=Paper=
{{Paper
|id=Vol-3026/paper13
|storemode=property
|title=Determinants of Wearable Healthcare Technology Usage in Vietnam
|pdfUrl=https://ceur-ws.org/Vol-3026/paper13.pdf
|volume=Vol-3026
|authors=Van-Anh T. Truong,Bao Quoc Truong-Dinh
}}
==Determinants of Wearable Healthcare Technology Usage in Vietnam==
https://ceur-ws.org/Vol-3026/paper13.pdf
Determinants of Wearable Healthcare Technology Usage
in Vietnam*
Van-Anh T. Truong1[0000-0002-8798-1934] and Bao Quoc Truong-Dinh1,2,**[0000-0002-4825-0717]
1 University of Economics, The University of Danang, Vietnam
2 Department of Business Administration, National Central University, Taiwan
{vananhtt,baotdq}@due.edu.vn
Abstract. The outbreak of the COVID-19 epidemic has been motivating people
to rethink health-related threats, causing the development of healthcare wearable
technology. Nevertheless, previous studies on smart devices focus too much on
the nature of wearables technologically. Moreover, there is a lack of research
examining consumers' physical and social elements in a relationship-oriented
country like Vietnam. Therefore, this study considers the antecedents of the ac-
tual use behavior of wearables users by inheriting the earlier models (i.e., TAPB,
TPB) to determine which and how internal and external factors affect consumer's
adoption of wearable devices in healthcare. The quantitative methodology with
Vietnam wearables users survey is conducted during the third wave of pandemic
spread in June 2021. We use the PLS technique to analyze the data from 143
questionnaires, representing the suitability of the research model. Except for
knowledge reflected via information search, all remaining dimensions positively
affect wearables adoption in healthcare. Some implications are proposed for fu-
ture directions theoretically and practically.
Keywords: smart healthcare, wearable technology, actual use behavior, design,
Vietnam.
1 Introduction
National sustainability can be perceived via upheavals like war, natural disasters, and
epidemics. The COVID-19 outbreak, therefore, has been changing many aspects of hu-
man life. As stated by the Grand View Research [1], this pandemic has extended the
usage of wearable medical technologies from monitoring ordinary healthy lifestyles or
health indexes to warning early signs of miner's viral infection symptoms.
* Copyright © by the paper’s authors. Use permitted under Creative Commons License Attrib-
ution 4.0 International (CC BY 4.0). In: N. D. Vo, O.-J. Lee, K.-H. N. Bui, H. G. Lim, H.-J.
Jeon, P.-M. Nguyen, B. Q. Tuyen, J.-T. Kim, J. J. Jung, T. A. Vo (eds.): Proceedings of the
2nd International Conference on Human-centered Artificial Intelligence (Computing4Human
2021), Da Nang, Viet Nam, 28-October-2021, published at http://ceur-ws.org
** Corresponding author.
Determinants of Wearable Healthcare Technology Usage in Vietnam 121
Technological wearables can be used as smart accessories, embedded clothes, even im-
planting or tattooing forms on the body with huge growth in market sizes, sales, ship-
ments, and usage [2]. They are considered electronic devices such as smartwatches,
wristbands, wearable fitness technology. Notably, smartwatches are well-known
among the other trends, including fitness trackers, smart glasses, hearables, smart cloth-
ing, skin patches, disease-orientated devices, AI/ML/cloud solutions/biosensors in
wearables [3].
Connect wearable devices number had increased double within 2016-2019 period
year and has been estimated up to one billion by 2022 [4]. This forecast includes both
healthcare devices and earwear technologies. According to Phaneuf [5], healthcare
wearables are willing to be worn by more than 80% of consumers. These technological
wearable devices are designed for enhancing user's experience and collecting their
health information (i.e., physical activities, heart rhythms, blood pressure, sleep, elec-
trocardiograms) which can be shared with healthcare professionals. Users can be ben-
eficial because of 89% reduction or prevention the health issues. Therefore, doctors,
healthcare staffs, and insurers are beneficial as well. Wearable healthcare devices are
desired by 35% of employers (wellness and insurance program), 88% of physicians
(health parameters monitoring), and hospitals (reduction of 16% of hospital costs within
five years, reduction of USD billions within coming 25 years) [6].
In Vietnam, the wearable technology market reached 65 USD trillions in 2020 with
the penetration of many big brands and even domestic distributors, especially towards
smartwatches [7]. Regarding low and middle-income countries like Vietnam, Brophy,
Davies, Olenik, Cotur, Ming, Van Zalk, O'Hare, Guder and Yetisen [8] suppose that
physical access to wearables could be the obstacle to this market growth. Besides, alt-
hough it is necessary to pay attention to the design aspect of healthcare wearables [9],
there is a lack of studies that mention at least the aesthetics attributes of these devices
and their influence on user's acceptance. Sharma and Biros [10] state the relationship
between usage and design of wearable devices via the functional and interactive fea-
tures. Additionally, previous research focuses on demographic differences or market
trends rather than user acceptance and actual use [11]. Hence, with a preventive health-
oriented approach, this study considers influential elements of actual use behavior of
healthcare wearables in the case of Vietnam. We try to solve the research problem re-
lated to which and to what extent the internal and external variables affect users' behav-
iors towards wearable technologies in healthcare. The empirical study is expected to
theoretically contribute to researches on developing countries. It also gives some im-
plications to increase performances of applying wearable tech and reduce the burden of
Vietnam's healthcare system.
2 Theoretical Background
2.1 Healthcare Wearable Technology
Healthcare wearable technology is considered novel wearables with advanced techno-
logical features that can track or monitor healthcare issues (i.e., heart rate, blood pres-
sure, exercise) biologically and physically to improve health behaviors [10]. It is the
122 Truong and Truong-Dinh
type of e-technology attached to wearable accessories in the healthcare and medical
industries [12]. The success of wearable devices shown via smart wearables acceptance
is helpful to predict IT devices' future directions [13].
The Technology Acceptance Model (TAM) proposed by Davis [14] is acknowl-
edged as one of the most influential models in theory regarding user's adoption of tech-
nology-related objects. Almost all extensions of the TAM on healthcare wearables re-
searches keep the core variables related to perceived usefulness (PU) and perceived
ease of use (PEOU), attitude, the intention with supplement variables, and some modi-
fications on external variables. Some external variables can be listed such as healthcare
professional trust [15]; initial trust, consumer innovativeness, compatibility, and health
interest [16]; task-technology fitness, characteristics of users and wearable devices, so-
cial influence factor [13]; technology readiness [17]; health-related variables, privacy
protection, consumer innovativeness, and reference group influence [18].
Towards m-health interventions for behavior change, Cho, Lee, Islam and Kim [19]
synthesize theories including behavioral learning theory (BLT), health belief model
(HBM), integrated theory of health behavior (ITHB), social cognitive theory (SCT),
transtheoretical model of behavioral change (TTMBH). These models are often used
along with extensions of the TAM models. Similarly, protection motivation theory
(PMT) and the theory of acceptance and use of technology 2 (UTAUT2) are combined
in studying healthcare wearables acceptance [20] in which technology, health, and pri-
vacy are the antecedents of healthcare wearable devices adoption.
In the research of Hwang, Chung and Sanders [21] on intelligent clothing, perceived
performance risk and environmental concern are supplementary, while the external fac-
tors are functionality, expressiveness, and aesthetics (FEA). The authors imply the at-
tention of product-makers should move from technical aspects to user-centric aspects,
especially compatible aesthetics depicted in the design.
The recent study on luxury fashion wearable technology of Blazquez, Alexander and
Fung [22] focuses on three-stage of attitudes, including cognition, affection, and cona-
tion. In these stages, PU and PEOU belong to functional dimensions besides individual
and social factors. On the other hand, PEOU is only mentioned as a component of in-
novation characteristics, while PU can be reflected via health-related information. In-
dividual traits can be considered moderators that affect the remaining relationships [23].
2.2 Research Framework
In this research framework, we do not focus on the prerequisite of technology ac-
ceptance in healthcare, as demonstrated in earlier researches. The prerequisite can be
any element related to perception on usefulness and ease of use that affecting actual
usage via attitude and consumer intention. The purpose of this study considering the
user's healthcare tech-related psychological process on wearable aspects as sufficient
conditions that can lead to actual use behaviors. We adapted and modified the work of
Lee and Lee [11] to introduce the conceptual framework (Figure 1). The framework
contains five antecedents of actual use behavior toward healthcare wearable technol-
ogy. The five antecedents are classified into two groups, internal and external variables.
Internal variables
Determinants of Wearable Healthcare Technology Usage in Vietnam 123
The internal variables are added in the research model by Lee and Lee [11], who
apply the knowledge, attitudes, practices, and belief (KAPB) model in healthcare wear-
able devices research. In the integrated theory of health behavior change introduced by
Ryan [24], knowledge and beliefs are essential dimensions that lead to self-manage-
ment behaviors. The more information about specific health behaviors and beliefs that
persons have, the more health behaviors are engaged.
Knowledge. Knowledge concept is not only understanding but the acquisition, man-
agement, and technological knowledge usage [11]. Individual traits such as self-effi-
cacy, motives, and usage patterns are the basement of healthcare technology design for
customization [25].
Hypothesis 1. Knowledge has a positive influence on actual use behaviors towards
healthcare wearable technology.
Attitude. According to Blazquez, Alexander and Fung [22], attitude can be consid-
ered subjective evaluation or individuals' learned tendency related to an object. Its fea-
tures (cognitive, affective, conative) reflect the process (of learning, feeling, and doing)
that effectively predicts actual behavior, especially attitude towards a specific behavior.
Hypothesis 2. Attitude has a positive influence on actual use behaviors towards
healthcare wearable technology.
Belief. Towards IT-related personal innovativeness, health belief that belongs to
health motivation is the antecedent of preventive health behaviors [23]. Besides, the
health belief model (HBM) is the fundamental theory in the study of Chau, Lam,
Cheung, Tso, Flint, Broom, Tse and Lee [12], Cheung, Chau, Lam, Tse, Ho, Flint,
Broom, Tso and Lee [18] on smart technology for healthcare in which perceived health
belief is the predictor of user's adoption.
Hypothesis 3. Belief has a positive influence on actual use behaviors towards
healthcare wearable technology.
External variables
Social factors. Social factors are mentioned in many studies on wearable technology
[22, 26, 27]. Social factors reflect consumers' psychology concerning their social
groups, including perceived conspicuousness and subjective norm towards specific be-
haviors. Individuals decide to act based on self-assessments due to their social relation-
ships [13] or reference groups [18].
Hypothesis 4. Social factors have a positive influence on actual use behaviors to-
wards healthcare wearable technology.
Design. In the book of Baisya and Das [28], the authors mention aesthetic attributes
in harmony with quality and other product elements as an attractive component leads
to purchase. Later, Mazzalovo [29] psychologically analyses the consumption aestheti-
cization linked with the product choice process. Depending on information processing
or receiving, it can be relevant to both internal (cognitive) and external (brand-mani-
fested) factors. The extent of aesthetics is based on product features. In hi-tech products
(technology-attached products), the aesthetics attributes are often known as design.
Playing the role of the visual communicator for a wearable product, design can directly
affect consumer's acceptance or actual use behaviors in healthcare wearables. The de-
sign of healthcare wearables mentioned by the earlier researchers [9, 30, 31] is the suit-
able representative of compatible aesthetic attributes that emphasized in the study of
124 Truong and Truong-Dinh
Hwang, Chung and Sanders [21], Kalantari [26], Jeong, Kim, Park and Choi [32] or
wearability and fashionableness mentioned by Chang, Lee and Ji [13].
Hypothesis 5. The design has a positive influence on actual use behaviors towards
healthcare wearable technology.
3 Research methodology
The measurement scales of all constructs in the study were adopted and minor modified
from prior validated scales in English-written literature. The back-translation was
adopted to translate all items to Vietnamese, and the pre-test process was performed to
modify the ambiguous items. All four-item of knowledge and both three-item scales of
attitude and belief were adopted from Johnston and Warkentin [33]; Ko, Moon, Kim
and Paik [34]. A four-item scale of social factors and actual use behavior were taken
from Johnston and Warkentin [33], Venkatesh, Morris, Davis and Davis [35],
Venkatesh, Thong and Xu [36]. Finally, a three-item scale of Hwang, Chung and
Sanders [21] was used for wearable design.
The field study was carried out in Danang city, Vietnam. The COVID-19 pandemic
increases the awareness of people in protecting and improving their health. Wearable
technology devices have become more popular among people. We collected the data
from sport and gymnastic centers in the city during a rare period of non-lockdown and
non-keep distance of 2021. Only respondents who currently use wearable technology
devices were invited to fill the questionnaire. Totally, 143 useable questionnaires were
satisfied to use for data analysis.
Our sample contained 38.5% male and 61.5% female. The respondents in a group
from 18 to 24 years old occupied 69.2% of the sample, while the group of 25 to 49
years old and the group over 50 accounted for 27.3% and 3.5% of the sample. 55.9%
of the participants in the sample were students, while employees and self-employed
accounted for 30.8% and 6.3%. Another occupation was 7% of the sample. The sample
showed the majority of respondents currently use a smartwatch, accounting for 67.3%.
Health tracking devices, heart rate monitor chest straps represent 16.4% and 13.3%,
respectively. Other types of devices related to mask, sensors, etc., define 3% of the
sample.
4 Data Analysis
We used the partial least square (PLS) technique to analyzed the data with SmartPLS
software [37]. Multiple criteria are used to test the measurement model, such as relia-
bility, convergent validity, and discriminant validity, with the results presented in Table
1. For reliability, the Cronbach's alpha of all constructs was higher than the threshold
of 0.5 after removing two items of knowledge construct. Besides, the convergent valid-
ity was confirmed to be satisfied by the assessment on two types of indices. First, both
composite reliability (CR) and average-variance-extracted (AVE) values were higher
than the suggested threshold of 0.7 and 0.5, respectively. Second, the item loadings of
all indicators were higher than the highest cross-loading with each other indicators and
Determinants of Wearable Healthcare Technology Usage in Vietnam 125
higher than the threshold of 0.5. As for discriminant validity, the values of the square
root of AVE exceeded the correlation of all constructs. As such, the preliminary analy-
sis supported the confirmation of the scale accuracy.
Table 1. Measurement accuracy assessment
Constructs Alpha CR AVE 1 2 3 4 5 6
1. Knowledge 0.763 0.894 0.809 0.899
2. Attitude 0.864 0.917 0.786 0.745 0.887
3. Belief 0.859 0.914 0.780 0.765 0.774 0.883
4. Social factors 0.885 0.921 0.744 0.665 0.688 0.837 0.863
5. Design 0.888 0.930 0.817 0.685 0.594 0.695 0.699 0.904
6. Actual use behav-
0.906 0.934 0.780 0.731 0.732 0.806 0.786 0.732 0.883
iors
The overall model fit was evaluated mainly through the R2 of endogenous contrast,
predictive relevance (Q2), and standardized root mean squared residuals (SRMR). The
value of R2 was 0.748, represented a substantial level [38]. The values of Q2 were
ranged from 0.376 to 0.618, all above the required value of zero [39]. The value of
SRMR was 0.053, which is lower than the threshold of 0.08 [40]. These indicated a
good model fit of the framework. A t-test using the bootstrapping procedure of 500
samples was applied to test the direct relationships, while Cohen's Indicator (f2) was
applied to measure their effect sizes. Hypothesis 1 was rejected because the p-value
was higher than 0.05. All other hypotheses (H2 to H5) were supported at least 95%
confidence level (Figure 1). The f2 values ranged from 0.038 to 0.086, indicating the
effect size of accepted hypotheses was medium.
Fig. 1. The conceptual framework of Actual Use Behaviors towards Healthcare Wearable Tech-
nology
5 Discussion and Conclusion
Except for knowledge of the internal components, all independent variables are the an-
tecedents of actual use behaviors towards healthcare wearable technology. It can be
seen that the proposed model is suitable for predicting healthcare wearables usage. This
126 Truong and Truong-Dinh
result confirms almost all the variables suggested by Lee and Lee [11]. Nevertheless,
the relationship between the knowledge of users and their actual use behavior is not
supported in the context of Vietnam. It means that the knowledge of health-related
wearables is not needed to be the prerequisite of smart devices usage. This type of
knowledge concerning with information search about healthcare wearables to manage
user's health. The search is conducted by positive-oriented people towards a healthy
lifestyle [41, 42]. This finding reflects the interesting insights from the Vietnam wear-
ables users whose actions are strongly influenced by external factors and attitude, belief
elements in the theory of planned behavior (TPB). In other words, consumers choose
to use healthcare wearables as long as they have a positive attitude towards buying
behavior and positive belief towards wearable technology usage. Hence, attitude, belief
on healthcare wearables usage should be considered instead of merely improving con-
sumers' knowledge.
Along with the spread of social networks and formalism, Vietnamese consumers are
pressured to look good and act based on social norms. Consequently, it is not difficult
to understand why social factors and the design of wearable devices positively influence
actual use behavior. Furthermore, in a relationship-oriented society like Vietnam,
health-related risks can be mentally and physically dependent on how strong individu-
als' relations with their social groups are. This nature can inspire producers, marketers,
managers, or medical professionals to think about the cooperative mechanism to boost
the wearables market. Despite the limitation in sampling due to the outbreak of the
COVID-19 pandemic, this finding represents the research contribution in understand-
ing the determinants of healthcare wearables use behavior, especially the external com-
ponents regarding social norms and design of devices in the Vietnam context. Further-
more, future research can examine the predictors of the continuum of usage and the
difference between patients and non-patients towards smart healthcare devices.
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