Age, Gender, and Technology Attitude as Factors for Acceptance of

Smart Interactive Textiles in Home Environments

Towards a Smart Textile Technology Acceptance Model

Philipp Brauner, Julia van Heek and Martina Zieﬂe

Human-Computer Interaction Center, RWTH Aachen University, Campus-Boulevard 57, 52074 Aachen, Germany

Keywords:

Digital Textiles, Smart Textiles, Smart Interactive Textiles, Technology Acceptance, UTAUT2, User Factors,

User Diversity.

Abstract:

Smart interactive textiles are the next frontier of ubiquitous computing and may serve as novel and accepted

interfaces that go beyond conventional human-computer interaction. Apart from the technical perspective, it

is important to understand if and under which conditions people adopt these technologies and which factors

constitute perceived barriers of technology acceptance. In this work we examine people’s attitudes towards

smart textiles and their relationship to the intention to use these products in their home environment. This

article provides a precise modeling of younger and older people in regard to expertise in interacting with

technology, their desire to control and automate functions in their home environment, and the evaluation of a

smart cushion for controlling the home environment, using the Smart Textile Technology Acceptance Model

that is derived from the Uniﬁed Theory of Acceptance and Use of Technology 2 model. This model was

applied for a speciﬁc smart textile product and the evaluation was focused on user-diversity, attitudes, and age.

The article concludes with open research questions and guidelines for practitioners to leverage the beneﬁts of

smart textile user interfaces.

1 INTRODUCTION

25 years ago Marc Weiser and his team at Xerox Parc

envisioned environments, in which smaller but in-

creasingly powerful technology penetrates everyday

objects and empowers us to seamlessly interact with

our periphery (Weiser, 1991). He coined the term

calm computing for technology which is in our fo-

cus when requested but dissolves into the background

when not and which can provide more details, more

information, or more capabilities and extends our pe-

ripheral reach (Weiser, 1991).

Today, we observe that a growing amount of de-

vices are interconnected and beginning to form the

Internet of Things (IoT) (Caceres and Friday, 2012).

Smart devices reacting to the resident’s presence, such

as light bulbs, robotic vacuum cleaners, connected

washing machines and refrigerators, or the heating,

enter consumer households. However, conventional

information and communication technology is usually

packed in metal, glass, or plastics and are often per-

ceived as artiﬁcial artifacts not harmonizing with our

natural habitat.

While the inventions of transistors, microproces-

sors, advanced sensing, and actuating technology are

rather new developments in human history, textiles

have accompanied us since the dawn of mankind.

Early traces of the use of textiles date back to 30.000

years B.C. (Robinson, 1970; Kvavadze et al., 2009)

and have accompanied mankind since then. Textiles

are usually perceived as warm, soft, smooth, and plea-

surable. We do not only use them as clothes, but also

for furniture, for example as bed linen, sofa coverings,

curtains, or cushions.

We believe that these two branches of human de-

velopment – ubiquitous computing and textiles – will

eventually converge and that smart interactive tex-

tiles will be the next frontier of ubiquitous comput-

ing. However, from the observation of various tech-

nology adoption processes its is known that some

technologies are adopted faster than others and that

some technologies are never adopted at all. Tech-

nology acceptance research aims at understanding the

factors which inﬂuence the likelihood of an adoption

and weight the factors promoting or diminishing the

acceptance of a product or service (Rogers, 2003).

Aging is one of the most important user fac-

tors that needs to be addressed in technology ac-

Brauner, P., Heek, J. and Zieﬂe, M.

Age, Gender, and Technology Attitude as Factors for Acceptance of Smart Interactive Textiles in Home Environments - Towards a Smart Textile Technology Acceptance Model.

DOI: 10.5220/0006255600130024

In Proceedings of the 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2017), pages 13-24

ISBN: 978-989-758-251-6

ceptance research, as the demographic structure of

many western societies is facing tremendous changes

through progress in medicine, nutrition, hygiene, and

a time of peace and prosperity (Giannakouris, 2008).

More elderly expect to live with dignity and self-

determination and they want to participate in the in-

creasingly digitized society (Mollenkopf et al., 2000).

Yet, numerous studies have identiﬁed that the elderly

are often less literate in interacting with modern in-

formation and communication technology, such as

computers (Selwyn et al., 2003), mobile phones and

tablets (Arning and Zieﬂe, 2007), or even using ticket

vending machines (Schreder et al., 2013).

In this article we combine these three lines – smart

interactive textiles, user diversity, and technology ac-

ceptance research – and identify user and system fac-

tors which relate to the acceptance and likely use of a

smart interactive textile. Speciﬁcally, we identify the

factors which contribute to the acceptance of a smart

textile cushion for controlling the home environment.

We focus on age as a user factor in the design of our

study and report the direct and indirect effects age

might have on the acceptance of smart interactive tex-

tile user interfaces.

The article is structured as follows: After this in-

troduction we present the current state of the art on

smart interactive textiles, technology acceptance re-

search, and research on acceptance of smart textiles

in section 2. Section 3 presents our research questions

and our research methodology. Section 4 presents the

ﬁndings of our empirical study while section 5 dis-

cusses these ﬁndings and their implications for further

design and development of smart interactive textiles.

Section 6 concludes this article with a brief discus-

sion of the limitations of this work and an outlook on

further research questions.

2 STATE OF THE ART

This section presents an overview of smart interactive

textiles in section 2.1, technology acceptance research

in section 2.2, and technology acceptance research on

smart interactive textiles in section 2.3.

2.1 Smart Interactive Textiles

In recent years, an increasing number of innovations

was developed combining textiles with electronic in-

teractive systems while concurrently different types

and designs of input were discussed (Cherenack and

van Pieterson, 2012).

One possible input option was evolved in the

project PinStripe, in which parallel conductive

threads were embroidered into clothes, for example

in the sleeves of a t-shirt, a jacket, or a pants leg (Kar-

rer et al., 2011). By pinching into the fabric with

the ﬁngers folds of varying size could be formed and

rolled. The connections between the parallel conduc-

tive threads could then be interpreted as a two dimen-

sional input device. A potential usage scenario was

controlling music during working out. Rolling the

fold moved the current playback position and the size

of the fold determined the speed of the movement.

As a further example, Google’s Project Jacquard

(Poupyrev et al., 2016) built on this idea, integrat-

ing conductive yarns into fabrics and designing a se-

ries of garments with interactive areas woven into

the garment. It is striking that smart textiles were

mainly considered in terms of smart clothes for med-

ical (e.g., monitoring of vital parameters) (Post et al.,

2000; Axisa et al., 2003; Paradiso, 2003), sport (e.g.,

monitoring of sport performance), or leisure contexts

(e.g., playing music or music control) (Helmer et al.,

2009). Other possible interactive textiles (e.g., as part

of furniture, such as carpets, blankets) have only been

rarely considered so far.

A ﬁrst technical approach and a recent applica-

tion of smart textiles in the home environment is

Gardeene! by (Heller et al., 2016). A conventional

curtain is equipped with parallel conductive threads,

allowing the system to sense either direct swipe ges-

tures or swipe movements in near proximity of the

curtain due to changes in the electromagnetic ﬁeld.

Intuitive swipe gestures are mapped to opening and

closing the curtain. This approach could and will

be transferred to other objects (e.g., blanket, cushion,

sides of chairs).

2.2 Technology Acceptance

Two of the most inﬂuential models for predicting fac-

tors that promote or reduce acceptance are the The-

ory of Reasoned Action (TRA) (Fishbein and Ajzen,

1975) and the Theory of Planned Behavior (TPB)

(Ajzen, 1991). Both models postulate a strong rela-

tionship between an individual’s Intention towards a

speciﬁc behavior and his actual Behavior. This be-

havioral intention is governed by personal attitudes,

subjective beliefs, and (in TPB) the individuals’ self-

efﬁcacy towards the behavior.

Based on this strong relationship between in-

tention towards a behavior and the actual behavior

Davis’ derived the well known Technology Accep-

tance Model (TAM) which assumes that Perceived

Usefulness and Perceived Ease of Use govern the At-

titude Towards Using, which on the other hand is

closely related to Intention to Use (ITU) and later

ICT4AWE 2017 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health

actual Use of a software (Davis, 1989). Despite its

predictive power, this model was tailored for and

evaluated with software for business contexts in the

early ’90’s. Thus, more sophisticated models were

developed over the time. A prominent example is

Venkatesh’s Uniﬁed Theory of Acceptance and Use

of Technology 2 (Venkatesh et al., 2012) which is

speciﬁcally adjusted to capture the features that gov-

ern the acceptance of consumer products or ser-

vices with voluntary usage. The model builds on

the seven dimensions Performance Expectancy (PE),

Effort Expectancy (EE), Hedonic Motivation (HM),

Social Inﬂuence (SI), Facilitating Conditions (FC),

Price Value (PV), and Habit (HB). The model was

able to predict 74% of the variance of the intention

to use a service and about 52% of the variance of the

actual use after four months.

2.3 Acceptance and Interactive Textiles

The research landscape in the intersection of technol-

ogy acceptance research and smart digital textiles is

relatively sparse due to the novelty of this technology.

A ﬁrst qualitative glimpse into the acceptance of

smart interactive textiles was delivered by Holleis et

al.. They investigated the suitability of different in-

put modes for controlling a media player on differ-

ent prototypical input devices. The study focused on

the preferred design of the interaction surface (e.g.,

visible buttons, ornaments, invisible buttons) and ac-

cepted body locations for performing these gestures

(e.g., hands, arms, legs, chest) (Holleis et al., 2008).

Within Google’s Project Jacquard (Poupyrev

et al., 2016) ﬁrst usability evaluations on smart tex-

tile interfaces focused on the recognition rates of ges-

tures (swipe right, swipe left, hold) under different

conditions (sitting, walking, standing) using small in-

teractive areas of a test jacket’s sleeve. The overall

recognition rate was 76,8% and varied signiﬁcantly

depending on the experimental conditions.

As wearables will probably be the ﬁrst interactive

textiles penetrating the market, a study examined the

acceptance of wearable smart textiles in different us-

age contexts (van Heek et al., 2014). The study re-

vealed that the evaluations were fundamentally differ-

ent between the use of smart textiles in leisure con-

texts (i.e., sports) and medical contexts (i.e., moni-

toring vital parameters). The study also detected that

factors of user diversity play a crucial role in the ac-

ceptance of wearable smart textiles, as the scenarios

were evaluated differently depending on age, previous

experiences, and the individual knowledge about the

technology. Speciﬁcally, age had a strong inﬂuence

on the perceived ease of using the technology as well

as on the perceived functionality and reliability of the

given product.

Furthermore, a more generic study on smart textile

interfaces used a Conjoint-based approach to iden-

tify which product features are most important for

the general acceptance of smart digital textiles (Hilde-

brandt et al., 2015). The most decisive attribute gov-

erning acceptance of smart textiles was the techni-

cal realization of these products. Speciﬁcally, users

disliked noticeable electronics in the devices and in-

stead desired a seamless integration into the fabric.

The second most inﬂuential attribute was the room, in

which the textile was used, showing that most users

preferred smart textiles in the living room, whereas

the use in the bedroom or kitchen was rather rejected.

Although the functionality, i.e. what the smart tex-

tile should be able to do, was the third most deci-

sive attribute, no clear preference regarding the three

provided usage scenarios (i.e., health, media control,

smart home) was articulated by the users. The least

important attribute was wearability, and the partic-

ipants of the study had a slight preference towards

smart interactive textiles that are integrated into non-

wearable devices (e.g., curtains, cushions, . . . ).

The comparison between wearable and non-

wearable devices was also investigated in another

study (Zieﬂe et al., 2014), in which user requirements

for different types of smart textiles were analyzed in

multiple contexts. First, the study revealed that the

importance of product properties – such as look, dura-

bility, washability, price – do not differ signiﬁcantly

between smart textiles based cloth and furniture. For

both, a pleasant-to-wear sensation or pleasant texture,

durability, and a fashionable look are perceived as the

most important evaluation criteria. Second, partic-

ipants preferred table surfaces, chairs or sofas, and

outerwear for integrating smart textile interaction sur-

faces, whereas smart carpets or curtains are perceived

as the least favorable option. Third, the most favor-

able functions that should be controlled by the inter-

action surface were identiﬁed as changing the music,

controlling interior lightning, or switching TV chan-

nels. Least favorable functions were controlling the

heating and locking and unlocking smart locks.

These aspects were deepened in a second study, in

which the motives, barriers, and conditions for using

smart textiles were investigated, comparing different

usage contexts (interactive textiles in bedroom, living

room, kitchen, and interactive textiles integrated in

clothes) (Zieﬂe et al., 2016). The results showed that

the use of smart textiles was evaluated differently de-

pending on the usage contexts: textiles in the kitchen

and bedroom were perceived as less enjoyable com-

pared with textiles in the living room and interactive

Age, Gender, and Technology Attitude as Factors for Acceptance of Smart Interactive Textiles in Home Environments - Towards a Smart

Textile Technology Acceptance Model

textiles integrated in clothes. Furthermore, Zieﬂe et

al. examined the inﬂuence of age on the acceptance

and intention to use smart textiles. The results re-

vealed signiﬁcant age differences for single but not

all acceptance factors (e.g., enjoying interacting with

the device, reliable recognition of gestures); however,

age revealed to be a less important predictor for ac-

ceptance. The study showed age differences regard-

ing rather generic smart textiles and hence, it is ques-

tionable, how and to which extent a concrete, speciﬁc,

and in the home environment integrated smart textile

would be accepted by younger and older participants.

The presented studies detected that smart interac-

tive textiles are predominantly accepted in the home

environment and speciﬁcally the living room. How-

ever, previous research in the ﬁeld of smart interactive

textiles clearly focused on smart wearable textiles,

while research on smart textiles in domestic environ-

ments was hardly investigated. Some studies showed

that user diversity factors inﬂuence the acceptance of

these technologies, for example, age, gender, and the

attitude towards technology. Consequently, our over-

arching research question of this article is if these ef-

fects are also present for smart textile surfaces in do-

mestic home environments and which evaluation di-

mensions govern their acceptance.

Based on ﬁrst functional demonstrators and ﬁnd-

ings of a focus group we speciﬁed a smart cushion as

an application scenario used in the remainder of this

work. This smart cushion can be placed in the living

room, for example on the sofa or armchair, and ges-

tures on the cushion can be used to control music or

light. The following section precisely describes our

research methodology and the investigated variables.

3 METHODOLOGY

In the following section, the questionnaire design, ap-

plied statistical procedures, and characteristics of the

sample are detailed. We chose a paper-and-pencil

questionnaire for our study in order to reach espe-

cially older participants.

The goal of this study is to develop and evaluate

a novel acceptance model for smart iterative textile

surfaces (Smart Textile TAM, STTAM) and to under-

stand the inﬂuence of age and other factors of user-

diversity on the acceptance of a speciﬁc smart textiles.

The study addresses three crucial research questions:

1. Which model-related dimensions are decisive for

the acceptance of smart textile interfaces?

2. Which user factors inﬂuence the acceptance re-

spectively behavioral intention to use smart textile

interfaces?

3. To which extent do speciﬁc user factors, such as

age and attitude towards technology, affect the ac-

ceptance and evaluation of a smart textile inter-

face?

3.1 Questionnaire Design

Questionnaire items were adapted from the UTAUT

2 model to the context of smart textile interfaces and

extended by constructs based on the ﬁndings of sev-

eral focus group interviews with 4-6 people carried

out prior to this study.

The ﬁrst part of the questionnaire addressed par-

ticipant’s demographic characteristics (age, gender,

educational level). Regarding user-speciﬁc aspects,

we asked for participants’ health status, such as

chronic diseases and physical restrictions. In addi-

tion, the participant’s previous experience with smart

textiles was assessed (two items, α = .791).

The questionnaire’s second part asked for par-

ticipants’ attitudes towards technology (TECH), tex-

tiles (TEX), and automation (AUTO). The respective

items were summed up and checked for item and scale

reliability. The attitude towards technology was mea-

sured on a scale based on Karrer et al. which cap-

tures the four dimensions Technical Enthusiasm (EN),

Experience of Technical Competency (COMP), and

Positive (POS) as well as Negative (NEG) Experi-

ence with technology with three items for each di-

mension (Karrer et al., 2009) . Furthermore, the Self-

efﬁcacy in Interacting with Technology (SET) was

measured on Beier’s scale (Beier, 1999) with four

items (α = .821). According to Bandura, the domain

speciﬁc self-efﬁcacy refers to an individual’s conﬁ-

dence to execute a speciﬁc behavior or to attain a spe-

ciﬁc goal (Bandura, 1982). It relates to an individual’s

choice of actions, the performance in these actions,

as well as the endurance in these actions if difﬁcul-

ties emerge. Various studies found a tremendous in-

ﬂuence of technical self-efﬁcacy on interacting with

computing technology (Selwyn et al., 2003; Arning

and Zieﬂe, 2007; Schreder et al., 2013).

As no validated scale for measuring the Attitude

Towards Textiles (TEX) exists, we built a new scale

with four items based on previous ﬁndings from fo-

cus groups. This scale achieved a sufﬁciently high

internal reliability for a newly developed scale (α =

.658). Furthermore, AUTO was queried using six

items (α = .828), which were also derived from the

results of prior qualitative investigations.

Afterwards, the participants were asked to con-

ceive a scenario, in which a cushion on the sofa in the

living room functioned as a remote control for the do-

mestic electronic devices light, music, and heating in

ICT4AWE 2017 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health

the whole home environment. The participants were

asked to imagine that electronic sensors were incor-

porated in the cushion, which were able to respond

to different hand gestures. For instance, the cushion

could be operated by stroking, kneading, grabbing, or

rolling and twisting of folds.

Subsequent to the scenario, the participants were

asked how they envisioned the cushion and how they

would rate it and its possible functions. For this eval-

uation, an adapted acceptance model based on the

UTAUT2-model was used (Venkatesh et al., 2012).

Our STTAM – Smart Textile Technology Acceptance

Model – incorporates the dimensions behavioral In-

tention To Use (ITU), Performance Expectancy (PE),

Effort Expectancy (EE), Hedonic Motivation (HM),

Social Inﬂuence (SI), Facilitating Conditions (FC),

Price Value (PV), and Habit (HB) from UTAUT2.

Furthermore and based on the results of previous

qualitative studies, the model was supplemented by

the dimensions Washability (WASH) and Technical

Conditions (TC), capturing technical aspects, such as

long durability or input efﬁciency. The participants

had to evaluate the respective dimensions on three or

four items each on a six-point Likert scale (0=strongly

disagree; 5=strongly agree).

Finally, the participants were able to indicate

which beneﬁts and barriers of the smart cushion are

most important to them and which household devices

they would like to operate in their own home environ-

ment.

Completing the questionnaire took about 15 min-

utes. Data was collected in Germany in spring 2015

by using a paper-and-pencil questionnaire in order to

address older people and to enable older people’s par-

ticipation in the questionnaire. Participation was vol-

untary and was not gratiﬁed.

3.2 Statistical Methods

All subjective measures were rated on six-point Lik-

ert scales. Data was analyzed using bi-variate cor-

relations of model- and user-related factors, Pear-

son’s χ

, uni- and multivariate analyses of variance

(ANOVA/MANOVA) as well as linear regressions.

The level of signiﬁcance was set to p = .05. Spear-

man’s ρ was used for bivariate correlations and Pil-

lai’s V was stated for the omnibus test of MANOVAs.

The effect size was reported as partial η

. The step-

wise method was used in the multiple linear regres-

sion and models with low standardized β were re-

moved between the runs. Models with high variance

inﬂation (V IF  1) were excluded. The whiskers in

the diagrams indicate the standard error.

Table 1: Inter-correlations of the ﬁve domains of technol-

ogy attitude: Self-efﬁcacy (SET), Enthusiasm (EN), Per-

ceived Competency (COMP), Positive Attitude (POS), Neg-

ative Attitude (NEG).

†

= p < .1, *= p < .05, **= p < .001.

EN COMP POS NEG

SET .659** .661** .507** -.305**

EN — .513** .560** -.198*

COMP — .541** -.316**

POS — -.266**

NEG —

3.3 Description of the Sample

A total of n=136 people from a rural area volunteered

to participate in our study and individually completed

the paper-and-pencil questionnaire. 12 incomplete

data sets were excluded, as only complete data sets

could be used for further analyses.

The participants (n=124) were on average 49.5

years old (SD = 16.2;min = 17; max = 86) with

45.2% males and 54.8% females. 29.3% of the partic-

ipants hold a secondary school certiﬁcate and 26.6%

an university entrance diploma. Moreover, 27.4%

completed junior high school, indicating the hetero-

geneity of the sample’s educational level. Only a

small percentage (14.8%) of the participants suffered

from chronic diseases (e.g., diabetes, allergies). Ad-

ditionally, 50.4% of the participants mentioned hav-

ing pets (48.7% have no pets). None of these factors

(educational level, health status, pets) correlated with

age or gender.

The participants were asked for their technology

attitude and experience in ﬁve dimensions. The cor-

relation analysis in Table 1 illustrates that all ﬁve di-

mensions of attitude towards technology are closely

interwoven. On average, the participants showed

a positive Perceived Competency in interacting with

technology (M = 3.7;SD = 1.2; min = 0; max =

5), a rather positive Technical Self-efﬁcacy (M =

2.9;SD = 1.2; min = 0;max = 5), and a slightly posi-

tive perceived technology Enthusiasm (M = 2.7; SD =

1.5;min = 0;max = 5). The participants’ evalua-

tion of a Positive Attitude Towards Technology (M =

3.3;SD = 1.0; min = 0; max = 5) was on average af-

ﬁrmative, while a Negative Attitude Towards Technol-

ogy (M = 2.5;SD = 1.2; min = 0; max = 5) was rated

neutrally.

Moreover, participants stated having a positive

Attitude Towards Textiles (TEX) (M = 3.4; SD =

1.1;min = 0;max = 5) and a slightly positive Atti-

tude Towards Automation (AUTO) (M = 2.9; SD =

1.6;min = 0;max = 5). However, previous ex-

perience with smart textiles was very low (M =

0.7;SD = 1.3;Min = 0; Max = 5). A correlation anal-

Age, Gender, and Technology Attitude as Factors for Acceptance of Smart Interactive Textiles in Home Environments - Towards a Smart

Textile Technology Acceptance Model

ysis revealed signiﬁcant relationships between gender

(dummy coded as 0 = male, 1 = f emale) and SET

(ρ = −.388, p < .01, sig.), gender and TECH (ρ =

−.313; p < .05, sig.), gender and TEX (ρ = .245, p <

.01, sig.) and gender and AUTO (ρ = −.255; p < .05,

sig.). Hence, women indicated to be less inclined

to technology than men, whereas they were more in-

clined to textiles than men.

Concerning age, signiﬁcant correlations were

found for the relationships between age and SET (ρ =

−.328, p < .01, sig.) as well as age and TECH (ρ =

−.278; p < .01, sig.) with the elderly being less in-

clined to technology than younger participants. More-

over, age was not related to AUTO (ρ = −.143, p =

.113 > .05, n.s.) and TEX (ρ = .023, p = .803 > .05,

n.s.).

4 RESULTS

The results are presented as follows: ﬁrst, the model-

related factors determining the acceptance of a cush-

ion as an example for smart textile interfaces are

presented. Second, user factors which inﬂuence

acceptance-relevant criteria are described and ana-

lyzed, applying a segmentation of user groups with re-

gard to the Attitude Towards Technology. Afterwards,

the results of the model-related factors are detailed for

the segmented user groups.

4.1 Model Based Evaluation of the

Smart Cushion

To understand which user factors and evaluation di-

mensions govern the intention to use the smart cush-

ion, a correlation analysis is conducted. Table 2 illus-

trates that all considered dimensions of the STTAM

are associated with the behavioral Intention to Use

(ITU) a smart cushion.

In particular, HB, HM, and PE are strongly re-

lated to ITU. SI, FC, PV, and TC are also signiﬁcant,

though slightly less important, determinants for ITU

and the acceptance of a smart cushion. In compar-

ison, WASH and EE have a lower impact on ITU.

To understand the key predictors for increased accep-

tance of a smart cushion in the home environment, a

step-wise multiple regression analysis with the evalu-

ation dimensions as independent variables and ITU as

dependent variable is calculated. The calculation re-

vealed three signiﬁcant models for the whole sample.

The ﬁrst model predicts 83.5% (adj. r

= .835) vari-

ance of ITU and is based on UTAUT2’s habit (HB)

dimension: i.e., participant’s behavioral intention to

use a smart cushion is higher, if they can envision to

use it regularly. The second model additionally con-

tains Hedonic Motivation (HE) and explains +2.4%

(adj. r

= .859) of the variance in ITU. The third and

ﬁnal model is based on Habit, Hedonic Motivation,

and Performance Expectancy (PE) an adds another

+0.3% (adj. r

= .862) explained variance. Table 3

presents the ﬁnal regression model.

4.2 Inﬂuence of User Factors

Table 2 also presents the correlations between the user

factors and the dimensions of the adapted UTAUT2

model. As it is shown, TECH and AUTO are associ-

ated with the intention to use: Participants being more

inclined to technology and with higher wishes for au-

tomation tend to have a stronger behavioral intention

to use smart textile interfaces. However, age (ρ =

−.044; p = .627 > .05, n.s.), gender (ρ = −.107; p =

.240 > .05, n.s.), and TEX (ρ = .104; p = .254 > .05,

n.s.) are not related to the ITU. Furthermore, it is

striking that gender is not related to any of the model’s

dimensions, while age is associated with EE. In addi-

tion, it is notable that TECH (which correlates with

gender and age (see chapter 3.4)) is related to almost

all dimensions except of PV (ρ = .133; p = .121 >

.05, n.s.) and WASH (ρ = .126; p = .149 > .05, n.s.).

As these correlative results lead to the assumption

that the attitude towards technology inﬂuences the ac-

ceptance of and the Intention to Use smart textiles,

a group segmentation was undertaken to analyze this

relationship in depth.

4.3 Segmentation of User Groups by

Attitude Towards Technology

To investigate the inﬂuence of attitude towards tech-

nology using factorial methods, we segmented the

sample using a k-means Cluster analysis with two

clusters and the ﬁve factors Self-efﬁcacy in Interacting

with Technology (SET), Enthusiasm Towards Tech-

nology (EN), Positive Attitude Towards Technology

(POS), Negative Attitude Towards Technology (NEG),

and Perceived Competency in Interacting with Tech-

nology (COMP).

The analysis converged after ﬁve iterations and

yielded in two clearly separated clusters. The ﬁrst

cluster contains 74 participants. As Figure 1 il-

lustrates, this group is characterized by a higher

Self-efﬁcacy in Interacting with Technology, higher

Perceived Competency, higher Enthusiasm Towards

Technology, and shows a more Positive Attitude as

well as a lower Negative Attitude Towards Technol-

ogy. In the following, this groups is referred to as

tech-savvy. In contrast, the second cluster with 54

ICT4AWE 2017 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health

Table 2: Inter-correlations of user factors (bottom) and the product’s evaluation (upper) on the Smart Textile TAM dimensions

(PE = Performance Expectancy, HM = Hedonic Motivation, HB = Habit, EE = Effort Expectancy, SI = Social Inﬂuence, FC =

Facilitating Conditions, PV = Price Value, WASH = Washability, TC = Technical Conditions, ITU = Intention To Use, TECH

= Attitude Towards Technology, TEX = Afﬁnity Towards Textiles, AUTO = Attitude Towards Home Automation).

†

= p < .1,

* = p < .05, ** = p < .001.

PE HM HB EE SI FC PV WASH TC ITU

PE — .689** .755** .348** .553** .520** .335** .351** .367** .764**

HM — .765** .313** .534** .577** .373** .335** .549** .791**

HB — .281** .662** .649** .445** .357** .493** .904**

EE — .167

†

.514** .207*

SI — .460** .363** .299** .313** .655**

FC — .342** .231* .337** .606**

PV — .246** .459**

WASH — .182* .304**

TC — .488**

Age -.236** .176

†

Gender

TECH .268** .288** .293** .430** .155

†

.407** .175

†

.327**

TEX .302** .156

†

.280**

AUTO .342** .343** .398** .307** .197* .372** .192* .198* .491**

Table 3: Linear regression table for Intention To Use (ITU)

based on HE (Habit), Hedonic Motivation (HM), and Per-

formance Expectancy (PE), r

ad j.

= .862.

Model B SE B β T

(const) -.614 .129 -4.761

HB .664 .070 .633 9.542

HM .272 .070 .237 3.876

PE .136 .068 .114 2.015

participants is characterized by lower Technical Self-

efﬁcacy, lower Perceived Competency, lower Enthu-

siasm, lower Positive Attitude, and a more Nega-

tive Attitude Towards Technology than the ﬁrst group.

Hence, this second group is referred to as tech-weary.

As depicted in Figure 1, the cluster member-

ship shows signiﬁcant differences for all ﬁve con-

sidered dimensions of Attitude Towards Technology

(p < .001).

Furthermore, Table 4 shows that the two technol-

ogy groups are closely related to the factors gender,

age, and Attitude Towards Automation. The tech-

savvy group consists to a greater part of men and is

comparatively younger than the tech-weary group. In

addition, the tech-weary group indicated to have a

more negative Attitude Towards Automation (AUTO)

than the tech-savvy group.

4.4 Acceptance of Smart Textiles

Depending on Technology Groups

Figure 2 illustrates the evaluation of the smart inter-

active cushion of both technology attitude clusters on

0.0

1.0

2.0

3.0

4.0

5.0

Self-efficacy

Technology

(p<.001)

Enthusiasm

(p<.001)

Perceived

Competence

(p<.001)

Positive

Attitude

(p<.001)

Negative

Attitude

(p<.001)

Level of Agreement (0=min; 5,=max.)

tech-savvy (n=74) tech-weary (n=54)

Figure 1: Differences in attitudes towards technology based

on identiﬁed clusters (whiskers indicate the SE).

the Smart Textile TAM model’s dimensions and the

overall intention to use.

First of all, one-way repeated ANOVA ana-

lyzes revealed that the tech-savvy (M = 2.2;SD =

1.5) and the tech-weary (M = 1.7; SD = 1.3) group

differed only slightly with respect to their Inten-

tion to Use (ITU) a smart cushion (F(1, 126) =

3.703, p = .057). Both groups did not differ with

regard to the dimensions Performance Expectancy

(PE) (F(1, 127) = 2.812, p = .096), Social Inﬂu-

ence (SI) (F(1, 127) = 1.114, p = .293), Price Value

(F(1, 127) = 1.112, p = .294), Technical Conditions

(TC) (F(1, 123) = 0.342, p = .560) and Washabilty

Age, Gender, and Technology Attitude as Factors for Acceptance of Smart Interactive Textiles in Home Environments - Towards a Smart

Textile Technology Acceptance Model

Table 4: Technology clusters (AUTO = Attitude Towards Home Automation, TEX = Afﬁnity Towards Textiles).

tech-savvy tech-weary

Sex 44m/29w 14m/36w χ

= 12.404, p < .001

Age 44.1 ± 15.1 55.0 ± 16.2 F(1, 119) = 13.528, p < .001

(17-78 years) (23-86 years)

AUTO 3.5 ± 1.3 2.0 ± 1.6 F(1, 123) = 34.537,p < .001

TEX 3.3 ± 1.1 3.5 ± 1.2 F(1, 126) = 1.612, p = .207 > .05

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

WA S H

ITU

Level of Agreement (0=min; 5,=max.)

tech-weary tech-savvy

Figure 2: Evaluation of the smart cushion on the Smart

Textile TAM dimensions by technology attitude clusters

(whiskers indicate the SE).

(WASH) (F(1, 123) = 1.735, p = .190). For all other

dimensions, signiﬁcant group differences were found.

Effort Expectancy (EE) (F(1, 126) = 20.357, p <

.001) and Facilitating Conditions (FC) (F(1, 126) =

8.126, p < .001) were signiﬁcantly more important

for the tech-savvy than for the tech-weary group.

The same pattern applies to Hedonic Motivation

(HM) (F(1, 126) = 3.905, p < .05) and Habit (HB)

(F(1, 127) = 4.732, p < .05), which were slightly

more important for the tech-savvy than for the tech-

weary group.

5 DISCUSSION

In this article we investigated the acceptance of smart

textiles as a medium for interacting with one’s home

environment, using a scenario with a smart cushion

as an example. We measured the acceptance and

the intention to use the device on a modiﬁed tech-

nology acceptance model that builds on Venkatesh et

al.’s UTAUT2 model and incorporates the (perceived)

washability and additional conditions as additional

evaluation dimensions.

5.1 Appraisal of Smart Textile TAM

(STTAM)

The results presented above and additionally illus-

trated in Figure 3 show that the intention to use a

smart interactive textile is governed by all of the con-

sidered nine dimensions of the proposed Smart Tex-

tile TAM (STTAM). The results show that the pro-

posed and adapted STTAM acceptance model oper-

ates and is applicable usefully for smart interactive

textiles as all dimensions have an effect on the accep-

tance and the intention to use smart interactive tex-

tiles.

The multiple linear regression calculated in Sec-

tion 4.1 also identiﬁed that habit, hedonic motivation,

and performance expectancy are the three strongest

predictors for the intention to use a smart cushion.

In the following the two newly added dimensions

washability and technical conditionsand their effect

on the intention to use of the STTAM are speciﬁcally

highlighted. The added evaluation dimension washa-

bility was found to have an impact on the overall ac-

ceptance and intention to use the smart cushion in the

home environment. Thus, we can conclude that devel-

opers of smart interactive textiles must consider and

ensure that their products are washable and easy to

clean. Additionally, feedback from our participants

in prior qualitative and quantitative studies (novices

as well as technological experts) showed the impor-

tance of washability issues as they were mentioned

each time and in various contexts. Hence, the results

suggest that the topic washability should also be ad-

dressed for marketing smart interactive textiles, e.g.

by presenting a seal that assures that the product can

be cleaned easily.

Furthermore, the second added evaluation dimen-

sion technical conditions was also found to have an

impact on the acceptance of and intention to use the

smart cushion in the home environment. The dimen-

sion’s high assessment illustrated in Figure 2 indi-

cated that our participants perceive the technical re-

alization of the smart cushion (i.e., input efﬁciency,

durability) as very important. However, the correla-

tion with the overall intention to use is signiﬁcantly

lower than many of the other considered dimensions,

such as the performance expectancy, or hedonic value

ICT4AWE 2017 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health

Effort Expectancy

Performance

Expectancy

Facilitating

Conditions

Hedonic Value

Washability

Technical

Conditions

Social Inﬂuence

Price Value

Habit

Attitude Towards

Technology

=.158)

Gender

(female=1,male=0)

Age

Intention To Use

=.862)

User factors Dependent variableEvaluation dimensions

-.313

-.278

.327

-.236

.176

.268

.288

.293

.430

.155

.407

.175

.764

.791

.904

.655

.606

.459

.304

.488

.207

Figure 3: Representation of the essential results with user’s factors on the left, evaluation dimensions in the middle, and

intention to use on the right (solid lines = sig. relationships, doubled lines = variables from the regression).

of the product. Nevertheless, it would be valuable

to address the technical realization in marketing and

to put the technical functioning of the device and its

longevity into the limelight.

5.2 Inﬂuence of User Factors

The study detected that age has a signiﬁcant inﬂu-

ence on the attitude towards technology. In our study,

elderly persons reported a tremendously lower over-

all attitude towards technology and consistently lower

scores for the ﬁve sub-dimensions self-efﬁcacy in

interacting with technology, perceived competency,

positive and negative attitude towards technology,

as well as enthusiasm towards technology. Like-

wise, women were also found to be less inclined to-

wards technology than men. Both ﬁndings ﬁt well

into the research landscape, as they are common

phenomenons in various technology acceptance re-

search studies (Busch, 1995; Arning and Zieﬂe, 2007;

Brauner et al., 2015).

The attitude towards automating processes at

home (AUTO) – i.e., inclination towards home au-

tomation – is related to gender, but not to age. As

AUTO was identiﬁed as the strongest user factor that

governs the intention to use the smart cushion at home

and even surpasses the inﬂuence of users’ attitude to

technology, this relationship should be investigated in

depth in future studies.

The newly developed scale for capturing an in-

dividual’s attitude towards textiles (TEX) achieved a

sufﬁciently high internal reliability and was also re-

lated to the user factor gender, with women being

more inclined towards textiles than men. More im-

portantly, TEX is positively associated with the hedo-

nic value attributed to the smart cushion. People with

a higher attitude towards textiles found the smart in-

teractive textile interface more pleasing, fun, and en-

tertaining. Nevertheless, the results of our study also

show that this positive inﬂuence of TEX on the hedo-

nic value does not carry over to the overall intention to

use the smart cushion. Therefore, further studies have

to elaborate whether an high attitude towards textiles

is decisive for adopting this novel input device or if

the adoption is unrelated to this factor.

Astonishingly, the intention to use a smart cushion

is neither related to age nor gender. Hence, we can

conclude that smart interactive textile surfaces have

the potential to serve as a novel and widely accepted

interaction device. The only user factors which pre-

dict the likely use of smart interactive textile surfaces

are the attitude towards technology and the desire to

control and automate other devices at home.

However, age does directly affect the attitude

towards automation and the attitude towards tech-

nology, which both have a strong inﬂuence on the

model’s evaluation dimensions and the overall inten-

tion to use. Hence, there is an indirect inﬂuence of

age on the evaluation of the smart cushion and its

likely adoption. Nevertheless, our study also revealed

Age, Gender, and Technology Attitude as Factors for Acceptance of Smart Interactive Textiles in Home Environments - Towards a Smart

Textile Technology Acceptance Model

that age is not the only decisive factor for the atti-

tude towards automation or attitude towards technol-

ogy, for example, as there are tech-savvy elderly and

tech-weary youngsters.

5.3 Deriving Guidelines

In the following, guidelines are suggested for the di-

mensions which are the strongest levers for increasing

the acceptance and likely adoption of smart interac-

tive textile surfaces in the home environment.

Increase perceived habituation: Bandura’s self-

efﬁcacy (see Section 3.1, (Bandura, 1982)) theory of-

fers several ideas to increase the perceived habitua-

tion of using these devices. For example, role models

in ads can show how a smart interactive textile can be

used in daily live to simplify certain activities. Also,

friends and family members may persuade people to

integrate these novel devices into their daily routine.

Increase hedonic value: To address the second

strongest predictor – the hedonic value of the inter-

active textile surface – the design of these novel input

devices should not only focus on simplistic usability

measures, such as efﬁciency and effective according

to ISO 9241/10 (DIN, 1998), but must also integrate

the aesthetic and the perceived fun when using these

devices (c.f., (Blythe et al., 2004)).

Increase perceived beneﬁts: Eventually, the per-

formance expectancy dimension refers to the per-

ceived beneﬁts of using the technology. For the smart

cushion, the perceived beneﬁt can be addressed by

providing clear examples of what the smart cushion

can be used for in his or her personal environment.

For instance, by giving examples how the light of the

living room can be adjusted to one’s personal mood

with an intuitive swipe gesture on the cushion or how

music or television can be controlled using the cush-

ion.

6 SUMMARY, LIMITATIONS AND

OUTLOOK

The study gives ﬁrst insights into the acceptance of

smart interactive textiles in the home environment and

on the impact of individual user factors on the ac-

ceptance taking a smart cushion as example. In the

presented study, the participants evaluated a ﬁctional

cushion and therefore, their evaluation is primarily

shaped by their beliefs, wishes, and fears than when a

real cushion would have been evaluated. Obviously, a

further study needs to address whether the identiﬁed

acceptance patterns remain similar when a tangible

and functional cushion is evaluated. This investiga-

tion would give us the opportunity to understand how

scenario-based evaluations differ from the evaluation

of concrete products.

Furthermore, the survey captures the snapshot of

the user’s evaluation at the time of the survey. We

therefore cannot conclude with certainty that the iden-

tiﬁed predictors of the intention to use successfully

predict the actual use of the product. Nevertheless,

the underlying concepts of Fishbein and Ajzen’s The-

ory of Reasoned Action (Ajzen, 1991; Fishbein and

Ajzen, 1975), Davis’ Technology Acceptance Model

(Davis, 1989) (see Section 2.2) and Venkatesh’s Uni-

ﬁed Theory of Acceptance and Use of Technology

(Venkatesh et al., 2012) have shown a very strong

relationship between the usage intention and the ac-

tual later use. As it was mentioned before, the pre-

sented study was focused on a single smart interac-

tive textile product – the cushion. Future studies will

also need to focus on different products with differ-

ent spectra of functions and characteristics to under-

stand which determinants for later use are universal

and which are tied to speciﬁc products. Our interdis-

ciplinary research project on smart interactive textile

surfaces has already developed a ﬁrst demonstrator:

The interactive curtain Gardeen! (Heller et al., 2016)

presented in section 2.1. A ﬁrst usability study found

the demonstrator to be very intuitive and easy to use

(high ease of use), though only a share attested the

curtain a high usefulness. Other prototypical demon-

strators are currently on a testing stage and will be

evaluated in the near future.

In addition, there are some limitations with regard

to the sample. This study’s sample was rather small

so that a replication of this study in a greater and more

representative extent would be useful. If the study

is replicated, it should be more balanced with regard

to age: here, the study aimed to reach older partic-

ipants leading to a comparatively “old” sample due

to the paper-and-pencil questionnaire. This was use-

ful to analyze older participants’ needs and wishes to-

wards smart interactive textiles. However, for future

studies, a balanced age-distribution covering all age

groups would be desirable.

Summarizing, the present study shows that smart

interactive textiles can be a novel and suitable inter-

action device for older and younger people alike. Al-

though an individual’s attitude towards technology is

a determinant for the projected acceptance, the rela-

tionship between age and attitude towards technol-

ogy is measurable, yet not decisive. Furthermore,

the presented guidelines enable designers and devel-

opers to build novel smart interactive textile products

with an increased acceptance and higher likelihood of

ICT4AWE 2017 - 3rd International Conference on Information and Communication Technologies for Ageing Well and e-Health

adoption and actual use. Therefore, this study con-

tributed to the vision of calm and ubiquitous comput-

ing (Weiser, 1991), as it could identify the user factors

that govern the acceptance of natural textile interac-

tion devices in people’s habitats, such as the smart

cushion, armchairs, or other textile surfaces.

ACKNOWLEDGEMENTS

The authors thank all participants for sharing their

thoughts on smart textile interfaces with us. Fur-

thermore, the research support of Jens Keulen and

Sarah Voelkel is highly acknowledged. This project

is funded by the German Ministry of Education and

Research (BMBF) under project Intuitex (16SV6270)

(Brauner et al., 2017) and by the Excellence initiative

of German state and federal governments (project Ur-

ban Future Outline).

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