Yang Lu
3
potential factors influencing users’ acceptance of one given IoT application or service. The majority of
the current studies were conducted within a specified research context or targeting a specific IoT
service, e.g. smart home (Bao et al., 2014; Kim, Park, & Choi, 2017; Marikyan et al., 2020; Park, Cho,
Han, & Kwon, 2017), smart healthcare/eHealth (Arfi, Nasr, Khvatova, & Ben Zaied, 2021; Arfi, Nasr,
Kondrateva, & Hikkerova, 2021; Karahoca, Karahoca, & Aksöz, 2017; Martínez-Caro, Cegarra-
Navarro, García-Pérez, & Fait, 2018; Pal, Funilkul, Charoenkitkarn, & Kanthamanon, 2018),
autonomous vehicles (Manfreda, Ljubi, & Groznik, 2021; Yuen, Cai, Qi, & Wang, 2021), and smart
city (Leong et al., 2017).
The majority of IoT acceptance and adoption studies were drawn upon technology acceptance
theories such as the Technology Acceptance Model (TAM) (Davis, Bagozzi, & Warshaw, 1989), the
Unified Theory of Acceptance and Use of Technology (Venkatesh, Morris, Davis, & Davis, 2003), the
Theory of Planned Behaviour (Ajzen, 1991), etc. Also, the most commonly tested dependent variable is
behavioural intention, which indicates the individual's readiness to perform a given behaviour (Davis et
al., 1989; Tscherning, 2012; Venkatesh, 2021). Evidence from previous studies supported that the two
fundamental constructs of TAM, i.e., perceived usefulness and perceived ease of use, significantly and
positively determine the users’ intention of using IoT applications and services (Bao et al., 2014; Gao
& Bai, 2014; Jang & Yu, 2017; Liew et al., 2017; Mital, Chang, Choudhary, Papa, & Pani, 2017; Park
et al., 2017).
IDT is one of the most influential theories in understanding technological evolution, postulated
that individuals’ degree of willingness of adoption is contigent on the individuals’ perceived
characteristics of the target innovation (Marikyan et al., 2020; Rogers, 1995; Tornatzky & Klein,
1982). More specifically, IDT explored and developed a comprehensive set of attributes of innovation
that significantly determine the adoption (Rogers, 1962). This set of attributes has been further revised
to six perceived characteristics of innovating, i.e. relative advantage, complexity, compatibility, result
demonstrability, visibility, and trialability (Moore & Benbasat, 1991; Rogers, 1983). The users appraise
the innovation characteristics after utilisation and reconsider the decision of continuous usage
(Marikyan et al., 2020; Rogers, 1995). This article aims to first test the effects of innovation
characteristics on user adoption of the IoT platform. Six hypotheses are put forward as follows.
First of all, relative advantage is a leading factor that determines the users' intention of adoption
(Abu-Khadra & Ziadat, 2012), referring to “the degree to which an innovation is perceived as being
better than the idea it supersedes” (Rogers, 1983). The “advantage” is often expressed in terms of
economic profitability, social prestige, convenience, and satisfaction (Karahoca et al., 2017; Rogers,
1983). However, whether an innovation is objectively advantageous has limited influence on the users’
adoption; instead, the individual's perception of the advantages determines the rate of adoption (Rogers,
1983). Perceived usefulness directly describes the perceived utilitarian value and functionalities of new
technology, which is defined as the degree to which an individual believes that using the technology
might enhance their performance in completing tasks (Davis, 1989; Davis et al., 1989). This study
employed perceived usefulness in testing IoT adoption intention.
An empirical study on the acceptance of smart lockers suggested that relative advantage has
positivi effects on the users’ attitude toward adoption (Tsai & Tiwasing, 2021). Beaides, perceived
advantage was also reported having positive invluence on the users’ perceived usefulness, perceived
ease of use, and behavioural intention of smart healthcare (Karahoca et al., 2017). Perceived usefulness
was also reported as having positive effects on the users’ attitude (Karahoca et al., 2017; Park et al.,
2017), behavioural intention (Bao et al., 2014; Gao & Bai, 2014; Liew et al., 2017; Mital et al., 2017;
Park et al., 2017), reuse intention (Jang & Yu, 2017), and satisfaction (Martínez-Caro et al., 2018) of
using the IoT. With the aim of investigating the users’ intention toward adopting the IoT, this study
hypothesised that
H1a: Perceived usefulness is positively correlated with users’ behavioural intention of using the
IoT.
Complexity refers to “the degree to which an innovation is perceived as relatively difficult to
understand and use” (Rogers, 1983), while perceived ease of use is defined as the degree to which an
innovation is perceived to be easy to learn and use (Moore & Benbasat, 1991). These two constructs
have a resemblance in concept (Moore & Benbasat, 1996; Venkatesh et al., 2003). Fundamentally, an
innovation that is perceived to be less complicated is more likely to be accepted and adopted (Davis et
al., 1989; Rogers, 1995). The effect of perceived ease of use on IoT acceptance and adoption is
arguable. The majority of studies have reported positive effects of perceived ease of use on users’
attitudes toward IoT, e.g., (Gao & Bai, 2014; Liew et al., 2017; Mital et al., 2017; Park et al., 2017).
However, the study of (Bao et al., 2014) did not show a significant effect while the studies of
(Karahoca et al., 2017; Tsai & Tiwasing, 2021) reported negative effects of complexity/perceived ease