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nt. J. Technology Marketing, Vol. 12, No. 3, 201
7
Copyright © 2017 Inderscience Enterprises Ltd.
Consumers’ adoption of wearable technologies:
literature review, synthesis, and future research
agenda
Mahdokht Kalantari
Department of Industrial and Systems Engineering,
Wayne State University,
4815 Fourth St. Detroit, MI, 48202, USA
Email: mahdokht.kalantari@wayne.edu
Abstract: Wearables devices have emerged as rapidly developing technologies
that have the potential to change people’s lifestyles and improve their
wellbeing, decisions, and behaviours as well as enhance core business
processes. However, the adoption of these devices has been relatively slow
when compared to mainstream technologies such as smartphones. Hence,
manufacturers and designers show a growing interest to understand the
influential factors in adopting these technologies. This will help them improve
the features and desirability of these devices in order to wow the consumers
and win them over. Researchers in various disciplines have studied consumers’
adoption of wearable technologies, such as smart glasses and smartwatches
using different theories and methodologies. The goal of this paper is to review
and synthesise the literature of consumers’ adoption of wearable technologies,
review the applied technology diffusion and adoption theories, identify the
influential factors in the adoption decision, and suggest directions for future
research.
Keywords: wearable technology; wearable devices; wearables; smart glasses;
smartwatch; smart clothing; fashnology; adoption; literature review; future
research.
Reference to this paper should be made as follows: Kalantari, M. (2017)
‘Consumers’ adoption of wearable technologies: literature review, synthesis,
and future research agenda’, Int. J. Technology Marketing, Vol. 12, No. 3,
pp.274–307.
Biographical notes: Mahdokht Kalantari is a Doctoral candidate in the
Department of Industrial and Systems Engineering at Wayne State University.
Her research addresses diffusion of innovations in professional networks, viral
marketing campaigns in social media platforms, and consumer’s engagement in
online brand communities.
1 Introduction
‘Wearable technologies’, ‘wearable devices’, or simply referred to as ‘wearables’ are
smart electronics or computers that are incorporated into different types of accessories as
well as items of clothing and can be worn on or attached to the body (Wright and Keith,
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2014). These devices are designed to provide the users with an integrated and seamless
experience that have long been expected from the computers.
The main functionality of wearable devices is to help consumers achieve a state of
connected-self by using sensors and software that facilitate data exchange,
communication and information access in real-time. For this reason, wearable devices are
a big part of the internet of things (Swan, 2012; Castillejo et al., 2013; Hiremath et al.,
2014; Wang, 2015; Sun et al., 2016).
Compared to smart phones and laptop computers, wearable devices offer consumers
more convenience. This convenience can be attributed to their light weight, accessibility,
possibility to use while the user is in motion, possibility to use non-keyboard commands
such as voice and hand gestures, and providing the user with control. Not only these
devices are generally perceived as ‘technology’, but many consumers also consider
wearables as ‘fashion’ or ‘fashnology’ (Hein and Rauschnabel, 2016). Wearables could
also surpass smart phones and laptop computers in performance and hence can potentially
replace these technologies in the future. Therefore, there has been an increase in
consumer’s awareness and knowledge about these devices as well as developer’s
inclination to release new wearable devices to the market (Park et al., 2014).
Wearable technologies have a large number of potential benefits that can dramatically
change the landscape of societies and businesses. These devices can improve individuals’
wellbeing and help them make better and more informed decisions. For example, using
wearables in medical centres could improve the accuracy of the health information
acquired and hence improve the success of medical procedures and patient’s safety.
Wearing health and fitness devices can lead to individual’s healthier behaviour and
consequently significant decreases in healthcare costs. In sports, wearables are used in a
new emerging practice called physiolytics which links wearable devices with data
analysis to provide quantitative feedback in order to monitor and improve sport’s
performance (Wilson, 2013). Wearables also provide great benefits in terms of assistive
services for the disabled community who have limited ability to operate technological
devices. Another great benefit of using wearable technologies is the improved safety and
security of children and elderly.
Wearables can also play an important role in improving core business processes and
saving companies millions of dollars by increasing efficiency in manufacturing, service
industries, and retail. Using wearables as hands-free guidance tools can help improve the
production rate in manufacturing companies (Abraham and Annunziata, 2013). Smart
clothing can be used to monitor the personnel who handle hazardous materials.
Wearables can speed up real-time access to information in order to enhance decisions and
actions in service industries. In retail, using wearable devices can create better customer
experience, expedite purchasing, provide customers with better access to deals, and give
them more real-time input that they can use to make purchasing decisions. In general,
wearables can be used as evolutionary tools for training the workforce. They can also be
used to provide remote customer service and technical support to solve customers’
problems more efficiently.
Despite all the advantages of wearables, and the fact that these devices are perceived
to be the next generation of core products in the IT industry (Chang et al., 2016), their
adoption has been slower than expected.
As the importance of wearables is expected to increase rapidly due to their
aforementioned benefits, consumers’ empowerment, and technological advancements, it
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is critical to identify the underlying factors that drive consumers’ and businesses’
decisions to adopt these devices. This knowledge will provide wearable designers and
manufacturers with helpful insights about the important features and capabilities that
should be incorporated in these devices in order to win over the consumers. It will also
help marketers come up with more efficient messages to promote wearables in marketing
campaigns so that they can address consumers’ main needs and concerns.
Various disciplines have studied the facilitators and barriers to the adoption of
wearable devices using different theories and approaches. The goal of this paper is to
provide an interdisciplinary literature review that synthesises the otherwise fragmented
research on consumer adoption of wearables, review and discuss the technology diffusion
and adoption theories used in the literature, identify and organise the influential factors in
adopting these technologies, and suggest topics that can be further investigated as future
research agenda.
In order to identify the underlying factors that expedite the diffusion of wearable
technologies and facilitate their adoption, more than 50 papers that have studied the
adoption and diffusion of wearable technologies have been reviewed. These papers were
selected from journals in the fields of engineering and technology, medicine and
healthcare, technology marketing, information technology, tourism, social and
behavioural sciences, and clothing and textile research as well as conference proceedings
in the related disciplines. All the research studies used in the literature review were
published between 2009 and 2017.
The rest of this paper is organised as follows: in the next section, the definition of
wearable technologies, their classification, and future trends will be discussed. Next,
technology acceptance theories that have been employed in the literature will be
reviewed. Afterwards, a synthesis and discussion of the underlying factors in consumers’
adoption of wearable technologies will be provided. Finally, the paper will be concluded
by a discussion of future research agenda.
2 Wearable technologies
2.1 Definition and classification
The term ‘wearable’ has a new meaning in today’s digital world. Wearables are no longer
just any item that can be worn or carried on the body. Nor any technology that can be
worn is called a wearable technology (e.g., traditional watches). A technology is
considered to be ‘wearable technology’ when not only it can be worn, but also has the
capability of incorporating information technology in order to be able to communicate
autonomously and process information on the go (Park et al., 2014). This capability is
basically what makes these technologies ‘smart’.
Wright and Keith (2014, p.204) define ‘wearable technology’ as “electronics and
computers that are integrated into clothing and other accessories that can be worn
comfortably on the body.”
Wearables cover a wide variety of devices such as smartwatches, smart glasses,
activity trackers, head-mounted displays, contact lenses, smart garments, smart
jewelleries (e.g., smart rings), headbands, bracelets, etc. Examples include Google Glass,
Microsoft HoloLens, Apple Watch, Pebble Smartwatch, Fitbit fitness tracker, Oculus Rift
virtual reality goggles, 9Solutions Real-Time Locating Systems, iKey wearable keyboard,
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and so on. Wright and Keith (2014) provide more extensive details on different types of
wearable devices and the major players in the market.
Wearables have a wide range of applications both for individuals and enterprises.
Their various uses include communication, information, education, entertainment, fitness
and health tracking, navigation, gaming, and assistive services. One of the important
applications of wearables is in marketing. These devices can be used to monitor
information about users and their surroundings; therefore, they can collect data about
consumer’s purchase behaviour, hobbies, activities, and location. Companies highly
value this information since it gives them consumer insights that they can use to enhance
customer experience.
Researchers and industry experts have proposed different classifications for wearable
devices. According to Mewara et al. (2016), two standards can be used to classify
wearable devices. These devices can be classified based on:
1 product forms (whether they are hand-worn, foot-worn, body-dressed or
head-mounted)
2 product functions (e.g., health and wellness, information consulting, etc.).
Park et al. (2014) suggested a more comprehensive taxonomy for classifying wearable
technologies based on features such as functionality (single vs. multiple), type (active vs.
passive), deployment mode (invasive vs. non-invasive), communication mode (wired vs.
wireless), field of use, and reusability (disposable vs. reusable).
In a market study published by Cognizant Technology Solutions Corp (Bhat et al.,
2014), wearable devices have been classified into five different groups based on their
functionality: fitness, medical, lifestyle, gaming and infotainment.
2.2 Future trends
The wearable technology market is growing rapidly and is expected to be the next
megatrend that will dramatically reshape the way we live and do business.
The Cognizant market research (Bhat et al., 2014) indicates that the market for
wearable electronics worldwide is expected to cross US$8 billion in 2018 which shows a
compound annual growth rate of 17.7% from 2013 to 2018. The largest market share can
be attributed to consumer applications (US$2 billion in 2012) whereas a 21% annual
increase from 2013 to 2018 is also expected for industrial applications. Furthermore, the
entire wearable devices market is expected to cross US$14 billion by 2018 which marks a
compound growth rate of more than 18% from 2013. Wearable devices are predicted to
have an accelerating penetration rate that accounts for 46% of the total addressable
market by 2018. Predictions also indicate that the healthcare sector will continue to be the
dominant sector in the wearable technology market (Wright and Keith, 2014). Another
industry forecast by CCS Consulting (Spencer, 2014) predicts that the smartwatch
shipments alone will exceed 68 million devices in 2018 compared to 4 million in 2013.
Despite all the hype and enthusiasm about wearable devices, these technologies have
not yet gone mainstream, and their diffusion has been slower than other technologies
such as smartphones. A PricewaterhouseCoopers (PwC) survey shows that 59% of the
respondents expressed concerns about these technologies. Although consumers
acknowledge that wearables offer enormous potential and endless opportunities, they are
not convinced that these technologies will have an added-value for them. Many people
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believe that these devices are luxurious toys that do not have a meaningful application
and hence are dispensable. Therefore, researchers and industry experts are interested to
explore consumers’ adoption decision process and determine the factors that can motivate
individuals and businesses to adopt and use wearable devices.
In the next section, an introduction and review of the technology diffusion and
acceptance theories that have been applied in the literature in order to understand the
underlying factors that influence consumers’ decisions to adopt wearable technologies
will be provided.
3 Technology acceptance theories
Studies that have investigated the influential factors in adopting wearable technologies
have applied different approaches, and most of them have based their study designs on
well-known technology acceptance and other consumer psychology theories in the
literature. In this section, the applied theories and approaches in the literature and their
relevance to wearable adoption will be briefly reviewed:
3.1 Technology acceptance model
The technology acceptance model (TAM) that was proposed by Davis (1989) is one of
the most highly validated and influential models among scholars who have investigated
the consumer’s acceptance of technological innovations in various contexts (King and
He, 2006; Ayeh et al., 2013). Davis (1989) proposed two factors that could jointly affect
consumer’s behavioural intention to accept and use new technologies: perceived
usefulness and perceived ease of use. He defined perceived usefulness as “the degree to
which a person believes that using a particular system would enhance his or her job
performance”, and perceived ease of use as “the degree to which a person believes that
using a particular system would be free of effort” [Davis, (1989), p.320]. Figure 1
presents the TAM model.
According to the TAM, when users perceive a technology or service to be easy to
operate, they form a belief that the technology is useful, and hence, their attitude towards
the technology will be positive. Of course, this could be a challenge in the diffusion of
wearable devices as this market is still in its nascent stage, and these devices may be
perceived as complex by many users.
Figure 1 Technology acceptance model
External
varia bles
Perceived
ease of use
Perceived
usefulness
Attitu de
toward
usin
g
Behavi oural
intention to
use
Actual
s
y
stem use
Source: Davis et al. (1989)
Consumers
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adoption of wearable technologies 279
A closer look at the literature of wearable technology adoption shows that the majority of
researchers in the field have utilised the TAM framework for their analysis (Choi and
Kim, 2016; Chuah et al., 2016; Kwee-Meier et al., 2016; Rauschnabel and Ro, 2016;
Chang et al., 2016; Lee, 2009; Chae, 2009; Kim and Shin, 2015; Arvanitis et al., 2011;
Hwang et al., 2016; Kalantari and Rauschnabel, 2017; Leue et al., 2014; Nasir and
Yurder, 2015; Spagnolli et al., 2014; Cheng and Mitomo, 2017; Hwang, 2014; Jang Yul,
2014; Krey et al., 2016; Jeong et al., 2016a; Hein and Rauschnabel, 2016). However,
many of these researchers have deemed it necessary to extend this model by
incorporating external variables such as perceived enjoyment, perceived aesthetics, and
perceived comfort in order to improve the explanatory power of the model. Particularly,
wearable technologies have different characteristics that can influence the adoption
behaviour; therefore, it is important to identify appropriate external variables that can
explain consumer’s decision in adopting these technologies (Tom Dieck and Jung, 2015).
According to Ayeh et al. (2013), adding external variables that are context-specific will
make the TAM framework more applicable to different technological contexts.
3.2 Unified theory of acceptance and use of technology
The unified theory of acceptance and use of technology (UTAUT) model is an extension
of the TAM that was proposed by Venkatesh et al. (2003). They integrated the TAM with
other decision-making theories such as theory of reasoned action (TRA), theory of
planned behaviour (TPB), social cognitive theory (SCT), and innovation diffusion theory
(IDT).
The UTAUT model refers to perceived usefulness as ‘performance expectancy’ and
perceived ease of use as ‘effort expectancy’. This theory has two other key constructs:
social influences that refers to norms and image regulation, and facilitating conditions
which is defined as “the degree to which an individual believes that an organizational and
technical infrastructure exists to support use of the system” [Venkatesh et al., (2003),
p.453].
Researchers have utilised the UTAUT model in order to get better insights about the
antecedents of consumer’s adoption of wearable technologies. Wu et al. (2016) combined
the UTAUT model with other theories such as IDT and TAM to explore consumers’
intentions of using smartwatches. Van Heek et al. (2014) used a model based on UTAUT
and TAM in order to understand consumers’ attitudes towards smart textiles.
3.3 Unified theory of acceptance and use of technology 2
In Venkatesh et al. (2012), extended the UTAUT model by incorporating three new key
constructs: hedonic motivation, price value, and habit since these constructs play an
important role in consumer’s behavioural intention to use new technologies. They named
this new model unified theory of acceptance and use of technology 2 (UTAUT2).
The UTAUT2 model has also been applied by researchers in the literature of adoption
and diffusion of wearable technology. Gao et al. (2015) combined the UTAUT2 model
with the protection motivation theory (PMT) and privacy calculus theory to investigate
the influential factors in consumer’s adoption of healthcare wearable technology. In
another study, Gu et al. (2016) applied the UTAUT2 to determine the influential factors
on consumer’s trust in wearable commerce.
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3.4 Theory of planned behaviour
Proposed by Ajzen (1985, 1991), the TPB is an extension of the TRA (Fishbein and
Ajzen, 1975; Ajzen and Fishbein, 1980) which has been used to study behavioural
intention and actual behaviour in various domains. The main purpose of the TPB model
was to improve the predictive power of the TRA model. According to the TPB model,
attitude towards the behaviour, subjective norms, and perceived behavioural control are
the factors that shape an individual’s intentions to perform a given behaviour as well as
their actual behaviour. Figure 2 presents the TPB model.
Figure 2 Theory of planned behaviour
Source: Ajzen (1991)
Wu et al. (2011) integrated the TPB model with the TAM in order to understand the
adoption of mobile healthcare by hospitals’ professionals. In order to improve the
explanatory power of their proposed model, they also included factors such as perceived
service availability and personal innovativeness in IT. In another study, Turhan (2013)
proposed a model to understand consumer’s acceptance of wearable technology,
particularly smart bra and smart t-shirt, by integrating the TPB model with the TAM. In
order to improve their model, they incorporated other factors such as normative beliefs,
need compatibility, relative advantage, self-efficacy, fear of technological advances, and
cost.
3.5 Uses and gratifications theory
Originally, uses and gratifications theory (U>) was developed by communications
scholars in order to better understand the reasons behind accepting new forms of media
by individuals. Researchers in the technology adoption discipline then applied this theory
in their research in an attempt to identify individuals’ motivations to adopt a new
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technology in order to seek gratifications and satisfy their specific needs (Stafford et al.,
2004; Mondi et al., 2008).
Rauschnabel et al. (2016a) applied the U> along with the TAM to identify the
factors that affect the adoption of smart glasses. In another study, Rauschnabel et al.
(2016b) analysed user’s perception of smart glasses and their purchase intentions using
the literature of technology acceptance as well as the U> and categorisation theory.
3.6 Innovation diffusion theory
Proposed by Rogers (1962), the IDT explains the underlying factors that affect the
dissemination of innovations and new technologies in societies. This theory suggests that
through a process called innovation-decision, individuals pass from obtaining knowledge
about an innovation to forming an attitude about it (Demir, 2006). This attitude will then
impact the individual’s decision to accept or reject the innovation.
Wu et al. (2016) applied the IDT along with TAM and UTAUT to study consumers’
intentions to use smartwatches. They added perceived enjoyment to their model to
improve its explanatory power.
A summary of the technology diffusion and adoption theories that were used in the
literature is provided in Table 1.
In the next section, the influential factors that have been identified in the literature to
be impactful on consumers’ adoption of wearable technologies will be introduced and
discussed in more detail.
Table 1 Summary of the technology acceptance theories applied in the literature
Study Sample Theories
TAM UTAUT UTAUT2 TPB U> IDT
Lee (2009) N = 683 √
Leue and Jung
(
2014
)
N/A
(
conce
p
tual model
)
√
Kim and Shin
(
2015
)
N = 363 √
Choi and Kim
(
2016
)
N = 562 (Korea) √
Jang Yul
(
2014
)
N = 385 (USA) √
Kwee-Meier
et al.
(
2016
)
N = 2,086 √
Rauschnabel
et al.
(
2016b
)
N = 226 (USA) √
Rauschnabel
et al.
(
2016a
)
N = 1,682 (USA) √
Hwang et al.
(
2016
)
N = 720 (USA) √
Nasir and
Yurder
(
2015
)
N = 730 (Turkey) √
Rauschnabel
and Ro
(
2016
)
N = 201
(
German
y)
√
Arvanitis et al.
(
2011
)
N = 91 √
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Table 1 Summary of the technology acceptance theories applied in the literature (continued)
Study Sample Theories
TAM UTAUT UTAUT2 TPB U> IDT
Chae (2009) N = 815
(
South Korea
)
√
Spagnolli et al.
(
2014
)
N = 110 √
Hwang (2014) N = 720 (USA) √
Gao et al.
(
2015
)
N = 462 (China) √
Gu et al.
(
2016
)
N = 266 √
Chuah et al.
(
2016
)
N = 226
(
Mala
y
sia
)
√
Cheng and
Mitomo
(
2017
)
N = 647 (Japan) √
Krey et al.
(
2016
)
N = 226
(
Mala
y
sia
)
√
Jeong et al.
(
2016
b
)
N = 327 (Korea) √
Wu et al.
(
2016
)
N = 212 (Taiwan) √ √
Van Heek et al.
(
2014
)
N = 172 √ √
Kalantari and
Rauschnabel
(2017)
N = 116 (USA) √
Hein and
Rauschnabel
(2016)
N/A
(conceptual model)
√
Turhan (2013) N = 1,412
(
Turke
y)
√
Wu et al.
(
2011
)
N = 800 √ √
4 Factors influencing consumer’s adoption of wearable technology
In this section, an introduction and discussion of the important factors that can influence
consumer’s adoption of wearable technologies will be provided. These factors have been
identified based on the review of the literature of wearable technology adoption. Some of
these factors are the fundamental constructs of the technology acceptance theories such as
TAM, UTAUT, UTAUT2 and TPB. Others are external variables that were incorporated
in these models with an attempt to improve their predictive power. Many of the variables
are context-specific; therefore, the various contexts in which these factors have been
analysed will also be discussed. The identified factors are categorised into five different
groups: perceived benefits, technology characteristics, social influences, individual
characteristics, and perceived risks.
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4.1 Perceived benefits
4.1.1 Perceived ease-of-use
Perceived ease-of-use (PEOU) is one of the basic elements of the TAM. PEOU is defined
as “the degree to which a person believes that using a particular system would be free of
effort” [Davis, (1989), p.320]. The PEOU construct is introduced in the UTAUT model
as ‘effort expectancy’. Davis (1989) suggested that when it comes to initiating the use of
a new technology, PEOU would be the major technical factor that affects user’s attitude
towards usage.
The TAM also suggests that perceived usefulness increases as consumers perceive the
technology as easy to use; therefore, perceived usefulness partially mediates the
relationship between PEOU and behavioural intention to use a new technology.
The effect of PEOU on behavioural intention to use wearable technologies has been
widely studied and confirmed in the literature in various contexts, such as: health and
fitness technologies (Wu et al., 2011; Jang Yul, 2014; Gao et al., 2015; Preusse et al.,
2016), smartwatches (Kim and Shin, 2015; Krey et al., 2016; Chuah et al., 2016), smart
glasses (Rauschnabel et al., 2015; Rauschnabel and Ro, 2016; Rauschnabel et al., 2016a;
Kalantari and Rauschnabel, 2017; Hein and Rauschnabel, 2016), smart clothing (Ko
et al., 2009), and GPS-based AR applications (Leue et al., 2014).
Among the studies that analysed the effect of PEOU on the behavioural intention to
use wearable technologies, some of them did not find such direct significant effects.
Rather, they found that PEOU affects the behavioural intention indirectly through
perceived usefulness (Chae, 2009; Turhan, 2013; Choi and Kim, 2016; Hwang et al.,
2016).
4.1.2 Perceived usefulness
Similar to PEOU, Perceived Usefulness (PU) is a fundamental construct of the TAM. PU
is defined as “the degree to which a person believes that using a particular system would
enhance his or her job performance” [Davis, (1989), p.320]. This construct is also
included in the UTAUT model as ‘performance expectancy’ and in the IDT model as
‘relative advantage’.
PU has been repeatedly found by the majority of research studies to have a significant
effect on consumers’ attitudes towards wearable technologies and their behavioural
intention to use them.
This finding has been replicated in various contexts, such as: smartwatches (Kim and
Shin, 2015; Choi and Kim, 2016; Wu et al., 2016), Augmented Reality Smart Glasses
(Rauschnabel et al., 2016a; Kalantari and Rauschnabel, 2017), smart clothing (Hwang
et al., 2016; Chae, 2009; Spagnolli et al., 2014), mobile fitness devices (Jang Yul, 2014;
Wu et al., 2011), and wearable commerce (Gu et al., 2016).
Some studies though concluded that PU has a significant effect on attitude towards
using the technology but does not have a significant effect on the behavioural intention to
adopt it (Krey et al., 2016; Chuah et al., 2016). Moreover, the basic premise of the TAM
model which indicates that PU partially mediates the relationship between PEOU and
behavioural intention holds in most of the studies (Hwang et al., 2016; Chae, 2009; Choi
and Kim, 2016).
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The important antecedents that affect consumer’s perception of the usefulness of a
technology have also been studied in various contexts. Hwang et al. (2016) found that in
the context of smart clothing, perceived compatibility and perceived comfort are
important predictors of PU. In a study to analyse the adoption of mobile fitness devices,
Jang Yul (2014) suggested that personalisation is an antecedent that drives PU. Jeong
et al. (2016a) studied the effects of user experience and perceived similarity of
smartphone on users’ perceived usefulness of smartwatches and found these to be
significant predictors of PU.
4.1.3 Price value
Price value is a founding element of the UTAUT2 model. Price value is the consumer’s
perception of the worth of the product. In fact, this construct refers to how consumers
assess the overall utility of a product based on their perception of the cost of the product
and the received benefits (Zeithaml, 1988). When a technology’s benefits outperform its
cost, the price value is positive (Venkatesh et al., 2012). Price value has long been known
as an influential factor in consumer’s decision to adopt new products and innovations. A
market research by PwC (2014) indicates that price is the leading factor discouraging
consumers from purchasing wearables in every demographic across every product type.
The reason for this reluctance is that many consumers are not convinced that wearables
offer value above what they get from their smartphones.
In the literature of consumer’s adoption of wearable technologies, price value has
been proven to have a significant effect on consumer’s intention to adopt these devices
(Lee, 2009; Yang et al., 2016; Preusse et al., 2016). Other studies have incorporated the
cost construct in their analysis and showed a significant effect for this construct on
consumer’s decision to adopt wearable devices (Leue et al., 2014; Kim and Shin, 2015).
Since the wearables market will be more saturated and competitive, prices will not be a
barrier for adoption in the long run. Therefore, manufacturers should focus on developing
devices that have a distinct advantage over smartphones in terms of their utilitarian
benefits.
4.1.4 Hedonic motivation
One of the fundamental variables of the UTAUT2 model is hedonic motivation.
Venkatesh et al. (2012, p.161) define hedonic motivation as “the fun or pleasure derived
from using a technology”. This variable has been conceptualised as ‘perceived
enjoyment’ in IS research. Common TAM variables such as PU and PEOU are known to
be extrinsic motivations for adopting a new technology that solely reflect its performance
outcomes. Davis et al. (1992) argued that intrinsic motives such as perceived enjoyment
could also affect the adoption behaviour. They defined perceived enjoyment as “the
extent to which the activity of using the computer is perceived to be enjoyable in its own
right, apart from any performance consequences that may be anticipated” [Davis et al.,
(1992), p.113]. Hedonic motivation or perceived enjoyment is particularly important
because intrinsic motivation is known to have a stronger effect on individual’s behaviour
comparing to extrinsic motivation. Many researchers have also confirmed perceived
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adoption of wearable technologies 285
enjoyment to be a powerful predictor of the behavioural intention to adopt technologies
in various settings (Moon and Kim, 2001; Ha et al., 2007; Rheingans et al., 2016; Stock
et al., 2016). Their results indicate that people want to use certain technologies both
because they enjoy the experience and because they find those technologies to be useful
in their lives.
Leue et al. (2014) used enjoyment in their extended TAM model to find out the basic
requirements for a GPS-based augmented reality application to be accepted by tourists.
They found out that enjoyment is one of the primary antecedents of perceived usefulness
and perceived ease of use, which in turn influences attitude, behavioural intention to use
and actual usage. Choi and Kim (2016) found a positive relationship between perceived
enjoyment and the intention to use smartwatches. Furthermore, their analysis revealed
that certain individual characteristics such as need for uniqueness and high level of vanity
would lead individuals to perceive smartwatches as more enjoyable. Yang et al. (2016)
also suggested that the importance of perceived enjoyment would vary between the actual
users and the potential users, and potential users cared more about the utilitarian purposes
rather than enjoyment. Rauschnabel et al. (2016a) suggested that in the situations where
smart glasses would be used at home or in public, their entertainment value would be an
antecedent for consumer’s usage intention. Wu et al. (2016) studied consumer’s intention
to use smartwatches and suggested that perceived enjoyment significantly affects attitude
towards smartwatches especially among individuals between 35 and 54 years old. In
another study, Gao et al. (2015) suggested that the users of fitness wearable devices pay
attention to hedonic motivation when they decide whether or not to accept them. The
importance of hedonic motivation was also confirmed by Gu et al. (2016) in the context
of wearable commerce.
It should be noted that there has not been a consensus in the literature about the
impact of hedonic motivation on user’s intention to use technologies. For example,
Jang Yul (2014) incorporated perceived enjoyment in the TAM framework in order to
analyse its effect on user’s intention to use mobile fitness applications; however, they did
not find a significant effect of perceived enjoyment on intention to use in their model.
This could be due to the fact that people may only expect to achieve their utilitarian needs
from some technologies, and intrinsic motives such as enjoyment do not play an
important role in their behaviour to adopt such technologies.
4.2 Social influences
4.2.1 Social norms and image regulation
It cannot be denied that one of the most important aspects of adopting new technologies
is helping individuals improve their image, express themselves, and differentiate
themselves from others (Southgate, 2003; Horton et al., 2012; Buenaflor and Kim, 2013).
This is especially true when the new technology is rare in the mainstream culture (Sundar
et al., 2014), or when the technology has fashion characteristics which so far has been the
case for most wearable technologies such as smartwatches, smart glasses, smart
garments, etc. Therefore, when it comes to making a decision about adopting these
technologies, users tend to be under the influence of their social networks. TAMs that
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were proposed after TAM such as TAM2, TAM3, the UTAUT model, and the IDT model
have all incorporated ‘social influences’ and ‘image’ as an influential factor in
individual’s adoption behaviour. Image is defined as “the degree to which use of an
innovation is perceived to enhance one’s image or status in one’s social system” [Moore
and Benbasat, (1991), p.195]. In addition, the TRA framework includes subjective norms
as a predictor for intention to use (Fishbein and Ajzen, 1975). Subjective norms address
how individuals perceive the opinions of their social network about performing a
particular behaviour which further emphasises the importance of image enhancement and
the social aspect of technology adoption.
Many of the researchers who have investigated consumers’ adoption of wearable
technologies have incorporated factors that address this social aspect of adopting new
technologies and found significant effects for social influences (Buenaflor and Kim,
2013; Yang et al., 2016). Kim and Shin (2015) analysed the main determinants of
smartwatch adoption. They included subcultural appeal that was adapted from Sundar
et al. (2014) in their model and hypothesised that smartwatches are viewed both as
utilitarian products and fashion products that have aesthetic attributes that can help users
express their characters and values. They found a significant effect for subcultural appeal
on user’s attitude and intention to use. The significant effect of social influences on
behavioural intention to use smartwatches was later confirmed by Wu et al. (2016).
Kwee-Meier et al. (2016) investigated the acceptance of wearable locating systems by
passengers. They discussed that the adoption of these systems could be prone to social
influences because people tend to perceive that these devices enhance survival
possibilities. Their analysis confirmed the effect of social influence (subjective norms and
image) on the intention to use. The importance of social influences on the adoption of
wearable devices has also been confirmed in other contexts, such as smart glasses
(Rauschnabel et al., 2015; Kalantari and Rauschnabel, 2017), smart clothing (Turhan,
2013), and health and fitness wearable devices (Wu et al., 2011; Jang Yul, 2014; Gao
et al., 2015; Canhoto and Arp, 2016).
One factor that can affect customer’s perception of social image is their propensity
towards adopting new technologies. Jeong et al. (2016b) found out that early adopters
perceive wearable technologies to be more influential on their social image comparing to
other groups of consumers. In another study, Choi and Kim (2016) found that perceived
self-expressiveness was a significant predictor of user’s intention to use smartwatches.
They defined self-expressiveness as “the extent to which a product or technology reflects
one’s personal characteristics” [Choi and Kim, (2016), p.780] based on a study by
Morrison and Johnson (2011). Hwang et al. (2016) also found expressiveness to be the
strongest predictor of PU for solar powered clothing.
It is noteworthy that the effect of social norms and self-image can vary based on the
product type and the conditions of the study. Rauschnabel and Ro (2016) studied
consumer’s adoption of augmented reality smart glasses but did not find a significant
effect for social norms (injunctive norms) on the adoption intention. They argued that this
result could be due to the pre-market nature of their study and could change over time.
Table 2 presents a summary of the papers that have studied perceived benefits and
social influences in their models.
Consumers
’
adoption of wearable technologies 287
Table 2 Summary of the papers that studied perceived benefits and social influences
Study Technology
Perceived benefits Social
influences
Perceived
ease-of-use
Perceived
usefulness
Price
value
Hedonic
motivation
Lee (2009) Wearable computers √ √ √
Yang et al.
(
2016
)
Wearable devices in
g
eneral
√ √ √ √
Preusse et al.
(
2016
)
Activity tracking
technolo
g
ies
√ √ √
Leue and Jung
(
2014
)
GPS-based AR
a
pp
lication
√ √ [√] [√]
Kim and Shin
(
2015
)
Smartwatch √ √ √ √
Choi and Kim
(
2016
)
Smartwatch √ √ √ √
Jang Yul
(
2014
)
Mobile fitness
a
pp
lications
√ √ (√) √
Kwee-Meier
et al.
(
2016
)
Wearable locating
s
y
stems
√ √
Rauschnabel
et al.
(
2015
)
Google glass √ √ √
Rauschnabel
et al.
(
2016a
)
AR smart glasses √ √
Jeong et al.
(
2016a
)
Smartwatch (√) √ √
Ko et al.
(
2009
)
Smart clothing √ (√) √
Hwang et al.
(
2016
)
Solar-powered
clothin
g
√ √ √
Nasir and
Yurder
(
2015
)
Wearable health
technolo
g
ies
√
Rauschnabel
and Ro
(
2016
)
AR smart glasses √ √ (√)
Buenaflor and
Kim
(
2013
)
Wearable devices in
g
eneral
√ √ √
Arvanitis et al.
(
2011
)
Mobile AR system (√) √
Canhoto and
Ar
p
(
2016
)
Health and fitness
wearables
√
Chae (2009) Smart clothing √ √
Spagnolli
et al.
(
2014
)
Wearable symbiotic
devices
(√) √
Page (2015) Google glass,
smartwatches and
√ √
Coorevits and
Coenen
Wearable fitness
trackers
√ √
Notes: √: studied and found a significant effect; (√): studied but did not find a significant
effect; [√]: proposed the effect conceptually but did not test it empirically;
√ (√): studied and found conflicting results.
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Table 2 Summary of the papers that studied perceived benefits and social influences
(continued)
Study Technology
Perceived benefits Social
influences
Perceived
ease-of-use
Perceived
usefulness
Price
value
Hedonic
motivation
Rauschnabel
et al.
(
2016
b
)
AR smart glasses √ √ √
Krey et al.
(
2016
)
Smartwatch √ √
Wu et al.
(
2011
)
Mobile healthcare
devices
(√) √
Chuah et al.
(
2016
)
Smartwatch √ √
Turhan (2013) Smart clothing √ √ √
Wu et al.
(
2016
)
Smartwatch (√) √ √ √
Gu et al.
(
2016
)
Wearable commerce √ √
Kalantari and
Rauschnabel
(2017)
AR smart glasses √ √ (√) √
Hein and
Rauschnabel
(2016)
AR smart glasses [√] [√] [√]
Gao et al.
(
2015
)
Health and fitness
wea
r
ables
√ √ √ √
Hwang (2014) Solar-powered
clothin
g
(√) √
Notes: √: studied and found a significant effect; (√): studied but did not find a significant
effect; [√]: proposed the effect conceptually but did not test it empirically;
√ (√): studied and found conflicting results.
4.3 Technology characteristics
4.3.1 Perceived quality
Like most products, consumers’ perception of the overall quality of a novel technology
plays a crucial role in their decision to adopt and purchase the technology.
In many cases, the quality perception is closely associated with consumer’s
perception of the manufacturer brand image (Keller, 1993). Rauschnabel and Ro (2016)
found a significant effect for brand attitude on consumer’s attitude towards adopting
smart glasses. The brand names of wearable technologies also have a positive influence
on enhancing their users’ social image (Yang et al., 2016). Ernst and Ernst (2016)
suggested that the expectation confirmation from the previous products of a manufacturer
positively affects the consumer’s adoption of smartwatches. Jeong et al. (2016a)
suggested that since consumers tend to associate the quality of novel products with the
existing products of a manufacturer, perceived similarity between the quality of existing
and expanded products has a positive effect on perceived ease-of-use and more
importantly on perceived usefulness of smartwatches. Leue et al. (2014) studied tourists’
acceptance of GPS-based augmented reality applications. They suggested that the quality
Consumers
’
adoption of wearable technologies 289
of the information provided by these applications has a significant effect on tourists’
perceptions of their pragmatic usefulness and hence will positively affect their intention
to adopt these applications. Similar findings were reported by Tom Dieck et al. (2016) in
the context of using smart glasses AR applications in art galleries. According to Kim and
Shin (2015), sometimes affective quality can be more predictive of perceived usefulness
and user’s adoption behaviour than utilitarian quality. Their study confirmed this
hypothesis in the context of smartwatch adoption.
4.3.2 Perceived aesthetics
As discussed before, wearable technologies such as smartwatches, smart glasses, and
smart clothing are oftentimes considered as fashion items; therefore, consumers tend to
choose these technologies according to their aesthetic attributes such as design, form,
colour, and texture mainly because these technologies can be a means of visual
communication (Chattaraman and Rudd, 2006; Page, 2015; Coorevits and Coenen, 2016).
The reason why the aesthetic attributes are effective in consumer’s decision-making is
that they influence both the cognitive attention and emotions that affect the consumption
pattern (Jeong et al., 2016a). Hwang et al. (2016) analysed the aesthetic attributes of solar
powered clothing and observed a significant effect of these attributes on consumer’s
attitude towards this technology. Yang et al. (2016) suggested that the visual
attractiveness of wearable devices strongly affects users’ perceived enjoyment and social
image.
Jeong et al. (2016a) also found a significant effect of aesthetic attributes on
consumers’ intention to accept smartwatches. In another study of consumer valuation of
smartwatches, Jung et al. (2016) suggested that in the mature stage of diffusion,
consumers have aesthetic needs that must be met by coming up with diverse product
designs.
4.3.3 Perceived comfort
It is no wonder that one of the most important aspects of the design of wearable devices
is their comfort. This construct addresses consumer’s satisfaction with the physical
attributes of the technology and their overall sense of physical well-being. These physical
attributes may include weight, bulk, flexibility, texture, elasticity, etc. Consumers would
not be susceptive of these technologies if they feel that the physiological effort of
wearing them would be high. Many researchers have incorporated consumer’s perceived
comfort as an independent variable in their models and confirmed its impact on
consumers’ ease-of-use perceptions and their attitudes towards wearable technologies
(Arvanitis et al., 2011; Buenaflor and Kim, 2013; Hwang, 2014; Hwang et al., 2016;
Spagnolli et al., 2014; Tom Dieck et al., 2016; Coorevits and Coenen, 2016). One of the
factors that affect the design considerations of wearables with regards to comfort is their
intervention with daily behaviour and activities (Coorevits and Coenen, 2016). In order to
assess the perceived comfort of wearable devices, Arvanitis et al. (2011) adapted a scale
from Fishbein and Ajzen (1975) that includes dimensions such as: emotion, attachment,
harm, perceived change, and movement.
The comfort construct could also overlap with the construct of perceived ease of use
of the wearable device.
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4.3.4 Perceived compatibility
Compatibility is one of the core constructs of the IDT. Rogers (1995, p.224) defines
compatibility as “the degree to which an innovation is perceived as consistent with the
existing values, past experiences, and needs of potential adopters”. Since this construct
addresses how well a certain technology fits into consumers’ lifestyles and prior
experiences, it can have a powerful impact on consumers’ perceptions of the usefulness
of the technology and their usage intentions (Choi and Kim, 2016).
Considering the importance of compatibility in the adoption behaviour, a few studies
have incorporated ‘perceived compatibility’ in their technology acceptance framework to
analyse consumers’ adoption of wearable technologies in various contexts, such as
wearable health technologies (Nasir and Yurder, 2015), smart clothing (Ko et al., 2009;
Turhan, 2013), and smartwatches (Wu et al., 2016; Choi and Kim, 2016). Yang et al.
(2016, p.260) defined compatibility as “the degree to which wearable devices comply
with other products’ (e.g., smartphones, PCs) technical functionalities, users’ business
needs and lifestyles”. They concluded that compatibility of wearable devices positively
affects PU which in turn leads to perceived value and consumer’s intention to use.
4.3.5 Visibility
Visibility or observability is one of the core constructs of the IDT. Fisher and Price
(1992) defined perceived visibility as the individual’s beliefs of the extent to which a
technology is noticed by other people. According to Rogers (2003), observability is
important in the dissemination and popularisation of a new technology. This
characteristic helps potential adopters of a technology to observe its uses and benefits
before making their decision to adopt and use the technology. This variable is particularly
important in expanding the adoption of wearable devices since these technologies are
worn on the body and can be seen and recognised by others (Canhoto and Arp, 2016).
In the literature of wearable technology adoption, the significant effect of visibility
has been confirmed in different contexts, such as smart clothing (Ko et al., 2009), health
and fitness wearables (Canhoto and Arp, 2016), and smartwatches (Krey et al., 2016;
Chuah et al., 2016). Visibility is often considered as an antecedent to social influences
and image since wearables that are visible can help individuals make an impression on
others in their social network (Krey et al., 2016). Of course, visibility can vary based on
different factors such as product type and usage conditions. Chuah et al. (2016) found a
positive relationship between individual’s perception of smartwatch visibility and their
attitude and intention to use smartwatches. Their findings suggest that smartwatches are
perceived as both technology and fashion or as they call it ‘fashnology’. Following this
terminology, Hein and Rauschnabel (2016) found that social cues have a more profound
effect on fashionistas comparing to technologists.
Table 3 presents a summary of the papers that have studied technology characteristics
in their models to understand consumer’s adoption of wearable technologies.
Consumers
’
adoption of wearable technologies 291
Table 3 Summary of the papers that studied technology characteristics
Study Technology
Technology characteristics
Perceived
quality
Perceived
aesthetics
Perceived
comfort
Perceived
compatibility Visibility
Yang et al.
(2016)
Wearable devices
in general
√ √ √
Leue and
Jung (2014)
GPS-based AR
application
[√]
Kim and Shin
(2015)
Smartwatch √
Choi and
Kim (2016)
Smartwatch √
Jeong et al.
(2016a)
Smartwatch √ √
Ko et al.
(2009)
Smart clothing √ √
Hwang et al.
(2016)
Solar-powered
clothing
√ √
Nasir and
Yurder
(2015)
Wearable health
technologies
√
Buenaflor
and Kim
(2013)
Wearable devices
in general
√
Arvanitis
et al. (2011)
Mobile AR
system
√
Canhoto and
Arp (2016)
Health and fitness
wearables
√
Spagnolli
et al. (2014)
Wearable
symbiotic devices
√
Tom Dieck
et al. (2016)
Wearable smart
glasses AR
application
√ √
Page (2015) Google glass,
smartwatches and
wristbands
√
Notes: √: studied and found a significant effect; (√): studied but did not find a significant
effect; [√]: proposed the effect conceptually but did not test it empirically.
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Table 3 Summary of the papers that studied technology characteristics (continued)
Study Technology
Technology characteristics
Perceived
quality
Perceived
aesthetics
Perceived
comfort
Perceived
compatibility Visibility
Coorevits and
Coenen
(2016)
Wearable fitness
trackers
√ √ √
Krey et al.
(2016)
Smartwatch √
Chuah et al.
(2016)
Smartwatch √
Turhan
(2013)
Smart clothing √
Wu et al.
(2016)
Smartwatch √
Hein and
Rauschnabel
(2016)
AR smart glasses [√]
Hwang
(2014)
Solar-powered
clothing
(√) √
Notes: √: studied and found a significant effect; (√): studied but did not find a significant
effect; [√]: proposed the effect conceptually but did not test it empirically.
4.4 Individual characteristics
When it comes to adopting innovations and novel technologies, all consumers do not
exhibit the same tendency for adoption (Parasuraman, 2000; Lin and Hsieh, 2007). Thus,
many researchers have reasonably argued that variables that address individual
differences should also be incorporated in the technology adoption models. In the context
of wearable technologies, many researchers have investigated the effects of these
individual difference variables. These variables can be categorised as socio-demographic
variables (age, gender), technology innovativeness, product involvement, technology
self-efficacy, and personality traits.
4.4.1 Socio-demographic variables
Socio-demographic variables such as gender and age are the most commonly studied
variables that address individual differences. Arvanitis et al. (2011) argued that gender
could play a moderating role in user attitudes. Their results indicate that females report
lower levels of satisfaction and perceived usefulness towards mobile augmented reality
systems for science education. Schaar and Ziefle (2011) reported a similar finding in the
context of smart clothing where women tend to have lower technical experience than men
in general and hence are more reluctant to adopt smart shirts. In their study of consumer’s
adoption of health and fitness wearables, Canhoto and Arp (2016) found that gender has a
significant effect on the way consumers perceive themselves to be early adopters of these
technologies. Rauschnabel and Ro (2016) included gender and age as additional
antecedents of the adoption of smart glasses in their TAM framework. They concluded
Consumers
’
adoption of wearable technologies 293
that female participants perceived lower functional benefits for this technology which
might be due to a lower level of knowledge about smart glasses; however, they did not
find a significant effect for age in their model. Kwee-Meier et al. (2016) did not find a
significant effect of age on the intention to use wearable locating systems; however, they
found a positive relationship between age and other factors such as trust and social
influence.
4.4.2 Technology innovativeness
Consumers’ innovativeness has been proposed as an antecedent to attitude and adoption
behaviour by various researchers (Nov and Ye, 2008; Leue et al., 2014; Jeong et al.,
2016b). Chae (2009, p.25) defines innovation as “the inclination of an individual to
accept innovation comparatively earlier than others within the social system”. There is a
general consensus in the technology acceptance literature that higher levels of technology
innovation is positively related to perceived usefulness, perceived ease of use, attitude
towards technology, and intention of use (Kim et al., 2005; Park, 2004; Kang and Jin,
2007). The reason for this can be explained by looking at the innovation diffusion
literature which characterises highly innovative individuals as active information seekers.
This will in turn help them deal with the uncertainty of new technologies better and hence
have a more positive adoption intention (Rogers, 1983, 1995).
In the context of wearable technology adoption, many researchers have investigated
the effects of technology innovativeness as an antecedent for adoption intention. Wu
et al. (2011, p.588) describe personal innovativeness as “an individual’s psychological
state of willingness to take a risk by trying out an innovation”. In their study which
considered the context of mobile healthcare devices, they concluded that personal
innovativeness in IT (PIIT) has a significant effect on PEOU and consumer’s attitude
which in turn affected their adoption behaviour. Jang Yul (2014) used the same construct
(PIIT) in a study to analyse the determinants of adopting mobile fitness applications. The
results confirmed the significant effect of PIIT on PU and PEOU which led to a more
positive attitude towards these applications and behavioural intention to use them. In
another study of smartwatch adoption, Choi and Kim (2016) found a positive effect of
Information Technology innovativeness on PU and PEOU. Kwee-Meier et al. (2016,
p.398) used a similar construct called ‘technical enthusiasm’ which they described as “the
enthusiasm perceived while dealing with electronic devices”. They found out that
technical enthusiasm has a significant effect on consumer’s intention to use wearable
locating systems. Rauschnabel and Ro (2016) also concluded that technology
innovativeness is a critical factor that influences consumer’s attitude towards smart
glasses.
4.4.3 Product involvement
Another individual variable that is fairly similar to technology innovativeness is
involvement. Several definitions have been provided for involvement in the consumer
behaviour literature. Here, the definition used is based on what was provided by Mano
and Oliver (1993, p.452) who define involvement as “the inherent need fulfillment, value
expression, or interest the consumer has in the product”. The level of consumer’s product
involvement depends on the degree to which the consumer perceives the product to be
personally relevant to them. Therefore, it has been considered as an important antecedent
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to adoption behaviour. Jang Yul (2014) found a significant effect for involvement on the
intention to adopt mobile fitness applications. In another study about smart clothing
adoption, Chae (2009) found that clothing involvement has a significant effect on
perceived usefulness of smart clothing and hence impacts user’s attitude and adoption
behaviour towards these technologies.
4.4.4 Technology self-efficacy
Self-efficacy does not address the actual skills that users have, but instead, it shows their
“judgements of their capabilities to organize and execute courses of action required to
attain designated types of performance” [Bandura, (1986), p.391]. Self-efficacy is one of
the main components of the SCT. When people have a positive belief about their ability
to accomplish a task, they are more likely to participate; whereas a low degree of self-
efficacy belief leads to avoiding a task. This is particularly important in adopting novel
technologies which can be relatively complicated to operate. Self-efficacy is positively
influenced by involvement because consumers who are more interested in new
technologies are more likely to have a better judgement of their abilities to use these
technologies.
Several researchers have investigated the impact of technology self-efficacy in the
adoption of wearable devices. Turhan (2013) found self-efficacy to be an important
antecedent of perceived behavioural control in the adoption of smart bras and smart
shirts. Jang Yul (2014) also confirmed the importance of self-efficacy in predicting the
adoption behaviour. Gao et al. (2015) found that the users of medical wearable devices
are affected by self-efficacy when they make decisions about adopting these devices.
4.4.5 Personality traits
Finally, personality traits can be another group of individual difference variables that can
explain user’s intention to adopt a new technology. Many researchers have investigated
the effects of personality on technology adoption (e.g., Vishwanath, 2005). Choi and Kim
(2016) found out that individuals with high levels of vanity and need for uniqueness
perceive smartwatches to be more appropriate to enhance their image and more enjoyable
to use, and hence have a more positive attitude towards adopting them. Rauschnabel et al.
(2015) hypothesised that human personality could affect consumers’ awareness and
intention to adopt smart glasses. They drew on the big five personality traits to analyse
the direct effects of openness to experience, extraversion, and neuroticism on consumer’s
awareness of Google glass and their moderating effect on consumer’s adoption intention.
Their results indicate that individuals who are more open to experiences are generally
more knowledgeable and more curious about smart glasses and hence are more likely to
use them whereas neurotic consumers are less likely to adopt smart glasses. Finally, they
found out that extraverts are more likely to adopt smart glasses if they believe that smart
glasses enhance their socialisation with their peers. In their study of wearable locating
systems, Kwee-Meier et al. (2016) suggested that individuals with higher neuroticism
have higher intentions for adoption.
Table 4 presents a summary of the papers that have studied individual characteristics
in their models for predicting consumer’s adoption of wearable technologies.
Consumers
’
adoption of wearable technologies 295
Table 4 Summary of the papers that studied individual characteristics
Individual characteristics
Socio-demographic variables
Study Technology
Age Gender
Technology
innovativeness Involvement Self-efficacy Personality traits
Leue and Jung (2014) GPS-based AR
application
[√]
Choi and Kim (2016) Smartwatch √ √
Jang Yul (2014) Mobile fitness
applications
√ √ (√)
Kwee-Meier et al.
(2016)
Wearable locating
systems
(√) √
Rauschnabel et al.
(2015)
Google glass √
Jeong et al. (2016) Smartwatch √
Rauschnabe l and Ro
(2016)
AR smart glasses (√) √ √
Buenaflor and Kim
(2013)
Wearable devices in
gener al
√ √
Arvanitis et al. (2011) Mobile AR system √
Schaar and Ziefle
(2011)
Smart clothi ng √
Canhot o and Arp
(2016)
Health and fitness
wearables
√
Chae (2009) Smart clothi ng √
Wu et al. (2011) Mobile healthcare
devices
√
Turhan (2013) Smar t clothin g √
Wu et al. (2016) Smartwatch (√)
Notes: √: studied and found a significant effect; (√): studied but did not find a significant effect; [√]: proposed the effect conceptually but did not test it empirically.
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4.5 Perceived risks and general concerns
One of the most important aspects of wearable technologies that immensely affects the
customers’ attitudes and can be a serious barrier for adoption is how these technologies
are perceived in terms of the risks they impose on individuals. Blackwell et al. (2001)
define perceived risk as consumer’s uncertainty about the potential positive and negative
consequences of the purchase decision.
These risks can have a higher adverse effect on adoption when the technology is new,
and hence there is more uncertainty associated with it (Rogers, 1995). An extensive
discussion of these perceived risks can be found in the literature of wearable technology
adoption. Different types of risks that can impact consumer’s decision are listed below:
4.5.1 Performance risk
Performance risk refers to consumer’s concerns about the failure of a technology to
perform as expected and the loss that will be incurred to them due to this failure. Several
researchers have identified performance risk to be a barrier in wearable technology
adoption (Ko et al., 2009; Nasir and Yurder, 2015; Hwang, 2014; Hwang et al., 2016).
Yang et al. (2016) suggested that perceived performance risk is an important factor for
potential users of wearable devices.
4.5.2 Security risk and privacy concerns
This aspect emphasises the importance of safe and secure data handling and storage.
Today’s consumers are concerned about privacy breaches and the potential loss of control
over their personal information. They need to make sure that their data is handled and
stored in a safe and secure manner. According to Mills et al. (2016), when it comes to
wearable devices, security is even more important because these devices are very
personal and intimate and fit wearer’s anatomy. Moreover, since many of them are
visible, the risks of theft are higher for these devices. In many cases such as medical
wearable devices, hacking the data could also lead to the malfunction of the device and
hence physical harm for the user. Consumer’s perception of the privacy risk of the
wearables can negatively affect their trust in these devices which could lead to decreased
adoption intention (Schaar and Ziefle, 2011; Spagnolli et al., 2014; Gao et al., 2015;
Nasir and Yurder, 2015; Kwee-Meier et al., 2016; Gu et al., 2016). In many cases,
obtaining anonymous data from the consumers should also be consented (Kwee-Meier
et al., 2016). This construct is closely related to consumer’s trust in technology. An
interesting finding with regards to the effect of privacy concerns on adoption intention of
smart glasses was proposed by Rauschnabel et al. (2016a). They found that people’s
concerns about others’ privacy have a significant negative effect on their adoption
intention; however, when it comes to their own privacy threats, the effect is not
significant.
4.5.3 Environmental concerns
Consumers are becoming increasingly conscious about environmental issues, and their
attitude towards protecting the environment plays an important role in their adoption
decision (Hwang, 2014; Hwang et al., 2016).
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4.5.4 Physical risk
This aspect of risk refers to consumers’ beliefs about the negative consequences of using
wearable technologies on their health and their threats to human life, such as radiation
emitted from smart glasses (Buenaflor and Kim, 2013) or the possibility of personal
injury and dangers to human body (Ko et al., 2009; Schaar and Ziefle, 2011; Nasir and
Yurder, 2015; Kalantari and Rauschnabel, 2017).
4.5.5 Social risk
Due to the visibility of wearable technologies such as smart glasses, consumers care
about the evaluation of their social group when they want to make a decision about
adopting these technologies (Ko et al., 2009). Generally, these devices can be associated
with intruding other people’s privacy especially because of their recording functionalities
(Weiz et al., 2016).
Table 5 A summary of perceived risks associated with the use of wearable devices
Study
Perceived risks and general concerns
Performance
risk
Privacy
risk
Environmental
concerns
Physical
risk Social risk Financial
risk
Yang et al. (2016) √ √
Ko et al. (2009) √ √ √ √
Hwang et al. (2016) √ √
Nasir and Yurder
(2015)
√ √ √
Buenalor and Kim
(2013)
√
Spagnolli et al. (2014) √
Hwang (2014) √ √
Gao et al. (2015) √
Rheingans et al. (2016) (√)
Stock et al. (2016) (√)
Rauschnabel, He, and
Ro (2016)
√ (√)
Kwee-Meier et al.
(2016)
√
Schaar and Ziefle
(2011)
√ √
Weiz et al. (2016) √
Kalantari and
Rauschnabel (2017)
(√) √
Notes: √: studied and found a significant effect; (√): studied but did not find a significant
effect; [√]: proposed the effect conceptually but did not test it empirically;
√ (√): studied and found conflicting results.
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4.5.6 Financial risk
Horton (1976, p.696) defines financial risk as the “net financial loss to the consumer
including the possibility that the product may be repaired, replaced, or the purchase price
refunded”. Consumer’s concern over the financial loss in buying wearable devices could
negatively affect their purchase intention (Ko et al., 2009; Yang et al., 2016)
Table 5 presents a summary of the perceived risks that are associated with the use of
wearable devices.
In addition to the aforementioned constructs, researchers have investigated the effects
of other factors on consumer’s intention to adopt wearable devices. Some of the variables
that have been found to have significant effects on consumer’s decision are mobility
(Kim and Shin, 2015), availability (Kim and Shin, 2015), user satisfaction (Arvanitis
et al., 2011), personalisation (Jang Yul, 2014), facilitating conditions (Spagnolli et al.,
2014; Gu et al., 2016), user experience (Coorevits and Coenen, 2016; Jeong et al., 2016a;
Hein and Rauschnabel, 2016), and Triability (Ko et al., 2009; Preusse et al., 2016).
5 Conclusions and directions for future research
To the best of the author’s knowledge, this is the first paper to provide a review of the
literature of the adoption of wearable technologies. By doing so, this paper synthesises
the otherwise fragmented academic and applied research on the influential factors in
wearable technology adoption and provides a framework for future research agenda.
5.1 Contributions and managerial implications
Wearable devices are expected to be the next megatrend of the technology market in the
near future; however, their adoption has been relatively slow comparing to similar
technologies such as smartphones. Wearable technology market is still in its early phase;
hence, researchers, manufacturers, and designers should better understand the factors that
drive consumers’ decisions to adopt these devices.
Previous academic and applied research on the wearable adoption antecedents is
fragmented due to the interdisciplinary nature of the subject, and various disciplines have
approached this subject using different adoption theories and models. This paper provides
an interdisciplinary literature review that synthesises the scattered research across these
disciplines, reviews the adoption theories that have been applied in previous research, and
organises the influential factors that drive consumer’s decision in adopting wearable
technologies. Figure 3 summarises these influential factors.
It should be noted that not all the aforementioned factors are influential when it
comes to adopting wearable devices, and some of them are idiosyncratic to certain
wearables and not to others. For example, hedonic motivation or perceived enjoyment is
more relevant in the adoption process of devices that are associated with fun and
entertainment, such as smartwatches and smart glasses rather than devices that are mainly
adopted for their functionality and utilitarian benefits such as health and wellness
wearable devices. Moreover, some of the wearables are considered more as fashion items
rather than technology such as smart clothing and smart glasses. This is usually due to
their higher visibility and the fact that they convey information about individual’s values
and lifestyle and can help with their self-expression and image regulation. Therefore,
Consumers
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adoption of wearable technologies 299
perceived aesthetics and design details have a greater impact on consumer’s decision to
adopt these devices. Some of the factors like perceived ease-of-use are more impactful
for certain devices because they are mainly adopted by older groups. For example, the
influence of perceived ease-of-use in health and wellness medical devices is higher than
smart glasses because they are more likely to be adopted by older individuals who have
lower levels of technology experience and innovativeness.
Figure 3 Summary of the factors influencing the adoption of wearable technologies
Perceived
Benefits
Perceived Enjoyment
Perceived Value
Individual
Characteristics
Socio-demographic
Variables
Technology
Innovativeness
Gender
Age
Prod uct In volveme nt
Technology
Self-efficacy
Perceived Risks
Privacy Ris k
Perf or ma nce R isk
Environmental Risk
Phys ical Ris k
Social R isk
Financial Risk
Perceived Use fulness
Perceived Ease-of-Use
Technology
Characteristics
Perceived Quality
Perceived Aesthetics
Perceived Comfort
Social Factors
Perceived
Compatibil ity
Visibility
Personality Traits
Social Norms and
Image Regulation
This paper also provides important managerial implications. The knowledge provided by
this review will help designers and manufacturers incorporate the important features and
capabilities in their devices in order to win over the consumers. It will also help
marketers employ more efficient strategies and use more persuading messages in their
marketing campaigns in order to address consumers’ needs and concerns.
Market research and interviews with potential consumers show that many of them see
wearable technologies as fancy toys rather than devices that could change and improve
their lifestyles. In today’s saturated market, people care more about technologies with
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meaningful applications rather than sheer innovations. Many consumers are not
convinced that wearables offer any added value especially if they already own
smartphones.
One of the drawbacks of wearable technologies is the lack of actionable data that
could help consumers make informed decisions. Many devices such as fitness trackers
provide consumers with informative rather than prescriptive data. Not only consumers
need to receive accurate information in real-time, they also need to be equipped with
insights that help them make better decisions or adjust their behaviour. They need a smart
device that they can wear all the time with a low risk of being lost, stolen or damaged;
otherwise, wearables will be perceived as irrelevant and dispensable.
Market research also indicates that consumers need wearable devices that are easy to
use and give them 24-hour connectivity. They prefer devices that are easily portable and
have a quick response time as well as the capability to connect with a growing group of
wearables (Bhat et al., 2014).
One approach for wearable manufacturers to overcome these problems is to follow a
‘human-centred design’ process. This process entails optimising consumer experience
and creating an easier means for the consumers to achieve their goals. Wearables that are
designed through this process will better fit into consumers’ lives and will provide them
with added values that cannot be easily obtained otherwise. When companies succeed to
wow consumers and win them over by optimally designing wearable devices, they can
also utilise this technology to enhance their core business processes such as marketing,
sales, and customer service.
5.2 Future research agenda
Based on the review and synthesis of the literature provided in this paper, it becomes
possible to frame an agenda for future research. Possible directions to better understand
the consumers’ adoption process and motivations to use wearable technologies can be
categorised into ‘advancing the existing research’ and ‘new research methodologies’.
5.2.1 Advancing the existing research
The current research endeavours in the area of consumers’ adoption of wearable devices
can be further improved by efforts such as studying other influential factors that can
impact consumer’s decisions, replicating the existing findings in various contexts and
different populations in order to find more generalisable results, and delving deeper into
some of the more arguable adoption antecedents such as privacy concerns and aesthetics.
These research directions will be further explained below.
Although most of the studies in the literature have focused on determining the
underlying factors that impact consumers’ adoption of wearable devices, there is still
need for extending these findings and improving the explanatory power of the acceptance
models by identifying additional antecedents of wearable adoption. Some constructs that
can be further tested are result demonstrability (i.e., whether the outcome of using the
device can be observed and communicated), mobility, and the experience of flow and
immersion when using these devices. Moreover, since referrals and product
recommendations are important predictors in the process of diffusion of innovations,
another construct that could be interesting to investigate is the consumer’s intention to
recommend the wearable device to their social network. Furthermore, many researchers
Consumers
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adoption of wearable technologies 301
that applied the TAM model in their studies have treated the TAM constructs such as
perceived usefulness as an explaining variable; however, the underlying factors of these
constructs themselves should be further explored.
As the wearable market expands, it will become more heterogeneous, and various
groups of users will prefer different types of wearable devices. Therefore, many
researchers suggest that the previous research findings should be tested and validated
within different research contexts and across more heterogeneous user groups so that
these results could be more generalisable and further supported. For example, previous
findings could be repeated across different cultures, different age cohorts, and groups
with different education levels and different technical expertise.
Some of the influential factors in the adoption of wearable devices such as privacy
issues and aesthetics are more subjective than others and hence arouse arguments that
need to be addressed.
As discussed in the previous section, privacy issues and concerns can have negative
effects on consumer’s adoption intention. Therefore, further research should be carried
out to understand how privacy concerns are mediated by social norms, and what kinds of
new policies in terms of privacy protection should be developed to mitigate public
privacy concerns about wearable devices. Perceived aesthetics and the design of
wearables have also been proved to affect consumers’ intention to adopt these devices.
Therefore it is critical to further investigate what ‘good design’ means for different
wearable devices. It is also important to understand if these devices should be designed as
fashion accessories or technological devices.
5.2.2 New research methodologies
A closer look at the literature of wearable technology adoption reveals the lack of
qualitative research methodologies in this area. In order to identify the underlying
attributes that drive consumers’ adoption, it is critical that future studies employ
qualitative research methodologies through conducting in-depth interviews before
moving on to quantitative testing. Since experience with technology is a key parameter in
consumers’ adoption, it is essential that consumers can touch, feel, and actually wear the
devices before they are interviewed about their attitude and tendency for adoption. Of
course, it will be ideal if there could be a longitudinal investigation to obtain more
information about how consumers develop attitudes towards wearable technologies over
time.
Furthermore, researchers who have used quantitative models primarily used a limited
set of technology acceptance theories such as TAM, UTAUT, etc., to investigate the
influential factors in the adoption process. Future studies can incorporate other
methodologies such as decision-making techniques (e.g., AHP) to explore the adoption
decision process.
Overall, our review offers a number of contributions. Various disciplines have studied
consumers’ adoption of wearables using different theories and approaches. This paper
provides a detailed review and synthesis of the literature. It identifies and synthesises the
underlying factors that impact consumers’ decisions in adopting these technologies. It
also provides a framework for future research inquiry in order to better understand
consumers’ requirements of wearable devices and the influential factors in making the
adoption decision.
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