementation stage in technology acceptance.32 This might be because that as users acquire sufficient skills in using a new technology, perceived ease of use is less likely to be an issue influencing user acceptance. However, both our and Holden and Karsh’s reviews26 found a strong impact of perceived ease of use on perceived usefulness, suggesting that an easy-to-use HIT is indeed able to reinforce usefulness perceptions for both physicians and consumers. In addition, we found relative large relationships between perceived usefulnessattitude (r = 0.76; β = 0.48), perceived ease of use-attitude (r = 0.69; β = 0.29) and attitude-behavioral intention (r = 0.67; β = 0.64). These findings support the original 14

Journal Pre-proof TAM and are consistent with a previous review.45 They indicate that attitude could still be a critical factor in TAM and its position in TAM theories may deserve reconsideration, although it has usually been excluded from its later versions (e.g., TAM2 and UTAUT). Our study identified a number of significant antecedents of core TAM constructs, such as subjective norm, self-efficacy, trust, perceived behavioral control, and facilitating conditions. First, while TAM2 suggests that subjective norm has a direct effect on behavioral intention over and above perceived ease of use and perceived usefulness for mandatory (instead of voluntary) technologies, our results support and also extend TAM2 in voluntary CHIT domain by showing that subjective norm exerted significant relationships with three core TAM constructs (i.e., perceived ease of use, perceived usefulness and behavioral intention). The underlying reason for the effects of subjective norm has been well explained by the internalization effect (i.e., one incorporates his/her important referents' belief into one's own belief structure) and compliance effect (i.e., one often chooses to perform an action when his/her important referents say he/she should, even though he/she may not like or believe in it) that were reported in previous studies.31,44 However, it should be note that this finding is inconsistent with results from physicians, who are less likely to be influenced by subjective norm.26,127 The reason for the difference seems obvious. Physicians usually maintain independent work and are capable of making their own judgment on technology use, and thus are more likely immune to peer influence.128 In contrast, consumers often lack professional knowledge to determine whether or not they should use a CHIT, and therefore they tend to ask for advice from important others (e.g., close friends, families and care givers). Trust has gained increasing attention in recent years and has been widely 15

Journal Pre-proof integrated into TAM under various technology settings, especially for safety critical systems such as automated vehicles, e-banking and online purchase.38,45 Our study found strong relationships for TRU–PU, TRU–PEOU and TRU–BI in the use of CHIT, one type of safety-critical technologies as well. These findings are consistent with the meta-analysis by Wu et al.,45 who also demonstrated the significant role of trust in technology acceptance. In fact, trust can reduce perceived technical complexity and uncertainty when interacting with a new CHIT. Moreover, trust may act as subjective endorsement from consumers that the technology can function as promised and expected, and therefore likely elicit favorable perceptions towards acceptance. While early TAM studies have linked self-efficacy to perceived ease of use,33 our results further indicate that self-efficacy is an important contributing factor to CHIT acceptance with its moderate-to-strong relationships with perceived usefulness, perceived ease of use and behavioral intention. A possible explanation for this finding is that consumers who feel more confident and capable of using CHITs are more skillful in using the technologies, and therefore they are more likely to perceive the technologies useful and easy to use. It also suggests that consumers’ general confidence in technology knowledge and ability may partly act as the basis for their judging criterion for CHIT acceptance. Finally, our study found consistent significant relationships between perceived behavioral control-behavioral intention and facilitating conditions-behavioral intention. Perceived behavioral control and facilitating conditions are proposed to shape an individual's behavioral intention in the Theory of Planned Behavior and UTAUT, respectively.29,32 Our results supported that both variables are important antecedents of behavioral intention to use CHITs. This suggests that people who feel 16

Journal Pre-proof that they have necessary resources and are in control of the process to use CHITs are more likely to accept the technologies.

4.2 Moderators Our findings reveal several moderators on TAM relationships. The results indicate that user type seriously affected the relationships, a finding consistently reported in previous reviews.41,44-47 For example, patients showed a larger relationship for perceived usefulness-attitude than general consumers, while general consumers yielded larger relationships for perceived ease of use-perceived usefulness and attitude-behavioral intention than patients. It seems to suggest that patients care more on the usefulness of the technologies, while for general consumers, perceived ease of use is a more influential factor. Technology type had a significant moderating effect on five of the seven pairwise relationships. There seems to be a pattern of non-mheath technologies displaying stronger effect sizes than mheath technologies in correlations such as perceived usefulness-behavioral intention, perceived ease of use-perceived usefulness, perceived ease of use-attitude and attitude-behavioral intention. It may indicate that these core TAM constructs have less relevance in mheath technologies, and that other contextual factors might be more relevant in determining consumer acceptance with the prevalence of mobile devices. Where the studies were conducted will also influence the pairwise relationships. Specifically, we found that perceived ease of use had a larger impact on perceived usefulness and attitude in Asian studies than in Western studies. This aligns with previous findings44,129 that perceived ease of use was more important in non-Western cultures. This interesting result appears to suggest the existence of cultural differences 17

Journal Pre-proof in relationships of TAM constructs, a speculation awaiting confirmation by future studies.

4.3 Implications Our findings have both theoretical and practical implications. Theoretically, our study supported the robustness of TAM relationships in CHIT domain. It indicates that TAM represents a good ground theory for studying factors that influence consumers’ decisions on whether they will accept or refuse to use a specific CHIT. The findings, through a comprehensive meta-analysis, reflected more precisely the magnitude of the relationships among TAM constructs in CHIT acceptance, compared with previous qualitative reviews.26,27,42 In addition, the successful identification of significant relationships between several antecedents and core TAM constructs shows promise to extend the models with additional theories and variables in varied CHIT contexts so that the models can capture unique contextual features of CHITs, better fit specific CHIT scenarios and have larger explanation power.26 Moreover, the finding that the relationships can be moderated by study characteristics help facilitate our understanding of TAM relations in specific demographic, technical and cultural contexts. Practically, our findings provide important implications for practitioners in the design and implementation of CHITs. First and foremost, the significant roles of perceived ease of use and perceived usefulness suggest that it may be insufficient for CHITs to only provide health/healthcare services to consumers. The technologies should provide the services in a way that could ‘please’ consumers and fulfil their healthcare requirements. Specifically, to be useful, CHITs should be designed to help achieve health promotion and service acquisition purposes, and enhance the 18

Journal Pre-proof effectiveness of healthcare activities. CHITs should also be created with usable interfaces and user-friendly navigations so that the technologies can be easily used. One of effective ways to increase interface usability can be careful adherence to human factors principles and iterative usability evaluation process in CHIT design.100,130 Second, relationships between antecedents and core TAM constructs suggest that, in addition to improve usefulness and ease of use in CHIT design, steps will also need to be taken in CHIT implementation to ensure that consumers have self-confidence in using the technology (self-efficacy), that consumers are in control of using the technology (perceived behavioral control), that using the technology is in line with beliefs from important others (subjective norm), that the technology can earn trust from consumers and eliminate uncertainty (trust), and that there are sufficient support resources and no barriers when using the technology (facilitating conditions). For example, for providers introducing a CHIT among communities, efforts should not only be aimed at targeting intended users for technology acceptance. Given the importance of subjective norm, relevant stakeholders, such as families and healthcare givers, should also be considered towards technology acceptance. It is important to convey clear messages to all relevant stakeholders that the technology can bring benefits in consumers’ health care activities in promotions like training programs and advertising campaigns. It is also worthwhile for technology providers to pay their attention to reinforce consumers’ self-efficacy in the use of CHITs. Simple hands-on tips, demonstration, and short training of how to use the technologies could be practical strategies to achieve such purpose. The identified moderating effects suggest that practitioners should recognize that different design and implementation strategies may be required when introducing different type of CHITs for different users. For instance, the CHIT acceptance process 19

Journal Pre-proof may differ much for specific patients compared with general consumers. For specific patients, perceived usefulness seems to be more important in determining CHIT acceptance, while perceived ease of use is more of a concern for general consumers. Therefore, design and implementation strategies successful for specific patients may not work well for general consumers. This suggests that, tailored design and implementation strategies will be required to fit specific user types. In addition, practitioners should also be aware that with the prevalence of mobile devices, perceived ease of use and perceived usefulness are now less important in shaping attitude and intention in mHealth than non-mHealth technologies. Finally, practitioners who plan to launch new CHITs in international markets should realize that technology adoption might differ by culture. For example, perceived ease of use is more important in Asian cultures than Western cultures. There is a need to identify the most relevant factors determining consumer acceptance in specific local markets to ensure the success of technology implementation.

4.4 Limitations and future studies The limitations of this study should be noted and can be addressed by future studies. First, there was moderate to high heterogeneity among trials in the subgroup analysis. This may indicate the existence of potential moderators that had not been examined in our study. However, the limited descriptions of study design and CHITs in the majority studies prevented us from assessing the roles of more moderators. Future studies are therefore advised to elaborate more of their experimental design and technologies evaluated, and to conduct their studies in consideration of potential moderators. Second, we meta-analyzed variables that were examined at least by three trials, and ignored less frequently examined variables. This may prevent us from 20

Journal Pre-proof understanding a full picture of antecedents of CHIT acceptance, which could be addressed by further systematic narrative review. Third, our study examined TAM relationships independently, and failed to consider interrelationships among the variables. This is because that we attempted to test a wide range of TAM relationships, but the relationships reported in original studies varied much, which prevented us from examining the relationships in a single model without losing samples. Future studies are suggested to examine TAM relationships in a holistic way with methods such as meta-analytic structural equation model to obtain a more precise estimation of causal relationships among TAM variables,131 when data on all the relationships examined are available. Moreover, it should be noted that the original path coefficients were extracted from diverse models across studies, and thus can be potentially biased. Although the impact of differences in the models on synthesizing results from path coefficients appears unclear54 or could be diluted,55 caution should be exercised in the interpretation of the synthesizing results. Finally, variability may exist in measures used between studies, as the generic definitions of TAM constructs might have been differently interpreted. Such variability is likely to affect the robustness of our results, and even challenge the degree to which the same constructs from different studies could be compared. One way to address the lack of consistent theoretical explication of the constructs, as suggested by Holden and Karsh,26 is systematic contextualization of TAM in healthcare through beliefs elicitation process, a preferred method to contextualize generic behavior theories to specific settings (use of CHITs) and population (consumers).132-134 Thus, future studies are also recommended to go through the theory contextualization process before their study implementation to improve the consistency in explication of TAM theories and constructs and in development of measures in CHIT domain. 21

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5. Conclusions This study represents the first application that quantitatively synthesizes studies on consumer acceptance of CHITs. It demonstrated that TAM represents a good ground theory for examining factors that influence consumer acceptance of CHITs. Our study also identified several important antecedents of TAM constructs and moderators, which can facilitate our understanding of TAM relations in varied CHIT contexts and guide future CHIT research and practice. Further efforts can be dedicated to contextualize the use of TAM theories in CHIT domain and to further examine factors that are able to moderate the model relationships.

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Journal Pre-proof Conflict of interest disclosures: None to declare.

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31

Journal Pre-proof Figure captions: Figure 1. Illustrations of the Technology Acceptance Model (TAM) and its updated versions. Figure 2. Study search and selection procedures. Figure 3. Moderating effects for relationships among TAM constructs.

32

Journal Pre-proof

(a) Theory of Planned Behavior (TPB) Attitude Subjective Norm

Behavioral Intention

Actual Use

Perceived behavioral control

(b) Technology Acceptance Model (TAM) Perceived Ease of Use Behavioral Intention

Attitude Perceived Usefulness

Actual Use

(c) Technology Acceptance Model 2 (TAM2) System/Jobrelated Variables Subjective Norm

Perceived Ease of Use Perceived Usefulness

Behavioral Intention

Actual Use

(d) Unified Theory of Acceptance and Use of Technology (UTAUT) Performance Expectancy Effort Expectancy Social Influence

Behavioral Intention

Actual Use

Facilitating Conditions

Figure 1. Illustrations of the Technology Acceptance Model (TAM) and its related versions.

33

Journal Pre-proof

6,644 records were identified through database searching 2,660 MEDLINE 3,041 Academic Search Complete 11 PsycARTICLES 932 PsycINFO

6,644 records were screened 6,504 records were excluded based on their titles and abstracts 140 Full-text articles were assessed for eligibility 78 articles were excluded 39 focused on health/medical professionals 14 not CHITs 9 not quatitative studies 7 without sufficient data for meta-analysis 5 not examined TAM-related theories 4 used the same sample

62 studies included for review 1,547674 MEDLINE 5 articles from the manual search 245 326 Academic Search Complete PsycINFOThe Cochrane Library 67107 studies, representing 68 individual trials, met the inclusion criteria andABI/INFORMWeb were included for review 1,18635 of Science

Figure 2. Study search and selection procedures.

34

Perceived ease of use

Larger for non-mHealth

Larger for Asian / general consumers / non-mHealth

Perceived usefulness

Attitude

Larger for Asian / general consumers / non-mHealth

Behavioral intention

Larger for specific patients

Larger for non-mHealth Figure 3. Moderating effects for relationships among TAM constructs.

35

Larger for Asian / non-mHealth

Usage behavior

Table 1. Definitions of variables in TAM-related models. Models that include the variable Variables Actual usage behavior

Definitions Actual technology use behavior in the context of technology acceptance (Davis et al., 1989)30

TPB TAM TAM2 UTAUT √ √ √ √

Behavioral intention

An individual’s intention or willingness to exert effort to perform a specific behavior (Davis et al., 1989)30

√

√

Attitude

An individual’s positive or negative feelings (evaluative judgment) regarding performing a specific behavior (Davis et al., 1989)30

√

√

Perceived ease of use (Effort expectancy)

The degree to which a user will find using a technology will improve performance in his/her intended tasks (Davis et al., 1989)30

Perceived usefulness (Performance expectancy)

The degree to which a user will find the use of a technology to be free of effort (Davis et al., 1989)30

Subjective norm (Social influence)

An individual’s perception of the degree to which important other people think he or she should or should not perform the specific behavior (Venkatesh and Davis, 2000)31

Self-efficacy

The degree to which an individual believes that he/she is capable to perform a specific task/job using the technology (Venkatesh and Davis, 1996)33

Trust

The willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party (Mayer, 1995)34

Perceived behavioral control

The perceived ease or difficulty of performing a specific behavior (Ajzen, 1988)29

Facilitating conditions

Objective factors in the environment that an individual agrees to make an act easy to accomplish (Venkatesh et al., 2003)32

TAM, Technology Acceptance Model; UTAUT, the Unified Theory of Acceptance and Use of Technology; TPB, the Theory of Planned Behavior.

36

√

√

√

√

√

√

√

√

√

√

√

√ √

Journal Pre-proof Table 2. Meta-analytic results for pairwise relationships. Pairwise relationships Path coefficients PU→ATT PU→BI PEOU→PU PEOU→ATT PEOU→BI ATT→BI BI→U SN→PU SN→BI TRU→PU TRU→BI SE→PEOU SE→BI FC→BI PBC→BI

No. of trials

Total sample Effect size size

19 51 40 18 39 22 10 8 18 3 3 5 5 5 5

5664 15452 12332 4914 13520 6834 2503 3823 5350 1312 963 1623 1976 1383 997

0.48*** 0.41*** 0.52*** 0.29*** 0.21*** 0.64*** 0.47*** 0.23*** 0.19** 0.43* 0.17*** 0.51*** 0.24** 0.11 0.14*

Confidence interval (95%) Lower Upper

Homogeneity (Q-value)

0.37 0.33 0.44 0.18 0.16 0.43 0.25 0.14 0.11 0.05 0.08 0.31 0.07 -0.06 0.02

488.48*** 1639.26*** 1228.38*** 305.48*** 383.49*** 2949.05*** 361.17*** 53.97*** 133.33*** 111.21*** 3.73 90.87*** 58.02*** 37.94*** 15.69**

0.58 0.48 0.59 0.40 0.26 0.78 0.64 0.32 0.26 0.70 0.25 0.67 0.40 0.27 0.25

I2

Egger’s regression test (t value)

96.32% 96.95% 96.83% 94.44% 90.09% 99.29% 97.51% 87.03% 87.25% 98.20% 46.40% 95.60% 93.11% 89.46% 74.51%

0.610 (0.550) 0.969 (0.337) 1.717 (0.094) 0.489 (0.632) 0.974 (0.336) 1.174 (0.254) 0.340 (0.743) 0.926 (0.390) 0.762 (0.457) 1.020 (0.494) 0.222 (0.861) 0.274 (0.801) 1.231 (0.306) 3.436 (0.041) 0.312 (0.775)

Correlations PU-ATT 12 4665 0.76*** 0.63 0.85 662.53*** 98.34% 1.053 (0.316) PU-BI 41 12184 0.66*** 0.59 0.71 1300.82*** 96.93% 0.169 (0.867) PEOU-PU 37 10719 0.65*** 0.57 0.72 1499.80*** 97.60% 0.367 (0.716) PEOU-ATT 11 3681 0.69*** 0.53 0.80 545.44*** 98.17% 1.040 (0.326) PEOU-BI 37 11135 0.58*** 0.49 0.65 1533.78*** 97.65% 0.439 (0.663) ATT-BI 13 4130 0.67*** 0.51 0.79 741.64*** 98.38% 0.242 (0.813) BI-U 10 2495 0.71** 0.33 0.89 1644.17*** 99.45% 0.257 (0.804) SN-PU 15 4984 0.52** 0.27 0.70 1613.80*** 99.13% 1.290 (0.219) SN-PEOU 10 2490 0.50*** 0.24 0.69 755.15*** 98.81% 1.151 (0.283) SN-BI 17 5457 0.57*** 0.38 0.71 1227.91*** 98.70% 0.571 (0.577) TRU-PU 4 1630 0.45 -0.40 0.88 901.12*** 99.67% 3.618 (0.069) TRU-BI 4 1630 0.32** 0.12 0.50 49.72*** 93.97% 3.791 (0.063) TRU-PEOU 4 1590 0.28** 0.07 0.47 52.55*** 94.29% 3.450 (0.075) SE-PEOU 6 2161 0.56** 0.24 0.77 393.88*** 98.73% 0.434 (0.687) SE-PU 5 671 0.46*** 0.22 0.65 141.44*** 97.17% 0.337 (0.758) FC-BI 5 1957 0.44*** 0.29 0.57 43.43*** 90.79% 2.373 (0.098) PBC-BI 4 825 0.57*** 0.32 0.75 50.09*** 94.01% 1.127 (0.377) PEOU, perceived ease of use; PU, perceived usefulness; ATT attitude; BI, behavioral intention; and U, actual usage behavior; SN, subjective norm; SE, self-efficacy; TRU, Trust; PBC, perceived behavioral control; and FC, facilitating conditions. * p < 0.05. ** p < 0.01. *** p < 0.001.

37

Table 3. Results of subgroup analysis for path coefficients among TAM constructs. Western

Country of origin a Asian

General consumers

User type Specific patients

mHealth

Technology type Non-mHealth

PU→ATT N Effect size (CI) QB

5 0.48 (0.10 to 0.74) 0.02

14 0.48 (0.39 to 0.57)

15 0.44 (0.42 to 0.47) 20.71***

4 0.58 (0.53 to 0.62)

5 0.52 (0.25 to 0.71) 1.18

14 0.47 (0.33 to 0.59)

PU→BI N Effect size (CI) QB

19 0.45 (0.32 to 0.57) 3.17

31 0.38 (0.28 to 0.48)

33 0.40 (0.29 to 0.50) 0.66

18 0.43 (0.32 to 0.53)

22 0.30 (0.28 to 0.32) 147.96***

29 0.47 (0.45 to 0.49)

PEOU→PU N Effect size (CI) QB

13 0.43 (0.40 to 0.45) 21.10***

27 0.52 (0.49 to 0.54)

25 0.55 (0.50 to 0.60) 9.57**

15 0.47 (0.44 to 0.50)

15 0.48 (0.46 to 0.50) 4.77*

25 0.52 (0.50 to 0.54)

PEOU→ATT N Effect size (CI) QB

4 0.21 (0.15 to 0.27) 14.13***

14 0.33 (0.31 to 0.36)

14 0.27 (0.14 to 0.39) 4.68

4 0.36 (0.12 to 0.56)

5 0.23 (0.17 to 0.29) 11.91**

13 0.33 (0.30 to 0.36)

PEOU→BI N Effect size (CI) QB

16 0.18 (0.10 to 0.26) 2.22

23 0.22 (0.15 to 0.29)

25 0.22 (0.15 to 0.28) 2.13

14 0.19 (0.10 to 0.28)

18 0.23 (0.15 to 0.31) 3.12

21 0.19 (0.11 to 0.26)

ATT→BI N Effect size (CI) QB

6 0.58 (0.55 to 0.61) 584.63***

15 0.81 (0.80 to 0.82)

18 0.75 (0.73 to 0.77) 189.33***

4 0.51 (0.47 to 0.55)

8 0.44 (0.41 to 0.47) 664.80***

14 0.81 (0.79 to 0.83)

BI→U N 5 5 4 6 3 7 Effect size (CI) 0.24 (0.18 to 0.29) 0.64 (0.61 to 0.68) 0.49 (0.44 to 0.53) 0.43 (0.38 to 0.47) 0.58 (0.53 to 0.63) 0.40 (0.35 to 0.44) QB 173.57*** 3.62 31.83*** a, Note that one study was conducted in South Africa (Cilliers et al., 2017), and was excluded in the subgroup analysis of PU→BI and ATT→BI for country of origin. PEOU, perceived ease of use; PU, perceived usefulness; ATT attitude; BI, behavioral intention; and U, actual usage behavior. N, Number of studies. QB, Between-group heterogeneity coefficient. CI, Confidence interval. * p < 0.05. ** p < 0.01. *** p < 0.001.

38

Table 4. Comparison of results from our meta-analysis with that from previous meta-analysis studies on technology acceptance. PEOU-PU

Technology Study

type

Na

nb

ES (95% CI)

nb

ES (95% CI)

PU-BI Na

nb

PU–ATT

ES (95% CI)

0.43 (0.40 to 0.46)

0.64 (0.43 to 0.78) 10 2503

0.47 (0.25 to 0.64)

0.55 (0.41 to 0.69) 9

1316

0.54 (0.39 to 0.68)

126 41954 0.71 (0.70 to 0.72) 103 33742 0.58 (0.57 to 0.59) 105 33767 0.81 (0.80 to 0.82) 55 19778 0.93 (0.92 to 0.95) 53 19771 0.67 (0.65 to 0.68) 46 17718 0.86 (0.85 to 0.88) 137 24110 0.44 (0.43 to 0.55) 60 16518 0.34 (0.32 to 0.36) 89 17895 0.55 (0.54 to 0.56) 54 9962 0.53 (0.51 to 0.55) 51 9048 0.45 (0.43 to 0.47) 44 8240 0.56 (0.54 to 0.58) 25 6059

0.46 (0.44 to 0.49)

37 10719 0.65 (0.57 to 0.72)

0.71 (0.33 to 0.89)

19

34

5392

0.13 (0.10 to 0.16)

9491

0.35 (0.33 to 0.37)

Na

nb

ES (95% CI)

Na

nb

8 2113 0.36 (0.32 to 0.40) 9 3046 0.26 (0.23 to 0.30) 10 3389

ES (95% CI)

79 23211 0.42 (0.37 to 0.46) 45 13391 0.48 (0.40 to 0.56) 40 11821 0.23 (0.17 to 0.28) 39 12106 0.51 (0.42 to 0.59)

39 13520 0.21 (0.16 to 0.26)

51 15452 0.41 (0.33 to 0.48) 19 5664 0.48 (0.37 to 0.58) 18 4914 0.29 (0.18 to 0.40) 22 6834

77 16123 0.49 (0.44 to 0.54)

56 12205 0.43 (0.37 to 0.48)

59 12657 0.59 (0.55 to 0.63)

All types

53 11538 0.55 (0.50 to 0.60)

40

38

7151

0.47 (0.42 to 0.52)

37 11135 0.58 (0.49 to 0.65)

7054

0.63 (0.58 to 0.68) 15 3493 0.67 (0.57 to 0.77) 16 3244 0.54 (0.41 to 0.67) 14 3214

41 12184 0.66 (0.59 to 0.71) 12 4665 0.76 (0.63 to 0.85) 11 3681 0.69 (0.53 to 0.80) 13 4130

PEOU, perceived ease of use; PU, perceived usefulness; ATT attitude; BI, behavioral intention; and U, actual usage behavior. a, The number of trials included in the meta-analysis. b, The number of participants included in the meta-analysis.

39

Na

0.54 (0.51 to 0.56) 9

56 16707 0.21 (0.17 to 0.25)

All types

Wu et al., 2011 All types Yousafzai et al., All types 2007 The present CHITs study

ES (95% CI)

BI-U

2663

67 12582 0.50 (0.46 to 0.55)

nb

ATT–BI

ES (95% CI)

67 12582 0.19 (0.15 to 0.23)

Na

PEOU–ATT

nb

Path coefficients King and He, All types 65 12263 0.48 (0.42 to 0.54) 2006 Baptista and Mobile 8 2871 0.53 (0.50 to 0.55) Oliveira, 2016 banking Chauhan and e-health 76 19986 0.46 (0.41 to 0.52) Jaiswal, 2017 applications The present CHITs 40 12332 0.52 (0.44 to 0.59) study Correlations King and He, 2006 Schepers and Wetzels, 2007

PEOU-BI Na

0.67 (0.51 to 0.79) 10 2495

Journal Pre-proof

Appendix 1. Electronic search strategy Databases were searched from 1989 to August 2018. The number in parentheses was the number of citations returned from the search. MEDLINE and Academic Search Complete via EBSCOhost Research Databases 1.

AB (technology acceptance OR TAM OR UTAUT OR (Universal Theory of Acceptance AND Use of Technology)) Academic Search Complete (10,407) MEDLINE (6,363)

2.

SU (technology acceptance OR TAM OR UTAUT OR (Universal Theory of Acceptance AND Use of Technology)) Academic Search Complete (536) MEDLINE (452)

3.

AB (health* OR medical* OR healthcare OR patient* OR eHealth OR mHealth OR mobile phone OR smart phone OR telemedicin* OR telemonitor* OR telehealth) Academic Search Complete (3,847,493) MEDLINE (7,087,577)

4.

SU (health* OR medical* OR healthcare OR patient* OR eHealth OR mHealth OR mobile phone OR smart phone OR telemedicin* OR telemonitor* OR telehealth) Academic Search Complete (2,224,002) MEDLINE (2,651,758)

5.

1 AND 3 Academic Search Complete (3,027) MEDLINE (2,844)

6.

2 AND 4 Academic Search Complete (64) MEDLINE (134)

7.

5 OR 6 Academic Search Complete (3,041) MEDLINE (2,660)

PsycINFO and PsycARTICLES via ProQuest 1.

ab(technology acceptance OR TAM OR UTAUT OR (Universal Theory of Acceptance AND Use of Technology)) PsycINFO (3,200) PsycARTICLES (31)

2.

su(technology acceptance OR TAM OR UTAUT OR (Universal Theory of Acceptance AND Use of Technology)) PsycINFO (1,117) PsycARTICLES (6)

3.

ab(health* OR medical* OR healthcare OR patient* OR eHealth OR mHealth OR mobile phone OR smart phone OR telemedicin* OR telemonitor* OR telehealth) PsycINFO (1,160,368) PsycARTICLES (33,796)

4.

su(health* OR medical* OR healthcare OR patient* OR eHealth OR mHealth OR mobile phone OR smart phone OR telemedicin* OR telemonitor* OR telehealth) PsycINFO (1,009,773) PsycARTICLES (39,380)

5.

1 AND 3 PsycINFO (759) PsycARTICLES (9)

6.

2 AND 4 PsycINFO (257) PsycARTICLES (2)

7.

5 OR 6 PsycINFO (932) PsycARTICLES (11)

40

Appendix 2. Study characteristics of the 68 trials. Total variance explained by model

Country

Technology evaluated

Technology type

Ahadzadeh et al., 2015

Malaysia

Internet use for health information

Non-mHealth General consumers

293

Andrews et al., 2014 Australia

Personal electronic health records

Non-mHealth General consumers

750

TAM and TPB

50.0%

Baulch et al., 2010 Beldad and Hegner, 2017 Bertrand and Bouchard, 2008

Australia

Online healthy weight website

Non-mHealth General consumers

143

TAM

44.0%

Germany

A fitness app

MHealth

General consumers

476

TAM

NA

Canada

Virtual reality therapeutic tool for mental health

Non-mHealth General consumers

141

TAM

85.0%

Bidmon et al., 2014

Germany

Mobile physician-rating apps

MHealth

958

TAM

40.0%

111

TAM

69.0%

User type

Patients

Sample size

Theory base

Study

Health belief model and TAM

38.0%

Borges and Kubiak, 2016

Germany

Continuous glucose monitoring patient systems

Patients (People with Non-mHealth diabetes)

Chang et al., 2015

Taiwan

Web-based appointment system

Non-mHealth Patients

140

TAM

51.0%

Chen et al., 2013

Taiwan

E-appointment system

Non-mHealth General consumers

334

TAM

66.0%

Cho, 2016

Korea South Africa

Mobile health Apps

MHealth

General consumers

343

TAM

59.0%

Mobile phone-based health information

MHealth

General consumers

202

UTAUT

36.0%

Slovenia

Home telehealth services

Non-mHealth General consumers

400

UTAUT

78.0%

Cilliers et al., 2018 Cimperman et al., 2016

TAM; Uses and Cudmore et al., 2011 USA

Healthcare web sites

Non-mHealth General consumers

192

gratification

84.1%

theory TAM, TPB and Deng et al., 2014 (1) China

Mobile Health Service

MHealth

General consumers

218

Value-attitudebehavior model

41

54.0%

TAM, TPB and Deng et al., 2014 (2) China

Mobile Health Service

MHealth

General consumers

206

Value-attitude-

61.0%

behavior model Deng, 2013

China

Mobile Health Service

MHealth

General consumers

435

Faqih and Jaradat, 2015

Jordan

Mobile healthcare technology

MHealth

Patients

366

TAM and Health Belief Model TAM

69.4% 48.0%

UTAUT2, protection Gao et al., 2015

China

Wearable technology in healthcare

MHealth

General consumers

462

motivation theory,

NA

privacy

calculus theory Guo et al., 2013 Hoque and Sorwar, 2017 Hoque et al., 2017

China

Preventive mobile health services

MHealth

General consumers

204

TAM

33.5%

Bangladesh MHealth services

MHealth

General consumers

274

UTAUT

NA

Bangladesh e-Health applications

Non-mHealth Patients

318

TAM

68.6%

Hoque, 2016

Bangladesh MHealth technology

MHealth

227

TAM

68.0%

Hossain et al., 2018 Hsiao and Tang, 2015 Huang, 2013

Bangladesh eHealth technology

Non-mHealth Patients

292

TAM

54.7%

Taiwan

Mobile healthcare technology

MHealth

General consumers

338

TAM

67.0%

Taiwan

Telecare technology

Non-mHealth General consumers

369

TAM

80.0%

Hung and Jen, 2012

Taiwan

Mobile health management services

MHealth

General consumers

170

TAM

80.0%

Jen and Hung, 2010

Taiwan

Mobile health service

MHealth

General consumers

100

TPB and TAM

64.1%

Jen, 2010

Taiwan

Mobile weight management services in a virtual community

MHealth

General consumers

311

TAM

63.8%

Jeon and Park, 2015

Korea

Mobile obesity-management applications

MHealth

General consumers

94

TAM

Na

Jewer, 2018

Canada

An emergency department wait-times website

Non-mHealth Patients

118

UTAUT

66.0%

42

General consumers

Jo et al., 2017 Kim and Chang, 2007

Korea

Accredited Online Health Information

Non-mHealth General consumers

500

TAM

NA

Korea

Health information websites

Non-mHealth General consumers

228

TAM

NA

Kim and Park, 2012

Korea

Multiple health information technology (Internet, phone, social networks)

Non-mHealth General consumers

728

TAM

83.0%

Kim et al., 2012

Korea

Online health information

Non-mHealth General consumers

449

TAM

NA

TAM Klein, 2006

USA

Electronic patient-physician communication

Non-mHealth Patients

143

Theory

and of 47.0%

Reasoned Action Klein, 2007

USA

Internet-based patient–physician portal

Non-mHealth Patients

294

TAM

39.0%

Koivumäki et al., 2017

Finland

Future Mydata-based preventive eHealth services

Non-mHealth General consumers

855

UTAUT

NA

Lai et al., 2008

USA

Computer-based education (Tailored interventions Patients (Patients living with Non-mHealth for management of depressive symptoms) HIV/AIDS)

32

NA TAM

Lanseng and Andreassen, 2007

Norway

Internet-based medical self-diagnosis application

Non-mHealth General consumers

470

TAM

83.0%

Lazard et al., 2016

USA

Patient portal

333

TAM

41.0%

Liang et al., 2011

USA

Online health information

Non-mHealth Patients Patients (Neurologically Non-mHealth disabled people)

330

TAM

45.0%

Lim et al., 2011

Singapore

Mobile web application contained health information

MHealth

164

TAM

44.0%

Lin et al., 2016

Taiwan

Wearable instrumented vest for posture monitoring

Non-mHealth General consumers

50

TAM

NA

Lin, 2011

Taiwan

Asthma care mobile service

MHealth

Patients (Asthma patients)

229

Lishan et al., 2009

Singapore

Female-focused general self-care applications

MHealth

General consumers

830

TAM

68.0%

Liu et al., 2013

Taiwan

A Web-based infertile personal health record system

Non-mHealth Patients

50

TAM

58.0%

43

General consumers

Health

Belief

Model and TAM

73.0%

Maass and Varshney, 2012 Martı´nez-Caro et al., 2013 Noblin et al., 2013 Or et al., 2011

Germany

Healthcare ubiquitous information systems

Non-mHealth General consumers

51

TPB, TAM

41.0%

Spain

Online health care services

Non-mHealth General consumers

256

TAM

77.0%

USA

Personal health record

Non-mHealth Patients

562

TAM

50.0%

USA

A web-based, interactive self-management website

Non-mHealth

Patients (Patients with chronic cardiac disease)

101

69.0% UTAUT

Quaosar et al., 2018

Bangladesh MHealth services

MHealth

General consumers

245

UTAUT

NA

Shareef et al., 2014

Bangladesh Mobile healthcare service

MHealth

Patients (Diabetic patients)

326

TAM

47.0%

Sun and Rau, 2015

China

Non-mHealth General consumers

346

TAM

45.0%

General personal health devices

UTAUT Sun et al., 2013

China

Mobile telehealth service

MHealth

General consumers

204

and

protection

44.0%

motivation theory

Tao et al., 2018 Tavares and Oliveira, 2016 Tavares et al., 2018

China

Online health information portals

Non-mHealth General consumers

201

TAM

45.0%

Portugal

Electronic health record patient portal

Non-mHealth General consumers

360

UTAUT

50.0%

Portugal

Electronic Health Record (EHR) portals

Non-mHealth Patients

386

UTAUT2

52.0%

Tsai et al., 2013

Taiwan

A fitness testing platform

Non-mHealth General consumers

101

TAM

NA

Social Tsai, 2014

Taiwan

Telehealth systems

Non-mHealth Patients

365

Theory,

Capital Social

Cognitive

36.0%

Theory, TAM Whetstone and Goldsmith, 2009

USA

Wilson and Lankton, USA 2004

Personal health records

Non-mHealth General consumers

542

Provider-delivered E-health

Non-mHealth Patients

163

44

TAM Motivational model and TAM

38.0% 70.0%

Wong et al., 2014

Hong Kong Internet use for obtaining health information

Xue et al., 2012

Singapore

Yan and Or, 2017

A computer-based chronic disease selfHong Kong monitoring system

Health informatics via a mobile phone-based intervention

Non-mHealth General consumers

98

TAM

27.0%

MHealth

700

TAM

88.4%

119

TAM and TPB

74.0%

General consumers

Non-mHealth Patients

Uses Yoo and Robbins, 2008

USA

Health-related websites

Non-mHealth General consumers

354

and

Gratifications,

43.0%

TPB, TAM Yun and Park, 2010

Korea

Disease information–seeking behavior on the Internet

Non-mHealth General consumers

212

TAM

NA

Zhang et al., 2017

China

Mobile health services

MHealth

General consumers

650

TAM

40.5%

Zhu et al., 2017

China

Mobile chronic disease management systems

MHealth

General consumers

279

TAM

49.4%

NA, not available.

45

Journal Pre-proof Acknowledgements This work received funding support from the Young Talents Foundation of Ministry of Education of Guangdong, China (grant no. 2016KQNCX143), the Natural Science Foundation of Shenzhen University (grant no. 827000228 & grant no. 827000033).

Journal Pre-proof Highlights 

TAM was a robust model in examining consumer acceptance of CHITs



A number of antecedents had significant relationships with the core TAM constructs



The antecedents included self-efficacy, subjective norm, trust, perceived behavioral control and facilitating conditions



TAM relationships could be moderated by study characteristics