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RESEARCH ARTICLE
Understanding consumers’ intentions to
purchase smart clothing using PLS-SEM and
fsQCA
Shucong Chen
1
, Jing YeID
2
*
1Department of Fashion and Accessory Design, College of Design, Jiaxing University, Jiaxing, China,
2Department of Fashion Design and Engineering, College of Design, Jiaxing University, Jiaxing, China
*yjwuse@gmail.com
Abstract
With the advancement of artificial intelligence (AI) and the Internet of Things (IoT), smart
clothing, which has enormous growth potential, has developed to suit consumers’ individual-
ized demands in various areas. This paper aims to construct a model that integrates that
technology acceptance model (TAM) and functionality-expressiveness-aesthetics (FEA)
model to explore the key factors influencing consumers’ smart clothing purchase intentions
(PIs). Partial least squares structural equation modeling (PLS-SEM) was employed to ana-
lyze the data, complemented by fuzzy-set qualitative comparative analysis (fsQCA). The
PLS-SEM results identified that the characteristics of functionality (FUN), expressiveness
(EXP), and aesthetics (AES) positively and significantly affect perceived ease of use
(PEOU), and only EXP affects perceived usefulness (PU). PU and PEOU positively impact
consumers’ attitudes (ATTs). Subsequently, PU and consumers’ ATTs positively influence
PIs. fsQCA revealed the nonlinear and complex interaction effects of the factors influencing
consumers’ smart clothing purchase behaviors and uncovered five necessary and six suffi-
cient conditions for consumers’ PIs. This paper furthers theoretical understanding by inte-
grating the FEA model into the TAM. Additionally, on a practical level, it provides significant
insights into consumers’ intentions to purchase smart clothing. These findings serve as valu-
able tools for corporations and designers in strategizing the design and promotion of smart
clothing. The results validate theoretical conceptions about smart clothing PIs and provide
useful insights and marketing suggestions for smart clothing implementation and develop-
ment. Moreover, this study is the first to explain smart clothing PIs using symmetric (PLS-
SEM) and asymmetric (fsQCA) methods.
Introduction
In the rapidly advancing fields of artificial intelligence (AI) and the Internet of Things (IoT),
the wearable device market has experienced rapid growth, offering a variety of products to ful-
fill the needs and desires of interested consumers [1]. According to MarketsandMarkets
(2019), the wearable technology market is projected to expand at a compound annual growth
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OPEN ACCESS
Citation: Chen S, Ye J (2023) Understanding
consumers’ intentions to purchase smart clothing
using PLS-SEM and fsQCA. PLoS ONE 18(9):
e0291870. https://doi.org/10.1371/journal.
pone.0291870
Editor: Mohammed A. Al-Sharafi, Universiti Tenaga
Nasional, MALAYSIA
Received: April 11, 2023
Accepted: September 7, 2023
Published: September 19, 2023
Copyright: ©2023 Chen, Ye. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Competing interests: The authors have declared
that no competing interests exist.
rate (CAGR) of 11.2% from 2016 to reach $56.8 billion by 2025. Wearable devices can be
found in a variety of industrial areas, including the fashion industry, where they are integrated
into clothing to enhance its value with electronic components. A prominent subcategory of
this technology is smart clothing, which integrates sensors and information technology into
garments [2]. Recently, smart clothing has seen increased growth. According to Marketsand-
Markets, the smart clothing global market is expected to grow from $1.6 billion in 2019 to $5.3
billion in 2024, for a CAGR of 26.2%. Additionally, Gartner (2019) predicted a surge in smart
clothing shipments from 5.65 million in 2018 to 19.9 million in 2022.
As a form of wearable device, smart clothing is next-generation clothing or accessories
empowered by information or electronic technology and wearable devices to offer the dual
functions of perception and feedback [2]. Smart clothing not only detects changes in the exter-
nal or internal environment but also responds to these changes through a feedback mechanism
[3]. With advancements in wearable devices and clothing as essential equipment in daily life,
smart clothing is receiving increasing attention from various perspectives. Some researchers
have investigated innovative technologies in related sectors, including electronic information
[4], sensors [5], and novel smart textiles [6]. Moreover, various researchers have paid attention
to the applications of smart clothing, including smart healthcare [7], baby and elderly monitor-
ing [8,9], sports and wellness [10,11], industry, defense and public safety [12,13], and envi-
ronmental interactions [14]. Moreover, numerous researchers have focused on the design of
smart clothing. For instance, Li et al. [15] defined design principles for smart clothing, includ-
ing intelligent module design and carrier design. Imbesi and Scataglini [16] proposed a user-
centered framework for designing smart clothing for older adults. Chen et al. [17] introduced
smart clothing design features, key technologies, and practical implementation methodologies.
Although the prospects and functions of smart clothing are promising, most researchers have
focused on smart clothing technology development and design, while few studies have
researched consumer needs, attitudes (ATTs), and purchase intentions (PIs) related to smart
clothing.
China, which is among the world’s fastest-growing regions for smart clothing, anticipates
total market revenue of $17.5 billion between 2023 and 2029. The Chinese government has
increased investment in research and development, talent training, and industry chain con-
struction to actively promote the development of the smart apparel industry. Additionally, the
government has issued a series of policy measures to support the smart clothing industry.
Under these circumstances, the number of smart clothing consumers in China is increasing.
Although this region has made significant progress in the development of smart clothing, it
still encounters important development challenges, and the adoption of smart clothing
remains in a relatively early stage. Furthermore, to the best of authors’ knowledge, research on
consumer needs and PIs in emerging regions such as China is limited.
Previous studies have examined smart clothing PIs from various perspectives. Mahmood
and Lee [18] explored how social influence (SI) and performance expectancy (PE) impact
elderly users’ adoption of health-monitoring smart clothing. Nam and Lee [19] introduced an
extended wearable acceptability range (WEAR) scale into the smart clothing field to investigate
consumers’ social acceptance of smart clothing. Ju and Lee [2] found that perceived risks and
unavailability lead to resistance to innovation in smart clothing. Park and Noh [20] investi-
gated the effect of price sensitivity and innovativeness on consumers’ smart clothing behav-
ioral intentions (BIs). Most related works have focused on consumers’ social, innovation, and
technology acceptance. Moreover, most social acceptability (SA) constructs are adopted from
the WEAR scale, while few studies have examined consumers’ needs to drive smart clothing
purchases.
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Lamb and Kallal [21] proposed the functionality-expressiveness-aesthetics (FEA) consumer
needs model, which is widely utilized in identifying and evaluating target consumers’ clothing
needs and is employed in designing any type of apparel. Lv et al. [22] explored the physiologi-
cal, functional, aesthetic (AES) and psychological needs of elderly Chinese users of smart cloth-
ing. According to Li et al. [15], consumers not only prioritize the functionality (FUN) of smart
clothing but also have heightened expectations regarding its appearance. Multiple Chinese
studies using varied surveys have indicated that in the purchase of smart clothing, the needs
are to improve uses’ health condition (HC) and increase their fashion sense, which also aligns
with the FEA framework. Additionally, various studies have utilized the FEA model as an ante-
cedent to investigate consumers’ needs for smart clothing, suggesting that the model con-
structs are feasible. Therefore, this work also incorporates the FEA model to explore the
essential intrinsic attributes of smart clothing. Notably, the FEA model has yielded contrary
conclusions across countries, and few studies have applied the FEA model to investigate Chi-
nese consumers’ BIs. Therefore, this paper aims to examine the applicability of the FEA model
in studying Chinese consumers’ behaviors toward smart clothing.
Various theoretical models, such as the theory of reasoned action (TRA) [23], the theory of
planned behavior (TPB) [24,25], the unified theory of acceptance and use of technology
(UTAUT) [18], and the technology acceptance model (TAM) [26,27], have been utilized to
examine smart clothing PIs. The TAM, which is a well-known theoretical framework for
understanding users’ motivations to accept new technology, has been widely applied in various
studies related to smart clothing BIs. Although this framework has significant explanatory
power, it may be too general to account for the specific factors of the phenomenon under
investigation. Most TAM-based studies either incorporate additional variables to enhance
overall their explanatory power or merge the TAM with other theories to solidify the theoreti-
cal foundation of the research model. Tsai et al. [28] extended the TAM by considering the
impact of perceived prevalence, technology anxiety (TA), and resistance to change (RC) on
patients’ wearable healthcare behavior. Additionally, few studies have examined the relation-
ship between perceived ease of use (PEOU) and PIs in a smart clothing context. Moreover, sev-
eral researchers have combined the FEA model with the TAM to investigate consumers’ smart
clothing PIs, but the conclusions have varied across studies. Furthermore, most research has
examined the relationships between AES attributes and consumers’ ATTs and BIs. Smart
clothing should be designed to look good and contain various features that consumers feel are
useful; additionally, the clothing should be easy to wear. However, few studies have examined
the relationships among AES attributes and PU and PEOU. Therefore, this work incorporates
consumers’ needs into the TAM to thoroughly understand how consumer needs influence BIs.
To fill the gaps noted above, this study uses mediation analysis to explore the relationship
between PEOU and PIs.
Methodologically, most previous studies on smart clothing PIs have employed symmetric
approaches, such as covariance-based structural equation modeling (CB-SEM). Few studies
have applied variance-based SEM (VB-SEM), commonly known as partial least squares struc-
tural equation modeling (PLS-SEM). In contrast to CB-SEM, PLS-SEM is based on a compos-
ite model [29] and offers more modeling flexibility with regard to modeling (e.g., formative
and reflective measurement models) and data needs (e.g., smaller sample sizes and nonnor-
mally distributed data) [30]. Few studies have employed VB-SEM to investigate smart clothing
PIs. Additionally, the traditional symmetric technique focuses exclusively on the individual or
net effects of antecedents and outcomes, overlooking the intricate configurations of variables.
To fill this gap, it is critical to assess the sufficiency and necessity of the factors under study
in achieving the expected outcome [31]. This evaluation should occur from the vantage point
of complexity theory and configurational models, thus making complexity theory an ideal
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theoretical framework for this research. As the most widely used asymmetric method, fuzzy-
set qualitative comparative analysis (fsQCA), is often utilized to identify models that are both
consistent and sufficient in predicting behavioral outcomes. Originally proposed by Ragin
[32], fsQCA integrates qualitative and quantitative methodologies and can be applied to inves-
tigate how various causal condition combinations may result in the same outcome, using con-
figuration analysis to explain complicated situations. With these considerations, the current
study applies complexity theory in conjunction with fsQCA to comprehend the causal patterns
underlying smart clothing PIs.
This paper explores the multifaceted influences on consumers’ intentions to purchase
smart clothing. Drawing from a review of the relevant literature, an integrated theoretical
model combining the FEA model and the TAM—both commonly used in smart clothing PI
research—is developed. This analysis, based on an online survey of 225 Chinese consumers,
employs both symmetric (PLS-SEM) and asymmetric (fsQCA) techniques to examine these
influences. This study starts with an investigation of the individual effects of each antecedent
using PLS-SEM, followed by mediation analysis to understand the relationship between PEOU
and PIs. Additionally, to facilitate a more accurate comprehension of the complex reality asso-
ciated with the diverse determinants and PIs, fsQCA is used for a more nuanced understand-
ing of causal factor configurations. This approach elucidates the complexities of intention that
cannot be fully explained by PLS-SEM alone. Insights gleaned from both PLS-SEM and fsQCA
contribute perspectives and offer practical marketing suggestions to boost consumer’s pur-
chase of smart clothing.
Based on the discussion above, the novelty of this paper can be summarized as follows.
First, this study specifically focuses on Chinese consumers, addressing the growing market in
China and filling the gap in research on consumer perspectives in this emerging region. Sec-
ond, this research examines some understudied relationships, including the relationships
among AES attributes and PU and PEOU, as well as the mediating effect of PU and ATTs on
the relationship between PEOU and PIs. By exploring these relationships, this research con-
tributes to advancing knowledge in the field and provides valuable insights for future research.
Third, based on PLS-SEM analysis, this research examines the mediating role of PU and ATTs.
Incorporating mediation analysis adds an additional layer of depth and strengthens the overall
findings of the PLS-SEM analysis, offering valuable insights into the factors shaping consum-
ers’ intentions toward smart clothing.
This work makes three core contributions. First, this research develops an integrated frame-
work that combines the FEA consumer needs model with the TAM to explore smart clothing
PIs in China’s burgeoning market. Second, this research enhances the theoretical understand-
ing of the complexities involved in consumers’ PIs. Methodologically, this work innovatively
employs an asymmetric technique (fsQCA) to supplement the traditional symmetric approach
(PLS-SEM) frequently employed in previous studies. Symmetric methods predominantly
focus on the individual and net effects of the determinants of smart clothing PIs. To the best of
the authors’ knowledge, this study represents the first application of a method integrating
PLS-SEM and fsQCA in the smart clothing context. Moreover, the hybrid approach can be
extended to address other issues in the clothing area. Finally, on a managerial level, the insights
derived from this study will help businesses and marketers develop effective strategies to con-
vince consumers to purchase smart clothing.
The remainder of the paper is organized as follows: Section 2 presents the theoretical foun-
dations, conceptual model, and hypotheses. The data and methodology are described in Sec-
tion 3. Section 4 presents the PLS-SEM and fsQCA results. Section 5 provides the key findings
and theoretical and managerial implications. Finally, Section 6 discusses the main conclusions
and limitations of this study as well as potential future research.
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Literature review and hypothesis development
Complexity theory
Complexity theory offers necessary tools for a significant shift in sociological practice,
acknowledging that human behavior is partly shaped by dynamic social processes, but remains
incompletely accounted for due to the distinct objective reality of humans. Complexity theory
has been employed across disciplines to elucidate the heterogeneity, nonlinearity and dynam-
ics inherent in complex systems [33]. Complexity theory has four basic principles: conjunc-
tion, equifinality, asymmetry, and causal asymmetry [34]. Conjunction implies that an
outcome is the result of a combination of interdependent conditions, rather than being attrib-
uted to a singular cause [35]. Equifinality denotes that multiple diverse antecedent configura-
tions can produce the same outcome [36]. Moreover, complexity theory recognizes the
principle of asymmetry by allowing for the existence of contradictory instances. Consequently,
the absence of antecedents that lead to high-scoring outcomes does not invariably result in low
scores. Additionally, another core principle of complexity theory is causal asymmetry [35],
where configurations resulting in high outcome scores and those leading to low scores are not
mere inverses of each other. Causal asymmetry extends beyond linear theory, emphasizing the
nonlinear relationships among the conditions that determine outcomes.
This study incorporates the principles of complexity theory by utilizing fsQCA, which
merges qualitative comparative analysis with fuzzy-set principles [36]. The aptness of fsQCA
for this study is supported by previous studies in similar nonlinear contexts [37]. This study
acknowledges the necessity of incorporating an asymmetric method alongside symmetric anal-
ysis to fully capture the complexity of smart clothing PIs.
Technology acceptance model (TAM)
In the current increasingly digital era, as new technologies continue to be developed and com-
mercialized, several theoretical models have emerged to explore factors influencing technology
adoption. In particular, the TAM, introduced by Davis [38], is one of the most important theo-
ries for assessing end-users’ adoption of new technology, which is derived from the TRA.
The TAM has various benefits with regard to assessing the factors that influence consumers’
intentions to utilize innovative technology. First, the TAM is a consistent measurement tool
and reflects empirical rationality and simplicity [39]. Second, the TAM accounts for the major-
ity of the variation in use intentions [40]. Third, the TAM has been used in several areas and
increases the reliability and relevance of the questions in questionnaires by providing a variety
of questions relevant to each factor [41]. Previous research has concluded that the TAM frame-
work provides insights to better understand consumers’ acceptance of new technology-related
applications by adding more external factors [42,43]. Therefore, this study adopts the TAM as
the main theoretical framework.
The TAM involves two key constructs, perceived usefulness (PU) and PEOU, both of which
are influenced by external variables and affect users’ ATTs and behaviors with regard to
accepting a new technology [38]. Some researchers have investigated the impact of PU and
PEOU on the intention to use wearable technologies [27,43]. The TAM incorporates ATTs as
a significant predictor of users’ acceptance of new technologies, while some studies on smart
clothing PIs have not considered ATTs.
Numerous studies have employed the TAM to investigate consumers’ BIs toward smart
clothing, as summarized in Table 1. Some conclusions have been invalidated in other coun-
tries. Moreover, limited studies have examined the mediating role of PU and ATTs in the rela-
tionship between PEOU and PIs.
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Perceived usefulness (PU). PU indicates the extent to which people feel that adopting a
new technology enhances their performance [38]. Previous studies have empirically supported
the effect of PU on consumers’ adoption of wearable technology [42,47]. According to Saleem
et al. [48], PU has been developed to influence users’ ATTs and intentions toward e-shopping
adoption. Thus, it is appropriate to investigate the influence of PU on consumers’ ATTs and
intentions to purchase smart clothing. Since smart clothing can meet consumers’ unique
needs in healthcare, work, and entertainment, it is important to understand its usefulness.
Accordingly, the following hypotheses is proposed:
H1a: PU positively affects consumers’ ATTs.
H1b: PU positively affects smart clothing PIs.
Perceived ease of use (PEOU). PEOU is highlighted as the degree to which individuals
believe that adopting a new technology will be simple [38]. If consumers do not need to spend
too much time or effort to master the wearing of smart clothing, smart clothing PIs will be
enhanced. Several researchers have found a positive relationship between PEOU and PIs [49].
The effects of PEOU on PU and ATTs have been analyzed in various studies. According to
Chuah et al. [50], PEOU positively and significantly impacts the PU of smartwatches as fashion
accessories. Kasilingam [41] discovered that PEOU had a significant and positive impact on
consumers’ ATTs toward c-commence chatbots. As PEOU is vital in the adoption of numer-
ous information technology systems, it is logical to expect the same for smart clothing. Hence,
the following hypotheses are suggested:
H2a: PEOU positively influences PU.
H2b: PEOU positively influences consumers’ ATTs.
Table 1. Smart clothing purchase intention-related research.
Author Foundation
theories
Country/
regions
Constructs Key findings Methodology
Bakhshian and
Lee (2022) [23]
FEA, TRA U.S. FUN, expressive (EXP), AES, tracking (TCK),
ATTs, SA, BIs
FUN!BI; EXP!ATT; EXP!BI; EXP!SA;
AES!ATT; TCK!ATT; TCK!SA; TCK!BI;
SA!ATT; SA!BI; ATT!BI.
CB-SEM
Wang and
Wang (2021)
[44]
FEA, TAM —— FUN, AES, compatibility (COM), PU, PEOU,
perceived performance risk (PR), ATTs, BIs
COM!PEOU; PEOU!PU; COM!PU;
FUN!ATT; AES!ATT; PU!ATT;
PEOU!ATT; FUN!BI; AES!BI; PU!BI;
ATT!BI.
CB-SEM
Mahmood and
Lee (2021) [18]
FEA, UTAUT U.S. FUN, EXP, AES, TCK, PE, effort expectancy (EE),
SI, HC, privacy concern (PC), BIs
EXP!PE; EXP!EE; EXP!SI; TCK!PE;
TCK!EE; SI!BI.
CB-SEM
Tsai et al.
(2020) [28]
TAM Taiwan TA, perceived ubiquity (PB), RC, PU, PEOU,
ATTs, BIs
PB!PU; PB!PEOU; PU!ATT. PLS-SEM
Noh et al.
(2016) [27]
TAM Korea,
China
Fashion innovation (FI), technology innovation
(TI), PU, PEOU, perceived enjoyment (ENJ),
perceived benefit, BIs
FI!PU; FI!ENJ; TI!PEOU; TI!ENJ;
PEOU!PU; ENJ!PU; PU!BI.
CB-SEM
Hwang et al.
(2016) [26]
FEA, TAM U.S. FUN, EXP, AES, PU, PEOU, PR, environmental
concerns, ATTs, BIs
FUN!PU; FUN!PEOU; FUN!PR;
COM!PU; COM!PEOU; COM!PR;
AES!ATT; AES!BI; PEOU!PU; PU!ATT;
PU!BI; PR!ATT; ATT!BI.
CB-SEM
Turhan (2013)
[25]
TPB, TAM Turkey Self-efficacy (SE), fear of technological advances,
cost, normative beliefs (NBs), PEOU, need
compatibility, relative advantage, perceived
behavioral control (PBC), subjective norms (SNs),
PU, ATTs, BIs
PEOU!PU; ATT!BI. CB-SEM
Chae (2010)
[45]
TAM Korea TI, clothing involvement (CI), PU, PEOU, ATTs,
BIs
TI!PU; CI!PU; CI!PEOU; PEOU!PU;
PU!ATT; ATT!BI.
CB-SEM
Chae (2009)
[46]
TAM Korea CI, PU, PEOU, ATT, BIs PEOU!PU; PU!ATT. CB-SEM
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H2c: PEOU positively influences consumers’ PIs.
Attitudes (ATTs). Based on the TRA, which is a belief-attitude-behavioral intention
model, ATTs dramatically influence consumers’ BIs. ATTs are defined as individuals’ inclina-
tions and feelings or evaluative reactions to a subject. PIs are a personal behavioral tendency in
terms of purchasing products or services. Furthermore, BIs can be influenced by ATTs in the
TAM. Various empirical studies have demonstrated the influence of ATTs on PIs [51,52]. In
line with such studies, the following hypothesis is constructed:
H3: ATTs positively affect PIs.
Mediating role of PU and ATT. In a widely cited TAM paper, Davis hypothesized indi-
rect effects of PU and PEOU on BIs through various mediators [38]. Specifically, PU was pro-
posed to mediate the relationship between PEOU and PIs, with various studies across domains
supporting this mediation [53]. Additionally, when consumers find smart clothing easy to
wear, they are inclined to wear it more frequently. This ease of use elicits positive emotions,
which in turn foster positive ATTs and enhance consumers’ PIs. Moreover, within smart
clothing contexts, few studies have examined the sequential mediating roles of PU and ATTs
in the relationship between PEOU and PIs. Therefore, the present study attempts to examine
the indirect effects of PEOU on PI through the sequential mediators of PU and ATTs. The fol-
lowing hypotheses are proposed:
H4a: PU mediates the relationship between PEOU and PIs.
H4b: ATTs mediates the relationship between PEOU and PIs.
H4c: PU and ATTs sequentially mediate the positive relationship between PEOU and PIs.
Consistent with the original research by Davis [38], this study extends current research by
examining ATTs as a mediator in the relationship between PU and PIs. If smart clothing pro-
vides more functions to meet consumers’ needs, consumers have a more favorable ATT
toward purchasing smart clothing. Several studies have obtained similar conclusions in other
contexts [54]. Hence, this study proposes the following hypothesis:
H5: ATT mediates the relationship between PU and PIs.
External factors of the FEA model
The distinction between smart and ordinary clothing lies in the fact that smart clothing focuses
on people’s actual needs [15]. Lamb and Kallal [21] proposed a user-centered model, namely,
the FEA model, in which all three dimensions should be considered when identifying end-user
clothing needs, and numerous studies have applied the FEA model in functional apparel and
fashion design [55–57]. All related articles are presented in Table 1, revealing that the FEA
model emerges as a commonly used model in research. Although the TAM is beneficial for
explaining the BI to utilize new technologies, additional factors pertaining to the specific tech-
nology must be considered to fully comprehend its acceptance in specific contexts. Therefore,
depending on the context of this research, this study applies the FEA model as the external var-
iable to examine the influence on consumers’ intentions to purchase smart clothing.
Functionality (FUN) attribute. The FUN attribute refers to usability and usefulness
related to wearable products performing their functions, which is crucial to consider when
designing wearable products that are accepted by consumers [19]. In the context of clothing,
FUN includes protection, thermal comfort, mobility, and safety, which are associated with
clothing practicality and whether users accommodate the technology [44]. To meet the needs
of consumers, smart clothing can currently implement various functions, such as positioning,
alarms, physiological monitoring, and interaction. Various studies have indicated that the
FUN attribute has a substantial impact on consumers’ use of smart clothing [26]. Therefore,
this paper hypothesizes the following:
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H6a: FUN positively influences PU.
H6b: FUN positively influences PEOU.
Expressiveness attribute. The EXP attribute relates to the communicative and symbolic
aspects of clothing in social contexts [56]. In this research, EXP is defined as the degree to
which the clothing aligns with an individual’s lifestyle, clothing, current needs, and social
image [58]. This means not only the consumers’ present values but also how well they are con-
sistent with the consumer’s current lifestyle. Consumers generally aim to minimize the effort
needed to utilize innovative technology, while high compatibility tends to positively impact
their willingness to adopt such technology. It has been suggested that the perceived EXP attri-
bute positively influences users’ adoption of smart clothing [23]. As a result, if smart clothing
aligns with the self-image that consumers wish to express, it may positively impact the factors
related to technology acceptance. Thus, this paper hypothesizes the following:
H7a: EXP positively influences PU.
H7b: EXP positively influences PEOU.
Aesthetics (AES) attribute. The AES dimension refers to style and design, by which a
product is supposed to look pleasing [21], and it includes novelty and beauty [55]. Creating
AES when developing product appearance can improve usability, durability, elegant image,
innovation, and positive user experience. According to Nam and Lee [19], design and consis-
tency with the user’s image influence the acceptance of smart clothing. Sonderegger and Sauer
[59] found that personalized design based on individuals’ aesthetic and formal preferences can
greatly affect acceptance and use tendency. Moreover, the unique design of wearables enables
users to immediately identify these devices [19]. In terms of smart clothing, the AES attribute
is a critical element for the wearability and acceptability of the final product. Given these con-
siderations, this paper proposes the following:
H8a: AES positively influences PU.
H8b: AES positively influences PEOU.
Based on the analysis above, this study constructs a theoretical model with the guidance of
the FEA model and the TAM, as presented in Fig 1.
Fig 1. Theoretical model.
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Methodology
The Ethics Committee of Jiaxing University approved the ethical aspects of the “Understand-
ing consumers’ intentions to purchase smart clothing using PLS-SEM and fsQCA” research
plan, including the research informed consent, risk assessment, emergency plan. The research
process underwent ongoing review for 12 months since approval.
All participants were protected and restricted by the Institutional Review Board (IRB). The
Ethics Committee of Jiaxing University provided ethical approval for the study. Prior to data
collection, all participants were informed about the purpose of the study, the benefits and risks
associated with participation, and how their data would be used. All the tests were conducted
after obtaining the participant’s consent, and the questionnaire was completed anonymously.
Variables and measures
In the present research, a quantitative survey questionnaire was employed to measure each
construct. The measurement of the variables comprises two parts. (1) The first part involves
the measurement of variables associated with the theoretical model, which contains seven
latent variables: FUN, EXP, AES, PU, PEOU, ATTs, and PIs. The items listed in Table 2 were
evaluated using a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree),
and the items were modified from previously validated measures to suit the analysis. (2) The
second part measures the sample demographics, including gender, age, education, and
monthly disposable income. After minor revisions, a pilot study involving 35 respondents con-
firmed the survey’s accuracy. All measurement items, as depicted in Table 1, were extracted
from established studies to ensure content validity.
Sample and data collection
In the scope of the current study, consumers who have bought and worn smart clothing are
the target participants. Since smart clothing is an emerging technology, it was difficult to col-
lect information about target consumers who wear such clothing. This research cooperated
with a company that specializes in the design, production, and sale of smart clothing and
commissioned it to collect consumer data. All participants were protected Institutional Review
Board (IRB), which placed restrictions on their data. Prior to data collection, the purpose, ben-
efits and risks of this research and the usage of data were explained to all participants. All tests
were conducted after obtaining the participants’ consent, and the questionnaires were com-
pleted anonymously. The data were collected employing the snowball sampling method. To
minimize sampling bias and errors, this study followed the guidelines outlined by Cohen and
Arieli [62], implementing several measures as follows: (1) This article initiated parallel snow-
ball networks that disseminated the electronic questionnaires across numerous WeChat cus-
tomer groups linked to the company. This study specifically targeted individuals who had a
comprehensive understanding of smart clothing, and it confirmed their smart clothing pur-
chase history, to enhance the diversity in the sampling process. (2) This article combined
snowball sampling with other methods, such as simple random sampling, to mitigate the sub-
jectivity associated with sample selection in snowball sampling. (3) Additionally, this work set
a limit on the number of times the questionnaire could be shared, permitting each participant
to distribute the questionnaire only once. (4) Moreover, this research put restrictions on IP
addresses, barring the same IP address from answering the survey questionnaire more than
once. The data were collected between September and November 2022. A total of 236 subjects
responded to the questionnaire. After removing missing data and duplicate answers, 225
usable responses were collected, yielding a 95.3% response rate. The final 225 responses exceed
the sample size requirements of (1) having at least ten times the maximum quantity of
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formative indicators employed to measure a single construct, and (2) having at least ten times
the largest number of structural paths pointing to any single latent construct within the struc-
tural model [63].
The demographic profile of the samples is presented in Table 3. Of the respondents, 183
were female and 42 were male. Regarding age, most respondents (86.2%) were 18–25, while
only 31 respondents were older than 26. Regarding their educational level, most (97.3%) held a
university degree or higher.
Common method bias. To mitigate any potential common method bias (CMB) arising
from the self-report questionnaire, Harman’s single-factor test was employed [64]. If one fac-
tor explains over 50% of the total variance, the threat of CMB is significant [65]. The results
indicate that a total of seven factors accounted for 71.23% of the total variance, with the first
Table 2. The constructs and measurement items.
Construct Items References
FUN The comfort of smart clothing is critical. [18,26]
The fit of smart clothing is critical.
The protection of smart clothing is critical.
Smart clothing is easy to wear and take off
Overall, I’m satisfied with the functionality of the smart garment.
EXP Smart clothing meets my needs. [23,26]
Smart clothing coordinates well with other clothing I own.
Smart clothing fits well with my lifestyle.
Smart clothing will make me a leader in adopting new technologies.
Wearing smart clothing will impress others.
AES To me, the color of smart clothing is critical. [18,60]
The texture of smart clothing is critical.
The design of smart clothing is critical.
Smart clothing is very fashionable.
Overall, I like the style of smart clothing.
PU Wearing smart clothing will improve my quality of life. [38,60]
Wearing smart clothing will improve my work efficiency.
Smart clothing will meet my needs.
Wearing smart clothing can effectively improve my life.
Overall, smart clothing is very useful.
PEOU Wearing and using smart clothing do not require much thinking. [26,38,60]
The uses of smart clothing are clear and easy to understand.
Smart clothing can be easily used.
The interaction with smart clothing is quite straightforward and simple to comprehend.
Overall, I think smart clothing is easy to use.
ATTs I like the idea of using smart clothing. [23,26]
It is wise to buy smart clothing.
Wearing smart clothing is an exciting experience.
Wearing smart clothing can be fun.
Overall, I have a positive attitude toward smart clothing.
PIs I will try smart clothing. [60,61]
I’m interested in purchasing smart clothing when it is available for sale.
In the future, I intend to buy smart clothing.
I think it’s worth buying smart clothing.
I will recommend that others buy smart clothing.
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factor accounting for 40.07% of the total variance and no general factor being higher than the
threshold of 50% [66]. In summary, there was minimal concern about CMB in this work.
Meanwhile, multicollinearity was tested using variance inflation factors (VIFs). All indepen-
dent variable VIFs ranged from 1.398 to 3.435, falling below the threshold value of 5.0 [30] and
indicating no multicollinearity issues in the current study.
Analytical approaches
Structural equation modeling (SEM) analyzes phenomenon-based structures using a confir-
matory approach that accounts for measurement error and therefore provides more reliable
conclusions about structural patterns for numerous indicators than other analysis methods,
such as linear regression [67]. VB-SEM is chosen in this work for the following reasons. First,
VB-SEM is a multivariate analytic method that possesses the capability to estimate causal mod-
els grounded in theoretical justifications [68]. Second, VB-SEM holds greater utility than
CB-SEM in determining the relationship variance between dependent and independent vari-
ables [68]. Third, VB-SEM imposes a rather lenient limitation on the data distribution, which
is more suitable for studies with nonnormal data and is quite robust to skewness [30]. Fourth,
VB-SEM can perform estimation with smaller sample sizes and achieve greater statistical
power than CB-SEM [69,70]. Fifth, VB-SEM easily handles reflective, formative measurement
models, and more complex models [71]. Finally, over the past decade, VB-SEM has been com-
monly applied in consumer behavior research, such as research on supply chain management
[72], environment management [73], education management [74], hotel management [75],
and marketing management [76]. Therefore, this study applied VB-SEM to perform data anal-
ysis. Following the Hair et al. guidelines [30], a two-step analytical procedure including mea-
surement and structural models was evaluated using SmartPLS 3.0.
Unlike SEM’s reliance on linear association and a symmetric “net effect”, fsQCA employs
an innovative configuration of causal antecedents to predict an outcome with either their pres-
ence or absence based on fuzzy set theory and fuzzy logic [77,78]. fsQCA is a suitable
approach for investigating complex complementarities and nonlinear relationships among
constructs because it can be used to study a small number of cases that mix qualitative and
quantitative methods. Additionally, configural analysis is used to investigate the configu-
rational relationship between multiple solutions and the desired outcome [79]. The analysis
was conducted using fsQCA 3.0 software. When using fsQCA software, the following three
Table 3. The characteristics of the respondents.
Variable Category Frequency Percentage
Gender Female 183 81.3
Male 42 18.7
Age 18–25 194 86.2
26–30 21 9.3
>31 10 4.4
Education High school 6 2.7
University 129 57.3
Graduate degree or above 90 40.0
Disposable income <CNY 1000 15 6.7
CNY 1001–1500 80 35.6
CNY 1501–2000 60 26.7
CNY 2001–3000 34 15.1
>CNY 3000 36 16.0
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steps were incorporated into the modeling process. First, calibration converted the data into
fuzzy sets by assigning values from 0 to 1. Second, necessary conditions identified determi-
nants that could potentially influence achieving the desired outcomes. Third, a truth table
algorithm was developed to report and interpret solutions.
Results
This research combined PLS-SEM with fsQCA to conduct data analysis. This combination has
been used in other areas [80,81] and offers a deeper understanding of the antecedents in the
smart clothing market context.
PLS-SEM results
Reliability and validity analysis. PLS-SEM was applied to investigate the measurement
model, including reliability, validity, and structural models. As indicated in Table 4, the Cron-
bach’s alpha values exceeded 0.7, and the composite reliability (CR) values ranged from 0.885
to 0.938, surpassing the recommended threshold of 0.7. These findings confirm high reliability
and internal consistency. To assess convergent validity, the average variance extracted (AVE)
was examined, it ranged from 0.578 to 0.751. The AVE values exceeded the cutoff value of 0.5,
demonstrating adequate convergence. Moreover, the square root of the AVE for each con-
struct, varying between 0.760 and 0.866, surpassed the corresponding correlation coefficients
[82]. Additionally, all heterotrait monotrait ratio (HTMT) values were below 0.9 (see Table 5)
[83], indicating excellent discriminant validity.
Analysis of the structural model. This paper used the bootstrapping method with a simu-
lation of 5000 random resamples to perform the path coefficient test. Table 6 represents the
hypothesis test results of the model. Overall, 9 out of 11 proposed hypotheses were confirmed
Table 4. Results of the reliability and validity of PLS-SEM.
Variables Cronbach’s alpha CR AVE FUN EXP AES PU PEOU ATTs PIs
FUN 0.840 0.885 0.607 0.779
EXP 0.873 0.909 0.670 0.394 0.818
AES 0.819 0.872 0.578 0.476 0.498 0.760
PU 0.894 0.922 0.703 0.373 0.726 0.415 0.838
PEOU 0.907 0.931 0.729 0.468 0.618 0.522 0.643 0.854
ATTs 0.891 0.920 0.697 0.413 0.594 0.436 0.657 0.631 0.835
PIs 0.917 0.938 0.751 0.283 0.485 0.319 0.574 0.496 0.716 0.866
Notes: The diagonal elements (in bold) represent the square root of the AVEs, while the off-diagonal elements show the correlations between variables.
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Table 5. HTMT ratio of PLS-SEM.
Constructs FUN EXP AES PU PEOU ATTs PIs
FUN
EXP 0.444
AES 0.589 0.570
PU 0.411 0.818 0.468
PEOU 0.520 0.693 0.601 0.712
ATTs 0.469 0.671 0.504 0.728 0.697
PIs 0.315 0.540 0.355 0.628 0.540 0.783
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by the data, as presented in Fig 2, which presents the explained variance (R
2
) of the endoge-
nous variables along with the path coefficients.
In the TAM, PU positively and significantly affected ATTs (β= 0.428, p<0.001) and PIs (β
= 0.179, p<0.05), thereby confirming H1a and H1b. PEOU significantly influenced both PU
(β= 0.315, p<0.001) and ATTs (β= 0.356, p<0.001), confirming H2a and H2b. However, the
results show that PEOU did not affect PIs (β= 0.006, p>0.05), failing to support H2c. ATTs
significantly influenced PIs (β= 0.595, p<0.001), supporting H3. Next, the examination of the
FEA model confirmed the influence of FUN on PEOU (β= 0.197, p= 0.001), confirming H6b,
whereas H6a (β= 0.029, p= 0.591) was not supported. This paper also observed that PU (β=
0.534, p<0.001) and PEOU (β= 0.435, p<0.001) were significantly affected by EXP, providing
support for H7a and H7b. AES positively influenced PEOU (β= 0.210, p= 0.001); thus, H8b
was supported. However, H8a (β= -0.030, p= 0.591) was not supported.
Importantly, path coefficients may not be quantified and evaluated until predictive power is
assessed. To check the model fit, the standardized root mean square residual (SRMR), which
represents the standardized difference between observed and predicted correlations [84], was
Table 6. Path results of the structural model.
Hypotheses Paths Coefficient (β)tvalue pvalue Supported
H1a PU ->ATTs 0.428 6.287 0.000 Yes
H1b PU ->PIs 0.179 2.539 0.011 Yes
H2a PEOU ->PU 0.315 4.787 0.000 Yes
H2b PEOU ->ATTs 0.356 4.661 0.000 Yes
H2c PEOU ->PIs 0.006 0.070 0.944 No
H3 ATT ->PI 0.595 7.179 0.000 Yes
H6a FUN ->PU 0.029 0.538 0.591 No
H6b FUN ->PEOU 0.197 3.183 0.001 Yes
H7a EXP ->PU 0.534 9.366 0.000 Yes
H7b EXP ->PEOU 0.435 7.542 0.000 Yes
H8a AES ->PU -0.030 0.537 0.591 No
H8b AES ->PEOU 0.210 3.402 0.001 Yes
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Fig 2. Path coefficient analysis.
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utilized. The acceptable cutoff SRMR value for PLS path models is 0.08 [85]. For the PLS path
model, the SRMR was 0.069, indicating a well-fitting model. Furthermore, the R
2
value and
predictive relevance (Q
2
) were used to evaluate the overall model quality. The structural model
accounted for 53.2% of the variance in PIs, 50.6% of that in consumer ATTs, 47.1% of that in
PEOU, and 58.9% of that in PU. These R
2
values indicate a predictive accuracy of the model
between moderate and strong [86]. Additionally, Stone–Geisser’s Q
2
values were employed to
assess predictive relevance, with values above zero indicating an accurate predictive capability
of the model. A Q
2
value of 0.391 was obtained, further validating the predictive applicability
of the proposed model.
Mediation analyses. This research conducted a complementary mediation analysis fol-
lowing the approach of Zhao et al. (2010) [87]. This analysis was implemented with 95% bias-
corrected bootstrap confidence intervals (CIs) with 5000 samples. As shown in Table 7, the
direct effect of PEOU on PIs was nonsignificant, but the indirect effects were significant since
the bias-corrected CIs excluded zero. Therefore, the results provide evidence that PU and
ATTs serve as indirect-only mediators of the relationship between PEOU and PIs, implying
that H4a, H4b and H4c were supported. This study employed a similar procedure to examine
the relationship between PU and PIs. Both the direct and indirect effects proved significant,
suggesting that ATTs serve as a complementary mediator in the relationship between PU and
PIs; hence, H5 was supported.
fsQCA results
Calibration. Since the variables were measured employing 5-point Likert scales, rescaling
was required. Based on the suggestions made by Fiss [77], the full membership threshold was
set to 5.0, the crossover point was set to 3.5, and full nonmembership was set to 1.0. Table 8
shows the results of this transformation and descriptive statistics.
Consistent with the calibration process above, the outcome variable “PIs” was calibrated as
“fs_PIs”, and the condition variables “FUN”, “EXP”, “AES”, “PU”, “PEOU” and “ATTs” were
calibrated as “fs_FUN”, “fs_EXP”, “fs_AES”, “fs_PU”, “fs_PEOU”, and “fs_ATTs”,
respectively.
Necessary conditions analysis. After the calibration, this research conducted a necessity
analysis, examining whether any of the six factors is necessary for consumers’ smart clothing
Table 7. Mediation effect test.
Panel A: H4
Hypotheses βtvalue pvalue Bias- corrected 95% CI Decision
Low High
Path estimate 0.006 0.070 0.944 -0.154 0.154 Not support
Mediation analysis
H4a: PEOU ->PU ->PIs 0.056 2.235 0.025 0.073 0.214 Support
H4b: PEOU ->ATT ->PIs 0.212 4.119 0.000 0.119 0.321 Support
H4c: PEOU ->PU ->ATTs ->PIs 0.080 3.425 0.001 0.044 0.142 Support
Total indirect effects 0.348 6.366 0.000 0.250 0.468 Support
Total effect 0.354 4.514 0.000 0.186 0.492 Support
Panel B: H5
Path estimate 0.179 0.071 2.539 0.042 0.318 Support
Mediation analysis
H5: PU ->ATTs ->PIs 0.255 0.052 4.870 0.164 0.372 Support
Total effect 0.434 6.361 0.000 0.301 0.567 Support
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PIs. If the consistency score surpasses 0.90, the condition is deemed “necessary” [88]. As
shown in Table 9, except for AES and PEOU, all other variables are necessary conditions for
consumers’ smart clothing PIs. Importantly, analyzing necessary conditions is only one com-
ponent of fsQCA, while examining sufficient causal combinations is essential.
Sufficient conditions analysis. The analysis of sufficient conditions was performed using
a truth table of 2
k
rows, where kdenotes the number of outcome predictors, and each row
denotes a combination of six predictors along with the frequency and consistency of each com-
bination [32]. Frequency was defined as the number of observations for every combination,
and because the sample of this study (225) was considered large (>150 cases), a general sug-
gested frequency threshold was a minimum of 3 [77]. Consistency was defined as the extent to
which cases responded to the set-theoretic relationships denoted by a combination, with a cut-
off point established at 0.80 [77]. Moreover, a minimum proportional reduction in inconsis-
tency (PRI) should be taken into account [89]. Therefore, this work set the consistency and
PRI consistency thresholds to 0.80 and 0.75, respectively. After calculating the consistency and
coverage for all configurations, this study discovered that sufficient configurations with consis-
tency and coverage exceed 0.8 and 0.2, respectively [36].
The standard analyses, produced by the fsQCA true table, reported complex, intermediate,
and parsimonious solutions [32]. The research proposed intermediate and parsimonious
Table 9. Necessity conditions.
Condition Smart clothing purchase intention
Consistency Coverage
fs_FUN 0.910 0.777
~ fs_FUN 0.464 0.886
fs_EXP 0.904 0.793
~ fs_EXP 0.491 0.886
fs_AES 0.825 0.884
~ fs_ AES 0.597 0.784
fs_PU 0.901 0.867
~ fs_PU 0.523 0.797
fs_PEOU 0.878 0.855
~ fs_PEOU 0.539 0.807
fs_ATTs 0.952 0.860
~ fs_ATTs 0.474 0.806
Note: “~” denotes the absence of a condition
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Table 8. Calibrations and descriptive statistics.
Configurational element [Range] Fuzzy set calibration Descriptive statistics
Full membership Crossover Full non-membership Mean S. D Min Max N-cases
FUN [1–5] 5.00 3.50 1.00 0.69 0.20 0.05 0.95 225
EXP [1–5] 5.00 3.50 1.00 0.55 0.23 0.05 0.95 225
AES [1–5] 5.00 3.50 1.00 0.67 0.20 0.05 0.95 225
PU [1–5] 5.00 3.50 1.00 0.61 0.22 0.05 0.95 225
PEOU [1–5] 5.00 3.50 1.00 0.61 0.22 0.05 0.95 225
ATTs [1–5] 5.00 3.50 1.00 0.65 0.20 0.05 0.95 225
PIs [1–5] 5.00 3.50 1.00 0.59 0.22 0.05 0.95 225
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solutions to distinguish between peripheral and core conditions [77]. Table 10 shows that each
of the six causal combinations can lead to PIs, and that six equifinal configurations exist, with
values ranging from 0.925 to 0.989. These configurations are as follows: Configuration 1:
~EXP *PU *ATTs; Configuration 2: FUN *AES *PU *ATTs; Configuration 3: FUN *PU *
PEOU *ATTs; Configuration 4: AES *PU *PEOU *ATTs; Configuration 5: ~FUN *AES *
~EXP *PEOU *ATTs; and Configuration 6: FUN *AES *EXP *PEOU *ATTs. The consis-
tency values were larger than 0.85, suggesting that all configurations were sufficient conditions
leading to PIs. Furthermore, the overall solution coverage was 88.1%, indicating that a consid-
erable share of the coverage was accounted for by the combinations related to consumers’
smart clothing purchase behaviors. Among them, solutions 2 and 3 accounted for 79.5% and
79.4% of cases leading to the outcome, respectively. The overall solution consistency was
0.915, suggesting that the six configurations could account for 91.5% of the cases with good
explanatory power.
As demonstrated in Table 10, the core conditions specified in the various configurations
were the presence of PU and ATTs. The findings indicated that consumers’ perception of use-
fulness and ATTs toward smart clothing were the most important significant conditions for
fulfilling consumers’ strong desires to purchase smart clothing.
Predictive validity. Validating the solutions for predictive validity is of utmost impor-
tance, as such validation assesses the extent to which the solutions accurately predict the values
of the dependent variable across samples. Following the guidelines of Pappas and Woodside
[36], the sample for this study was randomly divided into holdout samples and subsamples,
identical analyses were conducted, and identical cutoff points were selected for both sets of
samples, as described in the preceding sections. The solutions obtained from the subsample
are presented in Table 11. Next, M1 (FUN*PU*PEOU*ATTs) was tested with the holdout
Table 10. Configurations for smart clothing purchase intentions.
Condition 1 2 3 4 5 6
FUN • • Y •
EXP Y Y ●
AES • • ●•
PU ●●●●
PEOU • • ●•
ATTs ●●●●●●
Consistency 0.959 0.925 0.934 0.937 0.989 0.947
Raw coverage 0.542 0.795 0.794 0.788 0.386 0.739
Unique coverage 0.016 0.013 0.021 0.017 0.007 0.012
Solution consistency 0.915
Solution coverage 0.881
Notes: Black circles (●) indicate the presence of a condition; crossed-out circles (B) indicate its absence; large and small circles indicate core and peripheral conditions,
respectively; blank spaces indicate that the condition may be either present or absent.
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Table 11. Predictive validity by using fsQCA.
Models from subsamples Raw coverage Unique coverage consistency
M1: FUN*PU*PEOU*ATTs 0.8035 0.4796 0.9306
M2: ~FUN*AES*~EXP*~PU*PEOU*ATTs 0.3435 0.0195 0.9817
Solution coverage 0.8230
Solution consistency 0.9305
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sample data. Fig 3 indicates satisfactory consistency (0.937) and coverage (0.784), confirming
the predictive validity of the proposed model. These findings indicate that the model has reli-
able and robust predictive ability for the values of the dependent variable.
Discussion and implications
Discussion of the key findings
This study incorporates the FEA model, the TAM, and complexity theory to examine consum-
ers’ smart clothing PIs. Additionally, the PLS-SEM and fsQCA approaches are applied to test
these relationships.
Within the context of the consumers’ needs framework, the PLS-SEM results demonstrate
that the FEA model is an antecedent of PU and PEOU, which indirectly influence smart cloth-
ing PIs. FUN is confirmed as positively influencing PEOU, consistent with previous findings
[26]. However, its significant net effect on PU could not be identified. One possible explana-
tion is that many consumers do not fully recognize the concept of smart clothing and still mis-
take smart clothing for functional clothing [2]. Moreover, smart clothing remains in the early
stages of development, and some of the advertised functions do not meet consumer needs
well. Additionally, EXP is one of the most significant factors of the FEA model that influences
PU and PEOU. The significant net effect is aligned with that of previous studies, such as for
smartwatches [90]. Furthermore, AES has a favorable net influence on PEOU, aligning with
the findings of previous studies [91], but it has no significant net effect on PU. This reason
may be that smart clothing is aesthetically unappealing and, thus, undesirable given the electri-
cal components attached to or incorporated in the fabric.
Within the TAM framework, the PLS-SEM results show that PU and ATTs significantly
affect consumers’ PIs. The significant net effects of PU and ATTs on shaping PIs align with
previous studies, such as those examining electronic word of mouth (eWOM) [52] and online
shopping [48], which emphasize the significance of consumers’ perceptions of usefulness and
ATTs in achieving smart clothing PIs. Moreover, the findings show that PEOU has a nonsig-
nificant effect on PIs, while PU and ATTs mediate the relationship between PEOU and PIs.
Fig 3. Fuzzy plot of model 1 (Table 11) using the holdout sample.
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The reason for this finding is that consumers might place more emphasis on PU than on how
easy clothing is to use. If smart clothing is seen as providing significant benefits for consumers,
PEOU might not play a substantial role in PIs. Additionally, consumers may appreciate the
ease of smart clothing, and this appreciation may not transform into PIs unless it positively
affects their ATTs toward smart clothing.
Although the PLS-SEM results offer insights into the net effects of antecedents on out-
comes, fsQCA discloses several sufficient configurations of antecedent conditions. fsQCA
reveals six key configurations of complexity theory that can drive high PIs (see the solutions in
Table 10), and the results reaffirm that PU (consistency = 0.901) and ATTs (consis-
tency = 0.952) are both necessary and sufficient conditions to achieve smart clothing PIs. In
solutions 1 and 2, consumers who highly purchase smart clothing reported high FUN and AES
needs or low EXP, as well as high PU and ATTs. Solutions 3 and 4 indicated that high FUN or
AES needs, aligned with PU, PEOU, and ATTs, result in high PIs. Solution 5 indicated that
high AES, PEOU, and ATTs, as well as low FUN and EXP, could produce high PIs. Conversely,
solution 6 revealed that high levels of FUN, EXP, AES, PEOU and ATTs could also yield high
PIs. The fsQCA results demonstrate that FUN and EXP are necessary for smart clothing PIs.
Moreover, FUN is a condition in three of the six configurations (solutions 2, 3 and 6). Further-
more, EXP is a core condition in solution 6, while in solutions 1 and 5, the absence of EXP is a
core condition, which is partially consistent with the PLS-SEM results (H7a and H7b).
Although AES is not necessary, the presence of AES is a core condition in solution 5. More-
over, PU is the core condition in four of the six configurations (solutions 1, 2, 3, and 4) that
lead to high smart clothing PIs, validating the PLS-SEM result (H1b). Additionally, ATTs are
the core condition in all six configurations, and the result is congruent with H3.
The outcomes of the mixed-method analysis demonstrate that higher PU and ATTs could
contribute to smart clothing PIs, supporting H1b and H3. Furthermore, this analysis reveals
that PEOU significantly affects PU and ATTs, which is also consistent with similar findings on
e-learning [92] and online food [93]. Solutions 3 and 4 indicate that PEOU, PU and ATTs can
jointly lead to PIs, which supports the mediation hypotheses (H4a, H4b, and H4c). However,
some results obtained from fsQCA contradicted those of PLS-SEM. For instance, FUN and
AES had no impact on PU in the PLS-SEM analysis, while fsQCA confirmed the existence of
several realities (i.e., solutions 2, 3, 4, and 6) in producing the same outcome. These findings
were consistent with the equifinality principle, which states that more than one complicated
configuration of antecedent conditions can produce the desired outcome. In accordance with
the principle of causal asymmetry, fsQCA implied that the same antecedent within various
solutions can have opposing impacts on smart clothing PIs, which depends on how they inter-
act or combine with other attributes. Moreover, depending on the causal interactions, the
absence or negation of some conditions might result in similar outcomes. For example, solu-
tion 5 suggested that low FUN and EXP can boost consumers’ smart clothing PIs, provided
that the levels of AES, PEOU, and ATTs are high. PLS-SEM can confirm the predicted rela-
tionships between antecedents and PI outcomes but cannot provide such insights. Conse-
quently, complexity theory demonstrated predictive validity for modeling consumers’ PIs
toward smart clothing.
Research implications
Theoretical implications. This work explores the relationships among FUN, EXP, AES,
PU, PEOU, ATTs, and PIs, thus offering theoretical implications in two areas.
First, this research used a symmetric method, PLS-SEM, to investigate how various con-
structs are identified and grouped as determinants that predict smart clothing PIs. The
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findings also revealed that ATTs and PU are the most important and significant constructs.
Moreover, this research explored the underlying mechanisms of the mediation process. The
mediation test illustrated that PU and PEOU have indirect effects on PIs through ATTs. Addi-
tionally, prior research has extensively identified the positive influences of PU and ATTs on
PIs, while few studies have examined the effect of PEOU on PIs within the specific context of
smart clothing. Through mediation analysis, it can be concluded that PU and ATTs act as indi-
rect-only mediators between PEOU and PIs.
Second, in contrast to previous studies that primarily employed symmetrical modeling
(multiple regression, PLS-SEM) to examine the net effect of each isolated antecedent on con-
sumers’ intentions to purchase smart clothing, this research adopted a configurational
approach rooted in complexity theory. By employing fsQCA, a more comprehensive and
nuanced understanding of the phenomenon of consumers’ PIs toward smart clothing was
achieved. Unlike symmetric techniques, which fail to identify the intricate causal conditions
necessary for achieving the desired outcome, fsQCA enabled a more extensive exploration of
the complex interplay between constructs. By applying fsQCA, six distinct models were devel-
oped to elucidate how configurations of constructs influence users’ behavior in the smart
clothing context. Moreover, the fsQCA findings demonstrate alignment with complexity the-
ory principles in the smart clothing context. Hybrid approaches integrating PLS-SEM and
fsQCA empower researchers to delve into the complexities of consumer behavior in greater
depth and detail.
Managerial implications. The rapid growth of smart clothing, an emerging technology
recognized as a disruptive innovation in the garment industry, necessitates a strategic
approach for businesses in this sector, especially in the context of markets such as China. This
research provides valuable insights and important managerial implications for manufacturers
and marketers in this innovative sphere.
First, within the framework of consumers’ needs, this research highlights that FUN, EXP,
and AES greatly influence PEOU. Additionally, EXP has a great impact on PU. For instance,
Yang et al. [94] noted that smart clothing that is washable, stretchable, and flexible is more
acceptable to users and could meet the needs of society. The FUN of smart clothing can be
enhanced through modular designs, allowing products to serve multiple purposes based on
consumers’ needs. By leveraging the power of the IoT, big data, cloud computing, mobile com-
puting, and social media, smart clothing can provide a variety of intelligent features, such as
alarms, positioning, physiological monitoring and interaction, and entertainment. In fact, the
complex and sophisticated functions of smart clothing may not improve quality of life but
bring much inconvenience, causing consumers to lose interest in smart clothing over time
[15]. Fashion designers need to listen to consumers and integrate the latest fashionable ele-
ments and technical attributes into the design of smart clothing to make it innovative and aes-
thetically pleasing clothing, and consumers could participate in smart clothing design [44,95].
Additionally, special attention should be paid to the needs of specific groups, such as elderly
individuals. Through style and structural design, smart clothing can be made easy to wear. It is
crucial to remember that smart clothing needs to strike a balance between the FUN and AES
requirements of consumers to appeal to the broadest consumer group, thus increasing market
acceptability.
Second, this study confirms that PU and ATTs are critical factors in smart clothing PIs and
that the effect of PEOU on PI is mediated by PU and ATTs. These results suggest the need for
manufacturers to focus on conveying the benefits of their smart clothing products clearly and
fostering a positive brand image. Doing so can be achieved through comprehensive marketing
strategies such as detailed product demonstrations, user testimonials, and educational cam-
paigns. Notably, companies should also ensure that their products are user-friendly, featuring
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Consumers’ smart clothing purchase intentions using PLS-SEM and fsQCA
PLOS ONE | https://doi.org/10.1371/journal.pone.0291870 September 19, 2023 19 / 25
intuitive user interfaces, clear instructions, and robust customer support. Providing effective
solutions to potential issues such as smart clothing washing and maintenance can also enhance
PEOU. As demonstrated, ATTs play a critical role in predicting smart clothing PIs. Manufac-
turers and marketers should concentrate on measures to improve individuals’ ATTs toward
this innovative technology. For smooth entry into the market, businesses should emphasize
the user experience and engage in diverse marketing tools such as social media marketing [96]
and key opinion leader (KOL) marketing [97].
Conclusions, limitations and future research
Conclusions
This paper draws on the FEA model, the TAM and complexity theory to establish a theoretical
model for understanding the formation of consumers’ smart clothing purchase behaviors. The
study empirically analyzes survey-based data using two complementary analytical approaches,
variance-based PLS-SEM and case-oriented fsQCA, providing a comprehensive and in-depth
research perspective for understanding the formation mechanism of smart clothing purchase
behavior in China.
The net effects of each antecedent on PIs are analyzed through PLS-SEM. The PLS-SEM
results demonstrate that all FEA dimensions influence PEOU, while the EXP attribute does
not significantly impact PU. Additionally, all direct relationships within the TAM are signifi-
cant, except for the link between PEOU and PIs. Furthermore, PU and ATTs exhibit major
mediating roles across the proposed relationships.
Regarding the high levels of complexity theory, this theory is successfully extended to the
smart clothing context. The fsQCA approach delivers granular insight into how antecedents
interact to influence consumers’ smart clothing PIs, deriving six different configurations of
variables for achieving high levels of smart clothing PIs, with the combination of FUN, AES,
PU, and ATTs being the best solution.
In summary, this research enriches the theoretical understanding of the complex causality
and interactions explaining smart clothing PIs. The findings of this research offer valuable
guidance for enterprises and marketers in formulating and implementing marketing strategies
to encourage consumers’ smart clothing purchase behavior.
Limitations and future research
Several limitations to this research suggest meaningful future research directions. First, given
that this study was conducted in China, its results may not precisely align with purchase behav-
iors in other countries, due to significant cultural, economic, and ethnic differences. Future
research should validate the applicability of the model in both developing and developed coun-
tries with varied cultural contexts. Second, this study examined only the effect of the influenc-
ing factors of smart clothing products themselves and technology acceptance on PIs. Future
works should consider additional constructs, such as self-efficacy (SE), price and innova-
tiveness, to explore the influence of multiple factors on smart clothing PIs. Finally, this work
assessed the determinants of smart clothing PIs by drawing on the FEA model and the TAM.
Future studies should investigate antecedents from other theoretical lenses, such as innovation
diffusion theory (IDT).
Supporting information
S1 File.
(CSV)
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Consumers’ smart clothing purchase intentions using PLS-SEM and fsQCA
PLOS ONE | https://doi.org/10.1371/journal.pone.0291870 September 19, 2023 20 / 25
Author Contributions
Conceptualization: Shucong Chen.
Data curation: Jing Ye.
Formal analysis: Jing Ye.
Investigation: Jing Ye.
Methodology: Jing Ye.
Resources: Shucong Chen.
Software: Jing Ye.
Supervision: Jing Ye.
Validation: Jing Ye.
Writing – original draft: Shucong Chen, Jing Ye.
Writing – review & editing: Jing Ye.
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PLOS ONE
Consumers’ smart clothing purchase intentions using PLS-SEM and fsQCA
PLOS ONE | https://doi.org/10.1371/journal.pone.0291870 September 19, 2023 25 / 25
